esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Comparative Analysis of Water and Nanofluid Performance
_PEC_ value reveals the system's overall performance by evaluating the relationship between heat transfer enhancement and pressure drop increase. The graphs show the performance of four different combinations: Al TPMS with water, Al TPMS with nanofluid, Ag TPMS with water, and Ag TPMS with nanofluid at three different flow rates of 23.56 cm\({}^{3}\)/s, 27.49 cm\({}^{3}\)/s, and 35.34 cm\({}^{3}\)/s. Results indicate that particularly at 27.49 cm\({}^{3}\)/s flow rate, Ag TPMS nanofluid combination achieves the highest _PEC_ values of approximately 350. This high _PEC_ value proves that heat transfer enhancement dominates over pressure drop increase, demonstrating the system's most efficient operation under these conditions. Ag TPMS structure exhibits 25% to 30% higher _PEC_ values compared to Al TPMS structure across all flow rates. A notable observation is that nanofluid usage increases _PEC_ values in both TPMS structures. This increase indicates that enhanced heat transfer properties of nanofluid compensate for the increased pressure drop effect. The fact that PEC values do not approach zero confirms that all tested combinations operate at an acceptable performance level. Figure 12: Comparison of _Nu_ average with distilled water and nanofluid: (**a**) experimental, (**b**) numerical. Figure 13: _PEC_ distributions along the TPMS structures at different flow rates: (**a**) 23.56 cm\({}^{3}\)/s, (**b**) 27.49 cm\({}^{3}\)/s, (**c**) 35.34 cm\({}^{3}\)/s.
## 5 Technical Results
### Mechanical Testing Results
Figure 9A presents the displacement of each design at its peak load. Stiffer designs deflected less under similar loads. The best-performing Design H had a displacement of 17.43 mm at 1757 N. As a direct comparison, the worst-performing Design I had a displacement of 13.73 mm at 297 N. For a better comparison, the designs should be evaluated within the linear region of Design I at 250 N.  
Figure 8: Maximum peak load from component testing and predicted load from FEM analysis, comparing the nine different designs A* to I.  
Figure 7: Load–displacement curves that depict how each design deflected as the load increased up to the yield. Figure 9B shows the displacement of each design with 250 N applied. Despite all the approaches in the GD setup using the'maximize stiffness' objective, the range of displacements seen implies a variety of stiffnesses achieved. This is not fully representative, however, as Designs B, F, G, and H all increased in stiffness at higher loads, as shown in Figure 8. It is notable that Design F achieved the highest deflection, with 26.29 mm at peak load and 19.70 mm at 250 N, since this design was formed from the only GD setup approach to use frictionless constraints and was therefore designed for deflection. The population of designs had a mean stiffness of 48 N/mm with a standard deviation of 27 N/mm. Again, this shows a large spread in the performance of the structures. In summary, the results show a wide variation in predicted load, stiffness, energy absorption, and peak load, with the standard deviation of each metric across the population of designs greater than 50% of the mean in all cases. The 341 N standard deviation in predicted loads and 471 N standard deviation in peak loads, in particular, shows how the variability in AM material properties compounds with the variability in GD outcomes to produce a wide range of component performance.
# An Equal Precision Programmed Cymometer Design Using Low-Power Technique
## 4 Results and Discussion
### Performance Test
#### 4.3.3 Duty Cycle Test  
Under the condition that the duty cycle is 40% and 60% at an amplitude of 1 volt, Table 3 shows the duty cycle test with the input frequency discretely changing. After testing, the duty cycle error between the proposed design and the standard frequency is very small. These results prove that the duty cycle accuracy can be significantly improved  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Input Amplitude (mV)** & **Input Frequency (MHz)** & **100** & **50** & **20** & **5** & **3** & **1** \\ \hline
**30 mV** & **Display Frequency (MHz)** & 99.9940 & 49.9997 & 19.9997 & 4.9998 & 2.9999 & 1.0000 \\
**Error (\%)** & 0.0060 & 0.0006 & 0.0015 & 0.0040 & 0.0033 & 0.0000 \\
**Display Frequency (MHz)** & 99.9996 & 49.9998 & 19.9996 & 4.9997 & 2.9997 & 0.9999 \\
**Error (\%)** & 0.0004 & 0.0004 & 0.0020 & 0.0060 & 0.0100 & 0.0100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: High-frequency sine channel test.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Input Amplitude (mV)** & **Input Frequency (KHz)** & **500** & **400** & **300** & **200** & **100** & **50** \\ \hline
**30 mV** & **Display Frequency (KHz)** & 499.9960 & 399.9970 & 299.9996 & 199.9999 & 99.9998 & 49.9999 \\
**Error (\%)** & 0.0008 & 0.0075 & 0.0001 & 0.0001 & 0.0002 & 0.0002 \\
**1 mV** & **Display Frequency (KHz)** & 499.9997 & 400.0070 & 300.0050 & 199.9999 & 99.9998 & 49.9999 \\
**Error (\%)** & 0.0001 & 0.0018 & 0.0017 & 0.0005 & 0.0002 & 0.0002 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Low-frequency sine channel test.  
by an equal precision frequency measurement method. In addition, in terms of error, the experimental results show that the maximum error is only 0.05% when the input duty cycle is 40%, and the maximum error is only 0.5% when the input duty cycle is 60%.
## 7 Results and Discussion
### Limitations  
While Edge-FLGuard performs strongly across most dimensions, we acknowledge several limitations, as follows:  
* Convergence Delay under High Non-IID: Training becomes slower with highly imbalanced or disjoint client datasets.
* Vulnerability to Large-Scale Poisoning: The framework remains susceptible when >40% of clients are adversarial, despite basic filtering and fallback logic.
* Hardware Constraints: While tested on realistic edge platforms (e.g., Pi 4, Jetson Nano), true ultra-constrained deployment (e.g., microcontrollers) will require further optimization.  
Figure 6 illustrates the robustness of Edge-FLGuard against model poisoning attacks, showing the effect of increasing the percentage of adversarial clients on detection performance.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### Environmental and Functional Test
#### 2.5.2 End-to-End Test  
In addition to the functional verification of the COMM subsystem itself, end-to-end tests were performed to verify two-way communications between IDEASat and the NCU ground station. Tests were performed with ranges of 10 km and 40 km performed prior to and following FM integration. While initial tests were relatively successful, anomalously high bit errors in UHF beacon packets received by the ground station were encountered following successive tests. The spacecraft UHF transceiver ultimately failed to transmit, although it was noted that the transceiver was still capable of receiving commands transmitted by the ground station in this state. This fault necessitated partial disassembly of the spacecraft FM to de-mate the UHF transceiver and antenna. It was found that due to the poor impedance matching of the self-made antenna, the transmit line of the UHF transceiver was ultimately destroyed by the reflected energy from the antenna during signal transmission. After emergency servicing by the manufacturer and replacement of the UHF antenna with a properly impedance-matched model, the UHF transmission and reception functionality were successfully verified using a 40 km end-to-end test.  
Table 9 is the result of the 40 km end-to-end test, the packet format used by the satellite and the reception results from the ground station. Full packet loss means that the ground station did not receive the beacon packet sent by the satellite at all. Because the life beacon is composed of 9 packets, as long as one of the packets is missing, it is classified as partial packet loss. Packet error refers to any bit errors present in the received packet, as determined via the CRC-16 checksum test. Although the three packet formats could be received over a distance of 40 km, the lite beacon format was found to have the most complete content. Even if the packet error of life is 100%, lite could actually be parsed as 9 independent packets, so it was easier to parse the result than the other two formats. The shorter packets also forced continuous resynchronization and clock recovery, which benefitted signal acquisition in a similar manner as the AX.25 preamble that was not implemented [13].  
Figure 9: IDEASat flatsat layout.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 1 Introduction
When this happens, vehicles using FOXS-GSC can detect traffic congestion and calculate a new route based on the traffic knowledge acquired through messages sent by other vehicles during their journey. The main contributions of this work are twofold:  
* Design and evaluation of a multi-hop communication protocol that enables even those vehicles far from a cloudlet to receive and send information about their routes. * Design and evaluation of a system to detect traffic congestion and then calculate a new route based on traffic knowledge. FOX-GSC is compared to three baseline solutions found in the literature: DESTINy [16], CHIMERA [17], and PAN [18]. As an example, we consider the road network in Ottawa, Canada. The proposed strategy shows promising results in terms of network metrics. FOXS-GSC reduces the number of transited messages by more than 90% and maintains a tax of 20% more messages properly responded. In terms of traffic efficiency, FOXS-GSC reduces stopped time by up to 39% and travel time by up to 22%. The remainder of this work is organized as follows. Section 2 presents related works. Section 3 presents the proposed approach. Section 4 outlines the experimental methodology and presents the performance evaluation and results. Finally, Section 5 summarizes the conclusion and the future work.
## 3 Results
### Mathematical Modeling Results
#### 3.2.2 Autoregressive Models ARIMA  
Observational data \(\{x(t)\}\), \(\{p(t)\}\), \(\{z(t)\}\), \(t=1\),..., \(n\) can be considered as time series. Figure 11 shows a diagram of the dynamics of the normalized \(x(t)\) value. On the graph, the abscissa is time and the ordinate is \(x(t)=X(t)/S(t)\). For time series, there is a whole arsenal of linear stochastic models. Below, time series models are constructed using normalized data for the first indicator, i.e., according to the data for \(\{x(t)\), \(t=1\),..., \(n\}\). It is known that autoregressive models can be constructed only for stationary series. After identifying the trend:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(X\) & \(Z\) & \(P\) \\ \hline \(X\) & 1.000000 & 0.530344 & 0.522465 \\ \(Z\) & 0.530344 & 1.000000 & 0.486965 \\ \(P\) & 0.522465 & 0.486965 & 1.000000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample correlation coefficient matrix.  
Figure 10: Graph of polynomial smoothing of the dependence of OXZt.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(x\) & \(z\) & \(p\) \\ \hline \(x\) & 1.000000 & \(-\)0.676560 & \(-\)0.090046 \\ \(z\) & \(-\)0.676560 & 1.000000 & \(-\)0.147367 \\ \(p\) & \(-\)0.090046 & \(-\)0.147367 & 1.000000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample correlation coefficient matrix converted data.  
\[x_{trend}(t)=0.0874+0.0016t, \tag{6}\] \[\widetilde{x}(t)=x(t)-x_{trend}(t)\]  
The ARIMA(1,0,1) model is constructed:  
\[\widetilde{x}(t)=0.39\widetilde{x}(t-1)+\epsilon_{t-1}-0.12\epsilon_{t-1},\text { where }\epsilon_{t}\in N(0,1). \tag{7}\]  
The autoregressive model (7) and model (6) can be used to estimate the forecast for indicator \(x(t)\) several steps ahead. The coefficient 0.0016 of the model (6) indicates an increase in the number of people in the group with a negative attitude towards the project. The root-mean-square error of the model is: \(Q=\sqrt{\frac{1}{\tilde{\mu}}\sum\limits_{t=1}^{n}\left(x(t)-\tilde{x}(t) \right)^{2}}=0.0037\), where \(\tilde{x}(t)\)--model values \(x(t)\). Figure 11 shows a diagram of the dynamics of the normalized \(x(t)\) value (blue line) and model values (red line).  
After identifying the trend \(z(t)\):  
\[z_{trend}(t)=0.85+0.0016t, \tag{8}\] \[\widetilde{z}(t)=z(t)-z_{trend}(t)\]  
The ARIMA(1,0,1) model is constructed:  
\[\widetilde{z}(t)=0.44\widetilde{z}(t-1)+\epsilon_{t-1}-0.14\epsilon_{t-1}, \text{ where }\epsilon_{t}\in N(0,1).\]  
Using the continuous models built to describe the dynamics of negative and neutral messages, let us find estimates of the rate of the share of negative (\(\widetilde{dx}\)) and neutral (\(\widetilde{dz}\)) messages. According to (6) and (8), we can write \(\widetilde{dx}>\widetilde{dz}\), therefore \(\tilde{x}\succ\tilde{z}\).
# A Social Network Analysis on the Damaku of English-Learning Programs
## 3 Methods
### Social Network Analysis Procedure
#### 3.2.1 Community Detection  
After constructing the network, community detection was performed. Community detection aims to uncover naturally occurring groups or clusters within a network without prior knowledge of the number or size of these groups [17]. A common approach involves maximizing the modularity score, which evaluates the quality of a particular division of the network into communities [17]. This study follows a similar approach. According to research by [31], the Leiden algorithm outperforms the heuristic Louvain algorithm [32] in terms of both speed and the quality of community connectivity. Therefore, this study applies the Leiden algorithm to analyze 21 social networks, aiming to obtain the modularity and community structure of an integrated network comprising three types of nodes.  
Modularity measures the extent of assortative mixing, where nodes with similar attributes tend to form connections within the same community. A higher modularity value, approaching 1, indicates a strong presence of intra-community connections relative to a randomized network, thereby reflecting significant structural properties of the network [17].  
Figure 2: Gephi visualization of social networks: a case study. The detail of danmaku nodes is presented in Appendix A. Different colors in the figure represent different communities.  
The complexity of social networks means that factors such as the diversity and frequency of user-submitted comments can significantly impact the interpretation of interactions. High-centrality danmaku comments, for example, can create distinct communities with the user nodes from which they originate, while separating from other nodes within the same cluster. This separation enhances the understanding of danmaku interactions. Thus, the greater the number of communities identified by a community detection algorithm, the more diverse the underlying topics, reflecting a wider range of user behaviors and textual features. In contrast, a smaller number of communities indicate a more concentrated set of topics.
# Photosensitivity of Infrared Glasses under Femtosecond Laser Direct Writing for mid-IR Applications
###### Abstract  
Chalcogenide glasses are increasingly being developed for photonic applications, particularly in the field of mid-infrared optical imaging. In this paper, we study the photosensitivity of one chalcogenide to direct femtosecond laser writing and its possible applications to micro-optics. Particular attention has been paid to the creation of phase changes and related refractive index variations. This chalcogenide glass was compared with two commercial heavy oxide glasses: Schott SF10 and Corning 9754. We observed different types of permanent modifications depending on the writing speed and the laser pulse energy. Index variations were measured at \(\Delta n>+0.055\) in the chalcogenide glass while maintaining spectral transmission similar to the pristine materials before irradiation. This provides a good candidate to design optical components for infrared application and helps to unlock the potential of the femtosecond laser direct writing technique to implement at high writing speed and high index changes with low optical losses.  
optical glasses; chalcogenide; optical components; femtosecond laser writing; IR optics Article
## 4 Experiment
### Dataset
#### 4.1.2 Houston Dataset  
The Houston dataset offers a real scene located at the University of Houston campus and its surrounding urban areas, as illustrated in Figure 3. The data were captured using the ITRES-CASI 1500 sensor on June 23, 2012, from 17:37:10 to 17:39:50 UTC. The spatial resolution of the image is \(349\times 1905\) pixels, presenting large areas of shadow due to cloud cover. The dataset has a spectral range from 380 nm to 1050 nm, providing 144 bands and a spectral resolution of up to 4.65 nm. The data were taken from an average altitude of about 1676 m, ensuring a spatial resolution of 2.5 m. This dataset was made available by the 2013 Data Fusion Contest, managed by the IEEE Geoscience and Remote Sensing Society (GRSS), and includes 15 classes of interest.
## 3 Proposed Approach
### Machine Learning (ML) Driven Classification Accuracy  
ML driven classification accuracies to predict peak and off-peak bus passengers ridership within the HetNet environment in our Energy-Aware Framework is proposed. ML was invented from pattern recognition to automate machines for intelligent decision making while learning from history and adapt to the testing environment [21, 30]. For the optimisation of peak and off-peak activities in our framework, ML algorithms proposed in [21, 31, 32, 33] are used to model traffic identification and user association to the BSs for classification of moving patterns. The first classification mechanism is KNN, which is a non-parametric classifier that searches for K-points in its training set that are the nearest to its test inputs. It performs counting of its member classes and returns observational fractions as estimated values [6; 19]. Distance-based metrics for KNN algorithm are comprehensively discussed in Section 2.2. The second classification mechanism is Discriminant Analysis (DA), which is based on independent variables to perform predictions for classification individuals into groups with two objectives: (i) classification of new inputs by predictive equations; or (ii) predictions of individual variables to comprehend relationships [6; 19]. The third classification mechanism is Support Vector Machine (SVM). SVM model is also recognised as a large margin classifier with the set of inputs classification of high dimensions through the liner and non-linear mapping. Output results are reliant on a subset of the training data, known as support vectors [6; 19]. The model takes a decision based on boundaries to construct distance bound nearest training samples in a form of hyperplane. The fourth classification mechanism is Decision Tree (DT), which is often called the classification and regression trees (CART) model. DT recursively partitions the input space of individual local models in each resulting region. It can be represented by a tree with one leaf per region [6; 19]. The fifth classification mechanism is Naive Bayes (NB), which is another mobility classification algorithm that classifies vectors of discrete-valued features [6; 19]. It has class labels through which we demonstrate the training classes (peak and off-peak passengers) as a product which is called the NB model. Finally, our sixth classification mechanism is an Artificial Neural Network (ANN) that classifies the interconnected group of nodes/neurons consisting of input and output layers. Neurons learn the training data without being programmed with task-specific rules. Numeric weights are tuned on experiences to exploit best possible outcomes when learning the neural nets [6; 19].
## 4 Results and Discussion
### Numerical Approach to Evaluation of FoS
According to the data from the instrumental observations and numerical modeling, the zone of inelastic deformation and damage adjacent to the walls of the protective pillars has a finite length. In this zone, a significant concentration of stresses is observed. However, as they go deeper into the pillar, the stress tends towards a normal value in the virgin rock mass. Therefore, at a certain width of the pillar (or more), we can expect the absence of the influence of the anomalous zone of  
Figure 9: Distribution of vertical stress \(\sigma_{yy}\), equivalent plastic strain \(\gamma^{P}\), and factor of safety \(F_{s}\) along the line drawn through the center of the model in the dip direction of seam 3 after extraction of panel 2.  
inelastic deformations and damage on the stress distribution and, accordingly, \(F_{s}\), with all other factors being equal. In other words, the statistical weight of the points with a \"normal\" safety factor will cease to increase significantly, starting from a certain W/H ratio of the pillar.
# A Study on the Pantograph Slide Wear Model Based on Energy Dissipation
## 2 Calculation of Current-Carrying Frictional Energy Dissipation in the Pantograph-Catenary System
### Energy Generated by the Work Done by the Current  
For the rough surface of the sliding electrical contact in the pantograph-catenary system, it is assumed that there are n conductive spots between the pantograph slide and the contact wire. The contact resistance of the pantograph-catenary system can be calculated using the following equation [23]:  
\[R=\frac{\rho l}{A_{\rm r}}=\frac{\rho l}{n\pi a^{2}} \tag{1}\]In the equation, \(A_{\rm r}\) denotes the actual contact area of the pantograph-catenary system; \(\rho\) is the sum of the resistivities of the pantograph slide and the contact wire materials; \(l\) represents the contact length, i.e., the width of the pantograph slide; and \(a\) is the average radius of a single conductive spot.  
The hardness of the materials affects the actual contact area and the shape of the conductive spots in the pantograph-catenary system. Considering the contact pressure and material hardness, the contact hardness \(H\) is defined as [24]  
\[H=\frac{F}{A_{\rm r}}=\frac{F}{n\pi a^{2}} \tag{2}\]  
In the equation, \(H\) denotes the contact hardness of the pantograph slide.  
By combining Equations (1) and (2), the expression for the pantograph-catenary contact resistance can be derived as  
\[R=\frac{\rho lH}{F} \tag{3}\]  
When a current flows through the contact resistance, the work done by the current can be derived from [PERSON]'s law. The energy generated by the work done per unit contact area is given by  
\[E_{1}=\frac{Q}{A}=\frac{I^{2}Rt}{A}=\frac{I^{2}t}{A}\bigg{(}\frac{\rho lH}{F} \bigg{)} \tag{4}\]  
In the equation, \(Q\) denotes the energy dissipated by the Joule effect; \(A\) represents the nominal contact area of the pantograph-catenary system, i.e., the rectangular area of the contact surface; and \(t\) is the sliding time.  
Energy Generated by the Work Done by the Frictional Force and Current-Carrying Frictional Energy Dissipation in the Pantograph-Catenary System  
During the pantograph-catenary contact friction process, relative sliding motion occurs between the pantograph slide and the contact wire. The energy generated by the work done by the frictional force per unit contact area is given by  
\[E_{2}=\frac{\int_{0}^{x}F_{f}dx}{A}=\frac{\int_{0}^{t}\mu Fvdt}{A}=\frac{\mu Fvt} {A} \tag{5}\]  
In the equation, \(F_{f}\) denotes the pantograph-catenary frictional force; \(\mu\) is the coefficient of friction; and \(x\) is the sliding distance.  
Therefore, the pantograph-catenary current-carrying frictional energy dissipation can be calculated as the sum of Equations (4) and (5). The expression for the current-carrying frictional energy dissipation per unit contact area is given by  
\[E=E_{1}+E_{2}=\frac{t}{A}\bigg{(}\frac{\rho lHl^{2}}{F}+\mu Fv\bigg{)} \tag{6}\]