esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Nordic exercises are widely used to improve physical performance and prevent injuries; however, their applicability across different sports remains unclear. Objective: This study examined the effects of Nordic exercises on the lower limbs and their relationship with absolute strength and vertical jump performance across various sports disciplines (soccer, athletics, basketball, sport climbing, cycling, and takewondo). Methods: We conducted a quasi-experimental study involving 122 athletes (mean age 18.2 \(\pm\) 3.2 years), who were not in pre-competitive or competitive periods, distributed across the following disciplines: soccer (\(n\) = 24), sprinting/athletics (\(n\) = 20), sport climbing (\(n\)= 20), basketball (\(n\) = 24), takewondo (\(n\) = 14), and cycling (\(n\) = 20). Participants were randomly assigned to a control group (CG), \(n\) = 57, with regular training and an experimental group (EG), \(n\) = 65, that performed Nordic hamstring exercises (NHEs). The effects of a 7-week NHE program were assessed on absolute strength (measured with a dynamometer) and vertical jump performance (Vert). A mixed-design repeated measures ANOVA was used, considering the factors time, group, and sport discipline, with a significance level set at \(p\) < 0.05. Results: Both groups (CG and EG) presented homogeneous baseline values for absolute strength (dominant and non-dominant sides) and vertical jump (\(p\) > 0.05). The EG exhibited significant gains in dominant-leg strength (from 12.4 kg to 14.5 kg), non-dominant side strength (from 11.1 kg to 13.8 kg), and vertical jump (from 42.4 cm to 45.8 cm), with statistically significant differences between pre- and post-tests (\(p\) < 0.01). No relevant changes were observed in the CG. The repeated measures ANOVA confirmed a significant time \(\times\) group effect in all three variables (\(p\) < 0.01), although no significant time \(\times\) sport \(\times\) group interaction was found. The largest strength gains occurred in strength were observed in sport climbing, basketball, and athletics and in vertical jump in soccer. Conclusions: Absolute strength improvements were most notable in sport climbing, basketball, football, and sprinting. Vertical jump performance improved notably in soccer, sprinting/athletics, climbing, and basketball. These results justify incorporating NHEs into youth athletes' training, tailored to the specific demands of each sport.  
Nordic exercise hamstring strength; vertical jump performance; absolute strength; lower limb +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Physicochemical Properties
#### 3.2.1 Colour Analysis  
The results of the colour analysis of all rice flour samples are shown in Table 2. The L* value expresses the brightness, in which the L* value ranges from 70.99 to 94.34. The brightness of rice flour was more prominent in Bario Adan Halus, followed by TQR. Both varieties exhibited high L* values due to their white appearance. Moreover, Bario Tuan, Bario Merah Sederhana, and Bario Celum rice flour presented lower L* values due to the brown, red, and purple pigmented external layers of the rice grains, respectively. The a* and b* values ranged between \(-\)0.15 and 4.80, and between 1.20 and 14.46, respectively. A positive a* value represents red, whereas a negative b* value represents green. Based on the result obtained, a positive a* value was high for Bario Merah Sederhana due to the red pigmentation of the rice bran. On the other hand, b* values ranged from 1.20 to 14.46. Specifically, a positive b* value indicates yellow while a negative b* value represents blue. The colour appearance of TQR and Bario rice flour is shown in Figure 1. In conclusion, the colour profiles of rice flour samples are based on the rice bran's pigment. Rice grains with a brownish-red pigment are typically called red rice, whereas rice kernels with purple pigmentation are renowned as black rice [57]. Anthocyanins and proanthocyanidins or condensed tannins are common pigments in black and red rice, respectively [58].
## 4 Seismic Data Interpretation
### Seismic Time and Depth Sections  
The first step is to solve the velocity windows, which are given on the top of the seismic lines. As shown in the Base Map in Figure 2a, the Tola-01 well lies on the seismic line 875-KBR-230; therefore, the velocity window is solved for the KBR-230 seismic section. On the velocity window, one column is for the time in milliseconds, and the other column is for the RMS (root-mean-square) velocity. Then, the RMS velocity is multiplied with the one-way travel time in milliseconds to give the depth by using the formula S = VT. The one-way travel time is calculated by dividing the time by 2. This column gives the depth of each time during the recording of a seismic wave traveling under the surface. For more insight into the velocity window, the depth needs to be plotted against the time window [51; 52]. The velocity windows are first solved to obtain the time values, and then, the depth is plotted on the horizontal axis against the time on the vertical axis. By using the reasoning that the subsurface structures can be predicted by transforming the time into depth, the TVT chart is constructed. The perception of transforming time to depth is accurate to point out the subsurface structures because the structure is flat and the map is in terms of the two-way travel time (TWT) of sound waves. Thus, a map of the seismic time section is proposed to point out the structure in the subsurface. The depth is plotted with time, as shown in Figure 2b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Formation Name** & **Depth** \\ \hline Dunghan & 1080.46 m \\ Samana Suk & 1174.05 m \\ Datta & 1400 m \\ Warcha sandstone & 1810 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Formation top values of marked reflectors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Formation Name** & **Time** \\ \hline Dunghan & 0.9 s \\ Samana Suk & 0.96 s \\ Datta & 1.08 s \\ Warcha sandstone & 1.24 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Marked horizons obtained from time-depth chart estimated in Tola-01 well.  
Figure 2: (**a**) Base map of the study area. (**b**) Time-depth (TD) chart of the study area.  
Figure 4: Marked seismic horizons of 875-KBR-221 line.  
Figure 3: Marked seismic horizons of 875-KBR-220 line.  
Figure 5: Marked seismic horizons of 875-KBR-229 line.  
Figure 6: Marked seismic horizons of 875-KBR-230 line.
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 4 Experiments and Results
### Evolving Robust Analog Filters Using Genetic Algorithms: The Classical Robust Design
\begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Perturbation Magnitude**} & \multicolumn{3}{c}{**0.1**} & \multicolumn{3}{c}{**0.2**} & \multicolumn{3}{c}{**0.3**} & \multicolumn{3}{c}{**0.4**} & \multicolumn{3}{c}{**0.5**} \\ \cline{3-11}  & **Mean** & **Stderr** & **Mean** & **Stderr** & **Mean** & **Stderr** & **Mean** & **Stderr** & **Mean** & **Stderr** \\ \hline \multirow{3}{*}{\(f_{robust1}\)} & GP & 36.57 & 5.55 & 40.04 & 5.00 & 45.33 & 4.56 & 54.68 & 4.25 & 61.91 & 4.06 \\  & GPGARMS & 65.16 & 6.60 & 50.08 & 6.05 & 64.33 & 3.66 & 73.85 & 3.84 & 79.81 & 5.28 \\  & GPRMS & 48.83 & 3.99 & 51.36 & 3.67 & 56.09 & 3.46 & 63.91 & 3.52 & 72.71 & 3.72 \\  & GPRPE & 14.08 & 2.65 & 19.64 & 2.22 & 27.25 & 1.82 & 37.51 & 1.81 & 48.98 & 2.21 \\ \hline \multirow{3}{*}{\(f_{robust11}\)} & GP & 2.44 & 0.40 & 5.59 & 0.79 & 14.37 & 1.61 & 30.20 & 2.83 & 42.78 & 3.76 \\  & GPGARMS & 5.42 & 1.85 & 10.81 & 1.72 & 18.18 & 2.12 & 33.57 & 1.85 & 54.05 & 4.45 \\  & GPRMS & 1.35 & 0.34 & 4.58 & 0.54 & 13.58 & 1.05 & 28.46 & 1.95 & 40.88 & 2.64 \\  & GPRPE & 3.92 & 0.46 & 8.13 & 0.78 & 20.85 & 3.75 & 30.89 & 2.36 & 43.11 & 2.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance and robustness of evolved lowpass filters using GP, GPGARMS, GPRMS, and GPRPE. Another interesting observation is the performance of GPGARMS. For highpass filters, we showed that GPGARMS evolved filters with the highest type II robustness \(f_{robustII}\). However, it is not the case for the lowpass filter problem. The GPGARMS shows the worst results in terms of both Type I and Type II robustness measures. How could GA produce worse results than standard GP? We have two possible explanations. First, the fitness function for GPGARMS only uses 10 simulations to estimate the robustness. This may result in large estimation error, thus misleading the evolution. Since the robustness scores in Table 3 are calculated by 10,000 perturbed simulations, results produced by a misled GPGARMS could be even worse than its starting solutions. Another projection is that the parameters of lowpass filters are located in a much more rugged landscape, so a misled GPGARMS tends to drift away quickly from the highly coupled parameters of the starting solution evolved by GP. These explanations are under investigation and will be reported elsewhere. From these analyses, we find that, compared to evolutionary design where robustness is not considered, allowing topological variation during the evolution of robust systems can yield more robustness and a superior structure with respect to perturbations of the component parameters of the filters, through topological innovation. To further consolidate our observations, in the following section, we report a series of statistical tests run to verify the significance of the differences seen between these algorithms.
## 3 System Model
### Edge Computing  
UD \(m\) utilizes the allocated bandwidth \(\alpha_{m}B\) to upload task data for edge computing. Hence, the maximum achievable transmission rate can be calculated by [10]  
\[r_{m}^{up}=\alpha_{m}B\text{log}_{2}(1+\frac{p_{m}h_{m}^{2}}{\sigma^{2}})= \alpha_{m}R_{m}, \tag{5}\]  
where \(p_{m}\) represents \(m\)'s transmit power, and \(h_{m}\) denotes the channel gain between \(m\) and the AP. \(\sigma^{2}\) indicates the background noise power. Accordingly, the corresponding transmission delay can be expressed as  
\[t_{m}^{up}=\frac{l_{m}^{f}}{r_{m}^{up}}=\frac{l_{m}^{f}}{\alpha_{m}B_{n}\text {log}_{2}(1+\frac{p_{m}h_{m}^{2}}{\sigma^{2}})}. \tag{6}\]  
The BS allocates \(\beta_{m}Fe\) of its computing resources to process \(t_{m}^{f}\) after the transmission. In this case, the corresponding computation delay can be denoted as  
\[t_{m}^{com}=\frac{\rho_{m}^{f}l_{m}^{f}}{\beta_{m}F_{e}}. \tag{7}\]
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 5 Validation
### Validation Scenario  
The MTRR was validated during the integration and final demonstration for the SWARMs project. These tests were done at the Trondheim Biological Station (TBS). The test area used for the validation is shown in Figure 23.  
A Central Control Station (CCS) was deployed at the TBS facilities as the on-shore control operations room. The CCS consisted of five computers connected to the same private IP network. Figure 24 shows how the different parts of the system were distributed among the CCS computers. Note that the MMT and CAF computers are in charge of running the MMT and CAF applications respectively, and two more Personal Computers are used for plume and vehicle simulations.  
Regarding the middleware and the repositories of information, a virtual machine solution was chosen. Both the relational database and the Fuseki server used to query the ontology were deployed in the same virtual machine as the middleware.  
All the communications from the middleware to the vehicles, as explained in Section 3, were done through the CDT. If the mission was simulated, the CDT was configured to connect to the VDTs from the simulator PC at the same CCS. Should the mission use real vehicles deployed underwater, the CDT would be configured to communicate to the VDTs on board those vehicles. Therefore, from the point of view of the rest of the system, the use of the simulator or the real vehicle was transparent.  
Figure 23: Final SWARMs demo test area used for validation.  
The plume simulator [53] that is also shown in Figure 24 was used in the following way: when the Environment Reporter component from the middleware received a new report from the vehicles (either real or simulated), it queried the plume simulator for the concentration values corresponding to the position of the vehicle. The returned data, if any, were aggregated to the report sent to the MTRR.
## 4 Results and Analysis
In the typical scenario of a cloudy day, the output of the hydro-photovoltaic combined system of the traditional scheme is 4.97955 million kW and the output of the hydro-photovoltaic combined system with hedging is 4.97166 million kW. However, the peak-valley difference of the system under the hedging scheme is 59.68% lower than that under the traditional scheme, which effectively improves the stability of the combined system and makes the grid-connected system more friendly. In order to analyze the process of reservoir water level change by the hedging mechanism scheduling scheme established in this paper, Figure 15 shows the change process of reservoir water level, inflow, and power generation flow based on the hedging scheme in typical sunny scenarios. As can be seen from Figure 15, when the water level of the reservoir is lower than the flood limit water level of 695.00 m, the power generation will be operated according to the power generation needs and the excess water will be filled into the reservoir until the water level of the reservoir reaches the limit water level of 695.00 m in the flood season. When the water level of the reservoir has reached the flood season limit water level of 695.00 m, after the power generation is operated according to the power generation needs, in order to ensure the safety of flood prevention, all of the excess water is discharged, resulting in a \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Typical Scenes in Sunny Days**} & \multicolumn{2}{c}{**Typical Scenes in Cloudy Days**} \\ \cline{2-5}  & **Traditional Scheme** & **Hedging Scheme** & **Traditional Scheme** & **Hedging Scheme** \\ \hline Photovoltaic output & & 57.55 & & 49.96 \\ Hydropower output & 442.917 & 440.447 & 447.997 & 447.207 \\ joint output & 500.4670 & 497.997 & 497.955 & 497.166 \\ Maximum joint output & 26.341 & 23.462 & 28.998 & 24.513 \\ Minimum joint output & 14.897 & 17.817 & 12.897 & 18.021 \\ Peak valley difference & 11.444 & 5.645 & 16.101 & 6.492 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hydropower and photovoltaic joint output fluctuation value under the two schemes (10\({}^{4}\) kW). Figure 15: Diagram of reservoir water level and flow change process under the optimization operation plan. waste of water resources. It can be seen that, in a scheduling cycle, the reservoir uses the shared storage to raise the water level from the designed flood control level of 695 m to the controllable upper limit of the flood control level of 695.12 m and reduces the water level to the designed flood control level during the effective forecast period, so as to always maintain the flood control risk under control and increase the benefit.
# Development and Biomechanical Evaluation of a Back Exoskeleton to Support Lifting Tasks
## 3 Evaluation
### Experimental Design  
To measure body kinematics during the task, a mobile Xsens MTw Awinda System was used (Xsens Technologies BV, Enschede, The Netherlands). This device shows acceptable to excellent reliability and validity [22]. The muscle activity of the m. erector spinae and m. semitendinosus was recorded using a Myon 320 system (Myon AG, Schwarzenberg, Switzerland). The system exhibits good to acceptable reliability [23]. The sensor placement was conducted according to the SENIAM guidelines. To synchronize both systems, a trigger signal from Xsens was used to mark the start and stop of the EMG measurement.  
Initially, a calibration walk consisting of eight circular steps was performed to calibrate the Xsens system. Subsequently, each participant donned the exoskeleton and adjusted the fit of the hip belt and shoulder vast. The thigh straps were fastened around the thighs of the participants. After activating the system, the participants were given a five-minute familiarization period to adapt to the exoskeleton and reduce the likelihood of adopting unintended protective postures during use. During the exoskeleton condition, the system was set to provide full support at 100%. They completed a lifting task in which they lifted a 10 kg object with both hands using two handles attached to the left and right of the object from the ground and placed it on a platform in front of them at hip height (Figure 4). After lifting the load, each participant placed it back on the floor in the starting position. The task consisted of 12 repetitions, performed at a frequency of 15 lifts per minute. A metronome was used to maintain a constant speed, which provided an acoustic signal for lifting and lowering the weight. The task was repeated with and without the exoskeleton, while the trials were randomized and stopped after the first signs of pain or any abnormal functions of the exoskeleton.  
Figure 4: Lifting task (exoskeleton condition) from (**a**) ground onto (**b**) platform.
## 3 Online Root Fault Identification Method
### Feature Vector Extraction
#### 3.1.2 Matching Alarm Lists and Their Root Faults  
Every time one wind turbine is shut down due to a fault, manual maintenance is needed. Therefore, the root fault of each alarm list is recorded in the maintenance log. The total number of records is \(Q\). In theory, \(Q\) should be equal to the total number of alarm lists \(M\). However, \(Q\) is smaller than \(M\). This is due to the irregular work of operators in the industry. Some maintenance activities are missing.  
The end time of alarm lists and the start time of maintenance activities is used to match alarm lists and their root faults. The schematic diagram of the match criterion is shown in Figure 2. The end time of an alarm list should be earlier than the start time of maintenance activity. The alarm list corresponding to the maintenance activity is the last list. Ultimately, we obtain \(Q\) pairs of data, which are made up of alarm lists and their root faults.
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 2 Overview of Existing KF RAIM
### Protection Levels (PLs)
#### 2.2.2 PLs  
Equations for the vertical PL (VPL) are given as follows. They can be easily modified for the horizontal PL (HPL). Under the SF modes, VPL is  
\[\text{VPL}_{SF}=\sum_{j=1}^{2}\Big{(}\max_{i\in\text{SF modes}}VPE_{j,i}^{U} \Big{)}+VPE_{3}^{U}+\gamma_{s}\sigma_{v} \tag{14}\]  
The operation \"max\" represents the maximum of all terms underneath it. The probability \(P(\text{absolute VPE}>\gamma_{s}\sigma_{v})=P_{MD,\,sf}^{alloc}\). \(\sigma_{v}\) is the standard deviation of the error in the estimated vertical position. \(P_{MD,\,sf}^{alloc}\) is the allocated probability of missed detection for SF modes. Similarly, the VPL under DF modes is calculated. The final VPL is  
\[\text{VPL}=\text{max}(\text{VPL}_{NF},\ \text{VPL}_{SF},\ \text{VPL}_{DF}) \tag{15}\]  
In the preceding equation, \(\text{VPL}_{NF}\) stands for the VPL under no fault conditions.
# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
\begin{table}
\begin{tabular}{c c c c} \hline
**Acronym** & **Definition** & **Acronym** & **Definition** \\ \hline SG & Fifth Generation & NFV & Network Functions Virtualization \\ aSDmalloc & autonomic SDN allocator & NFVO & Network Functions Virtualization Orchestrator \\ CFS & Customer Facing Service & NSGA-II & Non-dominated Sorting Genetic Algorithm II \\ DSDVMEC & Dynamic Software Defined Vehicular Multi-access Edge Computing & ONOS & Open Network Operating System \\ DSRC & Dedicated Short-Range Communications & PGA & Packing and genetic algorithm \\ eNB & Evolved Node B & PSO & Particle Swarm Optimization \\ ETSI & European Telecommunications Standardization Institute & QoS & Quality of Service \\ gNB & Next Generation Node B & RAN & Radio Access Network \\ GWO & Gray Wolf Optimizer algorithm & RRH & Remote Radio Head \\ ILP & Integer Linear Program & RSU & Road Side Unit \\ IoT & Internet of Things & SDNN & Software Defined Mobile Network \\ IoV & Internet of Vehicles & SDN & Software-Defined Network \\ ITS & Intelligent Transportation System & URLLC & Ultra Reliable and Low Latency Communication \\ ITU & International Telecommunication Union & V2X & Vehicle-to-everything \\ LCAA & Low-complexity controller master assignment & VANET & Vehicular Ad hoc Network \\ LTE & Long Term Evolution & VIM & Virtualization Infrastructure Manager \\ MANO & Management and Orchestration & VNF & Virtualized Network Functions \\ M-CORD & Mobile Central Office Re-architected as a Datacenter & VNFPRA & VNF placement and resource allocation \\ MEC & Multi-access Edge Computing & WAN & Wide Area Networks \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of Important Acronyms.
## 4 Results
### System Accuracy  
#### 4.3.1 Masked Face Classification  
The masked face classification accuracy results are shown in Table 7. The results presented here are results of Approach 4 chosen in the tuning section (refer to Section 4.1). There are values present for correct detection of the correctly masked face, correct detection of the incorrectly masked face, the True Positive Rate (TPR), True Negative Rate (TNR), precision and recall. Considering the fact that all of these values are over 96% and that the dataset classes are balanced in terms of sample amount, this translates into a high-quality classifier.
# Joints Trajectory Planning of Robot Based on Slime Mould Whale Optimization Algorithm
###### Abstract  
The joints running trajectory of a robot directly affects it's working efficiency, stationarity and working quality. To solve the problems of slow convergence speed and weak global search ability in the current commonly used joint trajectory optimization algorithms, a joint trajectory planning method based on slime mould whale optimization algorithm (SMWOA) was researched, which could obtain the joint trajectory within a short time and with low energy consumption. On the basis of analyses of the whale optimization algorithm (WOA) and slime mould algorithm (SMA) in detail, the SMWOA was proposed by combining the two methods. By adjusting dynamic parameters and introducing dynamic weights, the proposed SMWOA increased the probability of obtaining the global optimal solution. The optimized results of 15 benchmark functions verified that the optimization accuracy of the SMWOA is clearly better than that of other classical algorithms. An experiment was carried out in which this algorithm was applied to joint trajectory optimization. Taking 6-DOF UR5 manipulator as an example, the results show that the optimized running time of the joints is reduced by 37.674% compared with that before optimization. The efficiency of robot joint motion was improved. This study provides a theoretical basis for the optimization of other engineering fields.  
slime mould algorithm; whale optimization algorithm; trajectory planning; meta-heuristic algorithm; zero-defect manufacturing