esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental ECT System  
The custom-built ECT probe assembly is mounted onto the rotary table of a motorized 4-axis stage via telescoping brass rods for automated translation and rotation. This motorized stage is controlled by two Velmex VXM-2 controllers that receive commands from a laboratory PC running custom MATLAB scripts. A coaxial cable connects the transmitter coil to a BK Precision 4064 dual channel arbitrary waveform generator to act as an AC voltage source operating at \(f=15\) MHz. Likewise, the receiver coil is connected to an AD8333 I/Q demodulator chip, which acts as a low-cost lock-in amplifier. In this case, the AD8333 chip is implemented using the AD833-EVALZ evaluation board, which contains all the necessary amplification, biasing, and connections for immediate use in the system. The reference signal for the AD8333, operating at \(4\times f=60\) MHz, is supplied by the second channel of the BK Precision waveform generator. Section 2.4 describes the mathematics behind the IQ demodulation and its relation to our study in more detail. Finally, the in-phase and quadrature output channels of the demodulator are sampled by the A/D inputs of an NI 6009 USB DAQ unit for processing by the computer in a MATLAB environment. Figure 3 shows the block diagram of the entire hardware system.  
Figure 1: Manufacturing of in-plane waviness in CFRP sample (**a**) forming wrinkle in transverse direction and (**b**) wrinkle rolled flat to form in-plane wave.  
Figure 2: Construction of custom ECT probe (**a**) without brass shielding/support and (**b**) with brass shielding/support.
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 4 Conclusions  
In the image recognition system of an autonomous vehicle, for safety, it is crucial for the system to judge the failure cases autonomously, which is a standard for Level 3 autonomous driving. This paper proposes failure detection network for road images segmentation using mIoU. Our mIoUNet uses CNN and FCL, which are commonly used in the existing classification network structure.  
The results on the Cityscapes dataset reveal 93.21% mIoU prediction accuracy and 84.8% failure detection accuracy. As a challenging task, HMG's SVM camera acquisition dataset, which is taken from different viewpoints, demonstrated 90.51% mIoU prediction accuracy and 83.33% failure detection accuracy.  
As a result of experimenting with many different FCL structure versions, an efficient 256 \(\times\) 12 \(\times\) 1 FCL structure with high accuracy and fast inference speed is implemented and assessed with a model trained using a modified loss function. Performance of the network improved due to an increase in the number of input channels. This phenomenon means additional information is provided and detecting failure situations can be improved.  
Finally, as a result of analyzing pictures with terrible error values, we observed that the proposed model successfully detects failure cases. We note the possibility that the performance of the proposed network can be improved according to the performance of semantic segmentation that creates the GT mIoU. This result suggests that a more accurate and robust semantic segmentation model results in better performance of the proposed model.  
Figure 15: Examples of false-positive images on the Hyundai Motor Group (HMG) dataset.  
Although it is meant to detect failure cases in road images in autonomous driving, the proposed method only evaluated the reliability of single images. We aim to study a failure detection network that can predict the reliability of each pixel in an image as future work. Lightening the network structure to ensure real-time performance can also be considered as a future work.  
Conceptualization, J.S. and W.A.; methodology, S.P. and M.L.; software, J.S. and W.A.; validation, J.S. and W.A.; formal analysis, J.S.; investigation, J.S. and W.A.; resources, J.S.; data curation, J.S.; writing--original draft preparation, J.S.; writing--review and editing, J.S. and W.A.; visualization, J.S.; supervision, M.L.; project administration, M.L.; and funding acquisition, J.S. and M.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Hyundai Motor Group (HMG) funded by the Hyundai NGV and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) (Grants No. NRF-2016R1D1A1B01016071).  
The Cityscapes data presented in this study are openly available in Cityscapes paper [37]. Hyundai Motor Groups data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Nonlinear Dynamics Analysis of Disc Brake Frictional Vibration
## 3 Dynamic Analysis of Disc Brakes
### Effect of Brake Pad Support Stiffness on the Non-Linear Characteristics of the System  
The stiffness of the structure is particularly important when the car is in motion, as it determines the safety, reliability, and comfort of the vehicle, among other indicators. This section uses the controlled variable method to analyze the effect of the support stiffness of the brake pad on its system vibration. The braking pressure is selected as 4000 N, the initial braking speed is 40 km/h, and the system stiffness and damping coefficients are shown in Table 2, to study the effect of different brake pad stiffness on system stability.  
Figure 19: Phase plane diagram (v = 10 km/h).  
Figure 20: Phase plane diagram (v = 60 km/h).  
Figure 18: Frequency spectrum diagram (v = 60 km/h).  
Figure 21 shows the bifurcation of the system vibration motion as the brake pad support stiffness \(k_{1}\) and \(k_{3}\) change from \(1\times 10^{7}\) N/m to \(6\times 10^{7}\) N/m. As the brake pad support stiffness increases, the chaotic characteristics of the brake pad diminish, effectively suppressing the random vibration of the brake pad, but with little effect on the brake disc, so this section only discusses the effect of the brake pad support stiffness on the vibration characteristics of the brake pad.  
The time domain and frequency spectrum of brake pad at different support stiffness were plotted. As shown in Figure 22, as the support stiffness increases, the amplitude of brake pad vibration decreases from 0.004 m to 0.002 m, indicating that increasing the support stiffness of the brake pad can suppress the random vibration characteristics of the brake pad. As can be seen from Figure 23, the main frequency of the brake pad does not change significantly as the support stiffness increases, and the amplitude decreases with the increase in support stiffness from 0.005 m to 0.0002 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(k_{2x},k_{2y}\)/(N\(\cdot\)m\({}^{-1}\)) & \(2.5\times 10^{7}\) \\ \(k_{23},k_{12}\)/(N\(\cdot\)m\({}^{-1}\)) & \(3.9\times 10^{7}\) \\ \(c_{1},c_{3},c_{2x},c_{2y}\)/(N\(\cdot\)s\(\cdot\)m\({}^{-1}\)) & 295 \\ \(c_{23},c_{12}\)/(N\(\cdot\)m\({}^{-1}\)) & 495 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System dynamics characteristics parameters (change the brake pad support stiffness).  
Figure 21: Bifurcation diagram of system motion when brake pad stiffness changes.  
The phase plane of the brake pad as the pad support stiffness changes were plotted, as shown in Figure 24. As the brake pad support stiffness increases, the range of phase plane trajectory lines gradually decreases and the random vibration characteristics of the system diminish. Consequently, the brake pad support stiffness can be selected in a relatively high range.
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 5 Web3602--Interactive Application Applying SDM Ontology  
In this study, we developed an application called Web3602[53], which operates based on an SDM Ontology-compliant dataset and was evaluated through experiments. This development is intended to verify the structure of the ontology from the viewpoint of the content creator and to help the adoption of the SDM Ontology.  
Web3602 is a web application that allows users to experience 360\({}^{\circ}\) videos and spatial audio. Free viewing using pre-recorded 360\({}^{\circ}\) videos on a web browser makes it possible to control the playback of audio sources based on the viewer's interactive operations. Moreover, Web3602 can be run without installation by simply accessing this URL ([[https://sdm-wg.github.io/web360](https://sdm-wg.github.io/web360) square-vue/#/]([https://sdm-wg.github.io/web360](https://sdm-wg.github.io/web360) square-vue/#/) (accessed on 1 June 2023)) from a PC, smartphone, or tablet browser. The source code of Web3602 is available on GitHub pages ([[https://github.com/sdm-wg/web360](https://github.com/sdm-wg/web360) square-vue]([https://github.com/sdm-wg/web360](https://github.com/sdm-wg/web360) square-vue) (accessed on 1 June 2023)).
## 5 Advanced MPPT Techniques
### Hybrid MPPT Techniques  
Hybrid MPPT techniques combine multiple MPPT algorithms to use their individual strengths and mitigate their weaknesses. They integrate different MPPT strategies to create a more robust tracking system to enhance the performance and adaptability of PV systems, particularly in environments with complex or rapidly changing conditions [29; 66].
## 6 Results and Analysis  
### Path Length Distribution Analysis  
Our experimental results demonstrate remarkable agreement between theoretical predictions and empirical measurements across all tested trie sizes. Figure 2 shows the path length distributions for different tries, revealing a clear right-skewed pattern. This pattern indicates that while most paths cluster around the average length (approximately eight nodes at the current scale), a significant tail of longer paths exists. This asymmetry has direct implications for proof sizes, as longer paths require more nodes to be included in state verification proofs. The right-skewed nature also aligns with theoretical expectationsfor tree structures storing cryptographically hashed data, where the uniform distribution of inputs creates logarithmic path length characteristics.  
For quantitative comparison, Table 1 presents the theoretical and experimental probabilities for different path lengths.  
Figure 2: Path Length Distribution across different scales shows the emergence of logarithmic behavior. The x-axis represents path lengths (number of nodes traversed), while the y-axis shows frequency on a logarithmic scale. Each subplot demonstrates (**a**) initial random behavior at a small scale (100 addresses), (**b**) emergence of a pattern at a medium scale (1000 addresses), (**c**) stabilization at a large scale (10,000 addresses), (**d**) further stabilization at 100,000 addresses, with reduced fluctuations and a clearer logarithmic trend, (**e**) near-convergence at 1,000,000 addresses, with minimal deviations and a consistent logarithmic pattern, (**f**) consistent logarithmic distribution at network scale (300,000,000 addresses).  
At Ethereum's current scale (\(N\approx 3\cdot 10^{8}\)), the path length distribution exhibits remarkable stability. The empirical results validate our theoretical predictions for path length probabilities:  
\[P(PL=7) = 0.350730\pm 0.000001,\] \[P(PL=8) = 0.585884\pm 0.000001,\] \[P(PL=9) = 0.059301\pm 0.000001.\]  
The scaling behavior of average path length follows our theoretical prediction (3) (Figure 2).  
Table 2 demonstrates how scaling impacts specific blockchain scenarios by showing the relationship between network growth and path length increases. The remarkably small differences between theoretical and experimental averages (\(\leq\)0.01) validate our model's predictive power across multiple orders of magnitude. This has direct implications for proof size estimation and optimization strategies as the network grows.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Path Length** & **Theoretical Prob.** & **Experimental Prob.** & **Difference** \\ \hline \multicolumn{4}{c}{For 100 addresses} \\ \hline
1 & 0.002386 & 0.000000 & 0.002386 \\
2 & 0.690504 & 0.684000 & 0.006504 \\
3 & 0.284479 & 0.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 2 Security AODV Routing Protocol  
The SAODV protocol divides the routing message into variable part and invariant part for processing according to the characteristics of hop by hop changes in the AODV protocol. The hop number field in the routing message is a variable part, which is authenticated by a hash chain. Other fields in the routing message are invariant parts, which are authenticated by digital signature [30; 31; 32; 33].
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Electroactive Polymer (EAP)
#### 2.3.3 Ionic Polymer-Metal Composite (IPMC)  
The IPMC material [135] consists of a fluoropolymer ion exchange membrane, which is sandwiched between two conductive precious metal layers (such as platinum, gold, etc.). The mechanism of the IMPC material is illustrated in Figure 8a. The two main characteristics of IPMC are low activation voltage and large bending strain caused by the movement of cations in the polymer matrix. IPMC demonstrates strong performance in a variety of sensing and actuating applications [136]. IPMC actuators have various advantages, such as low driving voltage, relatively fast response, large displacement, the ability to be activated in water or wet conditions, and easy miniaturisation [137].  
[PERSON] et al. printed a crawling robot with IPMC [138]. By applying voltage (<5 V), an IPMC-based actuator stretches and contracts, driving the robot to move along the pipe like a caterpillar or an inchworm (see Figure 8b). IPMC-based actuators have potential applications in the field of underwater robots. The research in [139] presented a fish-shaped robot that can imitate fish swimming in an S shape, and control the speed by the voltage  
Figure 8: (**a**) The mechanism of IPMC [135]. (**b**) An IPMC-made soft robot mimics a caterpillar moving along a pipe [138].  
frequency. When the input voltage frequency is 4.0 Hz, the speed is the fastest to reach 7 mm/s. The research in [140] put forward a bionic jellyfish robot, and the curve shape of the IPMC-based actuator can be successfully applied to generate smooth curve motions through heat treatment. When the excitation frequency is 1 Hz and 2 Hz, the vertical floating displacement of the robot is about 0.15 mm and 0.3 mm, and the floating speed is 0.057 mm/s and 0.045 mm/s. [PERSON] et al. designed a small capsule underwater robot [141]. The pectoral fin and caudal fin are made of IPMC. Under the condition of a symmetrical square wave with a driving voltage of 3 V and a frequency of 1.5 Hz, various swing combinations of the fins can be realised to drive the movement of the robot. The robot can swim forward at a speed of 4.83 mm/s and turn around at a speed of 4.5-5\({}^{\circ}\)/s.
# Design of NO\({}_{2}\) Photoacoustic Detection System Based on Finite Element Simulation
## 4 Experimental Verification and Discussion
### Atmospheric No\({}_{2}\) Detection  
During the period from 12:00 noon on 13 November 2023 to 12:00 noon on 24 November 2023, the air near the observation point of Anhui University of Science and Technology in Huainan City was monitored for 12 consecutive days by using the designed photoacoustic sensor. The measured time resolution was 1s, and the gas flow rate was controlled at 0.2 L/min. The Allen variance under the average time of 240 s was 0.82 ppb, which is basically consistent with the Allen variance under the average time of 206 s. In order to facilitate subsequent data processing, the average time was set to 4 min, and the results obtained are shown in Figure 16. The measured results were further averaged into hourly concentration data and then compared with hourly monitoring data released by the Huainan environmental monitoring point, which is about 7 km away from the measurement point. The results are shown in Figure 17. The data of the photoacoustic sensor were linearly fitted with the data of the environmental monitoring site in Huainan City, and the results obtained are shown in Figure 18, with the linear fitting degree of both reaching 0.801. Due to the limitation of the sensitivity of the sensor, the error of the monitoring data of the environmental monitoring station and the distance of several kilometers between the measuring points, there are some differences between the hourly data of the photoacoustic sensor and the environmental monitoring station, but it can be seen from Figures 19 and 20 that the two have roughly the same trend of change. For example, on 23 November, the NO\({}_{2}\) concentration in the Huainan area surged. The maximum concentration reached more than 40 ppb and remained at high levels for a period of time, and the photoacoustic sensor successfully detected this change.
## 3 Introduction to Related Technologies of the Scheme
### IPFS Storage Technology  
IPFS is a protocol with the following functions:  
1. Content addressing: Contrasting with the address addressing in the HTTP protocol, IPFS employs multiple hashes to uniquely identify a data block's content. Consequently, hash-based addressing is employed during searches.
2. Tamper resistance: The unique hash facilitates determining whether the data block's content has been tampered with.
3. Deduplication: Relying on the premise that identical data share the same hash, duplicate data are identified to conserve storage space.
4. Content distribution: Content distribution is achieved through BitTorrent, a content distribution protocol. BitTorrent utilizes content distribution and peer-to-peer technology to enhance the efficiency of large file sharing among users, reducing the burden on centralized servers.
5. P2P transmission: Transmissions primarily depend on libp2p, which is responsible for IPFS data's network communication, routing, exchange, and other functions.  
Compared with the traditional HTTP protocol, IPFS exhibits the following advantages:  
1. IPFS eliminates reliance on backbone networks and centralized servers, connecting all devices in the network through a file system. This enables swift access to files stored on the system from any location worldwide.
2. IPFS features historical version tracking, facilitating permanent data storage and ensuring all operations are reversible, akin to the widely used gift.
3. IPFS can transform the distribution mechanism of web content, decentralizing it and employing content delivery network (CDN) acceleration technology.
4. Within the IPFS system, files and data are unique. When a file joins the IPFS network, a unique encrypted hash value is assigned to the content based on calculations.
5. IPFS possesses a file duplication detection mechanism, thereby preventing resource redundancy issues.
## 3 Comprehensive Analysis of Issues of CIM-Based DMS Using RDB  
This section analyzes and describes issues arising from the introduction of an RDB to a CIM-based DMS. Traditional DMS platforms have primarily utilized RDBs. The DMS database is primarily used to store static network models and historical data regarding the operation of a distribution system, as well as for managing alarms, ensuring real-time operation, consistency, and reliability of the data managed in the database. RDBs store data in a fixed tabular format, making information simple and intuitively represented, thus easily perceivable by humans. By utilizing an RDB, it is possible to efficiently and systematically manage structured data in the form of tables. Owing to the inherent characteristics of RDBs, they can ensure data integrity and maintain consistency by continuously synchronizing information. By implementing queries effectively, it is possible to increase search speed by utilizing the fixed structural characteristics of the database. In some cases, joining tables can be used to improve search speed, but as the number of joined tables increases, readability decreases, and query execution time significantly increases. The time complexity for finding'm' elements in 'n' tables using queries in a traditional RDB is \(O(m\log_{2}n)\). As the complexity of the join query increases, additional parameters contribute to the time complexity of the query process. Despite the numerous advantages of RDB as a database type, CIM-based DMS can encounter various issues. In an object-oriented CIM-based DMS, using an RDB can lead to object-relational impedance mismatch, difficulties in adapting to information model changes, and decreased performance in TP.
# Movie Recommendation through Multiple Bias Analysis +
## 2 Related Works  
### MF: Matrix Factorization  
MF [8; 10] is a sort of model-based CF. It became known through 'NetFlix Prize 09' and it has been considered the best model to date. Although SVD [9; 10] is used because MF can decompose only square matrices, the two terms MF and SVD are used interchangeably.  
When a rating matrix \(\mathbb{R}^{m\times n}\) has been given for \(m\) users and \(n\) items, the core of learning is decomposing the original matrix \(\mathbb{R}\) into several \(f\)-dimensional matrices (MF: \(p_{u},q_{i}\in\mathbb{R}^{f}\), SVD: \(U,\Sigma,V\in\mathbb{R}^{f}\)) and ensuring that the decomposed matrices are to the original matrix. Equation (1) is the rating prediction, Equation (2) is the objective function, and the model is learned through Equations (3)-(5). \(p_{u}\) is the users' latent factor (users' vector), \(q_{i}\) is the items' latent factor (items' vector), \(\lambda\) is regularization, and \(\gamma\) is the learning rate.  
\[P^{\text{MF}}(u,i)=q_{i}^{T}p_{u} \tag{1}\]  
\[\min_{p,q}\sum_{(u,i)\in\tau}\Big{(}r(u,i)-P^{\text{MF}}(u,i)\Big{)}^{2}+ \lambda(\|q_{i}\|^{2}+\|p_{u}\|^{2}) \tag{2}\]  
\[\epsilon=P^{\text{MF}}(u,i)-r(u,i) \tag{3}\]  
\[q_{i}=q_{i}+\gamma(\epsilon\cdot p_{u}-\lambda\cdot q_{i}) \tag{4}\]  
\[p_{u}=p_{u}+\gamma(\epsilon\cdot q_{i}-\lambda\cdot p_{u}) \tag{5}\]  
SVD++ [8; 10] refers to a model in which bias and temporal score [31] were added to MF [9]. Equation (6) is the prediction of rated scores, Equation (7) is the objective function, and the model is learned through Equations (8)-(13).  
\[P^{\text{SVD++}}(u,i)=\mu+b_{u}+b_{i}+q_{i}^{T}\Big{(}p_{u}+|I_{u}|^{-0.5} \sum_{j\in I_{u}}y_{i}\Big{)} \tag{6}\]  
\[\min_{p,q}\sum_{(u,i)\in\tau}\Big{(}r(u,i)-P^{\text{SVD++}}(u,i)\Big{)}^{2}+ \lambda(b_{u}^{2}+b_{i}^{2}+\|q_{i}\|^{2}+\|p_{u}\|^{2}+\|y_{j}\|^{2}) \tag{7}\]  
\[\epsilon=P^{\text{SVD++}}(u,i)-r(u,i) \tag{8}\]\[q_{i}=q_{i}+\gamma\{\epsilon\cdot(p_{u}+|I_{u}|^{-0.5}\sum_{j\in I_{u}}y_{j})- \lambda\cdot q_{i}\} \tag{9}\]  
\[p_{u}=p_{u}+\gamma(\epsilon\cdot q_{i}-\lambda\cdot p_{u}) \tag{10}\]  
\[b_{u}=b_{u}+\gamma(\epsilon-\lambda\cdot b_{u}) \tag{11}\]  
\[b_{i}=b_{i}+\gamma(\epsilon-\lambda\cdot b_{i}) \tag{12}\]  
\[\forall j\in R(u):y_{j}=y_{j}+\gamma(\epsilon\cdot|I_{u}|^{-0.5}\cdot q_{i}- \lambda\cdot y_{j}) \tag{13}\]  
MF and SVD++ are implemented and distributed in many Open APIs. In this study, the experiments on MF and SVD++ were conducted using MyMediaLite [34].
# Watermarking Protocols: A Short Guide for Beginners
## 2 Requirements
### Security Requirements
#### 2.2.4 Dispute Resolution Problem  
The dispute resolution protocol is a sub-protocol of watermarking protocols. It is run by the content provider whenever a pirated copy of a copyrighted content is found in the market. Its task is to identify the \"traitor\", that is, the buyer who distributed illegal replicas of the protected content. In this regard, it is worth noting that, in the general practice of law, it is the accuser who has to prove the guilt of the defendant, not the reverse. Therefore, the sub-protocol should provide the content provider with the evidence necessary to make appropriate adjudications without involving the suspected buyer, and this is because of two main reasons: the first is that a suspected buyer is very unlikely to cooperate since he/she is presumed innocent until proved guilty; the second is that a malicious content provider could easily harass an innocent buyer by repeatedly requiring cooperation [8].