esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification
### Simulation Experiment
#### 4.1.2 Simulation Experimental Result
(**a**) Range systematic error estimation of UAV B; (**b**) azimuth systematic error estimation of UAV B; (**c**) elevation systematic error estimation of UAV B; (**d**) yaw systematic error estimation of UAV B; (**e**) pitch systematic error estimation of UAV B; (**f**) roll systematic error estimation of UAV B.  
Figure 6: Tracking effect of scenario one. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Distance** & **Azimuth** & **Elevation** & **Pitch** & **Yaw** & **Roll** \\ \hline UAV A & 1.0323 m & 0.0081\({}^{\circ}\) & 0.0054\({}^{\circ}\) & 0.0002\({}^{\circ}\) & 0.0002\({}^{\circ}\) & 0.0002\({}^{\circ}\) \\ UAV B & 0.9570 m & 0.0088\({}^{\circ}\) & 0.0054\({}^{\circ}\) & 0.0002\({}^{\circ}\) & 0.0002\({}^{\circ}\) & 0.0002\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Root mean square error of systematic error estimation. corrected tracking effect has a large jitter in the initial stage, and the relatively large error is due to the fact that the initial error estimation stage is not converged and the systematic error estimation deviation is large, resulting in the corrected data also deviating from the true value in the initial stage, but after the systematic error estimation is converged, a stable and accurate tracking of the target is quickly maintained. It can be concluded that the proposed method in this paper is feasible and achieves the purpose of improving the tracking accuracy quickly after spatial registration. To further illustrate the effectiveness of the proposed method, the positions of the observation platforms are changed, and UAVs A and B are set to move 900 m in the X direction, and the tracking effect at different observation positions is shown in Figure 7.  
Figure 7 shows that the new method can also obtain good position estimation results when constantly changing the observation positions, which proves the robustness of the method. Changes in the observed array position have a small effect on the tracking error as long as the application conditions are met. The result shows that the proposed method is applicable to a certain large range.
## 2 Experimental Setup and Methodology  
A hybrid data network based electronic parameter monitoring system was developed to monitor and transmit the operating parameters of the solar power plant. The data monitoring system consists of environmental and electrical parameter sensors for monitoring the solar power plant and environmental parameters. To monitor the ambient temperature, a DS18B20 temperature sensor was used, supporting communication through a 1-wire bus, which requires only one data line for communication with the central microprocessor. The current and voltage of the photovoltaic cells of the solar power plant were measured directly using the contacts of the PIC18F25K22 microcontroller and additional electronic modules suited for monitoring the electrical parameters (the modules for monitoring electrical parameters were built into the main printed circuit board and connected to a single electrical circuit together with the microcontroller). The programming code for the microcontroller was written using the C programming language. The printed circuit boards (PCBs) were (for both initial data collection and processing of transmitted data using the aforementioned programmable microcontrollers) designed using the software design tool Sprint Layout 6. The PCBs were manufactured by the Chinese company JLC PCB. To test the hybrid data transmission network, a solar power plant consisting of 3 solar modules was chosen, the electrical parameters of which were: power--290 W each, output voltage of each--32.5 V, maximum current--9 A. The solar modules employed are made of polycrystalline silicon. RS 485 interfaces and radio module system EBTYE OEM/ODM E32-433T20 DC SX1278 433 MHz UART Wireless Lora were used to transfer the data between individual parts of the system (between the central data processing unit and the main server). A database was created using the MySQL tool and the Python programming language to store the data on a central computer/server and to display it. For data control and monitoring, a mobile application with an artificial intelligence tool was created for devices with the Android operating system. Java programming technology was used to create the application. Wi-Fi (if the mobile device is close to this network source) and GSM mobile communication networks based on 4G or 5G technology were used for data transfer from the central server to the mobile device.
## 3 Results and Discussions  
### Simulation of the Proposed Unit Cell  
This study uses the commercially available three-dimensional (3D) full-wave FEM-based solver, Ansys HFSS, to design and optimize the anticipated FSS. Initially, a simple Jerusalem cross (JC) structure rotated around the axis at 45\({}^{\circ}\) is implemented, which resonates at 20 GHz, as depicted in Figure 3a. Then, in stage II, a square loop is incorporated at the ends of the JC, which increases its electrical length. Finally, rectangular strips are etched to form a loop structure. As a result, the resonant frequency shifts downwards. Thus, the MJCL acts as a bandstop filter at 11.9 GHz in the Ku-band, with an attenuation of 37 dB. Furthermore, in the case of the CMSL-FSS, a conventional square loop (SL) is simulated first, and its frequency response is depicted in Figure 3b. It is noticed that the SL resonates at the 10.5 GHz X-band frequency. Therefore, in step II, the SL is split into four segments and truncated to form a split-ring resonator (SSR) structure. It is observed that the resonance frequency considerably shifted upwards. Finally, these SSRs are joined together to form a compact corner-modified SL. Henceforth, the CMSL-FSS resonates at a significantly lower frequency than the square loop in step I. In addition, the CMSL structure assures more miniaturization and a good angular stability over the conventional SL when used with the MJCL in the co-planner design configuration. Figure 3c presents the frequency response of the FSS structure in the co-planner arrangement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{MJCL-FSS} & Parameter & a = b & c & d & e = f & r & R & u & v & S & L \\ \cline{2-13}  & Values & 1.00 & 0.75 & 2.50 & 0.50 & 1.00 & 0.90 & 0.25 & 2.00 & 4.65 & 4.70 \\ \hline \multirow{2}{*}{CMSL-FSS} & Parameter & g & m & n & q & x & y & z & P & - & - \\ \cline{2-13}  & Values & 0.50 & 6.75 & 5.25 & 0.35 & 1.35 & 0.50 & 5.75 & 7 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of FSS unit elements.  
Figure 3: Stepwise performance of FSS elements at normal incidence. (**a**) MJCL. (**b**) CMSL. (**c**) Shielding performances of the MJCL and CMSL individually and in the co-planner configuration.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 2 Background
### Review of Existing ML and DL Security Approaches in IoV
#### 2.5.1 Machine Learning-Based IDS
Experimental evaluation using real anonymized CAN data from a vehicle demonstrated that the approach achieves high detection accuracy with no false alarms. [PERSON] and [PERSON] [40] proposed an anomaly-based algorithm designed to detect malicious CAN message injections on the CAN bus of modern vehicles. The algorithm identifies anomalies by analyzing the sequence of message flows and is optimized for low memory and computational requirements, making it suitable for deployment on current ECUs. Its detection performance is demonstrated through experiments on real CAN traffic collected from an unmodified licensed vehicle, showing high detection accuracy and low false positive rates. However, the system struggles to detect replay attacks, achieving a detection rate of only 20 to 40%. [PERSON] et al. [41] introduced an intrusion detection algorithm for CAN bus systems that leverages the Hamming distance between successive payloads from different ID classes. Designed with minimal memory requirements, the method is well suited for deployment on modern vehicle ECUs. The approach was tested using traffic data collected from a legally operated, unmodified vehicle. Experimental results indicated near-perfect detection rates for message injection attacks involving fuzzing in both NoR-ange and SmallRange categories. However, its effectiveness significantly dropped for MidRange attacks, with detection rates between 20 and 30%, highlighting its limitations in identifying replay-based threats. [PERSON] et al. [42] proposed the use of a Hidden Markov Model (HMM) to identify abnormal vehicle behaviors. Real-world data was collected from legally operated vehicles, and the anomaly detection task was framed as a classification problem. The CAN bus data was processed as sequential observations using a sliding window technique, with each observation represented by a feature vector (e.g., speed, RPM, door status). The HMM was trained to model normal behavior and compute the posterior probabilities of observed sequences. If an observation sequence falls below a defined probability threshold, it is flagged as anomalous, indicating a potential unsafe state. [PERSON] et al. [43] proposed a machine learning-based fingerprinting approach that links CAN messages to their originating sources by exploiting unique physical-layer artifacts caused by hardware imperfections. The method extracts a feature vector of \(11\) statistical attributes from both the time and frequency domains and uses these to train a neural network classifier. Evaluated on data from 16 CAN channels and four identical ECUs sending the same messages, the system achieved detection accuracies of 95.2% for channel classification and 98.3% for ECU identification.
## 2 Materials and Methods
### Description of the MAS Model
#### 2.6.1 Input Data  
The input data come mainly from GIS files, which have been processed beforehand (digitisation, georeferencing, orthorectification).  
* A digital terrain model (DTM) representing the topography in raster format. The data used come from the RGE Alti(r) (Referentiel a Grande Echelle) with a resolution of 5 m \(\times\) 5 m provided by the IGN.
* A \"flow direction\" raster file, which for each cell identifies the cell in its vicinity that has the greatest downward slope, based on the DTM (resulting from a GIS processing in ArcMap).  
* Land use, derived from the digitisation of the images and aerial photographs described in the previous paragraphs, in vector format.
* The location of the facilities (fascines, hedges, ponds), in vector format.
* The location of roads in vector format, from the IGN BDtopo.
# Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications
## 4 Deep Q-Network for Energy-Efficient Resource Allocation
### Deep Q-Network  
A frequently used framework in the RL is a DQN [35; 36; 37; 38]. The DQN framework is a structure that includes a Q-network consisting of a deep neural network (DNN) in the Q-learning structure. In order to train the Q-network in the DQN framework, several learning methods need to be applied [39].  
In Q-learning, the Q-value means the expected return when reaching the terminal state from the state observed in time \(t\), as follows:  
\[Q(\mathbf{s},a)=\mathbb{E}[R_{t:T}|\mathbf{s}_{t}=\mathbf{s},A_{t}=a]. \tag{10}\]  
The Q-value is updated as follows:  
\[Q(\mathbf{s}_{t},a_{t})=Q(\mathbf{s}_{t},a_{t})+\alpha[r_{t}+\gamma\max_{a}Q( \mathbf{s}_{t+1},a)-Q(\mathbf{s}_{t},a_{t})], \tag{11}\]  
where \(\alpha\) denotes the learning rate. The agent's behavior is determined based on the \(\epsilon\)-greedy policy. The \(\epsilon\)-greedy policy is a method of randomly selecting an action if the randomly sampled value is lower than the value of \(\epsilon\) and selecting the action with the highest Q-value is greedy if it is high. However, because Q-learning uses a lookup table called a Q-table that stores Q-values in order to find the state and action pairs, it has several disadvantages: First, the probability of visiting the same state is very low. Second, a very large storage device is required to store an exponentially increasing number of state and action pairs. A DQN framework has been developed to overcome these disadvantages.  
As shown in Figure 3, the DQN framework calculates Q-values using a Q-network in which weights and biases are stored. Therefore, when an agent needs a Q-value that matches a state, the agent puts the state as input to the Q-network and obtains the appropriate Q-value as output. The loss function for training the Q-network is as follows:  
\[Loss(\mathbf{\theta},\mathbf{\beta}) = \sum_{\mathbf{s}_{t},a_{t}\in\mathbb{E}}(y-Q(\mathbf{s}_{t},a_{t },\mathbf{\theta},\mathbf{\beta}))^{2} \tag{12}\] \[y = r_{t}+\gamma\max_{a}Q(\mathbf{s}_{t+1},a,\mathbf{\theta},\mathbf{\beta}), \tag{13}\]  
Figure 2: The structure of the RL for the vehicular network.  
where \(\theta\) and \(\beta\) mean the weights and biases in the Q-network, respectively. \(Q(\mathbf{s}_{t},a_{t},\theta,\beta)\) means the Q-value. \(E\) is a mini-batch sampled from the experience replay memory that stores the state, action, and reward of the next-state tuples collected while the agent interacts with the environment. However, in order for the Q-network to perform an approximation function in the DQN framework, a training process that adjusts the weights and biases stored in the Q-network is required.
## 5 Proposal Architecture and Its Deployment
### Challenges and Solutions  
During the implementation, we encountered several challenges:  
* Increased latency due to TOR routing: Mitigated by optimizing message sizes and implementing efficient QoS strategies.
* Compatibility issues between MQTT clients and TOR: Resolved by developing custom adapters and thoroughly testing various client configurations.
* Maintaining performance under varying network conditions: Addressed through adaptive QoS mechanisms and robust error handling.
# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 3 Method
### Superpixel-Wise Fuzzy Clustering Modified by Edges  
We adopt SLIC [30] to obtain preliminary over-segmentation that provides the FCM technique with adaptive and irregular local spatial neighborhoods. Moreover, superpixel-wise clustering is able to achieve low computational complexity by reducing the number of processing elements. With the number of iterations, the desired number of superpixels, and compactness set to 20, 333, and 13, respectively, with SLIC, we can obtain an accurate over-segmentation for post-processing. Finally, the catheter zone \(R_{c}\) and the background zone \(R_{b}\) are both considered superpixels.  
The obtained local spatial information is incorporated into the objective function of the FCM algorithm, defined by:  
\[J=\sum\
olimits_{s=1}^{N_{s}}\sum\
olimits_{c=1}^{N_{c}}P_{s}u_{cs}{}^{m} \|q_{s}-v_{c}\|^{2},\text{ subject to }\sum\
olimits_{c=1}^{N_{c}}u_{cs}=1,\;0\leq u_{cs} \leq 1, \tag{8}\]  
where \(s\) is the superpixel index and \(N_{s}\) is the number of superpixels. \(P_{s}\) is the number of pixels in the \(s\)th superpixel \(R_{s}\). The mean intensity \(q_{s}\) in \(R_{s}\) is calculated as:  
\[q_{s}=\frac{1}{P_{s}}\sum\
olimits_{i\in R_{s}}I(i) \tag{9}\]  
Similar to Equations (2) and (3), the membership function \(u_{cs}\) and cluster center \(v_{c}\) are updated according to:  
\[v_{c}=\frac{\sum\
olimits_{s=1}^{N_{s}}u_{cs}{}^{m}P_{s}q_{s}}{\sum\
olimits_{ N_{s}}^{N_{s}}u_{cs}{}^{m}P_{s}} \tag{10}\]  
\[u_{cs}=\frac{\|q_{s}-v_{c}\|^{-2/(m-1)}}{\sum\
olimits_{k=1}^{N_{c}}\|q_{s}-v _{k}\|^{-2/(m-1)}} \tag{11}\]  
Figure 2: The flowchart of the proposed segmentation algorithm.  
Each superpixel belongs to the cluster that has the most of membership belonging. \(N_{c}\) takes the value of 3, and three clustering maps are obtained. The cluster denoted by \(B_{1o}\), which has the smallest white areas, consists of ROIs located inside the adventitia, calcification, and soft plaque areas. The cluster denoted by \(B_{2o}\) whose center is white contains ROIs belonging to shadow artifacts and soft plaque, as shown in Figure 2.  
After clustering, ROIs belonging to different tissues may still have weak connections in \(B_{1o}\). To fully cut these connections, \(B_{1o}\) minus an edge map \(B_{e}\) and is processed morphologically. The resulting map is denoted by \(B_{1}\).  
\[B_{1}=((B_{1o}-B_{e})\oplus b_{disc})\oplus b_{disc}, \tag{12}\]  
where \(b_{disc}\) is a disc-shaped structuring element. \(\odot\) and \(\oplus\) denote morphological erosion and the dilation operation, respectively.  
Edges are actually high-frequency areas in an image. If lower-frequency information is rejected by some sort of filter, higher frequencies will remain, which are edges [37]. \(B_{e}\) is computed according to the maximum magnitude response of a bank of 2D Morlet wavelet kernels with dimensions equal to \(1/32\) the size of the image, as shown in Figure 3.
## 4 Simulation Example
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(k\) & **100** & **200** & **500** & **1000** & **2000** & **3000** & **4000** & **5000** & **True Values** \\ \hline \(a_{01}\) & \(-\)0.17179 & \(-\)0.18574 & \(-\)0.31748 & \(-\)0.41405 & \(-\)0.61777 & \(-\)0.71905 & \(-\)0.76959 & \(-\)0.80135 & \(-\)0.80860 \\ \(a_{02}\) & \(-\)0.17111 & \(-\)0.11012 & \(-\)0.05496 & 0.01077 & 0.12643 & 0.17775 & 0.21975 & 0.24397 & 0.25514 \\ \(b_{00}\) & 0.37344 & 0.45616 & 0.62647 & 0.76016 & 0.91687 & 0.95693 & 0.98483 & 0.99692 & 1.00000 \\ \(b_{01}\) & 0.06673 & 0.04317 & 0.02695 & \(-\)0.05431 & \(-\)0.24467 & \(-\)0.
###### Abstract
In the first of them, the rolling motion of the ship in static conditions and sailing in beam seas without a resistant environment was studied. In the second one, similar research but in dynamic conditions was carried out, where the ship's navigational parameters (heading and speed) and loading conditions, which can be modified to waves coming from any direction, were analysed. In the present work, new mathematical models are developed. First of all, the ship's response when hit by a single external force is studied from a new point of view. In the present case, this single force is a certain wave that ceases instantly. Secondly, as this assumption is far from reality, the relationship between the different parameters of waves' full spectrum that a ship can face during sea passage is analysed. These two approaches, which correspond to the real ship's behaviour during the rolling motion in beam seas with resistance, are studied too. Finally, after studying the influence of waves coming from any direction, with the ship at non-zero speed, a useful guide for shipmasters based on the change of heading and ship's speed was provided to be useful when experiencing changing sea conditions in order to improve safety during navigation. Regarding the ship's characteristics which apply to the proposed models, the present research analyses the ship's loading conditions, expressed as metacentric height (GM), which in turn depends on the ship's hydrostatic values. However, as hydrostatic curves or tables would be very specific for each ship, here, the ship's natural rolling period (Td), a parameter very easy to obtain by any seafarer on board any ship, is analysed. Furthermore, it is known that, through different empirical formulas, there is a direct relationship between GM, Td, and the ship's beam. However, in order to make the present research applicable to both small fishing vessels and large merchant ships, the Td conditions of 8 s, 10 s, 12 s, and 14 s were studied. These values can be considered sufficiently representative of the majority of fishing and merchant fleets (regardless of their length) when they are operating under normal loading conditions. As the maximum initial rolling angle, \(8^{\circ}\) was selected as it is considered representative enough of an unfavourable initial situation. Concerning the sea state conditions, the full spectrum of all registered waves, with amplitudes from 0.08 to 22.92 m, and an average rolling damping factor of 0.015 was selected for modelling according to the literature.
## 5 Image Analysis  
Image analysis of closed cell aluminum foams is an important technique useful for understanding microstructure and porosity characteristics. Such properties are crucial for optimizing their performance in various applications. Image analysis can be used to quantify the porosity of the foam, which affects its insulation properties and structural performance. By analyzing the foam's microstructure, image analysis can also provide insight into its strength, stiffness, and energy absorption capacity, which are critical factors in some applications, such as aerospace and automotive engineering. Image analysis can help identify defects and inconsistencies in the foam's microstructure, allowing manufacturers to improve their production processes and quality control measurements. Furthermore, image analysis can aid in the development of new closed cell aluminum foam materials with tailored microstructures to meet specific application requirements. Image analysis also enables researchers to study the behavior of closed cell aluminum foams under different loading conditions, such as compression and impact, which can provide valuable insights for designing safer and more durable products. Finally, image analysis can be used to monitor the degradation of closed cell aluminum foams over time, helping to ensure their long-term performance and reliability in harsh environments.  
Lucia ScMeas software (supplied by Nikon) is an image analysis tool which can extract useful information for research and commercial applications. The software has a wide range of image processing and analysis tools, capable of performing automated feature extraction, segmentation, object recognition, 3D volume rendering and visualization, and statistical analysis. Users can import images from various sources and pre-process them with image resampling, noise reduction, and contrast enhancement. The software can detect edges, lines, shapes, and regions of interest in the image and perform object recognition by training the software to recognize specific objects or patterns. It also provides statistical analysis tools, including image statistics, correlation, and regression analysis.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 6 Informal Security Analysis
### Data Tampering  
An attacker can use any unauthorized means to modify the information, which can directly impact the integrity of the whole system. To overcome such an attack, we have made use of the hash function \(\mathcal{L}\)'s so when an attacker aims to tamper with the signature (DS) or the information (\(m\)), it will be spotted during verification since the computed hash will be entirely different. However, if an attacker tries to decrypt the cipher text C, it is not feasible because \(m\) is encrypted using \(PB_{\text{AP}}\) and logically an attacker cannot generate the decryption key \(\varphi_{\text{AP}}\). Therefore, our scheme effectively protects against data tampering.