esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation into the Thermal Performance of Personal Cooling Mechanisms
###### Abstract  
Environmental conditions, job tasks, and clothing choices influence the thermal comfort of workers. While it is impossible to control outdoor environmental conditions, selecting appropriate clothing for workers is feasible. Personal protective equipment often does not completely protect outdoor workers at high air temperatures. In such cases, cooling garments can help dissipate body heat and be worn with standard work clothes or uniforms. Currently, there is a lack of consensus in the literature regarding the characterisation of this type of garment. This study employed an innovative laboratory testing method that integrates a thermal manikin with a computer simulation program to assess the thermal sensation and comfort of various garment types in different activities and environmental conditions. This advanced approach enables a thorough evaluation of cooling garments that considers their physical properties and interactions with the human body. The findings confirm the efficacy of the tested cooling mechanisms, highlighting that the air circulation mechanism was the only one that consistently maintained user comfort across low, medium, and high metabolic activity levels. This study aims to assist users in selecting the most suitable cooling mechanism for the market based on the type of work or activity being performed.  
Keywords:personal cooling systems; thermal manikin; thermal comfort; heat stress; cooling vests; cooling garments +
Footnote †: journal: Applied sciences
# Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks
## 6 Experimental Results
Figure 11: Dependence graph of flow intensity of successfully transmitted data packets when time values of the realization of cyberattacks are different. 5. In the context of cyberattacks, the efficiency of detection and recovery of the operability of the data transmission route after an information impact essentially influences the time of successful data delivery (Figure 12). For example, if the time \(T_{di}\) of detection and neutralization of the cyberattack against network elements and terminal equipment of users is reduced by up to one second, the average data delivery time will be reduced by more than 10 times. This suggests the expediency and prospects of the early detection of cyberattacks, for example, using the methods described in [34]. Thus, experiments have shown that the proposed method allows analytical evaluation of the probabilistic behavior of the key parameters of DTN function (delivery time and real throughput of data transmission routes), depending on the following factors:  
* the intensity of information exchange;
* the volume of transmitted information;
* the effectiveness of route restoration in the event of failures;
* the laws of packet distribution, which are subject to information flows in the DTN;
* capacity of packet storage systems at switching nodes;
* the duration of the cyberattacks. Knowledge of the distribution laws that will obey the key parameters of the DTN operation allows one to make effective decisions on the management of the DTN in electric power systems. In this regard, the proposed method and its software implementation can be included in the DTN control system of electric power complexes as elements of mathematical and software support. With the help of such elements, it is possible to solve the problems of quickly predicting the behavior of the DTN under the influence of cyberattacks with various options for constructing the DTN.
# Optimizing Latent Factors and Collaborative Filtering for Students' Performance Prediction
## 4 Proposal for Improving The Prediction
### Efficient Tuning of Collaborative Filtering
#### 4.1.2 Genetic Algorithm  
Metaheuristics [42] are approximate algorithms that explore the space of solutions efficiently, by focusing the search on the vicinity of a promising solution. This is the rationale behind the application of metaheuristics to solve large optimization problems. Metaheuristics are classified as trajectory or population algorithms. Among population- based metaheuristics, Evolutionary Algorithms (EAs) [43] search the optimal solutions by tracing the evolution of individuals according to rules inspired by biological phenomena.  
Genetic Algorithms (GAs) [44] are popular EAs that perform stochastic searches after postulating an individual solution to the optimization problem. The individual \(X\) is composed of several decision variables \(x_{i}\) that define its genotype. In our case, we have two decision variables: \(x_{1}=\beta\) and \(x_{2}=\lambda\). A population is a set of individuals that evolves along generations. The genetic pool of the population is composed of the genotypes of their individuals. The population evolves by minimizing a fitness function \(f(X)\); in our problem, it is \(RMSE\).  
GA starts generating an initial population and evaluating its individuals. From here on, the population evolves along generations. Each generation consists of several sequential phases. The first phase assigns a fitness value to each individual. Next, the selection phase chooses the best individuals (parents) for crossover according to their fitness values. The third phase (recombination) crosses the parents to generate new individuals (offspring), thus updating their fitness values. After that, some individuals among the offspring develop mutations, their fitness being updated accordingly. The evaluation phase evaluates the offspring and eventually incorporates it back into the population (reinsertion phase). Then, the assignment phase starts again in order for the next generation to begin. The GA ends when it reaches a stopping criterion (usually a predefined number of generations).  
The GA was tuned for the experiments by selecting the following values for the main hyper-parameters, among others: population size of 150 individuals; 60 generations as the stopping criterion; (0.0001,10) as the range of search for \(\beta\); (0.0001,1) as the range of search for \(\lambda\); (2,200) as the range of search for \(K\); random initial population with a uniform distribution; rank as the fitness scaling function; stochastic uniform as the selection function; scattered crossover function; elite count (number of individuals that are guaranteed to survive to the next generation) of 5% of the population size; crossover fraction of 0.8; Gaussian mutation function; and 31 runs for each experiment.
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 1 Introduction
Features such as native support for Layer-2 (Ethernet) traffic, efficient transportation of TSN Ethernet traffic with compression schemes in the 5G network, End-to-End (E2E) time synchronization, Quality of Service (QoS) differentiation, priority class handling to map QoS flows of TSN traffic to 5G QoS, and translation functionalities for 5G-TSN interworking are supported. The 5G-ACIA (5G Alliance for Connected Industries and Automation) industrial forum has been investigating the detailed requirements, interfaces, and architectures that allow the integration of 5G with TSN-enabled networks in industrial use cases, bringing both automation industry and information communication technology players together [15]. We have carried out a prototype setup integrating 5G in a TSN network to evaluate 5G-TSN performance for industrial applications, mainly in real-time analysis of the communication. The industrial application requirements are defined for virtual Programmable Logic Controllers (PLC) in mobile robotics use cases communicating with cyclic and synchronous signals. This prototype setup consists of a 5G URLLC standard-compliant pre-commercial system and a wired network supporting different TSN standards. The integration of the 5G URLLC prototype system has been carried out as a virtual bridge between the TSN switches as described in [15]. The structure of this paper is as follows: Section 2 describes the mobile robotics use case and its requirements toward the communication infrastructure. In Section 3, the architecture and implementation of the 5G-TSN communication prototype are given. The detailed performance measurements carried out on the integrated prototype system for the use case requirements and a discussion of the results are presented in Section 4. Section 5 summarizes the paper and provides an outlook.
## 5 Discussion
Moreover, according to the results of the BIAS metric (Table 2), the ER and LR products underestimate precipitation (BIAS \(-\)0.14 and \(-\)0.16, respectively) while the FR product has a very low bias (0.01). However, when the ER and LR are used as forcing for the model, the simulation results are even better than those of the FR product for the NSE and BIAS metrics. One possible explanation is that the relationship between the quality of the precipitation dataset and the hydrological performance is not straightforward as described by previous studies due to the potential interactions between the model and the specific satellite-based rainfall products [1; 46; 47]. Thus, they should be used with caution in simulations over the XRRB. Satellite-based precipitation products are obtained through the indirect method of rainfall detection, and the errors of satellite-based precipitation products are also influenced by a variety of factors, such as precipitation variability, spatiotemporal sampling, instrumentation capabilities, and the retrieval algorithms used [48; 49]. Therefore, there are still many limitations to these satellite-based precipitation products, including sensitivity to precipitation types, underestimation of terrestrial rainfall, a tendency to miss snowfall, an inability to capture short precipitation events, and systematic bias in mountainous areas [50], which result in the relatively low accuracy of satellite-based precipitation products in some situations. Deviation correction studies of IMERG satellite products in the XRRB should be carried out in the future. In addition, we would like to characterize the influence of the input parameters on the hydrological simulation errors, which had also been reported in [51; 52; 53]. However,as the true values of the non-precipitation parameters are missing, it is hard to analyze the contributions of their errors to the final simulation errors. In this case, the errors associated with the precipitation parameters will be mainly considered. Table 6 displays the simulation error changes when the input precipitation parameters are shifted from ground-based observed values to satellite-based precipitation products. It can be seen that the average annual precipitation is 1938.9 mm, while the ER, LR, and FR are 1721.5 mm, 1681.3 mm, and 1975.1 mm, respectively. The relative change rate of ER, LR, and FR with respect to precipitation at the site (STA) is \(-0.11\), \(-0.13\), and \(0.02\). While the relative change rate of simulation by the different precipitation inputs (ER, LR, and FR) with respect to simulation by the STA is \(-0.06\), \(-0.08\), and \(0.11\). The relative contribution to the simulation error by the precipitation parameters is obvious, and 9% of the precipitation error will cause 8% simulation errors. It is necessary to quantify the relative contribution to the simulation error by the non-precipitation and precipitation parameters in the future study.
## 1 Introduction  
The photocatalytic water splitting into its constituents, hydrogen and oxygen, has attracted much attention as a clean and renewable source of fuel. In the past, various semiconductor materials have been investigated as water oxidation catalysts [1]. However, it is still challenging to develop effective catalysts that split water under sunlight. Recently, calcium manganese oxides have gained significant attention as water oxidation catalysts, due to their earth-abundant and environmentally friendly elemental constituents and also a structural similarity to the natural photocatalytic centre in plant cells. In natural photosynthesis, bio-electricity decomposes water into hydrogen and oxygen within a unique catalytic centre composed of a CaMn\({}_{4}\)O\({}_{5}\) cluster, in the oxygen-evolving complex (OEC) enzyme in the Photosystem II protein [2; 3; 4]. This natural process is the most efficient known, for the photo-generation of H\({}_{2}\) from water.  
Among many calcium manganese oxides, CaMn\({}_{2}\)O\({}_{4}\) contains structural sub-units of (Mn\({}_{4}\)CaO\({}_{x}\)), similar to the structure of natural CaMn\({}_{4}\)O\({}_{5}\) cluster in OEC [5; 6]. This has led to extensive investigation of catalytic water oxidation properties of CaMn\({}_{2}\)O\({}_{4}\)[7; 8; 9]. [PERSON] et al. reported an oxygen-reduction reaction(ORR) catalytic activity of CaMn\({}_{2}\)O\({}_{4}\) microspheres consisting of aggregated nanoparticles [7]. [PERSON] et al. demonstrated that one-dimensional CaMn\({}_{2}\)O\({}_{4}\) nanostructures, synthesised using a solvothermal method, enabled quasi-four-electron transfer in the ORR [8]. These studies were electrochemical in nature and therefore were conducted without any light irradiation. It is very important to study the water splitting in the presence of light because the use of natural sunlight, as the source of energy, is the final goal in practical applications.  
[PERSON] et al. studied light-driven oxygen evolution by using an aqueous suspension of CaMn\({}_{2}\)O\({}_{4}\) particles, however only in the presence of various sacrificial reagents [9]. Studies of photo-catalysts in the dispersed form are of significant interest, however, from a practical perspective, it is crucial to study the immobilisation of catalysts on conducting electrodes as an anode [10]. To the best of our knowledge, there is no report on the study of light-driven electrochemical activity of CaMn\({}_{2}\)O\({}_{4}\) as a fixed anode material without sacrificial reagents.  
In this study, CaMn\({}_{2}\)O\({}_{4}\) ultrafine powders were studied as an anode material in photo-electrochemical cells for their catalytic and photocatalytic properties in the water oxidation reaction. It has been reported that photocatalytic properties are greatly affected by the morphology of catalysts [11]. Therefore, we also investigated the effects of particle shapes on the photo-electrocatalytic properties of CaMn\({}_{2}\)O\({}_{4}\).
## 2 Materials and Methods
### Aligners Selected
#### 2.2.2 Mechanical Properties of EvoFlex  
EvoFlex is a multi-layered material introduced in 2019. It consists of three layers: two external layers of polyurethane and an internal elastomer layer, with a total thickness of 0.75 mm. Designed to improve the elasticity and shape-memory properties of F22 aligners while enhancing stability over time, EvoFlex exhibits unique mechanical characteristics. Its combination of layers provides high elastic deformation capacity, enabling an effective and consistent force application throughout orthodontic treatment. In stress-relaxation tests conducted at elevated temperatures to simulate 15 days of usage, EvoFlex demonstrated superior final stress retention compared to other aligner materials, with a final stress value of 6.5 MPa. The stress relaxation at 5 days was 49.7% and 60.8% at 15 days, markedly lower than traditional F22 aligners (84.3%) and materials such as PET-G (96.1%) and Durasoft (95.4%). This indicates that EvoFlex retains its orthodontic force longer, contributing to predictable tooth movement over extended periods. Moreover, the triple-layer structure of EvoFlex enhances its modulus of elasticity and yield point compared to monolayer materials like PET-G and traditional polyurethane aligners. While EvoFlex shares the benefits of low thickness (0.75 mm) and flexibility with F22 aligners, it demonstrates greater durability and a reduced rate of stress decay under prolonged use [19].
## 3 Materials and Methods
### Training BRB with Historical Data  
The objective of the optimization process is to find a set of parameters that reduce the classification error rate. This is achieved by minimizing the variance or deviation \(\zeta(P)\) between the output from BRBES, which is known as simulated output \((z_{m})\), and the output from the real system, known as observed output \((z_{m})\) as shown in Figure 2.  
A set of real-world dataset is used as the training data in the optimization process. The inference output of BRBES is compared with actual output for each training sample. Assume that T number of training samples are used to train the BRBES and BRBES provides inaccurate prediction for \(C\) samples using the parameter vector \(P\). Then, the classification error rate of the BRBES can be denoted as follows:  
\[\zeta(P)=\frac{C}{T} \tag{10}\]  
The parameter vector \(P\) consists of \(L\) rule weights \(\{\theta_{l},l=1,\ldots,L\}\), M attribute weights \(\{\delta_{m},m=1,\ldots,M\}\), and N belief degree \(\{\beta_{l,n},n=1,\ldots,N\}\) for each belief rule \(R_{l}(l=1,\ldots,L)\). Therefore, the parameter vector optimization model of BRBES can be described as follows:  
\[\textit{Minimize}\ \{\zeta(P)\} \tag{11}\]  
\[\textit{Subject to}\ \sum_{n=1}^{N}\beta_{l,n}=1,\ l=1,\ldots,L \tag{12}\]  
\[0\leq\beta_{l,n}\leq 1,\ n=1,\ldots,N;\ l=1,\ldots,L \tag{13}\]  
\[0\leq\theta_{l}\leq 1,\ l=1,\ldots,L \tag{14}\]  
\[0\leq\delta_{m}\leq 1,\ m=1,\ldots,M \tag{15}\]  
\[u(A_{i,j})-u(A_{i,j+1})\leq V_{i},\ m=1,\ldots,M;\ j=1,\ldots,J_{m}-1 \tag{16}\]  
\[u(A_{m,1})=ub_{m};\ m=1,\ldots,M \tag{17}\]  
Figure 2: Optimal learning process of BRBES.  
\[u(A_{m,l})=lb_{m};\ m=1,\ldots,M \tag{18}\]  
where Equation (11) is the objective function used to minimize the classification error, Equations (12) and (13) are constrain on belief degree in the BRBES; Equations (14) and (15) are on the antecedent attribute weights and the rule weights, respectively; Equation (16) is the constrain on the utility values of the referential values of the antecedent attributes in the BRB; and \(V_{i}\) denotes a small value to differentiate between two adjacent referential values for the \(i\)th antecedent attribute in the BRBES. \(lb_{m}\) and \(ub_{m}\) are the lower and upper bounds of the \(m\)th antecedent attribute in the BRB, respectively. To solve the constrained optimization problem mentioned above, the optimization algorithm should be used. A modified differential evaluation (DE) based optimization algorithm is employed in this paper to optimize the BRB which is described in the following section.
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
## 3 Silicon on Insulator (SOI) Detector for Microdosimetry
### TCAD Simulations of 3D Mushroom SOI Microdosimeters
This was caused by the fact that small amount of charge produced by alpha particles in the extremely low resistivity polysilicon filling the trench was collected by diffusion from a thin silicon region surrounding the SV. No charge collection was observed in this region for the Trenched-3D structure because the outside silicon was physically disconnected by an air gap. As mentioned in Section 3.1.2, it is important to keep in mind that the Trenched-3D approach requires that the trench is not a completely closed ring, thus still leaving a point of contact between the outside and the inside of the SV. This aspect cannot be easily simulated in 2D and would require a complex 3D structure. As the incident particle approached the edge of the SV (10 < x < 15 \(\upmu\)m), the charge collection rapidly increased, reaching a maximum of almost 100% charge collection efficiency (which corresponds to 1.5 MeV) for both structures. The Trenched-3D version exhibited a sharp transition, while for the trenched planar, a 2-\(\upmu\)m-wide penumbra was observed in the charge collection efficiency. The charge collection profile inside the SV was mostly flat in the region from 15 < x < 22 \(\upmu\)m; however, in the middle of the SV, the two structures behaved differently. The Trenched-3D structure was completely insensitive because of the presence of an empty columnar electrode, while the Trenched-planar structure only had a small reduction in charge collection efficiency due to the presence of the planar n+ implantation, causing some of the generated charge to be lost due to recombination. Figure 7: Comparison of the radial charge collection profiles for the Trenched-3D and Trenched-planar structures in response to a 5.5 MeV alpha particle for SOI thickness of 10 \(\upmu\)m.  
These numerical simulation studies showed that both the Trenched-3D and Trenched-planar designs are viable, although the Trenched-planar solution should be able to deliver better and more consistent uniformity in charge collection efficiency, i.e., higher definition of a cylindrical SV.
# Optimal Transport in Multilayer Networks for Traffic Flow Optimization
## 3 Results
### Results on Real Data  
We illustrate our model on a real 2-layer network of the city of Bordeaux, where the two layers are the bus and tram, respectively. Data are taken from [49]. We simulate a monocentric source matrix \(S\), i.e., \(p=0.0\), to asses the scenario where all the passengers travel to the city center; however, the results are similar for other values of \(p\) (not reported here). Optimal paths are extracted using our model for \(\beta_{1}=0.5\), \(\beta_{2}=1.5\), \(w_{2}=0.2\) and compared against the case where the tram network is absent. This can be simulated by setting a high value of \(w_{2}\), so that the cost on the tram edges makes it extremely unlikely to  
Figure 4: Results on synthetic data. We show the \(Gini\) w.r.t. the optimal \(||F_{e}||_{2}\) (y axis) vs. \(\beta_{2}\) (x axis) for synthetic 2-layer networks generated as in Section 3.1. Blue and red markers denote \(p=0.2,0.8\), respectively, \(w_{1}=1\) in all cases, while \(w_{2}=0.2\) (**left**) and \(w_{2}=0.8\) (**right**); \(\beta_{1}=0.5\) in all cases, except for the case where \(\beta_{2}=1\) for which \(\beta_{1}=1\). This case is the shortest path-like baseline. Markers are averages over 20 network samples and 50 source matrix samples (for a total of 1000 individual samples).  
use any tram connection (here, we use \(w_{2}=100\)). We measure the total percentage flux \(f_{2}=\sum_{e\in\mathcal{E}_{2}}||F_{e}||_{2}/(\sum_{e\in\mathcal{E}_{1}}||F_{e}|| _{1}+\sum_{e\in\mathcal{E}_{2}}||F_{e}||_{2})\) passing through layer 2. Remarkably, in this scenario, the tram network absorbs \(f_{2}=17\%\) of the total flow of passengers, even though the tram network contains only \(E_{2}=112\) edges, compared to \(E_{1}=2347\) bus edges. This allows to reduce significantly the traffic along the road network, as can be seen in Figure 5, and the road edges, and, in particular, those parallel to the tram line and close to the city center get thinner as more passengers use the tram. This also results in a higher \(Gini_{1}=0.26\) (calculated on edges in layer 1 w.r.t. \(||F_{e}||_{2}\)), compared to the \(Gini_{1}=0.23\) when the tram is absent: as the passengers use the tram, they decrease traffic on many road edges. While the traffic distribution on layer 1 gets more hierarchical (higher \(Gini_{1}\)), this does not necessarily lead to more traffic congestion. In fact, the total percentage flow \(f_{1}\) decreases, as we saw above. Additional plots can be seen in Figure A2.