esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset Construction  
We construct a sample dataset, keeping in mind the myriad characteristics that we want our cluster trajectories to project. Our aim is to provide values to features in a way where the temporal movement of clusters encompasses all possible variations across distinct matrices, including volatility, variability, and distance.  
Therefore, we curated a dataset that has the below characteristics:  
* A temporal dimension spanning the values 1 through 12.
* Three entities (\(X\), \(Y\), and \(Z\)) that are part of the ordinal column in our dataset.  
* Two continuous variables, \(f_{1}\) and \(f_{2}\), representing the metric of interest. We have 3 objects that are assigned values in the way described below:
* Country \(X\) exhibits a monotonically increasing trend with some variability for the feature \(f_{1}\) and a sudden increase in value for \(f_{2}\).
* Country \(Y\) demonstrates a monotonically decreasing trend for \(f_{1}\) and an oscillating trend for \(f_{2}\).
* Country \(Z\) displays a vacillating trend for \(f_{1}\) and a U-shaped change in direction for the feature \(f_{2}\).  
This diversity in patterns enables the subsequent analysis to demonstrate the methodology's capacity to capture different evolutionary trajectories. Our synthetic dataset is designed to exhibit diverse patterns across the following three formally defined metrics [36]:  
1. Volatility (\(\sigma_{v}\)): Measures categorical transition instability: \[\sigma_{v}=\sqrt{\frac{1}{(n-1)}\sum_{i=2}^{n}(|c_{i}-c_{i-1}|-\mu)^{2}}\] (1) where \(\mu\) is the mean absolute categorical change and \(c_{i}\) represents categorical value at time \(i\).
2. Variability (\(V\)): Quantifies range of categorical states experienced: \[V=\max(c_{i})-\min(c_{i})\quad\text{for }i\in\{1,2,\ldots,n\}\] (2)
3. Distance (\(d\)): Trajectory dissimilarity between entities using modified Hamming distance [16]: \[d(T_{i},T_{j})=\sum_{t=1}^{m}|c_{i,t}-c_{j,t}|\] (3)  
These metrics enable comprehensive evaluation of temporal pattern diversity across distinct evolutionary trajectories, supporting robust comparative analysis of multi-dimensional dataset dynamics [33].  
Table 1 presents the synthetic dataset constructed to demonstrate the methodology's capacity to capture diverse temporal evolution patterns. The dataset comprises three entities (\(X\), \(Y\), \(Z\)) observed over 12 time periods across two continuous variables (\(f_{1}\), \(f_{2}\)).  
Entity \(X\) exhibits a monotonic increase in \(f_{1}\) with a sudden escalation in \(f_{2}\), demonstrating systematic progression with structural breaks. Entity \(Y\) shows a monotonic decrease in \(f_{1}\) alongside oscillatory behavior in \(f_{2}\), reflecting declining trends with high-frequency volatility. Entity \(Z\) displays vacillating patterns in \(f_{1}\) and a U-shaped evolution in \(f_{2}\), capturing non-linear temporal dynamics.  
This controlled diversity enables a comprehensive evaluation of the clustering-based trajectory analysis framework across distinct evolutionary dynamics, ensuring robust testing of the methodology's discriminatory power.
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 2 Taxonomy of Blockchain-Based Multimedia Content Protection Systems
### Multimedia Content Protection Techniques
#### 2.6.2 Digital Rights Management (DRM)  
DRM systems have been developed to provide the secure delivery of digital content to an authorized receiver with restrictions (e.g., copying, printing or editing) on the usage of the content after delivery. A typical DRM system provides means for protecting content, creating and enforcing rights, identifying users, and monitoring of the content usage. A generic DRM architecture consists of three entities: content provider (responsible for generating the multimedia content, its metadata, and the corresponding content encryption keys, and encrypting the content), license provider (responsible for creating licenses and managing the content encryption keys), and a user (who has rights to access the content downloaded via a local software, called a DRM agent). DRM can be implemented as both software (Apple's FairPlay) and/or hardware (smart cards) solutions.  
A DRM system is designed to satisfy the following security requirements:  
* Unauthorized copying: It ensures that the digital asset is packaged in a secure manner so as to prevent unauthorized usage. This secure packaging is achieved by encryption.
* Secure distribution: It must securely distribute the digital asset to the authorized user.
* Conditional access: It must obtain the access conditions (licenses) specified by the owners of the protected content. The license consists of rights expression language, metadata or watermark, and a security mechanism to prevent users from circumventing DRM by modifying the access conditions.
* Tampering resistance: It must provide an effective tamper-resistant mechanism to process protected data and enforce content usage rights.  
The core technologies used by DRM to fight piracy include encryption, passwords, watermarking, digital signature and payment systems. Encryption and password technologies are used to control who has access to the content and how it is used. Watermarks and digital signatures are used to protect the authenticity and integrity of the content, the copyright holders, and the users. Digital watermarking complements DRM to ensure that the digital rights of the copyright holders are not violated. Unlike traditional DRM schemes that compress and encrypt single multimedia content into multiple copies with each copy targeted at a specific application, and provide single access-control, modern DRM systems have been proposed to support encryption of scalable code streams with multiple keys to allow multiple accesses. Since a watermark can be used to identify the original content owner, it discourages a user from misrepresenting the content as if it was his/her own as well as unauthorized distribution or sharing it illegally with unauthorized parties.
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 3 Performance Evaluation and Simulation Results
### Results
#### 3.2.2 End-to-End Delay  
Figure 3 shows the end-to-end delay results for all tested routing schemes. The graph shows that RACE-SM has the lowest latency among all routing protocols. This is because of how easy it is to move the sink. The source and the destination are the only two places where data transfer occurs. Using the sink-moving method by RACE-SM, sensor nodes send data directly to the sink node when the sink is in the communication range. This reduces the number of nodes between the source and the destination.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rounds** & **RACE-SM** & **EH-UWSN** & **MulSi-Co** & **EERBCR** & **MulSi** & **GCORP** & **EEDG** \\ \hline
0 & 20 & 20 & 20 & 20 & 20 & 20 & 20 \\
1000 & 50 & 120 & 155 & 155 & 110 & 142 & 135 \\
2000 & 155 & 420 & 472 & 470 & 250 & 510 & 521 \\
3000 & 333 & 470 & 718 & 820 & 420 & 855 & 870 \\
4000 & 510 & 1050 & 1130 & 1110 & 810 & 1310 & 1390 \\
5000 & 929 & 1220 & 1740 & 1790 & 1105 & 1910 & 2010 \\
6000 & 1399 & 1810 & 1920 & 2033 & 1510 & 2070 & 2100 \\
7000 & 1620 & 2020 & 2120 & 2170 & 1810 & 2230 & 2310 \\
8000 & 1833 & 2170 & 2219 & 2255 & 2020 & 2310 & 2410 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The energy consumption of all routing protocols.  
Figure 2: Energy consumption.  
The latency of the MuLSi and EEDG is less than that of the EERBCR, Mulsi-Co, GCORP, and EH-UWSN cooperative routing protocols. This is because one hop and a maximum of two hops are used instead of a multi-hop for data transmission between the source and destination. Latency is the same for EERBCR, Mulsi-Co, GCORP, and EH-UWSN because they all use multi-hops to send data between the source and the destination. Table 3 addresses the end-to-end delays of all evaluated routing schemes.
# An Automatic Transformer from Sequential to Parallel Java Code
## 1 Introduction
Secondly, data dependence analysis and control dependence analysis are carried out to check data dependencies among statements that could run in parallel. Two statements have a data flow dependence when the output set (i.e., the set of variables written) of the first statement contains data that are in the input set (i.e., the set of variables read) of the second statement [28; 29; 30]. Thirdly, a control flow graph (CFG) [31] is built, which represents all the paths that could be executed by a program. Such a CFG is then used to determine the two paths that might execute in parallel and to evaluate whether the statement numbers in each path are sufficiently large to possibly improve execution time. Finally, once a path that has passed all previous analysis steps has been found, the source code is refactored to run a new thread and insert synchronization points where needed. The code of the new version is generated automatically for developers to further modify or simply run it. Our primary original contributions are as follows. We present a comprehensive approach that (i) automatically transforms sequential Java source code into parallel code (in previous literature, the authors mainly tackled executable code); (ii) undertakes a detailed static analysis of the source code, comprising control flow, data dependence analysis, evaluation of Bernstein's conditions, and the assessment of the computational effort required; (iii) employs an a priori evaluation of the potential benefits of new threads by computing execution paths; and (iv) evaluates the benefits performed according to the context of the instructions that are potential candidates for parallel execution and the estimated computational effort required by such instructions. We performed several experiments to assess the benefits and correctness of our approach. We report on an experiment where the analysis steps were automatically executed on an application; the results of several tests executed on the refactored parallel version show that our transformation preserves correctness. The rest of the paper is organized as follows. Section 2 shows the state of the art and a comparison with our approach. Section 3 introduces the general approach and a high-level algorithm for analyzing code. Section 4 shows the static code analysis, revealing the context of method calls and data dependence. Section 5 describes the construction of the CFG for the analyzed method and the evaluation of instructions for parallel execution. Section 6 presents the analysis and transformation performed on a sample application and the execution results. Section 7 comments on the results and the limitations of the approach. Finally, our conclusions are drawn in Section 8.
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 3 The Proposed Method for Fault Diagnosis
### BPA Determination
#### 3.3.2 Support Degree Calculation of Propositions with Two Elements  
In this part, the support degree of the test model to compound proposition \(\{F_{i},F_{j}\}\) denoted as \(Sup(\{F_{i},F_{j}\})\) (\(i,j=1\), \(2\),..., \(M,i<j\)) is obtained.  
Considering the nature of the model curve, firstly let us calculate the integral interval:  
\[InMin=\min(\bar{X}_{i}+3\sigma_{i},\bar{X}_{i}-3\sigma_{i},\bar{X}_{j}+3\sigma _{j},\bar{X}_{j}-3\sigma_{j},\bar{X}_{T}+3\sigma_{T},\bar{X}_{T}-3\sigma_{T}), \tag{23}\]  
\[InMax=\max(\bar{X}_{i}+3\sigma_{i},\bar{X}_{i}-3\sigma_{i},\bar{X}_{j}+3\sigma _{j},\bar{X}_{j}-3\sigma_{j},\bar{X}_{T}+3\sigma_{T},\bar{X}_{T}-3\sigma_{T}). \tag{24}\]  
Then, the intersection area between the test model curve \(\mu_{T}(x)\) and the fault model curves \(\mu_{F_{i}}(x),\mu_{F_{j}}(x)\) is calculated:  
\[Area(\{F_{i},F_{j}\})=\int_{InMin}^{InMax}\min(\mu_{F_{i}}(x),\mu_{F_{j}}(x),\mu_{T}(x))dx. \tag{25}\]  
For example, \(Area(\{F_{i},F_{2}\})\) is as shown in Figure 2b if \(M=3\).  
Finally, the support degree \(Sup(\{F_{i},F_{j}\})\) of the test model to compound proposition \(\{F_{i},F_{j}\}\) is obtained:  
\[Sup(\{F_{i},F_{j}\})=\frac{Area(\{F_{i},F_{j}\})}{Area_{T}}, \tag{26}\]  
where the \(Area_{T}\) is the area under the test model curve \(\mu_{T}(x)\), \(Area_{T}=\sqrt{2\pi}\sigma_{T}\).
## 1 Introduction
These methods tend to increase the complexity of the gripping system and are less cost-effective due to additional high voltage electronics. In contrast, EA pads with exposed electrodes (as shown in Figure 1b) have been found to release objects faster than those with insulated electrodes [14]. However, only four types of materials were investigated in this study, and the effect of the EA base material was not investigated. During activation, the base material is also polarized, and if its dielectric relaxation property is slow, the residual charge on it can also affect the release of the object. To the author's knowledge, no work has been done on analyzing the effects of base materials on the de-electrodhesion speed in exposed electrode EA designs. In this study, the release performance of EA pads with exposed electrodes on eight lightweight materials and also the effect of the base material on the releasing performance were investigated. It is highly desirable to equip EA systems with quicker de-adhesion ability, as this could significantly improve the overall throughput of assembly lines where material pick-and-place tasks are involved and produce more efficient and faster locomotion speeds for crawling or climbing robots where EA is employed as their active adhesion and de-adhesion unit. We plan to draw insights from this comprehensive experimental study on speeding up the de-electrodhesion process of an EA gripper, to design future cost effective EA systems that can help speed up the pick-and-place procedure in assembly lines where lightweight and flexible products (e.g., flexible printed circuit boards) are involved and movement efficiency and speed are required. Micro robots, especially micro flapping-wing air vehicles (MFWAV) such as the Harvard RoboBee [8], have been demonstrated to be candidates for future environmental monitoring, structural health monitoring and security surveillance applications. This work could potentially guide future micro robot designs with integrated EA pads to allow a faster release response. The rest of this paper is organized as follows: in Section 2, the EA pad design and its fabrication process are introduced, and the experimental setup is explained; in Section 3, the experimental results are presented, key findings are discussed, and potential applications that can benefit from this study are listed; and finally, in Section 4, conclusions are drawn, and future work is discussed.
# Mobile Augmented Reality Serious Game for Improving Old Adults' Working Memory
## 3 Gamification
### Evaluation of MAR Serious Game Based on the Ocalysis Framework Core Drives  
In order to motivate the elderly and induce them to achieve their learning objectives, game mechanics and game design techniques were integrated. In this section, preliminary evaluations of the Ocalysis core drives were carried out by a primary verification questionnaire with five experts, and the results were obtained. The experts evaluated the system using a 5-point Likert scale. The experts repeated the game three to four times and provided scores and brief comments on the Ocalysis core drives. The average scores from the five experts were entered with an online tool, and graphs for individual core drives were generated as shown in Figure 3.  
* _Epic Meaning and Calling_--The MAR serious game was designed to improve cognitive abilities, focusing on working memory. In addition, this system can be used as a tool for measuring cognitive abilities.
* _Development and Accomplishment_--A 30-day protocol is provided so that changes can be observed by checking the list of games played by date and records on My Page.
* _Empowerment of Creativity and Feedback_--The user can interact with virtual objects in the AR environment, and the system provides visual effects. The system also provides a mini-map, navigation bar, and zoom animation when searching for virtual objects.
* _Ownership and Possession_--The users log in with their own accounts and provide personalized data. The users can read data scored based on the game results.
* _Unpredictability and Curiosity_--The difficulty for users gradually increases for 30 days. The system randomly provides the forms and shapes of objects with or without numbers.
* _Loss and Avoidance_--There is a timer at the top right of the screen so that game can be played in real time. The game can only be played once a day, and only the first performance record is kept. If it is assumed that the game is played every day, the time to complete the game and the user's cognitive abilities such as attention and working memory are closely related.  
Figure 3: Average of the score provided by experts to MAR serious game using the Ocalysis Tool.  
The average score of the Octalysis Tool in the MAR serious game was 240. Although the score seems to low, it is fairly balanced in both White Hat and Black Hat Core Drives. It also seems to have balance between Left Brain and Right Brain Core Drives, which refer to the Intrinsic and Extrinsic Motivation of the core drives, maintained in a proper balance.
## 2 Materials and Methods
### Anthropometric Measurements  
Manual anthropometric measurements included height, WC, and hip circumference (HC). Height (H) was measured with a portable stadiometer with the head in the horizontal Frankfurt plane and recorded with a precision of 0.1 cm (SECA 220, Hamburg, Germany). Anatomical WC was measured with a stretch-resistant tape that provides constant 100 g tension (SECA 201, Hamburg, Germany) while standing, in a horizontal plane, midway between the inferior margin of the ribs and the superior border of the iliac crest, to the nearest 0.5 cm [18,19]. HC was measured at the widest part of the hip bones of the buttock's region in centimeters to the nearest 0.5 cm. All measurements were repeated twice. To minimize inter-observer and inter-device variability, all measurements were performed under strictly standardized conditions (room temperature of 22 \(\mathrm{\SIUnitSymbolCelsius}\), air humidity of 45%) by well-trained researchers using the same device. If a difference of more than 1% existed between the first two measurements, a third measurement was taken, and the averages were calculated to exclude the value with the largest difference from the three measurements and calculate the mean using the two remaining measurements. The intra-tester technical errors of measurement (TEM) were 0.5%.  
Anthropometric indices were calculated based on anthropometric measures. WHR was calculated as WC (cm) divided by HC (cm), WHtR was calculated as WC (cm)/H (cm), and BMI was calculated as body weight (kg) divided by H (m\({}^{2}\)) [20, 21, 22, 23].  
According to the criteria of the International Diabetes Federation (IDF), the occurrence of central obesity in adults in the European population is diagnosed in the case of WC: \(\geq\)94 cm for males and \(\geq\)80 cm for females [20]. WHR was categorized as \(\geq\)0.90 for males and \(\geq\)0.85 for females, while WHtR was considered 0.5 for both sexes [20; 21]. Cut-off points for BMI are defined by the World Health Organization, with normal weight classified as a BMI between 18.5 and 24.99 kg/m\({}^{2}\)[23].
## 2 Materials and Methods
### Pollution Risk Evaluation Techniques
#### 2.6.2 Geological Accumulation Index Method  
The geo-accumulation index (\(I_{geo}\)) is utilized to assess the degree of heavy metal accumulation in soil and to distinguish between natural and anthropogenic sources. Developed in the late 1960s [38], this method considers environmental geochemical background values and the influence of natural activities on these baselines. The index serves as a foundation for pollution source identification. The calculation formula is as follows:  
\[I_{geo}=\log_{2}\biggl{(}\frac{C_{i}}{1.5B_{i}}\biggr{)} \tag{6}\]  
where \(C_{i}\) denotes the concentration of Cd in soil samples, and \(B_{i}\) represents the natural background value of Cd (0.056 mg/kg), representing the baseline element concentration in soil. The background values utilized in this study are based on background values obtained from a nationwide survey conducted in Hainan Province during the mid-1970s. These background values, derived from a uniform sampling strategy with 167 sampling points, have been widely adopted as a benchmark in previous studies to ensure comparability across different research efforts. A coefficient of 1.5 was used to accommodate possible variations in background values resulting from geological differences across regions. A higher \(I_{geo}\) value signifies increased pollution. When the \(I_{geo}\) surpasses 0, it indicates that the heavy metals in the soil primarily originate from anthropogenic activities rather than natural geological processes. The classification criteria are outlined in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(PI\) & **Classification Level** & \(I_{geo}\) & **Pollution Level** & \(E_{i}\) & **Ecological Risk** \\ \hline \(PI\leq 0.7\) & slightly clean & \(I_{geo}<0\) & No & \(E_{i}<40\) & Low (I) \\ \(0.7<PI\leq 1\) & clean & \(0<\ I_{geo}\leq 1\) & No to moderate & \(40\leq E_{i}<80\) & Moderate (II) \\ \(1<PI\leq 2\) & mildly polluted & \(1<\ I_{geo}\leq 2\) & Moderate & \(80\leq E_{i}<160\) & Considerable (III) \\ \(2<PI\leq 3\) & moderately polluted & \(2<\ I_{geo}\leq 3\) & Moderate to heavy & \(160\leq E_{i}<320\) & High (IV) \\ \(PI>3\) & heavily polluted & \(3<\ I_{geo}\leq 4\) & Heavy & \(E_{i}\geq 320\) & Extremely high (V) \\ - & - & \(4<\ I_{geo}\leq 5\) & Heavy to extreme & - & - \\ - & - & \(I_{geo}>5\) & Extreme & - & - \\ \hline \end{tabular} Abbreviations: - represents not available. The results of \(I_{geo}\) and related indices are closely tied to the selected reference standards. As different studies employ varying reference benchmarks, results should primarily be compared within the context of the same standards. When comparing across studies, conversions or adjustments may be necessary.  
\end{table}
Table 1: The evaluation criteria for \(PI\), \(I_{geo}\) and \(ER\).
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Folded Dipole Antenna  
The folded dipole antenna is one of the simplest and cheapest antenna extensively deployed in wireless transmission. It is compact and can be easily manufactured and installed. The design is based on the wires folded but with open ends. The folded dipole antennas are wide loop in nature. The radiation pattern of a folded dipole antenna is the same as that of a dipole antenna [47; 48; 49]. These antennas attributes help the antenna to gain traction, as dipole antennas are known to have high directivity and high gain.