esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Underground Mining Illuminance  
As we have presented in previous sections, one of the main benefits of VLC systems in underground mines is that they offer lighting and communication at the same time. Because in the system that we propose the optical \(T_{x}\)s are LED light sources, it is necessary to provide a mathematical lighting model for the \(T_{x}\)s. An important consideration is that this model is calculated only for direct light; therefore, in order to define the luminous flux \(Q\) as the optical power that the human eye can receive, the following expression is presented [11; 16]  
\[Q=683N_{T_{x}}\int_{380}^{720}S(\lambda)V(\lambda)d\lambda, \tag{40}\]  
where \(V(\lambda)\) is the sensitivity function of the human eye and \(S(\lambda)\) is the distribution of the radiation spectrum. The relationship between \(Q\) and the illuminance \(I(\phi)\) on the receiving surface is given by [11]  
\[I(\phi)=\frac{\delta Q}{\delta A_{p}}=\sum_{j=1}^{N_{T_{x}}}jI_{0}cos^{m}(\phi), \tag{41}\]  
where \(I_{0}\) is the center of light intensity of \(T_{x}\). This expression allows us to calculate the light intensity in the reception plane of the \(R_{x}\). However, a baseline is needed as a point of comparison for our analysis. Consequently, results are contrasted with the requirements of underground mining lighting standards, which indicate that the minimum light intensity requirements for these environments, is given by \(0.158\)\(W\)/m\({}^{2}\) for the luminous power density, and \(107.65\)\(\,\) lux for the illuminance [46]. Considering these values, the illuminance is obtained by simulation both in the mining roadway scenario and in the mine working face, as we can see in Figure 7a,b, respectively. Further, the average height of the mining workers is also considered. As, the reception plane of the \(R_{x}\) would be in their helmets, the illuminance is evaluated at \(1.67\)\(\,\) m above the floor in both scenarios. Also, all the parameters presented in Table 1 were considered for simulations.  
Note in Figure 7a that the illuminance values are in the range of [400-1300] lux. It is also observed that the illuminance peaks occur precisely in the location of the \(T_{x}\)s, which are in a row in this scenario, taking into consideration that the \(T_{x}\)s are located at \(3.7\)\(\,\)m. Secondly, in Figure 7b, to obtain the illuminance values, the location of the \(T_{x}\)s changes to have a rectangular order, in addition to the height of the stage being increased to \(4.7\)\(\,\)m. Due to these variations, since the \(T_{x}\)s are further from the mining helmets, it is observed that the illuminance is in the range between [200 and 650] lux, which is less than the previous scenario. However, in both scenarios it is possible to note that the illuminance standard for underground mine environments is met.  
Figure 7: Illuminance distribution of underground mining VLC scenarios for the (**a**) mining roadway and (**b**) mine working face.
## 3 Results and Discussion
### Temperature Measurements of Electrodes before and after tACS  
Figure 6 shows thermal images of rats measured before and immediately after 20 min of application of 10 Hz tACS with an electrical current of 1.5 and 2.0 mA. It was confirmed that the regions where an anode or a cathode was located on the head and back of rats had higher temperatures than other regions. However, even after 20 min of tACS application, the temperature did not increase above 41 \({}^{\circ}\)C. This implies that tACS using the electrical stimulator and electrodes employed in this study does not violate the regulation for the temperature conditions of skin-contacting medical devices.  
Figure 4: Histopathological slide scan images of H&E and Cresyl violet-stained brain tissue slides under each current condition (I = 0.0 (control), 1.0, 1.5, and 2.0 mA) of 10 Hz tACS. Red boxes indicate regions of sub-images segmented for quantitative analysis of a number of Nissl bodies. (T: Transcranial alternating current stimulation-applied region, C: Contralateral region).  
Figure 5: Quantitative comparison of the numbers of Nissl bodies (neuronal components) in sub-images of Cresyl violet-stained brain slide scan images. Although there was a difference in the number of Nissl bodies between the two regions in all the experimental cases, including the control (no current), the deviation was approximately 10%, indicating that there was no significant reduction in neuronal components due to brain damage.  
Figure 6: Thermal images of rats measured before and immediately after 20 min applications of 10 Hz tACS with an electrical current of 1.5 or 2.0 mA. Regions where an anode or a cathode was located on the heads and backs of rats had higher temperatures than other regions, but the temperature did not increase above 41 \({}^{\circ}\)C even after 20 min of tACS. The average, the maximum, and the minimum temperatures of regions where the anode and the cathode were attached are described on the right side of the thermal images.
## 2 System Specification
### Rules
#### 2.2.4 Saturation Rule  
If \(ND_{R}\) has been updated or the primary controller is not activated because the node degree error \(e_{ND}\) is within the acceptance region and also the controller output is saturated, then the output of the controller must be driven out of the saturation value. The appropriate new value for the controller output is unknown because the controller does not contain a model that relates its output with the node degree error, and also, this relation is non-linear and non-injective. The rule to achieve this is as follows:  
\[\begin{array}{l}\textbf{IF}\\ \{|e_{ND}(k)|-\xi_{ND}\leq 0\}\\ \textbf{AND}\\ \{CR_{f}(k)-CR_{max}\geq 0\}\\ \textbf{AND}\\ \{|ND_{R}(k)\
eq ND_{R}(k-1)|\ \textbf{OR}\ [e_{ND}(k)<e_{ND}(k-1)]\}\\ \textbf{THEN}\\ e_{ND}(k)=-(\xi_{ND}+\Delta CR_{min})\end{array} \tag{18}\]  
The introduction of a small disturbance to the node degree error forces the activation of the primary control function. This will force the controller to decrease the output trying to correct the error. This rule introduces the new parameter \(CR_{f}\), which is the value of \(CR\) being filtered to avoid punctual values of \(CR\) saturated, according to Equation (19).  
\[CR_{f}(k)=\frac{1}{2}[CR(k)+CR(k-1)] \tag{19}\]
## 9 WLAN Integration with 6G: Challenges and Opportunities
### Context- and Network-Aware Resource Allocation  
The heterogeneity of the 6G ecosystem is useful only if the resources can be allocated and managed effectively for the required users. When there are multiple WLANs in a given indoor space, assignment of a network to a particular user needs to be carried out considering the complete traffic load of the space, the QoS requirements of the user application, the current loading of the WLAN, the support for mobility, and other practical considerations. Hence, novel resource management algorithms are required to manage the resources associated with indoor WLANs in 6G deployment for the optimal operation and to satisfy the requirements of emerging applications. Further, regarding the complexity of the heterogeneity of networks used, the handover between networks needs to be carried out with the knowledge of user context and network performance. Handover mechanisms that can be used in WLAN converged networks need to be further explored to support highly reliable and uninterrupted access.
# Formation Mechanism of Metro Rail Corrugation Based on Wheel-Rail Stick-Slip Behaviors
## 4 Analysis of Wheel-Rail Stick-Slip Characteristics
### Vehicle-Track Space Rigid-Flexible Coupling Model  
#### 4.1.1 Establishment of Model  
The space rigid-flexible coupling model mainly includes vehicle (motor car) model, track model, and wheel-rail contact models. The vehicle model is composed of body, bogies, and wheelsets. The body and bogie are treated with rigidity, the wheelset is regarded as a flexible body, and the wheel adopts an LM worm tread. The body, bogie, and wheelset all consider six degrees of freedom, namely, longitudinal movement, lateral movement, floating, rolling, nodding, and shaking, and the whole vehicle model has 42 degrees of freedom. The body and bogies, as well as the bogies and wheelsets, were all connected by spring-damping elements to simulate the secondary suspension and primary suspension, and the stiffness and damping characteristics in six directions could be considered. The rail type in the track model was CHN60, and the fastener was also simulated by spring-damping elements, with stiffness and damping values in six directions. The under-rail structure composition and line layout of the track were set according to the target line so as to make the simulation results closer to reality. The general structural parameters of the vehicle model and track model are referred to the literature [34; 35].  
The improved algorithm CONTACT was adopted for the wheel-rail contact. Based on the Duvant-Lions variable principle, the friction rolling contact problem is transformed into the variational inequality, thus, the minimum residual energy expressed by the product of force and displacement on the contact patch is directly solved [36; 37].  
Based on the above vehicle model, track model, and wheel-rail contact model, the vehicle-track space rigid-flexible coupling model was constructed, and the schematic diagram is shown in Figure 5.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 8 Conclusions  
There are many benefits that are expected to be provided by the D2D communication when compared to the traditional cellular networks. D2D technologies show great potential to be the most favorable and promising paradigm for future networks. We have highlighted, in the survey, a detailed review of the current existing D2D technologies together with their characteristics, such as device discovery, interference management, security, mode selection, and power control. We review various proposed solutions with a goal to achieve a secure Device-to-Device (D2D) communication in 5G. We summarize the existing solutions by highlighting the issues or problems of the research and the proposed solutions. Based on the comprehensive analysis, we further identified the open problems and challenges that deserve future research. Even though D2D communication is considered a relatively new idea, the remarkable amount of studies and research in D2D has triggered various aspects of related research problems and challenges that can be explored in the future. The detailed discussion in this extensive review hopefully will help the future reader to gain a better understanding of D2D technologies, concepts, research gaps and future works that have been highlighted within the scope of D2D communication domain.  
Authors contributed equally to this work. Present a primer addressing to some of the problems, solutions, recent advances and challenges in D2D communications specifically in device discovery, interference management, privacy and security, power control, and mode selection domain. The author focuses on their problem areas, proposed methods, and techniques considered. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. This research is self-funded by the authors since it is part of the requirement for Research Method subject in University Putra Malaysia for postgraduate students.  
We thank the anonymous reviewers for their valuable comments and suggestions, which helped us to improve the content, organization, and presentation of this paper.  
The authors declare no conflict of interest.
# Performance Analysis of Magnetorheological Porous Fabric Composite
## 2 Preparation and Experiments
### Material Preparation  
In the preparation of MR porous fabric composite, the commercial MRF (MRF-G28; Chongqing Materials Research Institute Co., Ltd., Chongqing, China) is used as the working medium. The maximum shear stress of this MRF is larger than 50 kPa. The non-woven fabric (Chang Tai Non-woven Fabric Co., Ltd., Shenzhen, China) has been selected as the matrix to store MRF. Material parameters of MRF and non-woven fabric have been listed in Tables 1 and 2. Figure 1 shows a photo of the non-woven fabric used.  
The MR porous fabric composite is prepared through a three-step process. Firstly, the non-woven fabric has been cleaned. The commercial non-woven fabric is cut into small-size pieces, which is the same as the testing area of the rheometer. Then, non-woven fabrics with a fixed size are immersed in 95% ethanol solution (Shanghai Aladdin Co., Ltd., Shanghai, China) at room temperature for 2 h. The immersed fabric is dried at 80 \({}^{\circ}\)C for 1 h. This step is used to remove the impurities. In the second step, the cleaned non-woven fabrics will be pretreated. All cleaned samples are divided into three parts equally. These cleaned samples are immersed into lithium steaterate (LiC\({}_{18}\)H\({}_{35}\)O\({}_{2}\), as a commonly used stabilizer in industry, has excellent thermal stability; Purity: 90%; Shanghai Aladdin Co., Ltd., Shanghai, China) by ultrasonic dispersion to ensure the required homogeneity and promote combinations between lithium steater and internal fibers. Figure 2 shows the non-woven fabric being pretreated. The three beakers were respectively filled with 1 wt%, 1.5 wt% and 2 wt% lithium steaterate solution for soaking non-woven fabric. Thirdly, the pretreated non-woven fabrics are mixed into the special MRF for 1 h by ultrasonic dispersion. The extra MRF of the treated non-woven fabric is separated by centrifugation. Moreover, three test samples containing different mass fractions of lithium steaterate were prepared. Therefore, the pretreated MR porous fabric composite can be prepared with different pretreatments.  
The pretreated MR porous fabric composite can form the internal porous network to store MRF and protect particle chains. It can reinforce the performance compared with other conventional MRF.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Values** & **Property** & **Values** \\ \hline Density/kg\(\cdot\)m\({}^{-3}\) & 2960 & Shear stress at 0.5 T/kPa & \(\geq\)50 \\ Viscosity of zero-field (\(\hat{\gamma}\) = 51/s, 20 \({}^{\circ}\)C)/Pa\(\cdot\)s & \(\leq\)1.5 & Operating temperature/\({}^{\circ}\)C & \(-\)40\(\cdot\)150 \\ Bulk modulus/MPa & 200 & Magnetic properties/kA\(\cdot\)m\({}^{-1}\) & 379.64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of MRF-G28.  
Figure 1: Photograph of non-woven fabric.
## 3 Development of the Multi-Objective AutoML Model
### Performance of the Multi-Objective AutoML Model  
The performance metrics for both the training and testing datasets demonstrate that the finalized AutoML model reliably inverts the mesoscopic parameters \(E\), \(k_{s}/k_{nt}\), and \(E_{d}\) (Figure 6). The inversion accuracy on the testing dataset closely matched that on the training dataset, indicating strong generalization. Specifically, \(R^{2}\) exceeded 0.98 for \(E\), surpassed 0.70 for \(k_{s}/k_{nt}\), and remained above 0.52 for \(E_{d}\). These results confirm the model's robustness in retrieving linear parallel bond parameters from stress-strain responses.  
Figure 5: Architecture of the multi-objective AutoML model for inverting \(E\), \(k_{s}/k_{nt}\), and \(E_{d}\) of the linear parallel bond model.  
Figure 6: Inversion performance of the developed multi-objective AutoML model on training and testing datasets: (**a**) \(E_{s}\)(**b**) \(k_{s}/k_{nt}\); and (**c**) \(E_{d}\).  
The AutoML framework's ability to handle large, nonlinear datasets and to incorporate multiple independent variables (\(x_{1}\), \(y_{1}\), \(x_{2}\), \(y_{2}\), \(x_{3}\), \(y_{3}\), \(x_{4}\), \(y_{4}\), \(x_{5}\), \(y_{5}\), \(x_{6}\), \(y_{6}\), \(x_{7}\), \(y_{7}\), \(x_{8}\), and \(y_{8}\)) without presupposed physical relationships underpins its improved inversion accuracy. Through iterative learning rather than heuristic assumptions, the model achieves efficient, accurate predictions with controlled computational cost.
# MLLP-VRAIN Spanish ASR Systems for the Albayzin-RTVE 2020 Speech-to-Text Challenge: Extension
## 2 Challenge Description and Databases  
The Albayzin-RTVE 2020 Speech-To-Text Challenge consists of automatically transcribing different types of TV shows from the Spanish public TV station RTVE, and the assessment of ASR system performance in terms of Word Error Rate (WER) by comparing those automatic transcriptions with correct reference transcriptions [27].  
The MLLP-VRAIN previously participated in the 2018 edition of the challenge [28], in a joint collaboration with the Human Language Technology and Pattern Recognition (HLTPR) research group of RWTH Aachen University. The evaluation was carried out on the RTVE2018 database [29], which includes 575 hours of audio from 15 different TV shows broadcast between 2015 and 2018. This database is allocated into four sets: _train_, _dev1_, _dev2_ and _test_ (_test-2018_). Our systems won in both the open-condition and closed-condition tracks [30], scoring 16.5% and 22.0% WER points respectively in the _test-2018_ set.  
For the 2020 edition of the challenge, a single open-condition track was proposed, and system evaluations have been carried out over the _test_ (_test-2020_) set from the RTVE2020 database, which includes 78.4 speech hours at a sampling rate of 16 kHz from 15 different TV shows broadcast between 2018 and 2019 [31].
## 3 Improvement of DSOGI-PLL
### Improved DSOGI-PLL Based on AHE  
After comparing Equations (12) and (13), it is found that multiplying \(v_{\alpha\alpha}\) by the coefficient \(1/n\) and then adding it to \(qv_{\beta\alpha}\) can eliminate the nth harmonic of the voltage component on the \(\alpha\)-axis. Similarly, multiplying \(v_{\beta\alpha}\) by the coefficient \(1/n\) and then subtracting it from \(qv_{\alpha\alpha}\) can eliminate the nth harmonic of the voltage component on the \(\beta\)-axis. The newly obtained two-phase stationary voltages can be expressed as:  
\[v_{\alpha\beta}^{+-}=\left[v_{\alpha}^{+-}\quad v_{\beta}^{+-}\right]=\left( \frac{1}{n}-1\right)v_{\alpha\beta}^{+}+\left(\frac{1}{n}+1\right)v_{\alpha \beta}^{-} \tag{14}\]  
In the equation, \(v_{\alpha\beta}{}^{+-}\) represents the two-phase stationary coordinate system voltage without the nth harmonic component.  
Equation (14) indicates that the transformed two-phase voltages no longer contain the nth harmonic component, despite changes in the amplitudes of the positive and negative sequence components. In the presence of other harmonics in the power grid, their amplitudes will also change, but their harmonic orders will remain unchanged. Multiple AHE modules can be used to remove individual harmonics separately. After that, the obtained positive and negative sequence components are corrected to obtain a positive sequence component that is free of harmonics. The structure of the AHE module is illustrated in Figure 6.  
Figure 6: AHE Control Block Diagram.  
By setting the number of AHE modules and the value of n, the harmonics in the grid can be eliminated in sequence. For example, to target the main harmonics in the power grid, which are the 5 th, 7 th, and 11 th harmonics, it is sufficient to set \(n\) to 11 in Figure 7. The DSOGI-PLL structure based on cascaded AHE modules is shown in Figure 7:  
Each application of the harmonic pre-elimination module not only removes the nth harmonic but also modifies the amplitudes of the positive and negative sequence voltages. Consequently, it becomes essential to adjust the output of the DSOGI-PLL to precisely acquire the fundamental voltage components in the two-phase stationary coordinate system. The value of \(x\) in the diagram can be calculated using the equation provided below:  
\[x=\prod_{m=1}^{\infty}\left(\frac{1}{n}-1\right) \tag{15}\]  
For example, when dealing with the main harmonics in the power grid, which are the 5 th, 7 th, and 11 th harmonics, the correction coefficient \(x\) is calculated as follows: \(x=(1/5-1)\times(1/7-1)\times(1/11-1)=-48/77\).
## 5 Computational Experiments
### Problem Instances  
This study adapts the dataset proposed by [PERSON] et al. [39], which includes instances with 15, 25, 50, and 100 customer points, denoted as C15, C25, C50, and C100, respectively. The original dataset assumes a single type of energy replenishment infrastructure, termed an alternative fuel station (AFS), with a constant refueling duration. To emphasize heterogeneity between energy replenishment infrastructures, we modify the instances by introducing CI at the original AFS locations, allowing vehicles to partially charge on demand. Additionally, BSIs are randomly generated within a radius of 2 units around the CI, ensuring proximity between them. Thus, if a vehicle's current battery level supports visiting one type of infrastructure, it can typically access the other as well. This infrastructure layout enhances the potential for BSI to alleviate the capacity pressure on CI. The instances in this study satisfy the following characteristics:  
* The depot is located away from customer points and energy replenishment infrastructures.
* Energy replenishment infrastructures are centrally located among customer points, with a limited capacity of CI.
* A maximum duration limit is imposed on single-vehicle routes, prohibiting service beyond this limit.  
Under these conditions, vehicles departing from the depot often require mid-route energy replenishment for distant customer deliveries, typically restricted to a single replenishment due to route duration constraints.  
The capacity of CI varies across instances based on their scales. Following [PERSON] et al.'s settings [39], the capacities of CI for C15, C25, C50, and C100 are set to 1, 2, 3, and 8, respectively. Other key parameters are detailed in Table 1.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 9 Discussion
### Evaluation and Selection for Practitioners  
Practitioners ought to be reflective in their selection and comparison of fairness-aware methods. First, they must ascertain which notion of fairness is most appropriate for serving the ethical and legal aims of the application context. Second, models must be contrasted on multiple dimensions: fairness metrics (e.g., demographic parity difference), predictive performance (e.g., balanced accuracy or F1 measure), and application limitations (e.g., interpretability or scalability). Since trade-offs often cannot be avoided, such mechanisms as fairness-accuracy Pareto frontiers or risk analysis tailored to domains can be useful in guiding decisions. Practitioners are also cautioned to recognize how fairness interventions such as pre-processing (e.g., dataset rebalancing), in-processing (e.g., regularization), and post-processing (e.g., threshold tuning) work together, especially when deploying models into the real world.
###### Abstract  
This study aimed to improve the flavor of royal jelly residue via microencapsulation technology using Arabic gum and gelatin as wall materials. This microencapsulation technology showed a good encapsulation yield of \(85.71\pm 2.84\%\) and encapsulation efficiency of \(92.34\pm 3.17\%\). The intact structures of the microcapsules were observed using optical and scanning electron microscopes. The results of the simulated gastrointestinal digestion proved that the microcapsules were well-tolerated in the gastric environment (a release rate of \(32.95\pm 2.34\%\)). Both electronic nose and electronic tongue evaluations showed that microencapsulation improved the sensory index of the royal jelly sieve residue. After microencapsulation, the astringency, bitterness, and irritant odors of the royal jelly residue were reduced. Simultaneously, the release rate in the intestine was \(98.77\pm 1.91\%\), which demonstrated that microencapsulation would not prevent the human body from absorbing the royal jelly. The results from this study are expected to facilitate the development of mild flavor products made from royal jelly.  
Keywords:royal jelly; microencapsulation; in vitro digestion; sensory evaluation +
Footnote †: journal: Applied Sciences