esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 2 Background  
### IoV Communication Types  
The integration of detection systems, control interfaces, and computational tools transforms each automobile in the Internet of Vehicles into an intelligent entity. Each vehicle connects with other elements through a comprehensive communication framework, referred to as V2X. This connectivity aims to promote safer driving by minimizing collisions, providing route guidance, and delivering various information services. Vehicles in this network interact dynamically with all factors influencing their operation [4].  
Vehicle-to-Everything (V2X) mainly includes five types of connectivity: Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), Vehicle-to-Cloud (V2C), and Inter-Vehicle (Inter-V), as illustrated in Figure 2.  
Vehicle-to-Vehicle refers to wireless communication between vehicles, where each vehicle can exchange real-time information about speed, location, and direction with other vehicles. Vehicle-to-Infrastructure is the communication established between vehicles and various roadside infrastructure units (RSUs) to enhance transportation efficiency and safety. Vehicle-to-Pedestrian enables vehicles to monitor and interact with pedestrians and cyclists on the road, improving safety and avoiding accidents. Vehicle-to-Cloud involves communication that allows vehicles to connect and exchange data with cloud-based services. Inter-Vehicle (Inter-V) is a communication system that enables the sharing of data from a vehicle's internal sensors among all users inside the vehicle.  
Figure 2: IoV communication types.
## 1 Introduction  
Sleep-disordered breathing (SDB), including snoring and sleep apnea, tends to be induced by narrow upper airways and collapse of the throat. It results in daytime sleepiness, neurocognitive impairment, cardiovascular and metabolic disorders, and severe functional impairment that can even cause traffic accidents [1; 2]. Although continuous positive airway pressure (CPAP) therapy devices are highly effective for treating SDB by utilizing air pressure to maintain the upper airway, there are several issues such as adaptation and compliance problems as well as the risk of iatrogenic pneumothorax [3]. A mandibular advancement device (MAD) is a superior alternative to CPAPs for patients suffering from SDB because they mainly act by expanding the upper airway. MADs push the mandible forward, which pulls up the oropharyngeal wall and keeps the airway open, thereby making breathing easier while asleep. Cephalometric and radiographic analysis revealed that the retropharyngeal air space increased after MAD use [4].MADs are also effective in increasing the tension around the oropharyngeal area, which reduces its collapsibility [5; 6]. MADs are simple, easy to use, and convenient because they do not require surgery and thus eliminate the need to control postoperative pain and surgical side effects. However, the patient must wear the MAD every night to reduce the symptoms of SDB. According to previous studies [3; 7; 8], pain in the teeth and gingiva is one of the main factors in patient unwillingness to continue using an MAD. In addition, one of the most important side effects of MAD use are occlusal change following long-term treatment [9; 10; 11]. Therefore, to improve patient compliance, it is important to design an MAD that minimizes discomfort or pain by avoiding such side effects [12].  
Stress analysis can provide useful information to control MAD-induced discomfort and prevent occlusal or mandibular changes after long-term treatments. Finite element (FE) analysis is a mathematical method that uses computation to predict approximate values in a physical system that is highly susceptible to external factors. Recent studies have reported the use of FE analysis for dental braces, but such use for the evaluation of MAD-induced stress has yet to be explored [13; 14]. Therefore, the purpose of this study was to propose a novel MAD design to distribute stress among the teeth and gingiva and to use FE analysis to show positive outcomes such as a reduced risk of occlusal change and relief from discomfort. To carry out the optimized design, FE analysis was adopted to analyze the modeled stress distribution between a conventional MAD and the newly proposed design.
# AI-Driven Waste Management in Innovating Space Exploration
## 4 Smart Waste Management Technologies for Space  
The challenges of space waste management have driven the development of advanced technologies to enhance sustainability in missions beyond low-Earth orbit. Innovative systems utilising AI, robotics, and biotechnology are being created to maximise resource recovery, minimise waste volume, and optimise efficiency in the isolated environments of space missions. These systems strive to establish sustainable, closed-loop processes that significantly lessen reliance on Earth-based resources. Key innovations include AI-driven sorting and recycling mechanisms that improve waste processing efficiency and biotechnological reactors that convert organic waste into biogas and other valuable products. In situ resource utilisation (ISRU) employs local materials, such as lunar or Martian soil, for construction, reducing the need for Earth resupply missions [70; 71]. Advanced water recycling and CO\({}_{2}\) reduction systems, like the Sabatier process, are essential for regenerating vital resources. Technologies such as plasma arc pyrolysis are being explored to convert mixed waste into inert gases and energy. Automating waste handling and enhancing efficiency are crucial for enabling long-duration space missions and paving the way for self-sustaining waste management systems in future space exploration (Table 2).
###### Abstract
When CA is used as the electrolyte in electrokinetic remediation, the removal efficiency of lead in the soil increases from 15.47% to 56.85% compared with KCl. Although the EK remediation has achieved good effects, the application of this technology is restricted due to poor solubility of pollutants, weak desorption capacity, and polarization phenomenon [20]. Studies have been conducted on the application of adsorbents (Activated carbon, fly ash, and carbonized waste) as PRB fillers and on PRB coupling with electrodynamics as remediation. In the coupled system, such PRBs can effectively reduce the pollution of heavy metals to the electrolyte and the polarization phenomenon. In addition, the removal efficiency of heavy metals (such as Cu\({}^{2+}\), Cr\({}^{6+}\), Cd, etc.) is increased by more than 20% [20; 21; 22; 23]. Steel slag is characterized by porosity and large specific surface area and can release OH\({}^{-}\) and hydroxyl SOH in an aqueous solution. It is a good adsorbent for heavy metal ions. A large number of experiments have proven that steel slag can effectively remove Cd, Cu\({}^{2+}\), Zn\({}^{2+}\), and Pb\({}^{2+}\) from wastewater and other metal ions; the removal rate is more than 80%, or even close to 100%. However, when the EK-PRB method is used to remediate contaminated soil, there is no report on the use of steel slag as PRB filler, and its remediation effect needs to be studied. The objective of this paper is to offer insights into the removal efficiency of the combination of steel slag PRB and EK for copper-contaminated soil. The laboratory model tests involve a series of comparisons between EK and EK-PRB tests with various electrolytes solutions (KCl, HAc, CA, and PASP). The effects of steel slag PRB on soil pH value, contaminants removal efficiency, and residual heavy metal forms are investigated, and remediation performance through the EK-PRB system is analyzed. Meanwhile, the effects of KCl, HAc,CA, and PASP on the dissolution and transport of copper in the soil are compared, and the energy utilization efficiencies in different EK experiments have been evaluated.
## 5 New Trends and Future Research Directions in Enterprise Networking
### Network-as-a-Service (NaaS)  
With network-as-a-service (NaaS), organizations can scale network services up or down according to their needs, attributed to its flexible, subscription-based networking paradigm [9; 103]. Hence, NaaS streamlines networking by eliminating the need for capital expenditures and operational complexity. In addition, it separates infrastructure management, thereby enabling firms to rent network capabilities as required [104]. This pattern is consistent with a larger movement toward \"as-a-service\" models. For this reason, business firms can use specialized network services without the need for requisite knowledge, while concentrating on their core business operations. By utilizing enterprise 5G and cloud computing as the enabling technology, customized traffic flow can be created by NaaS to significantly improve the QoS requirements of diverse applications [95].  
Also, the enterprise may easily incorporate new applications that require wider expansion, allowing for flexible organizational growth (see Figure 10). Hence, NaaS paradigm eliminates the need for the IT department to determine or have information about network activities. Rather, networks and IT share a common knowledge of network services by relying on the same set of open APIs [103]. This gives networking domains more freedom to decide how best to develop, guarantee, and control the visibility of a network function, such as firewall-as-a-service (FaaS). However, this also depends upon which options best meet the needs of clients.  
Figure 9: Cloud computing deployment model.
# Mobile Robot Path Planning Considering Obstacle Gap Features
## 2 Path Planning Method with Clearance Characteristics
### The Experiment in the Real Environment  
In this study, indoor integrated navigation experiments were conducted using the Tracer robot developed by Agile Robotics, which was equipped with an on-board laptop. The robot runs ROS Noetic on Ubuntu 20.04 and carries various sensors like a laser radar and an IMU. It navigates in a VLP-16-equipped indoor environment with complex layouts and dynamic obstacles. The on-board laptop handles real-time data processing and algorithm execution, ensuring smooth robot operation. The Agile X Tracer robot, equipped with a laptop computer, is adopted to carry out indoor integrated navigation experiments in this study. As mature robot navigation algorithms, the A* and DWA algorithms in ROS-Nav are widely used in real hybrid path planning, so they are used as comparison algorithms for the proposed improved algorithm.  
Experimental scenario 1 is shown in Figure 9, which is a multi-bend scenario.  
It can be seen in  
Experimental scenario 2 is composed of multiple narrow gap scenes. It is shown in Figure 11.  
Figures 11 and 12 are the results of the navigation experiment. The trajectory angle generated by the algorithm in this study is smaller and smoother, and the attitude adjustment during the movement is more uniform, which is more suitable for the actual movement of the robot. It can be seen in Table 6 that there is a clear optimization of the movement time when the path length is close.  
This work emphasizes path planning in tight spaces, distinct from the Intelligent Follow Gap Method (IFGM) presented in [24], which focuses on U- and H-shaped obstacles. While IFGM combines the Intelligent Bug Algorithm and Follow the Gap Method for goal-oriented navigation and local minima avoidance, our approach targets small gap scenarios. Both methods offer unique solutions within the mobile robot navigation field.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **ROS-Nav Algorithm** & **Improved Algorithm** \\ \hline Path Length & 8.14 & 7.75 \\ Number of Turning Points & 5 & 3 \\ Movement Time & 46 & 39 \\ IAE (Path Length) & 0.39 & - \\ ITAE (Path Length) & 18.94 & - \\ ISE (Path Length) & 0.15 & - \\ ITSE (Path Length) & 7.33 & - \\ IAE (Runtime) & 7.00 & - \\ ITAE (Runtime) & 322.00 & - \\ ISE (Runtime) & 49.00 & - \\ ITSE (Runtime) & 2354.00 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Algorithm experiment results for Scenario 2.  
Figure 12: Experimental comparison diagram for Scenario 2.
###### Abstract  
We used atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS) to comprehensively study the growth and the cross-linking of dotriacontane (C\({}_{32}\)H\({}_{66}\)) nanof films that were deposited on a silicon wafer by the spin-coating process. It was found that the molecular structure of the nanofilms changed with C\({}_{32}\)H\({}_{66}\) concentration at the given spin speed, of which a monolayer of oriented C\({}_{32}\)H\({}_{66}\) molecules, formed at lower deposition concentrations, was composed of a perpendicular orientation state with the molecular long-chain axis perpendicular to the substrate surface and a parallel orientation state, while the perpendicular state was essentially dominant when the nanofilm was formed at higher deposition concentrations. The shortening of the first perpendicular layer in AFM topography could be attributed to the mixing of both parallel and perpendicular lamellas in the first layer. XPS analysis indicated that the average thickness of the layer almost linearly increased with the C\({}_{32}\)H\({}_{66}\) concentration. The monolayer of C\({}_{32}\)H\({}_{66}\) film could be cross-linked by a hyperthermal hydrogen-induced cross-linking (HHIC) at a few eV via kinetic collision to cleave C-H bonds. The water contact angle measurement of extensive HHIC on C\({}_{32}\)H\({}_{66}\) nanofilms disclosed that the static contact angle decreased with the treatment time (or fluence) and saturated after full cross-linking of the film.  
C\({}_{32}\)H\({}_{66}\) nanofilm; molecular orientation; cross-linking; hyperthermal hydrogen +
Footnote †: journal: Applied Science  
1 Shanghai Technical Institute of Electronics & Information, Shanghai 201411, China; [EMAIL_ADDRESS]  
2 Faculty of Materials Science and Technology, Kunming University of Science and Technology, Kunming 650093, China  
3 School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China; [EMAIL_ADDRESS]  
4 Solmont Technology Wuxi Co., Ltd., 228 Linghu Blvd., Tian'an Tech Park, A1-602, Xinwu District, Wuxi 214135, China; [EMAIL_ADDRESS]  
5 Surface Science Western, The University of Western Ontario, London, ON N6G 0J3, Canada; [EMAIL_ADDRESS]  
6 Shunde Graduate School, University of Science and Technology Beijing, Foshan 528399, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel: +86-172-8751-5437
# Whispered Speech Recognition Based on Audio Data Augmentation and Inverse Filtering
## 5 Materials and Methods
### Automatic Speech Recognition Systems
#### 5.3.1 HMM-Based ASR System  
The conventional ASR methodology relies on Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM). Three modeling units for the recognition of isolated words are usually considered: phonemes independent from context (monophones), phonemes with contextual dependencies (biphones or triphones), and whole words. The highest robustness in whispered speech recognition and the Whi-Spe database is demonstrated using monophone models [34].  
The most important configuration parameters in the feature extraction procedure are summarized in Table 2. The feature vectors were extracted using the HTK software (version 3.4.1) [35].  
The developed ASR system utilized HMM models with continuous density GMMs and diagonal covariance matrices. Models of monophones consist of 5 states (3 emitting states) with a left-to-right topology and without skips, as depicted in Figure 6.  
The models are initialized with the global mean and variance, with flat-start initialization. Baum-Welch re-estimation was performed for five cycles, and the number of mixtures was gradually increased up to 16. Phone-level transcription involved 32 monophones, encompassing 30 Serbian letters, the schwa phoneme, and silence. Although the phoneme schwa does not have a letter in Serbian, it is marked separately if it is next to the letter/r/. During the testing phase, the Viterbi decoder was employed to estimate the most probable state sequence. The ASR system was developed using the HTK toolkit, while MATLAB software (version R2016a) was utilized for generating scripts and configuration files, phonetic transcription, and logging the performance of the ASR system, which was evaluated within HTK.
## 3 Preliminary and Model
### Traffic Degradation Image Restoration Model  
In traffic degraded image restoration, the objective is to eliminate various environmental interference factors from the input degraded images as much as possible and restore clear scenes with high authenticity. During this process, it is crucial not to alter or lose the original traffic elements (vehicles, pedestrians, traffic signs), as the primary goal of most traffic environment perception tasks is to accurately identify the categories and locations of these key traffic elements.  
The stable diffusion model (SDM) faces significant challenges when applied to real-world traffic-degraded image restoration. Although SDM can guide the restoration process through text prompts, this approach encounters two major issues. First, text prompts with similar meanings may yield different latent features after being processed by the text encoder, which can introduce bias in SDM's understanding and lead to image generation along divergent directions. Second, SDM struggles to precisely control the details of generated images, such as inadvertently altering the original layout and content, which is unacceptable for traffic-degraded image restoration. Therefore, directly using text prompts to guide SDM for traffic image restoration is not feasible. Figure 4 demonstrates the results of SDM restoring traffic-degraded images under similar text prompts, all of which describe the removal of adverse environmental effects (dehaze, sand removal). The results show that SDM not only fails to effectively eliminate adverse factors but also causes uncontrolled changes to image content, making it difficult to ensure fidelity.  
In this regard, a traffic degradation image restoration model (TDIRM) based on stable diffusion is proposed to effectively restore degraded images in real traffic scenarios.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 3 Experiments and Results
### Tracking Accuracy Analysis in Different Conditions
#### 3.3.1 Vehicle Speed  
**Stop position error**. Table 2 shows that the error of the stop position increases as vehicle speed increases, and it is also found that there is a certain randomness of error even at the same speed, which relates to the stability of the speed control during each experiment. When there is a small speed fluctuation, this will have an impact on the positioning progress of the differential drive model.  
**Tracking position error**. Tracking position errors at different speeds can be seen in Figure 12, proving that the error is similar to that in the stop position, with the smallest error at 5 km. However, there is little difference between the error at 10 km and 15 km, with a larger error occurring at 10 km in some positions and a larger error at 15 km in others.
# Geosynthetic Reinforced Steep Slopes: Current Technology in the United States
## 6 Performance Measures & Cost Effectiveness
### Failure Modes  
[PERSON] and [PERSON] compiled and analyzed a database of 141 failed geosynthetic reinforced structures, in which there were 34 cases of excessive deformation and 107 cases of actual collapse [54]. Below are the main statistical findings:  
* All but one were privately owned (as opposed to publicly financed).
* 72 percent were in North America.
* 49 percent were 4 m to 8 m high.
* 90 percent were geogrid reinforced.
* 81 percent failed in less than 4 years.
* 62 percent used silt or clay backfill in the reinforced soil zone.  
* 75 percent had poor to moderate compaction.
* 98 percent were caused by improper design or construction.
* 58 percent were caused by internal or external water (the remaining 42 percent were caused by soil related issues).  
The major inadequacies in these failures were a lack of proper drainage procedures and a lack of adequate placement of plumbing within the reinforced soil zone. Another inadequacy was the use of fine grained silt and clay backfill soils along with insufficient placement and compaction during construction. This led to hydraulic pressures being mobilized behind or within the reinforced soil zone, which requires the use of back and base drains so as to dissipate the pressures and properly remove the water out of the front.  
[PERSON] et al. also investigated three failures on a geogrid reinforced slope on the approaching road to Chi-Nan University in Nantou, Taiwan [55]. The first slope failure occurred after rainfall infiltrated into the permeable gravel and was impeded by the underlying impermeable clay layer. The shear strength was reduced at the interface of the gravel and clay, initiating a slide when the toe of the slope was excavated to install reinforcement during construction. The second failure was caused by a strong earthquake, which generated stress and created a slide in the vicinity of the clay layer. The third failure occurred when abundant rainfall infiltrated into the reinforced slope during a heavy rainstorm. The infiltration was obstructed by the impermeable clay, producing transient water pressure and inducing slope failure behind the reinforced zone. Lessons learned from the study include carrying out a detailed site investigation, selecting permeable materials as backfill, installing drainage systems appropriately, and combining the design of a reinforced slope with other types of retaining structures to improve the system global stability.