esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Telepresence robots are gaining more popularity as a means of remote communication and human-robot interaction, allowing users to control and operate a physical robot remotely. However, controlling these robots can be challenging due to the inherent delays and latency in the communication systems. In this research paper, we propose a novel hybrid algorithm exploiting deep reinforcement learning (DRL) with a dueling double-deep Q-network (DDQN) and a gated recurrent unit (GRU) to assist and maneuver the telepresence robot during the delayed operating signals from the operator. The DDQN is used to learn the optimal control policy for the telepresence robot in a remote healthcare environment during delayed communication signals. In contrast, the GRU is employed to model the control signals' temporal dependencies and handle the variable time delays in the communication system. The proposed hybrid approach is evaluated analytically and experimentally. The results demonstrate the approach's effectiveness in improving telepresence robots' tracking accuracy and stability performance. Multiple experiments show that the proposed technique depicts improved controlling efficiency with no guidance from the teleoperator. It can control and manage the operations of the telepresence robot during the delayed communication of 15 seconds by itself, which is 2.4% better than the existing approaches. Overall, the proposed hybrid approach demonstrates the potential implementation of RL and deep learning techniques in improving the control and stability of the telepresence robot during delayed operating signals from the operator.  
telepresence robot; healthcare environment; remote management
# Slot Optimization Based on Coupled Airspace Capacity of Multi-Airport System
## 4 Algorithm
_Transp. Res. Part E Logist. Transp. Rev._**2012**, _48_, 1009-1022. [CrossRef]
* [PERSON] (2014) [PERSON]; [PERSON]; [PERSON] The time slot allocation problem under uncertain capacity. _Transp. Res. Part C Emerg. Technol._**2014**, _46_, 16-29. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] Evaluation and optimization of departure flight schedule stability of airport group. _J. Beijing Univ. Aeronaut. Astronaut._**2023**, _49_, 1331-1341. * [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON] Multi-airport Ground Hokling Problem Based on Airline Schedule Optimization: Models and Algorithm. _J. Southwest Jiaotong Univ._**2010**, _45_, 464-469. * [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] On-demand airport slot management: Tree-structured capacity profile and coadapted fire-break setting and slot allocation. _Transp. A Transp. Sci._**2024**, 1-35. [CrossRef]
* [PERSON] et al. (2025) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-Airport Capacity Decoupling Analysis Using Hybrid and Integrated Surface-Airspace Traffic Modeling. _Aerospace_**2025**, _12_, 237. [CrossRef]
* The International Civil Aviation Organization (2018) The International Civil Aviation Organization. _Collaborative Air Traffic Flow Management_, 2018 ed. ; Doc. 9971; The International Civil Aviation Organization: Montreal, QC, Canada, 2018. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems. _IEEE Access_**2021**, \(9\), 57757-57791. [CrossRef]

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# Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
## 2 Related Work
### Machine Learning in Adversarial Settings  
While progress has been made in the past few years in the domain of advanced machine learning techniques for security--including deep learning and data/pattern mining--several key challenges remain for which the state-of-the-art is yet to produce an adequate answer. The machine learning (ML) algorithms used in security analytics applications are also subject to attacks. As the interest in and use of machine learning for security applications increases, so will the awareness of cyber-criminals. When frequently updating an ML model to account for new threats, malicious adversaries can launch causative/data poisoning attacks to inject misleading training data intentionally so that an ML model becomes ineffective. For example, various poisoning attacks on specific ML algorithms [17; 18] were able to bypass intrusion detection systems. More recently, [PERSON] et al. [19] demonstrated how to automate poisoning attacks against malware detection systems. However, keeping ML models fixed can give rise to exploratory/evasion attacks to find the blind spots. Understanding the trade-offs between either keeping a machine-learning model fixed or updating the model, taking into account its complexity, is non-trivial.  
[PERSON] [20] investigated how deep learning can be applied for intrusion detection in the presence of adversaries. Given the fact that deep neural networks have been demonstrated to be vulnerable to adversarial examples in the image classification domain, he argued that deep neural networks also leave opportunities for an attacker to fool the networks into misclassification. He investigated how well state-of-the-art attack algorithms performed against deep learning-based intrusion detection, on the NSL-KDD dataset. He discovered different levels of effectiveness, as well as varying degrees of significance across features in terms of their rates of being selected to be perturbed by an adversary.  
More recently, similar research was done by [PERSON] et al. [21] in the area of intrusion detection for software-defined networks (SDN). They conducted SDN-based experiments with adversarial attacks against a deep learning detecting system, evaluating the fast gradient sign method (FGSM), Jacobian-based saliency map attack (JSMA), and JSMA reverse (JSMA-RE) schemes as perturbation functions for the adversarial attacks. They determined that JSMA is more suitable for use in perturbing testing data, whereas FGSM is more suitable for perturbing the training model.
## 3 Results and Discussion
This phenomenon is caused by lower encapsulation efficiency where more of the core material remains on the surface. It is evident that the increase of CMC in carrier material promotes encapsulation efficiency. Retention of phenolic compounds was evaluated using spectrophotometric assay and HPLC analysis. Obtained encapsulates showed a high phenolic retention rate ranked from 5.641 to 12.821 mg/g. The highest retention rate had a sample encapsulated with a mixture of all three carriers in the same proportion (MD2 GA2 CMC2), while the lowest had a sample encapsulated with pure MD (MD6). The content of three major phenolic compounds (hesperidin, naringin, and rutin) was measured using HPLC and presented in Table 4.  
Figure 1: Encapsulation efficiency of citrus byproduct encapsulates. Hesperidin was ranked from 1.010 to 2.826 mg/g, naringin from 1.398 to 2.592 mg/g, and rutin from 0.156 to 1.590 mg/g. Hesperidin and naringin are phenolic compounds well-known for their hydrophobic and low-soluble properties. Those properties limit their bioavailability and application in the food and cosmetic industry [37]. However, encapsulation with hydrophilic carriers can enable better physical properties, as proven previously. Retention of hesperidin was dependent on carrier usage (particularly, CMC content), where an increase in CMC led to higher hesperidin retention. A similar trend for naringin content and CMC as a carrier can be observed [38; 39]. It was previously shown that CMC can complex with phenolic compounds, enhancing physical properties and reducing interaction with proteins (reducing astringency). Hesperidin content also positively correlated with encapsulation efficiency (r = 0.86, \(p\) = 0.01), indicating that the main encapsulated phenolic compound is hesperidin while other phenolic compounds could be distributed on the surface of the encapsulates. Although an increase in carrier material can lead to the dilution of core material, surprisingly, other authors reported an increase in encapsulation efficiency probably due to an increase in the thickness of microparticle shell walls [40].
# Attention-Guided Wireless Channel Modeling and Generating
###### Abstract
Figure 1: Frameworks of GAN. In summary, our contributions can be concluded as follows:  
1. Proposing a novel approach named SE-GAN for wireless channel modeling and generating, capable of effectively modeling complex wireless environments and generating high-quality channel samples;
2. Introducing the channel attention mechanism into the field of wireless channel modeling for the first time, significantly enhancing the model's learning efficiency and the realism of generated samples;
3. Demonstrating the superiority of the proposed method over the existing techniques through extensive experimental validation. The rest of the article is organized as follows: Section 2 reviews the research status in related fields and introduces the classical methods in the field of channel modeling and generation. Section 3 describes the channel modeling and generation method proposed in this paper, focusing on the architectural details of the model used. In Section 4, the experimental simulation results are presented, and the performance of the proposed method is compared and analyzed. Finally, Section 5 concludes the paper and discusses the future research direction.
## 2 Materials and Methods
### Atmospheric Stability
#### 2.4.2 Turbulence Intensity (TI)  
Meteorological parameters related wind resources can be important influencing factors on wind turbine performance and load. Among them, the turbulence component is a representative parameter, and turbulence measurement is generally performed using equipment, such as cup anemometer, sonic anemometer, and LiDAR. Another detail is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stability Class** & **Wind Shear** & **TI 1** & **Richardson Number** & **Boundary Layer Properties** \\ \hline Strongly Unstable & \(\alpha<0.0\) & TI \(\geq 0.15\) & Ri \(<-0.86\) & Lowest Wind Speed/Shear, \\  & & & & Highly TI \\ \hline Unstable & \(0.0\leq\alpha<0.1\) & \(0.11\leq\) TI \(<0.15\) & \(-0.86\leq\) Ri \(<-0.1\) & Lowest Wind Speed/Shear, \\  & & & & High TI \\ \hline Near-Neutral & \(0.1\leq\alpha<0.2\) & \(0.08\leq\) TI \(<0.11\) & \(-0.1\leq\) Ri \(<0.053\) & Logarithmic wind profile \\ \hline Stable & \(0.2\leq\alpha<0.3\) & \(0.05\leq\) TI \(<0.08\) & \(0.053\leq\) Ri \(<0.134\) & High Wind Speed/Shear, \\  & & & & & \\  & & & & \\ Strongly Stable & \(\alpha\geq 0.3\) & TI \(<0.05\) & Ri \(\geq 0.134\) & Highest Wind Speed/Shear, \\  & & & & & \\  & & & & \\ \hline \hline \end{tabular} 1  
\end{table}
Table 3: Atmospheric stability indices criteria and boundary layer properties.  
that IEC 61400-12-1 requires measuring the horizontal/vertical component of a wind resource, so that the strength of turbulence can be analyzed with its horizontal/vertical component. Turbulence intensity is a detailed theory constituting the governing equation of turbulence along with the continuity equation and Navier-Stokes equation. When the flow is classified into the average value and the fluctuation value, this indicates the degree of fluctuation compared to the average value. The turbulence intensity can be calculated with Equation (7):  
\[\text{TI}=\frac{\sigma V}{V} \tag{7}\]
## 2 Outdoor Positioning in the IoT Ecosystem  
### Why IoT Location Matters?  
With the entrance of IoT to different Industrial and non-industrial systems, it is predicted that the number of IoT end nodes will reach 50 billion by 2022. Therefore, the future of systems known as IoT systems is shaped by intelligent and moving IoT end nodes. Furthermore, IoT systems are moving toward autonomous operation with direct interaction with the human, environment, and other systems [3]. For example, in an autonomous vehicle, the control system receives the achieved data of sensors as input. Based on receiving data, it sends appropriate vehicle manipulation commands (e.g., steering, accelerating, and braking) to actuators to achieve the goal travel path. To make this goal possible, it is important to achieve the location information of the vehicle. In addition, if the location estimation error becomes more than a specific threshold, it can make a wrong rotation decision. Wrong decisions in an autonomous vehicle can cause accidents and human harm. Accurate location estimation has become more critical in smart healthcare systems. In smart healthcare systems, the vital sign of use is determined using the adopted sensors. The measured vital signs are sent to the medical care center periodically. In an emergency, accurate location estimation of the user for faster and effective assistance is important. This issue becomes more complicated in Industry 4.0. The safe operation of Industry 4.0 is essential due to the widespread application of these systems in critical infrastructures. Autonomous systems in Industry 4.0 receive data from different mobile sensors which provide information about surroundings. Based on received data, control systems make a decision and sends control commands to actuators. In these systems, accurate location information of sensors and actuators is critical because decision errors in these systems can lead to catastrophic harm to humans, the environment, and other systems.  
According to the mentioned points, to provide a safe IoT system, it is important to provide location information of IoT end nodes. Therefore, besides a robust and reliable communication network, it is important to provide an accurate location estimation method [4].
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 2 Materials and Experimental Methods  
### Raw Materials  
#### 2.1.1 Soil  
The soil samples in this study were collected from two sections of an expressway construction site in Qinzhou City, Guangxi, named soil sample 1 (S1) and soil sample 2 (S2), respectively. The maximum dry density of S1 and S2 were 1.92 g/cm\({}^{3}\) and 1.95 g/cm\({}^{3}\), respectively, and the corresponding maximum water content were 10.4% and 12.4%, respectively. The liquid limit of S1 was 43.7%, and its plasticity index was 12.2%. The liquid limit of S2 is 59.6%, and its plasticity index was 31.9%. Referring to T0115-1993 in the Chinese specification (JTG 3430-2020), according to this method of soil classification of the plastic diagram, S1 was silty clay and S2 belongs to high liquid limit clay. The grading curves of S1 and S2 are shown in Figure 2. For S1, the coefficient of uniformity (Cu) and coefficient of curvature (Cc) were 2.9 and 0.8, respectively, and the Cu and Cc of S2 were 6.3 and 72.3, respectively, which were poor gradings. The XRD analysis results for S1 and S2 showed that there were more quartz components, and S2 also contained some components of CaCO\({}_{3}\), as shown in Figure 3a,b.  
Figure 2: The grading curves of soil and SL used in this study.
## 3 Preliminaries
### Federated Learning with Threshold Secure Aggregation  
#### Overview  
We consider \(n\) honest-but-curious servers (numbered from \(\mathrm{Svr}_{0}\) to \(\mathrm{Svr}_{n-1}\)), \(z\) clients (numbered from \(\mathrm{C}_{0}\) to \(\mathrm{C}_{x-1}\)), and a level server. Each client \(z_{i}\) has their own dataset with a size of \(l_{i}\), and the total data size across all clients is \(L\). Figure 1 is the proposed architecture. Prior to the beginning of each round of federated learning, the clients collectively decide a number \(t\), where \(2\leq n\) and \(1<t\leq n\), which means that \(t\) servers are selected from \(n\) servers for aggregation. The numbering of each server is then converted from decimal to \(t\)-base according to the clients' choice. After the conversion is completed, the length of servers' number is \(q\) bits, and the number of the \(\mathrm{Svr}_{1}\) is \(i_{0}\ldots i_{q-1}\).  
After each round of local training, the clients use secret sharing to split their model's gradients, then send the shares and dataset sizes to each server based on predefined rules.  
Figure 1: \(t\)-out-of-\(n\) federated learning architecture diagram. In this example, \(t\) is 2 and \(n\) is 4. After training the model locally, the client divides the gradients into 4 shares and distributes them to all servers. Each server aggregates the received shares. The selected 2 servers, the leftmost server and the rightmost server here, then return the aggregated shares to the client, which aggregates the \(t\) received shares to update the model.  
When all servers receive the shares and dataset sizes, \(t\) servers that meet the conditions are selected to aggregate the shares they received and return the results to each client. Finally, clients can update their models and start the next round of training by summing the gradients received from the \(t\) servers.  
We assume a passive and honest-but-curious secure threat model, where servers comply with all protocol specifications but may attempt to infer private information based on inputs. Moreover, attackers will try to obtain \(t\) servers that can recover the information to access private data.
## 4 Experiments
### Analysis of Experimental Results
#### 4.3.3 Ablation and Migration
liveness detection method: the Slim-ResCNN [24], the method in [25] based on the CNN architecture, and Inception [21], which combines the patch-label strategy, which is trained on a comprehensive fingermark dataset and tested on a signed fingermark dataset. The results are shown in Table 9, and under the same conditions, the detection accuracy of these methods for signed fingermarks is not satisfactory, and the lowest accuracy is lower than 90.0%, while the SFNet proposed in this paper, which combines the multi-probability labeling strategy and the image quality weight score fusion strategy, achieves the optimal test accuracy. Counterfeiters may use novel materials to carry out spoofing attacks on the liveness detection algorithm. The ability of a liveness detection algorithm to resist attacks with unknown materials is also one of the criteria for judging the robustness of the algorithm. Unknown materials refer to materials that do not exist in the algorithm's training dataset. In this paper, a small dataset was created based on five unknown materials, including glass glue, vulcanized silicone, glue, emulsion, and a bionic finger. Among them, the bionic finger was made of mixed materials to simulate a real finger, which has a flexibility and ductility very close to those of real skin, and has a similar appearance and touch to a real finger. The experimental results are shown in Table 10. It can be seen that SFNet can be effectively protected against attacks from unknown materials, which is better than other methods. It can be seen that SFNet shows a slight degradation in accuracy against bionic fingers, so further research will need to consider investigating how to defend against unknown materials with high ductility and flexibility.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 3 The Compression Framework  
A high-level description of our reduction/compression pipeline is described in Figure 2. The framework mainly comprises two pipelines--Primary Data (PD) reduction and Quantities of Interest (QoI) satisfaction. For PD reduction, we combine Tucker decomposition, an autoencoder (AE), and a product quantizer (PQ) for effective global compression. An error-bounded lossy compressor is used for residual post-processing after global learning to provide the guaranteed error bound on PD. For QoI satisfaction, we perform local constrained optimization on the reconstructed data from the PD reduction pipeline. This step enables us to directly control QoI preservation. We use another product quantizer to encode the Lagrange multipliers produced by this step. Each of these pipelines is now described in detail in the remainder of this section.
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 6 Conclusions  
To satisfy the needs of MECs for modern development and to further improve the quality of service, this paper proposes a novel DRL-based task-offloading strategy for rapid processing. We investigate a multiuser, multiserver MEC system and discuss the queue dynamic of tasks as a key point. Furthermore, the target is to reduce the total delay for each user in the MEC system. By using the parameterized value function for value estimating, this algorithm can address the formed problem and determine the offloading policy efficiently without any prior knowledge. Experimental data indicate that the proposed algorithm performs better on the users' delay than other algorithms.  
Limitations: In this paper, we study a DQN-based algorithm which has discrete action space and is not suitable for scenarios which demand continuous action space. Fortunately, we can extend the proposed algorithm for partial offloading problems in a new MEC system where tasks can be partially offloaded to both users and servers. We will realize the assumption in future experiments.  
Figure 5: Average delay evaluation with different number of MDs.  
Figure 6: Average delay evaluation with different deadlines.  
Author ContributionsProject administration and funding acquisition, Y.W.; Validation, X.L.; Writing--original draft, X.L.; Writing--review & editing, S.J. All authors have read and agreed to the published version of the manuscript.  
This researchwas funded by the National Natural Science Foundation of China, grant number U1805262, 61901117. This research was also funded by the Special Funds of the Central Government Guiding Local Science and Technology Development (2021L3010), by Key provincial scientific and technological innovation projects (2021G02006), by Natural Science Foundation of Fujian Province, China. (Grant No.: 2022J01169, 2020J01157, 2018J01569).  
Not applicable.  
Data Availability StatementData available on request.