esa-sceva/satcom-chunk-collection huggingface dataset:
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 2 Related Work
### Datasets
#### 2.3.1 Jilin-1 Dataset  
The Jilin-1 dataset is a satellite dataset that we constructed ourselves. Jilin-1 satellite remote sensing images possess characteristics such as high resolution and wide swath, being capable of obtaining high-definition remote sensing imagery with a panchromatic resolution better than 0.75 m, a multispectral resolution better than 3 m, and a swath width exceeding 40 km. Compared to drone remote sensing images, the Jilin-1 high-resolution remote sensing images can rapidly capture large-area land object information.  
The study area for the dataset was designated as the Chang'an District of Xi'an City, China (Figure 1). This region boasts a thriving economy, a high degree of urbanization, and diverse building types. Regarding area delineation, we aimed to select regions where buildings are relatively concentrated, particularly in the urban city center. The chosen date for the imagery was 13 July 2023, a day with clear weather, which minimizes the impact of weather conditions on image quality.  
We fused the panchromatic and multispectral images of the study area to obtain remote sensing images with a spatial resolution of 0.75 m. The images had three channels: red, green, and blue. In addition, with pixel values ranging from 0 to 255. We selected 16 areas with a high concentration of buildings to annotate the samples, ensuring a diverse range of building types and avoiding areas with too few building categories. We used a sliding window approach to segment the images and labels into samples of size 256 \(\times\) 256. All samples were then divided into training, validation, and testing sets at a ratio of 8:1:1. This resulted in 4005 training images, 500 validation images, and 502 testing images.  
Our dataset presents challenges for segmentation networks. The Jilin-1 remote sensing images are captured at a certain tilt angle, resulting in large shadow areas obscuring buildings. Additionally, the side contours of buildings are clearly visible. Drawing from the annotation standards of other public datasets and real-world cases, our annotation process disregarded shadow obstructions. At the same time, we considered precise side contours to be part of the building. The segmentation considered the entire building in the 2D image, as opposed to just the rooftop area. Furthermore, factors such as atmospheric radiation impacted the imaging of Jilin-1.
###### Abstract  
Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - \(\text{NIR}_{\text{V}}\) and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and \(\text{NIR}_{\text{V}}\)) and BRDF-corrected (\(\text{NDVI}_{\text{BRDF}}\), \(\text{EVI}_{\text{BRDF}}\), \(\text{EVI}_{\text{2 BRDF}}\), and \(\text{NIR}_{\text{V, BRDF}}\)) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. \(\text{EVI}_{\text{BRDF}}\) and \(\text{NIR}_{\text{V, BRDF}}\) had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.  
carbon cycle; gross primary production; eddy covariance; vegetation productivity; MODIS; vegetation activity; photosynthesis; remote sensing; light use efficiency; environmental stresses +
Footnote †: journal: Remote Sensing, 2019, 11, 1823; doi:10.3390/rs11151823
## 3 Results and Discussion
### Volatile Organic Compounds
Ketones comprised 3.0-13.3% of the total peak area of the volatile fraction of the different teas, with the highest proportion found in infusions from fermented samples, especially Samples 1, 2 and 3. Biogenetically, ketone components are derived from alcohols through oxidation reactions catalyzed by different enzymes, which exert dehydrogenase activity, and have a relevant contribution to the taste and fragrance of the essential citrus fruit oils [56]. In general, they contribute sweet, fruity, rose, and honey notes [53; 54]. \(\gamma\)-nonlactone was only identified in Sample 5 (fermented). \(\beta\)-damascenone, identified in the present study only in fermented teas, has been reported as key aroma compound in black tea [52], toasted mate leaf [49] and fruits, vegetables, and derived products, including wine, where it imparts a pleasant \"stewed apple\", \"fruity\" and honey-like character [61]. \(\beta\)-damascenone and \(\gamma\)-nonlactone have been cited as compounds responsible for prune aroma in prematurely aged red wines [62]. \(\beta\)-damascenone has also been previously identified in fresh robusta cherry pulp [46]. Pyrazines and furans, especially 5-methyl-furfurral, are heat treatment markers in dried fruits [44]. Lactones such as \(\gamma\)-nonalactone, are also typical of dried fruits like apricots [63]. Their presence in coffee cascara suggests the importance of the drying process for their overall aroma profile. Pyrazines (2,3,5 trimethyl-pyrazine and 2,6-dimethyl-pyrazine), which contribute roast, nut, cocoa, and caramel notes [53; 54], were only identified in one fermented sample. Furfural was identified in all samples, while 5-methyl-furral and 2-acetyl-furan were only identified in one fermented sample, giving bread, almond, sweet and woody characters [53; 54]. In general, alcohols, esters, aldehydes, acids, and ketones have been found to be predominant in dried fruits like raisins [64] and apricots [63]. Overall, the diverse volatile compounds identified in the cascaras reflected their nature as dried fruits [44]. A few alkanes were observed in the infusions, but they were not considered because they did not meet the peaks confirmation criteria. In general, these compounds have a limited effect on the overall odor characteristics of teas [23]. Additionally, small peaks of non-volatile compounds, such as caffeine, lycoxanthin, lycopene and astaxanthin, were identified most probably due to sublimation caused by heat exposure during volatile analysis.
## 1 Introduction
The maintenance of the performance of solar panels has been closely linked to strategies for managing soils, with preventive and cleaning methods playing a significant role. Various approaches for anti-dust coatings have been studied and tested as preventive measures [14; 15; 16]. [PERSON] et al. (2024) experimented with various configurations of anti-dust coatings and tracking systems in the desert region of Saudi Arabia, achieving a reduction in soiling losses of up to 85 % by combining anti-soil coatings with tracking that includes vertical night stowage [17]. There are different models in the literature that are used to estimate soiling losses, including both physical-statistical models and artificial intelligence models. Examples include an exponential model correlating dust deposition density with power losses [18] and an optical model that calculates dust deposition based on particulate mass concentration, precipitation, and other variables [19], and thereby determines the loss of light transmittance [20]. In the field of machine learning, the most widely used models are artificial neural networks. Among the main disadvantages of current models are their high dependence on the location where the data for the model were obtained and their inflexibility when changing the design region [21]. In this context, given the importance of modelling soiling losses in photovoltaic panels, in this work, we have developed various machine learning models tailored for this purpose. The focus has been on easily accessible meteorological variables, such as atmospheric temperature, wind speed, relative humidity, particulate matter (PM\({}_{2.5}\), PM\({}_{10}\)), and others, which are continuously monitored worldwide and available from satellites. These models aim to create reliable estimations and can be extrapolated to different locations. Specifically, models were initially developed and tested for the location of Almeria, Spain, and then evaluated for their ability to extrapolate to another location: Madrid, Spain. In particular, a global model was trained using combined data from both locations and demonstrated strong generalizability, achieving a root mean square error (RMSE) of 1.02% in predicting soil erosion losses. This result underscores the effectiveness and applicability of the approach in varying geographic and climatic conditions. This article is organised into several sections. The Materials and Methods Section details the data acquisition process, including the sources and types of meteorological data used, as well as the data processing techniques applied to prepare the data for analysis. It also describes the development of four different machine learning approaches used to predict soiling losses in photovoltaic panels. The results present the outcomes of these models, evaluating their performance in the initial location (Almeria), their extrapolation to a different location (Madrid), and the development and results of a global model trained on data from both locations. The Conclusions Section summarises the key findings of the study, emphasising the effectiveness and limitations of the developed models.
## 3 Results: Publication Analysis
### Relationships between Sense of Presence and the Study Variables
#### 3.4.3 Relationships between Sense of Presence and Conditions (Technology and User Profile)
In some cases, these relationships are also predictive of behavioral responses in users. For example, study [21] found that a higher level of presence and involvement, sensory fidelity and immersion predicted that users would experience greater comfort. However, these relationships arose only under heat stimuli, not cold. It should be noted that the relationship with presence occurred before adaptive interactions took place, while for its indicators it was later. In the cold condition, the absence of relationships was associated with less behavioral adaptation. Study [22] found that greater presence predicted better smell detection. Curiously, this occurred only with the measures taken during the experience, not with the post-experience measurement. This underlines the importance of examining when measurements were taken. In addition, study [26] revealed that spatial presence predicted a better user experience. Similarly, [5] showed that the level of presence experienced predicted affective responses. It is worth mentioning two studies, not included in the results analysis, that established that interesting predictive links exist between spatial/presence dimensions and behaviors. Study [27] found that social presence predicts greater socio-spatial interaction, establishing a circular relationship that feeds back depending on the intensity of the interactions and affective associations. Study [82] revealed that positive relationships existed between spatial presence, copresence and social presence, which become cross-predictors of each other. In addition, the users' tendency for involvement predicted greater spatial and copresence. Together, these findings highlight the value of examining predictive relationships between presence qualities and behavioral responses, which can reveal the existence of processes of mutual feedback between cognition, emotion and action in virtual environments. Figure 3 shows a graphic relationship between the types of environmental stimuli, scenarios and technology and user profile conditions that influence sense of presence, where the size of the circles is equivalent to the number of studies that explored the area.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 6 Performance Study
### Performance Comparison with Other Algorithms  
In this subsection, we consider the network performance of our algorithm, LPF and RAW under different values of node density, event rate and the number of sources. Note that the calculated duty cycle interval obtained from our algorithm is used for the LPF and RAW algorithms, since they are not able to find the optimal duty cycle interval.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 3 Trajectory Compensation Based on Stiffness Modeling
### Error Compensation Model for a 6-DOF Manipulator  
#### 3.3.1 Joint Stiffness  
Assuming that the force on the end of the arm is F\({}_{0}\), then  
\[\text{F}_{0}=\left[f_{x},f_{y},f_{z},m_{x},m_{y},m_{z}\right]^{T} \tag{17}\]  
where \(\left[f_{0x},f_{0y},f_{0z}\right]\) and \(\left[m_{0x},m_{0y},m_{0z}\right]\) represent three-dimensional force vectors and three-dimensional moment vectors, respectively.  
The deformation at the end due to the external force is \(X\):  
\[X=\left[d_{x},d_{y},d_{z},\delta_{x},\delta_{y},\delta_{z}\right]^{T} \tag{18}\]  
where \(\left[d_{x},d_{y},d_{z}\right]\) and \(\left[\delta_{x},\delta_{y},\delta_{z}\right]\) represent translational deformation and rotational deformation, respectively.  
When the force is within a certain range, the relationship between the force and the deformation satisfies a linear relationship, i.e., following [PERSON]'s law:  
\[\left[\begin{array}{c}f_{x}\\ f_{y}\\ f_{z}\\ m_{x}\\ m_{y}\\ m_{z}\end{array}\right]=\left[\begin{array}{cccc}k_{11}&k_{12}&\ldots&k_{16} \\ k_{21}&k_{22}&\ldots&k_{26}\\ \vdots&\vdots&\ddots&\vdots\\ k_{61}&k_{62}&\ldots&k_{66}\end{array}\right]\left[\begin{array}{c}d_{x}\\ d_{y}\\ d_{z}\\ \delta_{x}\\ \delta_{y}\\ \delta_{z}\end{array}\right] \tag{19}\]  
The stiffness matrix or flexibility matrix of the robot reflects the linear relationship between the end force \(F\) and the deformation \(X\) and varies with the change in shape and position. From the elements in the stiffness matrix, it can be seen that the force in a certain direction not only causes deformation in a certain direction but also causes deformation in other directions.  
The stiffness matrix can be obtained by combining the Jacobian matrix calculated in the previous section with the stiffness calculation formula:  
\[C^{-1}=J^{-T}(q)K_{q}J^{-1}(q) \tag{20}\]  
The inverse matrix of \(C^{-1}\) is \(C\), which is referred to as the flexibility matrix.
## 5 Discussion
### Biological Insight and Interpretability  
While our framework primarily emphasizes computational efficiency, it also yields biologically relevant predictions. For instance, secondary structure motifs such as \(\alpha\)-helices and \(\beta\)-strands tend to occur in recurring residue spans--typically between 8 and 15 amino acids for helices and 5 and 10 for strands. The empirical finding that 50-residue subsequences outperform shorter or longer segments aligns with known domain-level folding patterns in proteins, where secondary motifs often appear in clusters spanning 40-60 residues [44; 45]. This setting offers a trade-off: it is long enough to retain contextual dependencies yet short enough to fit efficiently in GPU memory. To provide biological validation, we selected a subset of proteins with known experimental annotations from the test set and compared their predicted secondary structures with the DSSP-annotated ground truth. Although our model is not explicitly designed for interpretability, the use of context-aware embeddings (ProtBERT) inherently embeds evolutionary and functional signals into the learned representations. Future integration with attention mechanisms or saliency maps could enable residue-level visualization of learned features, aiding interpretability in biomedical contexts.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dimension**} & \multicolumn{5}{c}{**Time (s)**} & \multicolumn{5}{c}{**Platform**} \\ \cline{2-9}  & **25** & **50** & **75** & **100** & **25** & **50** & **75** & **100** \\ \hline
1024 & 16804 & 19258 & 17987 & 30307 & CPU & CPU & CPU & CPU \\
512 & 25533 & 19439 & 31777 & 31714 & CPU & CPU & CPU & CPU \\
256 & 937 & 869 & 1240 & 1078 & GPU & GPU & GPU & GPU \\
128 & 1240 & 1108 & 1254 & 1104 & GPU & GPU & GPU & GPU \\
64 & 1090 & 1377 & 1366 & 1840 & GPU & GPU & GPU & GPU \\
32 & 905 & 954 & 524 & 1443 & GPU & GPU & GPU & GPU \\ \hline \hline \end{tabular}
\end{table}
Table 9: Training times (seconds) and platforms for Q3 classification.
## 2 Materials and Methods
### TOPSIS Method and Classification  
In order to successfully analyze and compare forest fire susceptibility data obtained by applying these two methods, the TOPSIS method (technique for order of preference by similarity to ideal solution) is used. Its purpose is to optimize the process of ranking variant solutions from the most optimal to the least optimal, taking into account the characteristics of evaluation criteria and values of priority coefficients associated to them. It implies the existence of a decision matrix, and the initial step of the procedure involves the normalization of the decision matrix for each of the criteria, separately (r\({}_{\rm ij}\)) [51; 52; 53]. The next step involves creating a weighted normalization decision matrix (a\({}_{\rm ij}\)), where the values obtained in the previous step (r\({}_{\rm ij}\)) and the values of the weight vectors (w\({}_{\rm j}\)) are multiplied for each criteria [51; 54].  
\[{\rm r_{ij}}=\frac{{\rm x_{ij}}}{\sqrt{\sum_{j=1}^{n}{x_{ij}}^{2}}} \tag{9}\]  
\[{\rm a_{ij}}=\;{\rm w_{ij}}\frac{{\rm x_{ij}}}{\sqrt{\sum_{j=1}^{n}{x_{ij}}^{ 2}}} \tag{10}\]  
Third step involves the identification of ideal variant (V\({}_{\rm j}\)+) and anti-ideal variant (V\({}_{\rm j}\)-) values for each criterion. These values are directly related to the goal of the analytical procedure and the nature of the criteria, i.e., they depend on whether maximization or minimization is performed for a certain criterion during the evaluation [52; 55].  
\[{\rm V_{j}}+ =\big{[}\big{(}{\rm maxV_{ij}}\;|\;{\rm j}\in\;{\rm J}\big{)}, \big{(}{\rm minV_{ij}}\;|\;{\rm j}\in\;{\rm J}\big{)}\big{]} \tag{11}\] \[{\rm V_{j}}- =\big{[}\big{(}{\rm minV_{ij}}\;|\;{\rm j}\in\;{\rm J}\big{)}, \big{(}{\rm maxV_{ij}}\;|\;{\rm j}\in\;{\rm J}\big{)}\big{]} \tag{12}\]  
Identification of the ideal and anti-ideal ranked variants is performed in order to calculate the value of the geometric distance of each of the variant solutions from the mentioned values, i.e., in order to identify the values of deviations represented by \(d_{i}\)+ \(i\)\(d_{i}\)-indicators [51; 52; 53; 55; 56].  
\[{\rm d_{i}}+ =\sqrt{\sum_{j=1}^{n}\big{(}{\rm V_{ij}}-{\rm V_{j}}+\big{)}^{2}} \tag{13}\] \[{\rm d_{i}}- =\sqrt{\sum_{j=1}^{n}\big{(}{\rm V_{ij}}-{\rm V_{j}}-\big{)}^{2}} \tag{14}\]  
In the final phase, the value of the performance index (\({\rm d_{i}}\)) is calculated for each of the variant solutions, in order to rank them from the most optimal to the least optimal [51; 52; 54; 55; 56].  
\[{\rm cl_{i}}=\frac{{\rm d_{i}}-}{\big{[}({\rm d_{i}}+)+({\rm d_{i}}-)\big{]}} \tag{15}\]  
Values of the performance index are in range from 0, for the most susceptible to forest fire occurrence, to 1, for the least susceptible. Zones of different forest fire susceptibility for the research area were classified based on the value of performance index according to Table 2.
## 2 Theoretical Background and Hypothesis  
### The Relationship between Entrepreneur and National Competitiveness  
Focusing on the relationship between entrepreneur and competitiveness, several researchers [14; 15; 16; 22; 23; 24] have pointed out that there is a direct relationship between them. In an environment which can be defined as being competitive, entrepreneurs will be more focused on finding opportunities and therefore will better contribute to regional economic growth [25]. Entrepreneurship is significant for a country's competitiveness and development because entrepreneurs create new businesses which generate new jobs,more competition and may even increase productivity through innovation [26]. However, [PERSON] et al. (2017) [16] have shown that different stages of the economy imply other characteristics of entrepreneurial activities and, therefore, the importance of entrepreneur depends on the stage of economic development and can have either a positive or negative impact on the global competitiveness of countries. Using enterprise policy as a tool for improving regional competitiveness or for addressing economic and social disadvantage can be efficient, but especially in the long term [24]. To date, there are no studies that have analysed the relationship between entrepreneurship performance and national competitiveness. Thus, for achieving one of the main purposes of the paper, we have formulated a set of hypotheses that will guide our future empirical analysis:  
**Hypothesis 1 (H1).**_Higher entrepreneurial performance is positively related to national competitiveness._  
**Hypothesis 2 (H2).**_The relationship between entrepreneurial performance and national competitiveness depends by the stage of economic development of the country._