esa-sceva/satcom-chunk-collection huggingface dataset:
# A V2V Identity Authentication and Key Agreement Scheme Based on Identity-Based Cryptograph  
[PERSON]  
Cellular vehicle to everything (C-V2X) is a technology to achieve vehicle networking, which can improve traffic efficiency and traffic safety. As a special network, the C-V2X system faces many security risks. The vehicle to vehicle (V2V) communication transmits traffic condition data, driving path data, user driving habits data, and so on. It is necessary to ensure the opposite equipment is registered C-V2X equipment (installed in the vehicle), and the data transmitted between the equipment is secure. This paper proposes a V2V identity authentication and key agreement scheme based on identity-based cryptography (IBC). The C-V2X equipment use its vehicle identification (VID) as its public key. The key management center (KMC) generates a private key for the C-V2X equipment according to its VID. The C-V2X equipment transmit secret data encrypted with the opposite equipment public key to the other equipment, they authenticate each other through a challenge response protocol based on identity-based cryptography, and they negotiate the working key used to encrypt the communication data. The scheme can secure the V2V communication with low computational cost and simple architecture and meet the lightweight and efficient communication requirements of the C-V2X system.  
C-V2X; V2V; identity authentication; key agreement; IBC
## 4 Experiment
### Parameter Settings  
The experimental configuration and network parameters were as follows:  
Figure 3: Schematic diagram of the deformation method.  
* Hardware equipment: NVIDIA GeForce RTX 3080 GPU;
* Operating system: Windows 10 Professional Workstation Edition;
* Software tools: Python 3.10 programming language, PyTorch 2.0 deep learning framework, and other third-party libraries;
* Batch size: 8;
* Initial learning rate: 0.001;
* Learning rate decay: multiply by 0.7 every 10 epochs;
* Optimization algorithm: Adam optimization algorithm [14];
* Evaluation indicators: L1 Loss (\(c=1\)) [15] and RMSE;
* Network module input and output feature parameters, as shown in Table 1.  
In the training process, in addition to the training time, the size of the dataset and number of iterations also affect the network. The results shown in Figure 4 are obtained through comparative experiments.  
As shown in Figure 4a, the loss value decreases rapidly during the first 200 iterations. Between 200 and 400 iterations, the loss values tend to converge. Between 400 and 1000 iterations, the fluctuation in the loss value is less than or equal to \(5\times 10^{-7}\), and the  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**No.** & **Module Name** & **Layer Name** & **Input Shape** & **Output Shape** \\ \hline
1 & Cloud Feature Module & Conv-BN-ReLU & [1028,3] & [1028,256] \\
2 & Control Point Feature Module & GRU & [20,3] & [20,256] \\
3 & Changed Control Point & GRU & [20,3] & [20,256] \\ Feature Module & GRU & [20,3] & [20,256] \\  & & & [20,256] & \\
4 & Connect Feature Module & Concat & [1028,256] & [1248,256] \\  & & & [20,256] & \\
5 & Convolutional Block & SA & [1248,256] & [1,256] \\  & Attention Module & & & \\
6 & Full Connection & FC & [1248,256] & [1208,256] \\
7 & Output Feature Module & Conv-BN-ReLU (3 groups) & [1028,256] & [1028,3] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network module input and output feature parameters.  
Figure 4: (a) Decline of loss during the iteration process; (b) statistical results of bias after deduction of all training data under different datasets.  
disturbance range is less than 1% of the loss value. This experiment indicates that the training results of the network are not significantly optimized when the number of iterations exceeds 400. Therefore, for the network training used in this study, the number of iterations was set to 400.  
In addition, we first conducted network training on datasets of different sizes, deduced the 3D non-rigid transformation of all data through the obtained network model, and finally determined the deviation (i.e., Euclidean Distance) between the deduction and actual results. As shown in Figure 4b, the experimental results show that when the dataset reached \(10^{5}\), the deviation statistical results tend to be stable and distributed more compactly. In contrast, when the dataset size is \(10^{5}\), Q3 (Upper Quartile), Q2 (median), Q1 (Lower Quartile), and the mean present the smallest values, and the upper and lower limits approach the minimum values. The detailed results of all deviation statistics are listed in Table 2.
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 7 Performance Evaluation
### Asymmetric Topologies  
We also evaluated our procedure in asymmetric scenarios in order to evaluate how well it improves the selection of the best route available. Given that the three estimation procedures perform similarly at low traffic loads, we focused on high traffic load conditions.
## 6 Conclusions  
This paper proposes a haze removal system that considers both atmospheric light and transmission maps and avoids the shortcomings of most state-of-the-art approaches. The proposed system comprises two essential parts, a simple \(AL\) estimation algorithm called \"A-Est\" that considers image blurriness and an efficient transmission map estimator based on a cascaded multi-scale CNN architecture. This architecture has two sub-tasks, one for generating a rough transmission (CMCNN\({}_{tr}\)) map and the other to refine it (CMCNN\({}_{t}\)). Although the proposed model is learned using hazy synthetic images, it can handle real-world hazy images effectively as well. Experimental results demonstrate that the proposed system estimated \(AL\) and t accurately, producing high-quality promising dehazing outcomes that are free from haze, without distorted colors, and improved contrast visibility. As future work, we will carry on with improving the proposed model, where we will integrate the atmospheric light estimation in it to further increase the speed of the dehazing system.  
Conceptualization, S.H.; Formal analysis, S.H., and D.W.; Funding acquisition, D.W.; Investigation, S.H.; Methodology, S.H.; Project administration, D.W.; Software, S.H. and D.W.; Supervision, D.W.; Validation, D.W.; Writing--original draft, S.H.; and Writing--review and editing, S.H. and D.W. All authors have read and agreed to the published version of the manuscript.  
The work presented in this paper was supported by the National Natural Science Foundation of China under Grant no. 61370201.  
The authors declare no conflict of interest.  
Figure 16: Visual comparison on nighttime hazy images: (**a**) hazy image; (**b**) [PERSON]’s method [14]; (**c**) [PERSON]’s method [28]; (**d**) [PERSON]’s method [26]; and (**e**) our method;.
# Approximate LSTM Computing for Energy-Efficient Speech Recognition
## 3 Similarity-Based LSTM Operation
### Pseudo-Skipping Method for Approximate LSTM Operations  
In order to further relax the computational costs in the algorithm level, we also present novel approximate computing of LSTM cell when the LSTM inputs are relatively similar but not enough to disable their cell operations. In this work, we define the additional threshold \(\theta_{\text{p}}\) to activate the approximate computing rather than the original LSTM cell when \(\theta_{\text{p}}<S_{t}<\theta_{\text{s}}\). To realize the approximate LSTM operations, more precisely, we adopt the concept of the delta-RNN scheme from [12], which calculates the difference vector between two consecutive inputs followed by the clipping operation, increasing the sparsity of matrix multiplications. As we only apply the delta computing for marginally-similar cases, it is shown in [14] that the LSTM cells activating this pseudo-skipping mode avoid operations from the inputs having large differences naturally. When the pseudo-skipping cases are detected, therefore, we can allow more aggressive approximation on the LSTM operations by reducing the computing resolution compared to the normal LSTM operations. In other words, as illustrated in Figure 4c, the time positions with large input changes automatically activate the original LSTM cells with high-resolution MAC operators, and the pseudo-skipping scenario is only used for handling the small number of changes between two inputs by exploiting the reduced computing precision. Combined with the cell-skipping method described in the prior subsection, the algorithm-level optimization can reduce the number of MAC operations of DNN-based speech recognition by 49% when compared to the original DeepSpeech processing, relaxing the computational complexity accordingly while maintaining the WER performance. To develop the hardware accelerator, we reduce half of the original computing resolution for approximate LSTM operations, allowing the high utilization of hardware resources.
## 5 Experimental Results
### Simulation Setup
The proposed SA-MCSDN algorithm achieves better performance than the HS-PSO and SA-FFCCP algorithms in terms of the impact of throughput (395.57 Kbps). Figure 11 shows the tradeoff between the algorithms mentioned above. The proposed SA-MCSDN algorithm achieves better performance than the HS-PSO and SA-FFCCP algorithms in terms of the impact of throughput (395.57 Kbps). Figure 10: Comparison of reliability. Figure 9: Comparison of average delay. Figure 12 illustrates a comparison of the algorithms listed above. The proposed SA-MCSDN algorithm achieves slightly better performance than the SA-FFCCP algorithm and slightly worse performance than the hybrid HS-PSO algorithm in terms of the impact of the cost metric (31.27). Figure 13 denotes the convergence of the proposed algorithm with the previously proposed algorithms (to a large extent) in terms of the fitness value (Table 2). Figure 11: Comparison of throughput. Figure 4.0 Comparison of cost.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 6 Discussion
### Computational Limitations of the MVT Model and Considerations for Real-Time Deployment  
The MVT model has demonstrated competitive performance in classifying malicious files based on visual representations. However, its architecture, built upon transformer mechanisms, imposes significant computational demands, particularly due to the self-attention layers applied over high-resolution images.  
During the training and validation phases, the model was executed on an NVIDIA RTX 3090 GPU with 24 GB of VRAM, which enabled efficient processing of 256 \(\times\) 256 byteplot images and large-scale data batches. While this experimental setting allowed for optimal parameter tuning and performance evaluation, it does not reflect the typical constraints of real-world operational environments, where hardware resources are often limited and real-time inference is required. The main limitations identified are as follows:  
* High memory and compute requirements: the self-attention mechanism scales quadratically with input size, increasing inference cost.
* Increased processing latency: acceptable in offline analysis scenarios but suboptimal for real-time detection or automated response systems.
* Challenges in deployment on embedded devices or resource-constrained infrastructures.  
To address these challenges and improve the operational feasibility of the model, we propose several future research directions. First, model optimization through compression techniques such as pruning, quantization, and knowledge distillation can significantly reduce computational complexity while preserving predictive performance. Second, the use of lightweight transformer-based architectures, such as MobileViT or TinyViT, offers promising alternatives for deployment in edge computing or mobile environments. Finally, a hybrid edge cloud strategy may be adopted, where initial preprocessing and infer ence are performed locally, while more computationally intensive analysis is delegated to cloud infrastructure.  
These strategies are intended to improve the model's deployability in real-time cybersecurity contexts, including threat detection systems, critical infrastructure monitoring, and IoT-based defense mechanisms.
## 3 Shift Strategy Modification Based on Vehicle Load and Road Slope  
### The Traditional Two-Parameter Economical Shift Strategy  
The economical shift strategy means that the shift timing selected by the vehicle should be for the minimum fuel consumption. The fuel consumption per hour of the vehicle is  
\[Q_{t}=\frac{b_{E}\cdot P_{E}}{3600}. \tag{8}\]  
When the vehicle is in the non-braking state (\(T_{B}\)\(=0\)), the engine output power can be described as  
\[P_{E}=\frac{T_{V}\cdot v_{V}}{\eta_{T}}=\frac{v_{V}}{\eta_{T}}\bigg{(}Mgf_{ \text{roll}}+\frac{C_{D}S_{V}}{21.15}{v_{V}}^{2}+Mg\sin\alpha+\delta Ma_{V} \bigg{)}, \tag{9}\]  
where \(\eta_{T}=\eta_{g}\cdot\eta_{TC}\) is the efficiency of the powertrain system. Substituting Equation (9) into Equation (8), we can get  
\[Q_{t}=\frac{b_{E}\cdot v_{V}}{3600\eta_{T}}\bigg{(}Mgf_{\text{roll}}+\frac{C_{ D}S_{V}}{21.15}{v_{V}}^{2}+Mg\sin\alpha+\delta Ma_{V}\bigg{)}. \tag{10}\]  
The relationship between vehicle speed and engine speed is  
\[v_{V}=\frac{0.377\cdot r_{w}\cdot\omega_{E}}{i_{g}i_{FD}}. \tag{11}\]  
From Equations (1), (2), and (11), we can know  
\[b_{E}=b_{E}(\theta,v_{V}). \tag{12}\]  
Combine Equations (10) and (12), we can know that the fuel consumption per hour \(Q_{t}\) is a function of vehicle speed \(v_{V}\), throttle opening \(\theta\), vehicle mass \(M\), and road slope \(\alpha\)  
\[Q_{t}=Q_{t}(\theta,v_{V},M,\alpha). \tag{13}\]  
The relationship between the vehicle fuel consumption \(Q\) and the fuel consumption per hour \(Q_{t}\) is  
\[Q=\int Q_{t}dt=\int Q_{t}dv_{V}\frac{dt}{d\sigma_{V}}=\frac{1}{a_{V}}\int Q_{ t}dv_{V}. \tag{14}\]  
As shown in Figure 4, the vehicle fuel consumption \(Q\) is the area enclosed under the fuel consumption per hour curve. The intersection of the two adjacent gears, as shown in Figure 4 point C, is the best fuel economy shift point (if there is no intersection, take the minimum position as the best fuel economy shift point at the same vehicle speed, as shown at points A and B).  
Integrating the shift points under different throttle openings, the traditional two-parameter economical shift curve of the truck without considering vehicle load and road slope (\(M=M_{\text{max}},\alpha=\alpha_{0}\)) is obtained, as shown in Figure 5.
## 4 Platform Architecture and User Interface
### Primary Features Detection  
The primary features detection module is comprised of five steps and is responsible for detecting urban features. Initially, the video frames from the depersonalization module are retrieved. The following step consists of the object detection and tracking process. Specifically, information regarding the bounding box coordinates of each detected object in the frame, the object class name, and the score representing the model's confidence in the detection is calculated, utilizing the detection models analyzed in Sections 3.4.1 and 3.4.2. Then, object tracking is applied to avoid counting the same object multiple times, using the mechanism described in Section 3.5. The tracking process assigns a unique identifier to each detected object in the video stream. In the next step, the location of each object is estimated, making use of the techniques documented in Section 3.6. Moreover, the process of estimating the road width and the existence of a sidewalk is performed using the semantic segmentation models and estimation techniques defined in Section 3.7. Finally, the results are transformed into JSON format and forwarded to the communication queue of the message broker for the Gateway service.
## 5 Results and Analysis
### Uncertainty Analysis (UA) of Models
#### 5.3.1 Lag-Based Models UA  
The visualization of interrelationships of the uncertain variables between the lag-based models for both catchments is shown in Figure 20 (Fal at Tregony) and Figure 21 (Teifi at Glanteifi). Figures 20a-c and 21a-c show the interrelationship of LB, UV and mean, SE and ME and WCB, respectively, for the models. For both catchments, LB, UV and mean (Figures 20a and 21a) and SE (Figures 20b and 21b) are lower for EO-ELM compared to other models. The ME (Figures 20b and 21b) is almost similar for all models except SVR and GBM (lower compared to other). From UA, it is observed that WCB (Figures 20c and 21c) also lower in EO-ELM. From UA, it is confirmed that EO-ELM has better prediction accuracy in the prediction runoff variable for both catchments.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 6 Discussion
### BIM Technology  
Among the various methods discussed, BIM Technology is widely used. This is because BIM provides a visual digital model of the building, which enables the collected data to have a carrier and compare with each other. BIM also has good simulation performance, can carry out 3D visual simulation of design, construction, and other solutions, find problems in the simulation, and solve the problem in the planning stage.  
In the digital management of construction projects, BIM is an important step. A major advantage of BIM is the comprehensive collection, linking, and provision of data for different planning, construction, and operation tasks. In the context of construction management, it is very common to apply 4D building model by connecting schedule activities with corresponding building elements. Based on 4D building model, construction sequence can be analyzed, and progress monitoring can be supported.
## 4 Discussion
Figure 12: Evolution (2016–2021) of the temporal coverage achievable using a multi-satellite monitoring system integrating L8-OLI, S2-MSI and Terra-ASTER observations. The plot shows for the Shishaldin volcano location the average temporal gap among observations. Note as the launch of Sentinel-2B at the end of 2017 dramatically reduced the temporal gap from 4 on average (maximum 9) to 2 (maximum 3) days. validation and training. The RASTer contribution to detect and map lava flows and other thermal features related to volcanic activity is particularly relevant, considering that AVA products are no longer available (since 2017), the NHI usage on ASTER data is limited to 2000-2008 ([PERSON] et al., 2017) and that other recent systems such as the ASTER Volcanic Thermal Output Database (AVTOD; [55]) operate over specific regions of interest and not globally. Indeed, ASTER also continues to play an important role in supporting volcanic activity monitoring from space at the time of writing. This is demonstrated by Figure 13, where lava flow (moving in E and SE direction toward Valle del Bove), associated with the powerful Mt. Etna paroxysmal of 16 February 2021 and the thermal activity at summit craters were correctly identified and mapped by RASTer.
# Time-Dependent Performance of a Multi-Hop Software Defined Network
## 2 Diffusion Model of an SDN Switch
### Steady-State Diffusion Model with General Interarrival and Service Processes
length probability density function is \(f(x)\), the probability density function of the packet's queueing delay is:  
\[f_{R}(t)=\int_{0}^{\infty}\Bigg{[}\frac{x}{\sqrt{2\Pi xt^{3}}}e^{-\left[\frac{2 \pi\beta}{x}+\frac{(x-\beta)^{2}}{2\pi t}\right]}\Bigg{]}f(x)dx. \tag{8}\]  
Figure 3 illustrates this result with a few curves of \(f_{R}(t)\) for different values of the traffic intensity \(\rho\), and with the parameters; \(C_{A}^{2}=C_{B}^{2}=1\), which have been used in all the examples of Figures 2-4. The mean delay experienced by a packet whose flow rule is contained in the flow table will be the sum of the queueing delay and processing time. If the mean queueing delay is \(D_{q}\) and the processing time is \(t_{p}\), then the mean packet delay at the switch \(D_{s}\) is:  
\[D_{s} = D_{q}+t_{p}=\int_{0}^{\infty}tf_{R}(t)dt+\frac{1}{\mu}. \tag{9}\]  
However, if a flow entry for an arriving packet is not found, then the packet is encapsulated and sent to the controller, which determines the flow rules, installs the flow entry for the packet, and sends back the packet in the packet-out message. In that case, the  
\[p_{N} = \varrho p_{0}e^{z(N-1)}\.\]0 Probability \(p_{N}\) of the buffer overflow; illustration of the solution given by Equation (4). \[p_{N} = \varrho p_{0}e^{z(N-1)}\.\]1 Density \(f_{R}(t)\) of the queueing time; see Equation (8). delay experienced by the packet, \(D\) is the sum of the delay in the switch and the delay at the controller \(D_{c}\)[27; 28; 29; 61; 62; 63]:  
\[p_{N} = \varrho p_{0}e^{z(N-1)}\.\]2  
The modelling of the controller's working mechanism to determine \(D_{c}\) is beyond the scope of this work and will be considered in future works, and in our numerical examples all the flows are known, i.e., \(p=0\).
# GPU-Accelerated Eclipse-Aware Routing for SpaceWire-Based OBC in Low-Earth-Orbit Satellite Networks
## 2 Problem Description
### Routing Problem in the LEO Network
Furthermore, while small-scale SpaceWire networks are relatively simple to design and operate, as the network size increases, traffic management and routing optimization become critical challenges. Therefore, performing thorough simulations before deploying the actual network is essential for verifying network performance and implementing effective routing strategies compatible with the SpaceWire protocol.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 3 Opportunities Given by System-Level Testing
### Opportunities Given by TID Tests  
Firstly, the opportunities resulting from performing the TID system-level radiation tests will be presented. These observations come from the Co-60 and X-rays irradiations performed on the PoL modules.
## 4 Experiments and Analyses  
### Frequency Response Characteristics of Anti-Corrosion Pipes  
When ultrasonic waves propagate in the pipes, the interaction between fundamental frequency and damage leads to the generation of higher-harmonics. This is a kind of leakage of energy. The energy of fundamental frequency is leaked to higher-harmonics in the propagation of ultrasonic wave. Therefore, usually the amplitude of higher-harmonics is lower than the fundamental frequency. To obtain an obvious result of the nonlinear effect, a sweep signal from 0 kHz to 500 kHz is performed to obtain the response characteristics. The sweep time is 1 s, and the amplitude of sweep signal is 1 V. The sweep sine responses are shown in Figure 5.  
Figure 5 shows that frequency response of the anti-corrosion pipe is stronger from 130 kHz to 250 kHz, and the strongest response is at 159.6 kHz. Therefore, the excitation frequency was 80 kHz, and the frequency of second-order harmonic was 160 kHz which almost has the strongest response, and the third-order harmonic was 240 kHz.  
In the damage detection experiment, the excitation signal was a string of sine wave with 6 V, and the excitation time was 0.001 s. The excitation signal was amplified 50 times through the amplifier. The sampling rate is 20 MHz. The galvanized steel pipe and polyethylene pipe were firmly bonded together. The side view of the anti-corrosive pipe is shown in Figure 6. The delamination damage was  
Figure 4: Lined anti-corrosion pipes with Lead Zirconate Titanate Transducer (PZT), T and R stands for excitation transducer and receiving transducer respectively.  
made by stripping the polyethylene-layer of the galvanized steel pipe. In the experiments, six types of delamination damages with lengths of 0 mm, 4 mm, 8 mm, and 12 mm, 16 mm, and 20 mm were investigated. The width of all damages is 40 mm. The delamination damage length was increased from 0 mm to 20 mm. For a damage state, the data collection was completed and the delamination damage length was increased to perform the next round of the data collection. The schematic illustration of delamination damage is shown in Figure 7.  
Figure 5: The received sweep signals. (**a**) time-domain; (**b**) frequency-domain.  
Figure 6: Side view of the anti-corrosive pipe.  
Figure 7: Schematic illustration of delamination damage. T and R stands for excitation transducer and receiving transducer respectively.