{"type": "FeatureCollection", "features": [{"id": "10.1155/2018/9736547", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:18:58Z", "type": "Journal Article", "created": "2018-05-09", "title": "Chronological Classification of Ancient Mortars Employing Spectroscopy and Spectrometry Techniques: Sagunto (Valencia, Spain) Case", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Forty-two mortar samples, from two archaeological excavations located in Sagunto (Valencian Community, Spain), were analysed by both portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF) and inductively coupled plasma mass spectrometry (ICP-MS) to determine major and minor elements and traces including rare earth elements (REEs). Collected data were crossed with those previously obtained from Sagunto Castle mortars, and principal component analysis (PCA) was applied to discriminate the construction phases of the unearthed buildings. REE permitted to ascribe most of the masonries to the Roman Imperial period. Moreover, a statistical model was built by employing partial least squares discriminant analysis (PLS-DA) in order to classify the mortars from Roman Imperial period and from Islamic period due to the problematic overlapping between these two phases. Results confirmed the effectiveness of the developed indirect chronology method, based on REE data, to discriminate among historic mortars from different construction periods on a wide scale including different Sagunto archaeological sites.</p></article>", "keywords": ["2. Zero hunger", "0601 history and archaeology", "QC350-467", "06 humanities and the arts", "Optics. Light", "energy dispersive X-ray fluorescence spectroscopy (pED-XRF); inductively coupled plasma mass spectrometry (ICP-MS)", "Analytical Chemistry; Atomic and Molecular Physics", " and Optics; Spectroscopy"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/130462/1/9736547.pdf"}, {"href": "https://iris.unica.it/bitstream/11584/248342/2/Ramacciotti%20et%20al%202018.pdf"}, {"href": "https://arpi.unipi.it/bitstream/11568/935316/1/P101%20Chronological%20Classification%20of%20Ancient%20Mortars.pdf"}, {"href": "https://doi.org/10.1155/2018/9736547"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Spectroscopy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1155/2018/9736547", "name": "item", "description": "10.1155/2018/9736547", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1155/2018/9736547"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "30592d0c330e6c206aa54bf98d50d122", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:25:07Z", "type": "Other", "title": "Spectra Fusion of Mid-Infrared (MIR) and X-ray Fluorescence (XRF) Spectroscopy for Estimation of Selected Soil Fertility Attributes", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R2p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R2p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R2p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R2p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.</p></article>", "keywords": ["Electrical and Electronic Engineering", "Biochemistry", "Instrumentation", "Atomic and Molecular Physics", " and Optics", "Analytical Chemistry"], "contacts": [{"organization": "Abdul Mouazen, Lalit Mohan Kandpal, Muhammad Abdul Munnaf, Cristina Cruz,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/30592d0c330e6c206aa54bf98d50d122"}, {"rel": "self", "type": "application/geo+json", "title": "30592d0c330e6c206aa54bf98d50d122", "name": "item", "description": "30592d0c330e6c206aa54bf98d50d122", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/30592d0c330e6c206aa54bf98d50d122"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-01T00:00:00Z"}}, {"id": "10.3390/s21092980", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:35Z", "type": "Journal Article", "created": "2021-04-25", "title": "Towards the Development and Verification of a 3D-Based Advanced Optimized Farm Machinery Trajectory Algorithm", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rost\u011bnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.</p></article>", "keywords": ["Agriculture and Food Sciences", "2. Zero hunger", "Technology and Engineering", "controlled traffic farming", "Chemical technology", "mission planning", "TP1-1185", "04 agricultural and veterinary sciences", "Biochemistry", "Article", "Analytical Chemistry", "soil compaction", "Atomic and Molecular Physics", "digital elevation model", "AGRICULTURAL ROBOTS", "0401 agriculture", " forestry", " and fisheries", "Electrical and Electronic Engineering", "and Optics", "coverage path planning", "controlled traffic farming; coverage path planning; digital elevation model; mission planning; soil compaction"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/21/9/2980/pdf"}, {"href": "https://www.mdpi.com/1424-8220/21/9/2980/pdf"}, {"href": "https://doi.org/10.3390/s21092980"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s21092980", "name": "item", "description": "10.3390/s21092980", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s21092980"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-23T00:00:00Z"}}, {"id": "10.3929/ethz-b-000278733", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:43Z", "type": "Journal Article", "created": "2018-07-06", "title": "Cost\u2013benefit optimization of structural health monitoring sensor networks", "description": "<p>Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost\uffe2\uff80\uff93benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information\uffe2\uff80\uff93cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.</p>", "keywords": ["Stochastic Processes", "structural health monitoring", "structural health monitoring; Bayesian inference; cost\u2013benefit analysis; stochastic optimization; information theory; Bayesian experimental design; surrogate modeling; model order reduction", "Chemical technology", "Cost-Benefit Analysis", "Bayesian inference", "Bayesian experimental design", "Uncertainty", "Bayes Theorem", "TP1-1185", "02 engineering and technology", "stochastic optimization", "Bayesian experimental design; Bayesian inference; Benefit analysis; Cost; Information theory; Model order reduction; Stochastic optimization; Structural health monitoring; Surrogate modeling; Algorithms; Monte Carlo Method; Nonlinear Dynamics; Stochastic Processes; Uncertainty; Bayes Theorem; Cost-Benefit Analysis; Analytical Chemistry; Atomic and Molecular Physics", " and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering", "Article", "surrogate modeling", "0201 civil engineering", "Nonlinear Dynamics", "model order reduction", "cost\u2013benefit analysis", "Monte Carlo Method", "Algorithms", "information theory"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/18/7/2174/pdf"}, {"href": "https://re.public.polimi.it/bitstream/11311/1085132/1/Sensors_2018b.pdf"}, {"href": "https://doi.org/10.3929/ethz-b-000278733"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000278733", "name": "item", "description": "10.3929/ethz-b-000278733", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000278733"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-06T00:00:00Z"}}, {"id": "11568/935316", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:08Z", "type": "Journal Article", "created": "2018-05-09", "title": "Chronological Classification of Ancient Mortars Employing Spectroscopy and Spectrometry Techniques: Sagunto (Valencia, Spain) Case", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Forty-two mortar samples, from two archaeological excavations located in Sagunto (Valencian Community, Spain), were analysed by both portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF) and inductively coupled plasma mass spectrometry (ICP-MS) to determine major and minor elements and traces including rare earth elements (REEs). Collected data were crossed with those previously obtained from Sagunto Castle mortars, and principal component analysis (PCA) was applied to discriminate the construction phases of the unearthed buildings. REE permitted to ascribe most of the masonries to the Roman Imperial period. Moreover, a statistical model was built by employing partial least squares discriminant analysis (PLS-DA) in order to classify the mortars from Roman Imperial period and from Islamic period due to the problematic overlapping between these two phases. Results confirmed the effectiveness of the developed indirect chronology method, based on REE data, to discriminate among historic mortars from different construction periods on a wide scale including different Sagunto archaeological sites.</p></article>", "keywords": ["2. Zero hunger", "0601 history and archaeology", "QC350-467", "06 humanities and the arts", "Optics. Light", "energy dispersive X-ray fluorescence spectroscopy (pED-XRF); inductively coupled plasma mass spectrometry (ICP-MS)", "Analytical Chemistry; Atomic and Molecular Physics", " and Optics; Spectroscopy"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/130462/1/9736547.pdf"}, {"href": "https://iris.unica.it/bitstream/11584/248342/2/Ramacciotti%20et%20al%202018.pdf"}, {"href": "https://arpi.unipi.it/bitstream/11568/935316/1/P101%20Chronological%20Classification%20of%20Ancient%20Mortars.pdf"}, {"href": "https://doi.org/11568/935316"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Spectroscopy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11568/935316", "name": "item", "description": "11568/935316", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11568/935316"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "1854/LU-8709527", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:18Z", "type": "Journal Article", "created": "2021-04-25", "title": "Towards the Development and Verification of a 3D-Based Advanced Optimized Farm Machinery Trajectory Algorithm", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rost\u011bnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.</p></article>", "keywords": ["Agriculture and Food Sciences", "2. Zero hunger", "Technology and Engineering", "controlled traffic farming", "Chemical technology", "mission planning", "TP1-1185", "04 agricultural and veterinary sciences", "Biochemistry", "Article", "Analytical Chemistry", "soil compaction", "Atomic and Molecular Physics", "digital elevation model", "AGRICULTURAL ROBOTS", "0401 agriculture", " forestry", " and fisheries", "Electrical and Electronic Engineering", "and Optics", "coverage path planning", "controlled traffic farming; coverage path planning; digital elevation model; mission planning; soil compaction"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/21/9/2980/pdf"}, {"href": "https://www.mdpi.com/1424-8220/21/9/2980/pdf"}, {"href": "https://doi.org/1854/LU-8709527"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8709527", "name": "item", "description": "1854/LU-8709527", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8709527"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-23T00:00:00Z"}}, {"id": "50|userclaim___::30592d0c330e6c206aa54bf98d50d122", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:25:50Z", "type": "Other", "title": "Spectra Fusion of Mid-Infrared (MIR) and X-ray Fluorescence (XRF) Spectroscopy for Estimation of Selected Soil Fertility Attributes", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R2p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R2p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R2p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R2p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.</p></article>", "keywords": ["Electrical and Electronic Engineering", "Biochemistry", "Instrumentation", "Atomic and Molecular Physics", " and Optics", "Analytical Chemistry"], "contacts": [{"organization": "Abdul Mouazen, Lalit Mohan Kandpal, Muhammad Abdul Munnaf, Cristina Cruz,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/50|userclaim___::30592d0c330e6c206aa54bf98d50d122"}, {"rel": "self", "type": "application/geo+json", "title": "50|userclaim___::30592d0c330e6c206aa54bf98d50d122", "name": "item", "description": "50|userclaim___::30592d0c330e6c206aa54bf98d50d122", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|userclaim___::30592d0c330e6c206aa54bf98d50d122"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=and+Optics&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=and+Optics&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=and+Optics&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=and+Optics&offset=7", "hreflang": "en-US"}], "numberMatched": 7, "numberReturned": 7, "distributedFeatures": [], "timeStamp": "2026-05-25T00:01:29.563395Z"}