esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
The data analysis was performed using Statistica, version 13.1 (StatSoft, Krakow, Poland). Descriptive statistics, including mean values, standard deviations (SD), and minimum (min) and maximum (max) values, were calculated for measurable variables. Data mining techniques were employed to match patients and healthy volunteers based on age, sex, weight, and height, as basic anthropomorphic properties. To assess the normality distribution and homogeneity of variances, Shapiro-Wilk tests and Levene's tests were conducted. In patients from four groups with incomplete spinal cord injuries at the cervical and thoracic levels, the mean values of parameters obtained from sEMG tests and clinical tests were compared using Student's \(t\)-test and the Mann-Whitney test. Statistical significance was determined at \(p<0.05\), indicating significant differences. Preliminary statistical analysis was conducted to determine the required sample size. The primary outcome variables from sEMG recordings of the anterior tibialis muscles were used, with a power of 80% and a significance level of 0.05 (two-tailed). The mean and standard deviation (SD) were calculated based on data from twenty subjects in both patient and healthy control groups. The sample size estimation software indicated a minimum requirement of 70 subjects in each group. To explore the correlations between [PERSON]'s or [PERSON]'s scale scores and the rsEMG or mcsEMG amplitude measurement results, respectively, the non-parametric [PERSON]'s rank correlation coefficient (rs) was employed. A significance level of \(p<0.05\) was considered statistically significant for rank correlation.  
Figure 1: The principles of sEMG methodology with pairs of bilateral electrodes placed bilaterally during recordings from a—abductor pollicis brevis (APB), b—recuts abdominis (RECT ABD), c—recuts femoris (RECT FEM), d—extensor digitorum brevis (EDB); (**A**)—sEMG performed at rest (rsEMG), (**B**)—sEMG performed during the attempt of maximal contraction (mcsEMG) in a healthy volunteer (**C**) and in patients with iSCI at different spinal levels (**D**–**F**). Each upper recording in (**C**–**F**) was recorded at rest, while the lower was during the attempt of the maximal contraction. Arrows in (**B**) indicate the signs of the 5-s attempt of maximal contraction. Calibration bars for the amplification and the time base presented in (**C**) are the same for all the recordings.
## 2 Related Work
### Spatiotemporal Prediction with ConvLSTM  
ConvLSTM [1] was used to capture spatiotemporal dependencies in video prediction. It extends the CNN-LSTM approach by incorporating a CNN-like convolutional operation within the LSTM for each time step.  
Figure 1 illustrates the construction of ConvLSTM, which can be mathematically expressed as in Equation (1). Here, '\(\otimes\)' denotes the convolution operation, and '\(\odot\)' represents the Hadamard product. The terms \(f_{t}\), \(i_{t}\), \(o_{t}\), \(c_{t}\), and \(\tilde{c}_{t}\) are gating mechanisms, where \(W\) and \(b\) are the weights and biases of the convolutional operator, respectively, and \(W_{f}\) is the weight of \(f_{t}\):  
\[\begin{split} f_{t}&=\sigma(W_{f}\otimes[X_{t},H_{t -1}]+b_{f})\\ i_{t}&=\sigma(W_{i}\otimes[X_{t},H_{t-1}]+b_{i})\\ o_{t}&=\sigma(W_{o}\otimes[X_{t},H_{t-1}]+b_{o})\\ \tilde{C}_{t}&=\tanh(W_{C}\otimes[X_{t},H_{t-1}]+b_ {C})\\ C_{t}&=f_{t}\odot C_{t-1}+i_{t}\odot\tilde{C}_{t}\\ H_{t}&=o_{t}\odot\tanh(C_{t})\end{split} \tag{1}\]  
ConvLSTM, in addition to short- and long-term modeling, replaces all linear operations within it with a convolutional layer to capture the spatial dependence. This structure was initially used to address the problem of precipitation prediction; the architecture initially employed for precipitation forecasting has since been applied to various other domains.  
The ConvLSTM was introduced to address the limitations of fully connected LSTMs, which lack the capability to capture spatial information. It is trained using the same methodology as the original LSTM; however, the input, output, and state layers are treated as three-dimensional tensors. The convolutional layers consist of convolutional filters rather than typical matrix multiplications, enabling the model to concurrently learn the temporal and spatial features. The convolutional layers identify and localize objects, whereas the subsequent recurrent layers track the movement of these objects across frames, effectively capturing spatiotemporal information. Various adaptations of this model have obtained impressive results in spatiotemporal prediction tasks.  
Figure 1: ConvLSTM cell.
## 1 Introduction
### System Model and Problem Formulation
#### 1.2.2 Downlink Scenario  
We first consider the downlink scenario where the BS transmits LoRa signals to the UAVs. There are \(K\) UAVs, each equipped with a single antenna. Additionally, the BS is equipped with \(M\) antennas. The downlink channel and beamforming vectors from the UAVs to the BS are denoted by \(\mathbf{h}_{\mathrm{d},k}\in\mathbb{C}^{M\times 1}\) and \(\mathbf{w}_{k}\in\mathbb{C}^{M\times 1}\), respectively, for \(k=1,\cdots,K\). Then, the received signal at time slot \(n\) is given by  
\[y_{\mathrm{d},k}[n]=\mathbf{h}_{\mathrm{d},k}^{H}[n]\sum_{i=1}^{K}\mathbf{w}_{ i}[n]x_{i}[n]+z_{k}[n] \tag{3}\]  
where \(z_{k}[n]\sim\mathcal{CN}(0,\sigma^{2})\) denotes the received noise at the \(k\)-th user. Using the orthogonality property of the LoRa modulated signals (i.e., \(\sum_{n=0}^{N-1}x_{s}[n]x_{s^{\prime}}^{*}[n]=0\) for \(0\leq s\
eq s^{\prime}\leq N_{s}-1\)[18]), the corresponding SINR can be derived as  
\[\mathrm{SINR}_{\mathrm{d},k}[n]=\frac{|\mathbf{h}_{\mathrm{d},k}^{H}[n] \mathbf{w}_{k}[n]|^{2}}{\sum_{i\
eq k}|\mathbf{h}_{\mathrm{d},k}^{H}[n] \mathbf{w}_{i}[n]|^{2}+\sigma^{2}} \tag{4}\]  
which is described in [22].  
In the downlink scenario, for each time slot \(n\), we aim to minimize the transmit power of the BS under the SINR constraints at the UAVs by optimizing the downlink beamforming vectors \(\{\mathbf{w}_{k}[n]:k=1,\cdots,K\}\), which is formulated as follows:  
\[\begin{split}\text{(P1)}:\min_{\{\mathbf{w}_{k}[n]\}_{k=1}^{K}} &\sum_{k=1}^{K}\|\mathbf{w}_{k}[n]\|^{2}\\ \text{s.t.}&\mathrm{SINR}_{\mathrm{d},k}[n]\geq \gamma_{\mathrm{d},k},\ \forall k\end{split} \tag{5}\]  
where \(\gamma_{\mathrm{d},k}\) denotes the SINR threshold for the \(k\)-th UAV. Note that the problem (P1) is a nonconvex optimization problem since the SINR constraints are all nonconvex.
# Reliability Evaluation for Clustered WSNs under Malware Propagation
## 5 Reliability Evaluation Method  
### Evaluating the Reliability of a Sensor Node  
The reliability of WSNs represents the probability that sensor nodes continue to perform tasks such as data sensing, transmission, and fusion over a particular period of time under stated conditions. In general, MTTF and MTBF (Mean Time between Failures) are typical ways to evaluate the reliability of pieces of hardware or other technology. Here, MTTF refers to the length of time that a device is expected to last in operation, whereas MTBF is the average elapsed time between a device's failures in operation. The difference between two terms is that MTTF is used for non-repairable devices, whereas MTBF is used for devices that can be repaired and returned to operation. In this work, we concentrate on WSNs where sensor nodes can hardly be repaired upon failure and we use MTTF for evaluating the reliability of a sensor node.  
We denote \(\mathcal{E}\) as the discrete state space where:  
\[\mathcal{E}=\{S,\ \widehat{S},\ I,\ \widehat{T},\ R,\ \widehat{R},\ D\} \tag{14}\]  
as illustrated in Figure 1. Let:  
\[\mathbf{Y}(t)=[Y_{S}(t)\ \ Y_{\widehat{S}}(t)\ \ Y_{I}(t)\ \ Y_{\widehat{I}}(t) \ \ Y_{R}(t)\ \ Y_{\widehat{R}}(t)\ \ Y_{D}(t)] \tag{15}\]  
be the state probability vector, where \(Y_{x}(t)\) denotes the probability that a sensor node is in state \(x\), \(x\in\mathcal{E}\), at time \(t\). Let \(\mathbf{P}\) be the \(7\times 7\) state transition matrix consisting of element \(p_{ij}\), \(i,\ j\in\mathcal{E}\). We find the state equation of a sensor node as:  
\[\frac{d\mathbf{Y}(t)}{dt}=\mathbf{Y}(t)\mathbf{P} \tag{16}\]  
We find the steady-state probability vector that is independent of \(\mathbf{Y}(0)\) (_i.e._, the initial state):  
\[\mathbf{Y}=[Y_{S}\ \ Y_{\widehat{S}}\ \ Y_{I}\ \ Y_{\widehat{I}}\ \ Y_{R}\ \ Y_{\widehat{R}}\ \ Y_{D}] \tag{17}\]  
by solving the system of seven equations where six of the seven equations are from:  
\[\mathbf{Y}\mathbf{P}=0 \tag{18}\]  
and the seventh equation is:  
\[\sum_{i\in\mathcal{E}}Y_{i}=1 \tag{19}\]  
Next, we compute a sensor node's MTTF from the steady-state probability vector. The discrete state space \(\mathcal{E}\) can be split into two disjoint sets \(\mathcal{E}_{\mathit{Use}}\) and \(\mathcal{E}_{\mathit{Disuse}}\), where:  
\[\mathcal{E}_{\mathit{Use}}=\{S,\ R\} \tag{20}\]
## 2 System Model and Channel Model
### Ultrasonic Array Signal Model  
As shown in Figure 1, the vehicle communicates through the RIS reflection signals in V2X communication when the direct link is blocked. Then, we consider the ultrasonic array stacked parallel to the RIS plane, and the ultrasonic sensor array elements correspond to the positions of the RIS elements one by one. The ultrasonic sensor spacing is half a wavelength. At the moment \(k\), \(N_{V}\) narrowband ultrasonic signals from the vehicle are incident to the \(N_{RIS}\) receiving array elements on the ultrasonic array. The two-dimensional incoming wave direction of the \(q\)-th ultrasonic signal \(\hat{\mathbf{x}}_{q}(k)\) is defined as \(\mathbf{\theta}_{q}(k)=[\beta_{q}(k),\varphi_{q}(k)]\). \(\beta_{q}(k)\in(-\pi/2,\pi/2)\) and \(\varphi_{q}(k)\in(0,\pi/2)\) are defined as the azimuth and pitch angles, respectively. Then, the ultrasonic signal received on the ultrasonic array is expressed as  
\[\hat{\mathbf{y}}^{\prime}(k)=\sum_{q=1}^{N_{V}}\{[\mathbf{b}(\varphi_{q}(k))\otimes \overline{\mathbf{v}}(\mathbf{\theta}_{q}(k))]\hat{\mathbf{x}}_{q}(k)\}+\hat{\mathbf{n}}^{ \prime}(k) \tag{31}\]  
where \(\mathbf{b}(\varphi_{q}(k))\) is the guiding vector of the array, \(\overline{\mathbf{v}}(\mathbf{\theta}_{q}(k))\)\(=\)\([1\), \(\mathbf{v}_{q}^{\mathrm{T}}(k)]^{T}\); \(\mathbf{v}_{q}(k)\) is the unit direction vector pointing to the target emission source, as shown in (32). The symbol \(\otimes\) denotes the Kronecker product and \(\hat{\mathbf{n}}^{\prime}(k)\) is the Gaussian noise generated by the process.  
\[\mathbf{v}_{q}(k)=\left[\cos\beta_{q}(k)\cos\varphi_{q}(k),\sin\beta_{q}(k)\cos \varphi_{q}(k),\sin\varphi_{q}(k)\right]^{T} \tag{32}\]  
\(N_{u}\) snapshots are taken and the ultrasonic output signal received by the ultrasonic array at moment \(k\) is represented as  
\[\hat{\mathbf{Y}}^{\prime}(k)=\mathbf{\
abla}\hat{\mathbf{X}}^{T}(k)+\hat{\mathbf{N}}^{\prime}(k) \tag{33}\]  
where \(\mathbf{\
abla}\)\(=\)\([\mathbf{\
abla}(\mathbf{\theta}_{1}(k)),\mathbf{\
abla}(\mathbf{\theta}_{2}(k)),\ldots,\mathbf{\
abla}(\mathbf{ \varphi}_{N_{V}}(k))]\)\(\in\mathbb{C}^{N_{RIS}\times N_{V}}\) is the directional matrix of the ultrasonic array, \(\mathbf{\
abla}(\mathbf{\theta}_{q}(k))=\mathbf{b}(\varphi_{q}(k))\otimes\overline{\mathbf{v}} (\mathbf{\theta}_{q}(k))\), \(\hat{\mathbf{X}}(k)\)\(=\)\([\hat{\mathbf{x}}_{1}(k),\hat{\mathbf{x}}_{2}(k),\ldots,\hat{\mathbf{x}}_{N_{V}}(k)]\)\(\in\mathbb{C}^{N_{u}\times N_{V}}\) is the ultrasonic signal matrix, and \(\hat{\mathbf{N}}^{\prime}(k)\)\(=\)\([\hat{\mathbf{n}}^{\prime}_{1}(k),\hat{\mathbf{n}}^{\prime}_{2}(k),\ldots,\hat{\mathbf{n}}^{\prime}_{ N_{u}}(k)]\)\(\in\mathbb{C}^{N_{RIS}\times N_{u}}\) is the Gaussian noise matrix.
### Analysis of System Application Scenarios
#### 2.1.2 Determining Configuration Goals  
The configuration of the PEDF system requires a flexible arrangement of various interconnected elements, such as photovoltaic modules, energy storage devices, and critical interactive devices. The various configuration options differ in terms of cost-effectiveness, low-carbon emissions, and reliability. Stakeholders, including property owners, asset management parties, operation and maintenance parties, and users, will evaluate the PEDF system based on these economical, low-carbon, and reliability factors [13; 14; 15]. Therefore, when optimizing and evaluating the PEDF system configuration, it is essential to consider various configuration objectives and constraints to achieve the optimal solution.
## 3 Results and Discussion
### Botanical Origin and Volatile Composition of Corsican \"Spring\" Honeys

Table 4: Chemical composition of volatile fraction of elementing and asphodel flowers. The 23 \"not-clementine\" honey samples (group II) were dominated by phenolic compounds (23.2%-60.4%) followed by linear compounds (11.3%-53.4%). The main compounds were phenylacetaldehyde **C14** (3.7%-36.2%), benzaldehyde **C9** (2.5%-18.4%) and methyl-benzene **C2** (1.5%-17.3%). Furanic compounds (average: 7.5%) were less abundant than in \"clementine\" honey (average: 26.2%), and acid components (average: 10.3%) were more abundant than in the \"clementine\" honeys (average: 6.8%). To our knowledge, only one previous report focused on the volatile fraction of asphal model unifloral honeys from Sardinia [18]. Methyl syringae was detected in asphalped nectar in high concentrations and was therefore considered a marker of asphalped honeys [19]. A low content of this component (**C42**: 0.1%-4.1%) was reported in the volatile fraction of \"spring\" honey samples (18-21, 24-30, 32, 33 and 36-41). Additionally, the amount of methyl syringae was unrelated to the presence of _Asphodelus_ pollen in the pollen spectrum. This result could be explained by the extreme \"under-represented\" type of _Asphodelus_ pollen in Corsican \"spring\" honeys and/or by other nectar contributions in these honeys. The sample 18 exhibited the association of _Cirrus_ sp. and _A. sinensis_; it was grouped with the \"not-clementine\" honey. In this sample, the citrus nectar contribution was less important than in \"clementine\" honeys in accordance with the lower concentrations of iliac aldehyde isomers. To our knowledge, the volatile composition of _A. ramosus_ subsp. _ramosus_ flowers is reported here for the first time (Table 4). The HS-SPME volatile fraction of asphalped flowers was dominated by oxygenated compounds, especially linear compounds. Nonanal (16.5%-38.2%), (_Z_)-3-hexenyl acetate (5.2%-41.8%), octanal (3.5%-12.6%), 1-octanol (5.7%-8.8%) and heptanal (3.1%-9.5%) were
# Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming
## 2 Materials and Methods
### Covariance Matrix Regularization
#### 2.3.1 Empirical Covariance Estimation  
Earlier spatiotemporal beamformer studies [21; 22; 28; 29] use the empirical covariance and inverse covariance, calculated as follows:  
\[\hat{C}_{\text{emp}}=\frac{1}{n-1}\sum_{i=1}^{n}\mathbf{x}_{i}\mathbf{x}_{i}^ {\intercal} \tag{6}\]  
\[\widehat{C^{-1}}_{\text{emp}}=\hat{C}_{\text{emp}}^{+} \tag{7}\]  
The Moore-Penrose pseudoinverse \({}^{+}\) ensures that a solution exists when \(\hat{C}_{\text{emp}}\) is singular. Figure 1a,b show examples of the empirical estimators of the covariance and the inverse covariance matrices, respectively. The empirical estimator suffers from performance and stability issues if the number of epochs \(n\) used for estimation is not much larger than the number of features _cs_[30; 31].
## 4 Conclusions  
The transition toward sustainable agriculture requires minimizing the use of chemical agents that contribute to environmental degradation and health concerns. Pulsed electric field (PEF) technology, a non-chemical approach, emerges as a promising solution for eco-friendly seed priming. In the present study, the PEF application markedly improved both the germination and seedling development in tomato seeds under various environmental conditions.  
The most rapid and pronounced germination response was observed on the seventh day following the exposure to 15.36 J of PEFs, reaching 72.81%, while the highest proportion of morphologically normal seedlings was noted at 17.28 J, achieving a rate of 94.62% (\(p\leq 0.05\)). Cold stress experiments (5 days at 24 \({}^{\circ}\)C) revealed that treatments with 1.92 and 10.88 J produced the highest germination rates, each at 46.67%. In saline conditions, the 17.28 J treatment yielded the most favorable outcomes, with germination reaching 62.00 \(\pm\) 0.90% under 100 mM of NaCl and 50.00 \(\pm\) 0.60% under 200 mM of NaCl, which are both statistically superior to the untreated controls (\(p\leq 0.05\)). Electrical conductivity (EC) measurements indicated a significant rise from 4 to 8 h of incubation across all samples, suggesting that the duration of the incubation had a more pronounced impact on ion leakage than the PEF dose itself. Additionally, microbial assessments showed that the PEF treatment at 17.28 J effectively eliminated detectable levels of total mesophilic aerobic bacteria and fungal contaminants, enhancing seed hygiene.  
The physiological enhancements observed may stem from electroporation-induced changes, such as the increased membrane permeability, improved water and oxygen uptake, and activation of metabolic enzymes, all contributing to a faster and more uniform germination. To build on these findings, future studies should prioritize large-scale field evaluations to assess the consistency of PEF effects under variable agronomic conditions. Furthermore, molecular and biochemical investigations are needed to clarify the pathways through which the PEF enhances the seed vigor and stress tolerance. Evaluating impacts on crop yield, quality, and soil health will be essential to confirm its viability as a replacement for traditional chemical-based seed treatments.  
G.A.E.: Conceptualization, data curation, data analyses, modeling, optimization, writing--original draft; B.Y.: Conducting test and analysis, writing first draft. All authors have read and agreed to the published version of the manuscript.  
This research was funded by TUBITAK, (Project no: 217O068).  
Not Applicable.  
Data Availability Statement: Data will be available upon request.  
The authors have declared no conflicts of interest for this article.
# The BCL Method for DSSC: Basis and Applications +
## 3 Mapping Electronic Density in the Ligand Excited State  
In order to build suitable dyes for dye-sensitized solar cell (DSSC) devices, a series of quinone-derivatized polypyridinic ligands with an acceptor character were designed [6]. The hypothesis was that the acceptor ligand in the complex should meet some basic requirements such as (a) bidentate (N, N)-type coordination to the metal, (b) the possession of planar geometry and (c) enhanced electron acceptor character. The ligands studied, shown in Scheme 1, were 12,17-dihydronaphtho-[2,3-h] dipyrido [3,2-a-2\({}^{0}\),3\({}^{0}\)-c]-phenylazine-12,17-dione, Aqphen, dipyrido[3,2-a:2\({}^{0}\),3\({}^{0}\)-c]benzo [3,4]-phenylazine-11,16-quinone, Nqphen, and uracil(5,6-diamino-1,3-dimethyl), Pptd.  
These ligands are rather planar, with the exception of pptd, and can be visualized as having a bidentate (N, N) phenanthroline-type coordination center fused to a pyrazine or phenazine portion. Their difference lies in the type and relative position of the corresponding quinonic groups. Based on these acceptor type ligands, different complexes can be built, containing other bipyridine-based bidentate ligands. Figure 2 shows that after receiving an electron, by direct reduction or by MLCT (metal-ligand charge transfer) excitation, the acceptor ligands in the complex should behave as a \"three step cascade\" with a \(\pi\)* ordering level following \(\pi\)* (phenanthroline) \(>\pi\)* (phenazine or pyrazine) \(>\pi\)* (quinone portion).  
Figure 2: The excited state redistribution of the electronic density after the excitation from the ground state to the metal ligand charge transfer acceptor.  
The ligand ppl was also studied as a model, considering that it represents the unsubstituted fragment (Scheme 1) that can be modified by introducing three kinds of fragments containing quinonic groups: naphtoquinone, anthraquinone and dimethyl pyrimidine quinone.  
One way to predict if the requirements mentioned above are present in a specific ligand is to study the nature of the lowest unoccupied molecular orbital (LUMO), which reflects the electron withdrawing capacity and the electronic distribution in the acceptor state. However, this is a virtual orbital and, hence, it cannot be accessed experimentally. This shortcoming can be overcome by adding a single electron to this empty level by means of an electrochemical reduction in the neutral molecule, giving rise to the single occupied molecular orbital (SOMO). The SOMO can be examined by two complementary approaches: one experimental, involving the reduction in the ligand and the subsequent analysis of the ESR pattern and hyperfine coupling constants, and the second is theoretical in nature, involving the calculation of suitable theoretical descriptors measuring the acceptor power and the delocalization of the unpaired electron along the molecular skeleton. The theoretical results would enable a picture of the acceptor state and to be constructed and correlated with the experimental data.
# Face Recognition and Tracking Framework for Human-Robot Interaction
## 2 Related Works
### Face Tracking Algorithms  
Visual object tracking has always been a research hotspot in computer vision, and face tracking is a special case. Face tracking is primarily a process of determining the position of the human face in a digital video or frame based on the detected face. This is challenging as the face is not the same during the time (video frames), but it may vary in pose and view. Moreover, other factors affected the face tracking in the actual scene and made it more complex, such as illumination, occlusion, and posture changes. On the other hand, face tracking has many advantages, such as counting the number of human faces in a digital video or camera feed and following a particular face as it moves in a video stream to predict the person's path or direction. Moreover, it can reduce the processing time needed for face detection and recognition.  
Many visual object tracking algorithms have been presented; however, Kalman filter [44] and template matching [45] are the most popular methods. In [46], [PERSON] et al. proposed simple online and real-time tracking (SORT) for multiple object tracking. SORT is a simple approach that associates objects efficiently for online and real-time applications by utilizing the Kalman filter and the Hungarian method. It achieves a favorable performanceat high frame rates of 260 Hz. In [47], [PERSON] et al. integrates SORT with the appearance information by employing a trained CNN to discriminate pedestrians on a large-scale person re-identification dataset, and called it Deep-SORT. This technique has improved the performance and reduced the number of identity switches through longer periods of occlusions.  
Recently, deep learning-based face tracking algorithms have been dominant, where the face tracking problem is solved as a binary classification problem for predicting a face or a non-face. [PERSON] et al. [48] proposed a multiple objects tracking algorithm that utilizes a multi-task CNN network (MTCNN) for face detection and fuses multiple features (appearance, motion, and shape features) for tracking. Despite the promising results achieved by deep learning-based face tracking algorithms, SORT has a higher frame rate with favorable accuracy due to its simplicity and ease of implementation.