esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 c-di-AMP Binding Molecules  
Since the diverse roles of c-di-AMP in cell signaling pathways depend upon its binding to target molecules, the investigation into the identification, structure, and function of c-di-AMP binding molecules is important to reveal the mechanisms of c-di-AMP activity in various signaling pathways. However, unlike c-di-GMP for which hundreds of binding proteins have been identified [32; 33; 55], a few c-di-AMP binding proteins have been discovered so far in bacteria (Table 2) [17]. Multiple strategies have been successfully employed to identify c-di-AMP binding proteins including UV-crosslinking of \({}^{32}\)P-labelled c-di-AMP with a library of purified proteins [55]. However, the most common strategy involves the use of c-di-AMP affinity columns to isolate binding proteins from bacterial cytoplasmic extracts followed by gel electrophoresis and mass-spectroscopy [17; 25; 48]. c-di-AMP binding is typically confirmed via standard affinity assays such as surface plasmon resonance (SPR) or pulse chase analysis [55], or more recently through an adaption of the differential radial capillary action of ligand assay (DRaCALA) first used to study c-di-GMP binding [17; 48]. These recently identified c-di-AMP binding proteins include enzymes, transporters and transcriptional regulators that are themselves allosterically regulated by c-di-AMP binding. The structural analysis of c-di-AMP binding proteins has also facilitated the identification of conserved c-di-AMP binding-domains, thus leading to the discovery of additional c-di-AMP binding proteins. In addition to binding proteins, c-di-AMP can bind to RNA riboswitches [56; 57; 58; 59; 60]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**c-di-AMP** & & & & & & \\
**Receptor** & & & & & & \\
**Proteins** & **Location in the** & & & & & \\
**(Species** & **Cell** & & & & & \\
**Originally** & & & & & & \\
**Identified in)** & & & & & & \\ \hline \multirow{6}{*}{DarR} & \multirow{6}{*}{C Cytoplasmic protein} & \multicolumn{3}{c}{DarR contains two domains: a} & \multirow{6}{*}{Protein Function} & \multirow{6}{*}{Proteins by Deletion or Overexpression of the Genes} \\  & & & C-terminal QacR-like domain, and an & & & \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{6}{*}{KtrA _(S. aureus)_} & Cytoplasmic protein & \multicolumn{3}{c}{KtrA possesses two RC domains, RCK domains, RCK_N and RCK_C.}
## 4 Findings
### Street Connectivity Model  
We conducted a hierarchical multiple regression (HMR) for street-length density around homes (Table 2). Street-length density around homes correlated with the number of commuting trips, the use of private motor vehicles for commuting trips, and the frequency of public transport use after socioeconomic variables (including age, daily activity, and income) were controlled for in the model. The number of non-commuting trips was removed from the model according to the highest \(p\)-values, but other non-significant variables, including commuting distance and the use of private motor vehicles in non-commuting trips remained in the model. The number of commuting trips, the frequency of public transport use, and the use of private motor vehicles are highly correlated with street-length density; there were no variables in the model that demonstrated only a marginally significant correlation. As Table 2 shows, one unit increase in street-length density around homes correlated with a 9% increase in the use of cars and motorcycles for commuting trips. Moreover, people who live in sprawled areas commute longer distances than those who live in compact areas (which have higher connectivity levels of the street networks). Table 2 also shows that R-square increased when socioeconomic variables were fixed in the model and the value of F changes was less than 0.05, and therefore is significant. In other words, controlling for age, daily activity, and income increased R-square in Model 2, and the model controlling for socioeconomic variables explains the better fit of the results. The HMR regression model also developed for street-length density around workplaces. The model controlled for age, daily activity, and income. Street-length density around workplaces in Lahore and Rawalpindi correlated with the frequency of public transport use, commuting distance, and the use of private motor vehicles in commuting trips. The number of commuting trips per week was removed from the model based on a
## 2 Numerical Simulation Calculation Model
### The Effect of Differential Ground Stress  
The magnitude and direction of the ground stress have an important influence on the expansion path and final morphology of the hydraulic fractures. For this reason, this subsection mainly analyzes the interaction mechanism between laminated joints and hydraulic fractures under different horizontal ground stress difference conditions (0, 2, and 6 MPa), and other parameters are the same as those in the benchmark calculations.  
Figure 10 shows the morphology of the seam network at the end of injection under a uniform horizontal ground stress. When the hydraulic fracture expands to the tip of the laminated seam, it continues to expand along the direction of the laminated seam.  
Figure 9: Displacement in the x-direction at the end moment of injection (magnified 200 times).  
Figures 11 and 12 show the geometry of the seam network formed when the ground stress difference is 2 and 6 MPa, respectively. Compared with the case where the difference in ground stress is 0 MPa, the hydraulic fractures still expand in the direction perpendicular to the minimum horizontal principal stress after intersecting with the laminae. When the difference between the overlying rock pressure and the minimum horizontal principal stress increases to 6 MPa, it can be seen that the hydraulic fractures tend to pass directly through the laminae joints, resulting in a decrease in the number of activated laminae joints. This is mainly because the greater the difference in ground stress is, the greater is the value of positive stress on the laminated joints, and the less likely they are to be activated.  
Figure 11: Crack pattern at the end of injection when the ground stress difference is 2 MPa.  
Figure 10: Crack pattern at the end of injection when the ground stress difference is 0 MPa.  
The crack lengths formed under different ground stress difference conditions were counted. The total crack length increased from 20.58 to 21.91 m as the ground stress difference increased from 0 to 2 MPa, but the total length of hydraulic cracks in the former was 7.97 m and the total length of laminated joints was 12.61 m, while the total length of hydraulic cracks in the latter was 12.97 m and the total length of laminated joints was 8.94 m. After the ground stress difference increased to 6 MPa, the total crack length was 16.76 m, among which the total length of hydraulic cracks was 15.62 m and the total length of laminar joints was 1.14 m. The above calculation results show that the existence of a certain ground stress difference is beneficial to communicate more laminar joints and form a more complex crack network, while an excessively large ground stress difference will reduce the complexity of artificial cracks.
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Quantitative Evaluation  
The experimental results of the proposed method are compared with those of other methods as follows. Table 3 provides the quantitative evaluation data for various low-light image enhancement methods applied to satellite images containing the Earth region, while Table 4 presents the results for single satellite images. In both tables, values highlighted in red indicate the best performance, whereas values in blue denote the second-best performance.
## 8 Results and Discussion
### Static Scenario #3  
The duration of the scenario is 3 min. In this scenario, a swept continuous wave (SCW) jamming signal is injected after 1 min from the start of the scenario and lasted for 1 min. The jamming signal parameters are summarized in Table 6.  
In the next figures, the shaded area represents the duration at which the jamming signal was enabled. Figure 11 shows that both ST and VT loops failed to track the carrier frequency of the satellites throughout the jamming duration. However, the vector-based receiver was able to rapidly re-lock on the signal after disabling the jamming signal without the need to go into re-acquisition mode. On the contrary, the scalar-based receiver failed to lock to the signal after the loss of some satellites such as PRN 30. Consequently, the scalar-based receiver had to go into re-acquisition mode, which is  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Jamming Signal Type & Swept continuous wave \\ Jamming-to-Signal Ratio & 45 dB \\ Start Frequency & 1575.43 MHz \\ End Frequency & 1575.55 MHz \\ Number of Frequency Hops & 12 \\ Dwell Time & 1 ms \\ \hline \hline \end{tabular}
\end{table}
Table 6: Jamming signal parameters.  
Figure 10: Doppler shift for scenario #2. PRN 21 (**left**), and PRN 29 (**right**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Scalar Tracking** & **Vector Tracking** \\ \hline Horizontal Position (m) & 12.5 & 3.39 \\ Horizontal Speed (m/s) & 5 & 0.15 \\ Up Velocity Component (m/s) & 6.13 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 5: RMSE values of scenario #2.  
computationally intensive compared to tracking. Figure 12 shows huge errors in the velocity solution encountered by the scalar-based receiver due to its inability to re-lock to the signal.
# Additive and Dominance Genomic Analysis for Litter Size in Purebred and Crossbred Iberian Pigs  
[PERSON]  
1 Departamento de Anatomia, Embirologia y Genetica Animal, Facultad de Veterinaria, Instituto Agorolimentario de Aragon (IA2), 50013 Zaragoza, Spain; [EMAIL_ADDRESS] (H.S.); marmatinezascliffeorgmail.com (M.M.-C.)1 Departamento de Anatomia, Embirologia y Genetica Animal, Facultad de Veterinaria, Instituto Agorolimentario de Aragon (IA2), 50013 Zaragoza, Spain; [EMAIL_ADDRESS] (H.S.); marmatinezascliffeorgmail.  
farmers crossing Iberian and Duroc varieties to cross boars from the Duroc variety and sows from the Iberian variety. Prolificacy, which is lower than that of white pig populations, is the major limitation in the intensive production of crossbred pigs from Iberian dams [4]. The INGA FOOD, S.A. company has developed a crossbreeding scheme between two Iberian varieties (Retinto-R- and Entrepelado-E-) that has created a hybrid sow called CASTUA-ER, which has an important heterosis effect in prolificacy [5]. In addition, the company has been developing a breeding scheme for increasing litter size through selection in the parental Retinto and Entrepelado populations.  
Theoretically, the optimal strategy for the selection of purebreds for crossbred performance is Recurrent Reciprocal Selection [6]; however, it has not been routinely used in pig breeding because it involves a delay in the generation interval. In fact, purebred parental populations are selected based on selection criteria calculated from purebred phenotypic information, and under the assumption that the genetic correlation between purebred and crossbred performance is high [7]. Those genetic correlations can be imperfect (<1) because of genotype-by-environment (GxE) interactions and the presence of non-additive genetic effects [7].  
Genomic information facilitates the analysis of crossbreeding data, even if genotyped and phenotyped individuals are not directly related [8], by the definition of an additive-dominance genotypic model that provides estimates of genotype x environmental interactions through genotypic correlations. In addition, the estimates of genotypic and dominance variances can be used to estimate the additive genetic correlation between purebred and crossbred performances. Backsolving, as proposed by [PERSON] et al. [9], provides an estimate of the SNP effects and allows us to calculate the amount of additive genetic variance associated with each genomic region in purebred and crossbred performances.  
This study estimated the additive and dominance genotypic variances and covariances, which were used to calculate the additive and dominance genetic variances and the genetic correlations between purebred and crossbred performances in the Retinto and Entrepelado populations. In addition, the distribution of the additive genetic variance within the autosomal genome for purebred and crossbred performance was quantified.
## 5 Discussion
Our research introduces a method that employs feature selection methods, specifically LASSO, in conjunction with Random Forest, resulting in a superior accuracy of 99.39%. In conclusion, this comparative analysis highlights the diverse strategies employed in EEG emotion analysis. Each method exhibits a notable performance, and our proposed approach, which incorporates feature selection methods for random forest, demonstrates particularly high accuracy. This study provides valuable insights into effective methodologies for accurate EEG-based emotion classification. Furthermore, our approach emphasizes the integration of the embedded-based feature selection method with random forest, enhancing model interpretability. These aspects are crucial for practical applications, where understanding the model's decisions. Figure 8 visualizes the correlation heatmaps of the EEG Emotion and DEAP datasets, focusing on the feature selection methods that demonstrated the best performance as mentioned in Tables 9 and 10. When comparing Figure 8b to Figure 8a, stronger positive and negative correlations can be observed, indicating the removal of unnecessary variables. Additionally, comparing Figure 8d,e with Figure 8c shows that positive correlations are more clearly observed. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Method** & **Label** & **Accuracy** \\ \hline \multirow{2}{*}{[13]} & \multirow{2}{*}{mRMR, RBF kernel} & Valence & 62.39\% \\ \cline{3-4}  & & & Arousal & 60.72\% \\ \hline \multirow{2}{*}{[13]} & \multirow{2}{*}{GA, SVM} & Valence & 56.69\% \\ \cline{3-4}  & & & Arousal & 53.46\% \\ \hline \multirow{2}{*}{This study} & \multirow{2}{*}{GA, XGBoost} & Valence & 64.65\% \\ \cline{3-4}  & & & Arousal & 63.52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative table of different DEAP classification methods. Figure 8: Correlation heatmaps: (**a**) before feature selection, (**b**) after feature selection for the EEG Emotion dataset, (**c**) before feature selection for the DEAP dataset, (**d**) after feature selection for the valence label in the DEAP dataset, and (**e**) after feature selection for the arousal label in the DEAP dataset.
## 2 Materials and Methods
### YOLOv10 Network Improvement
#### 2.2.2 Wavelet Transform Conv Optimization  
In the YOLOv10 network, Conv (convolution) serves as a crucial component for feature extraction, where the size of the receptive field depends on the dimensions of the convolution kernel and the number of network layers [20]. With the increase in the size of the convolution kernel, the receptive field will gradually expand, but this will also significantly increase the complexity of the calculation and the number of parameters. In the case of large receptive fields, the YOLOv10 network is prone to overfitting and the computational efficiency is reduced. Moreover, traditional Conv is more sensitive tohigh-frequency features while exhibiting a weak response to low-frequency features. In view of the above problems, this study optimizes the Conv in the YOLOv10 network. It aims to enhance the response of the network to low-frequency information and achieve a larger receptive field with fewer parameters, thereby improving the stability and efficiency of the model. The core steps of the improved WTConv (Wavelet Transform Conv) are illustrated in Figure 5 [21].  
WTConv first uses Haar Wavelet Transform to decompose the input image into a low-frequency component and three high-frequency components, and the spatial resolution of the component is 50% of the original image. This is expressed in Equations (8) and (9), where \(X_{LL}\) denotes the low-frequency component, and \(X_{LH}\), \(X_{HL}\), and \(X_{HH}\) denote the horizontal, vertical, and diagonal high-frequency components, respectively; \(f_{\rm LL}\) is the low-pass filter, while \(f_{LH}\), \(f_{HL}\), and \(f_{HH}\) represent a set of high-pass filters. Meanwhile, to increase the frequency resolution and reduce spatial resolution, the low-frequency component is further decomposed into multiple levels, as shown in Equation (10), where i indicates the current level, and \(X_{LL}^{(0)}=X\). To enable the convolution kernel to operate over a larger area and thus increase the receptive field, this study utilizes the WT (Wavelet Transform) to filter and downsample the input, and performs small-kernel depthwise convolution on different frequency maps. Finally, the convolved results of each frequency component are recombined through the IWT (Inverse Wavelet Transform) to generate the output image, as shown in Equation (11), where X and W denote the input tensor and the weight tensor of a k \(\times\) k depthwise convolution kernel, respectively.  
\[f_{LL}=\frac{1}{2}\begin{bmatrix}1&1\\ 1&1\end{bmatrix},\,f_{LH}=\frac{1}{2}\begin{bmatrix}1&-1\\ 1&-1\end{bmatrix},\,f_{HL}=\frac{1}{2}\begin{bmatrix}1&1\\ -1&-1\end{bmatrix},\,f_{HH}=\frac{1}{2}\begin{bmatrix}1&-1\\ -1&1\end{bmatrix} \tag{8}\]  
\[[X_{LL},X_{LH},X_{HL},X_{HH}]=\text{Conv}([f_{LL},f_{LH},f_{HL},f_{HH}],X) \tag{9}\]  
\[X_{LL}^{(i)},X_{LH}^{(i)},X_{HL}^{(i)},X_{HH}^{(i)}=\text{WT}\Big{(}X_{LL}^{(i -1)}\Big{)} \tag{10}\]  
\[Y=\text{IWT}(\text{Conv}(W,\text{WT}(X))) \tag{11}\]
# Compaction Characteristics and Mechanical Response of Gravel-Glass-Rubber Mixtures +
## 3 Experimental Procedure
### One-Dimensional Compression  
Material compressibility is one of the most important factors required in design considerations. For gravels with rigid particles, any change in volume is due to the rotation, movement, and rearrangement of non-compressible grains [43]. The compressibility of GGRM, however, is entirely different from that of hard-grain gravels due to significant differences in the elastic modulus of the rigid gravel grains, the rigid but crushable glass, and the soft rubber.  
The compression behavior of granular media is usually studied in conventional one-dimensional (1D) compression equipment [44]. Yet, practical difficulties are encountered when testing soils with large particles using conventional small-size compression apparatus. Therefore, in this study, the 1D compression behavior of GGRM was evaluated using an in-house-built medium-size compression cell, which can host specimens of 250 mm in inner diameter and up to 100 mm in height.  
Dry specimens were prepared by the tamping method at a dry density corresponding to 90-95% degree of compaction. In the tests, the nominal vertical stress varied incrementally from 0 to 200 kPa according to the loading pathway shown in Figure 3. At the end of each loading-unloading-reloading phase, the vertical effective stress was kept constant (creep) until the settlement increment became negligible (i.e., \(\Delta s\leq\) 0.02 mm/5 min). The measured vertical stress was corrected for the loss of transferred load (due mainly to material-wall friction) that was experimentally measured by using a load cell placed on the base plate of the compression device.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 5 Discussion
### Limitations  
While this study demonstrates the feasibility of using EEG to monitor distraction in controlled environments, several challenges must be addressed before applying this method to real-world coal mining operations. First, EEG signals are highly susceptible to noise and artifacts caused by environmental electromagnetic interference and physical movements, which are prevalent in mining settings. Future studies should explore advanced artifact-removal algorithms and robust hardware designs to mitigate these issues. Second, integrating EEG devices with miners' safety helmets presents significant design challenges. Current EEG systems, while effective in controlled environments, may lack the durability, comfort, and usability required for prolonged use in harsh mining conditions. Developing lightweight, wearable EEG devices specifically tailored for mining scenarios is a crucial step toward practical implementation. Additionally, miners perform diverse tasks with varying cognitive demands, necessitating further validation of the model's applicability across different scenarios. Future research could employ task-specific EEG feature selection or adaptive machine-learning approaches to address this challenge.
## 2 Scope and Objectives  
Despite the availability of modern evacuation methods, their efficacy is limited in underground mines by the range of human behaviors exhibited by miners during emergency situations. This study addresses this gap by providing insights into mine evacuation practices, identifying potential behavioral constraints, and proposing solutions to these constraints. The proposed approach employs a novel ABS to capture a wide range of miner dynamic behaviors during evacuations, providing a realistic modeling environment and assessing evacuation performance under multiple scenarios. Simulation scenarios are conducted to examine the effectiveness of the technology and understand potential human factors. The ABS evaluates and optimizes mine evacuation policies and procedures, including the development of evidence-based guidelines for emergency drills, evacuation route planning, and miner training programs. Additionally, ABS-based training procedures for miners and emergency responders provide immersive, interactive experiences that improve preparedness and response times during real emergencies. The contributions of the study are summarized as follows:  
* Conducting a comprehensive analysis of human behaviors during mine evacuations, identifying and evaluating key behavioral constraints overlooked by previous studies;
* Developing ABS, as an advanced simulation technique for dynamic and realistic circumstances, to model and optimize the evacuation process;
* Implementing an empirical data-driven method for simulating evacuation scenarios and incorporating real-world data on walking and running speeds under various conditions to provide a robust framework for examining and optimizing underground mine evacuation strategies;
* Evidence-based decision-making for individuals' safety in underground mines by addressing the human factor in mine evacuations and creating more effective and adaptive evacuation protocols.