esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
## 6 Experiment
### Quantitative Comparisons  
Figure 2 shows a quantitative comparison of the proposed OPV with state-of-the-art non-deep learning-based visual trackers. Our visual tracker considerably outperforms other methods, of which performance mainly comes from the effectiveness of the proposed OPV. Figure 3 shows a quantitative comparison of the proposed OPV with state-of-the-art deep learning-based visual trackers. Our visual tracker is comparable to other methods; our method is simple owing to complex deep neural networks not being employed.  
Table 4 compared the OPV with other visual trackers using our UAV datasets, which verified that the OPV outperforms other trackers if either small targets or severe backgrounds exist. Other trackers easily missed the targets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **1** & **5** & **10** & **15** & **20** \\ \hline AUC & 0.631 & 0.645 & 0.706 & 0.707 & 0.706 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Visual tracking performance of the proposed OPV according to values of \(c\) in (8).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathit{OPV_{g}}\) & \(\mathit{OPV_{g+a}}\) & \(\mathit{OPV_{g+a+w}}\) \\ \hline AUC & 0.674 & 0.683 & 0.706 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Visual tracking performance of the proposed object proposal voting (OPV) according to different settings for the terms in (8). \(\mathit{OPV_{g}}\), \(\mathit{OPV_{g+a}}\), and \(\mathit{OPV_{g+a+w}}\) denote the proposed visual tracker using position, position+appearance, and position+appearance+vote weight terms, respectively.  
Figure 2: Quantitative comparison on non-deep learning-based methods using the OTB dataset. We compared the proposed method (OPV) with Sparsity-based Collaborative Model (SCM), STRUCTured output tracking with Kernels (Struck), Tracking-Learning-Detection (TLD), Adaptive Structural Local sparse Appearance (ASLA), ConteXt Tracker (CXT), Visual Tracker Sampler (VTS), visual trackibg decomposition (VTD), Circulant Structure of tracking-by-detection with Kernels (CSK), Local Sparse appearance model and K-selection (LSK), and Locally Orderless Tracking (LOT).
## 3 Results and Discussion
### Variation of \(k\)  
The results for the variation of the dimensionless permeability, \(k\), can be found in Figure 8, while all other parameters take on the values identified in the Reference Case given in Table 1. Note that the velocity profiles are shown on a semi-log scale due to the significant reduction caused by the low permeability flows.  
For the higher permeability flows, the velocity profiles tend towards Navier-Stokes behaviour, with a variable viscosity. As \(k\) decreases, however, the resistance to flow through the porous medium increases, resulting in a flow that is dominated by the Darcy friction. Taking Equation (28) in the limit of small \(k\) and then substituting for \(f(p)\) from Equation (29) and rearranging gives the following:  
\[u(y)=\frac{k\sin\theta}{A\mu_{0}e^{(\frac{p}{\mu_{0}})^{n}}} \tag{42}\]  
The above equation, for the Reference Case with \(n=1\), will produce an inverse exponentially varying velocity profile across the channel. Near the edges of the channel, where the solution must satisfy the boundary conditions, the viscous shear terms become significant. As \(k\) becomes progressively smaller, the region of flow dominated by Darcy-type behaviour increases. As seen in Figure 8, the Darcy friction profiles are relativelysymmetric, and as \(k\) becomes smaller, they become nearly constant across the channel (except near the boundaries). For small values of \(k\), the viscous shear term, \(\mu\left(\frac{d\mu}{dy^{2}}\right)\), is nearly zero, except near the walls, and shows significant deviation from the constant value that would be expected in non-porous channel flow that would produce a parabolic velocity profile. The plot of the variation of \(\left(\frac{d\mu}{dy}\right)\left(\frac{d\mu}{dy}\right)\) can be found in Figure 8, where, since the variation of \(\mu\) is the same in all flows, the differences in the profiles are directly due to the changes in the velocity profiles resulting from the variation of \(k\). Note that this force changes sign approximately at the midpoint of the channel, where \(\left(\frac{d\mu}{dy}\right)\) changes sign to meet the no-slip boundary condition.
# Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels
## 4 Simulation Parameters  
The system model and simulator parameters for the DL and UL NOMA power domains in 5G networks with and without MIMO were implemented using the MATLAB software program. Tables 1 and 2 show the simulation parameters that are properly considered in the simulation model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Parameters** & **Values** \\ \hline
1. & Number of users & 4 users \\
2. & Transmit power & 0 to 40 dBm \\
3. & Bandwidth & BW 1 & 80 MHz \\  & & BW 2 & 200 MHz \\  & & User 1 & 800 m \\
4. & Distances & User 2 & 600 m \\  & & User 3 & 300 m \\  & & User 4 & 100 m \\  & & User 1 & 0.75 \\
5. & Power coefficients & User 2 & 0.188 \\  & & User 3 & 0.047 \\  & & User 4 & 0.011 \\
6. & Path loss exponent & & 4 \\
7. & MIMO & \(64\times 64\) & \\
8. & Modulation & QPSK & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulator parameters for the DL scenario.
# Integrated Photonics on Glass: A Review of the Ion-Exchange Technology Achivements
## 2 Ion-Exchanged Waveguides
### Waveguide's Performances  
The main characteristics when dealing with integrated optics waveguides are their spectral operation range, their losses that can be split between coupling and propagation losses, and their behavior with respect to light polarization.
# Simple K-Medoids Partitioning Algorithm for Mixed Variable Data
## 3 Demonstration on Artificial and Real Datasets
### Data Simulation
#### 3.1.2 Different Number of Clusters  
The artificial data generated four different numbers of clusters. The numbers of clusters were 3, 4, 8, and 10. In any number of clusters, the SFKM algorithm always gave the smallest Rand index and was different from the other two algorithms (Table 5). When the number of clusters was small, i.e., \(k=3\), and the GDF was Marweco, the Rand index of the SFKM was the highest (0.84) compared to other GDFs. Both PAM and SKM, however, had a higher Rand index (0.88), and it was statistically different from that of the SFKM. Meanwhile, if the number of clusters was large (\(k=10\)), the highest Rand index (0.91) of both the PAM and SKM was the Huang GDF. Although the Rand index of the SFKM algorithm is higher than the other one, the Rand index of the SFKM algorithm is higher than the other one.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Domination** & **Algorithm** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline \multirow{3}{*}{Numerical} & PAM & 0.74 & 0.82 & 0.73 & 0.84 & 0.83 & 0.85 & 0.82 \\  & SFKM & 0.72 \({}^{a}\) & 0.73 \({}^{a}\) & 0.71 \({}^{a}\) & 0.74 \({}^{a}\) & 0.71 \({}^{a}\) & 0.76 \({}^{a}\) & 0.71 \({}^{a}\) \\  & SKM & 0.74 & 0.80 & 0.73 & 0.84 & 0.83 & 0.85 & 0.82 \\ \hline \multirow{3}{*}{Binary} & PAM & 0.78 & 0.78 & 0.77 & 0.77 & 0.76 & 0.73 & 0.74 \\  & SFKM & 0.74 \({}^{a}\) & 0.73 \({}^{a}\) & 0.73 \({}^{a}\) & 0.74 \({}^{a}\) & 0.72 \({}^{a}\) & 0.73 \({}^{c}\) & 0.73 \({}^{c}\) \\  & SKM & 0.78 & 0.77 & 0.78 & 0.77 & 0.75 & 0.74 & 0.75 \\ \hline \multirow{3}{*}{\(c=3\)} & PAM & 0.78 & 0.76 & 0.78 & 0.69 & 0.68 & 0.67 & 0.74 \\  & SFKM & 0.71 \({}^{a}\) & 0.72 \({}^{a}\) & 0.71 \({}^{a}\) & 0.70 \({}^{a}\) & 0.68 & 0.67 & 0.70 \({}^{a}\) \\  & SKM & 0.77 & 0.75 & 0.78 & 0.69 & 0.68 & 0.67 & 0.74 \\ \hline \multirow{3}{*}{\(c=5\)} & PAM & 0.77 & 0.82 & 0.77 & 0.79 & 0.78 & 0.76 & 0.77 \\  & SFKM & 0.71 \({}^{a}\) & 0.75 \({}^{a}\) & 0.71 \({}^{a}\) & 0.79 & 0.79 & 0.76 & 0.69 \({}^{a}\) \\ \cline{1-1}  & SKM & 0.76 & 0.81 & 0.77 & 0.79 & 0.78 & 0.76 & 0.77 \\ \hline \hline \end{tabular}  
Significantly different (\(\alpha=0.05\)) from: \({}^{a}\) PAM and SKM, \({}^{a}\) SKM.  
\end{table}
Table 4: Rand indices of the different proportions of variables.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set** & \(n\) & \(p_{n}\) & \(p_{b}\) & \(p_{c}\) & \(k\) \\ \hline Iris & 150 & 4 & 0 & 0 & 3 \\ Wine & 178 & 13 & 0 & 0 & 3 \\ Soybean & 47 & 0 & 0 & 35 & 4 \\ Vote & 435 & 0 & 0 & 16 & 2 \\ Zoo & 101 & 1 & 15 & 0 & 7 \\ Credit approval & 653 & 6 & 0 & 9 & 2 \\ \hline \hline \end{tabular} \(n=\) number of objects, \(p_{n},p_{b},p_{c}=\) number of numerical, binary, and categorical variables, respectively, \(k=\) number of true clusters.  
\end{table}
Table 3: Details of the real datasets.  
SFKM was slightly lower than the other two, i.e., 0.90, it was statistically different. The result showed that SFKM was the most underperforming algorithm.
# Real-Time Control System for Model Railway Based on SIMIS W Interlocking System
## 4 Results
### System Responsiveness and Data Processing  
The goal of our system was to achieve a maximum system response time of 1.5 s. This limit was determined by knowing the maximum speed of the model trains on the track and the length of the isolated sections on the model railway. If the response time was longer than 1.5 s, there was a risk of losing synchronization between the model railroad and the control application. A train might pass through an isolated section, and the control system would not detect the train passing through the isolated section due to the system's long response time.  
System response testing was under laboratory conditions with one train on the line. This method was sufficient because the single-track topology of the intermediate station sections allows the operation of a maximum of two trains simultaneously. In addition,  
Figure 8: Sample of graphic symbols for switch [8].  
the manual control of two trains on the small track is difficult. In the future, only one train at a time will be controlled on the model railway.  
Several configurations for data processing were tested. The data processing time was measured by subtracting two timestamps recorded at the beginning and end of each data processing section. This test was run multiple times, and the average response time was calculated. The results of these tests are shown in Table 1. All data processing algorithms were initially implemented within the REST API interface, yielding an average processing time of 2.84 s. This response time was unacceptably high. Some data processing algorithms were transferred to the ILTIS-N applications to achieve a shorter response time. Processing algorithms were left in a single thread with other algorithms of the ILTIS-N application. Tests indicated an improved response time with this configuration; however, the ILTIS-N applications became unresponsive during data processing. Finally, further improvements were made by transferring the data processing algorithms to a separate thread within the ILTIS-N application, thus creating multithreaded data processing. This configuration significantly reduced the system's response time, which was, on average, 1.2 s, and resolved the responsiveness issues of the ILTIS-N application. This new approach improved the system response time by 60%.  
This reduction ensured that the ILTIS-N system consistently displayed an accurate, real-time representation of the model railway state, with no observable differences between the physical model and its visualized status. This improved latency has met our requirements for real-time operation, which is essential for railway signalling systems.
## 5 Simulated Jamming Results Analysis
### Low jamming Scenario  
Firstly, the performance of the GNSS receiver was assessed under low jamming conditions for both the first and second trajectory. It is worth mentioning that the positioning solution is extracted directly from the NovAtel PwrPak7 GNSS receiver without any further processing.
## 3 Problem Configuration
### Operational Profile  
Table 3 shows the definition of the operational profile. As in the reference AUVs, the payload and operational profile of the three models is the same. Because the information published by the manufacturer of the reference vehicles corresponds to a steady power demand, the same principle has been applied to this work. This results in the absence of batteries hybridized with the fuel cells. In any case, a small auxiliary battery is included in each pressure hull. These auxiliary batteries feed critical equipment, such as the Iridium connection during emergencies, enable a stand-by condition thereby feeding sensors and communication equipment if the power plant is off for a short time, and are used to start-up and shut-down the power plant.
## 2 Approach
### Spectral Difference  
Reference sensor noise, \(SNR^{*}\), was defined for an Earth surface reflectance of 5%, i.e., \(\pi R_{rs}\) = 0.05, regardless of wavelength. Equation (8) was then used to compute \(L_{typ}\). Note that while \(SNR^{*}\) was adjusted throughout the analyses for various reference conditions, \(L_{typ}\) remained constant.  
The spectral difference criterion, \(\varepsilon\), was defined as the difference in the top of the atmosphere radiance representing a reference condition and a test condition relative to sensor noise. Since \(SNR\) changes with scene radiance, the joint radiance variability \(\sigma\) when comparing the reference radiance, \(L_{sat}^{\prime}\), and a test radiance, \(L_{sat}\), was computed as  
\[\sigma=\sqrt{\left[\frac{L_{sat}^{\prime}}{SNR_{L}^{\prime}}\right]^{2}+ \left[\frac{L_{sat}}{SNR_{L}}\right]^{2}}. \tag{9}\]  
The between-spectra difference averaged across the spectral range of interest was computed as  
\[\varepsilon=\frac{1}{N}\sum_{i=1}^{N}\left|L_{sat}^{\prime}-L_{sat}\right|_{i }/\sigma_{i}, \tag{10}\]  
where \(N\) is the number of discrete spectral bands. If \(\varepsilon\) < 1, i.e., the average radiance difference was smaller than \(\sigma\), then the two spectra were regarded as indistinguishable from system noise. Equation (10) is consistent with the z-statistic for comparing the similarity of two normally distributed populations [41].  
Equation (10) does not offer an absolute measure of spectral discrimination. For example, wavelengths in the red and near-IR portions of the spectrum are absorbed at higher rates than those in the mid- and short-range portions of the visible spectrum, due to absorption by pure water. [PERSON] et al. [13] noted this effect and suggested that remote sensing approaches to coral reef environments are best achieved at wavelengths shorter than 580 nm. Likewise, coral reefs ecosystems are often sources of colored dissolved organic matter [42] that increases water absorption in the blue portion of the spectrum. As \(\varepsilon\) approaches the threshold value, \(\left|L_{sat}-L_{sat}^{\prime}\right|/\sigma\) will generally be greater than the threshold in some portions of the spectrum, likely in the mid-visible range where light transmission is a maximum, and less than the threshold in the blue and red regions of the spectrum.
# Improvement of Mesh Atomizer Performance by Electrolytic Polishing
## 3 Experimental Study on Electrolytic Polishing of Metal Sheet  
### Electrolyte Composition  
Traditional electrolytic polishing uses a strong acid composite solution, so there are a series of safety problems, such as in transportation, storage, preparation, operation, waste liquid pollution, corrosion, and hydrogen infiltration [28]. There are advantages in this experiment. Firstly, the amount of phosphoric acid and sulfuric acid were comparatively reduced. Secondly, chromium anhydride components were cleared out. Lastly, polyethylene glycol was added as a thickening agent to replace the traditional process containing chromium anhydride. Polyethylene glycol cannot only form a thick protective film on the surface of stainless steel, but also plays a significant role in regulating the viscosity of the solution. This is a more environment-friendly acid composite solution, thus avoiding the main problem of strong acid polishing, and provides convenience for electrolyte circulation and reuse [29; 30].  
The phosphoric acid-sulfuric acid system is the most used electrolyte at present. Phosphoric acid is the main component of an electrochemical polishing liquid; its chemical corrosion is weaker than that of sulfuric acid, but stronger than that of acetic acid, and it is easy to form a passivation film with metal. Moreover, the high viscosity of phosphoric acid can slow down the leveling rate and play a certain role in improving the metal surface quality. When the volume fraction is comparatively high, the activation effect is large, while the passivation effect is small, easily leading to stainless steel surface corrosion. In this experiment, only the burrs with a height of a micrometer were removed, so the volume fraction of phosphoric acid and sulfuric acid was low, and the electrolyte did not contain chromic acid, chloride, fluoride, and other harmful substances, which met the ROHS standard. Cationic surfactants can reduce the surface tension of liquid, with good washing, solubilization, leveling, corrosion resistance, and other characteristics. The addition of a small amount of glycerin can improve the viscosity of the polishing liquid, improve the brightness of the surface polishing of metal sheet, and reduce sensitivity to metal impurities [31; 32]. The electrolyte components used in this experiment are shown in Table 1.
## 2 Ground Profile Characterization Based on a Probabilistic Approach  
Geotechnical site investigation on granular deposit is mainly based on in situ tests, such as Standard Penetration Test (SPT) and Cone Penetration Test (CPT). These in-situ tests have the advantage of testing the material before it is disturbed by sampling.  
In many applications, a large soil investigation with many in-situ tests is available. In these cases, it is usual to find a large dispersion of the values, which is not a surprise if the ground profile is heterogeneous. However, even with a very homogeneous soil profile, the dispersion is usually larger than the one initially expected.  
Part of this dispersion is due to the measurement equipment. Including within this concept everything related to the use of different machines, different personnel, variations in the calibration of the equipment over time, etc. It is named herein as measuring method uncertainty.  
Another part can be explained by the random behavior of the material, which exits even if all the tests have been conducted on the same homogeneous material and at the same stress level. Causes for this spatial variability can be related to chemical reactions (dissolution and precipitation of minerals), physical conditions (wetting and drying processes), previously undergone stress states, etc. In this case, the test outputs can be understood as a sample of a statistical variable. If the sample is large enough, the statistical variable can be characterized by low uncertainty. It is named herein as statistical uncertainty.  
These two sources of uncertainty are addressed in this section so that they can be statistically quantified.
# PK-Judge: Enhancing IP Protection of Neural Network Models Using an Asymmetric Approach
## 4 Background
### Error Correction Codes (ECC)  
Error Correction Codes (ECC) [52] ensure data integrity under noise or transmission errors through two core operations:  
* Encoding: Transforms message \(\mathbf{m}\) (\(k\)-bit) to codeword \(\omega\) (\(N\)-bit, \(N>k\)) via \(\omega\leftarrow\text{Encode}(\mathbf{m})\), adding redundancy \(r=N-k\) for error resilience.
* Decoding: Recovers original message \(\mathbf{m}^{\prime}\) from corrupted \(\omega^{\prime}\) using \(\mathbf{m}^{\prime}\leftarrow\text{Decode}(\omega^{\prime})\).  
Common ECC types include Hamming codes for single-bit correction [53], Bose-Chaudhuri-Hocquenghem (BCH) codes for multiple random bit errors [54], Turbo codes for high error rates [55], Low-Density Parity-Check (LDPC) codes for large blocks [56], and Reed-Solomon (RS) codes for correcting multiple symbol errors, including bursts [52]. For our study, we selected Reed-Solomon coding due to its robustness against burst errors, efficiency in handling structured errors, and suitability for applications requiring high reliability, such as watermarking deep neural networks. The choice of RS parameters are detailed as detailed in Section 6.  
Figure 1: Digital signatures and public key infrastructure.
# Questionnaires or Inner Feelings: Who Measures the Engagement Better?
## 3 Results
### Analysis of the Questionnaires' Results  
Farmer4.0 simulator has achieved an overall level of users' engagement of 68.53%. Analyzing each contribution, the sub-scale with the highest level of involvement is perceived usability (PU), which reflects the perceived ease of navigating through the simulator by users. Conversely, the sub-scale with the lowest percentage value, only 57.55%, is focused attention (FA), suggesting that the application is slightly dispersive and not very involving.  
From the analysis of the correlation between the different factors of users' engagement, a \"very weak\" correlation between aesthetic appeal (AE)-PU, novelty (NO)-PU, and reward factor (RE)-PU, a \"weak\" correlation between endurability (EN)-PU, a \"moderate\" correlation between EN-FA, a \"strong\" correlation between AE-EN, AE-FA, AE-felt involvement (FI), AE-NO, AE-RE, EN-FI, EN-NO, FA-NO, and FA-RE, and a \"very strong\" correlation between FI-FA and FI-NO emerge. Instead, there is a very weak inverse correlation between PU-FA and PU-FI, and in general a low correlation between PU and all the other sub-scales. An explanation for this result could be that 7 of the 8 items in the perceived usability section have a negative meaning, and it may have been a nuisance to questionnaire participants. All positive and negative correlation values are shown in Table 3. The RE groups the EN, NO, and FI components, and for this reason, the correlation is calculated only among RE and the remaining FA, PU, and AE.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**User Engagement** & **Items** & **Cronbach’s \(\alpha\)** & **Mean** & **Mean \% 1** & **Median** & **Standard Deviation** \\ \hline Focused Attention & 5 & 0.90 & 4.03 & 57.55 & 4 & 0.58 \\ Perceived Usability & 8 & 0.80 & 5.54 & 79.21 & 62.5 & 0.51 \\ Aesthetics & 5 & 0.93 & 4.71 & 67.24 & 5 & 0.41 \\ Endurability & 5 & 0.78 & 4.29 & 61.22 & 4 & 0.68 \\ Novelty & 3 & 0.94 & 4.98 & 71.09 & 5 & 0.53 \\ Felt Involvement & 3 & 0.90 & 4.90 & 70.07 & 5 & 0.43 \\ Reward & 11 & 0.92 & 4.64 & 66.33 & 5 & 0.48 \\ Total & 29 & 0.93 & 4.80 & 68.53 & 5 & 0.39 \\ \hline \hline \end{tabular}  
* 1
* Corresponds to the percentage calculation of the mean (Mean/7*100).  
\end{table}
Table 2: Sub-scales of User Engagement (UE), number of items corresponding, and results of the analyses performed: Cronbach’s alpha, mean, mean %, median, and standard deviation.