esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigates how three technical factors--steering wheel tilt, torque, and cabin vibration frequency--affect driver posture. Heavy-truck drivers often suffer from musculoskeletal disorders (MSDs), mainly due to poor cabin ergonomics and prolonged postures during work. In countries like Ecuador, making major structural changes to cabin design is not feasible. These factors were identified through video analysis and surveys from drivers at two Ecuadorian trucking companies. An experimental system was developed using a simplified cabin to control these variables, while posture and muscle activity were recorded in 16 participants using motion capture, inertial sensors, and electromyography (EMG) on the upper trapezius, middle trapezius, triceps brachii, quadriceps muscle, and gastrocnemius muscle. The test protocol simulated key truck-driving tasks. Data were analyzed using ANOVA (\(p<0.05\)), with technical factors and mass index as independent variables, and posture metrics as dependent variables. Results showed that head mass index significantly affected head abduction-adduction (\(-8.12\) to \(-2.18^{\circ}\)), and spine mass index influenced spine flexion-extension (\(-0.38\) to \(6.99^{\circ}\)). Among technical factors, steering wheel tilt impacted trunk flexion-extension (\(-13.56\) to \(16.99^{\circ}\)) and arm rotation (\(-31.1\) to \(19.7^{\circ}\)). Steering wheel torque affected arm rotation (\(-30.49\) to \(-6.77^{\circ}\)), while vibration frequency influenced forearm flexion-extension (\(3.76\) to \(16.51^{\circ}\)). EMG signals showed little variation between muscles, likely due to the protocol's short duration. These findings offer quantitative support for improving cabin ergonomics in low-resource settings through targeted, cost-effective design changes.  
heavy-truck vehicle drivers; ergonomic; postures; EMG 2025157775
## 2 Materials and Methods
### System Test Conditions of the MPC Controller  
Together, the PLC and EC form the MPC controller. A flow chart of the two components and their tasks is shown in Figure 5. The PLC tasks are executed cyclically with cycle times of 10-100 milliseconds, i.e., virtually continuously compared to the discrete optimisation intervals of the EC every 15 min. Optimisation in the EC is triggered by Linux' system daemon in order to maintain precise MPC cycles.  
The implementation was tested with a system model in which the number of modular components (PVs, BESSs, and EVs) is two per category. For the tests, the measurement data and predictions from an existing production facility were used as scenarios. The configured device parameters for BESSs and EVs, used for the system tests, are shown in Table 1. The static feed-in tariff of \(c_{f}=0.14\,\mathrm{\upmu K}/\mathrm{kWh}\) is taken from German grid regulation and corresponds to the installed PV power plants at the facility. The dynamic electricity tariffs were taken from a German utility, which provides tariffs based on European electricity exchange prices. The price and scenario data were taken from 18 September 2020 and 19 September 2020.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Device Parameter** & **Unit** & **EV1** & **EV2** & **BESS1** & **BESS2** \\ \hline \(P_{ch,max}\) & kW & 11 & 13 & 2 & 2 \\ \(P_{dc,max}\) & kW & 0 & 0 & 2 & 2 \\ \(C\) & kWh & 77 & 77 & 16 & 11.04 \\ \(\eta_{ch}=\eta_{dc}\) & \% & 99 & 99 & 98 & 95 \\ \(\mathrm{SOC}^{24}_{1,tar}=\mathrm{SOC}^{24+192}_{7,atr}\) & \% & 10 & 30 & n/a & n/a \\ \(\mathrm{SOC}^{68}_{1,tar}=\mathrm{SOC}^{68+192}_{7,tar}\) & \% & 90 & 70 & n/a & n/a \\ \(\mathrm{SOC}_{\beta,min}\) & \% & n/a & n/a & 10 & 90 \\ \(\mathrm{SOC}_{\beta,max}\) & \% & n/a & n/a & 10 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Device parameters of EVs and BESSs in an exemplary production facility, which were used for the model composition in the system tests.  
Figure 4: UML class diagram for the modular model composition in the EC.
## 6 Results and Discussion  
In this section, we have conducted various experiments and tests to assess the performance of V-shaped time-varying-based HHO algorithms in solving the FS problem. The proposed BHHO algorithms were also compared to different optimizers. To achieve a fair comparison, the initial settings of all optimizers, such as population size, number of iterations, and number of independent runs, were unified by setting them to similar initials values.  
Eighteen popular benchmark datasets obtained from the UCI data repository are applied for evaluating the performance of the proposed FS approaches. Table 2 shows the details of the datasets comprising a number of features, classes, and instances in each dataset. Following the hold-out method, each dataset is arbitrarily split into two portions (training/testing), where 80% of the data were preserved for training while the rest was employed for testing. Furthermore, each FS approach was run for 30 trials with a randomly set seed on a machine with an Intel Core i5, 2.2 GHz CPU, and 4 GB of RAM.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **No. of Features** & **No. of Instances** & **No. of Classes** \\ \hline Breastcancer & 9 & 699 & 2 \\ BreastEW & 30 & 569 & 2 \\ Exactly & 13 & 1000 & 2 \\ Exactly2 & 13 & 1000 & 2 \\ HeartEW & 13 & 270 & 2 \\ Lymphography & 18 & 148 & 4 \\ M-of-n & 13 & 1000 & 2 \\ PenglungEW & 325 & 73 & 7 \\ SonarEW & 60 & 208 & 2 \\ SpectEW & 22 & 267 & 2 \\ CongressEW & 16 & 435 & 2 \\ IonosphereEW & 34 & 351 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of employed datasets.  
To study the impact of four types of time-varying V-shaped TFs on the efficiency of the BHHO optimizer, we provide comparisons between the results of HHO with four basic V-shaped TFs and those recorded by HHO with four time-varying V-shaped TFs. Furthermore, the best FS approach among tested basic and time-varying V-shaped based approaches was then compared to several state-of-the-art FS approaches comprising BGSA, BPSO, BBA, BSSA, and BWOA. The following criteria were used for the comparisons:  
* The average of accuracy rates obtained from 30 trials.
* The average of best selected features rates recorded from 30 trials.
* The mean of best fitness values obtained from 30 trials.
* F-test method is used for ranking different FS methods to determine the best results.  
Please note that in all reported tables, the best-obtained results are highlighted using a boldface format.
# Effect of Coupled Mechanical-Chemical Activation on Hydration Activity of Copper Slag Powder
## 1 Introduction
The hydration products of C-S-H and C-A-H were produced by hydration of SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) in a copper slag glass phase with lime. The 28 d strength of the new low-strength filling material reached 4.22 MPa, which can be used for underground filling materials [21]. However, the mechanism of coupled mechanical-chemical activation on the hydration activity of copper slag powder has not been fully revealed. In particular, it remains to be studied further to reveal the hydration activity of copper slag powder by means of hydration heat characterization. In order to further clarify the effect of coupled mechanical-chemical activation on the early hydration activity of copper slag powder, this work firstly studied the effect of mechanical grinding on the microstructure and particle size distribution of copper slag. The hydration activity of copper slag powder with different grinding time was characterized by the compressive strength activity index. The copper slag powder was milled for 90 min and CaO was employed as a chemical activator to stimulate the hydration activity. The hydration heat was adopted to characterize the effect of coupled mechanical-chemical activation on the hydration process of copper slag powder-based composite cement. The microstructure and hydration products of hardened cement paste were characterized by ESEM and XRD. The hydration mechanism of the mechanical-chemical coupling-activated copper slag powder composite cement was analyzed.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 3 Experimental Setup
### Load Cases  
Considering a wide range of test operating conditions of the belt drive system is vital to validate the calculated results from the thermal model. The types of pulley for the serpentine belt drive system vary considerably in terms of the radius and materials. To cover the range of potential pulley variations, three radii and two materials were selected for the experiments. The engine operation varies significantly from idle to the maximum RPM conditions, and hence, three speeds for the DN pulley were selected. Table 1 summarizes the test configurations to fully consider the significant variation in the engine operating conditions. The transmitted speed and torque of the belt drive system were controlled by altering the revolution of the DR pulley and torque load of the DN pulley. The temperature distributions of the belt system were measured under the designed working conditions for validating the thermal model using the above devices and equipment as a test facility.
# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 5 Simulation Evaluations
### Performance Comparison
#### 5.3.1 Impact of Network Scale  
We first evaluate the charging scheduling algorithm's performance with respect to the number of deployed nodes, which vary from 70 to 130. Another simulation parameters are set as default mentioned above. The results are shown in Figure 5a-c, respectively.  
Figure 5 illustrates performance curves of the algorithms. We can see that with the number of nodes deployed in the network increases, both disjointed and inactive time grow rapidly. For example, the total nodes' disjointed duration and total nodes' inactive duration of WCI are, respectively, about 8521 s and 10,776 s when 70 nodes are deployed, but these two durations are increased to, respectively, about 207,204 s and 913,592 s when 130 nodes are deployed. This is due to the increase of \(|N|\), the burden of the relay nodes, especially the neighbor nodes of the base station, are significantly increasing, leading to the frequent exhaustion of those nodes and the increase of disjointed and inactive time of the network. There exists a decreasing tendency of the network's data loss rate from \(|N|\)= 70 to \(|N|\)= 90. This is because when \(|N|\)= 70, the nodes are deployed sparsely, which causes the poorer the network connectivity and the more the data have been lost. When \(|N|\)= 90, an increasing tendency of the network's data loss rate exists, where the data loss is mainly caused by the relay nodes' frequent exhaustion. From the result, we can see that the total nodes' disjointed time, inactive time and networks data loss rate by WCI outperform the NJNP and TSP in all the cases. For example, when \(|N|\)= 100, the total disjointed time of network by WCI is 72%, the total inactive time is 70%, and the data loss rate is 69% of that delivered by the TSP. In contrast, when compared with NJNP, the three ratios are reduced to 42%, 34%, and 34%, respectively. Compared to WCI, CI has inferior performance in most cases. This is because that CI ignores the node's residual energy, leading to the result that the nodes with a little contribution in connectivity are rarely visited even if their batteries are depleted, which increases the  
Figure 5: Impact of \(|N|\) on the charging scheduling algorithms: (a) Total nodes’ disjointed time; (b) Total nodes’ inactive time; (c) Network’s data loss rate.  
inactive nodes' number and then causes additional data loss in the network. Compared to WCI and CI, BC considers the betweenness centrality of nodes, in which only a few nodes have higher betweenness centrality while the other nodes are 0 or lower value. Unfortunately, some potential critical nodes and edge nodes among these nodes exist with lower betweenness centrality. Thus, the mobile charger only charges a few nodes in the BC scheduling algorithm, leading to the poorer network connectivity and severer data loss than WCI and CI.
## 2 GEO Grid ASTER EVI Products
### Radiometric Calibration and Atmospheric Correction Modules
With the subscript \(b\) omitted for simplicity, the following equation is used for this partial correction [27, 28]:  
\[\rho_{ASTER}^{ac}=\frac{\rho^{*}}{1+S_{R}(\tau_{R}(Z)\cdot\rho^{*})} \tag{4}\]  
and  
\[\rho^{*}=\frac{\frac{\rho_{ASTER,b}^{TOA}}{\tau_{g\partial_{3}}(\theta_{s} \theta_{\
u},U_{\partial_{3}})}-\rho_{R}^{A}(\theta_{s},\theta_{\
u},\phi_{s- \
u},\tau_{R}(Z))}{T_{R1}(\theta_{s},\tau_{R}(Z))\cdot T_{R1}(\theta_{\
u}, \tau_{R}(Z))\cdot T_{gH_{2}o}\big{(}\theta_{s},\theta_{\
u},U_{H_{2}o}\big{)}} \tag{5}\]  
where  
\(\rho_{ASTER}^{ac}\)atmospherically-corrected reflectance;  
\(T_{g\partial_{3}},T_{gH_{2}o}\) gaseous transmittances for ozone and water vapor, respectively;  
\(U_{\partial_{3}},U_{H_{2}o}\) total amounts for ozone [cm-atm] and water vapor [g\(\cdot\)cm\({}^{-2}\)];  
\(\rho_{R}^{A}\) intrinsic reflectance (normalized path radiance) for molecular atmosphere;  
\(T_{R1},T_{R1}\) downward and upward transmittances for molecular atmosphere, respectively;  
\(S_{R}\) spherical albedo for molecular atmosphere;  
\(\tau_{R}\) molecular atmosphere optical depth;  
\(z\) ground elevation above sea level for the ASTER scene;  
\(\theta_{s}\) solar zenith angle at the ASTER scene acquisition time;  
\(\theta_{\
u}\) view zenith angle for the ASTER scene;  
\(\phi_{s-\
u}\) relative azimuth angle or the difference between the solar and view azimuth angles. The ASTER Grid atmospheric correction module requires three inputs, from which to compute the atmospheric parameters in Equations (4) and (5) to obtain \(\rho_{ASTER,b}^{ac}\): (1) ozone concentration, (2) column water vapor, and (3) ground elevation for the adjustment of surface pressure. The module completely relies on outside sources for the two required atmospheric information since the ASTER sensor was not designed to retrieve atmospheric information [29]. The module obtains ozone concentration information from global daily gridded total ozone data products derived by the TOMS-V8 total ozone algorithm from either Earth Probe Total Ozone Mapping Spectrometer (EP TOMS) or Ozone Monitoring Instrument (OMI) onboard the Aura platform [30], depending on the acquisition dates of ASTER scenes to be processed. Column water vapor contents are acquired from the MODIS daily atmosphere product (MOD08_D3) [31]. These EPTOMS, OMI, and MODIS global daily gridded products are stored on-line on the GEO Grid system and, thus, ozone and water vapor information can be obtained near instantaneously upon the execution of the atmospheric correction module. As for ASTER data, newly generated OMI and MODIS global daily atmospheric products are automatically pushed to the GEO Grid system from NASA Goddard Space Flight Center. Ground elevation information are, in contrast, obtained internally from the ASTER Digital Elevation Model (DEM) product, which is included in \"ASTER Data BETA\" data generated by the GEO Grid system [4].
## 3 Results and Discussions
### Damage Morphology  
#### 3.3.1 Damage Morphology of Three-Point Bending  
Figure 5 shows the macroscopic three-point bending damage morphologies of samples with different hybrid ratios. Damage first appeared on the compression surface under the impact point during the bending process. With the increase in load, the crack of the specimen expanded along the width direction, causing delamination and finally failure. By comparing the surface damage of samples with different hybrid ratios after bending failure, it was found that the most serious damage of S-1 occurred, with a large area of fiber buckling damage on the compression surface, matrix cracks, continuous fiber breakage on the tensile surface, obvious delamination between the panel and the core, and serious folding buckling. When carbon fiber is mixed in the glass fiber in the way of sandwich mixing, the panel can withstand greater loads and absorb more energy in the bending process, thus significantly improving the bending damage of the sample. Therefore, the damage of the sample is arranged according to the severity of S-1, S-2, S-4, and S-3. A comparative analysis of the damage patterns in samples 2, 3, and 4 reveals that the samples' bending resistance improves with increasing hybrid ratio. However, beyond a critical threshold (>0.67), the enhancing effect of promiscuity on bending properties diminishes significantly, ultimately transitioning to detrimental impact. Therefore, it is necessary to obtain the optimal confounding ratio with the bending property as the target value, so that the wall confounding has a negative effect.  
Figure 4: The low-velocity impact properties of samples with different hybrid ratios: (**a**) force–time curve; (**b**) impact process curve; (**c**) comparison of peak load; (**d**) comparison of EA.
# Quantum Multi-User Broadcast Protocol for the \"Platform as a Service\" Model
## 3 Multi-User Broadcast Protocol Based on _Qcm_
### Selective Repeating Process  
In this protocol model, the multicast router node acts as a key management system and authenticates the communication users in communication. The multicast router manages the security key for the communication users and authenticates the identity of users by arbitrating the quantum signature using the shared quantum state [25]. In the process of communication, the multicast router node conducts authentication occasionally to prevent the user from being attacked.  
Ideally, we assume that each photon emission is perfect. If the information transmission fails due to channel loss or eavesdropping, the multicast router will return a negative acknowledgment (\(NAK\)) to the nodes. After receiving the \(NAK\), the node will start the selective repeating process, thus ensuring the security and reliability of the communication. The strings are transmitted continuously as a block of \((4n+\delta)\), the quantum multicast router resends (or repeats) only those codewords that are negatively acknowledged. Since the strings must be delivered to the user in correct order, a buffer must be provided at the receiver to store the error-free qubits of received qubits after error detection. When the first negatively acknowledged strings are successfully received, the receiver releases the error-free qubits in consecutive order until the next erroneously received qubits are encountered. Sufficient qubits receiver buffers must be provided, otherwise the qubits buffers may overflow and quantum data may be lost.
## 3 Methods
### Time Series InSAR Processing of Muyubao Landslide  
Sentinel-1 is a two-satellite constellation with the prime objective of land. It is the first satellite developed by the European Commission and the European Space Agency (ESA) for the Copernicus Programme. This satellite was launched in April 2014. After half a year of trial operation, it began to be gradually used after October 2014. One goal of the mission is to provide C-Band SAR data continuity following the retirement of ERS-2 and the end of the Envisat mission. In this study, 37 images acquired by Sentinel-1a, in the period from 10 March 2016 to 13 September 2017 (Figure 5), were collected from the ESA [27]. We collected the precise orbit parameters of the Sentinel-1 in the ESA as well [28]. The angle of the orbit with the north is 12.16\({}^{\circ}\), and the average incidence angle of three sub-swaths are 34.01\({}^{\circ}\), 39.39\({}^{\circ}\) and 40.02\({}^{\circ}\), respectively.  
Interferograms are generated applying the two-track differential method [29]. The SRTM (Shuttle Radar Topography Mission) DEM (digital elevation model) covering the study area is utilized to generate single-look differential interferograms. Temporal and perpendicular thresholds are, respectively, set to 90 days and 1000 m for generating interferograms. Considering that these thresholds will generate many interferograms and result in a heavy computational burden, each image was mostly combined to three subsequent images for interferometric processing. Data processing was carried out in COMET-LiCSAR [30]. As a result, 97 interferograms were produced as the input for the SBAS analysis (Figure 6). The amplitude difference dispersion was applied to selected coherent targets, and it was set as 0.6. Subsequently, the time series displacement was obtained by applying the StaMPS SBAS method. Moreover, in order to reduce the effects of residual atmospheric artefacts, the obtained time series displacement within the Muyubao landslide has been referenced to a stable neighbor point (31\({}^{\circ}\)1\({}^{\prime}\)52.62\({}^{\prime\prime}\)N, 110\({}^{\circ}\)29\({}^{\prime}\)57.48\({}^{\prime\prime}\)E) [15; 31].  
The direction of displacement acquired by the SBAS method is the line-of-sight (LOS) of radar satellite. Before applying the measurement to the landslide analysis, the LOS displacements should be projected along real slope components to get actual vectors. [PERSON] et al. [32; 33] proposed a widely used projection method; the LOS displacement can be projected into the direction of the steepest descent slope. In this case study, a field investigation showed that the slip direction was 16\({}^{\circ}\). Hence, the LOS displacement was projected onto the downward direction of along the sliding direction.  
Figure 5: The time distribution of sentinel-1 images.  
Figure 6: Baseline network of the interferograms.