esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 4 Optimization of the Skeleton Cross-Section
### Numerical Example
To justify this conclusion, we start with the transfer function of the self-integration coil in Figure 2  
\[G(s)=\frac{U_{0}(s)}{I_{1}(s)}=\frac{M_{\text{s}}}{L_{\text{c}}C_{\text{c}}s^{ 2}+\left(\frac{L_{\text{c}}}{R_{\text{s}}}+R_{\text{c}}C_{\text{c}}\right)s+ \left(\frac{R_{\text{c}}}{R_{\text{s}}}+1\right)}. \tag{45}\]  
The frequency response \(|G(\text{j}\omega)|\) around the lower cut-off frequency \(f_{l1}\approx 15\) MHz and the uppercut-off frequency \(f_{h1}\approx 0.14\) THz of the presented example are plotted in Figure 15a and Figure 15b, respectively. It is seen that in both low-frequency and high-frequency ranges, the proposed cross-section yields the highest transmission gain, while the rectangular cross-section exhibits the worst performance. To further evaluate the dynamic performance of the coils, Figure 16 depicts the step response of the self-integration coil. As expected, the proposed cross-section corresponds to the quickest response, indicating the largest overall bandwidth of this design. For comparison, the oval and circular cross  
Figure 14: Upper part (\(z>0\)) of the optimal skeleton cross-section of Rogowski coils. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Shape of the** & **Perimeter** & \(R_{\text{c}}\) & \(L_{\text{c}}\) & \(C_{\text{c}}\) & \(f_{h1}\) & \(f_{h2}\) \\
**Cross-Section** & **(cm)** & **(\(\Omega\))** & **(\(\mu\)H)** & **(pF)** & **\(f_{h1}\)** & **\(f_{h2}\)** \\ \hline Rectangular & 4.22 & 0.0760 & 0.1044 & 1.230 & 1 & 1 \\ Circular & 3.93 & 0.0711 & 0.1044 & 1.151 & 1.069 & 1.034 \\ Oval & 3.85 & 0.0697 & 0.1044 & 1.128 & 1.090 & 1.044 \\ Proposed & 3.71 & 0.0671 & 0.1044 & 1.086 & 1.133 & 1.064 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different shapes of skeleton cross-section. sections exhibit very close comparisons, while the rectangular cross-section leads to the longest rising time and thus the most unfavorable performance. Last but not least, there are some extra benefits due to the reduction of the perimeter of the cross-section, including lighter weight and more compact structures.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 4 Challenges Faced by the Acoustic Signal in Underwater Communication Technology
### Challenges in MAC Protocol  
In Underwater acoustic sensor networks, medium access control is still an unsolvable problem. The challenges faced by the MAC protocols are listed below.  
1. Topology design: The underwater network's infrastructure always changes due to node mobility, node failure, adding new nodes, etc. The performance of MAC protocol depends on the topology design. So, topology design is a critical task in the underwater environment.
2. Sensor node deployment: In underwater networks, the sensor nodes are sparsely deployed in different places. Long-range communication depends on the availability of connections between the nodes. So, in the MAC protocol, the design of the node deployment is a critical issue.
3. Time synchronization: In the MAC protocol, the power cycling method works on time synchronization. In order to handle time certainty between nodes, time synchronization is necessary in MAC protocols.
4. Power wastage: The power wastage in sensor nodes is because of collisions while transmitting data. So, MAC protocols must be designed to avoid collisions between nodes.  
Other Issues: Also, the MAC protocol underwater gives rise to other problems, such as making connections with centralized networks, high delays for handshaking, collision avoidance problems, etc.
## 4 Performance Evaluation of Improved SOSK-Means
### Statistical Analysis
Table 12 shows the significant differences between ISOSK-means and the other competing algorithms under the DB and CS cluster validity indices. Out of 12 datasets, ISOSK-means recorded about 92%, 100%, and 83% significant differences in its performance from K-means, SOS, and SOSK-means under the DB index, and with a significant difference of 100%, 83% and 58%, respectively, under the CS index. This implied that the modification made to the existing SOSK-means algorithm contributed to the performance enhancement of the existing hybrid algorithm in solving cluster analysis problems.
### Methodology  
The methodology employed in this study encompasses four main areas related to cybersecurity in smart grids. First, it involves conducting a comprehensive analysis of smart-grid components and applications, examining the various types of cyberattacks that target these systems, and assessing their short-term and long-term impacts on the technological, economic, safety, and social aspects of smart grids. Real-world case studies are investigated to provide practical insights into the effects of cyberattacks on operational smart grids (Section 3.1). Second, the study uses the analytical hierarchy process (AHP) as a multi-criteria decision making (MCDM) technique to determine the optimal cybersecurity option. A set of cybersecurity measures, such as access control, authentication, encryption, IDPS, firewalls, SIEM, vulnerability assessment, and others, are evaluated based on multiple criteria, including security effectiveness, scalability, integration, and compatibility; performance impact; cost-effectiveness; manageability and usability; compliance and regulatory requirements; resilience and redundancy; vendor support and collaboration; future readiness; network segmentation; patch management; threat intelligence; and vendor and supply chain security (Section 3.2). Third, the research explores the difference between artificial intelligence (AI) and machine learning (ML), addressing their potential and challenges. AHP-MCDM is applied to find the optimal cybersecurity option that incorporates AI techniques and methods, such as machine learning, deep learning, natural language processing (NLP), genetic algorithms (GA), fuzzy logic, expert systems, swarm intelligence, Bayesian networks, and hybrid approaches. The criteria considered in this evaluation include security effectiveness, scalability, integration, and compatibility; performance impact; cost-effectiveness; manageability and usability; compliance and regulatory requirements; resilience and redundancy; vendor support and collaboration; future readiness; network segmentation; explainability; and transparency (Section 3.3). Finally, the study discusses the advantages (Section 4.1) and limitations of AHP-MCDM (Section 4.2), suggests improvements to its application in smart-grid cybersecurity (Section 4.3), and explores other MCDM techniques (Section 4.4) that can be implemented to assess cybersecurity options in smart grids.
## 2 Materials and Methods
### Feature Extraction
#### 2.4.4 Fiducial Features  
The ECG abnormalities can be detected using the features of the ECG signal, and it comprises the P-wave, time interval of the QRS complex (QRS) and T-wave, which includes the corresponding onset, offset and peak points and are represented as fiducial points, as shown in Figure 2[28]. The features of an ECG signal are nothing but the segments and intervals between the fiducial points [29]. In this experiment, the fiducial points are detected based on the the Pan-Tompkins QRS detector and the corresponding intervals, such as RR-I (ms), heart rate (bpm), QRS complex (ms), time interval measured from the beginning of the P wave to the peak of the R wave (PRQ) in ms, amplitude of the R wave (R-H) in mV, amplitude of the P wave (P-H) in mV, time interval measured after the S wave to the beginning of the T wave (ST) in ms, time interval measured from the beginning of the Q wave to the end of the T wave (QT) in ms and corrected QT interval (QTc) in ms from the ECG signals extracted using BIOPAC Acqknowledge 5.0 software (BIOPAC systems Inc., Goleta, CA, USA).
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 4 Programming and Testing
### Software Testing  
This section shows the results of the software implementation using AES-128 [17]. This test uses the standard S-box, Inverse S-box, Mixcolumns and InvMixColumns from NIST AES, and the explicit text is \"Learn to walk before you run\". The following chart includes the complete contents of the S-box and Mixcolumns, as well as the initial key and the result of each round key expansion.  
Table 7 [7] is the 10-round expanded key generated by the secret key, and Table 8 [4] is the S-box of the AES software test.  
\begin{table}
\begin{tabular}{c c c} \hline
**Items of Hardware Objects** & **AES-384 Bits** & **AES-512 Bits** \\ \hline Number of Slices & 4879 & 6371 \\ \hline Number of Slices FF & 2791 & 3791 \\ \hline Number of four-input LUTs & 8997 & 11,211 \\ \hline Number of bonded IOBs & 471 & 594 \\ \hline \end{tabular}
\end{table}
Table 6: The number of hardware objects used to implement AES-384/512.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline  & **00** & **01** & **02** & **03** & **04** & **05** & **06** & **07** & **08** & **09** & **0a** & **0b** & **0c** & **0d** & **0e** & **0f** \\ \hline
00 & 63 & 7c & 77 & 7b & f2 & 6b & 6f & c5 & 30 & 01 & 67 & 2b & fe & d7 & ab & 76 \\ \hline
10 & ca & 82 & c9 & 7d & fa & 59 & 47 & f0 & ad & d4 & a2 & Af & 9c & a4 & 72 & c0 \\ \hline
20 & b7 & fd & 93 & 26 & 36 & 3f & f7 & cc & 34 & a5 & e5 & f1 & 71 & d8 & 31 & 15 \\ \hline
30 & 04 & c7 & 23 & c3 & 18 & 96 & 05 & 9a & 07 & 12 & 80 & e2 & eb & 27 & b2 & 75 \\ \hline
40 & 09 & 83 & 2c & 1a & 1b & 6e & 5a & a0 & 52 & 3b & d6 & b3 & 29 & e3 & 2f & 84 \\ \hline
50 & 53 & d1 & 00 & ed & 20 & fc & b1 & 5b & 6a & cb & be & 39 & 4a & 4c & 58 & cf \\ \hline
60 & d0 & ef & aa & fb & 43 & 4d & 33 & 85 & 45 & f9 & 02 & 7f & 50 & 3c & 9f & a8 \\ \hline
70 & 51 & a3 & 40 & 8f & 92 & 9d & 38 & f5 & bc & b6 & da & 21 & 10 & ff & f3 & d2 \\ \hline
80 & cd & 0c & 13 & ec & 5f & 97 & 44 & 17 & c4 & a7 & 7e & 3d & 64 & 5d & 19 & 73 \\ \hline
90 & 60 & 81 & 4f & dc & 22 & 2a & 90 & 88 & 46 & ee & b8 & 14 & de & 5e & 0b & db \\ \hline a0 & e0 & 32 & 3a & 0a & 49 & 06 & 24 & 5c & c2 & d3 & ac & 62 & 91 & 95 & e4 & 79 \\ \hline b0 & e7 & c8 & 37 & 6d & 8d & d5 & 4e & a9 & 6c & 56 & f4 & ea & 65 & 7a & ae & 08 \\ \hline c0 & ba & 78 & 25 & 2e & 1c & a6 & b4 & c6 & e8 & dd & 74 & 1f & 4b & bd & 8b & 8a \\ \hline d0 & 70 & 3e & b5 & 66 & 48 & 03 & f6 & 0e & 61 & 35 & 57 & b9 & 86 & c1 & 1d & 9e \\ \hline e0 & e1 & f8 & 98 & 11 & 69 & d9 & 8e & 94 & 9b & 1e & 87 & e9 & ce & 55 & 28 & df \\ \hline f0 & 8c & a1 & 89 & 0d & bf & e6 & 42 & 68 & 41 & 99 & 2d & 0f & b0 & 54 & bb & 16 \\ \hline \end{tabular}
\end{table}
Table 7: S-box matrix.
## 5 Proposed Model
### Density-Based Clustering  
As the WSN nodes in the local gateway coverage are sparsely distributed and possess mobility within the coverage, the lifetime of the WSN nodes gets quickly drained out, thereby affecting the sensing rate in the SDWSN environment. To overcome those issues, the proposed research performs density-based clustering of WSN nodes. The nodes are clustered based on the density and mobility in the specific area within the coverage using the Divide Well To Merge Better (DWTMB) algorithm. The proposed algorithm was composed of three division stages and one merging stage.
# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## 1 Introduction
* This paper proposes information depositories for asynchronous communication in decentralized evacuation. * This paper verifies that integrating information depositories benefits decentralized evacuation. * This paper shows that already a small number of information depositories make a difference if these depositories are deployed at strategic locations such as staircases. * This paper compares the alternative approaches of fading memory and information depositories, showing that their mutual advantages can be combined. The research scope of this paper is to investigate a conceptual model of decentralized indoor evacuation with information depositories, while it leaves aside specific implementations of technologies. For example, a conceptual model may involve multiple modules such as simulation of the emergency event, localization and navigation, the sensing of the environment, risk assessment, and ad-hoc communication. This paper does not limit the implementation of each module to specific technologies. Any existing or emerging technologies can be plugged in. In this paper, sensing is assumed to happen on a mobile device in the background and is taken for granted. To explain and to investigate the conceptual model of evacuation with information depositories, this paper assumes that each evacuee carries a mobile device such as a smartphone, and evacuation happens in a departmental building after a fire broke out. The fire is spatially extended and varying over time. The rest of this paper is organized as follows. Section 3 develops the concept of immobile information depositories for roving, self-localizing and mapping agents. Section 4 implements the information depositories in an agent-based simulation of decentralized evacuation from an indoor environment. Section 5 presents the results of simulations, which are discussed in Section 6. Section 7 concludes this paper.
## 5 Results and Discussion
### Impact of Augmentation on Class-Specific Level: AP50  
As shown in Table 4, examining the class-specific AP50 results reveals that each augmentation technique produces variable improvements across the \"Non-helmet\", \"Non-mask\", \"Non-glove\", \"Non-vest\", and \"Non-shoes\" categories. Notably, \"Non-helmet\" detection lags behind other classes for all three architectures, underscoring the difficulty of accurately identifying helmets relative to other PPE types. Among the various augmentations, brightness emerges as the most effective, yielding higher average AP50 scores than even the sum of all techniques. Scale also contributes substantial gains, whereas translation and perspective offer comparatively modest benefits--suggesting that changes in object position or camera viewpoint are less critical for distinguishing PPE from the background in these datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Techniques** & **AP50** & **AP50:95** & **AP50** & **AP50** & **AP50** & **AP50** & **AP50:95** \\ \hline Original & 68.42 & 37.25 & 69.35 & 38.16 & 70.05 & 38.47 \\ Brightness & 71.23 & 39.13 & 72.21 & 40.43 & 73.12 & 40.82 \\ Contrast & 69.65 & 38.18 & 70.53 & 39.12 & 71.43 & 39.44 \\ Perspective & 68.45 & 37.42 & 69.44 & 38.28 & 70.11 & 38.58 \\ Rotation & 68.57 & 37.59 & 69.51 & 38.36 & 70.27 & 38.64 \\ Scale & 70.32 & 38.45 & 71.07 & 39.35 & 72.04 & 39.68 \\ Shearing & 69.87 & 38.23 & 70.68 & 39.02 & 71.63 & 39.27 \\ Translation & 68.38 & 37.24 & 69.43 & 38.27 & 70.09 & 38.55 \\ A sum of techniques & 70.98 & 39.55 & 71.98 & 40.72 & 72.55 & 40.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of average performance across different architectures under augmentation. From an architectural perspective, each backbone's design influences how it interacts with these augmentations. ViT's patch-based global attention can be more sensitive to local distortions (e.g., translation), while Swin Transformer's hierarchical windowed attention proves more resilient to shifts in object location and orientation. PVT's progressive shrinking pyramid stands out by balancing efficiency and accuracy, leading to stronger gains when brightness and scale are applied. Indeed, PVT achieves the most pronounced per-class boosts, especially under brightness (e.g., from 66.35 to 68.66 in \"Non-helmet\" detection and 71.33 to 77.14 in \"Non-shoes\"). ViT and Swin Transformer likewise benefit from brightness and scale, though to a slightly lesser extent. While combining multiple augmentations still elevates performance above the unaugmented baseline, brightness alone remains the single most impactful method for enhancing AP50 in non-PPE detection.
## 2 Materials and Methods
### Bloom Indices
#### 2.4.3 Cyanobacterial Bloom Indicator (CyaBI)  
As an additional step for the indicator, the CSA index was supplemented with in situ cyanobacterial biomass measurements in order to obtain the CyaBI index as described by Anttila et al. [17]. The part of the cyanobacterial biomass being an input for the CyaBI was calculated as the sum of three genera of the nitrogen-fixing and bloom-forming cyanobacteria: _Aphanizomenon_, _Nodularia_, and _Dolichospermum_[29]. The in situ biomass data were obtained from the Estonian Marine Institute monitoring of Estonian coastal areas, whereas the satellite-derived biomass was estimated from the Maximum Chlorophyll Index (MCI) values. The MCI is a line-height algorithm originally developed for MERIS [48], but due to spectral band continuity, it is also applicable to the OLCI [49]. Line-height algorithms are generally useful for estimating cyanobacterial blooms in optically complex waters because they are less hindered by issues of atmospheric corrections [50] as the MCI estimates chl-a from Level 1 satellite data. The MCI is not sensitive in waters where the chl-a is low  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Chl-a** & **Turbidity** \\ \hline
0—no algae & \textless{}5 & \textless{}25 \\
1—some algae & 5–11 & 2.5–4.5 \\
2—abundant algae & 11–27 & 4.5–7.5 \\
3—very abundant & \textgreater{}27 & \textgreater{}7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grouping conditions for estimating AB. Chl-a values are in mg m\({}^{-3}\) and turbidity in FNU.  
(\(<\)10 mg m\({}^{-3}\)) as reported by Binding et al. (2013) [51] but functions well in waters with high chl-a. The CyaBI consists of the normalized value of the two parameters, in situ cyanobacteria biomass and satellite-derived CSA. After normalization, an average of the two parameters is taken as the value for the CyaBI.
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 7 Experimental Results
### Case Study: Impact on Anomaly Detection  
To demonstrate the effectiveness of IoTBystander in improving security monitoring, we conduct a case study using our testbed, outlined in Table 12. We simulate five different anomaly scenarios, such as device component malfunction, cyberattacks, and the lack of monitoring on non-smart devices. The results show that, without IoTBystander, most situations where event integrity is compromised remain unresolved. In contrast, IoTBystander effectively handles all scenarios by leveraging dual-channel fusion, cross-verifying event integrity across both audio and network traffic.  
For example, in the case of the smart faucet (scenarios four and five), all methods detect that the faucet is left on for an extended period. However, IoTBystander provides a more context-aware decision by factoring in the presence of non-smart devices. If the homeowner is cooking nearby, the \"faucet-on\" event is deemed normal, whereas, if the homeowner is in another room working, it may indicate a potential water leak risk. This highlights how IoTBystander enhances the monitoring by incorporating both smart and non-smart devices, offering a more comprehensive view.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Accuracy** & **FPR** \\ \hline ESC-16 & 93.42\% & 5.18\% \\ PingPong & 97.71\% & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance of IoTBystander on public datasets.  
Although anomaly detection is used here as a representative task, IoTBystander is designed to serve as a versatile foundation for a variety of security applications, including attack detection, failure diagnosis, configuration debugging, and data provenance. By integrating insights from both audio and network traffic, IoTBystander improves the reliability of security monitoring, ensuring that event data are consistently verified and contextually informed for a broad range of security tasks.
## 4 Simulation and Results
It contains data with five different channel characteristics, and we used a channel (named as NCS1) measured in Norway. This is the channel in which broad Doppler spreading occurs and repeats for 60 cycles. We set the Doppler shift higher to make more visible the performance difference of the proposed method. Other detailed information is provided in the reference [31]. Figure 12 shows the bit error rate. The horizontal axis of the figure refers to the number of cycles. The bit error rate of the proposed method is approximately 0.07, which shows better performance than the conventional method.
###### Abstract
Capacitor calculations were made and the electric field distributions formed on the insulating materials were examined through finite element analysis. The completed prototype capacitors were tested in the high-voltage laboratory. Capacitor performance was analyzed with the help of partial discharge inception voltages (PDIV) using the OMICRON MPD 600 (Vorarlberg, Austria) device and dissipation factor (tan\(\delta\)) measurements using the OMICRON CPC100/CPTD1 (Vorarlberg, Austria) device. While performing tan\(\delta\) measurements, 2 and 4 kV voltages within the 50-400 Hz frequency range were selected. The effects of different insulating materials on capacitor operating conditions were revealed. The performances of the PLA, ABS, ASA, and PETG materials were analyzed comparatively. In light of the results, the superiorities of the tested materials were highlighted.
# Lempel-Ziv Parsing for Sequences of Blocks
## 4 Upper Bounds on LZ77 Parsings for Block Contractions
### Formalized Recursive Pirrase-Splitting Procedure  
We formalize the described recursive procedure as a function \(\mathsf{parse}(k,k^{\prime},h,p)\), which is called with parameters \(k\), \(k^{\prime}\), \(h\), \(p\) such that the substring \(t_{h,k}d_{h,k}t_{h,k+1}d_{h,k+1}\ldots d_{h,k^{\prime}-1}t_{h,k^{\prime}}\) from the \(h\)-shifted block parsing of \(s\) occurs before the position \(p\) and its copy occurs at position \(p\), i.e., \(h+|t_{h,1}d_{h,1}t_{h,2}d_{h,2}\ldots d_{h,k^{\prime}-1}t_{h,k^{\prime}}|<p\) and \(s[p..p^{\prime}]=t_{h,k}d_{h,k}d_{h,k+1}d_{h,k+1}\ldots d_{h,k^{\prime}-1}t_{h,k^{\prime}}\), for an appropriate \(p^{\prime}>p\). In order to process \(t_{i}\), we call \(\mathsf{parse}(k,k^{\prime},h,|t_{1}d_{1}t_{2}d_{2}\ldots t_{i-1}d_{i-1}|+1)\), where \(k,k^{\prime}\), and \(h\) are determined as explained above. The function emits phrases of the resulting parsing of \(s_{b}\) from left to right and works as follows:  
1. If the substring \(t_{h,k}\) starting at position \(p\) does not have a block-aligned earlier occurrence and \(|t_{h,k}|>b\), then, as was described above, we find numbers \(k_{0}\), \(k_{0}^{\prime}\), \(h_{0}\) such that \(t_{h,k}=t_{h_{0},k_{0}}d_{h_{0},k_{0}}t_{h_{0},k_{0}+1}d_{h_{0},k_{0}+1}\ldots d _{h_{0},k_{0}^{\prime}-1}t_{h_{0},k_{0}^{\prime}}\), and we first recursively call \(\mathsf{parse}(k_{0},k_{0}^{\prime},h_{0},p)\), thus processing \(t_{h,k}\), then we emit a one-letter phrase by contracting \(d_{h,k}\), and finally, call \(\mathsf{parse}(k+1,k^{\prime},h,p+|t_{h,k}d_{h,k}|)\), ending the procedure afterwards;
2. Otherwise, we compute the maximal number \(m\in[k..k^{\prime}]\) such that the substring \(q_{1}=t_{h,k}d_{h,k}t_{h,k+1}d_{h,k+1}\ldots d_{h,m-1}t_{h,m}\) starting at position \(p\) either has length of, at most, \(b\) or has a block-aligned earlier occurrence;
3. We emit the \(b\)-block contraction of \(q_{1}\) unless \(q_{1}\) is empty (which happens if \(m=k\) and \(t_{h,k}\) is empty);
4. If \(m=k^{\prime}\), we exit; otherwise, we emit a one-letter phrase by contracting \(d_{h,m}\) and call recursively \(\mathsf{parse}(m+1,k^{\prime},h,p+|q_{1}|)\). In the sequel, we refer to the steps of the splitting procedure as \"item 1, 2, 3, 4\". It is easy to see that the generated parsing of \(s_{b}\) after processing \(t_{1},d_{1},t_{2},d_{2},\ldots,d_{z^{\prime}},t_{z^{\prime}}\) is a correct LZ77 parsing. It remains to estimate its size.