esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Calibration of MFGWR  
A back-fitting algorithm is commonly used for the calibration of generalized additive models [21; 34; 35]. MFGWR is defined as follows (Equation (15)),  
\[\mathrm{y}=\sum_{\mathrm{j}=0}^{\mathrm{m}}\mathrm{f}_{\mathrm{bwj}}\Big{(} \mathrm{x_{j}}\Big{)}+\varepsilon \tag{15}\]  
where \(\mathrm{y}\) is a column vector of response variables, \(\varepsilon\) is a column vector of random error terms, and each of the additional components \(\mathrm{f}_{\mathrm{bwi}}\Big{(}\mathrm{x_{j}}\Big{)}\) are the product of the elemental multiplications of the local parameters \(\mathrm{\beta_{j}}\) and \(\mathrm{x_{j}}\) which is \(\mathrm{j}\)th column of \(\mathrm{X}\) (Equation (16)).  
\[\mathrm{f}_{\mathrm{bwj}}\Big{(}\mathrm{x_{j}}\Big{)}=\mathrm{\beta_{j}} \otimes\mathrm{x_{j}}=\left(\begin{array}{c}\mathrm{\beta_{1j}}\mathrm{x_{ j}}\\ \mathrm{\beta_{2j}}\mathrm{x_{2j}}\\ \vdots\\ \mathrm{\beta_{nj}}\mathrm{x_{nj}}\end{array}\right) \tag{16}\]  
Initial coefficient estimate \(\mathrm{\beta}\) and residual of MFGWR are taken from the calibration of the model by GWR [21]. The definition of the convergence criterion for bandwidth optimization is as follows (Equation (17)).  
\[\mathrm{SOC_{f}}=\mathrm{SQRT}\left[\sum_{\mathrm{j}=1}^{\mathrm{p}}\sum_{ \mathrm{i}=1}^{\mathrm{n}}\Big{(}\mathrm{f}_{\mathrm{ij}}^{\mathrm{new}}- \mathrm{f}_{\mathrm{ij}}^{\mathrm{old}}\Big{)}^{2}/\sum_{\mathrm{i}=1}^{ \mathrm{n}}\left[\sum_{\mathrm{j}=1}^{\mathrm{p}}\mathrm{f}_{\mathrm{ij}}^{ \mathrm{new}}\right]^{2}\right] \tag{17}\]  
The detailed description and implementation of the back-fitting algorithm can be found in [21; 25].  
Summarizing the above statement, in the case of spatially interactive transport flows, the MFGWR is defined as follows (Equation (18)).  
\[\mathrm{ln}\Big{(}\mathrm{M_{\mathrm{ij}}}\Big{)}=\mathrm{f}_{\mathrm{bw0}} \left(\mathrm{intercept}\right)+\mathrm{f}_{\mathrm{bw1}}\Big{(}\mathrm{ln} \left(\mathrm{Tpop_{i}}\right)\Big{)}+\mathrm{f}_{\mathrm{bw2}}\Big{(} \mathrm{ln}\left(\mathrm{GDP_{j}}\right)\Big{)}+\mathrm{f}_{\mathrm{bw3}} \Big{(}\mathrm{d_{ij}}\Big{)}+\varepsilon \tag{18}\]  
where \(\mathrm{M_{ij}}\) is the interaction intensity between \(\mathrm{i}\) and \(\mathrm{j}\), and \(\mathrm{Tpop_{i}}\), \(\mathrm{GDP_{j}}\) and \(\mathrm{d_{ij}}\) are explanatory variables. \(\mathrm{Tpop_{i}}\) and \(\mathrm{GDP_{j}}\) present the push and pull forces at the origin place \(\mathrm{i}\) and destination place \(\mathrm{j}\) respectively.
# New Tendencies in Wind Energy Operation and Maintenance
## 3 Overview of Wind Farm O&M Research
### Reliability (RELI)
A discrete event model was developed in [22] to simulate the operation, the failure occurrence and wind turbine maintenance, with the goal of determining the main factors influencing maintenance costs and the availability of the turbines in the wind farm. [PERSON] et al. [47] integrated a control platform based on the wind farm SCADA system to assist the work of the farm as a predictable, controllable system and reliable, high-quality generation was designed. To increase maintenance efficiency and increase availability, ref. [48] used an example of a failure in a gearbox after almost four years of operation and with nearly 7 weeks of lost availability. The paper identifies research questions that could produce a better maintenance regime for wind farms based on usage and remote condition monitoring. Early detection of failures via condition monitoring can help [49] to prevent major failures and significantly reduce associated costs. It also allows maintenance program optimization, reduced downtime, increased availability of assets and improved safety and operational reliability. The technology of the vertical axis wind turbine scale arose as a solution to reduce the overall costs of the life cycle and improve profitability [50] and competitiveness with other commercial technologies. However, durability and availability have always been the weak points of this technology [51]. The concept of reliability-centered maintenance may constitute [21] the basis for the development of quantitative models for maintenance or selection and optimization strategy, but it can also provide an assessment to further improve turbine design. The amount of information from SCADA systems for wind farms is enormous, so the use of neural networks to cope with all the information and try to detect failures in some equipment at an early stage [52] is a promising method. The training of the neural network represents a major disadvantage, but if these problems are solved, the results are very interesting, and failures can be detected several months [9] in advance. In this sense, with neural networks [53], it was found that there were significant relationships between gearbox failures and changing climatic conditions. [PERSON] et al. [54] have provided an optimization model based on real options and stochastic dynamic programming to maximize the availability of an offshore wind farm with a capacity for prognosis. Real options theory provides promising ways to address the economic aspects of prognostics and health management (PHM) after prognostic indication, and evaluation of the costs necessary to meet the availability requirements. When competitive pressures force asset managers to prioritize their maintenance, the risk-based methodology presented by [PERSON] et al. [55] is a rational, efficient and somewhat flexible method for the integral management of assets. In this sense, [PERSON]. [56] presents the methodology of how Bayesian networks can be used to learn this model when past data are available.
## 3 Novel Asynchronous Cooperative Diversity Scheme
### Mapping OFDM Frames into the Frequency Domain QSTBC  
The \(k\) th frame equivalent low pass received signal of the \(j\)th relay corresponding to the \(k\)th OFDM frame is expressed by:  
\[\mathbf{Y}_{j\,k}=h_{S\,R_{j}}\mathbf{X}_{k}+\mathbf{n}_{j\,k} \tag{3}\]  
where \(\mathbf{Y}_{jk}=\left[Y_{jk}(\mathrm{I}),\cdots,Y_{jk}(I),\cdots,Y_{jk}(L) \right]^{T}\), \(\mathbf{n}_{jk}=\left[n_{jk}(\mathrm{I}),\ \cdots,n_{jk}(I),\cdots,n_{jk}(L)\right]^{T}\) (\(1\leq j\leq 4,\ 1\leq k\leq 4\), \(L=N+N_{cp}\)) is the additive white Gaussian noise (AWGN) sample vector with each item being zero mean and unit variance at the \(j\)th relay.  
Each of the four frames of the received signal is allocated with the optimum power \(p\) (Here, the optimum means that if the source transmits the signals with half the total power P and the relays exploit the other half, the Chernoff bound on the pairwise error probability would be minimized [9]. Then, we assume that the transmitted power of source is \(P_{1}\), while the average transmitted power of each relay is \(P_{2}\) and let \(P_{1}=4P_{2}\). Therefore, we have \(p=\sqrt{P_{2}\left/\left(P_{1}+1\right)}\right.\)[7, 9]). Thereafter, the resulting signals are subtly reordered by the individual relay in such a way that the frequency domain transmitted signals \(\mathbf{Y}_{j,k}\), its complex conjugation version and its time-reversion version act like QSTBC in a multiple-antenna system, denoted by:  
\[\left[\begin{array}{cccc}p\mathbf{Y}_{11}&p\zeta(\mathbf{Y}_{12})&p\mathbf{Y}_ {13}&p\zeta(\mathbf{Y}_{14})\\ -p\mathbf{Y}_{22}^{*}&p\zeta(\mathbf{Y}_{21}^{*})&-p\mathbf{Y}_{24}^{*}&p\zeta (\mathbf{Y}_{23}^{*})\\ -p\zeta(\mathbf{Y}_{33}^{*})&-p\mathbf{Y}_{34}^{*}&p\zeta(\mathbf{Y}_{31}^{*} )&p\mathbf{Y}_{32}^{*}\\ p\zeta(\mathbf{Y}_{44})&-p\mathbf{Y}_{43}&-p\zeta(\mathbf{Y}_{42})&p\mathbf{Y} _{41}\end{array}\right] \tag{4}\]  
where \(\zeta(Y_{jk}(l))=Y_{jk}(L-l)\) (\(j\),\(k=1\),\(2\),\(3\),\(4\), \(l=0\),\(\ldots\), \(L-1\)) and \(Y(L)=Y(0)\).
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 6 Structure, Function and Regulation of Pkc1
### C-Terminal Phosphorylation by TORC2  
It was recognized quite some time ago [47] that the short C-terminal extension in Pkc1 contains sequences very similar to the conserved turn and hydrophobic motifs found in other yeast and mammalian AGC kinases that are now known to be phosphorylated by TORC2. Shortly thereafter, genetic data linked TORC2 function to the regulation of Pkc1 and its control of cell wall integrity. Mutants carrying temperature-sensitive alleles in different regions of the essential TORC2 component Avo3/Tsc11 displayed cell wall defects, as evidenced by characteristic rescue of their cell lysis defect by 1 M sorbitol, their decreased trypan blue staining (diagnostic of less phosphomannoprotein), and their increased sensitivity to killing by digestion by cell wall-degrading enzymes; moreover, _PKC1_ was isolated as a dosage suppressor of some of these _avo3\({}^{ls}\)_ alleles [217]. Yet, according to cumulative data compiled at the SGD, and unlike the hydrophobic motif (Thr662) in Ybk1, phosphorylation at the hydrophobic motif (Ser1143) in Pkc1 has not been detected in any global phosphoproteomic study. However, it has been reported that, in response to a non-physiological concentration (10 mM) of exogenous methylglyoxal, TORC2-dependent phosphorylation at both the turn motif (Thr1125) and Ser1143 is markedly stimulated [207]. Moreover, lack of phosphorylation at the turn motif site reduced phosphorylation at the hydrophobic motif site, and vice-versa [207], reminiscent of the interdependencies we have found among the C-terminal TORC2 phosphorylation sites in Ybk1 [39]. On the other hand, the biological significance of this response to such a high extracellular concentration of this very reactive and toxic aldehyde is questionable. Moreover, unlike a \(pkc1\Delta\) mutant, which is unable to grow in the absence of an osmotic stabilizer (e.g., 1 M sorbitol), cells expressing a Pkc1(T1125A S1143A) mutant as the sole source of this protein kinase, grew in normal medium without sorbitol (207). Thus, in Pkc1, phosphorylation of both its turn and hydrophobic motifs is dispensable for the growth of yeast cells on normal medium, whereas that is not the case for Ypk1 or Ypk2. In \(ypk1\Delta\)\(ypk2\Delta\) cells, expression of either Ypk1(S664A) or Ypk2(S641A), which cannot be phosphorylated at their turn motif, support only very poor growth compared to the same cells expressing either WT Ypk1 or WT Ypk2 (39; 67). So, it remains to be determined whether any physiologically relevant stress(es) or signal(s) modulate Pkc1 function by affecting its TORC2-mediated phosphorylation.
# Intelligent Monitoring Systems for Electric Vehicle Charging
## 3 Analysis of Current State and Implications  
Analysis of current research reveals several fundamental aspects of EV charging infrastructure: communication protocols, energy management, and user interfaces. Recent developments demonstrate progress in charging station placement optimization, distributed energy control systems, and urban EV integration frameworks. While cloud-based management solutions show quantifiable gains in operational efficiency and charging optimization, critical aspects like cybersecurity and privacy in networked charging stations remain only partially addressed [3]. The integration of these systems with future transportation infrastructure presents additional technical challenges [33].  
The literature includes extensive reviews of EV infrastructure, addressing fundamental technologies and implementation challenges [34], optimization of vehicle routing [35], strategic infrastructure deployment [36], power consumption forecasting [37], intelligent parking systems [38; 39], charging mechanisms [40; 41], and microgrid management approaches [42]. Recent developments continue to advance these capabilities [43; 44; 45].  
We identify five key requirements for effective EV charging infrastructure management based on the reviewed literature: (i) charging optimization: monitoring systems must ensure optimal power delivery by implementing condition-based maintenance and adaptive charging protocols that account for battery characteristics (i.e., real-time battery characteristic monitoring) and grid conditions [22; 46]; (ii) system reliability: redundant architectures and predictive maintenance algorithms (e.g., fault prediction) are essential to minimize downtime, enabled by distributed sensor networks and real-time analytics [18]; (iii) thermal monitoring: continuous temperature monitoring of connectors, power electronics, and cooling systems is required to prevent component degradation and ensure safety, with thermal anomalies preceding most major failures [19]; (iv) user interaction optimization: integration between charging status monitoring and behavioral analysis enables accurate prediction of charging duration and station availability (i.e., predictive scheduling), increasing user satisfaction [6]; (v) grid integration: real-time communication between charging infrastructure and grid operators (V2G-enabled systems) is necessary to implement load management and prevent network overload, improving power quality metrics [47]. These requirements emerge from both the technical limitations of current systems and the practical challenges observed in operational charging networks.  
This analysis indicates that effective charging infrastructure management necessitates integrated monitoring systems capable of simultaneously addressing multiple technical domains. The following section presents a framework for intelligent EV charging monitoring that implements these requirements through a layered architectural approach.
## 4 Data Processing and Analysis  
### Electrodermal Data Activity (EDA)  
EDA is considered a reliable physiological indicator for detecting changes in sympathetic nervous system (SNS) activity that result from active or passive stress conditions. EDA data measure changes in the electrical flow of skin conductance in response to sweat secretion. The decomposition of EDA data in its components can evaluate physiological responses to stress stimuli. EDA data can be split into two components: phasic (i.e., electrodermal response (EDR)) and tonic (i.e., electrodermal level (EDL)). The tonic component (EDL) includes the slow drift of skin conductance and often indicates SNS activity induced by long-term stressors. Meanwhile, phasic (EDR) is considered the fast-changing component of skin conductance, reflecting rapid SNS activity change due to external stressors.  
Figure 5: Experimental setup.  
Figure 6: Experimental procedures.  
The raw EDA data collected using an E4 emphatic wristband were pre-processed using segmentation and down-sampling procedures. Raw data collected from participants were segmented based on the timestamp for the experiment's stress loading and eye-tracking phase, and data segmentation was conducted for all three conditions (baseline, mental stress, and auditory stress). After segmentation, EDA data were down-sampled from 4 Hz to 1 Hz using Ledalab (V3.4.9), a MATLAB-based skin conductance data analyzer [90]. Down-sampling of EDA data was necessary to match the sampling frequency of heart rate, which was 1 Hz. Also, the down-sampling process prevents us from overestimating the significance of the statistical analysis. Artifact removal is a common practice while processing physiological data, as EDA data may contain artifacts and noise that can obscure the signals of interest. Therefore, this study applied a low-pass Butterworth filter to the raw EDA data for artifact removal and adaptive smoothening to remove unwanted noise. Ledalab, an open-source tool that provides an artifact removal function, was used to filtrate raw EDA data (Figure 7). For the decomposition of raw EDA data in its phasic (EDR) and tonic (EDL) components, a convex-optimization-based EDA model (cvxEDA) developed by [91] was applied (Figure 8). Before conducting decomposition, the EDA data of each participant were standardized using a z-score to omit individual variations.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 3 VRITHI: System Design and Characteristics
### System Setup  
Figure 4 illustrates three nodes that are deployed at different tiers as mentioned in Figure 1. Each of the nodes in the field has four key components: (1) IoT module, (2) sensor interfacing circuit, (3) power harvesting system, and (4) sensors. The IoT module uses the wasp motes from Libelium, Inc. (libelium.com, Zaragoza, Spain) integrated with a tiny sim5300E modem. The reason for choosing this module is that it has many add-on industrial interfaces, such as RS485, Modbus, etc., and also has many sensors components off the shelf for future use. The IoT module has a processing speed of 16 MHz and 8 KB of RAM. The node has an ultra-low power consumption of 7 uA during hibernation, 30 uA during sleep, and 17 mA during the active state. The sim5300E modem connects the cellular network to the IoT node, and the sensor interfacing circuit helps the IoT node to communicate with sensors. The modem has both voice and data connection functionalities. In the node, sensor drivers use 4 KB of RAM in the controller; processing routines use 2 KB leaving only 2 KB for network routines. The sensors used are pore pressure sensors, movement sensors, and moisture sensors. The sensors are buried inside the soil to sense deep earth movements and characteristics. The power system uses solar energy to function the node. It harvests energy for operating during the nights and rainy conditions.  
Figure 4: Field nodes (top-left to bottom-right): Node 2 (Tier1), Node 5 (Tier2), Node 10 (Tier3), and their components.
## 4 Results and Discussion
### Experimental Setup
#### 4.1.2 Hyperparameters and Training Settings  
In this research, to ensure the stable performance of the model across different datasets, we employed five-fold cross-validation to assess the robustness of the model. Through this method, the entire dataset was evenly divided into five parts, with four parts used for training the model and the remaining part used for testing. This process was repeated five times, each time with a different part used as the test set, thus ensuring that every data point had the opportunity to be used in testing. This cross-validation method reduces bias and variance in the model evaluation process, enhancing the accuracy and reliability of the assessment.  
For model training, we adjusted several hyperparameters to achieve optimal learning outcomes. Initially, we set a relatively high learning rate, \(\alpha=0.001\), to allow the model to converge quickly. As training progressed, we implemented a learning rate decay strategy,gradually reducing the learning rate to \(\alpha=0.0001\) to ensure that the model could make finer adjustments near the global minimum and avoid excessive oscillations. Batch size is also an important hyperparameter that determines the number of data samples computed in each forward and backward propagation. In this study, we set the batch size to 64, a compromise that ensures the utilization of computational resources while maintaining the stability and memory efficiency of the model training.  
The number of training epochs, also determined through multiple experiments, was set at 50 epochs. This number was adjusted based on the model's performance on the validation set. Typically, the performance of the model would stabilize after a few epochs, but to ensure that the model could fully learn all complex patterns in the data, it was necessary to increase the number of training cycles appropriately. Moreover, to prevent overfitting, we also incorporated an early stopping mechanism, which stops training if there is no significant improvement in the model's performance on the validation set over 10 consecutive epochs.
# Prediction of RF-EMF Exposure by Outdoor Drive Test Measurements
## 6 Conclusions  
In this work, we focus on the prediction of DL total RF-EMF exposure in the outdoor urban environment. A broadband (700 to 2700 MHz) drive test measurement was performed on the streets of Paris using a spectrum analyzer, i.e., Tektronix RSA306B, connected to a 3-axis dipole antenna via a switch. From the broadband measurements, E-fields for each frequency band of each operator were extracted and compared. The correlation between the E-fields indicates a strong correlation of E-field levels from bands belonging to the same operator. The measurement data were used to build an ANN model in order to predict the E-field strength. Eq900 was selected in order to account for the human body's capability of absorbing EM energy at different frequency bands and then to adequately represent the DL exposure. Then, two ANN models were built to predict total Eq900, considering \(N\) nearest BS with and without distinguishing operators. The results show that increasing \(N\) would increase complexity and computation time in training the ANN model while the performance of prediction is not always significantly improved. Moreover, we proved in the results that the hybrid model with \(N=3\) from model (b) is the best model for the current paper to use in assessing RF-EMF exposure levels in an outdoor urban environment.  
Conceptualization, S.W. and J.W.; data curation, T.M.; formal analysis, S.W.; investigation, S.W. and T.M.; methodology, S.W.; supervision, J.W.; validation, S.W.; writing--original draft, S.W.; writing--review and editing, S.W., T.M. and J.W. All authors have read and agreed to the published version of the manuscript.  
The current work was partly supported by the following projects: Beyond 5G granted by Bpif and MINIRE.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Method
### Model Design  
As Figure 1 shows, the CTDI model proposes three indexes to identify features that effectively distinguish between normal and attack network traffic. These indexes are:  
Classification Tendency Frequency Difference (CTFD): Measures the difference in the frequency of occurrence of each FV between normal and attack samples.  
Classification Tendency Membership Difference (CTMD): Employs FCM to cluster normal and attack samples, then calculates the membership difference between the two clusters for each FV.  
Classification Tendency Distance Difference (CTDD): Calculates the distance difference between the cluster centers of normal and attack samples for each FV.  
In addition, data preprocessing is applied to the original features. Minimax is applied to all original features to standardize their range from 0 to 1. Also, missing and unknown values are set to 0 in the original features.  
Figure 1: CTDI model design.  
For the feature extraction, the CTDI model first applies Auto Encoder to generate new extracted features from the dataset and then calculates each score of three indexes for both the original and new features. Finally, CTDI ranks the features based on their index scores and selects the top m% features for each index.  
**Ensemble Feature Selection (EFS)**: EFS is a method that combines the results of feature selection conducted using three CTDI indexes: CTFD, CTMD, and CTDD. To identify the most pertinent features, EFS first selects those that consistently appear among the top m% of features for each index. This ensures that the selected features have a high degree of representativeness. As a feature appears in two or more of the top m% features of CTDI indexes, it suggests that it has strong representativeness, and thus, this feature is chosen in CTDI. Table 2 summarizes the common abbreviations and notations in CTDI.