esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 2 Materials and Methods
### Sparsity-Based Classification
#### 2.4.2 Input Module  
The sparse EEG coefficients were taken as input to the 1D-convolutional layer. Here we used stride length as three instead of a pooling layer, as a large stride can result in adaptive learning of convolution kernel [26]. Next, we used a rectifier linear unit (ReLU) as an activation layer. The activation function either transforms the node's summed weight into the node's activation or lets the input pass out of the layer. ReLU is a linear function that allows only positive outputs while thresholding the remaining outputs to zero [27].  
While training a deep neural network (DNN) with many layers, the model might become susceptible to the structure of the learning algorithm and initial random weights. This is because the distribution of input layers may change when initial weights are updated after each minibatch. This disruption is known as 'internal covariate shifting'. Batch normalization (BN) was used to normalize the inputs for each minibatch addressing internal covariate shifting [28]. Therefore, this procedure helps alleviate the learning process and reduces the number of training epochs in the neural network. The sparse EEG coefficients of each class are passed through the 1D-convolutional layer with an input shape (N, 1). N is the number of sparse coefficients for each category.
## 5 Discussion
### Mechanism of Aperture Development
#### 5.3.2. Diagenesis  
Diagenesis determines the evolution of the reservoir's pore structure under buried depth conditions and has a direct control on the final physical properties of the reservoir [41]. The diagenesis types of the Fengcheng Formation reservoir are various, and mainly include compaction, pressure dissolution, dissolution, and metasomatism. Among them, the compaction effect is relatively strong, and with the increase in buried depth, the particles are arranged more closely, resulting in the surface fracture of some rigid particles to form fractures (Figure 12a). Pressure dissolution is mainly developed at the joints of rock particles, and suture structures can be seen in some areas (Figure 12b). The dissolution mainly develops inside the cement particles and between feldspar, quartz, and carbonate mineral particles, forming intergranular dissolution pores, intragranular dissolution pores, and dissolution fractures (Figure 12c). Metasomatism mainly involves the replacement of clay minerals by calcite, authigenic minerals by quartz, and carbonate minerals by siliceous minerals (Figure 12d). These diagenetic processes play a very important role in the development of reservoir pores, and directly or indirectly lead to the formation of micro-pores and micro-fractures.  
Figure 12. Schematic diagram of diagenesis. (**a**) Compaction; (**b**) pressure solution; (**c**) dissolution; and (**d**) metasomatism.  
As shown in Figure 13, Figure 13a is the variation curve of porosity with depth. The deeper the depth, the higher the pressure temperature and the stronger the compaction effect, so the overall porosity increases with the depth. However, due to the late diage-netic processes of dissolution and metasomatism, some minerals in the rock reservoir are metasomatized or dissolved, so that the deep reservoir with low porosity has high porosity. Among them, corrosion greatly promoted the formation of pores. As shown in b and c in Figure 13, the images were SEM images of positions with high porosity in deep strata, from which it could be seen that the development of pores was dominated by corrosion pores and fractures, and the matrix between mineral particles was mostly formed by corrosion.  
To sum up, diagenesis has a great influence on the development of pores and fractures. The main observation is that the porosity of the strata is reduced by early compaction, and the metasomatism and dissolution in the compacted strata promote the development of pores and fractures in the later period, so that the porosity of the strata begins to increase. However, due to the different mineral composition of the reservoir in the study area, the pores of plastic minerals such as clay minerals, after being compacted, are mostly of the narrow-slit type, while the pores of brittle mineral particles that can withstand the pressure will retain the original appearance of the solution pores, showing round, oval, or polygon shapes. If the pressure is too large, fractures will occur, resulting in the formation of structural fractures and other fractures, thus affecting the development of reservoir pores.
# Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning
###### Abstract  
This study presents a novel approach to passive human counting in indoor environments using Bluetooth Low Energy (BLE) signals and deep learning. The motivation behind this research is the need for non-intrusive, privacy-preserving occupancy monitoring in sensitive indoor settings, where traditional camera-based solutions may be unsuitable. Our method leverages the deformations that BLE signals undergo when interacting with the human body, enabling occupant detection and counting without requiring wearable devices or visual tracking. We evaluated three deep neural network models--Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN+LSTM architecture--under both classification and regression settings. Experimental results indicate that the hybrid CNN+LSTM model outperforms the others in terms of accuracy and mean absolute error. Notably, in the regression setup, the model can generalize to occupancy values not present in the fine-tuning dataset, requiring only a few minutes of calibration data to adapt to a new environment. We believe that this approach offers a valuable solution for real-time people counting in critical environments such as laboratories, clinics, or hospitals, where preserving privacy may limit the use of camera-based systems. Overall, our method demonstrates high adaptability and robustness, making it suitable for practical deployment in diverse indoor scenarios.  
B +
Footnote †: journal: Applied sciences  
_Article_
# Investigation of Pellet Properties Produced from a Mix of Straw and Paper Sludge
## 3 Experimental Research Results and Discussion  
### Calorific Values  
The higher calorific value (HCV) of samples, as one of the most important fuel indicators in terms of energy, was measured on a calorimeter LECO AC 500. The lower calorific value (LCV) of the samples was subsequently determined by the following equation [27]:  
\[LCV=HCV-r_{H2O}\left(W_{p}+8.94\cdot x_{H}\right) \tag{1}\]  
where \(r_{H2O}\) is the water heat of vaporization [kJ/kg], \(W_{p}\) is the water content in sample [% wt.] and \(x_{H}=0.01\cdot H_{h}\cdot B_{p}\); where \(H_{h}\) is the hydrogen content in the sample [% wt.], \(B_{p}\) is the volatile content in the sample [% wt.].  
From Figure 3, it is clear that straw pellets (S100) show the highest calorific value and the LCV of the sludge pellets (PS100) is around 5 MJ\(\cdot\)kg\({}^{-1}\). As expected, the calorific value decreases with increasing sludge content in the samples. The lowest LCV was recorded for the sample with the highest proportion of paper sludge (SPS-70/30).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Moisture (\%)** & **Volatile Matter (\%)** & **Ash (\%)** & **Fixed Carbon (\%)** \\ \hline SPS–90/10 & 5.81 & 69.05 & 10.32 & 14.81 \\ SPS–80/20 & 5.1 & 68.39 & 12.92 & 13.58 \\ SPS-70/30 & 6.64 & 66.93 & 13.91 & 12.51 \\ S100 & 6.87 & 71.3 & 5.02 & 16.81 \\ PS100 & 5.61 & 55.71 & 42 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proximate analysis of samples.  
The data shown above were confronted with the results presented in the work of [PERSON] et al. and around 1% variation was recorded in compared samples [16].
## 2 MCR Frequency Modulation Method and Laser Processing Platform  
### MCR Frequency Modulation Method  
The frequency modulation processing of MCR is the key processing technology to determine the product quality, and its frequency is almost only related to the physical properties of the wafer itself. The Sauerbrey equation [21] describes the relationship between the frequency of the MCR and its surface attachment:  
\[\Delta\text{f}=\frac{-2\Delta\text{m}f_{0}^{2}}{\text{A}\sqrt{\rho_{\text{q}} \text{I}_{\text{q}}}} \tag{1}\]  
where \(\Delta\text{f}\) is the frequency variation of MCR, \(\text{f}_{0}\) is the original resonant frequency of MCR, \(\Delta\text{m}\) is the mass change of MCR, A is the electrode area of MCR, \(\rho_{\text{q}}\) is the density of MCR, and \(\mu_{\text{q}}\) is the shear elastic modulus of MCR.  
In the frequency modulation process of MCR, A, \(\rho_{\text{q}}\), \(\mu_{\text{q}}\), and \(\text{f}_{0}\) in Equation (1) are all constant values. Frequency can only be changed by changing the mass of silver layer on the surface of MCR with the laser processing method. In the process, the electrode film on the surface of the MCR is processed by laser, then the mass of the silver layer is reduced by gasification to improve the frequency of the MCR until it reaches the target frequency. The MCRs before and after laser processing are depicted in Figure 1.
## 3 Results
### Service Delivery Model for Health Monitoring and Health Management Systems  
The service delivery model for HM and HMGT encompasses a range of activities, from data collection to actionable insights for maintenance and operational decisions. Table 4 outlines a comparative service delivery model for each, highlighting their processes, technologies, and outcomes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Service** & & **Aviation Health** & **Aviation Health** \\
**Component** & & **Monitoring** & **Management** \\ \hline \multirow{4}{*}{Data collection} & - & IoT devices and sensors collect & - & Utilizes the same IoT and sensor infrastructure as HM for data collection, with added emphasis on integration across different data sources for comprehensive analysis. \\  & - & \begin{tabular}{c} Data are transmitted in real time \\ or near-real time via satellite or \\ ground-based communication \\ networks. \\ \end{tabular} & - & Like HM but with enhanced \\ \cline{1-1}  & - & \begin{tabular}{c} IoT devices and sensors collect \\ real-time data from aircraft \\ systems. \\ \end{tabular} & - & \begin{tabular}{c} Utilizes the same IoT and sensor \\ infrastructure as HM for data \\ collection, with added emphasis \\ on integration across different \\ data sources for comprehensive analysis. \\ \end{tabular} \\ \hline \multirow{4}{*}{Data processing} & - & Basic processing to clean and \\  & & structure data for analysis. \\ \cline{1-1}  & - & Descriptive analytics to \\ \cline{1-1}  & & understand current and \\ \cline{1-1}  & - & historical aircraft performance. \\ \cline{1-1}  & - & Diagnostic analytics to identify \\ \cline{1-1}  & - & \begin{tabular}{c} Diagnostic analytics to identify \\ issues. \\ \end{tabular} & - & \begin{tabular}{c} Advanced processing techniques, \\ including data integration from \\ multiple sources and contextual \\ analysis. \\ \end{tabular} \\ \hline \multirow{4}{*}{Analysis} & - & \begin{tabular}{c} Descriptive analytics to \\ understand current and \\ historical aircraft performance. \\ Diagnostic analytics to identify \\ issues. \\ \end{tabular} & - & \begin{tabular}{c} Predictive analytics to forecast \\ potential failures or maintenance \\ needs. \\ \end{tabular} \\ \cline{1-1}  & - & \begin{tabular}{c} Prescriptive analytics to \\ recommend specific maintenance \\ actions. \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} Actionable \\ insights \\ \end{tabular} } & - & \
\begin{tabular}{c} Alerts andtifications about \\ current health status and \\ potential issues. \\ \end{tabular} & - &
\begin{tabular}{c} Detailed maintenance \\ recommendations, including \\ optimal timing and required \\ actions to prevent failures. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Service delivery model for HM and HMGT.
## 3 Results
### Cytotoxicity and Anticancer Activity of C. sativa Leaf Extracts  
The cytotoxicity of the _C. sativa_ extract was clearly affected by different factors such as concentration, exposure time, and cell line. Results showed high anticancer activity against gastric adenocarcinoma (AGS) and melanoma cells (A375) (Figure 8).  
\begin{table}
\begin{tabular}{c c c} \hline
**Precursor** & **EC Number** &
\begin{tabular}{c} **Is It Included in** \\ **CannGEM?** \\ \end{tabular} \\ \hline FNTA; protein farnesyltransferase/gernarylgeranyltransferase type-1 subunit alpha & 2.5.1.58 & No \\ \hline RCE1, FACE2; premyl protein peptidase & 3.4.22.- & No \\ \hline STE24; STE24 endopeptidase & 3.4.24.84 & No \\ \hline ICMT, STE14; protein-S-isopenrylcysteine O-methyltransferase/farsefase & 2.1.1.100 & No \\ \hline PCME; premylcysteine alpha-carboxyl methylesterase & 3.1.1.- & No \\ \hline PCYOX1, FCLY; premylcysteine oxidase/famesylcysteine lyase & 1.8.3.5 & No \\ \hline FOHSR; NAP+-dependent farnesol dehydrogenase & 1.1.1.216 & No \\ \hline FLH; NA+-dependent farnesol dehydrogenase & 1.1.1.354 & No \\ \hline FOLK; farnesol kinase & 2.7.1.216 & No \\ \hline K15793; acyclic sesquiterpene synthase & 4.2.3.49 & No \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 8: Cytotoxicity analysis of the _C. sativa_ leaf extract on different carcinoma and healthy cell lines. Results for carcinoma cell lines (A549, AGS and A375) after 24 (**A**) and 72 h of exposure (**B**). Results for healthy cell lines (Vero and HFF) after 24 (**C**) and 72 h of exposure (**D**).  
Cytotoxicity levels ranging from 50 to 90% for concentrations between 0.0125 and 0.05 mg/mL were observed in both cell lines. In contrast, for Vero and lung carcinoma cells (A549), these cytotoxicity levels were observed in concentrations between 0.025 and 0.05 mg/mL. This confirms less activity against A549 and significant toxicity against Vero cells. Surprisingly, results obtained for healthy skin fibroblasts (HFF) showed negligible toxicity in concentrations between 0.0004 and 0.025 mg/mL (below 10%).
## 2 Model of Confined Concrete and Fiber Deformation Capacity Limit  
### Model of Confined Concrete  
The first analytical expression linking the strength of confined concrete with the confinement pressure was proposed based on experimental results [62]. Since then, new models have been developed to predict the behavior of concretes confined using steel elements [63; 64]. These models are not valid for concrete elements confined with FRP due to the fact that steel has a significant plastic deformation capacity prior to fracturing; however, in the case of FRP, the behavior is elastic and linear [65; 66].  
In practice, the most commonly used models for predicting the breaking strain of concrete elements confined with CFRP are the ACI model [23] and the FIB model [22]. Table 1 shows both models for elements with a circular cross section, where \(f_{cc}\) is the strength of the confined concrete, \(f_{c}\) is the strength of the non-confined concrete, \(f_{1}\) is the effective stress of the confinement, \(\varphi_{f}\) is a reduction coefficient which the ACI takes as 0.95, \(E_{f}\) is the modulus of elasticity of the fiber, \(n\) is the number of layers, \(t_{f}\) is the thickness of the fabric, \(D\) is the diameter of the confined element, and \(e_{flu}\) is the ultimate strain of the fabric. It is necessary to reduce the fabric's ultimate strain value obtained in uniaxial tension testing for the calculations; this is because the performance of the fabric in a confined material differs from the ideal tensile strength testing situation due to the state of triaxial stress it is in, the curved shape of the jacket, the possible damage during execution, and the stress concentration produced by irregularities in the concrete. The ACI proposes a value of 0.55; the FIB does not propose any specific value and points out that it is necessary to carry out prior testing in order to determine this value.  
Both models involve the same variables: the geometrical and mechanical characteristics of the original element and of the reinforcement fiber. However, examination of both expressions reveals that whilst in the ACI model the increase in strength does not depend on the initial strength of the non-confined concrete, in the FIB model the increase in strength does depend on the strength of the non-confined concrete.
# Risk-Aware Wireless Positioning in
## 2 Preliminary: Various Wireless Positioning Schemes
### Time Difference of Arrival (TDoA)  
The TDoA scheme is a positioning scheme that uses the arrival time differences of positioning signal at the receivers [21]. Unlike the ToA scheme, the TDoA scheme only measures the absolute difference between the arrival times, so is easier to implement than the ToA scheme. The TDoA scheme is illustrated in Figure 3.  
Figure 3: The Time Difference of Arrival (TDoA) scheme.  
Figure 2: The Time of Arrival (ToA) scheme.  
There are three BSs of which the positions are \((X_{1},~{}Y_{1})\), \((X_{2},~{}Y_{2})\) and \((X_{3},~{}Y_{3})\). We denote by \((x,~{}y)\) the position of a source node and by \(d_{i}\) the position of the \(BS_{i}\). Then, from the arrival time difference, we can obtain the distance differences, that is, \(d_{i}-d_{j}\) for \(i\
eq j\). Therefore, the position of the signal source can be estimated from the intersection of hyperbolas.
# A Method for Predicting Radiated Acoustic Field in Shallow Sea Based on Wave Superposition and Ray
## 2 Theoretical Model
### Wave Superposition Theory
It should be noted that in practical applications, the matrix \(D\) may be an ill-conditioned matrix, and slight disturbance of vibration velocity will cause a dramatic change in virtual source strength. According to the acoustic inverse problem observed in a literature study, the Tikhonov regularization method based on L curve can effectively suppress the disturbance and effectively deal with the ill-conditioned matrix before solving the inverse problem [15]. For the same discrete processing method in Equation (4), the pressure at point \(P\) can be expressed as the superposition of \(I\) discrete point source radiated acoustic fields as shown as:  
\[v_{n}={v_{n}}^{+} \tag{2}\]2
## 5 Discussion
### The Role of Interactive Dialogue in Fatigue Recovery  
In recent years, the importance of interactive dialogue in alleviating driving fatigue and enhancing driver alertness has garnered increasing attention. This study finds that highly interactive voice dialogues, particularly humorous and Q&A-style dialogues, have a significant effect on fatigue recovery. This conclusion aligns with existing research, supporting the notion that interactive dialogue can activate drivers' emotions, improve their mental state, and enhance their attention [47]. Compared to mechanical, one-way information delivery, dialogues with emotional and cognitive interactions are more likely to trigger positive emotions in drivers, alleviate negative emotions and psychological stress, and thus effectively counter the effects of fatigue.  
In long-duration driving tasks, such as long-haul freight, taxi, or ride-hailing operations, drivers demonstrate a higher demand for voice dialogues that are both interactive and emotionally valuable. When the emotional expression of the voice system aligns with the driver's emotional state, drivers are less likely to have accidents, pay more attention to road conditions, and are more likely to engage in interactions [48]. Q&A-style dialogues are particularly common in these scenarios, fulfilling the need for information while also helping to divert attention and restore cognitive alertness. Additionally, voice interaction systems have an advantage over touch-based systems in reducing distractions and improving accessibility [49].  
However, the positive effects of interactive dialogue are not universally effective in all contexts. On the one hand, individual differences significantly impact the interactive experience. For instance, older drivers or those with a higher dependence on technology may tend to focus more on interface feedback during voice interactions, which can pose safety risks [50]. On the other hand, in cases of severe fatigue, excessive interaction or information input may increase the driver's cognitive load and exacerbate fatigue. Therefore, the content and frequency of interactions should be tailored to the driver's fatigue state: light, humorous dialogue or casual chats can aid fatigue recovery in mild fatigue, while unnecessary interventions should be minimized in severe fatigue to avoid cognitive overload [51].  
Furthermore, this study reveals that drivers from different occupational backgrounds and with varying individual characteristics have different preferences for voice interactions. Long-haul freight drivers tend to prefer a balance of functionality and interactivity in voice dialogues to alleviate fatigue from long hours of driving. In contrast, taxi drivers and private car owners are more inclined towards social and entertainment-oriented interactions. This finding confirms that human drivers exhibit specific social preference patterns during driving tasks [52].
## 4 Simulation Experiments
### Applicability of the Proposed Technique  
This subsection verifies the applicability of the proposed technique while considering only the RPC with robust estimation. In the previous simulations, the GNSS signal is assumed to have a constant Doppler frequency, which is not realistic in practice. Therefore, the GNSS signal is modified using the GNSS signal settings module in Figure 3 to test different conditions. Two types of common GNSS signal dynamics are considered, including uniform acceleration dynamics (denoted as Acc dynamics) and sinusoidal acceleration dynamics (denoted as Sin dynamics). These two types of GNSS signal dynamics are shown in Figure 8. In addition, different \(C/N_{0}\) conditions are also considered in this simulation. The \(C/N_{0}\) varies from 35 to 50 dB-Hz in steps of 5 dB-Hz. Other simulation parameters remain the same as in Section 4.2.  
Figures 9 and 10 show the carrier phase errors with and without phase compensation techniques at different \(C/N_{0}\) conditions under Acc and Sin dynamics, respectively. The following results can be derived from these figures. (1) From the original curves at different \(C/N_{0}\) conditions in the two figures, it can be seen that increasing \(C/N_{0}\) does not reduce the amplitude of the phase bias because the GNSS is still buried under the noise floor and it has a tiny effect on the array weight. However, increasing \(C/N_{0}\) can slightly reduce the amplitude of transient overshoots. (2) From the SPC and RPC curves at different \(C/N_{0}\) conditions in the two figures, it can be seen that the residual phase bias after compensation in these two phase compensation techniques decreases as the \(C/N_{0}\) increases. This suggests  
Figure 7: Carrier tracking errors with and without phase compensation techniques under the outlier contaminated case. (**a**) Carrier phase error. (**b**) Doppler frequency error.  
that a higher \(C/N_{0}\) makes the phase bias estimation more accurate and thus improves the performance. This is consistent with the results in Section 4.1. (3) Comparing the SPC and RPC curves at the same \(C/N_{0}\) conditions in the two figures, it is shown that the RPC has better phase compensation performance than the SPC, resulting in a significant improvement in measurement accuracy. This is because the RPC has better phase bias estimation than the SPC, as shown in Section 4.1. (4) The RPC shows the expected performance at different \(C/N_{0}\) conditions under different dynamics. By using the RPC, the phase distortions are significantly reduced after phase bias estimation and compensation. This proves the applicability of the proposed technique.