esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Elaboration of the Optimization Method
### Weight and Cost Objective Functions  
During the structural optimization, the weight and the cost objective functions were taken into consideration.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Steps** & **Process Description** \\ \hline
**Step 1.** & **Initialization—** In this step, an initial population \(P_{0}\) of size \(N\) is established, and the generation is set to an initial value of \(g=0\). The fitness values associated with individuals in \(P_{0}\) are calculated, and \(P_{0}\) is copied into an archive \(A_{0}\) of size \(N\). \\
**Step 2.** & **Generation—** A new generation is created by incrementing the generation counter (\(g=g+1\)) and selecting the parent population (\(P_{g}\)) from the previous archive (\(A_{g-1}\)). \\
**Step 3.** & **Sorting—** The population is sorted based on the specified objectives for the current generation. If optimizing for two objective functions, the first objective is chosen in the first generation, the second objective in the second generation, and so on. \\
**Step 4.** & **Grouping—**\(P_{g}\) is split into two groups consisting of stored individuals. \\
**Step 5.** & **Crossover and Mutation—** Crossover and mutation operations are performed on each group, producing two child individuals from two parent groups and then eliminating the parent groups. \\
**Step 6.** & **Assembly—** The child groups generated in Step 5 are assembled to produce a new population (\(P_{g}\)). \\
**Step 7.** & **Archive Renewal—\(P_{g}\) and \(A_{g-1}\) are combined to generate 2N individuals. Environment selection is then conducted to reduce the number of individuals to \(N\), and a new archive (\(A_{g}\)) is generated. \\
**Step 8.** & **Termination—** The terminal criterion is verified. If the criterion has been met, the process can be stopped. If not, the algorithm returns to Step 2. \\ \hline \hline \end{tabular}
\end{table}
Table 3: NCGA flow steps and descriptions.
# Dynamic Synthesis of Three -- Point Circle Peripheral Docking Technology Pose
## 3 Visual Positioning System  
### System Design  
Reconstructed vehicles may need to dock in both outdoor unobstructed environments and indoor environments such as an underground garage. For indoor docking environments, traditional GPS (Global Positioning System) navigation and positioning cannot be used due to building occlusion. At present, the main options are wireless positioning, laser Simultaneous Localization and Mapping (SLAM), visual SLAM, and landmark positioning. Wireless positioning often yields poor location accuracy due to multi-path interference. Laser SLAM technology is mature and has high precision, but its cost is high and its dynamic interference resistance is weak. Visual SLAM uses cameras to track visual features, and it has a low cost and strong adaptability, but low reliability. Landmark positioning utilizes natural and artificial landmarks, such as road signs, for positioning. Natural landmarks have good universality, but their computation is complex and not robust. In contrast, artificial landmarks are relatively simple to calculate and stable in their characteristics. However, the frequently used RFID (Radio Frequency Identification) tags and magnetic strip landmarks have a short working distance, limited fixed route movement, and poor flexibility.  
To avoid the above shortcomings, in this study, a robot positioning system based on vision is adopted for the reconstructed car to provide artificial beacons for indoor mobile positioning of the reconstructed car, achieving accurate and stable docking.  
The visual positioning system is composed of an artificial beacon, a visual module, and an embedded image processing platform. The beacon is made of infrared luminous material and is placed above the three locking holes of the passive vehicle. The infrared camera is embedded in the active vehicle body. The vision module is responsible for automatic exposure control and image data acquisition, and it sends the image to the processor module through the EPPI (Enhanced Parallel Peripheral Interface). The processor module is the core part of the whole system, and is responsible for receiving the image data sent by the camera module, running the automatic exposure control algorithm and  
Figure 5: Model of chassis dock structure.  
Figure 6: Turning radius of the whole vehicle.  
artificial beacon recognition algorithm, and outputting the final detection results to the actuator through the serial port for graphic comparison and movement. Figure 7 shows the working principle of the visual positioning system.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 3 Transmission Acceleration Using BoostTCP
### Improved Congestion Judging and Handling Mechanism  
Many congestion estimation and recovery strategies were developed for standard TCP over the last two decades to meet network requirements under different conditions. The fundamental premise was that a packet loss represented a result of congestion. However, this assumption does not hold for a transmission network with ultra-long-distance special-line optical fiber. In such a network, packet loss is typically caused by the BER of long-distance transmission, not by congestion-related factors. Therefore, standard TCP can frequently enter an excessively conservative transmission state. Meanwhile, when a network path contains deep-queue network devices, packet loss does not occur for a long period after the congestion occurs. The standard TCP is insensitive to congestion, resulting in excessive transmission, which not only affects network congestion but can also cause significant packet losses. As a result, the TCP enters a lengthy recovery phase for packet loss, resulting in transmission stagnation. All of these factors contribute to the poor performance of the standard TCP protocol for an ultra-long-distance optical fiber transmission network with a special line.  
Considering both packet loss and delay variation, the proposed BoostTCP algorithm can dynamically learn the network path characteristics of each specific connection during data transmission, including end-to-end delay and its variation characteristics, the arrival interval of feedback packets (ACK) at the receiving end and its variation characteristics, the degree of data packet reversal and its variation characteristics, delay and jitter caused by the security equipment's deep data inspection, and random packet loss caused by various factors. These characteristics are monitored in real time and analyzed holistically to derive precursor signals and available bandwidth that reflect congestion and packet loss along the TCP connection network path. They also determine the degree of congestion and the transmission rate, and show whether the congestion recovery mechanism is compatible with the available bandwidth on the current path and can achieve accurate and timely packet loss judgment and recovery.  
Based on network characteristics, the congestion degree and available bandwidth can be estimated accurately and in a timely manner. When congestion occurs, the transmission is realized based on the mentioned result. The unnecessarily slow data transmission rates caused by BER-induced packets can be avoided in an ultra-long-distance optical fiber transmission network with a special line. Specifically, the advanced congestion judgment and control algorithm of BoostTCP mainly uses the two following mechanisms: Prevent excessive conservative transmission and Prevent congestion deterioration.
## 2 Literature Review
### Conclusions of the Literature Review  
In our literature survey, we found that the geometry of lattice structures implies the use of small FEA elements which produce intractable FEA meshes. Consequently, the numerical analysis of large lattice structures is a complex (sometimes unfeasible) process, limited by its elevated computational cost.  
To alleviate the computational burden of the simulation of lattice structures, material homogenization is applied to produce regular domains that mimic the lattice domain. Following this approach, one can obtain fast and accurate approximations of the displacements field of the lattice domain. However, the stress-strain response cannot be directly obtained due to the geometric dissimilarities between the lattice and the homogeneous domains.  
Our goal with this paper is to contribute to the problem of the estimation of the stress-strain response in large lattice domains. For this purpose, we propose a methodology that integrates material homogenization and DOE. We use the DOE-based methodology in [5] to devise simple mathematical expressions (meta-models) to characterize the stress-strain of Schwarz primitive lattice domains. The inputs of the produced meta-models are displacement-based features that can be efficiently calculated using material homogenization instead of full FEA simulations. The meta-models developed in this article are not intended to be suitable in high precision contexts, but to produce rough and efficient estimations of the von Mises stress that allow fast pre-evaluation of designs.  
Particularly, we apply our methodology to estimate the von Mises stress under compressive loads for large (more than 100 cells) Schwarz primitive lattice structures. The meta-models use the strains of the boundary of the lattice cell to relate the displacement field of the homogeneous domain with the von Mises stress of the lattice domain.
## 7 Future Research Directions
### Sensing  
Since vision-based defect detection requires a large training dataset and still not be fully accurate in all conditions, a multi-sensor approach has been explored to detect surface defects that take input from various sensors, such as RADAR, thermal cameras, and accelerometers [86; 94; 95]. More research is needed in this area to identify other types of defects as well by fusing data from multiple sensors. For example, data from regular and thermal cameras can be combined to detect wet patches on ceilings and walls. Data from the accelerometer and LiDAR can be combined to measure the verticality of walls and the horizontality of floors.
## 3 Attacks in Blockchain
### Detecting and Mitigating Malicious Blockchain Attacks
#### 3.3.1 The 51% Attack  
A 51% attack occurs when a single entity or group gains control of more than 50% of a network's mining hash rate or staking power, enabling them to manipulate the blockchain. This attack is particularly relevant to Proof-of-Work (PoW) blockchains like Bitcoin and Litecoin, as well as smaller chains with lower hash rates. The attacker can double spend coins, prevent transactions from being confirmed, and disrupt the network's integrity.  
Detection TechniquesDetection techniques for 51% attacks focus on monitoring network hash rate distribution and identifying unusual patterns in block creation. Key methods include the following:  
* Hash rate monitoring: Continuous tracking of hash rate distribution among mining pools to detect centralization risks [68].
* Block propagation analysis: Observing block propagation times and orphan rates to identify potential manipulation [69].
* Network consensus monitoring: Analyzing deviations from normal consensus behavior, such as sudden changes in block confirmation times [70].  
Mitigation StrategiesTo mitigate the risk of a 51% attack, the following strategies are essential:  
* Decentralization of mining power: Encouraging a diverse and distributed mining pool ecosystem to reduce the risk of hash rate concentration [69].
* Network size and security: Increasing the overall hash rate and network size to make it economically infeasible for an attacker to gain majority control [68].
* Consensus algorithm enhancements: Transitioning to more secure consensus mechanisms, such as Proof of Stake (PoS), which are less susceptible to hash-rate-based attacks [70].
* Real-time alerts and response systems: Implementing systems to detect and respond to unusual network activity, such as sudden hash rate spikes or block reorganizations [69].  
By adopting these measures, blockchain networks can reduce the likelihood of a 51% attack and maintain the integrity and trustworthiness of their systems.
## 4 Results  
In this paper, the designed helium detector was applied to the Dongsheng gas field in the northern Ordos Basin, and helium concentration was determined in three wells in the same area, namely, the wells HJQ-H501, HJQ-H505, and K2-P2. The software used could automatically integrate the signal curve into the concentration standard curve and use the integrated values to obtain the helium concentration in the mud gas. It was found that the time required by the mud gas to pass from the mud tank to the helium detector was 3.5 min; this delay time was taken into account when processing the data.  
Helium detection in well HJQ-H501 started from the Shiqianfeng Formation, and the results showed that in the Lower Shihezi Formation, the helium concentration had a good correlation with the total hydrocarbon concentration. In this formation, the average total hydrocarbon concentration was 8.9%, with a maximum of 42.7%, and the average helium  
Figure 8: Instrument signal attenuation test.  
concentration was 190 ppm, with a maximum of 830 ppm. Meanwhile, hydrogen was also detected in the well, with a maximum concentration of 3.7%, and no carbon dioxide was detected. It is worth mentioning that a few high-helium-concentration layers were also detected above the main producing layer of the gas reservoir, in which the helium concentration was up to 199 ppm. Helium detection in well HJQ-H505 started from the Yan'an Formation, and no helium was detected during the whole drilling process. A high total hydrocarbon concentration was found in the Shanxi Formation and Lower Shihezi Formation, with a maximum concentration of 28.1% and an average concentration of 7.7%, and no hydrogen and carbon dioxide were detected. Helium detection in well K2-P2 started from the Lower Shihezi Formation, and no helium was detected during the whole drilling process. A high total hydrocarbon concentration was found in the Lower Shihezi Formation, with a maximum concentration of 71.3% and an average concentration of 24.8%. Hydrogen was not detected in the well, and carbon dioxide with a maximum concentration of 2% was detected. The above three wells are all located in the Dugujiahan area of the Dongsheng gas field and are relatively close to each other.
## 2 The Experimental Equipment and Materials
### The Materials for Grinding Wheel
#### 2.2.2 The Abrasive Grains  
The CBN abrasive grains exhibit strong chemical stability at high temperatures and are less prone to react with iron group elements than diamond [33]. Funek Superhard Materials Ltd.'s CBN-980T titanium-plated abrasive grains were used for this investigation. The surface of the CBN abrasive grains has a titanium coating. Titanium can enhance the bonding agent's capacity to cling to CBN abrasive grains by reacting chemically with both CBN and the bonding agent. CBN is a crystal made of nitrogen and boron atoms. Its hardness is second only to diamond, and its thermal stability and chemical inertness are far better. The grain size was selected from 100 to 120 mesh.  
\begin{table}
\begin{tabular}{c c} \hline
**Instrumentation** & **Model** \\ \hline Robot control system & KUKA ZH30/60 III \\ KUKA robot & KUKA-KR16-3 \\ Laser device & Raycham Laser RC52 \\ Laser generator & IPG YLR-500-WC \\ Powder feeder & RC-PGF-S \\ Laser head water cooling system & Tongfei MCWL-25 DTR-05-1225 \\ Ultrasonic generating equipment & BRANSON 2000 bdc \\ \hline \end{tabular}
\end{table}
Table 1: The specific parameters of experimental equipment.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Plane resolution & 0.12 μm \\ Resolution in z-direction & 10 nm \\ Grating reading capability & 0.8 nm \\ Laser wavelength & 405 nm \\ Maximum measured straight-line length & 100 mm \\ Types of measurement modes & 7 \\ Maximum imaging slope & 85\({}^{\circ}\) \\ Accuracy & \(\pm\)2\% \\ \hline \end{tabular}
\end{table}
Table 2: The specific parameters of laser confocal microscopy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Element** & **C** & **Si** & **Mn** & **Ni** & **Fe** \\ \hline Wt\% & 0.452 & 0.271 & 0.442 & 0.021 & Bal. \\ \hline \end{tabular}
\end{table}
Table 3: The chemical composition of AISI 1045 steel.
## 3 Q2: What Are the Challenges of the Current UIoT System
### Channel Characteristics of UIoT
#### 3.1.2 Energy Consumption and Storage  
UIoT nodes are designed with limited battery power, computational capacity and memory space [21]. Furthermore, the nodes consume more power for data gathering, processing and transferring. Compared to terrestrial networks, the nodes are rechargeable using solar energy. However, in UIoT networks, it is not easy to maintain or recharge due to the natural behavior of the environment. This may cause power constraints in UIoT networks.
## 4 Intraoral Molar Distalization Methods
### Tooth Tissue-Supported Intraoral Molar Distalization Methods
#### 4.1.5 Distal Jet Appliance  
The Distal Jet appliance was developed to prevent the rotational and tipping movements often observed in molars during intraoral distalization methods. The Nance appliance serves as the anchorage unit. Distalization is achieved through Ni-Ti springs attached to a thick wire, which is embedded on one side in the Nance acrylic and on the other side in the palatal tube of the molar band. Activation is performed by compressing the Ni-Ti springs toward the first molar. [PERSON] et al. [31] compared the maxillary dentoalveolar changes in patients treated with three distalization force systems (Jones Jig, Distal Jet and First Class appliances) using digitized models. The Distal Jet appliance promoted smaller mesial displacement of the premolars and greater expansion of the posterior teeth [31].  
An intraoral occlusal view of the Distal Jet appliance is shown in Figure 3.  
Figure 2: Transpalatal arch.
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
## 5 Discussion
This work is motivated by growing concerns about security threats to general-purpose devices. The study presents an analysis of different TEMPEST standards and configurations and highlights their importance concerning unintentional EM emissions. It also contributes to the field of EM data leakage prevention, providing valuable information on the latest trends in effectively reducing the risks of EM leakage.
## 3 Results and Discussion
### Evaluation of Antifungal Activity of Allicin
#### 3.1.2 Inhibition Zone Assay  
A special fungal-seeded agar was employed for this part of the investigation. The efficacy of this assay for assessing the antifungal activity of novel test compounds is described in the literature [49]. An adequate water solubility of the compound is a critical requirement for this method. It implies that the target substance must be able to diffuse through the water-based agar-solidified medium. Allicin treatment resulted in the inhibition of fungal growth even at the lowest concentration (2 mM). The efficacy of allicin varies in case of each fungal strain tested. There was clear zone for inhibition with sharp edges in case of _A. brassicicola_ (Figure 3b). Nevertheless, a greater antifungal activity was observed for other fungi with no sharp boundaries for each concentration of allicin (Figure 3a). It infers that there was almost complete inhibition of fungal growth around the wells containing allicin. On the other hand, fungi grow predominantly in the area without allicin (control).
## 4 Discussion and Conclusions  
### Discussion  
This study is subject to certain limitations. For instance, the overall distribution of land in the country results in inadequate sample sizes for categories such as shrub, wetland, and water, leading to unstable classification accuracy. Moreover, while the classification accuracy achieved in our experiment is relatively high, the diversity of the utilized categories is limited. Future efforts will concentrate on exploring methods to refine the classification of forest categories. This can be accomplished by conducting secondary classification of forests using the findings of this study, with a focus on broadleaf forests, coniferous forests, and mixed forests. Furthermore, the data sources utilized in this study solely comprise Sentinel-2 imagery. Future research may involve integrating multiple data sources for classification, leveraging the strengths of each to complement one another and enhance classification accuracy. The 3 DCNN model employed in this study effectively handles the issue of multi-spectral input channels for deep learning extraction in convolutional neural networks. Future endeavors may include experimenting with other models, such as ResNet, that utilize three-channel inputs, adjusting channel operations, and integrating attention mechanisms to further enhance classification effectiveness.  
Figure 10: Czech land cover classification map in 2018.
# Automatic Clustering for Improved Radio Environment Maps in Distributed Applications
## 2 Wireless Requirements and Applications
### Improving Wireless Coverage  
One of the main challenges in the next generation of wireless technologies is the accuracy of the prediction models used in REM construction. It is essential to predict an accurate REM in order to determine several aspects that contribute to the quality of wireless communication. For example, predicting an accurate map contributes to determining the median of the relationship between RSRP and logarithmic distance in a wireless environment. Currently, most of the work done in improving RSRP has focused on single path loss in the considered range of communication. The single path approach is considered simple since it does not depend on complex mathematical operations. However, this approach does not provide accurate results, since it does not consider all communication factors in a wireless environment. For instance, the single path model does not capture the essential components in computing the path, such as the exponent of path loss [35].  
Due to the challenges with a single path approach, multiple research works have considered other approaches to improve RSRP prediction. For example, the multiple-trend path loss model was explored to improve RSRP prediction capabilities. Some works targeted double-trend models where line-of-sight (LOS) possibilities were considered before and after the clearance distance. Other approaches looked at the median of RSRP values in the prediction process. This is because the median might be different, even with the same distance, mainly due to differences in environmental aspects, such as varying shadowing [39]. Clustering approaches have also been considered as a means to improve RSRP prediction. All these works, either on single or multiple approaches, emphasize the importance of finding an accurate prediction model and lowering the complexity of the prediction process at the same time.