esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
Then for each resident \(r_{j}\) that has \(h_{i}\) in her preference list, replace \(h_{i}\) with the linear order \((h_{i,1},h_{i,2},\ldots,h_{i,q_{h_{i}}})\). By transforming \(I\) to \(I^{(1)}\), the many-to-one matchings of \(I\) can now be viewed as one-to-one matchings of \(I^{(1)}\). Let \(\mu\) be a matching of \(I\). Create the corresponding matching \(\mu^{(1)}\) of \(I^{(1)}\) as follows: when \(h_{i}\) is matched to residents \(r_{j_{1}},r_{j_{2}},\ldots,r_{j_{k}}\) in \(\mu\) and these residents are arranged according to its preference, let \(h_{i,1},h_{i,2},\ldots,h_{i,k}\) be matched to \(r_{j_{1}},r_{j_{2}},\ldots,r_{j_{k}}\) respectively in \(\mu^{(1)}\). Notice that \(\mu\) and \(\mu^{(1)}\) have the same size. Moreover, it is easy to verify that this mapping is a bijection from the set of stable matchings of \(I\) to the set of stable matchings of \(I^{(1)}\). Now, suppose we want to compare \(\mu\) with another matching \(\sigma\) of \(I\). The task becomes equivalent to comparing \(\mu^{(1)}\) and \(\sigma^{(1)}\) in \(I^{(1)}\), and the symmetric difference technique described in the previous paragraph can now be applied. **Proposition 1**. : _In the HR instance \(I\), let \(\mu\) be a stable matching and \(\sigma\) be a maximum matching of \(I\). Then \(|\mu|\leq|\sigma|\leq 2|\mu|\)._  
Proof. : By definition, \(|\mu|\leq|\sigma|\). Now, construct \(I^{(1)}\) and the matchings \(\mu^{(1)}\) and \(\sigma^{(1)}\) from \(I\), \(\mu\) and \(\sigma\) respectively. Since \(|\mu|=|\mu^{(1)}|\) and \(|\sigma|=|\sigma^{(1)}|\), \(|\mu^{(1)}|\leq|\sigma^{(1)}|\). This means that in \(\mu^{(1)}\bigoplus\sigma^{(1)}\) there is a \(\sigma^{(1)}\)-alternating path. However, there cannot be a \(\sigma^{(1)}\)-alternating path that simply consists of one edge \((h,r)\) from \(\sigma^{(1)}\) because this means that \(h\) and \(r\) are acceptable to each other in \(I^{(1)}\) but are unmatched in \(\mu^{(1)}\)--_i.e._, \(\mu^{(1)}\) is not a stable matching of \(I^{(1)}\) since \((h,r)\) is a blocking pair. This contradicts the fact that \(\mu^{(1)}\) was constructed from \(\mu\), a stable matching of \(I\). Thus, in every \(\sigma^{(1)}\)-alternating path, the ratio of edges belonging to \(\sigma^{(1)}\) to those belonging to \(\mu^{(1)}\) is at most \(2\!:\!1\). Hence, \(|\sigma^{(1)}|\leq 2|\mu^{(1)}|\). It follows that \(|\sigma|\leq 2|\mu|\). The previous example shows that the bound in Proposition 1 is tight.
## 5 Conclusions  
To sum up, this study used the KDE algorithm to generate soft label information of the input data, which made knowledge distillation work and improved the performance of prediction models for bipolar disorder. To the best of our knowledge, this study is the first attempt to apply KDE to knowledge distillation for supervised machine learning. Another contribution of this study is that we used medical history information readily available from the electronic health record system to try to improve on the limitation of previous studies that needed to use special medical instruments to collect data. Furthermore, in view of the fact that most previous studies had sample sizes of less than 1000 data samples, we collected tens of thousands of data samples to improve the representativeness of the constructed prediction models. Finally, the soft label information generated by KDE provided correct data distributions, so they helped the decision tree algorithm select appropriate branching attributes to construct the prediction models. These branching attributes could be mapped back to specific disease diagnoses that are all associated with bipolar disorder. In conclusion, the KDE algorithm provided correct information of data distributions, and this information was applied to knowledge distillation to improve prediction models of bipolar disorder.  
Our study still has shortcomings that need to be improved. When using several machine learning algorithms to construct the diagnostic models of bipolar disorder patients, we set the parameter contents empirically or adopted the default parameter settings of the tool functions used. Therefore, one future prospect for research works is that we should use the systematic optimization mechanisms to select the appropriate parameter settings. Second, we need to collect more data to increase the sample size, as well as collect different data types. Since bipolar disorder occurs in approximately 1% of the global population [3], we must also adopt methods to deal with the imbalance of answer labels, e.g., bipolar disorder patients vs. healthy controls. Finally, considering the models constructed in this study to identify bipolar disorder patients, their predictive performance still has potential for improvement. Therefore, we plan to pursue the following works: selecting drug prescription information from the electronic health record system as feature attributes, using different machine learning algorithms to construct predictive models, and using advanced KDE methods to provide more accurate data distribution information [29; 30; 31; 32].  
Conceptualization, M.-H.Y.; methodology, Y.-S.T. and M.-H.Y.; software, Y.-S.T.; formal analysis, Y.-S.T.; data curation, Y.-S.T.; writing--original draft preparation, Y.-S.T. and M.-H.Y.; writing--review and editing, M.-H.Y.; supervision, M.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
# Evaluation of Structural Stresses of Mountain-Embedded Railway Systems
## 2 Simulation Model for the Mountain-ERS Track
### Mesh Convergence Analysis  
Mesh convergence analysis was conducted to increase the reliability of the results of the structural analysis model for mountain-ERS. Considering the geometries of the structural analysis model sections, a mesh convergence analysis was conducted on the following numbers of elements: 204,384 (element size = 25 mm), 137,078 (element size = 30 mm), 92,124 (element size = 34 mm), 61,014 (element size = 40 mm), 42,644 (element size = 45 mm), and 37,384 (element size = 25 mm). Point B was selected as the point to be evaluated for the mesh convergence analysis, where the longitudinal reinforcing bars were arranged inside the concrete panel. The results varied considerably, depending on the size of the elements and whether reinforcing bars were included. The value of S22 was the largest among the other stresses; therefore, the values at Point B were evaluated using the analyzed cases. As shown in Figure 14, as the number of elements increased, S22 tended to converge to a specific value. To obtain reliable results with an efficient structural analysis, a structural analysis model with 92,124 elements (element size = 34 mm) was chosen as the reference model.  
Figure 14: Analyzed mesh convergence results.  
Figure 13: Direction of stresses and examined points for behavioral analyses of the mountain-ERS track.
# Bond Strength Assessment of Concrete-Corroded
## 1 Introduction
However, thus far, a reliable method with high accuracy and efficiency is still required. In our previous study, a rigid body spring model (RBSM) was developed to simulate the mechanical performance of RC members after rebar corrosion [4; 39]. The crack width distribution was used as the input for the calculation. The relationship between the crack width on the surface and the corrosion level of the main reinforcement inside the concrete was proposed for the RBSM model based on the experimental findings. The cracking of concrete is subsequently simulated, as well as the residual mechanical properties of the corroded RC beams. The RBSM model shows considerable potential for structural performance evaluation. However, in reality, the relationship between the crack width on the surface of concrete and corrosion level of rebar is not certain because of complicated confinement. Therefore, the sensitivity and correlation of each parameter to the bond strength loss require further investigation to justify the model. Soft computing techniques are considered a promising tool to solve problems in civil engineering, which has been widely explored in various fields [40; 41; 42]. For example, it was used by [PERSON] et al. [40] to predict the compressive strength of FRP-confined circular reinforced concrete columns. In terms of performance assessment related to various factors, an artificial neutral network (ANN) is a good alternative because it can provide the optimal solution by minimizing the difference between observed and modeled data. This method was used by [PERSON] and his coworkers for many applications [43; 44; 45]. A scaled feed-forward multilayer perception ANN model with an error back-propagation algorithm of gradient descent and momentum (FF-BP-ANN) can achieve the most satisfactory prediction, and the results are useful for drawing strategies. It seems that this method could be a good option to solve the problem for bond strength evaluation. Therefore, in this study, the scaled FF-BP-ANN was first applied to assess the bond strength of concrete with a corroded rebar. Owing to the large differences in the confinement, cases of RC specimens with and without stirrups were considered. For unconfined cases, the relative bond strength was evaluated based on several parameters: corrosion level, crack width, cover-to-diameter ratio and concrete strength. For the cases with stirrups confinement, in addition to these parameters, the stirrups diameter-to-spacing ratio is also considered. In this study, the corrosion level was quantifiedas the mass loss percentage of rebar after corrosion. The existing test results were collected from literature [1, 26, 46, 47, 48, 49] for calculation and verification. Finally, a parametric analysis was conducted systematically and the sensitivity of each parameter on the loss of bond strength was examined. The results are expected to be meaningful to the understanding of bond strength loss and the mechanical performance evaluation of corroded RC structures.
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 3 Problem Formulation
### User Partitioning  
User partitioning in mmWave and THz bands is an extremely challenging task for enhancing throughput performance. An iterative algorithm is utilized to address optimal user partitioning, where users are partitioned into groups based on their distances and SINR values. The proposed modified k-mean algorithm for user grouping includes vital modifications to the standard k-means algorithm such that it can incorporate the shortest Euclidean distance criteria (high SINR) and the data rate constraint mentioned in (16a) for each group independently. It offers the advantages of low complexity, easy implementation, and fast convergence. Algorithm 1 describes the user grouping process step by step that may achieve faster convergence and enhance the overall mmWave system performance in terms of HO and mobility. The proposed modified K-means algorithm enables efficient user grouping by jointly considering both spatial distance and SINR levels, ensuring that each cluster maintains acceptable signal quality and proximity for effective UAV-RIS optimization. This two-stage clustering enhances the initial conditions for trajectory and phase shift optimization, reducing handover frequency and improving link stability. The primary advantage of this approach lies in its low computational complexity, fast convergence, and ability to incorporate QoS constraints. However, a potential limitation is that the initial cluster centroids may influence convergence to local optima, especially in highly dynamic user mobility scenarios.  
```
Input: Number of GUs, SINR threshold, sum rate threshold Initialization: Randomly generate each user's locations and initial data rates.
1:for every user \(u=1,\ldots,U\)do
2: Calculate Euclidean distance between the user and neighboring users.
3: Randomly assign data rate to each user using random distribution techniques.
4: Assign the user to the group with the highest SINR, minimum distance, and maximum data rate.
5: Repeat steps 2-4 for all users in the network until convergence.
6:endfor Until Groups achieve minimal inter-user distances, highest SINR, and maximum data rates. Output: Optimal User Groups
```  
**Algorithm 1**Proposed SINR/Distance-based User Partitioning Scheme  
The communication coverage area depends on UAV-RIS altitude; the higher the UAV-RIS altitude, the larger the coverage area, and vice versa. The optimal coverage radius, based on UAV-RIS altitude and beam angle in the mmWave network, can be expressed as  
\[radius(r)=h^{\text{UR}}*tan(\theta) \tag{17}\]  
where \(\theta\) is the half beam angle in the mmWave band and \(h^{\text{UR}}\) is the UAV-RIS height.
## 3 Results and Discussion  
### Degree of Incorporation of Bio-Based Monomers  
Thermosets were prepared with AESO-DBI or AESO-EC mixtures with mass ratios of 3:1, 2:1, and 1:1 (for clarity, samples will be discussed according to these mass ratios unless otherwise noted). The corresponding molar ratios of reactive functionalities are 2.1:1, 1.4:1, and 0.7:1 for the DBI series and 1.5:1, 1.0:1, and 0.5:1, comparable to previous studies of comonomer blends. Initiator and thermal curing cycles were similarly based on earlier studies of AESO reactivity [5,20,21]. Extraction with dichloromethane gave \(>\)99% recovery of insoluble material for the AESO-DBI series. For the AESO-EC series, \(>\)99% recovery was obtained for the 3:1 blend but was 95% for the 2:1 ratio and 90% for the 1:1 ratio. Further exploration of initiator and curing conditions would be necessary to improve the incorporation levels of EC. The mass recovery observations were in good agreement with infrared spectroscopic analysis.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 1 Introduction
### Satellite Phones  
There are many options for satellite receivers. You can choose between geosynchronous equatorial orbit (GEO) or low earth orbit (LEO) [7], both of which have advantages and disadvantages. Generally, GEO satellites have the advantage of uninterrupted coverage compared to LEO satellites. However, GEO-based phones use more energy when amplifying signals, and because satellites are placed above the earth [7], they cause more delay. There are four major satellite network companies on the market that provide customers with global services. They are Iridium, Globalstar, Inmarsat, and Thuraya [7]. Thuraya has provided a comparison sheet on six popular satellite phones [8] in terms of form factor, navigation system, reliability, and price. The phones compared are  
1. Thuraya XT-PRO DUAL
2. Thuraya XT-PRO
3. Thuraya XT-LITE
4. Iridium 9555
5. Iridium 9575
6. IsatPhone 2
# Single-Switch Non-Isolated Resonant DC-DC Converter for Single-Input Dual-Output Applications
## 2 Proposed Single-Switch SIDO ZVS-QRC Configuration Description
### Steady-State Analysis
\right\}\]  
\[V_{o}^{+}=V_{o}^{-}=\frac{1}{f_{s}}\int\limits_{t_{o}}^{t_{1}}\left[\left(V_{ g}+\frac{V_{g}}{2}\right)-\frac{\left(I_{g}+I_{o_{1}}+I_{o_{2}}\right)}{C_{R}}(t-t_{1 })\right]dt+\frac{1}{f_{s}}\int\limits_{t_{3}}^{t_{4}}\left(V_{g}+\frac{V_{g}}{ 2}\right)dt\]  
Hence  
\[V_{o}^{+}=V_{o}^{-}=\left(V_{g}+\frac{V_{g}}{2}\right)\left[1- \frac{1}{f_{s}}\left(t_{3}-\frac{t_{1}}{2}\right)\right]\Rightarrow V_{o}^{+ }=V_{o}^{-}=V_{g}\cdot\frac{\left(1-f_{s}\cdot\left(t_{3}-\frac{t_{1}}{2} \right)\right)}{f_{s}\cdot\left(t_{3}-\frac{t_{1}}{2}\right)}\] \[\Rightarrow V_{o}=2V_{g}\cdot\frac{\left(1-f_{s}\cdot\left(t_{3}- \frac{t_{1}}{2}\right)\right)}{f_{s}\cdot\left(t_{3}-\frac{t_{1}}{2}\right)} \tag{9}\]  
Table 1 summarizes the voltages and currents for the main components of the bipolar output ZVS-QR SEPIC-Cuk combination converter in terms of input voltage (\(V_{g}\)), the conversion parameter \(m\) and loads \(R_{Load_{1}}\), \(R_{Load_{2}}\) and \(R_{Load_{3}}\). From the above analysis, the following characteristics of the bipolar output ZVS-QR SEPIC-Cuk combination converter can be highlighted:  
1. Both outputs provide a step-down/step-up conversion ratio. 2. The turn-on and turn-off transitions of the main power switch occur at zero voltage, so switching losses are low. 3. Output voltages depend on load currents. 4. The \(L_{R}\)-\(C_{R}\) tank must be dimensioned so that the zero-switching condition is satisfied: \(\left(\left(V_{g}+\frac{V_{g}}{2}\right)<\left(I_{g}+I_{o_{1}}+I_{o_{2}}\right) \cdot Z_{o}\right)\).
## 4 Conclusions  
Purslane is an edible wild plant with great nutritional and medicinal potential. The results of this study show that the grinding technique can improve this potential by producing a fine powder from purslane. The use of a ball mill resulted in a powder with a higher proportion of fine particles (\(x<100\)\(\mu\)m) compared to other grinding techniques (knife and planetary ball). Extracts from purslane powder after ball grinding had the highest content of total phenols compared to extracts from freeze-dried, unground purslane (control), and extracts from powder after knife and planetary ball grinding. The gastrointestinal stability of the phenols was the highest in extracts from purslane powder after ball grinding following simulated digestion in the stomach and intestine. HPLC-DAD analysis revealed that protocetahuic acid, chlorogenic acid, and ferulic acid are the dominant phenolic compounds in purslane extracts. However, their content depends on the grinding techniques. Ferulic  
Figure 7: Free radical scavenging capacity (FRAP) extracts from unground and ground purslane. Values represent the means \(\pm\) standard deviations of three biological replicates and three technical replicates. Different letters indicate significant differences between the samples for each concentration separately (one-way ANOVA, Duncan test, \(p\leq 0.05\)). Control = freeze-dried, unground purslane; PPK = purslane powder after grinding with a knife mill; PPB = purslane powder after grinding with a ball mill; PPP = purslane powder after grinding with a planetary ball mill.  
acid was only dominant in the extract of purslane powder after planetary ball grinding. The extract of purslane powder after ball grinding had the highest antioxidant activity in all tested methods (DPPH, ABTS, and FRAP), which correlates positively with the particle size. Many studies have already proven that purslane can be used as a natural superfood or powerful dietary supplement in human nutrition and aquaculture, but the results of this study suggest its potential use in the form of very fine powder.  
Conceptualization, T.B.; methodology, T.B., M.B., I.S., D.D. and M.C.; formal analysis, A.B., D.R., T.B., I.S., M.B. and M.C.; investigation, T.B., D.R., M.B., I.S. and D.D.; resources, T.B. and I.S.; data curation, M.C., T.B., M.B. and I.S.; writing--review and editing, T.B., M.C., A.B., I.S. and M.B.; supervision, T.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
This research was partially supported under the project \"Functional integration of the University of Split Faculty of Science/Faculty of Chemistry and Technology/Faculty of Maritime Studies through the development of scientific research infrastructure in the building of the three faculties\" (KK. 01.1.1.02.0018).  
The authors declare no conflicts of interest.
## 1 Introduction
This method features acceptable control over the aspect ratio and density [23; 24], which are important parameters to take into account when employing AAO for nanostructured back-electrodes in \"substrate\"-type solar cells based on A\({}^{\text{II}}\)-BVI heterojunctions. Nevertheless, in order to achieve a compact and void-free NW-based metal/CdTe interface advantageously engaged within A\({}^{\text{II}}\)-BVI compound-based solar cells, one of the main challenges lies in seeking out a suitable CdTe deposition method to evenly fill the gaps through the NW array. Previous reports on embedding vertically-aligned NWs into an organic or inorganic matrix revealed that adequate optimization of the NW pitch and aspect ratio are required, and that the best strategy to fill the NW space is tremendously important, as the embedding-material should uniformly protrude through the NWs in order to secure a maximized interface area with superior electrical properties [24; 25; 26]. To the best of our knowledge, optimization studies of the morphological and structural quality of CdTe thin films capable of uniformly penetrating a dense array of vertically-aligned metallic NWs remain limited. In this work, a fabrication protocol with great potential for low-cost and effective preparation of CdTe-embedded Cu nanowire arrays is described. The method relies on a multi-step process in which the CdTe film is deposited on a dense array of vertically-aligned Cu NWs; these are previously prepared through an electrochemical synthesis procedure within a supported AAO nanoporous template. We subsequently carried out comparative evaluations of the morphological and structural quality of the CdTe thin films obtained by different techniques, namely, vacuum thermal evaporation (VTE), radio-frequency magnetron sputtering (RF-MS), and electrochemical deposition (ECD). The results show promising results when employing the electrochemical synthesis approach for CdTe thin film deposition under optimized processing conditions. Hence, the presented work deserves consideration in the future, as it demonstrates that this is a reliable and cost-effective method for production of absorber layer/collecting electrode CdTe/Cu nanostructured interfaces for affordable and more efficient \"substrate\"-type solar cells based on A\({}^{\text{II}}\)-BVI heterojunctions.
## 3 Error Model of the Odometry  
As it has been aforementioned, odometry suffers from different kind of errors. The one introduced by tire wear has a very low frequency and may be compensated by calibration of the step value. It may be represented as a calibration error with the form \(\omega(k)\delta r\), where \(\omega(k)\) is the angular rate of the wheels at instant \(k\) and \(\delta r\) the difference between the real wheel radius, and the nominal value \(r\).  
Errors due to mechanical and electrical noises can be grouped together in a common term with a Gaussian characteristic and zero mean, \(n(k)\)-\(N(0,\sigma_{\alpha od})\). We have tuned the value of \(\sigma_{\alpha od}\) to 0.26 m, corresponding with the odometry step in our vehicle setup.  
A wheel slide appears when the wheel rotates slower than the value calculated according to the current vehicle speed. Wheel slip is the opposite case, when the wheels rotate faster than they would do in normal friction conditions.  
Although both effects may partially compensate one another in a long term, the estimation of the vehicle pose at a given time suffers from slips and slides. In addition, slides appear more often than slips in car navigation. This is the reason why we focus on slides in this paper. Nevertheless, analogous considerations apply for slippages.  
The characterization of the odometry error may be completed now to represent slides in the sensor model  
\[\varepsilon_{dt}^{slide}(k)=\delta^{slide}(k)+\omega(k)\delta r+n(k) \tag{1}\]  
where \(\delta^{slide}(k)\geq 0\) stands for the sliding error, that may be also developed as  
\[\delta^{slide}(k)=(1-scv)\times c(k) \tag{2}\]  
being \(c(k)\)-\(N(0,\sigma_{\alpha do})\), and \(scv\) (slide compensation value) varying from 0 (maximum compensation) till 1 (minimum).