esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Tensor Solution to the Problem of Inter-Domain Routing with Normalized Quality of Service
## 3 Decomposition Model of Inter-Domain QoS Routing in Communication Network
When the router \(R_{i}^{p}\) from the \(p\)th domain is the \(k\)th packet flow receiver, only the last equation of system (2) will be simplified and will look as follows:  
\[\sum_{W_{i,j}^{p}\in W^{p}}x_{j,i}^{p,k}=1.\]  
In addition, to prevent links overload on the \(p\)th network domain, it is necessary to fulfill the following conditions:  
\[\sum_{k\in K}\lambda_{req}^{k}x_{i,j}^{p,k}\leq\varphi_{i,j}^{p},\ p=\overline {1,N}. \tag{3}\]  
Let us denote as \(\lambda_{i,j}^{p,k}=\sum_{k\in K}\lambda_{req}^{k}x_{i,j}^{p,k}\) the average intensity of the \(k\)th packet flow transmitted in the link \(W_{i,j}^{p}\in W^{p}\). Then, when implementing multipath routing, the routing variables are imposed by the following constraint:  
\[0\leq x_{i,j}^{p,k}\leq 1. \tag{4}\]  
Variables (4) are the coordinates of the routing vectors, \(\overrightarrow{x}_{p}^{k}\), that set the result of the solution for the \(k\)th flow routing problem in the \(p\)th domain. During the distributed calculation of vectors \(\overrightarrow{x}_{p}^{k}\) across each \(p\)th domain, it is essential to ensure the structural and functional connectivity of inter-domain routes that are traverse multiple routers of different domains. In order to provide inter-domain connectivity, models (1)-(4) introduce the following inter-domain interaction conditions [22; 23; 24]:  
\[\mathrm{C}_{p,q}^{k}\overrightarrow{x}_{p}^{k}=\mathrm{C}_{q,p}^{k} \overrightarrow{x}_{q}^{k},\ p,q=\overline{1,N},\ p\
eq q,\ k\in K, \tag{5}\]  
where \(\mathrm{C}_{p,q}^{k}\) is the interaction matrix of the \(p\)th and \(q\)th domains of the \(m_{p,q}\times m_{x}^{p,k}\) size, \(m_{p,q}\) is the number of routers through which the boundary between the \(p\)th and \(q\)th domains passes, and \(m_{x}^{p,k}\) is the number of coordinates \(x_{i,j}^{p,k}\) of the \(\overrightarrow{x}_{p}^{k}\). Therefore, for the network structure shown in Figure 2 that consists of two domains, the boundary passes through two routers. In the designations of the first domain (Figure 2), the border routers are \(R_{3}^{1}\) and \(R_{4}^{1}\), while in the designations of the second one, the border routers are \(R_{1}^{2}\) and \(R_{2}^{2}\). In the gaps of links, their bandwidth is shown.
## 3 Net Impact and Capturing Risks
### Energy and Work Validation  
With the decoupling of the implemented models within the aforementioned sequential modelling strategy, both the energy of the tethered net and the spacecraft must be checked individually.  
Starting with the tether-net system, the net's mechanical energy (here denoted as partial total energy) is plotted for this case study in Figure 15.  
Initially, it can be observed that the energy of the net decreases due to the net deployment energy damping, similar to what was observed in [10; 15]. Furthermore, the total partial energy is observed to strongly drop at 0.9 s, 4.5 s and 5.25 s. These are due to the energy loss caused by the damping process of contact and the decrease in the bullets' velocity. After the last significant step decrease at 5.25 s, the mechanical energy continues to decrease due to the net wrapping around Envisat being in continuous contact, and thus, contact and net damping.  
The last validation step is related to Envisat's rotational energy as can be seen in Figure 16. The same has been performed for the translational kinetic energy, with the addition of model verification tests using a simplified constant force and moment case for verification. However, for the sake of brevity, only the rotational energy is presented in this paper.  
Figure 15: Total partial or mechanical energy of the net during the impact and capturing process as a function of time. Alongside the latter are the kinetic and elastic energies.  
Three essential aspects can be observed in Figure 16:  
1. Total rotational energy is approximately constant with a maximum variation of 1 J at \(t\approx 7.1\) s.
2. Due to the rotational instability around the \(X\)-axis and the predicted effect of the modelling strategy itself, the rotational energy exceeds the total rotational energy from 4.5 s to 5.2 s by \(\sim 5\%\).
3. The system's energy is entirely conserved without variation during the artificial extension from \(t=7.5\) s.  
As was mentioned in Section 2.6, (1) and (2) were expected due to the fact that the modelling strategy assumptions resulted in a small violation of momentum and energy conservation. Additionally, this effect is related to the contact dynamics themselves, which have been found in the literature [15] to be unstable due to their high dependence on initial impact penetration \(\delta\). However, due to the significant difference in mass between the net and the Envisat, this effect can be seen to be negligible. The correct implementation of the modelling strategy is confirmed by (3), as during this artificial extension, the net's impact forces are exponentially decreased and Envisat's dynamics are dominated by its initial conditions from \(t=7.5\) s onwards.
## 2 Materials and Methods
### Body Composition and Muscle Quality Assessment  
In a separate testing session, participants arrived at the laboratory after an overnight fast. They underwent a dual-energy X-ray absorptiometry (DXA, Lunar Prodigy, GE Healthcare(r) Chicago, IL, USA), with enCore 2009 software (version 13.20.033) scan of their whole body, following the standards of the International Society for Clinical Densiometry (ISCD). Bone-free lean mass (BFLM) of the forearms was determined by defining the Region of Interest (ROI) using anatomical landmarks, specifically the humero-radial joint line at the proximal end and the radio-carpal joint line at the distal end. Muscle quality was assessed as the ratio of peak isometric force measured in the half-crimp, three-finger drag, and handgrip tests to the forearm BFLM, assumed as forearm muscle mass.
# Enhancing Recognition Memory in Virtual Memory Palaces Using Worlds-in-Miniature
## 3 Pilot Study
### Pilot Study 1--Simple Memory Palaces  
The first pilot experiment investigated the differences in memorization performance between rote memorization and the virtual memory palace. The virtual environment was developed using Unity Engine and experienced via a Meta Quest 3 VR headset. The virtual memory palace consisted of 10 rooms randomly placed within an open 3D space, with road connections between the rooms. Each room contained eight unique symbols randomly selected from a pool of approximately 2000 icons. The world was designed to limit spatial dispersion by ensuring minimum and maximum distances between rooms (see Figure 1a).  
Participants explored the virtual memory palace for 5 min, during which symbols were displayed in fixed locations within the rooms. Participants had no time limit for observing each symbol and could spend as much time as needed to memorize them before moving to the next location. The symbols were observed through the VR HMD in a first-person perspective, simulating a natural exploration experience (Figure 1b). In contrast, participants in the rote memorization condition observed symbols statically displayed on a 2D screen within the VR environment without moving (Figure 1c). The eight symbols were displayed simultaneously on a static whiteboard screen, and participants could navigate between different sets of symbols using UI (user interface) controls, specifically by pressing left or right buttons to flip through pages.  
During the memory palace condition, participants physically navigated through the virtual space using VR controllers to move between rooms and observe symbols. In the rote memorization condition, participants remained stationary and observed symbols sequentially on the 2D display. A total of six participants, recruited from the university's graduate program, participated in both conditions for 5 min each. Immediately after each task, participants were tested on their ability to recall and recognize symbols by identifying whether a given symbol was part of the target set or not.  
The results of this pilot study showed that participants using the virtual memory palace achieved a memorization accuracy of 90%, compared to 85% for rote memorization. The virtual memory palace demonstrated slightly higher memorization performance. However, participants reported that exploring the unfamiliar virtual memory palace caused significant fatigue and was perceived as a waste of time. Additionally, they reported that the open spaces between rooms caused confusion and dizziness.
## 3 Problem Statement
### Vehicle Interaction Model  
In the considered scenario, the modeling of the CAV-to-CAV and the CAV-to-HDV interactions were developed in this paper. The CAVs equipped with V2X communication devices could exchange information, including the vehicle position, speed, acceleration, orientation, destination with the vehicles within the communication range, and road infrastructure, i.e., vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Based on the road infrastructure, the HDVs' state information could be observed and transferred to the CAVs [31]. Unlike the CAVs, the HDVs have the liberty to maneuver themselves under the premise of obeying the traffic regulations. However, they cannot send any acknowledgment of their interactions. In this paper, the HDVs were modeled as cautious drivers and they slowed in response to the dangerous movements of the vehicle ahead. The intelligent driver model (IDM), which imitates human driving behavior, was used to control the HDVs. Consequently, the acceleration of an HDV could be anticipated based on its current speed and headway distance.
## 4 Implementation  
### Pixel Eb/N0 Computation and Noise Modeling  
In CCD/CMOS, the pixel noise in cameras can be approximately modeled using Equation (1), according to [23].  
\[n\sim N(0,\delta(s)^{2}) \tag{1}\]  
where \(s\) represents the value of a pixel, \(\delta^{2}(s)=s\times a\times\alpha+\beta\), \(a\) is the mark and space amplitude, and \(\alpha\), \(\beta\) specify the fitting factors obtained in the experiments. In our implementation, we used model-fitting coefficients in our system, which were able to predict experimentally [26]. The pixel \(E_{b}/N_{0}\) in the receiver side was estimated using Equation (2) [26], with the assumption that one symbol contains one bit.  
\[Pixel\frac{E_{b}}{N_{0}}=\frac{E[s^{2}]}{E[n^{2}]}\approx\frac{a^{2}\times \Delta}{a\times\alpha\times\Delta+\beta} \tag{2}\]  
where \(E_{b}\) is the bit energy, \(N_{0}\) represents the noise density, \(s\) is the value of a pixel, \(\Delta=T_{\text{exp}\,\text{{source}}}/T_{bit}\) is the ratio of camera exposure time to the bit interval, and \(\alpha\), \(\beta\) are the fit parameters.  
Because of the difference between camera types on the market, each camera should indicate its evaluation. For instance, a theoretical estimation of pixel SNR is illustrated in Figure 4 as a nonlinear curve that used Equation (2). The shutter speed of a rolling shutter camera is estimated to be \(10\) kHz.  
Figure 4: The theoretical relationship between Pixel Eb/No and image amplitude.  
Figure 3: Manchester code patterns and the received signal from two adjacent LEDs (**a**) 1 impulse, (**b**) 0 impulse, (**c**) the received signal.
## 6 Conclusions  
The presented study dealt with the complex analysis of the bike-sharing service in a small urban area in Slovakia using the example of the city of Zilina. The aim was to assess the current state of this service, identify its strengths and weaknesses, and suggest possible improvements. The analysis was carried out by combining spatial methods in the GIS software QGIS, statistical summarization of non-spatial analysis, and a questionnaire survey among users of this service.  
Spatial analysis showed that the current coverage of bike-sharing stations in Zilina is insufficient, especially in some municipality districts, such as Zavodie, Banova, Budatin, and Borik (marked in the figure). Although the stations in the city center have a high level of use, some peripheral locations are not served, which limits the potential users of this service, as shown in the analysis. The survey questionnaire confirmed that the availability of stations and the price of the service are key factors for using the service. Most respondents agreed that expanding the bike-sharing network in Zilina would be beneficial.  
The analysis shows that for bike-sharing to function effectively in Zilina, it is necessary to:  
1. Expand the network of stations to areas with insufficient coverage.
2. Improve cycling infrastructure and connect existing cycle routes.
3. Consider the possibilities of further improvements, such as the introduction of electric bicycles.
4. Improve the information campaign to attract new users.  
The findings of this study provide useful recommendations for responsible entities managing bike-sharing in Zilina, as well as for city authorities dealing with the development of sustainable mobility. The implementation of the proposed measures could lead to a more efficient use of bike-sharing and increase its benefits for inhabitants.  
Conceptualization, S.K., K.O.B. and M.H.; methodology, S.K. and K.O.B.; software, S.K. and M.H.; validation, S.K. and K.O.B.; formal analysis, S.K. and K.O.B.; investigation, S.K. and K.O.B.; resources, S.K. and K.O.B.; data curation, S.K. and K.O.B.; writing--original draft preparation, K.O.B.; writing--review and editing, S.K. and K.O.B.; visualization, K.O.B.; supervision, K.O.B.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This publication was created thanks to support from the Institutional Research of the Faculty of Operation and Economics of Transport and Communications, University of Zilina 0342U-4/2025--Inovaitve vzdelavanie projektoveho manazmentu pre udrzatefnu a inteligentnu dopravu v EU v sulade s Priemyslom 5.0.  
Ethical review and approval were waived for this study by the EURAXESS Centre of the University of Zilina due to the full anonymization of the date with no possibility of retrospective deanonymization of individuals.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### Slice-Based QoS Manager Solution
#### 4.6.1 Open vSwitch Role  
There are mainly two ways of providing QoS with OvS. For traffic that ingresses into a switch, OvS can perform policing [20], which is the action of dropping packets whenever the allocated network resources have been exceeded. When traffic is egressing from a switch, OvS supports traffic shaping. Unlike policing, shaping slows down the packet transmission or reception instead of dropping the packet, inserting it in queues, waiting to be dispatched. It is essential to mention that OvS does not implement QoS by itself, taking advantage of Linux QoS features previously built in its kernel.
## 2 Sensors
### State-of-the-Art Research on BSN Status
#### 2.1.1 Classification of Sensors
Non-self-adapting sensors, which are simple to design and need no consideration of self-adjusting function, are widely used in BSNs at present. Because of the requirements of complexity and accuracy enhancing, self-adapting methods will be gradually applied to design of sensors.
## 3 Experimental Results and Discussion
### Dynamic Compressive Strength
#### 3.3.1 Effect of the Strain Rate on the Dynamic Compressive Strength

Table 17: The dynamic impact test of basalt fiber-reinforced concrete on a loading rate of 9.8 m/s. Figure 19: The stress–strain curve of basalt fiber-reinforced concrete under three loading rates. Figure 18: The stress–strain curve of ordinary concrete under three loading rates. With the increase of the loading rate, the impact energy increases rapidly, the internal energy of concrete increases rapidly, and it does not have time to release; therefore, the dynamic compressive strength of concrete is increased.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)  
In a typical web application, the architecture is divided into multiple layers to enhance modularity, maintainability, and scalability [24]. Examples of layered web applications are Data Access Layer--Application Logic Layer--Presentation Layer [23] or Resource Layer--Service Layer--Presentation Layer [47].  
We propose a novel architectural model called the **Holistic Security and Accessibility Layered Architecture (HSALA)**, which consists of five key interconnected layers: the **Data Layer**, **Persistence Layer**, **Application Layer**, **Content Presentation Layer**, and **Accessibility Enhancement Layer**.  
Each of the abovementioned layers also has an adjacent layer of security: the **Data Security Layer**, **Persistence Security Layer**, **Application Security Layer**, **Content Presentation Security Layer and Accessibility Layer**. This combination ensures that security considerations are integrated at every level of the architecture, embodying the holistic approach of HSALA. Figure 1, presented below, illustrates the HSALA model and how the layers interact with each other
## 4 Modeling and Results
### Results
#### 4.3.4 Experiment 3: Handwritten Kanji Recognition (300 Classes)  
The next experiment focused on evaluating the effectiveness of all the examined approaches for recognizing 300 kanji classes. Figures 10-13 in Appendix A illustrate the accuracies and losses achieved using the proposed approaches. The training dynamics for the 300-class experiment (Figures 10-13) reinforce the observed trends. Rapid initial convergence occurs across the methods, but Algorithm 2 maintains its advantage in achieving a lower loss and a higher accuracy rate upon stabilization. It should be noted that in this experiment, we doubled the number of kanji classes compared to those in the studies conducted in [12]. The results of this investigation are presented in Table 7.  
An in-depth evaluation of the kanji recognition performance uncovered the highest and lowest accuracy rates for the different training strategies, which are presented below:  
* Vanilla Preact ResNet-18: The lowest accuracy rate for % (65.63%) and the highest for and some others (100%);
* Preact ResNet-18 with data augmentation: The lowest accuracy rate for % (69.01%) and the highest for and some others (100%);
* Algorithm 1: The lowest accuracy rate for % (62.82%) and the highest for, and some others (100%);
* Algorithm 2: The lowest accuracy rate for % (67.61%) and the highest for and some others (100%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Accuracy** & **mAP** & **MAA** \\ \hline Vanilla Preact ResNet-18 & 96.20\% & 92.63\% & 94.99\% \\ Preact ResNet-18 with data augmentation & 96.22\% & 92.90\% & 95.18\% \\ Algorithm 1 & 93.41\% & 89.51\% & 93.13\% \\ Algorithm 2 & **97.92\%** & **96.23\%** & **97.56\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance indicators for all the investigated approaches in the case of the 200-class recognition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Accuracy** & **mAP** & **MAA** \\ \hline Vanilla Preact ResNet-18 & 95.35\% & 90.42\% & 93.81\% \\ Preact ResNet-18 with data augmentation & 95.40\% & 90.26\% & 93.76\% \\ Algorithm 1 & 92.83\% & 87.51\% & 92.31\% \\ Algorithm 2 & **97.62\%** & **95.60\%** & **97.11\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance indicators for all the investigated approaches in case of the 300-class recognition.
## 6 Analysis and Discussion
#### 6.1.1 Influence of Surrounding Rock Strength  
The effect of surrounding rock strength on the normal service and ultimate state-bearing capacity of the secondary lining was analyzed by extracting the \(P_{0.3}\) and \(P_{\text{max}}\) values for different grades of the surrounding rock. As shown in Figure 13, for the secondary lining with a reinforcement ratio of 0.6% and a thickness of 0.8 m, the \(P_{0.3}\) and \(P_{\text{max}}\) values were obtained under Grade III, IV, and V surrounding rock conditions.  
For \(P_{\text{max}}\), the decrease in bearing capacity is minimal across the surrounding rock grades. The \(P_{\text{max}}\) for Grade V surrounding rock is only 1.95% lower than that for Grade III surrounding rock, indicating that the surrounding rock grade has a negligible impact on \(P_{\text{max}}\). However, for \(P_{0.3}\), the values for Grade III, IV, and V surrounding rocks were  
Figure 12: Relation curve between crack width and reinforcement ratio of a 1.35 m thick secondary lining of Grade V surrounding rock.  
2.97 Mpa, 2.83 Mpa, and 2.62 Mpa, respectively, resulting in a maximum decrease of 11.52%. This suggests that the secondary lining structure is more likely to reach its normal service state under Grade V surrounding rock conditions, which makes it more susceptible to structural damage during normal operation. Therefore, the analysis of reinforcement ratio, thickness, and other parameters for the secondary lining should prioritize Grade V surrounding rock in future designs.
## 4 A Unified Frequency Model for Multi-Machine Systems
### Structural Singular Values and Robust Performance Issues
#### 5.1.2 Robust Performance Criterion  
In addition to robust and stable performance, some robust performance requirements should be met when controlling systems with uncertainties. Assuming that the H\({}_{\infty}\) norm of the transfer function between the input \(w\) and the output \(z\) of the system \(M\) is used to evaluate the robust performance, the criterion of the performance is:  
\[\left\|\frac{z}{w}\right\|_{\infty}\leq 1 \tag{23}\]  
This performance judgment problem can be transformed into a robust stability judgment according to the following theorem. For the uncertain system shown in Figure 10, if \(M\) is stable, the input is \(w\), and the output is \(z\), then the necessary and sufficient conditions for the system to be robust and stable and to satisfy Equation (22) are:  
\[\begin{split}\mu_{\Delta^{\prime}}[M(j\omega)]\leq 1,\forall \omega>0\\ \Delta^{\prime}=diag[\Delta(s),\Delta f],\left\|\Delta f\right\|_ {\infty}\leq 1\end{split} \tag{24}\]  
where \(\Delta f\) is a virtual complex perturbation matrix inserted between the input and output.
## 1 Introduction
### On-Chain and Off-Chain Data Storage Paradigms  
Data storage capacity and performance in blockchain systems depend on the chosen storage methods [1; 2]. On-chain storage ensures immutability and reliability by storing data directly on the blockchain; however, due to high transaction costs and scalability limitations, it is not suitable for large datasets [17; 18; 21; 22]. Off-chain storage, on the other hand, offers cost and performance advantages by keeping data outside the blockchain but introduces risks related to data integrity and access control [12; 15]. Hybrid approaches integrate both on-chain and off-chain solutions to establish a balanced model that optimizes security, cost, and scalability [6; 16; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37].