esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Intersection Delay Fairness Analysis
### Delay Model Fairness Evaluation
#### 3.3.1 Information Entropy  
In 1948, [PERSON] introduced the concept of \"information entropy,\" addressing the quantification issue of information. For an uncertain system \(Y\), if its source symbols have \(n\) possible values with corresponding probabilities \(P_{1},\ldots,P_{i},\ldots,P_{n}\), and each occurrence of values is independent of others, then the average uncertainty of the source should be the statistical mean (\(E\)) of the individual symbol uncertainties (\(-\)log \(P_{i}\)), known as information entropy and denoted as Equation (16):  
\[H(X)=E(-\text{log }P_{i})=-\sum_{i=1}^{n}P_{i}\text{log }P_{i} \tag{16}\]  
In Equation (16), the base of the logarithm is not specified and is typically chosen as 2, e, or 10. Different bases represent information units differently. In this paper, the base e is selected.  
Information entropy can be used to evaluate the equilibrium of a system. The closer the individuals are to each other, the less significant the differences and the larger the information entropy, indicating a more balanced system. Conversely, smaller entropy values imply greater system uncertainty and higher information content. When \(\exists P_{i}=1\), entropy is minimal; when and only when \(P_{i}=\frac{1}{n}\), entropy is maximal. For a four-phase delay problem, this is calculated as approximately \(-\sum_{i=1}^{4}\frac{1}{4}ln\frac{1}{4}\approx 1.386\).
# Predictive Model for the Optimized Mixed-Air Temperature of a Single-Duct VAV System
## 5 Results and Discussion
### Predictive Model-Based Economizer Control  
1. Hourly AHU mixed-air temperature control status  
A predictive model-based economizer control was developed and evaluated by a simulation. Figure 6 shows the change in the mixed-air temperature set point and the cooling energy consumption according to the application of the predictive model-based control for one day during the simulation. At this time, it was confirmed that the mixed-air temperature set point value was not constant but continuously changing.  
Figure 5: ANN model regression using test data: (**a**) Load prediction model; (**b**) Energy prediction model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Load Prediction Model** & **Energy Prediction Model** \\ \hline CV(RMSE) & 21.8\% & 20.6\% \\ R\({}^{2}\) & 0.96 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Verification of the ANN model.  
The predictive model-based economizer control for the optimized mixed-air temperature set point was evaluated in terms of its energy use along with the existing control, which kept the mixed-air temperature set point value constant. Figure 7 shows the evaluation results; the results are shown for 23 days in a month (31 days), excluding weekends, when the system was not operating. When controlling with the predictive model-based control, the cooling energy was reduced in all sections compared to the base operation control. The monthly cooling energy consumption with the base operation was approximately 1,620,865 kJ. On the other hand, the monthly cooling-energy consumption when operated by the predictive model-based control was about 859,997 kJ, saving about 760,868 kJ of energy. In addition, the day with the greatest energy savings was 3 March (the 3 rd data point in Figure 7), in which approximately 110,639 kJ of energy were saved.  
The energy consumption in the buildings was evaluated for the cooling coil, fan, the chiller, and the pump. Compared to the base control, when controlled by the predictive  
Figure 6: Hourly mixed-air temperature set point and cooling energy consumption.  
Figure 7: Comparison of cooling coil energy consumption.  
model-based control, the energy consumption of the chiller and pump were reduced, whereas the fan energy consumption increased. The fan energy consumption was 823,486 kJ with the base control and 823,926 kJ with the predictive model-based control. The energy increase amount was 440 kJ, indicating a 0.05% increase rate. Furthermore, the chiller energy consumption was 2,374,292 kJ and 1,600,102 kJ in the base and predictive model-based control, respectively. The energy consumption of the pump in the base and predictive model-based control was 1,083,547 kJ and 915,127 kJ, and 168,420 kJ of energy was saved. According to the predictive model-based economizer control of the building, the total energy saving in the building was approximately 3,194,029 kJ--showing an energy saving of approximately 28.9% (Figure 8).
## 2 Temperature Field and Thermal Error Modeling of Nut-Rotating Ball Screw Transmission Mechanism
### Thermal Resistance Network Analysis of Nut-Rotating Ball Screw Transmission Mechanism
#### 2.4.1 Heat Transfer Analysis  
In this study, the transmission component used in the dual-drive mechanism is the DIR-type nut-rotating ball screw pair, which is a device integrating the single nut ball screw with the supporting bearing. In other words, the outer ring of the supporting bearing in the nut assembly can be integrated with the flange in the nut assembly. In addition, the inner ring of the supporting bearing is integrated with the outer ring of the rotating nut. The heat source distribution of this mechanism shows the difference with that of conventional mechanisms with the nut and the screw being driven, respectively. According to thermodynamic research and achievements on bearings and conventional screw drive mechanisms, both domestically and internationally, thermal analysis is performed on the dual-drive mechanism.  
The dual-drive nut-rotating ball screw pair is an open heat transfer system. During the operation, there is a large amount of heat generated due to power loss and heat dissipation. According to the law of energy conservation, the entire dual-drive ball screw system ultimately reaches a thermal equilibrium state. In mechanical transmission systems, heat transfer mainly occurs in the form of thermal conduction and convection, and thermal radiation can be ignored. Additionally, part of the heat generated by power loss is transferred to other components in contact through thermal conduction, while another part is transferred to the internal air or external environment of the system through thermal convection.  
Taking the dual-drive nut-rotating ball screw pair as the research object, the heat transfer of the ball screw pair system under the action of the screw nut heat source and the nut component bearing heat source is first analyzed, as displayed in Figure 3. Similarly, the effects of other heat sources are explored.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 3 Methods
### Clustering Approach for Segment Generation  
The derivation of the TOW and TFV was done based on both pixels and segments. Among other advantages, such as a reduction of speckle, the usage of segments allows for the reduction of intra-class variability, which can be caused by the high spatial level of detail of the S-1 data. Clustering represents a common method to perform image segmentation under consideration of multi-spectral and multi-temporal data [63]. Cluster algorithms have already been used as segmentation methods for the derivation of TFV [64; 65; 66]. The grouping of pixels into segments was implemented for the study area using a k-means cluster algorithm. k-means is an iterative, unsupervised method that assigns each pixel to a cluster using the minimum distance. This technique is widely used due to its simple implementation. In addition, k-means produces relatively high-quality clusters with low computational effort [67; 68; 69].  
Segmentation by k-means was carried out in several steps. First, multi-temporal clustering was performed. Therefore, a SAR time series data stack, containing the VV and VH polarizations, is used. This allows one to integrate the temporal component and both polarizations for the generation of clusters. Second, a spatial component was integrated by means of spatial clustering based on the multi-polarised SAR image at the analysed date of the flood event. In order to combine the multi-temporal and spatial information, both cluster images, multi-temporal and spatial, were intersected with each other.  
In contrast to other segmentation methods such as multiresolution segmentation [24; 41], k-means clustering requires the definition of only a single parameter: the number of clusters. In order to determine this parameter for both the multi-temporal cluster and spatial cluster image, a range for the number of clusters between 5 and 100 has been defined. Thereafter, all possible combinations of the number of clusters for multi-temporal and spatial clustering were performed. Although all possible combinations for the defined areas were tested, it turned out that the combination of 10 multi-temporal clusters and 5 spatial clusters was the best cluster number combination in terms of classification accuracy and computation efficiency. Finally, the combined clusters were examined for their spatial independence and split into their spatially connected segments. The resulting segmented image was used beside the pixel image as a basis for the classification of TOW and TFV.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 3 Experimental Setup  
Tests were conducted on an 18650 Sony VTC 6 battery pack with 4 cells in series. The specifications of the individual cell are listed in Table 3. The NCA cells were selected due to their higher risk of thermal runaway when compared to other cell chemistries such as lithium cobalt oxide (LCO) or lithium iron phosphate (LFP) [39]. The battery pack had a capacity of 3 Ah with a maximum voltage of 16.8 V. Each cell weighed approximately 46.4 \(\pm\) 1.5 g, and the entire battery pack weighed 195 \(\pm\) 3 g. Each cell was considered fully charged when the voltage reached 4.2 V, and fully discharged it reached 2.5 V. According to the datasheet, a standard cycle was defined as charging the battery under CCCV at 3 A of current followed by 0.167 h rest and discharging under constant current discharge at 3 A of current followed by 1.0 h rest.
## 2 Related Works
### Spatial-Temporal Graph Neural Networks  
Spatiotemporal graph neural networks (GNNs) have become a common model for traffic forecasting due to their ability to handle spatiotemporal relationships effectively. MT-MGNN [35] combined Graph Attention Networks and Diffusion Graph Neural Networks into a multi-graph neural network, extracting complex features from various graphs by inputting adjacency matrices of different types of metro networks. MST-GRT [36] proposed a multi-graph neural network that aggregated metro passenger flow data from smaller to larger time granularities, enhancing the model's predictive performance. [PERSON]. [37] designed a novel framework for metro passenger flow prediction, STP-TrellisNets+. This network can capture spatial correlations in passenger flow and both long-term and short-term correlations, while also addressing the flow discontinuity issue. Ref. [38] introduced the Spatiotemporal Multi-Graph Convolutional Network (ST-MGCN) by encoding the non-Euclidean pairwise relationships between regions into multiple graphs and utilizing multi-graph convolution to explain the impact of distant regions on the current regions. Ref. [39] extended this idea by adding the correlation of graph structure between preceding and succeeding time steps, proposing the Spatiotemporal Synchronous Graph Convolutional Network (STSGCN). Ref. [40] proposed a multi-level attention network to model the dynamic spatial-temporal dependencies among multiple geographic sensor time series. Ref. [41] integrated spatial-temporal attention mechanism and spatial-temporal convolution to capture dynamic spatial-temporal correlations and spatial patterns. Ref. [42] proposed an adaptive graph spatial-temporal transformer network to model the cross-spatial-temporal correlations on the spatial-temporal graph directly using local multi-head self-attention. Ref. [43] proposed a spatial-temporal memory-augmented multi-level attention network to explicitly model both short-term and long-term dependencies at different spatial scales. These methods primarily focus on the local spatial dependencies of graph network nodes, neglecting dynamic global spatial dependencies, which may result in the loss of information from distant nodes. Therefore, we attempt to capture local spatial dependencies using GCN while employing dynamic global attention to implicitly capture the dynamic influence of any global node pairs. Additionally, we use series decomposition to further extract the temporal features of both long-term and recent-term passenger flows, aiming to explore the potential of guiding passenger flow prediction from the perspective of series decomposition.
## 2 Problem Formulation  
In the phase retrieval problem, we consider an estimation problem of a \(N\)-dimensional complex signal vector \(\mathbf{x}\) from \(M\) magnitude-squared linear measurements \(\mathbf{y}\). A basic phase retrieval model with Fourier measurements is related as:  
\[y_{i}=\left|\sum\limits_{n=0}^{N-1}x_{n}e^{-\frac{2\pi i(n-1)(i-1)}{M}}\right|^ {2},i=1,\cdots,M. \tag{1}\]  
This formulation (Equation (1)) is equivalent to the matrix-vector multiplication \(y_{i}=\left|\mathbf{F}_{i}\mathbf{x}\right|^{2}\), where \(\left|\cdot\right|^{2}\) denotes the element-wise absolute squared value, \(\mathbf{F}\in\mathbb{C}^{N\times N}\) denotes the discrete Fourier transform (DFT) matrix with elements \(\mathbf{\phi}^{(n-1)(i-1)}=\left(e^{-2\pi\gamma/M}\right)^{(n-1)(i-1)}\), and \(\mathbf{F}_{i}\) is the \(i\)th row of \(\mathbf{F}\). Here the signal \(\mathbf{x}\) is known to be sparse, and the measurements \(y_{i}\) are known. To recover the signal \(\mathbf{x}\) with known sparsity level \(s\) from the given measurements \(y_{i}\), we consider a minimizing the sum of squared errors cost as:  
\[\min_{\mathbf{x}}\sum\limits_{i=0}^{N-1}\left(\left|\mathbf{F}_{i}\mathbf{x}\right|^{ 2}-y_{i}\right)^{2}\ s.t.\ \|\mathbf{x}\|_{0}\leq s. \tag{2}\]  
The matrix-vector multiplication \(\mathbf{F}_{i}\mathbf{x}\) is the fast Fourier transform (FFT), which can be written in terms of the autocorrelation function, i.e.,  
\[\begin{array}{ll}\left|\mathbf{F}_{i}\mathbf{x}\right|^{2}&=\sum\limits_{n=0}^{ N}\mathbf{\phi}^{(n-1)(i-1)}x_{n}\sum\limits_{v=0}^{N}\mathbf{\phi}^{-(v-1)(i-1)}x_{v} ^{*}\\ &=\sum\
olimits_{n=0}^{N}\sum\limits_{v=0}^{N}x_{n}x_{v}^{*}\mathbf{\phi}^{(n-v)( i-1)}\\ &=\sum\
olimits_{k-N}^{N}\mathbf{\phi}^{\text{km}}\sum\limits_{v=-\text{max}(k,0)}^{ \text{min}(N+k,N)}x_{n}x_{n-k}^{*}\\ &=\sum\
olimits_{k=-N}^{N}\mathbf{\phi}^{\text{km}}g_{k},\end{array} \tag{3}\]  
where \(g_{k}=\sum\limits_{n=\text{max}(k,0)}^{\text{min}(N+k,N)}x_{n}x_{n-k}^{*}\), \(k=N\), \(\cdots,-1,\ 0,\ 1,\cdots,N\) is the autocorrelation sequence of \(\mathbf{x}\), and obviously \(g_{k}\) can be obtained by the inverse DFT of \(\mathbf{y}\). Here, referring to GESPAR [44], we assume that no support cancelations occur in \(g_{k}\), then we divide the support of the signal \(\mathbf{x}\) into two parts: \(S_{1}\) and \(S_{2}\). \(S_{1}\) denotes the known indices in the support, and \(S_{2}\) is the unknown indices in the off-support.  
With these notations, defining \(\mathbf{A}_{i}=\Re(\mathbf{F}_{i})^{T}\Re(\mathbf{F}_{i})+\Im(\mathbf{F}_{i} )^{T}\Im(\mathbf{F}_{i})\), Equation (2) can be rewritten as:  
\[\begin{array}{ll}\min_{\mathbf{x}}f(\mathbf{x})&\equiv\sum\limits_{i=0}^{N-1}\left( \mathbf{x}^{T}\mathbf{A}_{i}\mathbf{x}-y_{i}\right)^{2},\\ &s.t.\ \|\mathbf{x}\|_{0}\leq s,\\ &S_{1}\subseteq supp(\mathbf{x})\subseteq S_{2},\end{array} \tag{4}\]  
which will be the studied formulation in the next section.
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 4 Modeling of the Rain and Fog Effect in the Virtual LiDAR Sensor
### Calculation of Extinction Coefficients  
The extinction efficiencies \(Q_{ext}\) and the drop-size distribution calculated in the previous section are used to calculate the extinction coefficients \(\sigma_{ext,rain}\) and the backscattered coefficients \(\beta_{back,rain}\). The extinction coefficients \(\sigma_{ext,rain}\) can be written as:  
\[\sigma_{ext,rain}=\frac{\pi}{4}\int_{0}^{\infty}N(D)Q_{ext}D^{2}\,dD, \tag{15}\]  
where \(N(D)\) is the rain distribution and \(D\) is the drop size diameter [29]. As mentioned earlier, we have used the rain distribution of Marshall-Palmer [24] and the NIED rain simulator [5].
## 3 Proposed Method
### LDA Applied  
LDA is a supervised learning algorithm. By maximizing between-class scatter and minimizing within-class scatter, LDA effectively reduces high-dimensional data to lower dimensions, simplifying the feature space and improving computational efficiency. In binary classification problems, it is also referred to as Fisher discriminant analysis (FDA) [29]. However, there are some distinctions between LDA and FDA. LDA assumes that the data of each class follows a Gaussian distribution, with identical covariance matrices that are full-rank [30]. Additionally, when handling fault modes with clear class distinctions, LDA can provide high classification accuracy. The main steps of LDA are outlined as follows:  
_Step 1_: The signal data are input (\(X=[(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})]\)), where any sample (\(x_{i}\)) is an \(n\)-dimensional vector and \(y_{i}\) represents the fault-class label. _Step 2_: The mean vector (\(\mu_{i}\)) for each class in the dataset and the overall mean vector (\(\mu\)) are calculated. The within-class scatter matrix (\(S_{\omega}\)) and the total scatter matrix (\(S_{t}\)) are computed, and the between-class scatter matrix (\(S_{b}=S_{t}-S_{\omega}\)) is computed. _Step 3_: Eigenvalue decomposition is performed on matrix \(S_{\omega}^{-1}S_{b}\), and the eigenvalues are sorted in descending order. The feature vectors corresponding to the top \(k\) eigenval  
Figure 3: The design of the IMFE algorithm.  
ues are selected, and the \(n\)-dimensional samples are reduced to \(k\) dimensions using the feature matrix (\(x^{\prime}=\omega^{T}x\)).
## 5 Results and Discussion  
This section provides a detailed analysis of our experimental findings and the results obtained from anomaly detection, both qualitatively and quantitatively. Following the semi-supervised anomaly detection method, the U-TFF model was trained exclusively on normal data. The training dataset comprised time series data collected over a duration of 35 min at a frequency of 3 HZ. This dataset included various sensor readings, including the position, speed, current, and voltage of all five motors. These values were recorded at each time instant using the side-channel mechanism. Energy consumption was calculated as the product of the current and voltage.  
Figure 5 presents a segment of the collected energy consumption data. The index of the motor corresponds to its relative position in the manipulator, where the end effector is labeled as Motor 5 and the base motor of the robotic manipulator is denoted as Motor 1. Notably, significant variations in energy consumption can be observed among the differentmotors, with motors 2 and 3 exhibiting higher energy consumption than others. The training dataset was randomly divided into 80% for training and 20% for validation to ensure the model's robustness and generalization.  
The sliding window size was determined to be 3, meaning that each data segment includes one current value and two previous values for each motor. Therefore, at each time the dimension of input to the proposed network is 15. Considering that a larger sliding window size leads to better detection performance but lower response speed, this value was selected in order to achieve a balance between detection accuracy and inference speed. The encoder of the network was developed using three TFF blocks with output dimensions of 12, 9, and 6, respectively; correspondingly, the decoder also consisted of three blocks, with output dimensions of 6, 9, and 12, respectively.  
The model training utilized the Adam optimizer to minimize the loss function defined in Equation (1), with 1000 epochs completed on a GPU workstation equipped with an Intel(R) Xeon(R) Gold 5220R CPU @ 2.20 GHZ and two NVIDIA RTX A6000 GPUs (Santa Clara, CA, USA).
# Towards the Use of Big Data in Healthcare: A Literature Review
## 4 Results
### Big Data and Analytical Skills  
BD can offer a major competitive advantage for healthcare providers, especially with regard to reducing therapeutic mistakes by analyzing patient data [120; 122]. In fact, software that uses BD efficiently can flag any inconsistencies between a patient's medical history and their drug allergies with the medications they are taking, thus alerting the referring physician to discontinue therapy. BD can also identify chronically ill citizens, providing them with preventive actions to avoid clogging emergency channels, such as the emergency room [117; 118; 123]. To seize these important opportunities, however, it is necessary to make a considerable investment in healthcare organizations, for example, in the hiring of analytics experts--that is, professionals capable of identifying problems from the data and proposing the most appropriate solutions [23; 124; 125]. [PERSON] et al. (2021) highlighted the crucial role of intangible elements, especially of the intellectual capital in the health sector. The dissemination of new knowledge and specialized skills promotes the sharing of best practices in the health sector. The result is a reduction in mortality rates, better outcomes in terms of cost minimization, and a reduction in hospitalization periods [126; 115; 127]. [PERSON] et al. (2018) proposed a classification of job roles and skill sets needed in the BD and AI era. This classification provides valuable support for business leaders and human resource managers in the selection process and in developing the skills needed to make the most of BD [72; 128]. The management of a highly variable amount of data in real time requires not only new tools and methods but also the development of new knowledge and skills that are essential for converting data into a strategic resource and for implementing new management practices or a new organizational culture across the entire organization. A lack of data analytics skills among existing employees may increase data entry errors that could result in placing information in the wrong record, losing valuable information, and limiting the value a business can derive from the data that it captures [129]. BD analysis is essential in defining the patient diagnosis. Therefore, doctors and nurses' understanding of data undoubtedly has a positive impact on the rapid recovery of patients in hospitals [62; 63]. These are professionals with technical skills and multidisciplinary knowledge who can manage a huge volume of data and extract useful information to ensure adequate social and healthcare and support the restructuring of healthcare processes [64; 65]. Furthermore, process innovation and efficient scheduling are key to addressing bottlenecks in healthcare management [64; 119]. Table 5 shows top ten articles per citations on theme 3 \"Big Data and analytical skills\".