esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Strutless Double-Row Structure for Deep Excavation: Working Mechanism Study and Analysis
## 3 Analysis of Experimental Results
### Earth Pressure Analysis
#### 3.2.1 E\({}_{\text{ff}}\)  
Figure 19 displays the distribution of E\({}_{\text{ff}}\) under each type of structure. The earth pressure in each structure exhibits a nonlinear pattern, with lower values at both ends and higher values in the middle. The maximum value of earth pressure is distributed approximately 250 mm away from the excavation face. Previous experimental studies have shown that the earth pressure in front of the pile typically forms a triangular distribution, with the maximum soil pressure occurring at the bottom of the pile [43; 44]. The difference in our findings can be primarily attributed to the excessive embedment depth of the experimental pile, where the soil at the base of the pile and its immediate surroundings are less influenced by excavation, leading to this distribution pattern. The earth pressure at the base of the pile is found to be between the values predicted by [PERSON]'s active and passive earth pressure theories, as revealed by the figure. E\({}_{\text{ff}}\) increases with wider row spacing in  
Figure 17: Pit bottom uplift (H = 500 mm).  
Figure 18: Earth pressure diagram.  
the DRPW structure. Conversely, in the case of ARPW structures, the earth pressure in front of the piles decreases as row spacing increases. The earth pressure distribution in front of the DRPW structure is closer to the [PERSON] passive earth pressure curve, while that in front of the ARPW structure more closely resembles Rankine static earth pressure. Comparing the maximum earth pressures of the DRPW and ARPW structures at 2D, 4D, and 6D row spacings, ARPW exhibits significantly lower pressures (11.2 kPa, 7.9 kPa, and 5.2 kPa, respectively) compared to DRPW (12.2 kPa, 17.1 kPa, and 26.5 kPa, respectively). The reductions are 8.1%, 53.8%, and 80.3%, respectively, indicating that the ARPW structure is more effective in controlling soil pressure. Both structures exert lower earth pressures than the SPW structure.
## 5 Results, Analysis, and Discussion  
### The Process of Signal Propagation  
Figure 8 displays the simulated displacement contour plot in the z-direction before and after the stiffener is used at the given time instants after the AE signal with \(f_{\text{c}}\) = 150 kHz encountering the stiffener at \(h\) = 16 mm.  
With reference to Figure 8, the stiffener interferes with the signal propagation. This figure clearly shows that the stiffener influences the signal propagation. The excited AE signal dominated by the L(0,1) mode spreads around at the beginning and then interacts with the stiffener. A mode conversion can be observed, which interferes with the propagation of the original signal, as shown in Figure 8b-d. These scattered waves with multiple modes and other waves (including the wave reflected from edges and the wave traveling around the circumference) were then received successively by R and T. Although their propagation phenomenon is complicated, it should be noted that the color of L(0,1) is darker than that of L(0,2), which indicates that the L(0,1) mode accounts for the dominant component in propagation.  
Figure 6: Schematic diagram of experimental system.  
Figure 7: Schematic of the experimental equipment: (a) an overall diagram of the experimental system; (b) a physical diagram of specimen 2.  
A qualitative description about the interaction between the L(0,1) mode and a change in the height of the stiffener is presented below. Figures 9 and 10, respectively, show the time amplitude relations acquired by R and T for the reflection and transmission of the incident L(0,1) signal with a stiffener at different heights. The incident wave and reflected waves can be easily distinguished in the response received at R, while the response received at T is all transmitted waves. This confirms that not only do reflection and transmission occur but also multiple modes are generated via the \"T-shape\" transformation along with mode conversion when the incident wave encounters the stiffener. Different heights of the stiffener and the difference between the wave velocities of each mode wave lead to the different arrival times of scattered waves reaching R and T. Then, these waves overlap with each other at receivers, which explains the responses presenting different superposition shapes, as shown in Figures 9b-f and 10b-f.  
Figure 8: The simulated displacement contour in the z-direction as the AE signal propagates in the cylindrical shell with the stiffener when \(f_{\rm c}\) = 150 kHz and \(h\) = 16 mm.  
Figure 9: The partial reflected waveform in the \(z\)-direction acquired by R (\(f_{\rm c}\) = 150 kHz).
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI  
Despite advancements in mitigation techniques, several challenges persist [6]. The rapid expansion of wireless technologies, including 5G networks and Internet of Things (IoT) devices, has increased RF spectrum congestion, raising the likelihood of interference. Additionally, the reliance on satellite-based navigation systems like GPS makes aviation systems vulnerable to jamming and spoofing. Developing robust alternatives and enhancing existing systems is critical to overcoming these challenges.  
Test Equipment Used to Investigate RFI in Aviation. The detection and mitigation of RFI in aviation require the use of specialized test equipment. These tools are integral for identifying sources of interference, analyzing their impact on aviation systems, and validating mitigation strategies to ensure the safe and efficient operation of Aircraft.  
The detection and mitigation of RFI in aviation rely on specialized tools that ensure system integrity and operational safety. These tools help identify interference sources, analyze signal disruptions, and validate mitigation strategies. Their role is crucial in maintaining reliable communication, navigation, and surveillance systems. Details of these tools are discussed below.
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 3 Improved PSO Algorithm
### Complexity Analysis  
Based on the classical particle swarm optimization algorithm, the APSO algorithm introduces improvement measures such as composite chaotic mapping initialization population, adaptive inertia weights, elite particle learning mechanism, and mutation strategy for ordinary particles. These measures aim to improve the search efficiency and solution quality of the algorithm and avoid falling into local optimal solutions.  
Algorithm 1 shows the computational framework for the algorithm APSO. Lines 4-6 and 7-18 are the parameter initialization stage and iterative optimization stage, respectively. The first stage usually involves setting parameters such as population size, spatial dimension, maximum number of iterations, learning factor, velocity boundary, and position boundary. The second stage divides the particles into two populations, dynamically adjusts their adaptive inertia weights, and then iterates over all particles to update their velocities and positions, calculate fitness values, and update both individual and global optimal solutions. The time bottleneck of the algorithm lies in the lines 7-18 because there exist two nested for loops, which dominate the time complexity of \(O(n^{2})\).  
Figure 2: Flowchart of the improved algorithm APSO.
# Robustness of Deep Learning Models for Vision Tasks
## 3 Adversarial Attacks and Defenses
### Adversarial Defenses
#### 3.2.3 Randomization  
Recently developed defense techniques tend to rely on randomization schemes to mitigate the effects of adversarial examples on the input and feature domains. Inspired by these types of defense techniques, DNNs are robust to random perturbations. Defense  
Figure 22: Overview of defense mechanism based on a transfer of knowledge contained in probability vectors through distillation. The main idea behind distillation is that the knowledge acquired by the DNN during training is not only encoded in the weight parameters but also in the probability vectors generated by the network.  
methods based on randomization involve randomizing adversarial perturbations using a random effect.  
[PERSON] et al. [143] proposed a defense method called RRP to combat adversarial effects. The method involves transforming the input through random resizing and padding to make it difficult for an attacker to calculate the gradient of the loss on the input. This is achieved by adding two layers after the input layer of a neural network, which converts the input image to multiple resized images, and then randomly pads them with zeros as shown in Figure 23. The final image is selected at random for classification. The authors showed that this defense method requires no fine-tuning, does not compromise accuracy on clean examples, and can be used in conjunction with other defense techniques such as adversarial training.  
This defense technique showed robustness against white-box attacks such as FGSM, BIM, DeepFool, and CW. It was second in the 2017 NIPS adversarial example defense challenge [143]. [PERSON] et al. [143] believed that random resizing and padding had a large enough computational space to deter attackers. However, [110] and [PERSON] et al. [144] discovered that the technique was based on gradient masking [110] and could be defeated using expectation over transformation (EOT) [145].
## 3 Methodology Applied in this Research
The new VRET experiment used drivers between the ages of twenty to sixty years old to minimize age factors affecting the experiment outcomes. A survey pre-test question removed subjects who had experienced traumatic traffic-related accidents to avoid the possibility of subjects  
Figure 3: Sample road conditions for levels 0–4 scenario designs. Figure 2: The five-level driving scenarios. having panic attacks during the experiments. A total of 130 randomly selected subjects were asked to fill out the driving behavior questionnaire to measure their fear of driving. After excluding 9 invalid questionnaires, the 121 surveyed subjects are median-split into two groups: 60 subjects ranked with high fear of driving and 61 subjects with low or no fear of driving. Among the 60 high-fear drivers in the waiting queue, 30 subjects were randomly drawn and invited to participate in the VRET experiment as the treatment group (TG). The control group (CG) of 61 subjects with low or no fear of driving was for VRET effectiveness comparison, who did not participate in the VRET experiment. The 30 randomly selected subjects as the TG to participate in the VRET experiment (sample size of 30) provided the appropriate sample size required for experiment validity [22]. The proposed clinical experiment flowchart is shown in Figure 5. Each subject participates in the VRET experiment twice, with an interval of one week between experiments. In the first experiment, the treatment begins with an introduction by the psychotherapist. An instructional short film is shown and explained to the subjects so that they fully understanding the experimental process and the equipment. The film provides detailed introductions about the experimental objective, method, procedure, expected risk, and relevant disclaimer information. Subjects are equipped with IoT sensors which measure their biodata, including temperature, respiration, heart rate, and skin conductance. The subjects' biodata are collected without receiving any VR stimulation to establish a baseline-state biofeedback. After signing the disclaimer, the subject begins their VR driving simulation and the biofeedback data are collected using biosensors. On the first day of the experiment, the subjects practice level 0 until they are familiar with the VR environment and then advance to level 1 and level 2. When the subject finishes level 2, the first day of the experiment ends. One week later, subjects are invited to the second stage of the experiment. The subjects are equipped with biosensors and start from level 0. After getting used to the VR environment, the subject will advance to level 3 and level 4. When both immersive treatments are finished, subjects are asked to complete the post-treatment survey to  
Figure 4: Subject selection flow chart. measure the subject's mental state after the VRET treatments. During the experiment, if a subject experiences panic, vertigo, or any feeling of motion sickness, an emergency stop can be triggered to immediately stop the experiment session as a safety precaution.
# Metal Recovery from Wastes: A Review of Recent Advances in the Use of Bioelectrochemical Systems
###### Abstract  
The rapid technological advancements and the shift towards clean energy have significantly increased the demand for metals, leading to an increasing metal pollution problem. This review explores recent advances in bioelectrochemical systems (BES) for metal recovery from waste, especially Acid Mine Drainage (AMD) and Electrical, Electronic Wastes (EEW) and waste from smelters, highlighting their potential as a sustainable and economically viable alternative to traditional methods. This study addresses the applications and limitations of current BES recovery techniques. BES, including microbial fuel cells (MFCs), microbial electrolytic cells (MECs), and Microbial Desalination Cells (MDCs), offer promising solutions by combining microbial processes with electrochemical reactions to recover valuable metals while reducing energy requirements. This review categorizes recent research into two main areas: pure BES applications and BES coupled with other technologies. Key findings include the efficiency of BES in recovering metals like copper, chromium, vanadium, iron, zinc, nickel, lead, silver, and gold and the potential for integrating BES with other systems to enhance performance. Despite significant progress in BES application for metal recovery, challenges such as high costs and slow kinetics remain, necessitating further research to optimize materials, configurations, and operational conditions. The work also includes an economic assessment and guidelines for BES development and upscale. This review underscores the critical role of BES in advancing sustainable metal recovery and mitigating the environmental impact of metal pollution.  
metal; recovery; removal; remediation; bioelectrochemical systems; techno-economic feasibility +
Footnote †: journal: Applied sciences
# Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions
## 4 Cryptography Approaches for WMSNs  
Many works in recent years have proposed security mechanisms based on cryptography for wireless multimedia sensor networks. Such works have addressed different particularities of those networks, optimizing encryption for one or more media types. In this context, we have surveyed and summarized some of the most relevant works in this area, classifying and comparing them as expressed in Table 3.  
Figure 3: Example of selective encryption for audio stream. The 15 papers presented in Table 3 are a good representation of how cryptography issues in wireless multimedia sensor networks have been addressed in the last few years. Considering papers from 2009 to 2016, those works have proposed promising solutions for the demand of cryptography in WMSNs, giving good hints about how such solutions may evolve in coming years. As can be seen in Table 3, cryptography in wireless multimedia sensor networks may be focused on one or more media types and employing different compression algorithms. Moreover, different cryptography algorithms may be employed, which leads us to consider that there is not a single solution that will always be effective for WMSNs. Actually, due to the resource constraint nature of typical wireless sensor networks, most summarized works employ symmetric cryptography when protecting multimedia data. Additionally, although more robust sensor nodes may be deployed in modern applications, using, for example, Raspberry Pi hardware [68], symmetric encryption should still be considered in many cases, but it puts some pressure on how cryptography keys will be hidden and distributed over the network. Future works should be concerned with such issues. The great number of feasible solutions push us to consider the scope and particularities of the target applications, which may indicate the most appropriate solutions for the desired multimedia cryptography. Therefore, we conclude that there is not a single solution that is effective for all scenarios, but there will be different solutions that present promising results depending on the nature of the sensing applications. As is already expected for general wireless sensor networks, cryptography should be application-centric, with multimedia cryptography solutions adjusted to the particularities of the target monitoring application. In short, the presented works are good indications of how cryptography will be performed in wireless multimedia sensor networks and what can be expected in the coming years.
# Revisiting the Analysis of Radiative Mid-Range Wireless Link for Pacemakers
## 3 Results
### Wireless Link Analysis Based on Related Formulas
#### 3.2.2 Rotational Scenarios  
The applicability of the formulas is examined for the case of the rotational scenario. The power transmission is evaluated for the case in which the implanted antenna is fixed  
Figure 5: A comparison of the power transmissions obtained from the related formulas and the full-wave simulation FEKO at 400 MHz between: **(a)** the two identical reader patch antennas outside the human body, **(b)** the reader patch antenna outside the human body and the implanted patch antenna immersed with 4 mm thickness of the human skin, and **(c)** the reader patch antenna outside the human body and the implanted patch antenna immersed with 8 mm thickness of the human skin.  
while the other one rotates with an angle \(\theta\). The integral coupling formula is more flexible for the estimation of the power transmission due to the movement of the antenna. The integral coupling formula can be used to compute the power transmission in rotational scenarios, while the formulas originated from the Friis formula are restricted to accurately estimate the rotational scenarios. The integral coupling formula was primarily used for the evaluation of the rotational scenarios. The separation distance was set as \(1\lambda\) and the rotation angles of the reader antenna varies from \(0^{\circ}\) to \(45^{\circ}\), which are slightly wider than the valid angle presented in [34]. A comparison of the integral coupling formula and the full-wave simulation tool FEKO is shown in Figure 6. The similarity of the two graphs is observed in terms of the shapes with the different rotation angles. A maximum deviation between the integral coupling formula and the full-wave simulation FEKO is approximately 0.8 dB. The integral coupling formula demonstrates its applicability to the diverse scenarios which are widely used in the biomedical applications.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 2 Preliminaries
### Blockchain Technology in Agriculture and Animal Husbandry: An Overview
#### 2.2.5 Decentralized Peer-to-Peer Networks in Agriculture and Animal Husbandry  
Peer-to-peer (P2P) networks operate on a structure that is decentralized, where each computer in the network acts simultaneously as both client and server [80]. Unlike the conventional client-server model, P2P networks are decentralized, with every node contributing to data processing and transmission. This design significantly reduces the load of data transmission and storage, thereby enhancing system efficiency and reliability.  
Decentralized peer-to-peer (P2P) networks enable direct data sharing among agricultural stakeholders. For example, in a crop trading scenario, a P2P network allows farmers to directly share real-time pricing and product availability with buyers, bypassing intermediaries. In livestock farming, P2P networks can facilitate collaborative data sharing between farmers and veterinarians, enabling faster responses to health issues and improving animal welfare. This decentralization enhances collaboration and efficiency in the agricultural ecosystem.  
P2P networks are distinguished by several architectural types:  
Pure peer-to-peer architecture: In this architecture, all nodes are equal, with no fixed network structure or central control point, and resource discovery relies on flood querying or random searches. Suitable for resource sharing and distributed searching, like the early Gnutella network.  
Structured peer-to-peer architecture [81]: This architecture utilizes predefined topological structures (like Distributed Hash Tables (DHT)) to organize nodes and manage resources, providing an efficient and predictable method of resource location. Suitable for scenarios requiring precise searches and data consistency, such as certain distributed database systems.  
Hybrid peer-to-peer architecture [82]: This architecture combines characteristics of pure P2P and client-server models, often with some central servers or special nodes assisting in resource discovery and network coordination. Suitable for scenarios that combine decentralized advantages with centralized management efficiency, like the BitTorrent network.  
Each P2P architecture offers unique benefits but has limitations, making them suitable for different network environments and application needs. Understanding these architectures is crucial for making informed decisions in P2P network design and implementation. The schematic diagrams of the above three types of p2p networks are shown in Figure 5.  
Figure 5: Three types of P2P networks.
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 3 CNN-NLP Architecture Design for LSB Steganography Detection  
The proposed model integrates two main components, a CNN specialized in steganography detection using the LSB method [22; 23; 26] and a semantic validation module implemented via NLP. This combination enables the classification of images as containing steganography, and also the verification of whether the extracted hidden content is linguistically consistent in Spanish, thereby increasing the system's reliability in forensic contexts. The model consists of four main stages, described below.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 2 Related Work
### Motivation of Our Works  
In summary, current research on network slicing, satellite navigation and resource allocation is poor in terms of the converged control and real-time requirement emphasized by the integrated space-ground communication system, which motivates this paper.