esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
The COP data were post-processed using MATLAB software (MATLAB R2023b, Natick, MA, USA). Key COP metrics, including anterior-posterior (AP) and medial-lateral (ML) displacement, were calculated from the collected data. From the COP data, features (root mean square (RMS) and peak-to-peak displacement (MAXD)) were extracted and are described below in Equations (1) to (6).  
AP COP time series without offset:  
\[\text{AP}=\text{APo}-\frac{1}{N}\text{$\sum$}|AP[n]| \tag{1}\]  
ML COP time series without offset:  
\[\text{ML}=\text{MLo}-\frac{1}{N}\text{$\sum$}|ML[n]| \tag{2}\]where APo/MLo: AP/ML COP path relative to the origin of the force platform coordinate system AP/ML and N = number of data points.  
AP Root Mean Square (RMS\({}_{\text{AP}}\)):  
\[RMS_{AP}=\left[\frac{1}{N}{\sum\text{AP}[n]}^{2}\right]^{1/2} \tag{3}\]  
ML Room Mean Square (RMS\({}_{\text{ML}}\)):  
\[RMS_{ML}=\left[\frac{1}{N}{\sum\text{ML}[n]}^{2}\right]^{1/2} \tag{4}\]  
where  
N is the total number of data points,  
\(\sum\) represents the summation over all data points.  
AP Peak-to-peak Displacement (MAXD\({}_{\text{AP}}\)):  
\[\text{MAXD}_{\text{AP}}=\text{max(AP)}-\text{min(AP)} \tag{5}\]  
ML Peak-to-peak Displacement (MAXD\({}_{\text{ML}}\)):  
\[\text{MAXD}_{\text{ML}}=\text{max(ML)}-\text{min(ML)} \tag{6}\]  
These COP metrics provided quantitative measures of postural stability and balance control, facilitating comparisons between pre- and post-training performance.  
After pre-processing in MATLAB, the RABT participants' data were tabulated and post-processed using Microsoft Excel (Version 16.26). Pre- and post-training trials were pooled for each test condition, from which mean and standard error of the mean (SEM) were computed. In Excel, BESS errors were analyzed by calculating the mean errors across stance conditions to assess deviations in balance performance. This approach aimed to determine whether stance deviations (BESS errors) changed as a function of increasing task difficulty (BESS test conditions) when comparing post- to pre-training results. The BESS errors were calculated both for individual stance conditions and as a total across all conditions. For each intervention, pre- and post-training averages (means) and SEMs were computed across trials for multiple balance performance parameters, including BESS errors, center of pressure (COP) metrics related to displacement.  
In terms of statistical analysis, the data for both the RABT and VR balance training interventions were systematically organized, processed, and analyzed to evaluate changes in balance performance among older adults following training. The statistical analysis was conducted using MATLAB (MATLAB R2023b, Natick, MA). To evaluate the VR-based and RABT interventions, changes in balance performance metrics (defined above) were analyzed for both groups. [PERSON]'s \(t\)-test (Independent Samples) comparisons of pre- and post-intervention outcomes. Statistical results included \(p\)-values (\(p<0.05\)) to provide a thorough analysis of the findings.
## 5 Additional Comment  
It should be noted that the presented WEDES system in this paper is different from a traditional Inductor-Capacitor (LC) compensation wireless power transfer (WPT) system or a traditional battery energy storage system (BESS). Instead, it is a combination of both systems. Table 2 shows a comparison between the presented WEDES system in this paper, conventional WPT system [18; 21; 22], and conventional BESS system [23; 24; 25]. It is shown that the presented WEDES system combines the advantages of both the conventional WPT system and BESS system, and can achieve bus voltage regulation and SOC balancing, and allow for fast and safe exchange/swapping of smaller and lighter battery modules with WPT technology.
## 5 Technical and Economic Analysis of the Test Design
### Durability  
In terms of durability, both the test design and the original plan require corrosion protection for the steel structure, necessitating reserved access for future inspections and painting. However, in the test design, the concrete poured inside the chord members protects the internal steel tube from corrosion, restricting the need for corrosion treatment to the exterior of the steel tubes, resulting in lower anti-corrosion and maintenance costs. Additionally, the concrete-filled composite section of the test design provides superior fire resistance compared to the original hollow steel tube section. The concrete inside the steel tubes absorbs a significant amount of thermal energy, enhancing the structural fire resistance and slowing down the steel tubes' temperature increase. Furthermore, the use of ultra-high-performance concrete improves the strength and ductility of the structure. Coupled with the interaction between the steel tubes and concrete, this enhances the ductility of the composite components, increases their energy dissipation capacity, and provides exceptional seismic performance.
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 4 Results and Discussion
### Influence of Wellbore Section Length
#### 4.3.3 Analysis of Purely Vertical and Purely Horizontal Conditions  
In this section, we assume the wellbore to be purely horizontal and purely vertical, directly comparing the pressure transmission patterns between the two. Assuming both wellbores are 3000 m long and other conditions remain unchanged, the simulation results are shown in Figure 12. As is evident from Figure 12, under the influence of gravity, pressure attenuates faster in a purely horizontal wellbore, with the average fluctuation amplitude of pressure reduced by about 13% compared to a purely vertical wellbore and the maximum fluctuation amplitude reduced by about 17%.  
Figure 10: Impact of the horizontal section length on downhole pressure.
## 4 Results and Discussion
### Flow Induced Vibration of Two Staggered Cylinders
#### 4.2.1 Vibration Characteristics Partition  
The time-domain and frequency-domain characteristic curves of displacement of the two cylinders were analysed. It was found that the vibration characteristics of the cylinders changed with the gap distance and reduced velocity. The time-domain and frequency-domain curves are depicted in Figure 9. The amplitude curves were directly output through CFD calculation, and the frequency curves were obtained by the fast Fourier transform (FFT). When the reduced velocity was small (\(U^{*}=3\)), the two cylinders were of periodic vibration mode. The amplitude-time curve of the cylinder presented a standard sinusoidal curve with very small amplitude. The two cylinders vibrated at the same frequency, and the main frequency of the vibration was single and stable, as shown in Figure 9a. With the slight increase of staggered spacing and reduced velocity, the two cylinders were still in the mode of periodic vibration. The difference is that the mode of periodic vibration was period-doubling referred by [PERSON] [30], as shown in Figure 9b. When the staggered spacing increased to \(T/D=0.8\), the downstream cylinder was still in periodic vibration mode, while the upstream cylinder was in multi-periodic vibration mode. Both vibrated together at the same frequency, as shown in Figure 9c. When the reduced velocity increased to a certain extent (\(U^{*}=6\)), the vibration mode of the cylinder changed to quasi-periodicity mode. At this time, the vibration of the downstream cylinder was stronger and its displacement was larger, as shown in Figure 9d.  
Figure 8: Vortex structure of the two stationary cylinders at different spacing ratios at \(Re=6000\)–\(14,000\).
# Improved Durability of Wood Strand-Based Panels Using Guayule
## 2 Materials and Methodology
### Testing of the Specimens  
Different physical bonding, mechanical, and biological experimental testing properties were conducted to understand the effects of guayule resin on the WSB panels such as tensile, internal bond (IB), water absorption (WA), thickness swelling (TS), and termite and fungal decay resistance. As the guayule solution was applied on wood strands during the manufacturing process for in-situ specimens, it could affect the bonding and, in turn, the mechanical performance of WSB panels. Therefore, in-situ specimens were submitted to tensile, internal bond (IB), water absorption (WA), and thickness swelling (TS) tests following sections 10, 11, and 23 of ASTM D1037 [33]. The tensile specimens were dog-bone-shaped, and detailed dimensions can be found in ASTM D1037, section 10 [33]. The results of these tests were compared to those of control specimens that were submitted to the same tests in our previous research [18]. For brushing and spraying techniques, guayule was applied on the fabricated WSB specimens in a thin coating; therefore, the IB and tensile tests were not conducted on these two treatments, since guayule, as a thin coating, does not affect the mechanical properties of these treated WSB specimens.  
To evaluate the effect of guayule treatment on the biological degradation performance of the pine WSB panels, which was the main objective of this study, AWPA E1-23 and AWPA E10-22 were followed [31; 32]. The average dimensions and number of specimens per treatment group can be found in Table 1. All specimens were conditioned for a minimum of 4 weeks at approximately 25 \({}^{\circ}\)C with a relative humidity of 65% before testing.
# A Bayesian Control Chart for Monitoring Process Variance
## 3 The Construction of the Shewhart-Type Variance Chart with the Bayesian Approach
### The Parameters of the Prior Distribution of \(p\) and Process Variance Are Known  
In this subsection, we first develop the variance chart under known process variance \(\sigma_{0}{}^{2}\) and parameters of the prior distribution of \(p\), \(\alpha_{0}\), and \(\beta_{0}\). For the in-control process, the monitoring statistic, \(M_{t}\), defined as the number of \(Y_{j}\)'s \(>\sigma_{0}{}^{2}\) at time \(t\), follows from the beta-binomial distribution with parameters \(n\), \(\alpha_{0}\), and \(\beta_{0}\), written as:  
\[M_{t}\,\sim Beta-Binomial(n,\,\alpha_{0},\,\beta_{0}). \tag{12}\]  
The mean and standard deviation of \(M_{t}\) are  
\[E_{0}(M_{t})=n\times\frac{\alpha_{0}}{\alpha_{0}+\beta_{0}} \tag{13}\]  
and  
\[SD_{0}(M_{t})=\sqrt{\frac{n\alpha_{0}\beta_{0}(\alpha_{0}+\beta_{0}+n)}{( \alpha_{0}+\beta_{0})^{2}(\alpha_{0}+\beta_{0}+1)}}, \tag{14}\]  
respectively.  
Thus, we propose a Shewhart-type Bayesian variance chart with the following limits:  
\[CL_{M}=\frac{n\alpha_{0}}{\alpha_{0}+\beta_{0}},\]  
\[UCL_{M}=\frac{n\alpha_{0}}{\alpha_{0}+\beta_{0}}+3\sqrt{\frac{n\alpha_{0} \beta_{0}(\alpha_{0}+\beta_{0}+n)}{(\alpha_{0}+\beta_{0})^{2}(\alpha_{0}+ \beta_{0}+1)}}, \tag{15}\]  
\[LCL_{M}=\frac{n\alpha_{0}}{\alpha_{0}+\beta_{0}}-3\sqrt{\frac{n\alpha_{0} \beta_{0}(\alpha_{0}+\beta_{0}+n)}{(\alpha_{0}+\beta_{0})^{2}(\alpha_{0}+ \beta_{0}+1)}}, \tag{16}\]  
and plot \(M_{t}\), \(t=1\), \(2\),.... If either \(M_{t}\geq UCL_{M}\) or \(M_{t}\leq LCL_{M}\), the process is deemed to show some out-of-variance-control signals.
## 4 Numerical Simulation of Slope Stability under Different Vegetation  
### Strength Reduction Method (SRM)  
The strength reduction method is commonly used to assess the factor of safety (FS) of a slope. It involves gradually decreasing the shear strength parameters (c and \(\varphi\)) until slope failure occurs, i.e., FS = 1.0. In this study, the SRM was introduced to analyze the effect of vegetation roots on slope stability during different growth periods. The calculation formula is as follows:  
\[c_{m}=c/F \tag{2}\]  
\[\varphi_{\mathrm{m}}=\arctan(\tan\varphi/F) \tag{3}\]  
where \(c_{m}\) is the effective soil cohesion (kPa); \(\varphi_{m}\) is the effective internal friction (\({}^{\circ}\)) of soil; and \(F\) is the strength reduction coefficient. Commonly, the initial value of \(F\) is generally set at 0.5, so that the slope first reaches stability under gravity. Then, by increasing the \(F\) value, the soil is gradually weakened until the specified failure criteria are met.
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 2 Attacks on VSS
### Privilege Escalation Attack  
In a multiple user architecture of any application or device network, access permissions to its users are restricted. Users at different levels have different permissions. In Android user applications or surveillance applications, components such as service, content provider, broadcast receiver and activity may be able to use privilege escalation to receive more permissions than required or desired. Two variants of privilege escalation are Vertical Privilege Escalation and Horizontal Privilege Escalation. Vertical Privilege Escalation: bugs and design flaws can be applied to allow the smartphone user to execute higher level applications or functions. Even a process, for instance, may use a bug in the system kernel and run functions with system privileges. There must be at least one process running with system rights to enable another lower-level process to escalate. Horizontal Privilege Escalation: the user and applications are located at the same permission level. Privilege escalation takes place if a user or an application can access data or functions of another user or application. One of the Android built-in security features is the Android application sandbox. It is a technique to manage and separate the user applications from the critical system resources and applications. Privilege escalation attack bypasses sandbox restriction by running malicious code at run time [37]. An application which is \"non-privileged\" can still access files of \"privileged\" system applications such as geo-location, user passcode, battery status, camera permission, etc. Similarly, in a video surveillance system, an attacker can exploit the firmware default port and login information and access the device as a user with privileged rights [38]. In such a scenario, companies could do nothing but recommend their customers apply newer firmware and use stronger passwords. The prevalent attacks on different parts of the VSS infrastructure are outlined in Figure 5. Table 2 gives information on different types of attacks, their description and examples of how such attacks are conducted by the attackers.
# Fully Open-Source Meeting Minutes Generation Tool
## 4 Selection of Open-Source Models
### Selection of Summarization Models  
Each transcribed text from the segmented video is summarized to help readers quickly grasp the presentation's main points. The summary is generated using an abstractive approach, leveraging the _language model (LM)_ to produce a coherent and concise output. Experiments were conducted to select the most suitable model from _HuggingFaceHub_. Table 3 shows the results.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{_SSIM_[43]} & \multicolumn{4}{c}{**Histogram Comparison [44]**} \\ \cline{3-6}  & & **Bhattacharyya** & **Chi-Square** & **Correlation** & \\ \hline Precision & 100\% & 35.29\% & 26.32\% & 38.46\% & 11.36\% \\ Recall & 100\% & 60.00\% & 100.00\% & 50.00\% & 100.00\% \\ F1 Score & 100\% & 44.44\% & 41.67\% & 43.48\% & 20.41\% \\ Duration (s) & 180.85 & 296.91 & 309.79 & 297.13 & 173.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of image comparison algorithms.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Component** & _Wav2 Vec_[46] & _HuBERT_[47] & _MMS_[48] & _Whisper_[49] \\ \hline _WER_ & 51.23\% & 26.92\% & 19.38\% & 4.41\% \\ Duration (s) & 6.484 & 16.044 & 104.832 & 4.912 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of _WER_ and duration among audio-to-text models.  
Table 3 suggests that the _BART_ model outperforms _PEGASUS_ and _T5_, achieving the highest _ROUGE-1_, _ROUGE-2_, and _ROUGE-L_ scores, and the fastest computational time. _BART_'s _bidirectional_ encoder and _auto-regressive_ decoder enable it to effectively process information from both the beginning and the end of sentences, allowing it to generate more coherent summaries. Despite its relatively complex architecture, _BART_'s high ability summarizing ability contributes to its computational efficiency. Based on these results, _BART_ is the most suitable model for generating summaries in the system.
###### Abstract  
This study investigates Hispano-Muslim plasterworks, exemplified by the Cuarto Real de Santo Domingo, the Madraza, and the Alhambra in Granada, focusing on cleaning methods to remove oil-based repaintings without damaging the original polychromies. To this end, samples replicating traditional materials (gypsum coating, pigments, and binders) and techniques (tempera painting) were prepared and subjected to an artificial ageing protocol (AAP). Subsequently, cleaning tests aimed at removing the oil repaintings were performed to recover the original polychromies. Analytical techniques, including Fourier Transform Infrared Spectroscopy (FTIR), Gas Chromatography-Mass Spectrometry (GC-MS), and colorimetry, were employed to evaluate ageing effects and cleaning efficacy. Results revealed significant chromatic alterations in vermilion and azurite bound with animal glue, while ochre remained comparatively stable. Chemical analyses indicated marked binder deterioration, including protein denaturation in animal glue and oxidation/polymerization in linseed oil. Cleaning tests demonstrated that both a heptane-acetone gel and a novel polyamidoamine-glycine (PAAGLY) treatment effectively removed oil-based repaintings while preserving the original layers. These findings highlight the critical role of pigment-binder interactions in conservation strategies and advocate for selective, minimally invasive restoration methods.  
decorative plasterwork; oil removal; artificial ageing protocol; PAAGLY; FTIR; GC-MS +
Footnote †: journal: Applied sciences
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Accurate Channel Estimation for mmWave Systems  
For IRS-assisted mmWave systems, this study suggests a \"two-stage LAMP network with row compression (RCTS-LAMP)\" to address the combined estimation problem of direct and cascaded channels. Specifically, by utilizing the deep unfolding technique, the suggested RCTs-LAMP is a model-driven neural network that combines the benefits of \"deep learning (DL)\" and \"compressive sensing (CS)\". In this way, we can recover the direct and cascaded channels with CS under low training overhead, and the joint optimization of DL and DL may greatly enhance the estimation performance. We employ an innovative technique termed row compression two-stage learning-based accurate multi-path processing network with \"received signal strength indicator association strategy\" (RCTS-AMP) to estimate the channel in both direct and indirect pathways. They propose a correlation based on the intensity of the received signal indication to ensure that clients obtain the optimal average energy.  
The technique takes into account the differences \"between LOS and non-LOS mmWave\" broadcasts that use the \"received signal strength indicator-based association strategy (RSSI-AS)\". For channel estimation, we used the novel method called row compression two-stage learning-based accurate multi-path processing network with \"receivedsignal strength indicator-based association strategy\" (RCTS-AMP- RSSI-AS)\", prior to exploring the particulars of the proposed RCTs-LAMP. This section briefly analyzes the LAMP network developed to address the sparse data issue in signal recovery. Table 2 delineates the notations of symbols.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 6 Experimental Result
### Number of Path Handovers COMPARISON  
This simulation scenario contains 50 TS flows with bandwidth requirements varying around 10 Mbps. The proposed LCP algorithm, and the comparison algorithms DT-DVTR, TGM, LSHADE, and ILP, are used to allocate routes for these 50 TS flows. The routing results are analyzed to find the total number of path handovers during the entire network cycle period.  
Table 3 shows the number of path handovers of ten flows and the average path handovers for all flows. We only list ten flows here since the remaining forty flows witness the similar results. LSHADE has the highest number of path handovers, followed by TGM and LCP, while DT-DVTR performs the best. This is because the LCP algorithm finds the longest-lasting path and uses the auxiliary graph to find the routing paths that have a smaller number of path handovers than TGM and LSHADE. However, this value is larger than DT-DVTR because DT-DVTR aims to find the path with the fewest path handovers in each time slot.  
The summation of the number of path handovers and the maximum number of path handovers in Table 3 are shown in Figure 6; DT-DVTR has the lowest summation value, followed by LCP and TGM.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Flow**} & \multicolumn{5}{c}{**Path Handovers**} \\ \cline{2-6}  & **LCP** & **DT-DVTR** & **TGM** & **ILP Solution** & **Lshade** \\ \hline
1 & 27 & 13 & 46 & 16 & 42 \\ \hline
2 & 23 & 27 & 51 & 18 & 41 \\ \hline
3 & 20 & 4 & 28 & 22 & 38 \\ \hline
4 & 28 & 13 & 38 & 6 & 27 \\ \hline
5 & 16 & 6 & 8 & 10 & 26 \\ \hline
6 & 18 & 6 & 14 & 8 & 32 \\ \hline
7 & 25 & 3 & 37 & 15 & 38 \\ \hline
8 & 22 & 17 & 32 & 26 & 41 \\ \hline
9 & 35 & 13 & 41 & 12 & 38 \\ \hline
10 & 22 & 15 & 44 & 21 & 39 \\ \hline Average & 19.22 & 8.3 & 22.98 & 11.1 & 25.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Path handovers in each TS flow routing result.
# Design, Implementation, and Field Testing
## 4 System Implementation
### Alerts and Feedback
To be robust against occasional jitters and measurement errors, a transition between \(S1\) and \(S2\) is made only after a \(n\) consecutive frames (in our study, \(n=10\)) have all shown the consistent configuration. As a tradeoff for the increased robustness, the detection time for the starting and ending of a non-compliant activity is longer. With \(n=10\), the detection time is 330 ms compared with 33 ms if a single frame is used. This delay is quite acceptable because a typical non-compliant activity lasts several seconds or longer.