esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Profile Retrieval
#### 2.5.4 Cloud Layer
The inversion is performed iteratively refining a global cloud LIDAR Ratio, until the integrated extinction profile matches \(VOD_{\text{cloud}}\). The LIDAR Ratio is,nevertheless, constrained to between limits of 5 Sr and 120 Sr. If the ratio fails to converge within a maximum number of iterations, the algorithm uses the nearest limit and rescales the extinction coefficients. In Figure 17, we show the result of a cloud layer identification and inversion on the elastic 355 nm line, obtained during a clear night at the ORM. Typical for a clear astronomical night, the ground layer is hardly detectable here, particularly in the UV part of the light spectrum in which the Cherenkov light is detected by the CTAO telescopes. From about 5 km above ground extending to \(\sim\)7.5 km, a typical structured cirrus cloud has entered the LIDAR's field-of-view. The cloud layer algorithm has correctly identified the existence of a cloud layer identifying those fit \(\left(\chi_{i}^{2}/N_{\mathrm{dof}}\right)^{\mathrm{min}}\) (Equation (46)), which exceed the threshold of 3.5 (marked as the dark red parts of the \(\log 10(\left(\chi_{i}^{2}/N_{\mathrm{dof}}\right)^{\mathrm{min}})\) line) and found the part of the free troposphere just below the cloud (lower dark violet line marked as \(C_{\mathrm{base}}\)). Together with the equivalent part of the free troposphere above the cloud, marked as \(C_{\mathrm{base}}\), the VOD could be determined using Equation (49). The iterative Klett inversion converged on a Lidar ratio of 19.9 sr, a typical value for the cirrus clouds observed above the observatory (see [9]) and the extinction coefficient profile shown on the right side.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 6 Open Issues and Future Research Directions
### Adversarial Training and Attacks  
Adversarial training and attacks present significant challenges for deepfake detection, particularly as adversarial examples can easily deceive Deep Neural Networks (DNNs). Adversarial attacks cause concern due to their transferability across models, allowing inconsistencies designed for one deepfake detector to potentially bypass others, posing a critical threat, as attackers could exploit open-source models to create adversarial input that can evade. To address this, advanced defences like denoising-based approaches, such as D-VAEGAN [66], have shown promise by projecting adversarial examples into a low -dimensional latent space and reconstructing clean images without retraining the detection model. However, deepfake detectors must also contend with the variability in data pre-processing and generation techniques. Future research needs to focus on developing more adaptable defences that can effectively mitigate the cross-model transferability of adversarial attacks while maintaining robust real-time performance.
# Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
## 2 Related Work
### Deterministic Latency Name Resolution  
Designing an ICN name-resolution system that can effectively meet the high scalability and deterministic latency requirements of 5G-IoT is extremely challenging. Figure 1 shows the DLNR framework. It is a potential solution proposed by [PERSON] et al. [23]. DLNR develops a locally enhanced name-resolution mechanism to provide a one-to-many relationship between an identifier and locators with the constraints of scopes or distances to achieve deterministic low latencies in a limited domain by accelerating the name-resolution process. It supports flat-name based NRS schemas, and the distributed name solution nodes in this schema are organized by a tree structure. By contrast with current NRS approaches, DLNR focuses on the underlying network and partitions the network entities into several nested hierarchical elastic areas (HEAs) according to their different deterministic latency constraints. There is a dedicated name-resolution node called the HEA Manager (HM) in every HEA. The HM is responsible for establishing, storing the mapping of identifiers to locators and it provides the primary name-resolution service to requesters in the HEA. In DLNR, the physical network is partitioned into multilayer overlay networks, corresponding to multiple level HEAs. A higher-level HEA consists of several lower-level HEAs. Different level layers of HEA have different latency constraints. The higher level means a broader coverage and a higher latency upper bound. HEAs in the same level hold the same latency with no area overlap. It is worth noting that [PERSON] et al. carefully analyzed the proportion of latencies contributed by each procedure in the name-resolution event. They found that the transmission latency was much greater than that of the other steps. Therefore, they used the transmission latency as the organization basis of the system structure, which is also used in this paper.  
The design of DLNR effectively addresses the 5G-IoT requirements. The method of the hierarchical organization improves the scalability and satisfies differentiated demands for latency. The partitioning of HEAs realizes the name resolution of deterministic latency, which limits the queries in the domain and reduces the query traffic between domains. The nested tree structure improves the management efficiency. The area partitioned according to the latency can be constructed ela  
Figure 1: The nested structure of deterministic latency name resolution (DLNR). The physical network is partitioned into several hierarchical elastic areas (HEAs). These HEAs are nested organized, and each HEA has an HEA Manager (HM) to provide a name-resolution service. Each layer is constrained by an upper bound of latency.  
in DLNR are typical multilayer overlay networks, and a reasonable partition for each layer and the placement of name-resolution servers are the keys to the whole architecture working efficiently.
## 2 Overall System Design  
### Functional Requirements  
In satellite laser communication, the Sun-transit phenomenon refers to the alignment of the Sun, satellite, and ground terminal, during which strong solar background radiation enters the receiver's field of view, resulting in a sharp drop in the signal-to-noise ratio (SNR) and possible communication interruption. This phenomenon is periodic and transient, with interference typically lasting several minutes. Sunlight in the spectral neighborhood of the 1550 nm communication band (1540-1560 nm) still emits considerable radiance, posing significant interference risks--particularly under low signal power conditions or with large receiving apertures.  
To investigate system-level responses under such conditions, a ground-based simulation platform is required to generate solar background radiation with high spectral consistency, tunable intensity, and spatial uniformity. Coupled with a weak signal generation module, such a system should recreate co-propagated interference scenarios within narrow angular fields. The design must fulfill the following key requirements: (1) spectral output matching the communication band while accounting for source wavelength and power variation; (2) continuously adjustable irradiance to simulate various interference levels; (3) uniform far-field beam profile across the receiver field of view; and (4) support for \(\pm 3^{\circ}\) incident angle modulation to mimic dynamic solar alignment during transit events.
# Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management
## 3 Methodology
### Machine Learning Based on a Data Analysis Algorithm for AI Management
Each convolutional layer is followed by a max-pooling layer with a kernel size of \(3\times 3\) and local response normalization. This set of convolutional layers is followed by two fully connected layers, each with 4096 incoming connections and a hyperbolic tangent activation function. A dropout of 0.5 is applied across these two fully connected layers during the training to offset the residual over-fitting. Finally, we obtain a fully connected layer with two incoming connections and a soft-max activation output [33]. The experiment was performed using a dataset of 9852 images from [PERSON] et al. [33], and the testing was conducted on 2931 images. The experiment was performed using an NVIDIA GeForce GTX 2080 Ti with 12 GB onboard memory and a deep learning framework [34] on an Intel Xeon CPU E3-1230 v6 with 32 GB RAM. From the results, it was noted that FireNet could predict the test data with an accuracy of 91.2%. Figure 6 displays the grad class activation map (CAM) results, elucidating how the computer differentiated between a fire and a non-fire. This demonstrates that this algorithm can be used for automatic fire detection and can suggest the appropriate response.
# DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization
## 3 The Proposed Work  
A methodology framework is proposed in this section, and it utilizes ACO as a metaheuristic optimization technique for feature selection. ACO is incorporated to address four advanced types of ML: XGBoost, KNN, RF, and CNNs. These ML algorithms have been chosen because of their demonstrated performance in conducting analyses on complex high-dimensional datasets and their performance established in the research community within the field of network traffic analysis. Additionally, the CIRA-CIC-DoHBrw-2020 dataset has been used as an evaluation benchmark dataset since it is a full and well-known dataset containing malicious DOH and benign DOH traffic. In addition to data preprocessing, several measures, including data balancing, data cleaning, normalization, and other fundamental activities for binary and multiclassification tasks, are likewise applied. Figure 2 provides a clear view of the proposed workflow and illustrates a detailed diagram of the methodology.  
All experiments were conducted on a system with an Intel Core i7 processor, 32 GB RAM, and a 512 GB SSD running Windows 11. The implementation was carried out using Python 3.11 within the Anaconda distribution, utilizing the Spyder IDE. Commonly used libraries such as scikit-learn, pandas, matplotlib, and imbalanced-learn were employed for model development. No GPU acceleration was used during experimentation.  
While our system improves DoH tunneling detection, critical awareness of its limitations and potential misuse is essential. Adversaries may attempt to evade detection by perturbing features selected via ACO (e.g., altering query timing or domain distributions). To address this, future work could integrate adversarial training to harden the model against evasion attacks. Additionally, though our method analyzes encrypted DoH traffic metadata, we emphasize that it does not decrypt user payloads, thereby preserving privacy. Strict access controls and audit mechanisms should be enforced during deployment to prevent abuse. Proactive collaboration with cybersecurity ethics boards could further ensure transparency and accountability.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 3 Results
### Evacuations in Historical District
#### 3.4.1 Evacuation Distance and Duration  
In both critical evacuation scenarios, evacuation distances increase rapidly during the initial 15 min, followed by stabilization. Ultimately, the average evacuation distances converge, with a final discrepancy of 1.6%. Scenario 2-600 has a higher proportion of evacueues within the \(\leq\)100 m range (18.33% vs. 12.16%), while Scenario 16-600 shows a higher proportion within the 100-500 m range (87.19% vs. 80.76%).  
Regarding evacuation duration, the 16-600 scenario exhibits a longer average evacuation duration, exceeding that of 2-600 by approximately 52.2 s overall. Scenario 16-600 has a slightly higher percentage in the 200-400 s range (20.10% vs. 18.93%), while Scenario 2-600 has a higher proportion in the \(\leq\)200 s range (21.40% vs. 14.51%). However, in the \(>\)800 s range, 16-600 also has a higher percentage (32.60% vs. 28.46%).  
Analysis of evacuation time and distance over time highlights the first 15 min as the most critical period, during which both metrics increase rapidly. Due to the greater evacuation pressure on roads in the latter case, wider evacuation pathways are required to accommodate the increased demand.
## 3 Propagation Characteristics of the NUC Beams in Anisotropic Turbulence Along the Horizontal Links
### Spectral Density of the NUC Beams Propagation through Anisotropic Turbulence
However, when the aperture size is in the range of  
Figure 7: **(a–c)** Variation of the _PIB_ of the NUC beam as a function of the propagation distance, **(a)** in free space; **(b)** in the presence of isotropic turbulence; **(c)** in the presence of anisotropic turbulence; **(d)** Variation of the parameter \(P\) against the propagation distance. 0.05 m \(<D<\) 0.15 m, the situation is reversed, i.e., the detector could receive more beam energy using the Gaussian beam. If the aperture size increases further, there is almost no discrepancy between the Gaussian beam and the NUC beam because all the power carried by the beams is captured by the detector. Figure 8b clearly shows that the increase of the anisotropic factor \(\mu_{x}\) leads to the reduction of the advantage of the NUC beam. Figure 8c indicates that only if the structure constant \(C_{n}^{2}\) is smaller than approximately 3 \(\times\) 10\({}^{-17}\) m\({}^{-2/3}\), a higher part of the power of the NUC beam can be received in the output plane, compared to that of the Gaussian beam.
## 7 Results and Discussion
### Validation of the FAEWM  
In order to verify the validity and reliability of the FAEWM, we use the simulation results obtained from the measured traffic volume data. As shown in the Data Collection section, the traffic volume is 3188 veh/h, and the diverging ratio is 51.44%. The simulation results of the four schemes with measured traffic data are listed in Table 23.  
From Table 23, it can be seen that Scheme 1 is the best in terms of TTC and PET, Scheme 2 is the best in terms of number of conflicts and CSI, Scheme 3 is the best in terms of travel time, delays, fuel consumption, and CO emissions, while Scheme 4 performs average in all aspects. It can be seen that Scheme 3 performs better in terms of traffic efficiency, while Schemes 1 and 2 perform better in terms of safety.  
Based on the scores obtained for each solution under different traffic configurations (Figure 10), it can be observed that Schemes 1 and 2 have similar scores under measured traffic conditions. However, as the traffic volume continues to increase, the score for Scheme 3 gradually improves and surpasses Schemes 1 and 2. It is evident that around the measured traffic volume, the difference in traffic efficiency between Scheme 1 and Scheme 2 is not significant compared to Scheme 3, but there is a significant safety improvement. Therefore, when the traffic volume is relatively low, Schemes 1 and 2 receive higher scores. However, as the traffic volume exceeds the maximum capacity of Schemes 1 and 2, the improvement in traffic efficiency for Scheme 3 leads to an increase in its score, making it the optimal solution.  
In summary, the results obtained using the FAEWM are consistent with the actual situation and verify the reliability of the FAEWM method.
# Three-Dimensional Velocity Field Interpolation Based on Attention Mechanism
## 5 Conclusions  
The precise construction of three-dimensional velocity fields occupies an indispensable position in seismic exploration, playing an irreplaceable role in insight and interpretation of underground geological structures. This paper constructs a network model based on a joint attention mechanism, JointA 3 DUnet, which innovatively integrates technologies such as dilated convolution, triple attention, and channel attention. This effectively expands the receptive field, enhances information interaction between dimensions, and adapts well to the irregular horizontal and vertical distribution of geoscience data. Additionally, the introduction of transfer learning further optimizes the network's interpolation performance.  
Through a series of detailed and in-depth experimental verifications, this paper extensively compares and evaluates the performance of the 3 DUnet and JointA 3 DUnet  
capture the characteristics of sparse data and reconstruct them more accurately has always been our research goal. We will also explore the application of JointA 3 DUnet in other domains and data, continually refining the network architecture, and building a better interpolation network.  
In general, the JointA 3 DUnet model not only enhances the accuracy of three-dimensional velocity field interpolation but also opens up new possibilities and perspectives for research and application in related fields, demonstrating the tremendous potential and broad prospects of deep learning in the field of earth sciences.  
Conceptualization, X.Y. and G.H.; Methodology, X.Y. and C.Z.; Validation, L.W. and Y.L.; Data curation, M.S.; Writing--original draft, M.C. All authors have read and agreed to the published version of the manuscript.  
We would like to acknowledge the sponsorship of the National Natural Science Foundation of China (Grant No. 42374144), the National Natural Science Foundation of China (Grant No. 42130812), and PetroChina Science and Technology Major Project (Grant No. 2021 DJ0403).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality concerns related to the data.  
Author [PERSON] was employed by the company PetroChina. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from PetroChina. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
## 4 Low-Cost Experimental Implementation Using on-Premises LTE/4G Network
### Digital Signage Scalability  
In this subsection, we analyzed an example of how we can expand the system by controlling the outputs of the deployment boards and displaying sensor data from the deployment boards on the \"Node-RED\" Dashboard. The Node-RED software runs on an Ubuntu Server, and communication between the deployment board and the Node-RED software was achieved with the MQTT protocol. A smoke sensor detector was installed on the module that controls the digital signage LEDs on Deck 8 (Figure 12). Figure 12 depicts the block diagram of the publish-subscribe communication between the MQTT clients and the MQTT broker.  
If the smoke sensor detects smoke, the module publishes to the Deck 8/way1 topic of the Broker a new message \"Smoke detected\" (1a). At the same time, the module subscribes to the Deck 8/way1 topic (1b), and the second MQTT client (Node-Red dashboard), which has already subscribed to the topic, publishes a message to the module \"Digital Signage RED\" (2). Therefore, when a new message is published to Deck 8/way1 topic, the module receives the \"RED\" or \"GREEN\" messages and turns the LED strips red or green. Following this method, we can add another type of safety sensor that can automatically change the state of the Led strips and inform the dashboard. Except for safety sensors, there is another option to add people count sensors, to help the crew direct the passengers to the muster station. Thus, we can tackle some people's congestion problems. In case of an emergency evacuation there are specific routines for each deck. Passengers are not allowed to inform  
Figure 11: Experimental digital signage. (**a**) SECURE; (**b**) NOT SECURE.  
the digital signage system for safety reasons, because the evacuation plan is guided by the crew. The digital signage dashboard can help the crew to execute the evacuation plan.
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
###### Abstract  
The Third Generation Partnership Project (3 GPP) has specified Cellular Vehicle-to-Everything (C-V2X) radio access technology in Releases 15-17, with an emphasis on facilitating direct communication between vehicles through the interface, sidelink PC5. This interface provides end-to-end network slicing functionality together with a stable cloud-native core network. The performance of direct vehicle-to-vehicle (V2V) communications has been improved by using the sidelink interface, which allows for a network infrastructure bypass. Sidelink transmissions make use of orthogonal resources that are either centrally allocated (Mode 1, Release 14) or chosen by the vehicles themselves (Mode 2, Release 14). With growing interest in connected and autonomous vehicles, the advancement in radio access technologies that facilitate dependable and low-latency vehicular communications is becoming more significant. This is especially necessary when there are heavy traffic conditions and patterns. We thoroughly examined the New Radio (NR) sidelink's performance based on 3 GPP Releases 15-17 under various vehicle densities, speeds, and distance settings. Thus, by evaluating sidelink's strengths and drawbacks, we are able to optimize resource allocation to obtain maximum coverage in urban areas. The performance evaluation was conducted on Network Simulator 3 (NS3.34/5G-LENA) utilizing various network metrics such as average packet reception rate, throughput, and latency.  
cellular V2X; 5G V2X; sidelink; 5G NR; 3 GPP; connected and automated vehicles 2023
# Design and Simulation of On-Orbit Assembly System Based on Insect-Inspired Transportation
## 6 Conclusions  
This paper proposes a robotic assembly solution for on-orbit assembly in space, featuring a lightweight and compact structure that enables the assembly of large components in the microgravity environment. The assembly process is composed of three stages: free-load cruising, load cruising, and load assembly, utilizing a quadrupedal robot's movement and a manipulator arm's grip to perform assembly tasks. Any two feet of the robot can be combined for bipedal movement, while a single foot can hold and assemble components. The study established the robot's motion model and the dynamic model for the assembly process, completing dynamic simulations for all three stages. The simulations analyzed the influence of flexible elements on assembly units, and a simple adjustment was made for their oscillation. Results show that robots with a volume of 1% of the assembly unit can complete on-orbit assembly and effectively regulate the components' flexible oscillation. However, the study has limitations, and the adoption of more advanced control algorithms could further improve the efficiency and stability of the assembly process. This research is of great significance to future on-orbit assembly tasks in space.  
Conceptualization, Y.S.; methodology, Y.S.; software, Y.S.; validation, Y.S. and G.G.; formal analysis, Z.N.; resources, Z.D.; data curation, Y.L.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S.; visualization, Y.S.; project administration, X.H. and Z.D.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China [No. 62073229], Jiangsu Policy Guidance Program (International Science and Technology Cooperation), the Belt and Road Initiative Innovative Cooperation Projects (No. BZ2021016), and EDL fund of Beijing Institute of Space Mechanics and Electricity (Grant No. EDL19092127).  
The data that support the findings of this study are available from the corresponding authors upon reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.