esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of the Experiment
### Datasets
#### 4.3.2 Training Sample Size Exploration
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample Size** & **ATEC** & **BQ** & **LCQMC** & **PAWS-X** & **STS-B** & **AVG** \\ \hline
438,650 & 25.49 & 40.15 & 43.51 & 11.31 & 28.21 & 29.73 \\
394,785 (90\%) & 25.15 & 43.72 & 46.18 & 11.13 & 30.49 & 31.33 \\
350,920 (80\%) & 24.83 & 40.86 & 46.73 & 11.14 & 29.12 & 30.53 \\
307,055 (70\%) & 25.05 & 40.97 & 45.67 & 12.61 & 27.81 & 30.42 \\
263,190 (60\%) & 27.91 & 44.06 & 50.87 & 8.83 & 29.61 & 32.25 \\
219,325 (50\%) & 25.67 & 44.2 & 50.35 & 10.27 & 28.77 & 31.85 \\
17,544 (40\%) & 26.89 & 44.32 & 50.8 & 11.85 & 27.51 & 32.27 \\
131,595 (30\%) & 27.94 & 45.97 & 52.95 & 10.43 & 26.78 & 32.81 \\
87,730 (20\%) & 28.77 & 46.93 & 56.58 & 13.67 & 28.53 & 34.89 \\
43,865 (10\%) & 30.59 & 48.26 & 58.97 & 10.67 & 25.7 & 34.83 \\
30,000 & 30.33 & 47.85 & 58.95 & 10.92 & 27.41 & 35.09 \\
25,000 & 31.62 & 48.79 & 58.72 & 9.9 & 26.92 & 35.19 \\
20,000 & 31.81 & 49.04 & 61.77 & 11.43 & 24.97 & 35.80 \\In summary, 10,000 randomly selected samples were chosen as the training set in this paper. The number of training samples for the three models in the other experiments in the later sequence is based on this criterion.
## 5 Experimental Results and Discussion
### Estimated State Transition Probabilities
#### 5.3.1 Log-Likelihood Ratio  
The log-likelihood ratio (LLR) is frequently utilized to assess the fitness of a statistical model to a given dataset in statistical modeling. As the term log-likelihood indicates, it is the natural logarithm of likelihood, which means that it is the likelihood expressed as a logarithm. Therefore, it is possible to determine the BWA's convergence by repeatedly running the algorithm until subsequent estimates of the state transition probabilities show only negligible differences. For a detailed mathematical analysis of the LLR and its representation, please refer to [45].  
The LLR plots for the models that were realized for both the weakly interrupted and strongly interrupted scenarios are depicted in Figures 12, 13, 14, and 15, respectively. These figures show that the models begin to converge at the second iteration with a negative LLR value close to zero, confirming the model's accuracy.
## 4 Results
### Structural Model  
To evaluate the structural model with the second-order variable following the steps proposed by [PERSON] et al. [65], the aggregated scores were used to model the second-order construct of ITC. The evaluation of the model's goodness of fit should be performed at the beginning of the model evaluation before examining the structural model [61]. To assess the goodness of fit, the SmartPLS 4 software was used [67], which provides the standardized  
\begin{table}
**ITC** & & & \\
**FP** & 0.572 & & \\
**EO** & 0.704 & 0.703 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Discriminant Validity. Heterotrait—Monotrait ratio (HTMHT) criterion.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **HCS** & **MITC** & **TITC** & **FP** & **EO** \\ \hline
**HCS** & 0.895 & & & & \\
**MITC** & 0.711 & 0.871 & & & \\
**TTTC** & 0.692 & 0.839 & 0.879 & & \\
**FP** & 0.397 & 0.563 & 0.458 & 0.847 & \\
**EO** & 0.547 & 0.570 & 0.509 & 0.586 & 0.729 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Discriminant Validity, Fornell–Larcker criterion.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Second Order Construct** & **Items** & **Loads** & **AVE** & **CR** & **CA** & **Rho_A** \\ \hline \multirow{3}{*}{ITC} & MITC & 0.940 & & & & \\  & TITC & 0.924 & 0.899 & 0.909 & 0.937 & 0.832 \\ \cline{1-1}  & HCS & 0.870 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: First-order loads in the second-order ITC construct.  
root mean square residual (SRMR) as the appropriate measure for model fit. For the proposed model, the SRMR value is 0.079, a result that indicates a good model fit [69]. The proposed structural model is evaluated through the path loadings and the R\({}^{2}\) values. The PLS bootstrapping technique was applied using 10,000 samples, as suggested by [PERSON] and [PERSON] [70], and the path loadings and \(p\)-values for the relationships described in the hypotheses were calculated. The results and paths are shown in Figure 2. Table 8 shows the results of the hypotheses, and all the proposed direct relationships are supported by appropriate path coefficients and \(p\)-values.  
The R\({}^{2}\) values represented in Figure 3 suggest that the model explains 39.2% of the FP and 35.1% of the ITC.
## 7 Conclusions  
In FANETs, the routing mechanism is crucial for cooperative and collaborative network functions. Several routing protocols for FANETs have been developed in several studies over the past few years. Diverse FANETs mobility models, routing protocol design challenges, network topologies, and different types of communication links have been reviewed and presented. In addition, several existing and innovative topology-based routing protocols for FANETs have been thoroughly reviewed and compared. The 22 topology-based routing protocols have been categorized into four categories: proactive, reactive, hybrid, and static routing protocols. The routing protocols were then compared in terms of characteristics, various routing mechanisms, mobility models, routing performance measurements/parameters, simulation tools involved in the development, and application scenarios. Based on our comparison, each routing protocol has its own advantages/disadvantages, and suitability to specific applications. Further, a low density of UAV nodes must be considered when developing topology-based routing protocols in FANETs. Moreover, three-dimensional space, time-dependent and path-planned mobility models are widely adopted for various FANETs application scenarios. Finally, opportunities and challenges related to FANET deployment were highlighted in this article.  
**Author Contributions:** Conceptualization, A.H.W. and R.N.; formal analysis, A.H.W. and A.A.S.; methodology A.H.W., R.N. and A.A.S.; resources R.N., M.H.A. and A.H.W.; software, A.H.W. and A.A.S.; supervision, R.N. and A.A.S.; writing--original draft, A.H.W., R.N. and A.A.S.; writing--review and editing, R.N., M.H.A. and M.A.K.; validation, R.N. and A.A.S.; investigation, M.A.K.; funding, R.N., M.H.A. and A.H.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** We acknowledge the financial support from Collaborative Research in Engineering, Science and Technology (CREST), under the grant T23C2-19 (UKM Reference: CREST-2020-001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 3 Preliminaries and Problem Formulation
### Anonymous Entropy  
It is pointed out in [17] that entropy can measure the uncertainty of target location in the location set. In this paper, we use entropy to evaluate the degree of anonymity.  
Here, we consider set \(\mathcal{G}\) including \(k\) locations, \(\mathcal{G}=\{(x_{0},y_{0}),\)\((x_{1},y_{1}),\)\(\ldots,\)\((x_{k-1},y_{k-1})\}\). The service request probability at location \((x_{i},y_{i})\) is \(q_{i}\), the candidate probability of location \((x_{i},y_{i})\) is \(p_{i}\). If the vehicle user at location \((x_{i},y_{i})\) request service \(u\), the service semantics at location \((x_{i},\)\(y_{i})\) is \(e_{i,u}\), and the request probability of service \(u\) at location \((x_{i},\)\(y_{i})\) is \({q^{\prime}}_{i}{q^{\prime}}_{i}=q_{i}e_{i,u}\), \(i=0,\)\(1,\)\(\ldots,\)\(k-1\), \(u=1,\)\(2,\)\(\ldots,\)\(U.\) Hence, the anonymous entropy is defined as  
\[H=-\sum_{i=0}^{k-1}p_{i}log_{2}p_{i}, \tag{1}\]  
where  
\[p_{i}=\frac{{q^{\prime}}_{i}}{\sum\limits_{i=0}^{k-1}{{q^{\prime}}_{i}}}. \tag{2}\]  
According to the mathematical property of entropy, it is required that the candidate probabilities of \(k\) locations be the same to achieve the maximum entropy. That is, if \(p_{i}=1/k\), \(i=0,\)\(1,\ldots,\)\(k-1\), the maximum of anonymous entropy of set \(\mathcal{G}\) is log\(2k\).
# Enhancing Unmanned Marine Vehicle Security: A Periodic Watermark-Based Detection of Replay Attacks
###### Abstract
A recursive watermark algorithm significantly safeguarding against replay attacks with remarkable efficiency and saving the control cost by embedding rapid, detectable watermarks in hard real-time channels was introduced in [29]. In [30], a watermark-based encoding and decoding method were also proposed to detect replay attacks on UMVs with less control cost. It is worth noting that while incorporating watermark signals into control systems of UMVs aids in replay attack detection, the approach also compromises control performance [25; 26; 27; 28; 29; 30]. The advantages and limitations of different existing methods can be seen in Table 1. Therefore, it is interesting to study the schedule of watermarks to use control costs more effectively. Inspired by the above-mentioned discussions, in this article, a periodic watermark signal method against replay attacks for UMV systems is investigated, which can precisely detect replay attacks and minimize the impact of the watermark signal on the control system's performance. Significant contributions of this paper are highlighted as follows. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detection Method** & **Timestamp** & **Watermark** & **Vulnerable** & **Precise** & **Cost-Effective** \\ \hline Ref. [19] & ✓ & & ✓ & & \\ Ref. [20] & ✓ & & ✓ & & \\ Ref. [25] & & ✓ & & ✓ & \\ Ref. [26] & & ✓ & & ✓ & \\ Ref. [27] & & ✓ & & ✓ & \\ Ref. [27] & & ✓ & & ✓ & \\ Ref. [29] & & ✓ & ✓ & & \\ Proposed Method & ✓ & & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and Limitations of Different Existing Methods. 1. The proposed method utilizes the relative positions of watermark signals to detect replay attack, diverging from traditional watermarking methods in [30; 31]. Compared with existing, this method significantly lowers control costs by reducing watermark signal variance. 2. A novel periodic watermarking-compensation mechanism is constructed based on a Gaussian signal, contrasting with binary watermark signals employed in [32]. Comparatively, this mechanism can effectively enhance the detection rate of replay attacks. Throughout this article, the superscripts 'T' and '-' denote the transposition and the inverse of a matrix.
# Research on VVC Intra-Frame Bit Allocation Scheme Based on Significance Detection
###### Abstract  
This research is based on an intra-frame rate control algorithm based on the Versatile Video Coding (VVC) standard, considering that there is the phenomenon of over-allocating the bitrate of the end coding tree units (CTUs) in the bit allocation process, while the front CTUs are not effectively compressed. Fusing a Canny-based edge detection algorithm, a color contrast-based saliency detection algorithm, a Sum of Absolute Transformed Differences (SATD) based CTU coding complexity measure, and a Partial Least Squares (PLS) regression model, this paper proposes a CTU-level bit allocation improvement scheme for intra-mode code rate control of the VVC standard. First, natural images are selected to produce a lightweight dataset. Second, different metrics are utilized to obtain the significance and complexity values of each coding unit, the relatively important coding units in the whole frame are selected, which are adjusted with different weights, and the optimal adjustment multiplicity is supplemented into the dataset. Finally, the PLS regression model was used to obtain regression equations to refine the weights for adjusting the bit allocation. The proposed bit allocation scheme improves the average rate control accuracy by 0.453%, Y-PSNR by 0.05 dB, BD-rate savings by 0.33%, and BD-PSNR by 0.03 dB compared to the VVC standard rate control algorithm.  
video coding Versatile Video Coding (VVC); intra rate control; bit.allocation; significance detection Article Research on VVC Intra-Frame Bit Significance Detection April 2024 14 471. [[https://doi.org/10.3390/app14010471](https://doi.org/10.3390/app14010471)]([https://doi.org/10.3390/app14010471](https://doi.org/10.3390/app14010471))
## 10 Research Challenges and Future Directions
### Transparency and Explainability  
Understanding the rationale behind an AI agent's annotation decisions is critical for multiple stakeholders. Developers rely on this insight for debugging, users depend on it to build trust, and regulatory frameworks often require it, especially in sensitive fields such as healthcare, where image classification outcomes must be justified. Although LLM agents are capable of generating explanations for their annotations, there is no assurance that these explanations accurately reflect the model's internal decision-making process. The model may produce reasoning that appears plausible but diverges from the actual computational pathway used to generate the label.  
An emerging research direction involves embedding self-explanation directly into the annotation process, treating chain-of-thought reasoning as a core output subject to separate evaluation. Structurally prompting models to express their reasoning before assigning labels, and conditioning label acceptance on the clarity and validity of that reasoning, improves transparency and overall system accountability [169]. Techniques from explainable AI provide further support, including saliency mapping for text inputs and visual segmentation for images, which help identify the features that influenced a model's classification decision. Future annotation systems are expected to present visual explanatory elements, such as heatmaps or highlighted regions, to justify model outputs and support efficient human verification.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Short Range Communication Technologies
#### 4.3.2 ZigBee  
ZigBee is another low-cost and low-energy consumption standard-based wireless short-range communication technology. It operates at 2.4 GHz within the ISM band (868 MHz in Europe and 902-928 MHz in North America). It uses the IEEE 802.15.4 standard, which uses 128-bit Advanced Encryption Standard (AES) for secure data transfer between ZigBee sensors and networks. The method of media access is CSMA/CA, and it uses mesh topology for networking. Zigbee has successfully been implemented in several research works, as seen in [49], where the authors used a ZigBee-based ECG monitoring system to acquire patient data for forward transmission to a remote base station for telemedical usage. Other articles on the use of ZigBee communication include [59; 60; 62; 93; 100; 101; 102]. The strengths of ZigBee technology include:  
1. Low latency;
2. High data security;
3. Collision avoidance and retransmission, ensuring more reliable data transmission;
4. Support for multiple network topologies, such as point-to-point, star, and mesh topologies;
5. Support for unlimited nodes in a network;
6. Scalability;
7. Low energy consumption;
8. Ease of implementation;
9. 128-bit AES encryption ensures data security.
## 3 Results and Discussion  
### Odor Evaluation  
SE-SAFE and SPME were used for isolating the volatile constituents from CSS. In order to confirm if the odorants contributing to the characteristic odor of CSS had been extracted, the odors of the isolates obtained were evaluated by three well-experienced evaluators. The results showed that both the liquid extract obtained by SE-SAFE and the fiber of SPME had the same overall aroma profile as CSS. They had caramel, cooked potato, smokoy, sour and floral notes. The odor intensity of isolates obtained via SE-SAFE was stronger than that of SPME fiber. That is, the extraction methods used were appropriate.
## 3 Preliminaries
### Security Model for CLAS Scheme  
As proposed first in [31], in CLAS we assume two types of adversaries termed _Type 1 Adversary_, \(A_{1}\), and _Type 2 Adversary_, \(A_{2}\). Here, \(A_{1}\) acts as a dishonest user and \(A_{2}\) acts as a malicious KGC on the other hand. _Type 1 Adversary_: \(A_{1}\) adversary does not control the master key but is allowed to replace public keys at will, with any desirable value of its choice. _Type 2 Adversary_: \(A_{2}\) adversary has access and controls the master key but cannot replace the public keys of users.  
The classical security model proposed in [PERSON] et al. [52] presents a security adversarial model for certificate-less key agreement schemes. The model is defined as a game between a challenger, \(C\), and an adversary defined by a probabilistic polynomial-time Turing machine,\(A\in\{A_{1},A_{2}\}\). Thus, \(A\) has full control of the communication channel of all parties and parties only respond to queries from \(A\) and cannot communicate directly with each other. As a controller of the communication channel, \(A\) has powers to actively carry out the following actions, such as relaying, modifying, delaying, interleaving, deleting all the messages flowing in the system.
## 5 Simulation Results  
Considering an MAS with six agents, the desired distances between each pair of adjacent agents are set to \(d=\big{[}\frac{\pi}{8},\frac{\pi}{2},\frac{3\pi}{8},\frac{\pi}{2},\frac{\pi}{ 3},\frac{\pi}{6}\big{]}^{T}\), which satisfy (6) and the initial values of the MAS are randomly generated to satisfy (2). In addition, the unique normalized positive left eigenvector of \(L_{d}^{T}\) with respectto eigenvalue 0 is \(\xi=[0.0625,0.0.25,0.1875,0.25,0.1667,0.0833]^{T}\). In order to verify the effectiveness of the proposed strategy, the simulated event detection is implemented in the form of sampled data. Here, according to [26], the sampling period \(h\) in real-time control is set to \(0.2s\). To ensure the condition (36) hold in real-time control, the permitted range \(\sigma\) is set to \(0.9\).  
Simulation results are shown in Figures 2 and 3. Figure 2a shows the evolution of the difference between the event-triggered angular distance and the expected counterpart, Figure 2b reveals each agent's own event sequence. Figure 3 illustrates the fluttering of the measurement error \(\|y_{i}(t_{k}^{i})-y_{i}(t)\|\). The results indicate that the desired circle formation can be asymptotically achieved under the proposed control law. From Figure 2b, it can be seen that under the self-triggering algorithm in Theorem 2, \(V(t)\) is exponential to 0, and the interevent times of each agent are strictly greater than some positive constants. Additionally, according to Figure 2, we calculate the average inter-event time overall mobile agents resulting to \(h_{avg}=0.6724\). We conclude that the proposed method with an average sampling period of \(h_{avg}\) has the advantages of reducing the number of control updates and communication frequencies.  
Figure 2: Simulation results of circle formation for MASs: (**a**) The evolution of \(y_{i}(t_{k}^{i})-d_{i}\) for \(i=1,2,...,N\) when \(h=0.2s\). (**b**) The sequence of event-triggered times for \(i=1,2,...,N\) when \(h=0.2s\).
# Power Modeling and Experiment Study of Large Flapping-Wing Flying Robot during Forward Flight
## 2 Materials and Methods
### Experiment Setup  
This paper analyzes and compares model data with actual flapping data from the wind tunnel experiment, which provides an environment that is steadier than outdoor flight. The experimental setup of the FWAV is shown in Figure 6 and the test platform system in Figure 7. The actual power consumption of the flapping-wing system is calculated by measuring the input current and voltage, and the experimental conditions are changed by controlling the flapping frequency, angle of attack, and wind speed. The test platform system includes the angle of attack adjustment platform, sensors, mounting bracket, HIT-Hawk, and optical platform. The wind tunnel at Harbin Institute of Technology (Shenzhen) was applied to test the HIT-Hawk prototype. The wind tunnel presents as a 24 m \(\times\) 6 m \(\times\) 3.6 m test section with a maximum wind speed of 35 m/s. The wind tunnel provides a 1.84 \(\times\) 10\({}^{-5}\) Pa-s kinematic viscosity of air and the Reynolds number is 5.73 \(\times\) 10\({}^{4}\)-1.39 \(\times\) 10\({}^{5}\).  
In the experiment, the six-dimensional force/moment, wing flapping angle, current, and voltage of the FWAV under incoming flow (5 and 10 m/s) in the wind tunnel were recorded by LabVIEW to calculate the actual power consumed for the power model verification. The M3816 BH six-dimensional force sensor from SRI was used for measuring the  
Figure 6: Block diagram of signal acquisition and motion control system principles for the wind tunnel experiment platform.  
Figure 7: Diagram of the installation of the wind tunnel test platform.  
lift and thrust; the AS5048 module was used as an angle sensor to measure the position of the wings and to calculate the flap frequency; the current sensor and voltage sensor with a resolution of 12 bit were ACS711 KLCTR-12 AB-T and MCP6L01, respectively. From the product of voltage and current, it is easy to obtain the overall power consumption during the experiment. The NI-6343 acquisition card with highest sampling frequency of 250 kS/s is utilized to acquire current and voltage together with force data. Since the maximum flapping frequency of the designed FWAV is 5 Hz, the sampling frequency used for the experiment is set to 1000 Hz.  
Wingspans of 1.8 and 1.6 m were used in the FWAV, see detailed parameters in Table 1. The average chord length of the FWAV was designed to be 0.29 m. The flapping frequency varied in the range 1.5 to 3.5 Hz with a step size of 0.5 Hz. The angles of attack were set to be 0\({}^{\circ}\), 5\({}^{\circ}\), 10\({}^{\circ}\), 15\({}^{\circ}\), and 20\({}^{\circ}\). The motion control was achieved by the STM32 platform.  
To ensure the consistency of the experiments, the wings were tested with the same fuselage skeleton and drive unit. The fuselage skeleton was fastened on a mounting bracket that was mounted on an optical platform. The sensors in the experiment were reset after installing the wings to be tested before turning on the wind tunnel.
## 4 Simulation Results
### Control Method Performance Comparison  
To confirm the convergence of the OSMC, a comparison is made between the OSMC, the nonsingular objective sliding mode control (NOSMC) [6], and traditional sliding mode control. The simulation results are presented in Figures 5-7.  
Figure 5: The tracking of positions for six joints near the initial system position.  
When the initial position of the system was \(\mathbf{q}=[0.032\ 0.032\ 0.032\ 0.032\ 0.032\ 0.032\ 0.032]^{\text{T}}\), the articulation position was tracked, as shown in Figure 5. OSMC had a small convergence time when the initial state of the system was close to the equilibrium point. Table 1 shows the time required for the tracking error from the near end of the system to decrease to less than 0.01. As shown in Table 1, OSMC performed better than NOSMC and the SMC multi-joint manipulator in terms of convergence speed.  
The initial position is \(q=[0.32\ 0.32\ 0.32\ 0.32\ 0.32\ 0.32]^{\text{T}}\). Figure 6 shows that when the initial state of the system was far from the equilibrium point, OSMC still had a shorter convergence time than NOSMC and SMC. Table 2 indicates the duration required by the three controllers to decrease the manipulator's position tracking error to under 0.01, starting from a position far from the equilibrium point. It demonstrates that OSMC exhibited superior convergence speed compared to NOSMC and SMC.  
Figure 6: The tracking of positions for six joints far from the initial system position.  
Figure 7: Compensated and uncompensated six joint position tracking error curves.