Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning

Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.
Integrating User-Centered Design with the Agile Software Development Methodology for a Cultural Heritage Information System

This paper reports on the findings of a user study on the design ideas for the Sarawak Digital Cultural Heritage Progressive Web App (PWA). The study elucidates the integration of User-Centered Design (UCD) in the Agile software development methodology. Five users were instructed to map their ideas on how they envisioned the application and the features it would have based on a brief description of the system as a one-stop center cultural heritage information for the general public in Sarawak while encouraging conservation and preservation of material culture. The study found that users prefer a simple and pleasing design with an emphasis on visual representation, and a straight forward and intuitive navigation to minimize cognitive load, Further study should explore the effectiveness of the integration of UCD for the Cultural Heritage Information System from the design stage to the evaluation stage.
Global Spatial and Temporal Variation of the Combined Effect of Aerosol and Water Vapour on Solar Radiation

This study aims to calculate the combined and individual effects of the optical thickness of aerosols (AOT) and precipitable water vapour (PWV) on the solar radiation reaching the Earth’s surface at a global scale and to analyse its spatial and temporal variation. For that purpose, a novel but validated methodology is applied to CERES SYN1deg products for the period 2000–2019. Spatial distributions of AOT and PWV effects, both individually and combined, show a close link with the spatial distributions of AOT and PWV. The spatially averaged combined effect results in a −13.9% reduction in irradiance, while the average AOT effect is −2.3%, and the PWV effect is −12.1%. The temporal analysis focuses on detecting trends in the anomalies. The results show overall positive trends for AOT and PWV. Consequently, significant negative overall trends are found for the effects. However, significant positive trends for the individual AOT and the combined AOT-PWV effects are found in specific regions, such as the eastern United States, Europe or Asia, indicating successful emission control policies in these areas. This study contributes to a better understanding of the individual and combined effects of aerosols and water vapour on solar radiation at a global scale.
The effects of the Covid-19 pandemic seen through the lens of the Italian university teachers and the comparison with school teachers' perspective

In this paper, we report one of the first investigations conducted at the National level with university teachers, with the aim to capture their perceptions about the capability of the learning ecosystems to react to the lockdown imposed by the pandemic and the recourse to on-line learning. The study, conducted about two months after the beginning of the lock-down, shows that: a) learning ecosystems reacted promptly and in a satisfactory manner to assure the didactic continuity at both the systemic and individual level; b) the teaching activities were mainly confined to transmissive ex-cathedra lectures in the attempt to reproduce standard university dynamics; c) the working load increased with respect to face-to-face activities; d) the intention to use on-line learning in the future is driven by preconceptions rather than experiences and by the capability to manage one’s own time. The comparison with the outcomes of a similar study conducted with school teachers shows that the latter adopt a broader spectrum of didactic activities (although they still tend to remain in their comfort zone), experienced a heavier increase of the working load, and were more influenced by the situation they experienced. Although both teachers categories recognized the relevance of digital pedagogy, in the case of school teachers - as shown by the causal structure of the variables considered in our studies - it should be urgently included in teacher education curricula, while in the case of the university teachers it appears to be a possible route to support integration of on-line activities with standard face-to-face ones.
Ionospheric Turbulence and the Equatorial Plasma Density Irregularities: Scaling Features and RODI

In the framework of space weather, the understanding of the physical mechanisms responsible for the generation of ionospheric irregularities is particularly relevant for their effects on global positioning and communication systems. Ionospheric equatorial plasma bubbles are one of the possible irregularities. In this work, using data from the ESA Swarm mission, we investigate the scaling features of electron density fluctuations characterizing equatorial plasma bubbles. Results strongly support a turbulence character of these structures and suggest the existence of a clear link between the observed scaling properties and the value of the Rate Of change of electron Density Index (RODI). This link is discussed, and RODI is proposed as a reliable proxy for the identification of plasma bubbles.
Beamforming of LOFAR Radio-Telescope for Passive Radiolocation Purposes

This paper presents the results of investigations on the beamforming of a low-frequency radio-telescope LOFAR which can be used as a receiver in passive coherent location (PCL) radars for aerial and space object detection and tracking. The use of a LOFAR radio-telescope for the passive tracking of space objects can be a highly cost-effective solution due to the fact that most of the necessary equipment needed for passive radiolocation already exists in the form of LOFAR stations. The capability of the radiolocation of planes by a single LOFAR station in Borowiec is considered to be ‘proof of concept’ for future research focused on the localization of space objects. Beam patterns of single sets of LOFAR antennas (known as tiles), as well as for the entire LOFAR station, are presented and thoroughly discussed in the paper. Issues related to grating lobes in LOFAR beam patterns are also highlighted. A beamforming algorithm used for passive radiolocation purposes, exploiting data collected by a LOFAR station, is also discussed. The results of preliminary experiments carried out with real signals collected by the LOFAR station in Borowiec, Poland confirm that the appropriate beamforming can significantly increase the radar’s detection range, as well as the detection’s certainty.
A Critical Analysis of Red Ceramic Blocks Roughness Estimation by 2D and 3D Methods

The method of measuring the roughness of ceramic substrates is not consensual, with unsuccessful attempts to associate roughness with the adhesion of coatings because the ceramic blocks have different areas of contact, shapes, and dimensions of the roughness as well as the extrusion process influences the mechanical anisotropy of the block. The goal of this work is a quantification and comparison of roughness data obtained by 2D and 3D methods, evaluating the variations of results between the measurement methods and formulating a critical analysis regarding the quality of the information obtained with each method. For this propose, four sets of ceramic blocks with different firing temperature were produced, in order to provide groups of blocks with different surface topographies in which the roughness was estimated. The roughness measurements were made in 4608 regions, resulting in 1536 values using 2D method and 3072 values using 3D method. In the 2D method for ceramic blocks, the measurement orientation strongly influences the result, depending on the measurement position and orientation. The 3D method generates a higher average value and allows to identify roughness variations typical of the ceramic block. The roughness estimation of a ceramic block surface must be done using the 3D method.
Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.
Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data

Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled the fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-sensor data will have a specific strength in the further classification of nemoral forest landscapes owing to the distinct seasonal patterns in the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and a random forest classifier trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90%, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34–74%. Species groups with coniferous species were the least confused, whereas the broadleaf groups, especially Quercus species, had higher error rates. The results are applied in Danish national accounting of greenhouse gas emissions from forests, resource assessment, and assessment of forest biodiversity potentials.
CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection

In the wake of developments in remote sensing, the application of target detection of remote sensing is of increasing interest. Unfortunately, unlike natural image processing, remote sensing image processing involves dealing with large variations in object size, which poses a great challenge to researchers. Although traditional multi-scale detection networks have been successful in solving problems with such large variations, they still have certain limitations: (1) The traditional multi-scale detection methods note the scale of features but ignore the correlation between feature levels. Each feature map is represented by a single layer of the backbone network, and the extracted features are not comprehensive enough. For example, the SSD network uses the features extracted from the backbone network at different scales directly for detection, resulting in the loss of a large amount of contextual information. (2) These methods combine with inherent backbone classification networks to perform detection tasks. RetinaNet is just a combination of the ResNet-101 classification network and FPN network to perform the detection tasks; however, there are differences in object classification and detection tasks. To address these issues, a cross-scale feature fusion pyramid network (CF2PN) is proposed. First and foremost, a cross-scale fusion module (CSFM) is introduced to extract sufficiently comprehensive semantic information from features for performing multi-scale fusion. Moreover, a feature pyramid for target detection utilizing thinning U-shaped modules (TUMs) performs the multi-level fusion of the features. Eventually, a focal loss in the prediction section is used to control the large number of negative samples generated during the feature fusion process. The new architecture of the network proposed in this paper is verified by DIOR and RSOD dataset. The experimental results show that the performance of this method is improved by 2–12% in the DIOR dataset and RSOD dataset compared with the current SOTA target detection methods.
Performance and Feasibility of Drone-Mounted Imaging Spectroscopy for Invasive Aquatic Vegetation Detection

Invasive plants are non-native species that can spread rapidly, leading to detrimental economic, ecological, or environmental impact. In aquatic systems such as the Sacramento-San Joaquin River Delta in California, USA, management agencies use manned aerial vehicles (MAV) imaging spectroscopy missions to map and track annual changes in invasive aquatic plants. Advances in unmanned aerial vehicles (UAV) and sensor miniaturization are enabling higher spatial resolution species mapping, which is promising for early detection of invasions before they spread over larger areas. This study compared maps made from UAV-based imaging spectroscopy with the manned airborne imaging spectroscopy-derived maps that are currently produced for monitoring invasive aquatic plants in the Sacramento-San Joaquin Delta. Concurrent imagery was collected using the MAV mounted HyMap sensor and the UAV mounted Nano-Hyperspec at a wetland study site and classification maps generated using random forest models were compared. Classification accuracies were comparable between the Nano- and HyMap-derived maps, with the Nano-derived map having a slightly higher overall accuracy. Additionally, the higher resolution of the Nano imagery allowed detection of patches of water hyacinth present in the study site that the HyMap could not. However, it would not be feasible to operate the Nano as a replacement to HyMap at scale despite its improved detection capabilities due to the high costs associated with overcoming area coverage limitations. Overall, UAV-based imaging spectroscopy provides comparable or improved capability, and we suggest it could be used to supplement existing monitoring programs by focusing on target areas of high ecologic or economic priority.
Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers.
Protein fluctuations explored by inelastic neutron scattering and dielectric relaxation spectroscopy

Glasses, supercooled liquids and proteins share common properties, in particular the existence of an energy landscape and the presence of two types of fluctuations, α and β. While the effect of α fluctuations on proteins has been known for a few years, the effect of β fluctuations has not been fully understood. By comparing neutron-scattering data on the protein myoglobin with β fluctuations in the hydration shell measured by dielectric relaxation spectroscopy, we show that the internal protein motions are slaved to these fluctuations. We also show that there is no ‘dynamical transition’ in proteins near 200 K. The rapid increase in the mean-square displacement with temperature in many neutron-scattering experiments can be quantitatively predicted by β fluctuations in the hydration shell.
Ionosonde Observations of Spread F and Spread Es at Low and Middle Latitudes during the Recovery Phase of the 7-9 September 2017 Geomagnetic Storm

This study presents observations of nighttime spread F/ionospheric irregularities and spread Es at low and middle latitudes in the South East Asia longitude of China sectors during the recovery phase of the 7–9 September 2017 geomagnetic storm. In this study, multiple observations, including a chain of three ionosondes located about the longitude of 100°E, Swarm satellites, and Global Navigation Satellite System (GNSS) ROTI maps, were used to study the development process and evolution characteristics of the nighttime spread F/ionospheric irregularities at low and middle latitudes. Interestingly, spread F and intense spread Es were simultaneously observed by three ionosondes during the recovery phase. Moreover, associated ionospheric irregularities could be observed by Swarm satellites and ground-based GNSS ionospheric TEC. Nighttime spread F and spread Es at low and middle latitudes might be due to multiple off-vertical reflection echoes from the large-scale tilts in the bottom ionosphere. In addition, we found that the periods of the disturbance ionosphere are ~1 h at ZHY station, ~1.5 h at LSH station and ~1 h at PUR station, respectively. It suggested that the large-scale tilts in the bottom ionosphere might be produced by LSTIDs (Large scale Traveling Ionospheric Disturbances), which might be induced by the high-latitude energy inputs during the recovery phase of this storm. Furthermore, the associated ionospheric irregularities observed by satellites and ground-based GNSS receivers might be caused by the local electric field induced by LSTIDs.
Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have shown significant improvements in remote sensing image scene classification for powerful feature representations. However, because of the high variance and volume limitations of the available remote sensing datasets, DCNNs are prone to overfit the data used for their training. To address this problem, this paper proposes a novel scene classification framework based on a deep Siamese convolutional network with rotation invariance regularization. Specifically, we design a data augmentation strategy for the Siamese model to learn a rotation invariance DCNN model that is achieved by directly enforcing the labels of the training samples before and after rotating to be mapped close to each other. In addition to the cross-entropy cost function for the traditional CNN models, we impose a rotation invariance regularization constraint on the objective function of our proposed model. The experimental results obtained using three publicly-available scene classification datasets show that the proposed method can generally improve the classification performance by 2~3% and achieves satisfactory classification performance compared with some state-of-the-art methods.