esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Our Findings
### Attack Categories and Their Impact Results Across the Included Studies
\begin{table}
\begin{tabular}{l l l} \hline
**Reference(s)** & **Attack Category** & **Impact Result** \\ \hline
[103; 104; 105; 106] & AI/ML Security & Unreliable vulnerability detection, financial exploitation risks, missed security flaws in contract audits, and unpatched vulnerabilities in deployed contracts. \\ \hline
[107] & Application & Undetected execution faults in Web3 applications, leading to potential security breaches. \\ \hline
[69; 70; 108; 109; 110; 111; 112; 113; 114; 115; 116] & Authentication or & User errors, private key leaks, unauthorized access attempts, patient data exposure, healthcare service disruptions, legal liabilities, data breaches, loss of intellectual property, regulatory penalties, compromised data integrity, financial loss, reputation damage, operational disruption, unauthorized vehicle use, financial fraud, identity theft, data theft, and unauthorized smart contract execution. \\ \hline
[117; 118; 119; 120] & Blockchain Security & Performance degradation, increased attack surface, data tampering risks, election result manipulation, transaction bottlenecks, and reduced performance. \\ \hline
[121] & Centralization & Anonymity loss and privileged operations due to centralized control. \\ \hline
[122] & Cloud Security & State continuity violations in cloud-based systems. \\ \hline
[123] & Code Injection & Database manipulation through malicious code injection. \\ \hline
[124; 125] & Code Reuse & Propagation of known security flaws and widespread deployment of vulnerable contracts. \\ \hline
[126] & Critical Infrastructure & Grid data manipulation in critical infrastructure systems. \\ \hline
[127; 128; 129] & Cross-Chain & Unintended contract behavior exploitation, cross-chain transaction fraud, and privacy leaks. \\ \hline
[130] & Cryptocurrency & Bitcoin loss due to script flaws in cryptocurrency systems. \\ \hline
[68; 131; 132; 133; 134; 135; 136; 137] & Cryptographic Attack & Unauthorized data access in smart grids, data breaches, loss of user trust, legal liabilities, data theft, espionage, compromised encryption, and privacy leakage. \\ \hline
[138; 139; 140] & Data Integrity & Loss of critical digital evidence, unauthorized modifications, and compromised tenant data. \\ \hline
[141; 142; 143; 144] & Data Security & Data loss, unauthorized access, medical data breaches, identity theft, and loss of control over information. \\ \hline
[145; 146; 147; 148; 149; 150; 151; 152] & Denial of Service & Service disruption, network security compromise, financial losses, blockchain slowdown, transaction delays, and smart contract failure. \\ \hline
[153] & Digital Asset Theft & Player data compromises in digital asset systems. \\ \hline \end{tabular}
\end{table} 
Table 5: Cybersecurity attacks and their impacts based on their attack categories.
## 4 Case Study
### Result of Noise Reduction (Highpass)  
As shown in Figure 12, the high-frequency noise components extracted using a high-pass filter were utilized during the DAE training process to remove noise from the raw data. The results of noise reduction using high-frequency noise are presented in Figure 12, and these are compared with the results obtained using Gaussian noise in Figure 5.  
In the case of normal data, some peak points were suppressed; however, the overall vibration amplitude range increased significantly compared to the Gaussian noise-based model. On the other hand, for fault data, the amplitude range remained similar to that of the Gaussian noise case, and the presence of peak points was also comparable.  
Figure 11: (**a**) High-frequency normal data (FFT). (**b**) High-frequency failure data (FFT).  
Figure 10: (**a**) High-frequency normal data. (**b**) High-frequency failure data.
## 2 Current Studies and Technologies for V2X Services
### Relevant Use cases and Requirements for V2X Services  
[PERSON] et al. [28] describe the benefits of vehicles cooperating through V2X communication. They define descriptions and requirements of some relevant use cases which would be supported by future V2X communications systems. Among relevant use cases presented, some of them are bandwidth-demanding applications with high link reliability estimated to reach 99%. Considering latency, the authors mentioned that a low latency with a value below 10 ms is required for most of relevant uses cases. Therefore, these relevant uses cases require a high throughput of tens of Mb/s per vehicle. In addition, [PERSON] et al. [4] provide a survey of the service flow and conditions of the V2X services based on LTE systems. They also discuss relevant scenarios suitable for an operational LTE-based V2X services system. Their work reveals some challenges such as high mobility and high density of vehicle which would bring a great impact in designing practical and technical solutions to satisfy the requirements of V2X services.
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
###### Abstract  
Grassland is one of the most widely distributed ecosystems on the Tibetan Plateau (TP) accounting for about 60% of the total area. The grassland degradation has spread throughout the TP, and the scope and degree are increasing. The inconsistency of multi-source data poses a great challenge to accurately obtaining information about grassland degradation on the TP. This study used five land cover products and six vegetation indexes to analyze the spatial-temporal change in grassland area and quality at the pixel level across the TP from 2000 to 2020. Then, 279 observed grassland degradation points that were collected from 86 published papers were used to verify the grassland degradation information. The grassland fusion product demonstrated that the grassland area increased by 8.84% from 2000 to 2020, and the rate of grassland degradation exceeded the rate of grassland greening during 2010-2020. The superimposed six vegetation indexes showed that 25.88% of the grassland quality has been degraded on the TP from 2000 to 2020. In Changdu City, Ganzi Tibetan Autonomous Prefecture, Gannan Tibetan Autonomous Prefecture, Yushu Tibetan Autonomous Prefecture, Abu Tibetan and Qiang Autonomous Prefecture, Rikaze City, Shannan City and Nagou City, the grassland quality degraded by more than in 20% and the degraded grassland area exceeded 2000 km\({}^{2}\). The observed grassland degradation points were mainly distributed in the northeastern and central parts of the TP. The consistency of six vegetation indexes with the observed grassland degradation points on the TP was 56.63%, with solar-induced chlorophyll fluorescence (SIF) being more effective than other vegetation indexes for monitoring grassland degradation on the TP. In general, the degradation of grassland on the TP has been a looming problem in recent decades.  
Keywords:grassland degradation; grassland area; grassland quality; multi-source data; Tibetan Plateau +
Footnote †: journal:
## 3 Research Model and Hypotheses
### Types of Teaching Tasks  
Gamified classrooms often quantify student performance through points, badges, and leaderboards to create a more competitive atmosphere [26]. Online education is often delivered via e-learning platforms (e.g., _Kahoot!_), where teachers only need to select the appropriate question type and answer interface and enter the assessment content to complete the design of the teaching session. Once students have completed the questions, teachers can assess their performance directly based on \"rewards\" from the electronic platform. These platforms support different types of classroom activities, such as individual (competitive) and group (collaborative) learning tasks, as well as subjective and objective questions. An empirical study by [PERSON] et al. [39] found that different types of gamified online learning activities affect the online motivation of university students, with students being more motivated to engage in collaborative tasks. Whereas the enjoyment of gamified activities for online learning is distinct from traditional teaching models, we are more interested in whether some types of instructional tasks can influence the extent to which gamification features affect students' learning intentions. Therefore, we set \"type of instructional task\" as a moderating variable and hypothesized that:  
**Hypothesis 6 (H6):**_The type of teaching task moderates the relationship between VAK learning style and students' intention to participate in an online gamified classroom._  
In summary, to explore the behavioral intention of college students to participate in an online gamified classroom, we constructed a theoretical model for this study (Figure 1) which includes five latent variables: Vak learning styles, Perceived learning tasks, Perceived usefulness, Perceived enjoyment, and Types of teaching tasks and Intention. On the whole, the first four variables are related to individual factors, e.g., student learning styles, perception of the classroom, and students' willingness to engage in learning behavior. Vak learning styles, as the dependent variable, directly influences students' behavioral intention to participate in online gamified classrooms. Since human behavioral intention can be influenced by a variety of internal and external factors, one or more mediating variables may exist in the causal relationship between students' Vak learning styles and their behavioral intention. We fully analyzed the original technology acceptance model (TAM) and TAM applied to gamification research and clarified the latent variables and relationships among the variables proposed in previous studies. We finally constructed a model structure of college students' behavioral intention to participate in online gamification classrooms. The hypotheses in the model will be verified by SmartPLS4 software in the Methodology section.
## 1 Introduction
extended the complex parameter Rao test to include the case of nuisance parameters and also derived the Wald, Gradient, and Durbin tests for complex-valued parameters in a recently published paper [20]. In the present paper, we also derive the complex parameter Wald test as a parallel task and apply it to the problem of testing the bandedness of a covariance matrix. The Rao test and Wald test are asymptotically optimal detectors for large data records. The complex parameter Rao test requires a lower computational cost than some other detectors, i.e., the generalized likelihood ratio test (GLRT) and Wald test. This is because it does not require the MLEs of unknown parameters under the alternative hypothesis \(H_{1}\). This property can be desirable in high-dimensional multivariate signal processing [19], as low latency is a key performance indicator in such systems. The Rao test strikes a good balance between performance and computational cost. The complex parameter Rao test proposed by [PERSON] and [PERSON] has been applied to multiple problems of radar and communication signal processing [19]. The Wald test is another very useful detector in addition to the Rao test. It is useful in radar target detection tasks, including but not limited to the detection of point targets, extended targets, and multiple-input/multiple-output radar targets in homogeneous, partially homogeneous, and heterogeneous environments [21; 22]. In general, it is an equivalent large-data-record test that has the same asymptotically optimal detection performance as the GLRT and the Rao test. For finite-data records, however, it is not guaranteed to have the same performance as the GLRT [23; 24; 25; 26]. In some cases, compared to the GLRT, the Wald test might be more robust when a mismatch exists and may have a lower computational complexity [22]. An example of its application can be found in adaptive detection for frequency diverse array multiple-input/multiple-output radar [27]. This paper is organized as follows: Section 2 formulates the problem of testing the bandedness of a covariance matrix; Section 3.1 derives the complex parameter Rao test detector for testing the bandedness of the Cholesky-factor matrix; and in Section 3.2, we derive the general complex Wald test for the complex-valued parameter hypothesis testing problem. In Section 3.2.3, the complex Wald test for the bandedness testing problem is derived. Examples and computer simulations for evaluating the Rao and Wald detector's performance are given in Section 4. In addition, the equivalence between complex Wald and Rao tests for the ubiquitous complex Gaussian linear models is proved and analyzed in Section 4.2. Finally, conclusions are drawn in Section 5.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 4 Security Analysis
### Resistance to User Tracking Attack  
In terms of user tracking attacks, when an attacker eavesdrops on the delivered messages in different sessions, and then the attacker can confirm that two messages are from a fixed user according to a stable pseudo-identity being used. In our proposed scenario, the user Ui's pseudo-identity would change in different sessions. Therefore, the attacker cannot ensure that any two messages are from the same user. The results show that the scheme can resist the user tracking attack.
# Seizure Detection in Medical IoT: Hybrid CNN-LSTM-GRU Model with Data Balancing and XAI Integration
## 4 Experimental Results
### Limitations and Challenges  
There are many limitations and challenges in this study, such as the following:  
* **Lack of External Evaluation:** The framework's performance, while impressive, is primarily evaluated using internal validation. The absence of external evaluation on diverse datasets limits its generalizability and raises concerns about its applicability to broader populations.
* **Synthetic Data Bias:** Although SMOTE effectively addresses class imbalance, it may introduce biases by generating synthetic samples that do not fully capture the variability and complexity of real-world data.
* **Sensitive Medical Data Handling:** Managing sensitive medical data presents significant challenges, necessitating strict compliance with data privacy regulations such as HIPAA or GDPR and the implementation of robust anonymization and security measures.
* **Computational Complexity:** The hybrid model's complexity, combining CNN, LSTM, and GRU architectures, may result in high computational demands, potentially limiting its deployment on resource-constrained devices, especially in real-time Medical IoT (MIoT) environments.
* **Scalability and Integration:** Integrating the model into existing MIoT frameworks and ensuring scalability across diverse medical applications and hardware configurations remains challenging.  
Addressing these limitations in future work is critical to enhancing the model's robustness, generalizability, and practical utility in real-world medical scenarios.
# A Local Discrete Feature Histogram for Point Cloud Feature Representation
## 4 Experimental Results
### Performance Evaluation of the LDFH Descriptor  
We compare our LDFH descriptor with several representative descriptors, including FPFH [22], SHOT [13], TOLDI [15], and SDASS [19]. Additionally, to investigate the influence of different geometric features on the LDFH performance, we design two variants of LDFH. The first variant replaces the normal lateral angle \(\phi\) with the angle between the LMA of neighboring points and the \(x\)-axis of the LRF, named LDFH-X. The second variant replaces the normal lateral angle \(\phi\) with the azimuth angle, named LDFH-AZ. The rest of the computation process remains the same as in LDFH. According to the LDFH parameter setting process, the values of the LDFH-X parameters \(N_{r},N_{\theta},N_{\psi},N_{\phi},\lambda_{1},\lambda_{2}\), and \(\lambda_{3}\) are set to 8, 9, 13, 2, 1.8, 1.4, and 0.7, and the values of the LDFH-AZ parameters \(N_{r},N_{\theta},N_{\psi},N_{\phi},\lambda_{1},\lambda_{2}\), and \(\lambda_{3}\) are set to 7, 12, 11, 5, 2, 1.9, and 1. Table 2 displays the parameter settings for the seven descriptors.
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 5 Experimental Results and Discussion
### Test of Different Threads in Interferogram Generation Process  
For the interferogram generation, the two images were read from the two DDR3 SDRAM chips, with the phase stored in DDRA and the coherence coefficient stored in DDRB. When the thread of coarse registration was 1, we set the threads of interferogram generation as 1, 2, 4, and 8, respectively, and the results of the tests are shown in Table 6. \(T_{\text{c}}\) was the measured time in the coarse registration process, and \(T_{g}\) was the estimated time in the interferogram generation process. \(T_{i}\) was the sum of \(T_{g}\) and \(T_{c}\). In practice, the loss of efficiency in storage space could not be avoided, leading to the differences between Tables 2 and 6. It could also be seen from Table 6 that the processing time rose nearly linearly as the threads increased from 1 to 4. Moreover, the \(T_{g}\) of 8 threads was close to that of 4 threads, indicating that the dual-channel storage structure hampered the system's processing capacity.  
The calculation results of \(\
abla G\) are shown in Figure 9. In the figure, \(\
abla G<0.8<3\) meant that there was no balanced scheme. Moreover, it could be seen from the gap between \(\Delta R_{\text{LUT}}/R_{\text{LUT}}\) and \(\Delta T/T_{i}\) that resource changes would sharply deteriorate the processing time.  
In addition, there would be a slight difference between measured \(\
abla G_{T}\) and theoretical \(\
abla G_{B}\) due to the efficiency loss in interface or logic, as shown in Table 7. From this table, the relative error \(\varepsilon\) did not exceed 10% when the thread was 1, 2 and 4, which verified our analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scheme** & **Thread** & \(R_{i}\) & \(R_{\text{LUT}}\) & \(R_{i}\)/\(R_{\text{LUT}}\) (\%) & \(T_{\text{c}}\) (ms) & \(T_{g}\) (ms) & \(T_{i}\) (ms) \\ \hline \(a^{\prime}\) & 1 & 10,477 & 433,200 & 2.42 & 8.91 & 710 & 718.91 \\ \(b^{\prime}\) & 2 & 20,914 & 433,200 & 4.83 & 8.91 & 357 & 365.91 \\ \(c^{\prime}\) & 4 & 41,723 & 433,200 & 9.63 & 8.91 & 186 & 194.91 \\ \(d^{\prime}\) & 8 & 83,048 & 433,200 & 19.17 & 8.91 & 165 & 173.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test results of interferogram generation with different threads.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & \(\
abla G_{T}\) (\(\
abla G_{B}\)) & \(\varepsilon\) (\%) \\ \hline \(\Delta c^{\prime}b^{\prime}(\Delta cb)\) & 0.0545 (0.0503) & 8.29 \\ \(\Delta c^{\prime}a^{\prime}(\Delta ca)\) & 0.0267 (0.0252) & 6.02 \\ \(\Delta b^{\prime}a^{\prime}(\Delta ba)\) & 0.0250 (0.0247) & 1.28 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between \(\
abla G\) in theory and practice.  
Figure 9: \(\
abla G\) of schemes in interferogram generation with different threads.
# A Driving Behavior Planning and Trajectory Generation Method for Autonomous Electric Bus
## 3 Map Building and Global Path Planning
### Conversion of the Coordinate System  
The built map is based on WGS-84 coordinate system, which is not suitable for local planning and control. WGS-84 coordinate system is transferred to local coordinate. Figure 4 displays the three types of coordinate systems. 'East-north-up' coordinates are a coordinate system where the \(X\) axis is latitudinal, the \(Y\) axis is longitudinal, and the \(Z\) axis vertical.  
Figure 3: Map-building in special path.  
\((B,L,H)\) represents the GPS data received from GPS/IMU which is the coordinate origin. \(B\), \(L\), and \(H\) represent longitude, latitude, and geodetic height in geocentric coordinate system. Then convert it to rectangular coordinate system \(P(X_{P},Y_{P},Z_{P})\) by Equations (1)-(3)  
\[\left[\begin{array}{c}X_{P}\\ Y_{P}\\ Z_{P}\end{array}\right]=\left[\begin{array}{c}(R_{n}+H)\cos B\cos L\\ (R_{n}+H)\cos B\sin L\\ \left[(R_{n}+H)-e^{2}R_{n}\right]\sin B\end{array}\right] \tag{1}\]  
\[R_{n}=a^{2}/(a^{2}\cos^{2}B+b^{2}\sin^{2}B)^{1/2} \tag{2}\]  
\[e=(a^{2}-b^{2})/a \tag{3}\]  
where \(R_{n}\) is radius of curvature in prime vertical and \(e\) is the first eccentricity of ellipsoid. \(a,b\) are semimajor axis of ellipsoid and semiminor axis of ellipsoid, respectively. \(P(x_{Q},y_{Q},z_{Q})\) is a point in rectangular coordinate. Then, the point \((x_{QP},y_{QP},z_{QP})\) in east-north-up coordinates is given by (4).  
\[\left[\begin{array}{c}x_{QP}\\ y_{QP}\\ z_{QP}\end{array}\right]=\left[\begin{array}{ccc}-\sin L&\cos L&0\\ -\sin B\cos L&-\sin B\sin L&\cos B\\ \cos B\cos L&\cos B\sin L&\sin B\end{array}\right]\left[\begin{array}{c}X_{ Q}-X_{P}\\ Y_{Q}-Y_{P}\\ Z_{Q}-Z_{P}\end{array}\right] \tag{4}\]  
Finally, we can get the \((x^{\prime},y^{\prime})\) in local coordinate by (5) and provide the \((x^{\prime},y^{\prime})\) for local path planning.  
\[\left\{\begin{array}{c}x^{\prime}=x_{op}*\cos yaw-y_{op}*\sin yaw\\ y^{\prime}=y_{op}*\cos yaw+x_{op}*\sin yaw\end{array}\right. \tag{5}\]  
where, _yaw_ is the heading angle of the bus.
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 3 Ad Hoc On-Demand Distance Vector (AODV)  
AODV is a reactive routing protocol which specifies the route between a source and destination at the time when the route is needed. Each node in the network maintains a routing table of routes to known nodes. However, as the network topology changes, these routes become invalid. In addition, new nodes may join the network which then need to build new routing routing tables. To handle this process, AODV includes two phases: route discovery and route maintenance.  
_Routte discovery_ is the process by which a valid route between a source and destination pair is found [34]. The routing discovery process in AODV operates as follows:  
1. A source node that has data to send to a destination node will first check its stored routing table. If the routing table contains a valid route, then data transmission begins; otherwise, the source initiates an RREQ packet. The RREQ packet contains: source IP, destination IP, source sequence number, destination sequence number and broadcast ID number.
2. When a node receives an RREQ packet, it checks its own stored routing table. If it has a valid route to the destination then it sends an RREP packet to the source. The RREP packet contains the following information: destination IP, destination sequence number, hop count, lifetime and source IP. If the node does not hold a valid route to the destination then it broadcasts the RREQ packet within its transmission range.
3. Once the source node has received a RREP packet, it starts transferring data using that path.  
_Routte maintenance_ is used when routes become broken due to nodes moving out of range or leaving the network. If this happens, then the following steps are taken to recover the broken route.  
1. The upstream node sends an RREP packet to notify the source that the route is no longer valid. An RREP packet contains: source IP, unreachable destination IP and destination sequence number.
2. All intermediate nodes on the route mark it as invalid.
3. On receipt of an RERR packet, the source starts a new route discovery process.