esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Results Analysis
### Comparison of Model Experiments
#### 3.3.2 Experimental Results and Analysis on the CamVid Dataset  
To verify the efficiency of the proposed ESNet, this section trains and evaluates the ESNet on the CamVid scene segmentation dataset and compares and analyzes it with several current fast segmentation methods. The evaluation indicators used include: Mean Intersection over Union (MIoU), model parameters, and Frames Per Second (FPS).  
Compared with some current lightweight real-time semantic segmentation methods, the overall segmentation performance of the proposed ESNet structure on the CamVid test set is shown in Figure 16.  
Figure 16: Segmentation comparison results of ESNet on the CamVid test dataset.  
Figure 15: Comparison of recognition results of various segmentation models: (**a**) recognition results of various buildings; (**b**) recognition results of various traffic facilities; (**c**) recognition results of various traffic signs.  
In Figure 16, the experimental results reveal that compared with segmentation networks of multiple lightweight real-time images, the parameters of the ESNet model are around 1.2 m, the highest FPS value is around 90 Hz, and the highest mIOU value is around 70%. It can be seen that ESNet significantly improves segmentation accuracy while maintaining faster forward inference speed.  
To further analyze the prediction accuracy of the ESNet structure on each category, the prediction accuracy of ESNet on the CamVid test set for each category is shown in Figure 17.  
Figure 17 demonstrates that in the seven semantic categories of the sky, buildings, pillars, roads, sidewalks, trees and traffic signs, the segmentation accuracy of the ESNet model is the highest at around 98% and the lowest at around 40%. In the segmentation test of each category, the ESNet model has the highest segmentation accuracy. This means that the performance of the ESNet model is better than other models.
## 4 Performance Evaluation
### Transmission Time Analysis  
The Euclidean distance between two symbols on hexagonal time-frequency lattice is less than on the rectangular lattice structure. Thus, the same number of symbols can be transmitted with a shorter transmission time compared to the rectangular lattice. For this, the Euclidean distance between symbols has to be taken into account to handle the interference between symbols. Bite error rates based on time-frequency lattice allocation are studied very well in [27; 28]. Accordingly, based on the distance between symbols, transmission time decreases by about 13%. On the other hand, more symbols can be transmitted in the same transmission time. According to LTE standards, the transmitted signal is designed into subframes of 1 ms duration. Each frame consists of 14 or 12 OFDM symbols that depend on whether standard or extended cyclic prefix is used [29]. On the other hand, if frame structures are adopted to hexagonal allocation, frame duration decreases. The comparison of the optimum frame  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **The Number of Multiplications** \\ \hline \hline GFDM [23; 24] & \(MKL+M\text{Klog}_{2}K\) \\ Proposed Hexagonal GFDM & \(ML+M\text{Klog}_{2}K\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The analysis of the computational complexity of the transmitter according to the number of multiplications.  
Figure 4: Comparison of the proposed hexagonal GFDM and traditional GFDM model for \(K=16\). The proposed method has less computational complexity.  
duration time of rectangular and hexagonal lattice structure based on the LTE frame structure is given in Table 2.
# Leveraging Large Language Models for Sensor Data Retrieval
## 3 Architecture of the System
### Data Gathering
#### 3.1.1 Data Identification  
Before diving into the data identification process, it is crucial to ascertain the data prerequisites necessary to accomplish the desired objectives and select a suitable output format for subsequent utilisation. For example, the objective of the data gathering process could be to acquire weather sensor data from a particular region in CSV format.  
After defining the objective, it is necessary to pinpoint web portals that facilitate the attainment of the goal established in the previous step. A range of options exist for retrieving data from sensors, including Shodan ([[https://www.shodan.io/](https://www.shodan.io/)]([https://www.shodan.io/](https://www.shodan.io/)), accessed on 15 January 2024), Censys ([[https://search.censys.io/](https://search.censys.io/)]([https://search.censys.io/](https://search.censys.io/)), accessed on 15 January 2024), and ZoomEye ([[https://www.zoomeye.org/](https://www.zoomeye.org/)]([https://www.zoomeye.org/](https://www.zoomeye.org/)), accessed on 15 January 2024). These portals provide different tools, including APIs and filtering capabilities. It should be noted that a large portion of these data are in formats that make their reuse difficult, such as HTML.  
One of the problems with these portals is the access limitations. APIs usually have access limits, and there are restrictions on the number of search filters that can be applied. Moreover, not all the geographical regions are covered equally. For insntace, ZoomEye is specially focused in China. For these reasons, it is necessary to properly verify that the chosen portal is suitable for the objectives established.  
The process of data identification requires querying sensors in the selected portals. To this end, specific search parameters are required to extract pertinent information. Different  
Figure 1: Two steps in the data gathering process: data identification and data transformation.  
filters can be applied in the search for sensors with specific features. In the case of Censys, geographic area can be narrowed down by using filters like location.city: Madrid, Spain, and sensors can be identified by using tag:IoT or tag:Sensor. It should be noted that the definition of filters changes from one portal to another. Thus, it is required to know their specific syntax; although, all portals share a common syntax basis.  
In the case of Censys, applying the previous filters reveal that most sensors have HTTP or MQTT ([[https://mqtt.org/](https://mqtt.org/)]([https://mqtt.org/](https://mqtt.org/)), accessed on 15 January 2024) services enabled. Usually the open ports are 80 and 1883 (used by MQTT). Moreover, filtering for a response status code of 200 (successful response) using response.status is necessary to avoid defunct or malfunctioning sensors. These filters allow retrieving sensor data that are mainly shared in HTML format, combined with XML or JSON responses.
## 1 Introduction
As missile controllers, Proportional Navigation Guidance (PNG) controls and their variations were widely used to let the pursuer capture the evader [1; 3; 4; 5; 6]. PNG laws enable the pursuer to capture the evader by driving the angular rate of LOS near zero as time passes [4]. But, PNG laws do not make the angular rate of LOS stay at zero at every time index. Note that PNG laws only considered a pursuer which moves with a constant speed. This paper proves that utilizing our chasing controller, the distance between the evader and the pursuer monotonically decreases, regardless of evader's maneuver or acceleration, if the following assumptions are satisfied: (1) the pursuer speed is bigger than the evader speed; (2) the pursuer can predict the evader's location within two time steps in the future. Our controller works as follows. The locations of both the pursuer and the evader are accessed at every time index. Considering a robot control system, the location of the pursuer is estimated in real time, since a robot (pursuer) can access the movement of itself utilizing Inertial Navigation Sensor (INS) or Global Positioning System (GPS). The pursuer uses sensor measurements, such as radar, to measure the evader's location in real time. Based on the accessed evader locations, the pursuer predicts the evader's location two steps forward in time. We acknowledge that sensor measurement noise exists as the pursuer measures the evader's location in real time. Moreover, predicted evader location is related to evader maneuvers and is not easy to conjecture in an accurate manner. The effect of prediction error on the performance of our motion camouflage controller is analyzed in Section 4.2. Since the maximum acceleration of the evader is bounded, we can assume that the evader's motions are smooth, so the evader's trajectory curves are derivative. Under this assumption, Section 4 presents a fitting method to predict the evader's location two steps forward in time. After predicting the evader's location two steps forward in time, the pursuer calculates its velocity command, while ensuring that the LOS does not rotate at the next time index. Using deduction, the angular rate of LOS is zero at every time index. In Section 5, the effectiveness of our chasing controller is demonstrated utilizing MATLAB simulations. This paper is organized as follows. Section 2 presents several definitions and assumptions before presenting our main results. Section 3 presents our chasing controller. Section 4 presents a method to predict the evader location two steps forward in time considering noisy environments. Section 5 introduces MATLAB simulation results to demonstrate the performance of our chasing controller. Section 6 provides Conclusions.
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
###### Abstract  
This study investigates the effects of wind-wave processes in a coupled wave-ocean circulation model on Lagrangian transport simulations. Drifters deployed in the southern North Sea from May to June 2015 are used. The Eulerian currents are obtained by simulation from the coupled circulation model (NEMO) and the wave model (WAM), as well as a stand-alone NEMO circulation model. The wave-current interaction processes are the momentum and energy sea state dependent fluxes, wave-induced mixing and Stokes-Coriolis forcing. The Lagrangian transport model sensitivity to these wave-induced processes in NEMO is quantified using a particle drift model. Wind waves act as a reservoir for energy and momentum. In the coupled wave-ocean circulation model, the momentum that is transferred into the ocean model is considered as a fraction of the total flux that goes directly to the currents plus the momentum lost from wave dissipation. Additional sensitivity studies are performed to assess the potential contribution of windage on the Lagrangian model performance. Wave-induced drift is found to significantly affect the particle transport in the upper ocean. The skill of particle transport simulations depends on wave-ocean circulation interaction processes. The model simulations were assessed using drifter and high-frequency (HF) radar observations. The analysis of the model reveals that Eulerian currents produced by introducing wave-induced parameterization into the ocean model are essential for improving particle transport simulations. The results show that coupled wave-circulation models may improve transport simulations of marine litter, oil spills, larval drift or transport of biological materials.  
Lagrangian transport modelling; coupled wave-ocean models; ocean drifters; wave-induced processes; model skills +
Footnote †: journal:  
0021
# Creep Model of Weakly Cemented Soft Rock Considering Damage and Secondary Development in FLAC3D
## 2 Experimental Study on Weakly Cemented Soft Rock  
### Microstructure of Weakly Cemented Soft Rock  
To identify the mesostructural characteristics and creep mechanisms of weakly cemented soft rock at the microscale, roof rock samples were collected from a roadway at a mining site in western China for X-Ray Diffraction (XRD) analysis and Scanning Electron Microscopy (SEM). The experimental setup is shown in Figure 1. A D8 Advance X-ray powder diffractometer (Produced in Qingdao, China) and a Nova NanoSEM450 ultra-high-resolution field-emission scanning electron microscope (Produced in Qingdao, China) were used for the XRD and SEM analyses, respectively. The purpose of these analyses was to determine the mineral composition and microstructural features of the weakly cemented soft rock and to understand their implications for its mechanical behavior. The scanning results are presented in Figures 2 and 3.  
As shown in Figure 2, X-Ray Diffraction (XRD) analysis indicates that clay minerals constitute the highest proportion (40.4%) of the sample, followed by secondary minerals such as quartz, potassium feldspar, plagioclase, and siderite. The clay minerals are primarily composed of chlorite, kaolinite, illite, and illite/smectite mixed-layer minerals.  
The SEM imaging results, presented in Figure 3, show that the sample has a loose, porous microstructure with poor cementation and sporadic primary fractures, indicative of weak overall structural integrity. These images further confirm the abundance of clay minerals, consistent with the XRD findings. The clay minerals--mainly kaolinite, illite, and illite/smectite mixed layers--are predominantly interconnected through face-to-face contacts and cementation bonds. Some clay particles fill the interstitial spaces between quartz and feldspar grains, forming blocky aggregates and detrital textures, which together contribute to the highly porous and weakly bonded nature of the rock.
## 2 System Model
### Architectures for Forwarding NB-IoT Frames over a Satellite Relaying Node
#### 2.2.3 Case 3: Detect-and-Forward Relaying of NB-IoT Frames Encapsulated in DVB-S2(X)  
The third case consists in encapsulating the NB-IoT encoded bits into DVB-S2(X) frames. Next, the LDPC coded bits from DVB-S2(X) are 4-PAM modulated, pulse-shaped, time-packed, and sent to the satellite. There, the time-packed interference is removed (see Section 2.3), and the LLRs for the 4-PAM modulation are computed and fed into the soft-LDPC decoder of the DVB-S2(X) [33]. After that, the NB-IoT encoded bits are desencapsulated from the DVB-S2(X) frames, mapped to QPSK symbols, OFDM modulated, and forwarded to the IoT device. There, the LLRs of the QPSK symbols are first computed, and then used by its soft-Viterbi decoder to detect the message of the gateway.  
Let \(c_{\text{iot,gw}}\) be the NB-IoT convolutional encoded signal at the terrestrial gateway, which is encapsulated into the DVB-S2(X) frame. Due to the difference in the payload size of a NB-IoT and a DVB-S2(X) frame, several NB-IoT convolutional codewords can be packed together into the input message of the DVB-S2(X) physical layer frames. We recall that at the physical layer, a DVB-S2(X) frame is first BCH encoded and then LDPC encoded. The BCH encoding is used to remove the possible error floors of the LDPC decoding, which aims at compensating the impairments that introduce the communication channel (i.e., the optical feeder link in our case). Let us assume that \(c_{\text{dvb,gw}}\) denotes the input frame to the DVB-S2(X) encoder at the gateway. Then, it can convey up to \(P\) NB-IoT convolutional-coded frames as \(c_{\text{dvb,gw}}=\big{[}c_{\text{iot,gw}}(0)\cdots c_{\text{iot,gw}}(P-1) \big{]}\). Next, the DVB-S2(X)-coded frames are 4-PAM modulated, E/O converted, and sent to the satellite on the optical feeder link. At the satellite, the O/E conversion is carried out, and the received signal samples are matched-filtered and equalized to mitigate the ISI introduced by time-packing. Likewise, in _Case 2_, the two LLRs are computed per 4-PAM symbol, i.e., \(LLR_{b_{0}}\) and \(LLR_{b_{1}}\) (see Section 2.3). However, in _Case 3_ these LLRs are introduced into the soft LDPC decoder, not in the soft-Viterbi decoder as it was used in _Case 2_. After that, the NB-IoT encoded bits are de-encapsulated from the decoded DVB-S2(X) frames, QPSK mapped, OFDM modulated, and forwarded to the IoT terminal over the radio access link. Finally, at the IoT device, the processing for detecting the transmitted message is the same as the one that was explained for _Case 1_.
## 3 Proposed Method
### Achieving CVD Compensation with ALCC-Glasses
#### 3.3.3 Distortion Function  
Figure 9a shows the response curves of the user perspective camera when the monitor was turned off and a single-channel color was displayed on the OST-HMD. For the \(\mathbf{I}_{H}\), the input image to HMD, we used \(\mathbf{I}_{D}\) to represent the distorted image that was observed by the user. As can be seen in Figure 9a, even with single-channel color input, the observation has value in all three channels. This is the side color effect [14]. Figure 9b shows the relationship between side colors and main colors, with the main color value along the horizontal axis and the side color value on the vertical axis. We can observe that  
Figure 7: Transmittance curves generated by displaying \(\mathbf{I}_{Li}\) on the LCD.  
the side colors have a linear relationship to the main colors. Therefore, the main color of each channel is denoted as \(R_{R}\), \(G_{G}\), and \(B_{B}\), and each side color is represented as follows:  
\[\begin{cases}R_{G}=a_{R_{G}}R_{R}+b_{R_{G}}\\ R_{B}=a_{R_{B}}R_{R}+b_{R_{B}}\\ G_{R}=a_{G_{R}}G_{G}+b_{G_{R}}\\ G_{B}=a_{G_{B}}G_{G}+b_{G_{B}}\\ B_{R}=a_{B_{R}}B_{B}+b_{B_{R}}\\ B_{G}=a_{B_{G}}B_{B}+b_{B_{G}}\\ \end{cases}. \tag{3}\]  
Here, \(R_{G}\) and \(R_{B}\) stand for side colors of the R-channel, \(G_{R}\) and \(G_{B}\) for the G-channel, and \(B_{R}\) and \(B_{G}\) for the B-channel. In this way, when \(I_{H}\) is estimated, we only consider the main color components of each channel. Given a distorted image \(I_{D}\), we have  
\[\begin{bmatrix}R_{I_{D}}\\ G_{I_{D}}\\ B_{I_{D}}\end{bmatrix}=\begin{bmatrix}R_{R}\\ R_{G}\\ R_{B}\end{bmatrix}+\begin{bmatrix}G_{R}\\ G_{G}\\ G_{B}\end{bmatrix}+\begin{bmatrix}B_{R}\\ B_{G}\\ B_{B}\end{bmatrix}\]  
with Equation (3), which can be rewritten as  
\[\begin{bmatrix}1&a_{G_{R}}&a_{B_{R}}\\ a_{G_{R}}&1&a_{G_{B}}\\ a_{B_{R}}&a_{B_{G}}&1\end{bmatrix}\begin{bmatrix}R_{R}\\ G_{G}\\ B_{B}\end{bmatrix}=\begin{bmatrix}R_{I_{D}}\\ G_{I_{D}}\\ B_{I_{D}}\end{bmatrix}-\begin{bmatrix}b_{G_{R}}+b_{B_{R}}\\ b_{R_{G}}+b_{B_{G}}\\ b_{R_{B}}+b_{G_{B}}\end{bmatrix}. \tag{4}\]  
\[\begin{bmatrix}1&a_{G_{R}}&a_{R_{B}}\\ a_{G_{R}}&1&a_{G_{B}}\\ a_{B_{R}}&a_{B_{G}}&1\end{bmatrix}\text{ and }\begin{bmatrix}b_{R_{G}}+b_{R_{B}}\\ b_{G_{R}}+b_{G_{B}}\\ b_{B_{R}}+b_{B_{G}}\end{bmatrix}\text{ are known from the experiment (Figure 9b), we can compute \(\begin{bmatrix}R_{R}\\ G_{G}\\ B_{B}\end{bmatrix}\) by solving Equation (4). With the color \((R_{R},G_{G},B_{B})_{RGB}\), we compute the required input \(I_{H}\) for the OST-HMD by solving the polynomial equation with the Newton-Raphson method.  
Figure 8: Observed color data plot for the decay of light through the system.
# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 5 A Neural-Symbolic Implementation of T-Pattern
### Discussion of the Results  
The experimental results show that the T-pattern model enables smart IoT objects to estimate confidence values in their partners for three trust issues (honesty, security, and expertise) derived from reliability values, with an average error ranging from 5% to 15% with respect to actual values, demonstrating that the model has a sufficient accuracy. Accuracy improves with network size: in networks with 16,000 smart objects, the average error is approximately 5%. In particular, in our simulations, larger networks collected more interactions (the number _i_), thus providing a more precise inference mechanism during training with respect to small networks. This results do not represent a limitation for small networks because, as we verified in our simulations, even smaller networks, sooner or later, will reach a number of interactions to decrease the measured error.  
Figure 4: Percentage error \(e(SEC)\) for different values of number of smart IoT objects \(n\) and number of groups \(k\).  
Figure 3: Percentage error \(e(HON)\) for different values of number of smart IoT objects \(n\) and number of groups \(k\).  
We also observe that the average error increases with the number of groups, as a higher number of groups complicates the inference process. While the average error with only 5 groups ranges from 5% to 11%, it increases to between 11% and 15% with 20 groups. We highlight that increasing the number of groups imply increasing the number of logical rules that the inference engine has to extract. It has been demonstrated in [12] that the effectiveness of the logical rules in representing the data with which the neural network is trained decreases with the number of logical rules; thus, this result was expected.  
As we further discuss in our conclusion, we aim to improve this particular aspect in the near future. We finally observe that the experimental results are consistent across all trust issues derived using the T-pattern model.
## 2 System Model and Problem Formulation  
### System Model  
We consider an IoV network architecture consisting of a set of vehicles \(V=\{v_{1},v_{2},\ldots,v_{N}\}\) and a central server \(S\), as illustrated in Figure 1. Each vehicle \(v_{i}\in V\) is equipped with multiple sensors, communication modules (e.g., cellular, Wi-Fi, DSRC [36]), and onboard computing units. These capabilities enable vehicles to collect local data, perform computations, and transmit model updates to the central server or other vehicles in real time. This architecture lays the groundwork for our trust-based mechanism, which ensures that only reliable vehicles contribute to the federated learning process, as detailed in subsequent sections.  
Each vehicle \(v_{i}\in V\) generates local datasets \(D_{i}\) containing data points \((x_{ij},y_{ij})\). Here, \(x_{ij}\) denotes the \(i\)-th sensor on vehicle \(v_{i}\) that produced the data, and \(j\)-th represents the index of the data point collected by the sensor, while \(y_{ij}\) represents the corresponding label or action. FL enables vehicles to train local models \(LM_{i}\) on their datasets \(D_{i}\) while maintaining data privacy by transmitting only model updates to the central server \(S\). The server aggregates these updates to refine the global model \(GM\)[7]. While this approach preserves privacy and leverages distributed intelligence, it requires mechanisms to filter contributions from trustworthy vehicles to safeguard the global model's integrity against malicious participants.  
The dynamic nature of IoV networks, characterised by fluctuating network conditions, varying vehicle states, and evolving environmental contexts, requires a trust evaluation mechanism. Vehicles continuously move in and out of coverage areas, face changing resource availability, and encounter different road or traffic conditions. Against this backdrop, an effective trust management strategy must integrate multiple factors and remain flexible in assigning their importance over time, ensuring that the global FL model is trained using updates from genuine, reliable sources.  
Figure 1: IoV architecture.