esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 V2H and V2G
### Technological Developments
#### 4.3.1 Denmark  
Denmark has the world-leading wind power generation industry, with 42% of its domestic energy generated by wind power up to 2015. Several research projects related to V2G were initiated as early as 2009. Between 2009 and 2013, Denmark launched a partially government-sponsored research project called the EDISON project on the eastern island, Bornholm, to lay the foundation for the turbine power system, which will be completed by 2025. Wind power will provide half of the total energy supply for the island, while the V2G system will serve as an alternative to avoid damage to the island's electricity grid. The EDISON project demonstrated the full-stack solutions offered by Danish and international companies to develop the EV integrated system, including network construction, market solutions, and the optimization of the interaction between diverse energy technologies [65, 66]. Moreover, the Danish electricity system creates a perfect experimental platform for testing and optimizing such projects. After the EDISON project, the Nikola project was launched as a Danish research and demonstrative project to establish a framework for interacting with EVs and the electricity system in the campus laboratory environment. The project was completed in 2016 as a collaboration among DTU, NUVVE, and Nissan. The latest Parker project, launched in 2016, aimed to supply electrical services to the Danish electrical system from EVs serving as the operational fleet [67]. The experimental project, carried out by the DTU, NUVVE, and INSERO organizations in collaboration with the automotive original  
Figure 5: Concept of V2G and V2H [64].  
equipment manufacturer called Nissan and Danish Convoy holder, has the following innovative features:  
1. Evaluating advanced smart grid services, such as V2G, by mass-produced vehicles and charging stations;
2. Using a vehicle-assisted power system to coordinate supply and demand;
3. Verifying the incorporation of vehicles into the power system vertically from distribution to market to optimize power system operation.
# Hyperbolic Asynchronous Method of a Radio Navigation Technique
## 4 Theoretical Investigations
### Analysis of Theoretical Investigation Results
The resulting images are shown in the following Figure 17.  
The results of the studies presented in the histograms also confirm previous conclusions. This is especially true for the significant influence of the bandwidth on the quality of the tested asynchronous mode of the hyperbolic localization system. Summarized here are the results of the theoretical research carried out. Table 1 summarizes the calculated error values \(ME_{\delta}\), \(MSE_{\delta}\) and \(RMS_{\delta}\) for three reference stations and for different bandwidths. The comparison of errors in Table 1 also confirms the previous conclusions that the use of an increased number of reference stations in a locating system using the hyperbolic asynchronous method--for this purpose, using radio signals with the highest possible spread spectrum width--results in a clear improvement in the efficiency of such a system. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **1 MHz** & **2 MHz** & **4 MHz** & **8 MHz** & **10 MHz** \\ \hline ME\({}_{\delta}\) & 27.86 & 18.78 & 15.75 & 15.14 & 15.09 \\ MSE\({}_{\delta}\) & 1035.39 & 510.20 & 378.02 & 345.61 & 342.95 \\ RMS\({}_{\delta}\) & 32.18 & 22.59 & 19.44 & 18.59 & 18.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of errors \(ME_{\delta}\)\(MSE_{\delta}\) and \(RMS_{\delta}\) for three reference stations and for different bandwidths. Figure 17: Absolute error \(\delta_{d}[m]\) histogram for three reference stations and for different bandwidths.
## 5 Results and Analysis  
Experimental validation explicitly correlates with the three-phase framework design. The pruned EEG classifier demonstrated sufficient temporal resolution to support real-time control, with empirical measurements confirming stable operation under hardware constraints. Subsequent inverse reinforcement learning successfully captured risk-aversion patterns, as evidenced by policy trajectories avoiding high-risk zones even in unmapped environments. Most critically, the integrated EIRL framework maintained baseline path efficiency while significantly reducing collision probabilities compared with conventional approaches, validating the synergy between neural decoding and learned reward structures.
# Potential Role of Vitamins A, B, C, D and E in TB Treatment and Prevention: A Narrative Review
## 2 Results
### Vitamin B Complex
#### 2.2.4 Vitamin B6 (Pyridoxine)  
Vitamin B6 is well-known for its antioxidant properties, which include the capacity to scavenge reactive oxygen species (ROS) [30]. A heteromeric PLP synthase comprised of Pdx1 and Pdx2 was found to produce pyridoxal 5-phosphate (PLP), the bioactive form of vitamin B6, in MTB. A B6 auxotrophic MTB mutant is produced when the pdx1 gene is damaged. By eliminating the cofactor during exponential growth or stationary phase, the necessity of vitamin B6 synthesis for pathogen growth and survival in culture is proven [31].  
Vitamin B6 (pyridoxine) supplementation during isoniazid (INH) therapy is necessary to prevent the development of peripheral neuropathy [28; 32].
# Two NEH Heuristic Improvements for Flowshop Scheduling Problem with Makespan Criterion
## 4 Second Improvement Method NEH-SS2
### Method Description  
As presented in Algorithm 1, a second choice is performed at each loop of the NEH heuristic. Indeed, there is no ambiguity in terms of retaining the best sequence if there is only one best sequence. However, if more than one sequence can be \"the\" best sequence, it could be worth looking at each of them. The second choice improvement proposed in this paper is based on this idea. It consists of making a list of the best intermediary solutions, and treating equally all of the equivalent partial sequences of the second choice, when possible.  
The algorithm is described below (Algorithm 2). For the purposes of illustration, only two lists of partial schedules, _LPS1_ and _LPS2_, are included. The list _LPS2_ is filled with the best partial schedules of the current iteration. If the number of equivalent partial schedules is greater than _Size_LPS1_, then a set of partial schedules is randomly extracted and copied to _LPS1_, in order to limit the number of tested combinations at each step of the algorithm process. Each of the selected partial lists is then treated in the next step of the algorithm. These two lists make it possible to cope with different ranges of problems, in terms of the number of jobs, number of machines, difficulty, and computation time. In order to adapt the heuristic to a problem with a high number of jobs, it is necessary to avoid increasing too widely the computing time. This is the reason why suitable values of _Size_LPS1_ have been investigated.  
```
Rank jobs in decreasing order of total processing time // (First point sort) Remove first job of ranked list and insert it as the first element of partial sequence \(k=1\) GiveSize_LPS1 and Size_LPS2 Do{ Take the first job of ranked list For (each partial sequence of _LPS1_) Evaluate all of \(k\)+1 possible partial schedule Keep the best partial sequence(s) in _LPS2. // (Second point choice)_ // If there is only one best partial sequence, we save only one partial sequence. End For \(k=k+1\) If (number of best partial sequences in _LPS2_\(\leq\)_Size_LPS1_) Copy_LPS2 in _LPS1 Else ChooseSize_LPS1_ sequences in _LPS2_ Copy them in _LPS1 End If EmptyLPS2 } While (\(k\leq\) number of jobs to schedule)
```  
**Algorithm 2**(NEH-SS2 improvement heuristic)
## 3 Proposed Hybrid JADE-DO Algorithm
### Mathematical Formulation of the Hybrid JADE-DO Method
#### 3.2.2 Main Loop  
The main loop repeats until the maximum number of iterations, Max_iter, is reached. Each iteration proceeds with the following steps.  
(1) Fitness evaluation  
Every individual \(i\) in the population is evaluated as follows:  
\[\text{fitnessP}(i)=f\big{(}P(i,\cdot)\big{)}, \tag{12}\]  
where \(f\) is the objective function.  
(2) Parameter sampling  
For each individual \(i\), the crossover rate \(CR(i)\) is sampled from a normal distribution,  
\[CR(i)\sim\mathcal{N}(uCR,0.1), \tag{13}\]  
and the scaling factor \(F(i)\) is sampled from a Cauchy distribution:  
\[F(i)\sim\text{Cauchy}(uF,0.1). \tag{14}\]  
Both \(CR(i)\) and \(F(i)\) are constrained to ensure that they lie within permissible ranges. (3) Mutation and crossover (JADE)  
A mutant vector \(V(i,:)\) for individual \(i\) is generated as follows:  
\[V(i,:)=P(i,:)+F(i)\left(X_{\text{pbest}}-P(i,:)\right)+F(i)\left(P1-P2\right), \tag{15}\]  
where \(X_{\text{pbest}}\) is a randomly selected individual from the top \(p0\%\) of the population, and \(P1\) and \(P2\) are randomly chosen solutions drawn from the population and the archive \(A\), respectively.  
Crossover produces a trial vector \(U(i,:)\) according to the following:  
\[U(i,j)=\begin{cases}V(i,j),&\text{if rand}\leq CR(i)\text{ or }j=\text{jrand},\\ P(i,j),&\text{otherwise},\end{cases} \tag{16}\]  
where jrand is a randomly chosen index that ensures at least one component comes from \(V(i,:)\).  
(4) Boundary handling and selection  
The trial vectors \(U\) are restricted to \([\text{lb},\text{ub}]\) by  
\[U =\max(U,\text{lb}), \tag{17}\] \[U =\min(U,\text{ub}), \tag{18}\]  
where the max and min operations are applied element-wise. If the fitness of \(U(i,:)\) is better than \(P(i,:)\), the trial vector replaces the original vector. In such cases, the associated \(CR(i)\) and \(F(i)\) are labeled as successful.  
(5) Adaptation (JADE)  
The mean crossover probability \(uCR\) and mean scaling factor \(uF\) are updated after each iteration using the following:  
\[uCR =(1-c)\,uCR+c\,\text{mean}(\text{Scr}), \tag{19}\] \[uF =(1-c)\,uF+c\,\Big{(}\frac{\sum(\text{Sf}^{2})}{\sum(\text{Sf})} \Big{)}, \tag{20}\]  
where \(c\) is a learning rate parameter, Scr is the set of successful crossover rates, and Sf is the set of successful scaling factors from the current iteration.
## 3 Materials and Methods
### Studied Cases for the Proof of Concept (POC)
#### 3.1.1 Case 1  
In the studied case 1, the exchange and handling of DCCs was tested by mimicking a realistic calibration chain from a national measurement standard of an NMI to a working standard of a pharmaceutical manufacturing company. The main objectives for the POC were achieving an efficient and easy-to-use method for managing calibrations and the related data exchange between organizations. The physical quantities for which the harmonized file formats were agreed on within the consortium were temperature and pressure, as the working standard in question is capable of measuring both quantities. In addition to Aalto University, the organizations involved were:1. VTT MIKES, the Finnish NMI;
2. Vaisala Oyj, an instrument manufacturer with an accredited laboratory and a provider of calibration services;
3. Orion Oyj, a pharmaceutical company;
4. Beamex Oy Ab, a field calibration instrument and CMS provider.  
Figure 3 shows the POC setting, where different colors highlight the systems and operations of the organizations involved.  
In case 1, a separate sub-schema was developed based on the DCC schema version 2.4.0 to limit the options for presenting the data only to those deemed necessary for the DCCs that were created as examples for testing.
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture  
[PERSON]  
1 Department of Industrial and Systems Engineering, School of Mechanical Engineering,  
Beijing Institute of Technology, Beijing 100081, China; [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (X.W.)  
[PERSON]  
2 School of Mechanical Engineering, North University of China, Taiyuan 030051, China 2*  
systems [12; 13]. Service paradigms in agriculture bring new issues and motivate new research in the design of maintenance service networks. First, in agriculture, there is no cross-level service in the multi-level maintenance service network. That is, spare part centres are built to supply spare parts to service stations, and service stations are in charge of maintenance service for failed machinery. Thus, spare part centres cannot supply maintenance services for failed machinery directly, which is different from other service networks. In addition, different from discrete demand points in previous studies, maintenance demands in agriculture are spread across a wide geographical area. Thus, manufacturers always cluster some agricultural production areas into several service units, and maintenance demands in service units are calculated based on all failed machinery in these production areas. All service demands in the same service unit can be served by one service station. The region-based service region districting problem is a special problem in agricultural maintenance. Lastly, an adjacent production area always means a similar climate and crop-ripening time, so service units in connected areas allocated to the same service station can improve service efficiency. Contiguity constraints should be considered in this research to ensure that service units in a service region and service district are contiguous.  
Thus, this paper considers the location-allocation problem in the multi-level maintenance service network, and this research aims to develop a practical solution approach to determine the location of spare part centres and service stations while determining the allocation results of service vehicles.  
The contributions of this research are threefold. First, in agriculture, service stations and spare part centres need to be located considering hierarchical relationships while determining the service area of these service facilities. A defined number of service vehicles is expected to be allocated to these selected service stations to provide maintenance service for failed machinery simultaneously. Thus, a multi-level facility location-allocation problem (MLFLAP) in agriculture is proposed in this paper. Second, an MILP-based solution model is formulated for minimal service costs. Contiguity criteria are introduced to ensure that the service region served by one service station is contiguous, and multi-level constraints are applied to prohibit cross-level service. Lastly, a Benders decomposition approach with several improvements is developed to address the large-scale cases in reality efficiently, and sensitivity analysis is conducted for decision making in practical application.
###### Abstract  
Recent studies have shown that lower-limb pylemetric training can effectively enhance muscle strength and explosiveness, which are particularly important for improving jumping ability. The purpose of this study was to evaluate the effects of pylemetric training on vertical, lateral, and horizontal jumping abilities, and their subsequent impact on basketball shooting performance and sports injury prevention. A quasi-experimental design was used, recruiting 30 male college-level basketball players from Taiwan, who were randomly assigned to an experimental group (\(n=15\)) and a control group (\(n=15\)). Both groups participated in 2 h of basketball training daily, while the experimental group additionally engaged in pylemetric training twice a week. The results revealed significant improvements in the experimental group in several key areas, including rate of force development (RFD), ground reaction force (GRF), jump height, jump distance, and both horizontal and vertical forces, across vertical, lateral, and horizontal jumps. Specifically, vertical jumps required the highest ground reaction force, followed by lateral jumps, with horizontal (step-back) jumps requiring the least. The optimal angles for the resultant force during take-off were found to be between \(66.1^{\circ}\) and \(66.8^{\circ}\) for lateral jumps, and between \(56.2^{\circ}\) and \(57.2^{\circ}\) for step-back jumps, while vertical jumps did not show significant variation in take-off angle. In terms of basketball performance, the experimental group demonstrated significantly better post-test results in all three types of jump shots, with the highest accuracy observed in the vertical jump shot, followed by the lateral jump shot, and the lowest in the step-back jump shot. Furthermore, the experimental group experienced a substantial reduction in sports injury rates, with the injury rate decreasing to 6%. These findings indicate that pylemetric training not only enhances jumping performance, but also contributes to injury prevention by strengthening lower-limb muscles. This study provides a theoretical basis for coaches to develop comprehensive training programs that improve athletic performance and reduce injury risk.  
vertical jump; lateral jump; horizontal jump; pylemetric training; injury prevention Article
## 4 Simulation Results and Analysis
### Gain Performance  
The antenna demonstrates a peak realized gain of 9.91 dBi, consistent with a linear array configuration with directional beamforming. This level of gain is consistent with expectations from a six-element series-fed patch array, where each element constructively contributes to directivity. The gain difference stems from whether the array is measured standalone or with the integrated power divider. Simulated ideal gain (array only): \(\sim\)9.91 dBi and measured gain (fabricated prototype with integrated divider): 11.51 dBi. The gain value indicates efficient radiation and low feed loss, despite integrating a Wilkinson-type power divider. High gain ensures effective signal reception/transmission over longer distances, ideal for point-to-point 5G backhaul or automotive radar. The gain G of an N-element uniformly spaced array is calculated using Equation (12), which is approximately equal to 11.53 dBi.  
\[\text{Garray}\approx\text{Gelement}+10\text{log}10(\text{N})\ 11.53\ \text{dBi}\ \text{(ideal)} \tag{12}\]  
Real-world losses (-1.5 dB due to dielectric, conductor, and feed) justify the measured 9.91 dBi.
## Materials and Methods
### AFM and SEM Imaging  
To prepare hMSCs for AFM imaging, the cells were grown on glass coverslips in the aforementioned medium. Near the confluency, the cells were treated with 5 mg/mL HAp nanoparticles. After the incubation period of 72 h, the cells were fixed for 15 min in 4% paraformaldehyde, and transported to the measurement site. AFM images were taken on a Pacific Nano Technology (Santa Clara, CA, UAS) Nano-R Atomic Force Microscope (Target Technology Company, Irvine, CA, USA). The close contact mode was used to acquire all the images on the Nanorule+ software platform (Pacific Nano Technology, Santa Clara, CA, USA). Gold-coated tips were placed onto the cantilevers prior to the imaging. The closed-loop control system of the instrument was set to compensate for the creep of the piezoelectric translator of the scanner with position sensors attached to it. The resolutions in the \(x\) and \(y\) directions were \(<\)2 nm and the noise level in the \(z\) direction was \(<\)0.13 nm. The maximum range of the scanner was 80 \(\upmu\)m in the \(x\) and \(y\) directions, and 8 \(\upmu\)m in the \(z\) direction. No image processing was performed, aside from the artificial coloring by adjusting the color saturation and hue. Field emission scanning electron microscopy (FE-SEM) was performed on a Carl Zeiss (Oberkochen, Germany) ULTRAPlus microscope at the electron acceleration voltage of 3 kV. The samples were sputtered with gold prior to the SEM analysis of the nanoparticle size and morphology.
## 5 Performance Evaluation  
### Simulation Model  
A set of computer experiments has been conducted based on 3 GPP's NR V2X specifications under various network settings. An autonomous and connected vehicle scenario (i.e., a potential use case) has been created using the ns-3 [23] discrete event network simulator. Note that ns-3 is a free and open-source software licensed under the GNU GPLv2 license. It provides a simulation engine for users to conduct simulation experiments, (i) to perform studies that are more difficult or not possible to perform with real systems, (ii) to study system behavior in a highly controlled, reproducible environment, and to learn about how networks work [52].  
The simulated highway scenario is shown in Figure 3. The simulation network consists of a highway of three lanes where, in each lane, at any point in time, one vehicle playsthe role of transmitter, and others act as receivers. At the default settings, the inter-lane distance is 4 m and, in each lane, 11 vehicles are set 20 m apart. The generated vehicles follow a constant velocity mobility model with a default speed of 38.89 m/s. A transmitting vehicle generates packets every 100 ms interval, and the whole simulation is conducted for 20 s. The explicit parameter settings for the conducted simulation are shown in Table 2. Other than these explicitly listed parameters, the simulation was conducted under the simulator's default settings.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Default** \\ \hline Number of vehicles per lane & 11 & \\ \hline Number of lanes & 3 & \\ \hline Inter-vehicle distance & 20 m & \\ \hline Freeway max. vehicle speed & 38.89 m/s & \\ \hline Broadcast message size & 100\(\sim\)1500 bytes & 700 bytes \\ \hline Transmission rate & 10 Hz & \\ \hline Carrier frequency & 5.89 GHz & \\ \hline Channel bandwidth & 10 MHz & \\ \hline Numerology (\(\mu\)) & \(\mu\) = 0\(\sim\)2 & \(\mu\) = 0 \\ \hline MCS & 0\(\sim\)28 & 14 \\ \hline Transmit power & 5\(\sim\)35 dBm & 23 dBm \\ \hline Communication range & 100\(\sim\)1000 m & 600 m \\ \hline Mobility model & Constant velocity mobility & \\ \hline Simulation time & 20 s sim time & \\ \hline \end{tabular}
\end{table}
Table 2: ns-3 simulation parameters.  
Figure 3: Highway simulation scenario in ns-3.