esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This cross-sectional study examined gender differences in physical activity (PA), body mass index, sedentary behavior (SB), and academic performance (AP) in school-aged children in Qatar. **Methods:** Fifty-two schoolchildren (age: 11.9 \(\pm\) 0.6 years) were assigned to gender-adjusted groups (female; n = 29). AP was calculated using the grade point averages (GPA) in Mathematics, Science, and Arabic. Additionally, the Short Form of the International Physical Activity Questionnaire (IPAQ) was utilized. **Results:** Four parameters (peak height velocity [PHV], science, Arabic, vigorous PA) showed relevant gender effects (\(p\) < 0.05 and \(\eta_{\mathrm{P}}\)\({}^{2}\) > 0.10). The largest gender effect was calculated for PHY (\(p\) < 0.001, \(\eta_{\mathrm{P}}\)\({}^{2}\) = 0.45). PHY was markedly higher for males (1.65 \(\pm\) 0.90) than for females (0.36 \(\pm\) 0.37). Females showed a higher performance level in science (82.9 \(\pm\) 8.61 vs. 77.0 \(\pm\) 8.76) and Arabic (80.9 \(\pm\) 8.25 vs. 73.0 \(\pm\) 8.22). A significant gender difference (\(p\) = 0.002, \(\eta_{\mathrm{P}}\)\({}^{2}\) = 0.18) was evident for vigorous MET-minutes/week (males: 1318 \(\pm\) 932 vs. females: 646 \(\pm\) 525). In conclusion, males exhibited the greatest amount of moderate and vigorous PA. Females spent the greatest time sitting. Arabic was different by gender, and science AP was dependent upon gender. **Conclusions:** These data suggest subjects in middle school are sensitive to gender, but not necessarily influenced by PA or SB at this age. Encouraging a school-based program and an after-school health club characterized by gender sensitive strategies consisting of a health class and physical education curriculum support the unique needs, interests of academic performance, and motor skills to improving health related fitness for girls compared to boys.  
physical activity; anthropometrics; academic achievement; sedentary; schoolchildren
## 3 Mismatch Error Estimation and Calibration Method
### Four-Channel TIADC Memory Nonlinear Offline LS Calibration  
The use of the LS method in the estimation of mismatch errors in TIADC systems has matured, and the error parameters can be estimated by least squares fitting of the overdetermined equations consisting of the input and output signals [21]. Define the input matrix consisting of a known input signal \(x\) as:  
\[x=\left[\begin{array}{cccccccc}1&x(1)&x(5)&x(9)&x(1)^{2}&x(1)x(5)&x(1)x(9)&x( 5)^{2}&x(5)x(9)&x(9)^{2}\\ 1&x(2)&x(6)&x(10)&x(1)^{2}&x(2)x(6)&x(2)x(10)&x(6)^{2}&x(6)x(10)&x(10)^{2}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 1&x(n)&x(n-4)&x(n-8)&x(n)^{2}&x(n)x(n-4)&x(n)x(n-8)&x(n-4)^{2}&x(n-4)x(n-8)&x(n- 8)^{2}\end{array}\right] \tag{13}\]  
Define the unit sampling sequence matrix:  
\[C_{m}=\frac{1}{4}\left[\begin{array}{cccc}\sum_{k=0}^{3}e^{j(1-m)\pi k/2}& \ldots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&\sum_{k=0}^{3}e^{j(n-m)\pi k/2}\end{array}\right] \tag{14}\]  
Define:  
\[X=[C_{0}x,C_{1}x,C_{2}x,C_{3}x] \tag{15}\]  
Define the 10 Volterra series kernel for the \(m\)th channel as:  
\[h_{m}=\left[\begin{array}{cccccc}h_{m,0}&h_{m,1}(0)&h_{m,1}(1)&h_{m,1}(2)&h _{m,2}(0,0)\\ h_{m,2}(0,1)&h_{m,2}(0,2)&h_{m,2}(1,1)&h_{m,2}(1,2)&h_{m,2}(2,2)\end{array}\right] \tag{16}\]  
Define:  
\[h=[h_{1}\ h_{2}\ h_{3}\ h_{4}]^{T} \tag{17}\]  
Write the output matrix as:  
\[Y=[y(1)\ y(2)\ \cdots\ y(n)]^{T} \tag{18}\]  
At this point, the input-output relationship of the TIADC system can be expressed as:  
\[Y=Xh \tag{19}\]  
The solution of the Volterra series kernel becomes a problem of solving the coefficients of the superdeterminant Equation (19). The LS method solves the superdeterminant equation as follows:  
\[\overset{\wedge}{h}=\left(X^{H}X\right)^{-1}X^{H}Y \tag{20}\]  
where \(X^{H}\) is the pseudo-inverse of the matrix \(X\), i.e., at this point the singular matrix \(X^{H}\) and the singular matrix \(X\) satisfy:  
\[\left\{\begin{array}{l}X^{H}XX^{H}=X\\ XX^{H}X=X^{H}\end{array}\right. \tag{21}\]  
At this point, \(\overset{\wedge}{h}\) is the least squares solution of \(h\).
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 4 Implementation and Performance Evaluation
### Analysis on Experimental Results
#### 4.3.1 Performance Comparison of Scheduling Algorithms
 & **PQ** & **WFQ\_RR** & **WFQ\_RRE** & **WFQ\_TE** & **WFQ\_TPE** & **WFQ\_TP** \\ \hline
1 & 0.041 & 0.036 & 0.034 & 0.033 & 0.034 & 0.032 \\
2 & 0.042 & 0.035 & 0.035 & 0.032 & 0.033 & 0.03  
Regarding the delay and jitter of WFQ-based scheduling approaches, Table 6 shows the average delay and average standard deviation of service delay of the LLQ [21] and the proposed WFQ-based algorithms. Observe that comparing to the jitter of LLQ with PQ [21] within 1 s time interval, LLQ with CBWFQ [21], WFQ_TE, WFQ_TP, and WFQ_TP algorithms achieve 17.07%, 19.51%, 17.07%, and 21.95% lower transmission jitters, respectively. Similarly, compared to the delay of LLQ with PQ [21], LLQ with CBWFQ [21], WFQ_TE, WFQ_TP, and WFQ_TP algorithms achieve 29.68%, 40.47%, 40.11%, and 43.88% fewer delays, respectively.
## 1 Introduction
However, they noted that deformation twinning, dendritic segregation, and precipitates had a minimal impact on HE behavior. Additionally, their study suggested that selective laser melted (SLM) 316L stainless steel could be a viable option for hydrogen applications, owing to its high yield strength and resistance to HE. [PERSON] et al. [20] investigated the impact of post-build processing--including annealing, hot isostatic pressing (HIP), and HIP followed by 30% cold working--on the hydrogen embrittlement (HE) behavior of additively manufactured (AM) 316L stainless steel produced via laser powder bed fusion (LPBF). The study assessed HE susceptibility through uniaxial tensile testing at both room temperature and \(-\)50 \({}^{\circ}\)C. Their findings revealed that the AM material exhibits directional sensitivity in both hydrogen-charged and uncharged conditions. Among the processing conditions, HIP-treated specimens demonstrated the highest resistance to HE. [PERSON] et al. [18] investigated how variations in processing parameters during the LPBF process and subsequent heat treatments influence grain boundary evolution in 316L stainless steel. Their findings indicated that differences in recrystallization behavior, driven by additive manufacturing conditions, are primarily due to changes in dislocation density and chemical inhomogeneity within the as-solidified microstructure. [PERSON] et al. [19] found that distinctive microstructural features--including cellular structures, elevated dislocation density, melt pool boundaries, and deformation twins--play a crucial role in influencing martensitic transformation, as well as hydrogen diffusion and transport, thereby impacting the material's susceptibility to hydrogen embrittlement. With the growing demand for hydrogen applications, additive manufacturing (AM) technologies offer enhanced design flexibility and customization advantages [7; 19]. This study investigates the performance of AM 316L stainless steel alloys, fabricated using selective laser melting (SLM), for HHO gas production and hydrogen storage applications. The research compares the performance of as-built and post-heat-treated AM materials with that of as-rolled stainless steel, evaluating their effectiveness as electrodes in an electrolyzer operating at 60 \({}^{\circ}\)C under varying currents. Additionally, the alloys were tested for hydrogen embrittlement (HE) through tensile and Charpy impact toughness tests before and after hydrogen charging. To simulate hydrogen storage conditions, mechanical testing was conducted at room temperature (to simulate gaseous hydrogen storage) and in liquid nitrogen (to simulate liquid and cryo-compressed hydrogen storage). The fracture surfaces of different material conditions were analyzed using scanning electron microscopy (SEM). Ultimately, the study identifies the most suitable alloy for both electrode and hydrogen storage applications.
## 4 Results and Discussion
### Model Training and Testing  
The calculation model utilizes the BP neural network to calculate the comprehensive semantic similarity by weighting Edge-Counting, Feature-based, and Information-Content similarities, thereby avoiding the subjectivity of manual weighting. To ensure the scientific validity and accuracy of weight settings, the model is trained using a training dataset, and during the training process, the SHO algorithm is employed to optimize the BP neural network [26]. The specific flowchart is illustrated in Figure 5. It is worth mentioning that within the ontology model, certain concepts may have multiple meanings or interpretations. Taking the concept of \"bridge pier\" as an example, this concept is relevant in various types of bridges, but the bridge piers in different bridges have distinct meanings, functions, and structures. To address this issue, we create each distinct meaning as an instance in the ontology model. When calculating the similarity between a regular concept A and a concept B with multiple meanings, we independently compute the comprehensive similarity between concept A and each instance associated with a specific meaning related to concept B. Subsequently, weights are assigned based on the relationships between these meanings and concept B, with a higher relationship relevance leading to larger weights. Finally, the obtained similarities are weighted according to these weights to derive the comprehensive similarity between concept A and concept B.  
In Figure 5, the similarity evaluation values are semantic similarity scores determined by domain experts, which are considered as the target for prediction. By arranging and evaluating the similarity of various concepts in pairs within the bridge ontology model, multiple sets of data are obtained. Among them, 300 sets of data are selected, with 270 sets used as training samples to train the BP neural network model, and the remaining 30 sets used as a test set to assess the accuracy of the experimental results. The training regression curve of the BP neural network is depicted in Figure 6, showing a high degree of fit with the actual data points and demonstrating excellent predictive capabilities.  
After inputting the three types of similarity data of the testing samples into the trained BP neural network, the comprehensive ontology semantic similarity output results were obtained. The comparison curve and error curve between the results obtained by SHO-BP algorithm and the similarity evaluation values are shown in Figure 7. The neural network's predicted results exhibited a small error compared to the evaluation values, demonstrating high accuracy.
## 2 Multi-GNSS ZTD Estimation in Real-Time
The ISB and IFB parameters \(d_{rE}\), \(d_{rC}\), \(d_{rR_{k}}\) are estimated as constant over one processed day. The ionospheric delays \(\mathbf{F}_{r,1}^{s}\) are taken as estimated parameters for each satellite at each epoch by using dual-frequency raw phase and pseudorange observations. The phase delays \(b_{r}\) and \(b^{s}\) will be absorbed by phase ambiguity parameters, and the phase ambiguities \(\overline{\mathbf{N}_{r}^{s}}\) are estimated as constant for each continuous arc. The tropospheric ZWD \(Z_{w}\), is modeled as a random walk process and the noise intensity is about 5-10 \(mm/\sqrt{h}\). The variance component estimation weighting method is also applied in this study. The multi-GNSS data processing strategies for retrieving RT tropospheric delays are summarized in Table 1.  
With \(Z_{h_{r}}\) and the estimated tropospheric parameter \(Z_{w}\),, the slant total delay can be reconstructed according to,  
\[\widetilde{T}_{r}^{s}=M_{h_{r}^{s}}\cdot Z_{h_{r}}+M_{w_{r}^{s}}\cdot Z_{w_{r}}+\varphi \tag{7}\]  
where \(\widetilde{T}_{r}^{s}\) is the reconstructed slant total delay, \(\varphi\) denotes the post fit phase residual, which includes residual tropospheric delays due to the tropospheric asymmetry. In this study, pressure provided by the Global Pressure and Temperature 2 (GPT2) model is used to calculate the a priori ZHD so as to derive the ZTD (PPP-derived ZTD) [25].
# Aveiro Canyon Head (Portugal) Submarine Slope Instability Assessment
## 4 Data Acquisition and Processing
### Predisposing Factors  
In this study, 5 predisposing factors were considered to have influence in the trigger of instabilities events along the canyon slopes (Table 1). Since direct observation of the terrain is not possible, the predisposing factors were recognized in the available information about the study area and reclassified in a geographic information system (GIS) environment. Each factor was divided into variables, which resulted in classified maps of categorical variables. The most important predisposing factor, the base and from which the others were derived, with the exception of superficial sediment mapping, was bathymetry (derived from the hydrographic data). The predisposing factor bathymetry was divided into 9 classes, with an interval of 300 m.  
Mean slope angle, aspect (direction of slope face exposure) and surface curvature were obtained with the 3D Analyst tool from ArcGIS software. The mean slope angle was reclassified in 5 degree intervals, originating 11 classes, while the aspect was divided into 9 classes, each within 22.5\({}^{\circ}\) azimuths. The seabed surface curvature represents the shape of the surface of the slope and may be concave, convex or flat. Positive values indicate that the terrain surface is concave, negatives represent a convex surface, and zero values correspond to a flat surface. Consequently, the curvature DBM was reclassified into 3 classes: concave (positive values), convex (negative values) and flat (zero). In order to use the sedimentary cover as a predisposing factor, it was necessary to model/extrapolate to the remaining study area the sediment maps covering the near external shelf and upper slope (until 700 m depth). This mapping was based on 64 superficial sediment samples collected by a Smith-McIntyre superficial sampler under the scope of the SEPLAT program and DEEPCO project. At 2500 m depth, the first 20 cm of 3 cores were also included. Nevertheless, it must be emphasized that superficial sediment mapping was based in an irregular sampling strategy, which may induce some local errors, especially in deeper areas. Results obtained with both methods for the bathym factor (Table 2) indicate that the area most prone to instability is located between the depths of 900 and 2100 m (higher values). This depth range also represents the \"centre\" of the canyon slope, where 80% of the slides were identified. As expected, the lowest values were observed in the shelf. However, below 2400 m depth, the probability of occurrence of downslope movements is also lower due to the flatter surface of the slope base.
## 3 Security and Privacy of Computing Paradigms
### Edge-Related Aspects
#### 3.2.2 Edge Data Privacy  
In Edge computing, accessing the system does not reflect trust. Averagely accepted systems store important data, resulting in critical privacy leakage. Examples of clients' data stored are personal information, location, and identity. The focus areas to be discussed herein any order include privacy, identity, and location privacy safeguarding [73].  
Edge computing always raises much concern in stark contrast to other existing computing models protecting information. This is because the challenges, e.g., leakages relating to Edge data privacy, are daunting. An Edge information center, services, infrastructure suppliers, and even certain clients are the potential weak link or at least establishments you cannot fully trust with such interwoven computing/cellular networks. With regard to this, the act of keeping safe the private information of clients is an obligation that requires very close attention [74]:  
* Protection of Data Privacy: At the Edge nodes, huge amounts of data belonging to clients are retrieved from applications and other users' pieces of equipment. This collected information is then processed and analyzed. Despite the trustworthiness of the Edge computing nodes, they can still display some level of vulnerability. Classified information such as an individual's medical data must be top secret. Therefore, information privacy protection is very important to avoid leakage at the nodes of Edge computing [75].
* Identity Privacy: Compared to the Cloud systems, especially Mobile Cloud, Edge models still lack adequate research attention in protecting the identity of customers well. Identity privacy protection is a major concern for several organizations and even individual customers. The third-party identity-designed model is said to still pose vulnerability [76].
* Location Privacy: Several software and services from Worldwide Web render functional capabilities based on location. For a client to gain access when they want to use the services in Edge computing, that client must deliver their location as required by the service provider [77; 78]. One of the particularly concerning fears is breaching data location through possible leaks. Different researchers gave some solution schemes on how to deal with issues on data leakage. A dynamic distribution in location privacy protection was presented in a mobile model of social internet platforms. This model can sort out visitors with low trust levels within a certain range of social interactions. It performs this by dividing customers' data location (unidentifiable) and personalities in individual storage systems. This separation enables the service provider to hide customers' location data safely. The importance of this model is that even if an attacker manages to breach one of the storage facilities, for example, data location, it will not pose a major threat since the identity of the client is not leaked or exposed [79].
# Single-Shot Time-Lapse Target-Oriented Velocity Inversion Using Machine Learning
## 2 Methodology
### Illumination Study  
In order to assess whether the single-shot geometry would be able to extract information from the reservoir region, we conducted an illumination analysis. Ray tracing [23] is the conventional technique for illumination, but representing the wavefronts as rays is  
Figure 1: P-wave velocity model and acquisition geometry used to model the synthetic database. The rectangle indicates the reservoir region.  
an approximation given the finite frequency of seismic signals. Instead, we performed the illumination analysis by migrating the \"rabbit ear\" component of the image condition of reverse-time migration (RTM). Higher migration magnitudes correspond to better illuminated regions of the subsurface.  
We show in Figure 2 the illumination maps obtained by migrating different subsets of traces at a time. The target reservoir region is depicted in this figure by a rectangle. In Figure 2a, we depict the illumination that corresponds to traces 11-20, on the left side. In Figure 2b, we show the illumination corresponding to the five smallest offset traces (traces 22 to 27). Figure 2c corresponds to the illumination of traces 21 to 30, and Figure 2d to traces 31 to 40. We do not show the illumination corresponding to the far-offset traces 1 to 11 or 41 to 49, because these traces weakly contribute to illuminating the target area. The conclusion from these three panels is that the reservoir region is well illuminated by the employed single-shot acquisition geometry.
## 4 Energy Management Strategies
### Rule-Based Strategy and Operating Modes  
The RB strategy considers a set of \"If-Then-Else\" rules based on driving patterns, SoC, and the conditions of requested versus available power. The set of desired objectives can be well-thought-of while defining these intuition-based control rules. These rules need to be included in the EMS block to work in tandem with the OB power split technique. The EMS must provide a flexible operation of the ICE in efficient operating points to meet the requested driving demands and satisfy the charging requirements. To this end, by using a speed-torque-efficiency look-up table, the EMS subsystem rules link the instantaneous split torque to its corresponding speed, leading to the highest efficiency on the engine map. The rules update the operating modes by considering the status of the demanded loads, required speeds, available energy from sources, and SoC values. The considered modes are categorized as follows.  
If the requested power is greater than the available power by the ICE, it needs to be supplied in a \"hybrid-traction\" mode in which the required traction power will be supplied by the ICE assisted by the power drawn from the battery.  
There is an opportunity to charge the battery by a part of the ICE power, if the total requested power is lower than the available ICE power. In this condition, the vehicle works in \"engine-traction and battery-charging\" mode in which the ICE partially supplies the requested traction power while its remaining part will charge the battery to maintain the SoC, considering its allowable minimum and maximum window range.  
In the \"battery-only\" mode, a threshold of requested speed will be considered to turn off the ICE, and the battery pack supplies all the requested power, seeing that the battery SoC does not violate its minimum allowable value (i.e., predefined by the rules).  
While braking, the battery charge can be sustained either in \"hybrid battery-charging mode\" or \"regenerative-braking mode\", satisfying the maximum allowable SoC value. In the \"hybrid battery-charging\" mode, the battery is charged using the energy delivered by the ICE, aside from the energy that is partially recuperated from braking. However, in the \"regenerative-braking\" mode, the ICE is turned off and only the kinetic energy of braking is converted to its electrical form to supply the battery.
## 3 Related Works
### Transmission Power Control and Routing
In the active probing mechanism, if a certain number of packets is transmitted successfully, the transmission power is decreased by one unit. If any of the packets are not acknowledged, the nodes increase the transmission power. These two proposals are based on the same assumption that a link is reliable if it is able to successfully transmit a certain number of consecutive packets. Our preliminary study shows situations in which this definition of a good link is not useful. Neither of these two proposals is integrated with RPL. [PERSON] et al. [25] adjust the transmission power to keep the size of the parent table as close as possible to a predefined threshold. Their proposed DODAG-oriented method was designed to be executed offline. The authors also mention the problem of the initialization of ETX, which can be optimistic/pessimistic.