esa-sceva/satcom-chunk-collection huggingface dataset:
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 4 Experimental Results
### Signal Acquisition Protocol  
In order to validate the architecture, the Rectus Femoris and Vastus Lateralis muscle were measured and the subject's skin was prepared for electrode placement. This procedure minimizes the attenuation and possible distortion of EMG signals. Following the recommendations of the SENIAM project guidelines [23], the electrodes are placed at the midpoint of the imaginary line connecting the superior iliac spine and the top of the patella.  
The subject is initially positioned seated on a table, with the knees slightly flexed and the upper body slightly inclined. This position facilitates knee extension without thigh rotation during measurements. For data capture, the subject extends the knee while an assistant applies pressure against the leg above the ankle in the direction of flexion. This induces tension in the target muscle. An initial capture is performed without muscle activation, followed by ten normal measurements for each test. Each measurement lasts 30 s, with the first 5 s without activation, followed by intervals of 5 s of muscle activation (maximum possible force) and 5 s of relaxation. This cycle is repeated three times per capture. A 1-min rest is allowed between captures.  
A total of ten recordings are performed, as described above. The prototype is configured to acquire data at 8 kSPS with a gain of 12. This gain was selected by recording the maximum voluntary contraction. The approximate total duration of the test is three hours. This time includes explaining the protocol to the subject, preparing the area where the electrodes will be placed, and conducting ten signal recordings, each with an average duration of twenty minutes.  
It is important to note that irregular contact between the electrodes and the skin, the interface between the conductor cables and the electrodes, and the connection to the ADS1298 module may introduce potential artifacts. This protocol was followed for the experimental validation of the proposed architecture, ensuring the accuracy and reliability of the obtained measurements.  
Finally, the functional validation was performed by measuring a single muscle group, the Rectus Femoris and Vastus Lateralis. However, the remaining channels were configured to acquire the internal test signal generated by the ADS1298. This means that while  
Figure 2: Prototype developed based on AFE ADS1298 and TE0727 manufactured by Trenz Electronic GmbH, based in Bünde, Germany SoC connected. Its size is 3 cm \(\times\) 6.5 cm \(\times\) 2.6 cm.  
data are acquired and processed on these channels, it does not correspond to meaningful EMG information.
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 3 Influence Analysis of Error Source
### Sampling Delay Error  
Transmit and receive channels not only affect the amplitude and phase of signal, but also produce different delays on the signal [20]. The delay inconsistency between channels leads to the mismatch of four images at range and further affects the application of PolSAR data. The sampling delay error can be measured by the delay difference of V relative to H channel. As shown in Figure 1, these differences of transmit and receive channel are denoted as \(t_{T}\) and \(t_{R}\), respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Amount of Data** & \begin{tabular}{c} **Number of** \\ **Covered Beams** \\ \end{tabular} & \begin{tabular}{c} **Covered Look** \\ **Angles (deg)** \\ \end{tabular} & \
\begin{tabular}{c} **Options of Bandwidth** \\ **and Pulse Duration** \\ \end{tabular} &
\begin{tabular}{c} **Time Span** \\ **(months)** \\ \end{tabular} \\ \hline
30 & 16 &
\begin{tabular}{c} Almost U \\ (18.9–42.75) \\ \end{tabular} & 6 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data information for the real situation analysis of channel imbalances, where U means uniform distribution.  
Figure 2: Imbalance of transmit and receive channel extracted by internal calibration: (**a**) phase imbalance of transmit channel; (**b**) phase imbalance of receive channel; (**c**) amplitude imbalance of transmit channel and (**d**) amplitude imbalance of receive channel.  
Similar to the extract of channel imbalance in Section 3.1, the sampling delay error can be obtained by comparing the time difference at the peak value of four internal calibration signals after pulse compression. Delay errors of 30 data groups are shown in Figure 3 including statics of all results where the data and number of data is same with the Figure 2. Similar to the imbalance of transmit/receive channel, the \(t_{R}\) is more volatile than \(t_{T}\). However, all the delay errors are less than 1 ns. As the minimum sampling interval of range is 15 ns, it can be considered that the channel delay of GF-3 satellite has good consistency and that the influence of sampling delay error can be ignored.
# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 1 Introduction
#### Sign Language Alphabet and Stance of Sign Language Speakers  
It is important to draw a distinction between sign language as such and fingerspelling. Every sign language has its own grammar and vocabulary. However, the syntax of many of them is based on signs that are equivalents of whole words or phrases in a spoken language. The fingerspelling alphabet, on which the Polish version of the gestures presented in this article are based, is a supplement to the sign language. Fingerspelling is also used to make signs corresponding to counters, numbers, or punctuation. These sign language signs are made solely with the hands, two for British Sign Language and one for Polish Sign Language, among others, and do not require facial expressions or poses. Spelling in sign languages is most used when pronouncing proper names such as first names or street names or when learning other sign language signs [5].  
The stance of the sign language community towards sign-language-to-speech glove-translators should also be made clear here. Many of them have a negative attitude towards this type of device. This is mainly due to bulkiness and low efficiency in sign language recognition [6]. While the first of these obstacles can be overcome by striving for miniaturization of such devices, for example, by reducing the number of sensors used (as described in this paper), the low effectiveness is due to the inability of the gloves to capture other important factors of sign language such as facial expression or pose [7]. However, although the data glove may not be the ultimate solution to the problem of translating sign language into speech, mainly due to the reasons stated above, it seems it is currently an excellent device for recognizing hand movements. The readings obtained with them and the databasescreated from them will allow for the development of more complete and accurate training sets for sign and sign word recognition algorithms, thus increasing their effectiveness in future implementations.  
It should also be noted that the gestures referred in this work are not letters of the Polish Sign Language manual alphabet. It was assumed that the main difference between letters is their main, static form. However, such an assumption makes it necessary to classify the handshapes as gestures rather than letters of PSL due to lack of dynamic stages of presentation of individual characters.  
The next section describes articles on applications of data gloves in gesture recognition and sign languages. Section 3 describes the design of the glove and the research methodology, and the results are presented in Section 4. The article concludes with a summary of the results in Section 5 and a brief discussion undertaken in Section 6.
## 3 Mudstone Electrochemical Modification Tests Program  
### Mudstone Samples Characteristics  
The sample used in this experiment is mudstone taken from the north slope of the quarry at the Shengli West No. 1 open-pit coal mine of Guoneng Beidian Shengli Energy in Inner Mongolia Autonomous Region, China. The mineral composition of the mudstone samples was determined using a SmartLab X-ray powder diffractometer, revealing the contents of montmorillonite, kaolinite, illite, and quartz to be 31%, 35%, 16%, and 18%, respectively. The montmorillonite, kaolinite, and illite in these mineral components have good water absorption properties. Montmorillonite and illite expand upon exposure to water, whereas kaolinite, while capable of absorbing water, does not undergo significant expansion. If this type of mudstone exists at a dip angle in the slope, it can significantly contribute to rock deformation and landslide formation.  
Mudstone has low permeability and good water absorption. During this experimental study, the water content of the mudstone was controlled at 25%, making it unsaturated. The motion state of water driven by no voltage can be described by [PERSON]'s equation for the continuity of water seepage in unsaturated soil and rock. The soil hydraulic characteristics of unsaturated mudstone include the soil moisture characteristic curve and the permeability coefficient curve. The soil water characteristic curve (SWCC) describes the variation in the soil water matrix potential with soil water content, while the permeability coefficient curve represents the relationship between the permeability coefficient and matrix suction. The soil  
Figure 1: Structure of the study on the preferential method of electrochemical modification of chemical agents.  
water characteristic curve provides a foundation for studying the strength, permeability, and constitutive theory of unsaturated soil.  
In this study, the mudstone sample's soil water characteristic curve was fitted using the VG parameters. As the moisture content decreases, the soil water characteristic curve transitions through different stages: initially dominated by capillary action, followed by water film adsorption and finally firm adsorption. The electrochemical drainage consolidation and modification of mudstone will further alter its soil water characteristic curve, a topic for future exploration in this study.
## 2 Related Work
### Emotion Classification Methods  
In order to provide a comparison to our method, we chose studies that classified human emotions with scales of Valence and Arousal in Table 2. It also lists the classification accuracy and the number of subjects. As seen in Table 2, the most commonly used emotional classification methods include k-Nearest Neighbor (k-NN, used in [15; 39]), Support Vector Machine (SVM, used in [14; 40; 41; 42]), Random Decision Forest (RDF), Bayes Neural Networks (used in [43]) and Neural Networks (used in [44; 45]). These methods are all used as baseline methods for comparison with our method, with details given in Section 5.2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author and Study** & \begin{tabular}{c} **Emotion** \\ **Classification Basis** \\ \end{tabular} & **Subjects** & **Accuracy** & **Classification Method** \\ \hline [PERSON] [14] & \begin{tabular}{c} Valence and Arousal \\ (2 classes) \\ \end{tabular} & 10 & 81\% & SVM \\ \hline [PERSON] [41] & \begin{tabular}{c} Valence and Arousal \\ (3 classes) \\ \end{tabular} & 30 & 66.7\% & SVM \\ \hline [PERSON] [40] & \begin{tabular}{c} Valence and Arousal \\ (2 classes, respectively) \\ \end{tabular} & 28 & 81.3\% & SVM \\ \hline [PERSON] [15] & Valence(2 classes) & 12 & 71.3\% & k-NN \\ \hline [PERSON] [39] & Valence (2 classes) & 9 & 82\% & SVM, k-NN \\ \hline [PERSON] [42] & \begin{tabular}{c} Valence and Arousal \\ (2 classes) \\ \end{tabular} & 15 & 82\% & SVM \\ \hline [PERSON] [43] & \begin{tabular}{c} Valence and Arousal \\ (2/3 classes respectively) \\ \end{tabular} & 32 & \begin{tabular}{c} 66.6\%, 66.4\% (2) \\ 53.4\%, 51.0\% (3) \\ \end{tabular} & Bayes neural network \\ \hline [PERSON] [44] & \
\begin{tabular}{c} Valence and Arousal \\ (2 classes, respectively) \\ \end{tabular} & 32 & 74.12\% & C-RNN \\ \hline Our Method &
\begin{tabular}{c} Valence and Arousal \\ (4 classes) \\ \end{tabular} & 32 & 75.21\% & CLRNN \\ \hline \hline \end{tabular}  
* SVM, Support Vector Machine; CNN, Convolution Neural Networks; RNN, Recurrent Neural Networks; C-RNN, CNN+RNN; LSTM, Long Short-Term-Memory; CLRNN, CNN + LSTM RNN.  
\end{table}
Table 2: Survey of the studies on emotion classification methods with EEG signal 1.  
It is noteworthy that most of the methods listed in Table 2 classify emotions statically, except for the method used in [44] where the LSTM RNN was adopted to learn from the EEG features incrementally and dynamically. Another point worth noting is that only CNN is suitable for automatically extracting features from the image out of these methods. These two points are the reason for selecting CNN and LSTM RNN as parts of our classification method. The second column of Table 2 shows the classification basis and the number of the difference classes in the previous studies. As we can see, previous studies have basically divided emotions into categories two to three. In this study, we divided the emotion state into four classes. All the studies in Table 2 classify the emotion by Valence and Arousal. The third column of Table 2 shows the number of subjects included in the evaluated dataset.
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
## 2 Related Work
### Comparison of Ongoing Missions with Present Research  
An analysis of previous research and successful missions is shown in Table 1. The table compares the slant range distance, minimum elevation angle, RSSI, SNR, data rate factor and network topology mode. The ongoing mission parameters were taken from the community website TinyGS [12]. The proposed study shows that the spacecraft carrying a LoRaWAN gateway with ADR, even with the minimum gain, has signal integrity and a high receive strength. Moreover, a comparison has been made with the LoRa payload alongside the LoRaWAN gateway, which this study uses to allow the star topology of the network with ADR.  
The main contribution of this paper is to investigate the maximum possible range of the LoRa signal while communicating to the LoRaWAN gateway on the satellite. It was tested by attenuating the signal at its maximum fade range, which will be explained in Section 4. To further improve the link availability, an ADR scheme is added to the communication subsystem, so when the signal is reaching its maximum range and unable to keep the link stable, the system will automatically adjust the quality by changing the spreading factor.
## 3 Modeling and Data Sources
### Model Evaluation and Validation
#### 3.2.3 Uncertainty Analysis  
In this study, the stepwise regression model was employed to identify key meteorological factors influencing the occurrence and affected area of forest fires in Yunnan Province. To ensure the model's reliability, a comprehensive analysis of the uncertainty in model parameters and input data was conducted. Initially, a bootstrapping approach with 1000 examples was used to evaluate the model coefficients, calculating their standard errors and 95% confidence intervals from these empirical distributions to ensure the stability of the model parameters. Subsequently, the uncertainty in model predictions was assessed by  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Autocorrelation Function** & **Partial Autocorrelation Function** \\ \hline AR (p) & tail dragging & P-step cut-off tail \\ MA (q) & q-step cut-off tail & trailing tail \\ ARMA (p,q) & tail trailing & trailing \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model characteristics.  
estimating the 95% confidence intervals of the predicted values, using standard error and residual distribution analysis [37].
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 2 Literature Review
### Mathematical Model-Based Approaches  
Some mathematical models for LoRaWAN have been developed in the literature [32; 33; 34; 35; 36]. The authors in [32] aimed to analyze and evaluate the LoRaWAN channel access operation in terms of the PER and packet loss ratio (PLR) in a confirmed mode. The results in [32] reveal that PER and PLR increases with an increasing load in the network. The authors in [33] further enhanced the work presented in [32] by proposing a mathematical model based on retransmissions. The proposed model accurately estimates the PER based on the offered traffic. The proposed method was verified through simulation by finding the probabilities of the distribution of data rates. The simulation results show that by considering the retransmissions, the model accuracy is significantly improved. Furthermore, the model presented in [33] was enhanced in [34] by taking into account the capture effect along with retransmissions. The authors assumed that the different SFs are entirely orthogonal. The performance of [34] has been further enhanced by [35] by proposing MCS allocation to satisfy the different Quality of Service requirements of IoT applications. However, the reception paths at the GW and duty cycle limitations both at EDs and GW were neglected. To take into the reception paths and duty cycle limitations, the authors proposed a mathematical model in [36]. The performance evaluation of the proposed model shows an improved packet success ratio when compared with [34].
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 5 Performance Analysis of RICC
### Network Reliability of RICC
#### 5.2.2 Weighted End-to-end MFR  
**Theorem 5.** In RICC, the weighted end-to-end MFR of the network is calculated, which reflects the universality of the data collection reliability with the RICC approach:  
\[MFR^{w}_{c2e}=\int_{0}^{R}\int_{0}^{2\pi}MFR^{x}_{c2e}\times x\times d\theta \times dx \tag{35}\]  
Figure 11: Comparison of end-to-end MFR.  
**Proof.** Considering network node that is \(x\) m far from the sink, where \(\left|x\in\left\{0,\ldots,R\right\}\right.\), we take a sector of fan-shaped ring \(\theta\) with an angle \(d\theta\) and a width of \(dx\). The area of the sector is \(xdxd\theta\). The probability of losing a packet from the generating node to the sink node, i.e., the end-to-end MFR, is \(MFR_{e2e}^{w}\), see Equation (34). Integral to the end-to-end MFR in the entire network, the network weighted end-to-end MFR is as follows:  
\[MFR_{e2e}^{w}=\int_{0}^{R}\int_{0}^{2\pi}MFR_{e2e}^{x}\times x\times d\theta \times dx \tag{37}\]  
Figure 12 is introduced to show a comparison of the weighted end-to-end MFR between two schemes. In RICC, only nodes that are very close to the sink have the same MFR as in NCCC, and these nodes are only a small proportion of the whole network. Therefore, the weighted end-to-end MFR in RICC is much lower than that in NCCC; specifically, the percentage reduction is at least 50%.
## 3 Results and Discussion
### Carboxylic Acids
Lactic acid was identified as the primary non-volatile organic acid that increased gradually throughout fermentation. It has been observed that kefir microorganisms were able to increase the lactic acid concentration along with other acids present in the fermentation medium (\(p<0.05\)). Pyruvic acid was generated in significant amounts due to the presence of various strains of lactic acid bacteria during CWK fermentation. Pyruvate is a critical intermediate in the homo- and hetero-fermentative pathways and can be reduced to lactic acid. Different strains of lactic acid bacteria break down pyruvate differently. _L. brevis_ degrades pyruvate to lactate and acetate (thus increasing both lactic acid and acetic acid concentrations in the medium [58]. Along PC1, CWK samples fermented for 96 h had the highest positive scores that corresponded to the high positive loading of succinic acid (Figure 2B). This observation was supported by the ANOVA results, where succinic acid was significantly the highest in 96 h fermented samples (\(p<0.05\)) (Supplementary Table S1-B). [PERSON] et al. [59] reported a correlation between the presence of _L. plantarum_ in the fermentation medium with succinic acid production. Fermented CWK Sets 4, 5, 6, 8, and 9 that contained added sucrose had high lactic acid concentrations. This was consistent with their rapid growth when consuming sucrose during the early stage of fermentation. Sucrose was reported to be the preferred substrate for lactic acid bacteria during sauerkraut fermentation in studies conducted by [PERSON] et al. [60] and [PERSON] et al. [61], with better utilization observed compared to fructose or glucose. [PERSON] et al. [62] also found that sucrose was gradually degraded into glucose and fructose for lactic acid production.
## 3 Proposed Method
### Magnify Pinpointing-Based Encryption (MPBE)
#### 3.2.2. Generation Phase  
Let \(v\) be the \(n\)-bit string SL for the user, and let \(V\subseteq\{1,\ldots n\}\) be the collection of all \(i\) for which \(v_{i}=1\). \(V\) is split into two different, namely \(V=\{v_{1},v_{2},\ldots v_{m}\}\) and \(\{V_{r_{1}},V_{r_{2}},\ldots v_{r_{m}}\}\) such that \(m+r_{m}=n\), where \(V_{r_{1}}\) stands for a random value that is introduced to the suggested approach to increase security. The private key corresponding to identity \(v\) is obtained by selecting a random value, as indicated in Equation (8). \(u^{\prime}\prod\limits_{i\in V}v_{i}\) is the formula for group operation during the key generation:  
\[d_{v}=\left(u^{\prime}\prod\limits_{i\in V}v_{i}\right) \tag{8}\]  
\(U=\{u_{1},u_{2}|\ldots u_{n}\}\) and V = \(\{v_{1},v_{2}\ldots v_{m}\}\) such that \(m<n\). Now, a polynomial function using the Lagrange coefficient method is created and we perform polynomial interpolation. We can conceal \(v\), which can be effectively reconstructed from the available data points with the aid of polynomial interpolation. For the suggested strategy, the polynomial equation and sagrange coefficient is  
\[\Delta_{i,v}(x)=\sum\limits_{i=0,k\in V}^{n}\left(\prod\limits_{0<i<n,i\
eq i }\frac{x-x_{j}}{x_{i}-x_{j}}\right)y_{k} \tag{9}\]  
where \(x=u_{i}\) and \(y=v_{k}\).  
Every user identity's random set \(u_{i}\) is generated once, and each identity's Lagrange coefficient is generated using the same \(u_{i}\) value. M-terms of identity will be used by authority value as a result of which a challenger will never learn the authentic user's original identity. As a result, it will be challenging to retrieve or deduce the key created for a specific _SL_. What if every user identity and \(U\) value are identical? The challenger will be unable to infer anything from the key in such case since \(\Delta_{i,v}(x)\) produces an error of zero. This situation is unique. The greatest error between any two subsequent nodes will be shown, and the error created will be zero.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method
### Feature Fusion Module  
In the field of person Re-ID, feature fusion is an important process for combining features obtained from different modalities into a coherent and comprehensive representation (cross-modality features), which significantly enhances the accuracy and reliability of a Re-ID system.