esa-sceva/satcom-chunk-collection huggingface dataset:
# The Resilience of Vegetation to the 2009/2010 Extreme Drought in Southwest China
## 3 Results
### The Resilience of Vegetation Growth and Productivity to the Extreme Drought  
The extreme drought event during 2009/2010 significantly inhibited vegetation growth and productivity in the 4 year base period (Figure 4a-c). Regions that suffered from intensive NDVI reductions were found in eastern Yunnan, western Guizhou, and central Guangxi, whereas the central and eastern Guizhou and western Yunnan were less affected. The spatial distribution of the LAI reduction was similar to that of NDVI, but the magnitudes of change were larger. Unlike NDVI and LAI, decreases in GPP were mainly found in Guangxi and Guizhou, and Yunnan was less affected. The spatial distribution of NDVI, LAI, and GPP resistance calculated with 2 year and 8 year base periods was similar to that of the 4 year base period (Figures S2a-c and S3a-c).  
Generally, resistance and recovery showed opposite spatial distribution patterns. Using the 4 year base period, we found that the recovery of NDVI, LAI, and GPP was high in Yunnan and Guizhou, where the resistance was low (Figure 4a-f). Such patterns were also found in the resistance and recovery of NDVI, LAI, and GPP in the 2 year and 8 year base periods (Figures S2a-f and S3a-f). The spatial distribution of resilience showed that  
Figure 4: Spatial distribution of resistance, recovery, and resilience (4 year base period). (**a-c**) show the resistance of the detrended NDVI, LAI, and GPP, respectively. (**d–f**) show the recovery of the detrended NDVI, LAI, and GPP, respectively. (**g–i**) show the resilience of the detrended NDVI, LAI, and GPP, respectively.  
the values of NDVI, LAI, and GPP after the extreme drought were lower than those before the extreme drought in Yunnan and Guizhou (Figures 4g-i, S2g-i and S3g-i).  
The resistance, recovery, and resilience of different vegetation types to the extreme drought showed large differences (Figures 5, S4 and S5). Evergreen broadleaf forest suffered the least suppression during the extreme drought period. Cropland was greatly susceptible to the extreme drought and suffered the most dramatically negative impact (Figures 5a, S4a and S5a). The recovery of NDVI, LAI, and GPP was relatively higher in cropland than in other vegetation types (Figure 5b, Figures S4b and S5b). The resilience results showed that the NDVI and LAI values of all vegetation after the extreme drought were lower than those from before the extreme drought, indicating that vegetation showed successively low growth after the extreme drought (Figures 5c, S4c and S5c).
# A Methodology for Controlling Bias and Fairness in Synthetic Data Generation
## 4 Experiments
### Metrics  
To compare the impact of variables on the classification node and to study the amount of bias existing in the datasets, two well-established and widely used metrics in the field of data fairness have been proposed, i.e., mutual information (MI) and demographic parity (DP).  
Mutual information [18] is a measure of dependence or \"mutual dependence\" between two random variables, namely \(X\) and \(Y\). As such, the measure is symmetrical, meaning that \(I(X;Y)=I(Y;X)\). In particular, it measures the average reduction in uncertainty about the samples of \(X\) that result from learning the samples of \(Y\). The mutual information between two discrete random variables \(X,Y\) jointly distributed according to \(p(x,y)\) is given by:  
\[I(X;Y)=\sum_{x,y}p(x,y)log\frac{p(x,y)}{p(x)p(y)}\]  
where \(x\) and \(y\) are the samples of \(X\) and \(Y\), respectively.  
Similar to [22], MIs between node \(Z\) and each of the other nodes depicted in Figure 4 have been calculated and used as a key metric to determine the mutual importance between pairs of variables.  
Figure 4: Representation of the network used to produce the results.  
While MI measures the impact of variables, demographic parity corresponds to the mathematical definition of fairness. Although, in most cases, _demographic parity_ is used to establish the amount of bias hidden in a machine learning classifier; in this case, the metric will be used to discuss the amount of bias in the generated synthetic datasets. The definition of DP is as follows. Given a binary sensitive attribute \(X\) with possible values \(0/1\) and a node \(Y\), a dataset is fair, i.e., does not discriminate with respect to attribute \(X\), if \(p(Y|X=0)\simeq p(Y|X=1)\). This metric will be later used to demonstrate how changing the weights of the edges causes the datasets to be more or less fair.
# Structure and Principles of Operation of a Quaternion VLSI Multiplier
## 5 Hardware Implementation of Quaternion Multiplication
### Implementation on FPGA
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Resources**} & \multicolumn{2}{c}{**Utilization**} & **Available** \\  & **Direct Multiplying** & **Fast Multiplying** & **Resources** \\ \hline LUT & 62 (0.1\%) & 512 (0.81\%) & 63,400 \\ \hline FF & & & 12,680 \\ \hline DSP & 17 (7.08\%) & 8 (3.33\%) & 240 \\ \hline IO & 257 (90.18\%) & 257 (90.18\%) & 258 \\ \hline BUFG & 1 (3.13\%) & 1 (3.13\%) & 32 \\ \hline _Energy_ & & & \\ _consumption_ & & & \\ \hline clocks & 0.001 W (1\%) & 0.001 W (1\%) & \\ \hline signals & 0.007 W (5\%) & 0.013 W (9\%) & \\ \hline logic & \(<\)0.001 W (\(<\)1\%) & \(<\)0.005 W (\(<\)3\%) & \\ \hline dsp & 0.016 W (11\%) & 0.008 W (5\%) & \\ \hline io & 0.121 W (82\%) & 0.122 W (79\%) & \\ \hline dynamic (sum of the above) & 0.145 W (61\%) & 0.149 W (62\%) & \\ \hline static & 0.091 W (39\%) & 0.091 W (38\%) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Usual implementation of direct and fast quaternion multiplication obtained for a 50 MHz clock.
# Differential Proteomic Analysis of _Listeria monocytogenes_ during High-Pressure Processing
## 3 Results
### The Influence of HPP on Differential Expression of L. monocytogenes Proteomes  
The Pearson correlation coefficients for the same treatment among the three replicates were 0.965-0.986. The more differentially expressed patterns were obtained when the control was compared with the H4 or H2, for which the coefficients were 0.879-0.900 and 0.904-0.921, respectively. The H2 and H4 correlation coefficients were as high as 0.952-0.972, implying that 200 and 400 MPa only caused marginal changes in _L. monocytogenes_ proteomes (Figure S1). In total, 380 proteins were quantitated with high confidence and proteins with fold changes of \(\geq\) 2 and \(p\) < 0.05 in comparison with the control group were considered to bethe differentially expressed proteins (DEPs). The total number of DEPs was 56 and 73 for H2/C and H4/C, respectively (Figure S2). The number of upregulated DEPs was 39 and 46 for H2/C and H4/C, respectively (Figure S2). The number of downregulated DEPs for H2/C and H4/C was 17 and 27, respectively (Figure S2). According to volcano plot analysis, upregulated DEPs exhibited a greater number than downregulated DEPs in H2/C and H4/C (Figure 2a). The DEPs accounted for 14.7 and 19.2% of total identified proteins for H2/C and H4/C, respectively. Six DEPs, namely 30S ribosomal protein S21 (H2/C: 3.41; H4/C: 4.52 log\({}_{2}\) fold), translation initiation factor IF-3 (H2/C: 3.75; H4/C: 4.08), 50S ribosomal protein L24 (H2/C: 3.36; H4/C: 4.08), 30S ribosomal protein S13 (H4/C: 3.90), 30S ribosomal protein S12 (H4/C: 3.47) and 30S ribosomal protein S20 (H4/C: 3.42), had more than 10-fold changes (equal to log\({}_{2}\) value 3.32). Four DEPs, namely D-alanyl carrier protein (H4/C: \(-\)5.20), UPP0473 protein lmo1501 (H2/C: \(-\)3.69; H4/C: \(-\)3.50), 50S ribosomal protein L33-1 (H2/C: \(-\)4.27) and ribosome-binding factor A (H2/C: \(-\)4.10), had less than 0.1-fold changes (equal to log\({}_{2}\) value \(-\)3.32) (Figure 2a and Table 1). Figure 2: (**a**) Volcano plot of changes in the levels of identified _L. monocytogenes_ proteins analyzed using label-free quantitative proteomics under the H2 and H4 treatments. Note: C, control group; H2 and H4, HPP group (200 and 400 MPa). The dashed line of y-axis is \(p\)-value = 0.05. The dashed lines of x-axis on the left and right side, respectively, are fold change = \(-\)2 and 2. Blue dots were upregulated DEPs; orange dots were downregulated DEPs; the others were not DEPs. (**b**) Hierarchical clustering heatmap of differentially expressed proteins (DEPs) of the H2 and H4 treatments. The rows represent individual proteins with gene name on the right of each corresponding row. Red bars indicate upregulated proteins and green bars indicate downregulated proteins. The k-means clustering algorithm identified the 9G, 9 GE, 3G and 3 GE groups.
## 1 Introduction
### Background
Furthermore, Industry 4.0 introduced advanced digital technologies, but its implementation has primarily focused on productivity and automation, sometimes overlooking the human aspect. Additionally, traditional safety systems typically adopt a reactive approach, addressing incidents only after they occur, rather than proactively identifying and mitigating potential risks. Hence, in the manufacturing context, it is crucial to prioritize workers' well-being and longevity to ensure and sustain manufacturing for future generations, industrial revolutions, productivity, and business continuity [20]. OHS is vital in manufacturing, particularly for small and medium-sized enterprises (SMEs) [33], since a safer, healthier workplace supports well-being and boosts worker productivity [34]. Safety 4.0 is a concept that applies Industry 4.0 technologies to proactively prevent accidents, enhance safety and productivity, and reduce work-related stress and mental health risks [7; 27; 35]. Digital solutions can effectively address hazardous situations and enhance safety performance [33]. For instance, Operator 5.0 is an emerging concept that envisions a smart operator using advanced interfaces and data systems to ensure sustainable manufacturing operations and workforce well-being [13; 16]. Moreover, businesses are increasingly recognizing OHS as a strategic priority essential for enhancing productivity, reducing costs, and achieving overall financial success [26; 36]. Effective employee health management is cardinal for enterprise development and sustained growth [37]. Thus, safety is a top priority, and companies are digitally transforming it through both technological and managerial approaches [38]. Despite advances in immersive and intelligent technologies, workplace accidents, ergonomic injuries, and mental stress remain prevalent in the manufacturing industry. Many existing safety protocols and training methods are often passive and outdated. The emergence of the Digital Twin (DT) and Industrial Metaverse (IM) enables the transition from reactive to predictive safety management. These technologies offer real-time insights, immersive training environments, and intelligent decision support, creating opportunities for enhanced ergonomics, reduced risks, and improved workforce engagement. This study is motivated by the urgent need to explore how the combination of Digital Twin (DT) and Industrial Metaverse (IM) technologies can actively address these ongoing safety issues. This research aims to bridge the gap between traditional safety approaches and the emerging paradigm of human-centric, predictive safety approaches enabled by advanced digital ecosystems. Hence, this study is motivated by the pressing need to bridge the current gap in OHS practices using these transformative digital capabilities.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 5 Discussion
### Model Performance  
To evaluate the model's effectiveness and generalizability, we employed a dual-evaluation approach using both pooled and individual datasets. The pooled dataset assessed the model's ability to generalize across diverse data. Our results demonstrated a significant accuracy improvement from 61% to 84% when trained with optimized features, alongside notable enhancements in precision, recall, and AUC metrics. On the individual datasets, the model exhibited strong adaptability to different subjects, with average accuracy increasing from 70.55% to 95.2% following feature optimization. These findings underscore the importance of identifying features that reliably distinguish between attention states. Moreover, they affirm the positive impact of feature reduction on optimizing computational efficiency and boosting classification performance.  
Despite extensive research on EEG-based distraction detection in various fields [25; 49; 50], studies on distraction recognition among coal miners remain limited. Our research fills this gap, demonstrating the feasibility of using EEG data to monitor the distraction state of coal miners. The robust performance of the distraction recognition model demonstrates the feasibility of distinguishing between focused and distracted states in coal miners and highlights its potential utility in real-world applications. These findings provide a foundation for integrating EEG-based monitoring systems into safety protocols, potentially enabling real-time attention tracking to mitigate risks associated with distraction. Furthermore, embedding this technology into wearable safety equipment, such as helmets, could significantly enhance hazard recognition capabilities in high-risk environments.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 3 Analyzed Cryptographic Methods and Primitives
### [PERSON] (RSA)
#### 3.2.2 Integrity and Authentication in RSA  
To achieve integrity and authentication of the exchanged data we leveraged the digital signature technique. Each sensor will be featured by a couple of keys, a public and a private one. For the case of integrity and authentication alone, without considering confidentiality, packets, being sent by each sensor to the server entity are composed of the plaintext message along with the digital signature computed upon the message using the private key of the sender, ensuring, in this way, that no one can impersonate him/her (unless their private key is leaked, but we have to always suppose that). On the other hand, when the server receives the message along with the digital signature, it has to verify this signature. In order to do that, it has to apply the RSA algorithm to the tag, in order to retrieve the original content of the tag itself, and then compare it with the plaintext received. If the messages are the same it means that no one has modified it during its transmission over the channel. Considering, now, all the security properties apply at once as shown in the Figure 5.
## 4 2.5D Map Representation and Relocation
### Proposed 2.5D Map  
Based on previous sections, the obstacle detected in the 2D laser plane and the image features detected by the camera can be aligned in one map, which can be represented as:  
\[M=\{m(x,y)\}, \tag{20}\]  
\[m(x,y)=\Big{\{}m_{g}(x,y),feature\_list(x,y)\Big{\}}, \tag{21}\]where \(feature\_list(x,y)\) represents the features in the corresponding vertical space of the grid \((x,y)\). If there is no feature detected above grid \((x,y)\), \(feature\_list(x,y)=null\).  
A general form of \(feature\_list(x,y)\) could be:  
\[feature\_list(x,y)=\{f\_list(x,y,z_{1}),f\_list(x,y,z_{2}),\ldots,f\_list(x,y, z_{n})\}, \tag{22}\]  
where \(z_{1},z_{2},\ldots z_{n}\) denotes the vertical height on which there is an image feature located above the grid \((x,y)\), and \(f\_list(x,y,z)\) is a list pointing to features in the feature bag. It should be denoted this expression is quite different from traditional methods, as there could be multiple features on a single space point \((x,y,z)\). That seems odd, but necessary in practice. When the camera is moving while SLAM, the image feature of one place is actually changing because of the huge change of visual angles and illuminations. In the history of image processing, researchers have been struggling with such changes and developed various features that are \"invariant\" to scaling, rotation, transformation and illumination, so that they can track the same place of the object and do more other works. In the expression of the proposed 2.5D map, considering the huge change of visual angles and illuminations while SLAM, the feature in one place may change greatly, as a result, we assume one place could have multiple features.  
As shown in Figure 5, a 2.5D Map combing with dense obstacle representation on the 2D grid plane and sparse features representation in 3D space above the plane could be obtained, through the combination and optimization with LiDAR and camera sensors. A list of key-frames with poses and visual words extracted while SLAM processing is also attached to the map.  
In the map, in order to simplify the calculation, the resolution is uninformed by the grid map, for example, the smallest grid cell could be 5 cm \(\times\) 5 cm \(\times\) 5 cm. In that case, there may be multiple feature points with different feature value detected in one cell. That situation is also considered in the map representation.
## 3 Proposed Methods  
This section explains four data detection techniques that rely on mutual data to improve the LLR during the detection process to enhance the system's performance. Before doing so, we first examine the distributions of readback signals obtained from staggered BPMR media, as shown in Figure 5.  
Three readback signals were obtained from three read heads (H1, H2, and H3), and the distribution of the sample amplitudes of each readback signal is depicted in Figure 5 without electronic noise. The dots denote the data obtained by sampling the readback signal. The red dots represent the samples obtained from the desired \(l\)-th track, whereas the black dots represent the samples from the \((l-1)\)-th and \((l+1)\)-th tracks. As a result, we note that the sample amplitude distribution retrieved from the desired track using H2 was more explicit than that from H1 and H3. This implies that the signal carrying the desired data from H2 was significantly stronger than that from H1 and H3.  
It is assumed that the data samples produced from H1 and H3 at the \(k\)-th bit can help enhance the quality and reliability of the \(k\)-th bit obtained from H2, which is the desired readback signal. After SOVA detection, three mutual samples of soft information representing one recorded bit are obtained. These data samples are referred to as mutual data information. To improve the LLR in the detection process, we propose four techniques that utilize mutual data information, which are described as follows.
## 2 Methodology
### Data Preprocessing  
After interpolating, the resulting dataset consisted of 782 data points, each with its corresponding Longitude, Latitude, pressure, chlorine, and flow values. Initial analysis involved generating histograms to understand the distributions of the features, as shown in Figure 2. To avoid mistaking normal locations as leaks, any data point with pressure, chlorine, or flow values that are 3 standard deviations above their respective means were dropped. This was carried out following the assumption that values at the higher extreme are not representative of leaks, but rather are exceptional conditions to water pipes that are irrelevant to the analysis.  
A feature, called 'ratio_pf', was created by dividing the pressure by flow for a better representation of the data points. This relationship could be more indicative of outliers. Further, it was important to scale or normalize the parameter values to avoid one feature overpowering the other. To determine the most suitable method of scaling, statistical tests were run to check the hypothesis that the features follow a Gaussian Distribution.  
Figure 2: Pressure, flow, and chlorine before and after dropping extremes.  
Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling tests were run, and the results can be seen in Tables 1 and 2 below.  
Since the \(p\)-values of all features for both the Shapiro-Wilk and Kolmogorov-Smirnov tests were below 0.05, we could reject the null hypothesis. This was further proven by the Anderson-Darling statistic for all features being below the critical value of 0.783. This conclusion allowed us to normalize the parameters using MinMaxScaler, rather than StandardScaler. After normalization, the data were ready to be fed through the machine learning model.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### Implementation Framework for Multi-Regional Load Forecasting Integration
#### 2.5.1 Multi-Regional Integration Framework Overview  
Our integration framework consists of four key components: (1) data collection and validation, (2) multi-source data preprocessing, (3) provincial forecast generation, and (4) weighted forecast fusion. Figure 2 illustrates this integrated approach, which preserves existing provincial forecasting systems while introducing a systematic evaluation and fusion methodology at the grid level.  
The framework addresses data heterogeneity challenges through standardized preprocessing protocols and accommodates temporal inconsistencies through dynamic time-delay compensation. Most importantly, it implements our evaluation index system to quantify  
Figure 2: Multi-regional data preprocessing flow chart.  
provincial forecast reliability, which then informs the PCA-based weighting algorithm for optimal integration.
## 2 Materials and Methods
### Selection of Optimal Model-Based Genetic Algorithm-Based Multi-Objective Approach  
Genetic algorithms (GAs), which are derived from the principles of natural selection and genetics, constitute a highly effective optimization technique for solving complex, multi-objective problems, such as the optimization of engineering systems. The fundamental approach of GAs is to enhance a population of potential solutions, with the goal of identifying outcomes that are either optimal or near-optimal results.  
The main operators of a GA include selection, crossover, and mutation operators. A random population is initially generated in the GA optimization process. The fitness function, which evaluates the quality of each population member, is calculated to transfer the randomly generated individuals. Subsequently, the selection operator is employed to select new individuals according to fitness function value. New offspring are then generated using the crossover operator, and the fitness function is recalculated for each offspring. This iterative process is repeated until criterion is met.  
During this study, the determination of optimal battery ECM model parameters (pulse time gap, discharge pulse time, C-rate, and rest time) is considered as a multi-objective optimisation problem with constraints. The purpose of the optimisation process is to minimize total RMSE errors between real and simulation model data corresponding to 30%, 50%, and 90% SoC values under different environmental conditions.  
Three objective functions were defined in MATLAB 2023a Software, and GAMULTIOBJ function was utilized to achieve the Pareto optimal solution set. These functions are given as follows.  
\[\begin{array}{rl}f_{90\%\ SoC\ RMSE\ val.}&=\min.\sum_{i=1}^{N}PT_{i},DPT_{i },Crate_{i},RT_{i}\\ f_{50\%\ SoC\ RMSE\ val.}&=\min.\sum_{i=1}^{N}PT_{i},DPT_{i},Crate_{i},RT_{i} \\ f_{30\%\ SoC\ RMSE\ val.}&=\min.\sum_{i=1}^{N}PT_{i},DPT_{i},Crate_{i},RT_{i} \\ &-10\leq T_{env.}\ \leq 40\text{w}\end{array} \tag{11}\]  
where \(PTG,\ DDT,\ Crate\), and \(RT\) are the pulse time gap, discharge pulse time, C-rate, and rest time, respectively. The subscript \(i\) demonstrates the number of cases. \(T_{env.}\) is the environmental temperature.  
A simplified diagram illustration of used experimental methods and all other models is presented in Figure 3.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Summary of EKF** \\ \hline
1: Initialization: \(x(\mathbf{0})=E[x(\mathbf{0})]\) \\ Error Definition: \(\widetilde{x}_{\mathbf{0}}=x_{0}-\hat{x}_{0}\) \\ Covariance matrix: \(\mathbf{P}(\mathbf{0})=E\Big{[}(x_{0}-\hat{x}_{0})(x_{0}-\hat{x}_{0})^{T} \Big{]}=E\