esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Validation  
### Implementation of the Algorithm  
The algorithm is implemented in a PC-based dynamic VAD platform with software written in the C++ language. An electromagnetic tracker (Polhemus FASTRAK) is used to detect the orientation of the subject's (user's) head in three degrees of freedom (turning around left-right, front-back, and up-down axes) in real time. According to the temporary orientation of the subject's head, the target source position with respect to the subject's head is calculated, and the synthesized binaural signals are updated. The update rate and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{M^{\prime\prime}}\) & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{4}\) & \(\mathbf{7}\) & \(\mathbf{11}\) & \(\mathbf{13}\) \\ \hline \(\eta_{m}(\%)\) & 69.9 & 81.1 & 90.7 & 95.7 & 98.9 & 99.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cumulative percentage variance of energy for various numbers of direction-related modes (left ear).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{N^{\prime\prime}}\) & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{4}\) & \(\mathbf{6}\) & \(\mathbf{8}\) \\ \hline \(\eta_{n}(\%)\) & 70.7 & 82.2 & 91.9 & 97.2 & 99.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cumulative percentage variance of energy for various numbers of time-related modes (left ear).  
Figure 2: Mean relative reconstruction error across all distances on the horizontal plane (left ear): (a) three-dimensional surface image; (b) color-scale image.  
system latency time of the dynamic VAD are 60 Hz and 25.4 ms, respectively. The details of the dynamic VAD are referred to [12].  
The binaural signals are synthesized according to the algorithm in Section 4 and analysis in Section 5. The HRIRs used in binaural synthesis are described in Section 5.1. According to the analysis in Section 5.2, the tensor decomposition of minimum-phase HRIRs is truncated to \(M^{\prime\prime}=13\) direction-related eigen modes and \(N^{\prime\prime}=8\) time-related eigen modes. Therefore, a common set of \(N^{\prime\prime}=8\) convolvers or filters is used for synthesizing the signal of each ear.  
The resultant binaural signals are reproduced by an in-ear headphone (Etymotic Research ER-2). Because the ER-2 headphone exhibits a flat magnitude response measured at the end of an occluded-ear simulator up to 16 kHz and the signal component above 16 kHz contributes little to localization, the equalization of the headphone to eardrum transmission is omitted.  
Here, non-individual HRIRs are used in binaural synthesis because it is difficult to use individualized HRIRs in most practical applications. Moreover, previous studies indicated that individualized HRTFs have a significant influence on the directional (front-back and vertical) localization and slight influence on distance perception [27]. However, when the dynamic localization cue is included in dynamic VAD, the influence of individualized HRIR (spectral cue) to directional localization decreases due to the redundancy among different directional localization cues.
# Compressible Diagnosis of Membrane Fouling Based on Transfer Entropy
## 2 Background of Membrane Fouling  
### Membrane Fouling  
Membrane fouling refers to the increase in water resistance and the decrease in permeation flux caused by the deposition of pollutants on the membrane surface or in its pores. The mechanisms of membrane fouling mainly include (1) plugging of membrane pores by colloid and SMP, fouling adhesion, and gel layer formation; (2) formation and consolidation of a cake layer; (3) variation in pollutants due to long-term functioning of the reactor; and (4) osmotic pressure effect. The membrane fouling has different characteristics, which can be divided into three types: (1) removable fouling, which usually generates in the filter cake layer and can be removed by physical means; (2) irremovable fouling, which usually requires chemical cleaning to remove the pore blockage; and (3) irreversible fouling, which cannot be removed by any cleaning operation. In addition, there are various methods for studying membrane fouling, with the Hermia model being the most widely used. This semiempirical parametric model assigns physical significance to its parameters, which is effectively described by this model. The generalized Hermia model is a form of a nonlinear differential equation. It displays the dynamic of membrane fouling and its complex relationships between factors of membrane fouling. According to these characteristics, it is difficult to design a diagnosis method to diagnose the root cause of membrane fouling accurately.
## 3 Modeling Method of PV DC Arc Based on AR Model
### The Best Order for the AR-Based Model  
Another important thing is to determine the best order for the proposed AR-based model, which entails a tradeoff of bias and variance, and a poor choice can result in a virtually useless estimator. The Akaike Information Criterion (AIC) can achieve this goal by providing an asymptotically unbiased estimate of the difference between the various fitted AR models and the truth. Furthermore, this can be carried out without knowing the true model. The AIC is defined as:  
\[\text{AIC}=-\frac{2}{N}\ln L+\frac{2p}{N} \tag{7}\]  
where \(L\) is the maximized likelihood and \(N\) is the sample size. \(p\) is the number of independently adjusted parameters in the candidate model. Since the proposed AR(\(p\)) model is a Gaussian one, AIC reduces to  
\[\text{AIC}=\ln\hat{\sigma}_{t}^{2}+\frac{2p}{N} \tag{8}\]  
where \(\hat{\sigma}_{p}^{2}\) is the maximum-likelihood estimate of \(\hat{\sigma}_{t}^{2}\), which can be determined by  
\[\hat{\sigma}_{p}^{2}=\Bigl{(}1-\left|k_{p-1}\right|^{2}\Bigr{)}\hat{\sigma}_{ p-1}^{2} \tag{9}\]where \(k_{p}\) is the reflection coefficient as per Equation (3). The value of the AIC represents the goodness of fit. The smaller the value is, the better the model fits the data. In order to ensure that the established model has sufficient estimation accuracy and low computational burden, it is necessary to use the measured data from PV inverters #A, #B, and #C to determine an optimal order. The original data are collected under the normal operation of inverters with a total of 4800 samples, and each sample lasts for 10 ms. According to the analysis of the generation mechanism of arc and the time-frequency domain characteristics of arc current, random characteristic is one of the typical inherent characteristics of the arc. The observation in a short time window shows that the operating noise of the inverter is relatively stable. The results of AR model estimation are consistent in normal operation. The arc signal is non-stationary and the model estimates for each window vary greatly. We used normal data to establish the AR model. Then, the AIC values can be obtained as per Equations (8) and (9) for a given order from 1 to 20, as shown in Figure 4. It can be seen from Figure 4 that the best order for the proposed AR-based model can be located at 12 th, for which order we can always obtain the minimum AIC value, that is, the \(p\)-value is 12.
# Differences in Body Fat in Athletes Categorized by Resting Metabolic Rate
###### Abstract  
The purpose of the study was to examine differences in body fat percentage (BF%) across groups stratified by resting metabolic rate (RMR) when normalized to body weight. National Collegiate Athletic Association Division III athletes (\(n=190\); Age: \(19.8\pm 1.4\) year; Body Mass: \(79.3\pm 20.2\) kg; Height: \(175.0\pm 9.3\) cm, Body Mass Index: \(25.6\pm 4.9\) kg/m\({}^{2}\)) participated in this cross-sectional mixed cohort study. Body composition was assessed using air displacement plethysmography. RMR was assessed using indirect calorimetry. For each sex, tertiles were determined and used to create low, moderate, and high relative RMR groups as follows: low (M: \(<\)26 kcal/kg; F: \(<\)24 kcal/kg), moderate (M: \(26.1\)-\(29.0\) kcal/kg; F: \(24.1\)-\(27.0\) kcal/kg), and high (M: \(>\)29.1 kcal/kg; F: \(>\)27.1 kcal/kg). The mean \(\pm\) standard deviation RMR male and female athletes was \(27.9\pm 3.2\) and \(25.9\pm 2.8\) kcal/kg when expressed relative to body weight. When stratified by sex, males in the low RMR group had significantly higher BF% values than those in the moderate (mean difference, [95% confidence intervals]) (7.2, [2.4, 12.0] kcal/kg; \(p<0.01\)) and high RMR groups (7.7, [2.9, 12.5] kcal/kg; \(p<0.001\)). Female athletes in the moderate RMR group had higher body fat percentages than those in the high RMR group (mean difference, [95% confidence intervals]) (5.8, [2.4, 9.2] kcal/kg; \(p<0.01\)). Female athletes in the moderate relative RMR group had higher BF% values than those in the higher relative RMR group (3.3, [\(-0.1\), \(6.7\)] kcal/kg; \(p=0.049\)). Both male and female athletes with a low relative RMR had a higher BF%.  
metabolism; body composition; sports; body mass; metabolic adaptation
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 4 The Anticipated Cloud Storage Structure
### Proposed Framework Functions  
The multi-layer encoding system incorporates several design elements to ensure robust data protection and privacy. Together, the system elements form a comprehensive framework for secure data processing and transfer. The system includes the cryptographic capabilities provided by the cloud service provider (CSP), as illustrated in Figure 2. Each component of the system performs specific tasks to effectively handle data encryption, decryption, and transfer. The administrator plays a crucial role in conducting audits and assessments to maintain the integrity and safety of the system [73; 74]. This includes verifying user identities, examining transmitted data for potential privacy breaches, and ensuring compliance with established security protocols.  
The user interface serves as the gateway for users to interact with the services of the cloud more efficiently. It provides a user-friendly environment where users can input data, manage their privacy settings, and perform various operations. The data controller ensures that data is properly managed and facilitates seamless access when needed [75]. The pre-encoding and the data-processing functions are responsible for transforming the plain data into a suitable format for the initial encryption layer. This step prepares the data for the subsequent encryption process, optimizing its security and confidentiality.  
The cryptographic system is the core component of the multi-layer encryption system. It establishes a second layer of encryption, utilizing advanced cryptographic algorithms provided by the CSP [76]. This additional layer enhances the confidentiality of the data, making it more resistant to unauthorized access and ensuring robust data protection throughout the system. Therefore, the authors can say that the multi-layer encryption system incorporates various design elements and functions to ensure secure data processing and transfer. The user interface, data controller, pre-encryption, and cryptographic system all work in harmony to safeguard sensitive information and protect user privacy within the system [77].  
Figure 2: Privacy-preserving framework.
# Study on the Co-Evolution Mechanism of Key Strata and Mining Fissures in Shallow Coal Seam Mining
## 5 Verification of Mining Fissure Evolution Parameters  
The upward mining fissure is caused by the fracture of the key strata, which causes the tensile stress or shear stress of the overlying rock to reach the ultimate bearing capacity of the overlying rock, thereby generating fissures. At the same time, the strength of the key strata is much greater than that of the overlying strata, so the minimum fissure length of the overlying strata must exceed the breaking height of the key strata, that is, \(a\geq 16\) m, which is taken as 16 m in this paper. The specific parameters of the occurrence characteristics of rock and soil layers in Hanjiawan Coal Mine are shown in Table 3.  
1. In order to simplify the calculation process, the given data in the paper are selected for calculation, and the mining fissure lag distance \(L_{Z}\) can be obtained as \[L_{Z}=\sum_{j=1}^{m}h_{j}\cot\beta_{j}+\sum_{k=1}^{n+s}h_{k}\cot\beta_{k}=36.84 \text{ m}\] From Formulas (22) to (48), the fissure evolution parameters of the mining face up and down mining can be obtained as (2) The depth \(H_{\text{c}}\) of the downward type I mining fissure is \[H_{\text{c}}=\frac{\frac{E_{\text{c}}}{1-\mu}-2c\tan(45+\phi/2)}{\gamma\Big{(} \frac{\mu}{1-\mu}-\tan^{2}(45+\phi/2)\Big{)}}=6.03\text{ m}\] (3) The height \(H_{\text{st}}\) of the upward type I mining fissure is \[H_{\text{st}}=\lambda a\bigg{[}\cos^{2}\frac{\beta_{\text{c}}}{2}\bigg{(}1+3 \sin^{2}\frac{\beta_{\text{c}}}{2}\bigg{)}\bigg{]}\cdot\cos\beta_{\text{c}}=38.35\text{ m}\] (4) The height \(H_{\text{sr}}\) of the upward type II mining fissure is \[H_{\text{sr}}\geq 5.77a=92.32\text{ m}\] Therefore, according to the sum of the depth of down-going type I mining fissures and the height of up-going type I and type II mining fissures, the relationship with the buried depth of key strata is as follows: \[\left\{\begin{array}{l}H_{\text{c}}+H_{\text{st}}=44.38\text{ m}\leq H_{ \text{gd}}\\ H_{\text{c}}+H_{\text{sr}}=98.35\text{ m}\geq H_{\text{gd}}\end{array}\right.\] It can be seen that when the mining height of the No.2-2 coal seam in Hanjiawan Coal Mine is 4.3 m, the downward type I mining fissures and the upward type I mining fissures are not connected, and it is not easy to produce water inrush, sand inrush, and air leakage in the process of working face mining. The downward type I mining fissures and the upward type II mining fissures are connected, and sudden disasters are prone to occur in the working face. The results are similar to those measured by field drilling, which verifies the rationality of this study.
# Therapeutic Potential of Gramicidin S in the Treatment of Root Canal Infections
## 2 Results  
### Susceptibility of Tetracycline-Resistant _E. faecalis to GS_  
According to the data of the Clinical and Laboratory Standards Institute [17], enterococci are defined to be resistant to tetracycline if the minimum inhibitory concentration (MIC) values are \(\geq\)16 \(\upmu\)g/mL and they are called susceptible at MIC \(\leq\) 4 \(\upmu\)g/mL. Table 1 shows the resistance of the _E. faecalis_ DSM 2570, which was isolated from urine and represents a quality control strain for antimicrobial susceptibility testing [18], the WW4 cheese isolate and four tetracycline-resistant clinical isolates, designated TRE1-TRE5. For comparison, the clinical root canal isolate WW6, which is susceptible to tetracycline (MIC \(<\) 1 \(\upmu\)g/mL) but forms robust biofilms (Figure 1A), was also studied (Table 1). Tetracycline inhibits protein synthesis, and its action is therefore bacteriostatic. GS is a bactericidal antibiotic peptide. As shown in Table 1, its MIC, minimum bactericidal concentration (MBC), and minimum biofilm inhibitory concentration (MBIC\({}_{90}\)) are mostly in the range of 8-16 \(\upmu\)g/mL for all studied _E. faecalis_ strains. This small difference between inhibitory and bactericidal concentrations as well as the concentrations required for the inhibition of biofilm formation, explains the great bactericidal activity of GS, which gives bacterial cells no chance to remain alive or to respond to the stress caused by GS treatment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Resistance/Susceptibility (\(\mu\)g/mL) to**} & & & \\ \cline{2-5} \cline
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## 2 AUV Model
### AUV Rigid-Body Model  
The equations of motion of the vehicle involve the rigid-body dynamic terms and components related to hydrodynamic forces and moments. The model includes the three main effects including restoring forces (depending on vehicle geometry and mass), the added mass (due to forced harmonic vehicle motion induced by force/moment pressure) and the damping terms (caused by skin friction and vortex shedding). The nonlinear dynamics model of the AUV may be represented in a compact matrix form introduced in Appendix A and expressed as follows:  
\[M\dot{v}+C(v)v+D(v)v+g(\eta)=\tau_{A} \tag{1}\]  
where \(M\) is the mass matrix component, \(C\) represents the Coriolis effects, \(D\) represents the dumping effects, \(g\) corresponds to restoring forces and moments, \(\tau_{A}\) represents actuators thrust, \(v\) is the vector of linear and angular velocities and \(\eta\) is the array of linear and angular positions of the AUV. A detailed description of the elements of Equation (1) is given in Appendix A. The numerical values of the parameters are collected in Appendix B.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 3 Methodology
### \((2d)^{2}pca\)  
\((2d)^{2}pca\)[21], G2 DPCA [22] and RC2 DPCA [23] are three representatives of existing bilateral 2 DPCA algorithms. All of them project an image onto the two lateral eigenvectors simultaneously in order to calculate the feature matrix. Their only difference lies in the way to calculate bilateral eigenvectors. Since \((2d)^{2}pca\) shares the same eigenvectors with the two unilateral 2D algorithms but the eigenvectors of G2 DPCA and RC2 DPCA are quite different, we focus on \((2d)^{2}pca\) in our paper. By this way, we can make a fair comparison between this bilateral 2D algorithm and the two unilateral 2D algorithms.  
To make the most of bilateral information, \((2d)^{2}pca\)[21] directly takes the projection matrices \(\mathbf{U}\) and \(\mathbf{V}\) from 2 DPCA and A2 DPCA, respectively. For a test image sample \(\mathbf{\widetilde{X}}\), its feature matrix projected by \((2d)^{2}pca\) is  
\[\mathbf{V}^{T}(\mathbf{\widetilde{X}}-\mathbf{\widetilde{X}})\mathbf{U}\in \mathbb{R}^{l\times k}. \tag{14}\]The reconstruction image of sample \(\widetilde{\mathbf{X}}\) reconstructed by (2D)\({}^{2}\)PCA is  
\[\mathbf{V}\mathbf{V}^{T}(\widetilde{\mathbf{X}}-\overline{\mathbf{X}})\mathbf{ U}\mathbf{U}^{T}+\overline{\mathbf{X}}\in\mathbb{R}^{h\times w}. \tag{15}\]  
(2D)\({}^{2}\)PCA overcomes the limitations of the two unilateral 2D algorithms by projecting the rows and columns of images onto two lateral eigenvectors simultaneously. [PERSON] and [PERSON] [21] demonstrated that compared with the two unilateral 2D algorithms, (2D)\({}^{2}\)PCA achieves the same classification accuracies on the Olivetti Research Laboratory (ORL) face database and achieves higher classification accuracies on a partial Face Recognition Technology (FERET) database. However, its formulation mixes bilateral information from images together and may lead to information loss. The other two unilateral algorithms, i.e., G2 DPCA [22] and RC2 DPCA [23], have the same problem.
## 3 Proposed Improved Hybrid SSA with an EDO (ISSAEDO) Algorithm
### Proposed Improved Hybrid SSA with the EDO (ISSAEDO) Algorithm  
This section introduces a novel approach called ISSAEDO which combines an improved SSA and the EDO method based on the DE technique, along with a bound-constraint adjustment mechanism. The hybrid method that has been suggested facilitates the accurate and dependable determination of the ideal values for various PV models.
## 5 Results and Industrial Interpretation
### Classification: State and Mode Prediction
The ordering criteria \(\Delta\), is equal to V – D. The columns State and Mode have the average MCC figures for the method using \(10\times 10\)CV. (*) represents this method has an average MCC that is significantly worse than the one for the best-ranked method. (+) represents this method has an average MCC that is significantly better than the one for the best-ranked method. The best MCC for State and Mode are highlighted in bold. In short, the high performance that Bagging of Random Balance achieved for Mode prediction, and LogitBoost for State, will guarantee that this model can identify these two process parameters. The combination of both can surely identify the main production activity of the machining center. Some examples of these combinations are: (1) reference fixation of a new workpiece (idle state and manual mode); (2) workpiece milling operation (working state and automatic mode), test of a new milling program (idle state and MDI mode), and load/unload of a workpiece (stopped state and Pass mode). Therefore, the prediction model can extract useful information on how the machining time is distributed, not from an Engineering Department planning report, but from the real workshop reports on the daily situation.
## 3 Macro-Control and Micro-Autonomy Pathfinding Strategy
### Centralized Planning with Modified A\({}^{*}\)
#### 3.2.2 Enhancements for AGV Path Finding  
In order to adapt A\({}^{*}\) for AGVs in a manufacturing environment and better provide pre-planning guidance and expert experience for the subsequent distributed planning strategy, this article incorporates several modifications to the A\({}^{*}\) algorithm, including the following:  
1. AGV-Referenced Action Space: The action space is updated to move forward, rotate 90\({}^{\circ}\) left, rotate 90\({}^{\circ}\) right, rotate 180\({}^{\circ}\) back, and then wait. A\({}^{*}\) now uses specific rotating actions to apply turning penalty corrections.
2. Directional Spatial Nodes: Instead of treating locations as simple X-Y coordinates, each AGV state includes directional information (North, East, South, West) and represents it using directional spatial nodes. Accordingly, when adding nodes to the open list or close list, the algorithm also considers the directional attributes of the nodes.
3. Modified Heuristic Function: A rotation penalty \(p(n)\) is introduced. It is based on the expected number of rotation actions and is combined with the Manhattan distance to form a revised heuristic function \(h^{\prime}(n)\). This allows for a more accurate estimation of the value of each node and helps reduce unnecessary turns.  
The AGV-Referenced Action Space and Directional Spatial Nodes ensure consistency with our subsequent experiments and provide direct guidance for the distributed planningstrategy. The directional penalty adjusts the heuristic function to account for rotation costs and avoid unnecessary rotation.  
In our model, which accounts for AGV orientation, the Manhattan distance only estimates the number of forward moves the AGV should take. We therefore introduce \(p(n)\) to estimate the number of rotation actions required to modify the heuristic function \(h(n)\). \(p(n)\) represents the estimated number of rotation actions that an AGV would need in an ideal obstacle-free scenario to move to the target node. The specific calculation method for \(p(n)\) is detailed in Table 2.  
The heuristic function is revised to the following:  
\[h^{\prime}(n)=|x_{n_{var}}-x_{n_{var}}|+|y_{n_{var}}-y_{n_{var}}|+p(n) \tag{10}\]  
The evaluation function is updated as follows:  
\[f(n)=g(n)+h^{\prime}(n) \tag{11}\]