esa-sceva/satcom-chunk-collection huggingface dataset:
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 7 Cooperative Localization  
Consider a network of \(n\) vehicles numbered \(1\) to \(n\). A vehicle \(i\), at sample time \((k)\), utilizes absolute measurements to propagate its pose using the discrete form of (\(\mathcal{T}\)):  
\[x^{i}(k+1)=g^{i}(x^{i}(k))u^{i}(k)+\gamma^{i}(x^{i}(k))\eta^{i}(k) \tag{24}\]  
where \(x^  
where \([H^{b}\ \ H^{t}]\) is defined in (17). The cross-covariance terms modifies the innovation covariance matrix as follows:  
\[\begin{split} S_{bt}(k+1)&=H^{b}{\rm{p}}^{bb-}{H^{b} }^{\top}+H^{t}{\rm{p}}^{tl-}{H^{t}}^{\top}\\ &+H^{b}{\rm{p}}^{bt-}{H^{t}}^{\top}+H^{t}{\rm{p}}^{tb-}{H^{b}}^{ \top}+R_{b}\end{split} \tag{28}\]  
Utilizing (27) and (28), the Kalman gains along with the states and covariance terms for all vehicles \(i=1,\cdots,n\) are updated as follows:  
\[\begin{split} K^{i}(k+1)&=[{P^{it}}^{-}{H^{t}}^{ \top}+{P^{ib}}^{-}{H^{b}}^{\top}]{\varsigma}^{bt-1}\\ \hat{\varsigma}^{i+}(k+1)&=\hat{\varsigma}^{i-}+K^ {i}\hat{\varsigma}^{b}\\ P^{i+}(k+1)&=P^{i-}-K^{i}S^{bt}{\rm{K}}^{i\top}\\ P^{j+}(k+1)&=P^{j-}-K^{i}S^{bt}{\rm{K}}^{j\top} \end{split} \tag{29}\]  
where \(j=1,\cdots,n,\ j\
eq i\). The Kalman gain, due to its semi-definite nature, ensures that a decrease in localization error is achieved by any relative measurement, as long as the measurements are not too noisy and the EKF linearization is accurate. The step-by-step procedure for the centralized CL algorithm can be found in [14].  
The structure of the EKF-based CL algorithm is illustrated as a diagram in Figure 3 for better understanding of the procedure.
# Optimizing Flight Delay Predictions with Scorecard Systems
## 7 Featured Application  
The scorecard-based approach to flight delay prediction developed in this study provides a practical and accessible analytical tool for stakeholders in the air transportation industry. Its simplicity, interpretability, and scalability make it particularly suitable for integration into real-time operational environments, where decision-making must be both fast and explainable.  
Unlike complex statistical models or black-box machine learning algorithms, scorecards allow for a transparent presentation of the influence of key operational variables--such as departure time, airport traffic, or weather conditions--on the probability of a flight delay. This transparency is critical for adoption by airline managers, airport coordinators, and regulatory bodies, especially in environments where model interpretability is a regulatory or operational necessity.  
The scorecard can be implemented as part of airport management systems or airline scheduling platforms to continuously assess delay risk during various stages of flight preparation and execution. For example, based on available pre-departure information, operators can use the scorecard to flag flights with a high predicted delay rate (PDR). Such alerts could trigger proactive actions--such as aircraft reassignment, crew schedule adjustments, or early passenger communication--that help mitigate the impact of potential delays.  
Moreover, the lightweight nature of the scorecard model makes it ideal for deployment in environments with limited computational resources, such as smaller regional airports or mobile airline control platforms. The model requires only a basic input dataset and delivers near-instant predictions that are easily interpretable by non-technical staff. This aligns well with the increasing demand for democratized access to analytics tools within operational teams.  
The application of scorecards also supports benchmarking and performance monitoring efforts. By aggregating predicted and actual delay rates over time, airlines can identify bottlenecks, seasonal patterns, or systemic inefficiencies. Additionally, the interpretability of the model allows it to serve as a validation or calibration layer for more advanced AI/ML models. As explainable artificial intelligence (XAI) becomes increasingly important in regulated industries, scorecards can offer a foundation for trustworthy model governance and auditing.  
In the broader context of logistics and transport optimization, the adoption of scorecards exemplifies the shift toward hybrid analytical approaches--combining simplicity with data-driven precision. The method presented in this paper offers an innovative, cost-effective, and practical solution to the pervasive problem of flight delays, with potential for further extension into multimodal logistics, passenger flow management, and infrastructure planning.
# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 5 Experiments and Results
### Watermarking Insertion and Extraction Performance Analysis  
We tested our algorithm on the 49 grayscale images shown in Section 3.1 to insert and extract the LOGO watermark (Figure 2a) and the image MetaData (Figure 2b).  
In Table 2, we present the metrics' averages by applying the algorithm to the 49 grayscale images. In addition, in Table 3, we show only the results using six representative images. However, the results were similar for the other images. As representative images, we selected the following images commonly used to test image-processing algorithms, and at the same time, they represent the variability of both low and high spatial frequencies: _Baboon, Barbara, Boat, Cameraman, Lena_, and _Peppers_. The experimental results using these representative images are shown in Figure 13, where each pair shows the original image on the left and the watermarked image on the right.  
Figure 12: Sensitivity analysis of the scaling factor. (**a**) NCC (solid red line) and SSIM (dashed blue line) values of the watermarked image versus \(\alpha\). (**b**) Enlargement of the left-upper rectangle region (black dotted line) of (**a**) showing the defined limit value of \(\alpha\).  
Tables 2 and 3 show, through the values of the metrics (PSNR, MSSIM), that the watermark is visually imperceptible, so both the LOGO and the image MetaData did not present perceptible changes. In relation to the extracted watermark and the MetaData, all  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Images**} & \multicolumn{4}{c}{**Insertion (Image Watermarked)**} & \multicolumn{4}{c}{**Extraction (LOGO and MetaData)**} \\ \cline{2-11}  & **MSE** & **PSNR (dB)** & **NCC** & **SSIM** & **MSSIM** & **MSE** & **PSNR (dB)** & **NCC** & **SSIM** & **MSSIM** & **BER** \\ \hline
49 & 7.1710 & 40.2051 & 0.9987 & 0.9999 & 0.9994 & 0 & \(\gg\)60 & 1 & 1 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metrics’ averages using 49 grayscale images, showing the metrics over the watermarked images and the metrics of the LOGO and MetaData extracted.  
Figure 13: Results of original images and their watermarked images without attack. (**a,c,e,g,i,k**) correspond to the original images _Baboon_, _Barbara_, _Boat_, _Cameraman_, _Lena_, and _Peppers_, respectively. (**b,d,f,h,j,l**) correspond to the watermarked images.  
metrics demonstrated that we can recover them perfectly. Among the six representative images, the best results were for the _Peppers_ image; after inserting the watermark, the quantity of the pixels modified was 5.8020, and the rest of the metrics demonstrated that there were no visual changes. The _Cameraman_ image presented the highest MSE value and the lowest PSNR value, but also these values indicated that the watermark was not perceptible.
## 5 Simulation Results and Discussion
### Performance Comparison of PFL with Traditional FL Methods
Some other recent works that have utilized a threshold latency as a metric for performance evaluation are [94; 95]. The percentage of latency violations in PFL and conventional FL is illustrated in Figures 5 and 6. Here, we vary the number of vehicles (\(V\)) from 1 to 100; the vehicle velocity is set as 60 km/h and 80 km/h; and \(R_{L}\) is set as 2 km and 4 km. The local learning rate for each vehicle is initially set to 0.005 and increased gradually with periodic increments. The number of global model and local model update iterations is set to 2000 and 1000, respectively. In each training round in PFL, 40-70% of vehicles are selected to participate in the training of the global model at the UAV. The subset of vehicles selected by the UAV is based on the similarity of data distributions in the model hyper-parameters. Out of two vehicles with high similarity in their data distributions, only one vehicle is selected by the UAV. This saves redundant computations and lowers latency. As noted from Figures 5 and 6, the percentage of latency violations lies between 25 and 30% for FedAvg and between 23 and 31% for FedSGD. For the PFL approach, for a similar number of vehicles, the percentage of latency violations lies between 18 and 28%, indicating a gain in performance. PFL uses an adaptive weighting approach that compares  
Figure 5: Variation in percentage of latency violations with number of vehicles (\(V\)) in FedAvg and PFL scenarios. the statistical similarity in model weight parameters of different vehicles as well as vehicles and the UAV. If two vehicles have a high similarity, they are treated as a single cluster, so the UAV can communicate with any of the vehicle out of the two. If there are three such vehicles in a cluster, the UAV can communicate with any one out of these three vehicles as all three vehicles are communicating similar data. Moreover, in a TTI, more than one such cluster can exist. So, to not miss different types of information transmitted by different vehicles, the UAV should communicate with at least one vehicle from each cluster. The lower latency in packet transmissions is further noted from Figure 7. The average delay experienced by a packet in PFL and conventional FL is illustrated in Figure 7.
## 2 Related Work
### Correlation Techniques in WSN  
Data correlation is characterized among sensor nodes especially under densely deployments, affecting network's performance. Focus by researchers on data correlation in WSN has influenced designing of efficient routing protocols [51]. As more and more sensors are being deployed in the environment to provide sensing data to support various Internet of Things (IoT) applications, use of temporal and spatial correlations among different sensor data identifies the usefulness and correctness of each sensor data for different applications under IoT [52]. Utilization of temporal correlation of mobile sensing nodes on crowdsensing for fine-grained urban environmental monitoring has been achieved by fusing the sensory data with correlated environmental information [53]. According to [PERSON] and [PERSON] [54], their model built on Spatial and Temporal Data Correlation Algorithm for Data Aggregation overcame the challenge of data aggregation during flooding. With the dynamic characteristics of WSN, spatiotemporal data correlation approaches have been applied to data collection, aggregations, dissemination and network evaluation [9; 55].  
Similarly, studies have shown data correlation techniques can monitor detect fault and anomalies in WSN [7], predict link quality and energy efficient routing methods [51; 54]. Densely deployed sensor nodes are prone to severe data redundancy due to the readings of neighboring nodes. High data redundancy leads to more energy consumption of the network. Large scale deployment causes congestion during transmission that leads to data losses and data retransmission is costly to WSN because of their limited energy budget [8]. [PERSON] et al. [51] proposed a correlation aware opportunistic routing protocol that reduces redundancy by aggregating correlated data from selected forwarding nodes and the opportunistically forwarding the aggregated data to the sink. Here, we show how data correlation technique can detect faulty, anomalies and outliers within the WSN.
# Spam Review Detection Techniques: A Systematic Literature Review
## 3 Assessment and Discussion of Research Questions
#### 3.1.1 Review Datasets  
Availability of a dataset is the key starting point of any spam review detection research. The key issue in the spam review detection problem is the availability of the labeled dataset. It has been observed from existing studies that only one labeled hotel review dataset is available [15], but it has only review text and no other features are available, such as review posting time and review rating. Researchers need to have access to the labeled dataset to train a classifier so that it may classify an unknown review as spam or not-spam. The alternative approach is to use an artificially created review dataset by using synthetic review spamming [16]. [PERSON] et al. [15] have used Amazon Mechanical Turk (www.mturk.com) (AMT) for collecting labeled datasets through online workers (called Tuckers) to write fake hotel reviews (by giving one dollar for each review) in order to represent a few hotels in a positive light and some negatively. According to [PERSON] et al. [4], the process of labeling has not provided improved accuracy for spam review detection on real-life datasets. Table 6 lists review datasets used by different researchers and show total reviews, number of reviewers, and number of products for each dataset. It is observed by the review of the literature that all review datasets are not publicly available, and many times researchers use crawlers to gather required data. It has also been observed that most of the researchers used Amazon.com e-commerce website datasets in their works, as it is the biggest e-commerce platform to have product reviews, and the second largest review dataset is available from tripadvisor.com, which is an online hotel booking website. Additionally, the researchers working in the spam review detection area use these datasets provided by such websites. Through a few datasets, such as Amazon.com ([[http://jmcauley.ucsd.edu/data/amazon](http://jmcauley.ucsd.edu/data/amazon)]([http://jmcauley.ucsd.edu/data/amazon](http://jmcauley.ucsd.edu/data/amazon))), Dianping ([[http://liu.cs.uic.edu/download/dianping](http://liu.cs.uic.edu/download/dianping)]([http://liu.cs.uic.edu/download/dianping](http://liu.cs.uic.edu/download/dianping))), Resellarrating (www.trustpilot.com/review/www.resellerratings.com), and Datatang ([[http://www.datatang.com](http://www.datatang.com)]([http://www.datatang.com](http://www.datatang.com))), products reviews and hotel review datasets are freely available, however the problem of unlabeled data exists in their datasets as well.  
Based on an analysis of existing studies, it is observed that very limited real-world labeled datasets are available. Hence, there is a need to have publicly available labeled standard review datasets that may be used by researchers for analyzing and comparing the results of different spam review detections techniques.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 2 Literature Review
### Spectrum Handoff
#### Hybrid Handoff  
In order to assure the requirement of the wide bandwidth and to achieve the improved results in terms of QoS and spectrum aggregation (SA), an approach is required which permits a PU or a CRU to concurrently use various spectrum groups [53]. CRU is a clever user who can sense and use vacant spaces by probing the radio atmosphere. This class of sensing builds probably to join the free channels by spectrum aggregation. In [54], the author presented a scheme named hybrid handoff, which is founded on dynamic spectrum aggregation (DSA) to discover the manners of a CRU through handoff. Each time the CRU performs a handoff to BC when PU visits back to its certified channel. According to CRU, all the channels are BCs except the present channel (can be either utilized or idle). When PU arrives at the present channel; on the one hand, if the channel is free, the communication of CRU can be smoothly moved into it while on the other hand if all the channels are assessed to be busy, the CRU will wait till the completion of the PU communication. In [55], authors developed a spectrum-management scheme and estimated the performance in varied spectrum environment (opportunistic and centralized CRNs). Authors considered a determined threshold period for spectrum handoff delay to improve the performance for both opportunistic and negotiated situations with backup channels. In [54], authors describe that the CRU should select the probability of minimum handoff to reduce the handoff delay and the number of handoffs. The main application of this method makes it achievable to give superior QoS. The level of SH gap is minimized whenever the BCs are utilized; it guides towards the utilization of channels which are continuously vacant (most of the time).  
Table 1 summarizes the handoff strategies in a comparative manner, which are also discussed in the above subsections. The strategies are compared on the basis of channel backup property, bandwidth utilization and handoff delay. The fuzzy-based analytic hierarchy process (FAHP) is a scheme with a full backup property in which BC is kept (all the time) with the ongoing operating channel. The bandwidth utilization becomes low whenever the handoff delay is at the minimal level. STBC and SHCP schemes have the middle course between full backup and no backup therefore the bandwidth utilization is at an average level while handoff delay is minimal. Since the fuzzy-based scheme has no BCs, the handoff delay can be maximized when the channel underutilization is at its minimal level. DFHC [18], M/G/1 [21] and Zigbee [52] are reactive handoff schemes with no channel backup facility, therefore, the handoff delay is at maximum level and the channel underutilization is at its minimal level. DSA [53] is a hybrid handoff scheme which is considered as a middle course between proactive and reactive handoff schemes. In DSA, the bandwidth utilization is at an average level, however, the handoff delay can be at its maximum level.
# A New Multi-Physics Coupled Method for the Temperature Field of Dry Clutch Assembly
## 3 Multi-Physics Coupled Simulation
### Thermal-Solid Decoupling Model  
For the pressure plate, rivets, and bolt, the hexahedral element is used in the meshing of the model of the clutch assembly to improve accuracy. For the diaphragm springs and the transmission plates, the tetrahedral element is the only option to use in the meshing because of its complex structure. The whole FE model consists of 253,280 nodes and 73,814 elements, as shown in Figures 6 and 7, respectively.  
For reducing the computational resource and achieving long-scale simulation, the thermal-structure bidirectional coupling is decoupled with two steps of sequential unidirectional coupling. In the first step, the structure analysis of the clutch assembly is performed under the normal clamp force of the clutch. The contact pressure of the friction surface of the pressure plate is obtained. The time step of the simulation is specified, for example, 0.1 s during the engagement sliding process, and a time step of 1 s is specified during the disengagement process. Within the time-step-specified range, the contact is assumed to remain unchanged. Then the frictional heat generated can be easily calculated based on the contact pressure. Then, applying the heat flux to the surface of the pressure plate, the thermal analysis of the clutch assembly is conducted. The thermal deformation of the pressure plate is achieved correspondingly. In the second step or the next time step, the structure analysis of the deformed clutch assembly under the normal clamp fore of the clutch is performed successively, where the deformation of the pressure plates by the previous step is applied in the model as the initial displacement condition. And the contact pressure and heat flux of the deformed clutch plate are conducted as the first step. Then the thermal analysis of the clutch assembly is performed successively, where the temperature distribution from the previous step is applied to the model as the initial thermal condition. These two steps are repeated until the running time reaches the set value. The regulation of the time step in the simulation can compromise the accuracy and computational resource of the simulation. The following Figure 8 illustrates the flow of the decoupling technique.
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
## 3 Preliminaries
### Operation Process of SACA  
The following four steps comprise the entire operation process of SACA:  
Step 1: each active sensor sends a packet to the base station. Sleep sensors do not need to send packets to the base station.  
Step 2: the base station starts to count the total number of packets from the active sensors, determines whether the total number of active sensors (\(Q_{1}\)) is close to the target value (\(Q_{0}\)), and calculates the difference value (\(d\)) between \(Q_{1}\) and \(Q_{0}\). The difference value d is added to the feedback packet and sent to the active sensor.  
Step 3 (adjustment procedure): the sensor executes the adjustment procedure for the weight values of the sensors. The active sensor determines whether to change its state (for example, maintain the original state or change from the ON state to the OFF state)according to the difference value \(d\) in the feedback packet. If the number of active sensors is too small (i.e., \(Q_{1}-Q_{0}<0\)), set \(W_{i}\) directly to 1, so that the sensors should remain in their original state. If the number of active sensors is too large in the current round (i.e., \(Q_{1}-Q_{0}>0\)), set \(W_{i}\) directly to 0, letting the sensor change to the sleep state. If the difference value d equals 0 (i.e., \(Q_{1}-Q_{0}=0\)), the sensors change to the sleep state, and then the process goes to step 4.  
Sleep sensors do not need to receive feedback packets from the base station. They increase their weight value, \(W_{i}\), in each round, as their main purpose is to turn on the active state to replace the active sensors. To increase the value of \(W_{i}\), we set an activation parameter(\(\sigma\)) and the sleep sensor increases its \(W_{i}\), and then the process goes to step 4.  
Step 4 (transform procedure): after the sensors finish the adjustment procedure for their weight value, they execute the change-of-state procedure. The sensors randomly generate a probability value \(r\) and compare it with their own weight value \(W_{i}\). When \(W_{i}>r\), the sensor switches to the active state; otherwise, it changes to the sleep state. The probability value of \(r\) is between 0 and 1, and the larger the probability value of \(r\), the greater the probability that the sensor is in a sleep state.  
The four steps mentioned above, shown in Figure 5, represent one round. After the system executes several rounds, the total number of active sensors converges to the target value.
# A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper
## 5 Conclusions
In view of the previous indications, a possible connection is found between the proposed classes to create a hybrid mixture to realize a high impact on the channel performance enhancement. This conclusion is built on the fact of dispersion, which is a relative problem with antenna arrays, channels, and subcarriers that cannot be resolved from one side and neglected from the other two. Therefore, for future research, a new technique must be recognized to find a complete solution that takes into the account beam squint effects at all communication system parts, thus leading us to fill the gaps in the developments in mmwave communication system for 5G and beyond. Investigation and original draft preparation, M.Q.A., supervision, methodology and review, A.H.M., Conceptualization, review and editing, R.A.A.-A. All authors have read and agreed to the published version of the manuscript. Authors thank the support of the higher education ministry from Iraq. There is no conflict of interest.