esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture
### Drone Three-Dimensional Coordinate Calculation
Other parameters are as follows:  
Point G: The true north position of the sphere; point O: Center of the sphere; R: Average radius of the earth (\(\sim\)6371 km);  
(\(W_{A}\), \(I_{A}\)): The values of the longitude and latitude of point A obtained by the GPS of the dual-axis device;  
\(\Downarrow\) (Azimuth angles of point A, B and G): Obtained by the magnetometer and gyroscope of the nine-axis attitude meter in the dual-axis device;  
\(\vartDelta\): The angle between the two points B and G and the ground connection \(\overline{GO}\);  
\(\vartDelta\): The angle between the two points A and G and the ground connection \(\overline{GO}\);  
\(\vartDelta\): The angle between the two points A and B and the ground connection \(\overline{GO}\);  
\(\vartDelta\): The spherical distance between the two points of point A (dual-axis device) and B (drone aerial location); that is, the length of minor arc \(\vartDelta B\) in the arc generated by the intersection of the planes passing through the three points A, O, and B, and the ball. Figure 6: Schematic diagram of the calculation of the height of the drone on the image concentric point by the attitude meter and the laser ranger. \(\mathcal{L}\)c is the metric angle:  
\[\mathcal{L}\mathrm{c}=\ \frac{\hat{A}B}{R}\times\frac{180^{0}}{\pi}. \tag{9}\]  
Since the spherical distance between point A (dual-axis device) and point B (drone aerial location) is much smaller than the length of R, the spherical distance \(\hat{A}B\) between two points can be approximated as the linear distance between two points \(\overline{AB}\), so Equation (9) can be corrected to:  
\[\mathcal{L}\mathrm{c}=\ \frac{\overline{AB}}{R}\times\frac{180^{0}}{\pi}. \tag{10}\]  
\(\mathcal{L}a\) and \(\mathcal{L}\theta\) can be converted by the sphere longitude and latitude coordinates [21], and Equation (11) and Equation (12) can be obtained as follows,  
\[\mathcal{L}a=cos^{-1}(sinA_{w}\times cos\mathcal{L}\mathrm{c}+cosA_{w}\times sin \mathcal{L}\mathrm{c}\times cos\psi), \tag{11}\]  
\[\mathcal{L}\theta=sin^{-1}\left(\frac{sinc\times sim\psi}{sina}\right). \tag{12}\]  
Since the drone flight altitude at point B is within a few kilometers above the ground, the length between the two points A and B and the point O of the center of earth can be regarded as the same, and \(\mathcal{L}\theta\) can be regarded as the longitude difference between the two points A and B. Thus, the value of the longitude and latitude of point B (\(W_{B},J_{B}\)) is the longitude and latitude coordinates of drone, where:  
\[J_{B}=J_{A}+\mathcal{L}\theta. \tag{13}\]  
As described above, the longitude and latitude values of the drone flying in the air, which is locked by the dual-axis device and the flight altitude h, can be calculated and acquired, then transmitted wirelessly to the ground control center for the attack tasks of direct attack or tracing and entrapping.
# Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy
## 4 Results and Discussion
### Factors Leading to Low Resistivity  
To discuss the reasons for the low resistivity in the LHS reservoir, various factors were considered. The factor of additional conductivity can be excluded, since Vsh is very low and conducting minerals have not been mentioned in the mineralogical description of the reservoir. The factor of a high irreducible water saturation can also be excluded. Althoughthe microbially induced calcium carbonate precipitation (MICP) data were unavailable for LHS, they were available for the Upper Haushi sandstone (UHS) reservoir, which also has similar low resistivity with a tight section at the top. The irreducible water saturation (Swir) in UHS according to capillary curve analysis was below 20% in the measured range of pressure. For comparison, in another tight sandstone sample, Swir was 18% at a porosity of 5.7% and a permeability of 5.6 mD [32; 33].  
The resistivity of the formation is influenced by both the resistivity of the fluid and the rock itself. Fluids with high salinity typically demonstrate lower resistivity. Additionally, a well-developed porous system combined with fissures can contribute to lower resistivity by reducing resistance to electrical current flow.  
When combined with high-salinity formation water, this porous system results in low resistivity readings overall. The cementation factor of 1.6, the Kv/Kh of 1, the dispersed clay type, and the high porosity of LHS indicate homogeneous sandstone with even grain packing and good connectivity. The fact that the resistivity is high in the oil-producing Dolomite Drain of the Upper Haushi reservoir despite its differing pore geometry supports this conclusion [14].
## 5 Results and Discussions
### Comparative Analysis of Variables Importance Method  
Figure 12 and Table 4 compares the prediction performance of the RF (%IncMSE, IncNodePurity), SVM, GBM, and before VIM method using the 9 features selected by VIM method based on RF. For each model run, 80% of the total data set is used to train the prediction model, and 20% of the total data set is used to test the model. The prediction performance for the training and testing set is compared. The statistical performance indicator (example: RMSE, MAPE) of trained model by k-fold validation are found to be 0.639, 22.94% for RF(before VIM), 0.354, 22.29% for RF (VIM %IncMSE), 0.262, 16.80% for RF (VIM IncNodePurity), 0.350, 22.87% for GBM (VIM IncNodePurity), and 0.297, 20.03% for SVM (kernel function). It is found that the RF (VIM IncNodePurity) performs the best in terms of prediction accuracy, which suggests that this approach method is a useful and robust tool for forecasting the 3 years gas production in shale reservoir. It should be noted that the RF (VIM %IncMSE) method also gives a comparable prediction (RMSE) ability compared to the various method.  
However, as shown in the boxplot in Figure 12, the problem of reproducibility of the test sets forecast results has been found, and more than 70% of the APE, which has been found to be an outlier, affects the overall accuracy of the model. In contrast, RF (VIM %IncMSE) shows more stable results. Therefore, instead of various supervised learning algorithms, it is recommended that one develops a production forecast model using RF (VIM %IncMSE).
# Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale X-ray Security Images
###### Abstract
Conventional image augmentation techniques, which we refer to from hereafter as image transformation, include rotating, cropping, flipping, shearing, resizing, and applying any transformation to the original image. Recently, techniques based on Generative Adversarial Networks (GAN) [23], which we refer to from hereafter as image synthesis, have been used to augment image datasets by generating completely new and unseen images that are sampled from the training set distribution. In the context of imbalance, image augmentation of the minority class is considered an oversampling approach, wherein the goal is to either balance the dataset or allow the learning algorithm to see the minority class more frequently. In this paper, we investigate the effects of using GAN-based image augmentation approaches to improve the performance of a threat detection model based on Faster-RCNN [24] on a large-scale and imbalanced X-ray security dataset, also referred to as a practical X-ray dataset. We adapt a Deep Convolutional GAN (DCGAN) model [25] to generate new X-ray images of threat objects and a Cycle-GAN model to translate X-ray images of threat objects from their camera image counterparts. We create new X-ray security images by overlapping the new X-ray images of threat objects to benign X-ray images of baggage scans. These new images are used to augment the training set. The main contributions of this paper are as follows: (a) the use of image augmentation specifically to address the class imbalance problem in a practical X-ray dataset, (b) the development of an image augmentation approach that takes into account the internal distribution of each threat object and their instances, (c) the exhaustive evaluation of image synthesis approaches against image transformation, and (d) the combination of image synthesis and image transformation approaches to enlarge the X-ray security image dataset. Experiment results indicate that image synthesis significantly improves the FPR by up to 15.3% on the dataset that closely resembles a practical X-ray dataset. The FPR is further improved by up to 19.9% on the same dataset by combining image synthesis and image transformation. Conversely, we observed that when the model already achieves a relatively high TPR, usingany image augmentation approach does not provide any further improvements. Overall, the best performance is achieved when image transformation is used simultaneously with any image synthesis approach, which altogether balances a high TPR and a low FPR. This paper is organized as follows: Section 2 briefly reviews the related works. Section 3 provides a description of the properties of the imbalanced dataset. Section 4 discusses the augmentation approaches used in this study. Section 5 describes the details of the experiments, evaluation criteria, and results. Section 6 presents the conclusions of this study.
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 4 Experiment and Result Analysis  
### Environmental Setup and Data Collection  
We conducted experiments by deploying 34 pseudolite base stations (BSs) in a 4.6 km long highway tunnel to evaluate the performance of the proposed model, as shown in Figure 5. Due to the curvature of the tunnel, the average spacing between adjacent base stations was 144 m, with a maximum gap of 203 m. These base stations were installed at the center of the tunnel ceiling [14], as shown in Figure 6. Meanwhile, receivers were installed on the test vehicle (Figure 6), and six sets of path data were collected at various times, resulting in 70,424 positioning signals. The precise locations of the base stations were obtained using a laser total station.  
For the Enconv1d model, two path datasets (containing 24,298 positioning points) were used for model training, with 80% of the data allocated to the training set and 20% to the validation set. The remaining four path datasets (46,126 positioning points) were used for model performance testing. The experiments used PyTorch 1.8 on an i9-13900 quad-core CPU with 16 GB of RAM and an NVIDIA RTX 4060 GPU with 8 GB of VRAM.  
Figure 5: Schematic of the test tunnel and the base station installation point.
# A Deep-Learning-Based Approach to the Classification of Fire Types
## 2 Related Work
The primary objective was optimizing firefighting operations and enhancing pilot safety using advanced technologies. Machine learning optimized firefighting strategies, focusing on parameters like plane velocity, angular velocity, initial position, particle size, sprayer amplitude, release times, and drop rates. The study argued for the feasibility of using a computational framework combining meshless discrete element modelling and machine learning in optimizing aerial firefighting operations. The work of [22] aimed to develop a real-time fire detection system that identifies the presence of fire and classifies the type of fire based on the surrounding environment. The authors used the YOLOv5 model for both fire detection and classification. Two object detectors were trained separately: one for detecting fire and the other for identifying flammable objects in the environment. Based on the US standard of fire classes, the system classified fires as A, C, and K. The intersection-over-union (IoU) scores are calculated to associate detected fire with the identified flammable objects, determining the fire class. The authors created a comprehensive dataset for each label and further trained the YOLOv5 model to identify various objects like curtains, couches, and laptops, which are potential sources of fire. The system was tested on a video dataset of 150 samples. In summary, the literature reveals significant interest among researchers from various fields in improving the accuracy of early fire detection systems. Extensive efforts have spanned a wide range of methods for recognizing fire symptoms, utilizing both historical fire data and real-time sensor-based data. The existing literature indicates that machine learning and rule-based approaches have achieved notable effectiveness, as evidenced by the high levels of accuracy reported (see Table 1). Our research integrates and extends previous work by introducing an additional dimension, emphasizing fire recognition and the automatic classification of fire types. This advancement has the potential to significantly aid firefighters by enhancing the efficiency and effectiveness of their operations, enabling them to determine the most appropriate firefighting methods for different fire scenarios.
## 3 Results and Discussion
### Batch Adsorption Capacity  
The adsorption data satisfactorily fitted to the Langmuir adsorption isotherm (R\({}^{2}\) = 0.80 - 0.83). The Langmuir maximum adsorption capacities (mg/g) calculated from the isotherms for sulphate, phosphate, and nitrate were 86.9, 38.6, and 11.1, respectively. The adsorption capacity for phosphate of 39 mg/g was among the highest values reported in the literature for most ion exchangers [15]. For example, [PERSON] and [PERSON] [27] developed an anion exchange resin from soybean hulls consisting of lignocellulosic by chemical modification with an organic nitrogen compound. The authors found that this resin had a Langmuir adsorption capacity of 19.5 mg/g at pH 7.0. They compared this value with values reported for a commercial cellulose-based resin of 14.3 mg /g and for a commercial synthetic resin, Amberlite IRA-400 which had a value of 32.2 mg/g. [PERSON] _et al._ [28] produced an ammonium functionalised mesoporous silica compound called MCM-48 and reported that it had a maximum adsorption capacity of 15.5 mg/g at pH 6.0. [PERSON] _et al._ [29] discovered that Purolite A500P anion exchange resin had a Langmuir adsorption capacity of 21 mg/g for phosphate at pH 7.5. These literature values were obtained from experiments conducted on synthetic waters containing only phosphate, whereas the current study was conducted using MBR treated water containing other anions as well. It is interesting to note that despite the competition from other anions in the solution the phosphate adsorption capacity was higher than the above literature values.
# A Survey on Blockchain-Based Federated Learning
## 3 Blockchain-Based Federated Learning  
In the traditional FL architecture, a central server is responsible for collecting, aggregating, and broadcasting the new global model, which may lead to the following problems: (a) The stability of the central server might be affected by cloud service providers; (b) the central server might show favoritism towards certain clients; and (c) a malicious central server might poison the model or collect private information from clients. To address these issues, the most direct solution is to remove the central server and let the client nodes handle the corresponding tasks [66; 67; 68]. This requirement aligns well with the inherent characteristics of blockchain technology. Recent studies have used the blockchain's distributed storage architecture as the foundational framework for FL [69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79]. By designing protocols on the upper layer of the blockchain, they implement the task of model aggregation running for clients. At the same time, the rational incentive mechanism in the blockchain provides a technical solution to enhance the enthusiasm of all participants to actively participate in FL model training.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Development Trend of Encoder-Decoder Systems
It is worth continuing in-depth research on better integrating ViTs with encoder-decoder architectures, leveraging ViTs' powerful remote dependency modeling capabilities, and achieving more accurate semantic segmentation through end-to-end training. Figure 6: Brief structure diagram of a transformer. 5. Guiding Semantic Segmentation with Prior Knowledge: In many practical applications, images often exhibit specific structures and prior knowledge. For example, in medical image segmentation, we usually have some understanding of the target structure's shape and location. Therefore, introducing this prior knowledge into encoder-decoder architectures can guide the model toward learning more reasonable semantic segmentation results. This can be achieved by adding prior knowledge constraints to the loss function or designing specific prior knowledge modules. 6. Adaptive Domain Adaptation: In practical applications, domain differences may cause performance fluctuations in the model across different data sets. To address this issue, we suggest researching an adaptive domain adaptation method based on encoder-decoder architectures to learn the mapping relationship between source and target domains to improve the model's generalization capabilities in the target domain. 7. Integration of Multimodal Information: In many practical scenarios, besides RGB images, other modalities of data such as depth information and spectral information can be obtained. This multimodal information provides a richer context for semantic segmentation. Therefore, we propose incorporating multimodal information into encoder-decoder architectures to improve segmentation performance. Specific approaches may include designing multimodal fusion modules or using multitask learning to simultaneously learn feature representations of different modalities. In summary, image semantic segmentation technology based on encoder-decoder architectures has achieved significant success in many visual tasks. However, to further improve its performance in the field of semantic segmentation, more exploration and research are needed.
# Correlation of Gravity and Magnetic Field Changes Preceding Strong Earthquakes in Yunnan Province
## 2 Data Processing and Accuracy
### Calculation Method
#### 2.3.1 Grid Interpolation  
The Yunnan area was gridded according to the longitude difference of 0.1\({}^{\circ}\) and the latitude difference of 0.1\({}^{\circ}\), and kriging interpolation was performed on the grid points to calculate the gravity change value and the total geomagnetism value of the grid points.  
\[\hat{z}_{0}=\sum_{i=1}^{n}\lambda_{i}z_{i} \tag{2}\]  
In Formula (2), \(\hat{z}_{0}\) is the estimated value of the measuring point \((B_{0},L_{0})\). That is, \(\hat{z}_{0}=z(B_{0},L_{0})\), where \(\lambda_{i}\) is the weight coefficient. A set of optimal coefficients can meet the minimum difference between the estimated value at measurement point \(\hat{z}_{0}\) and the true value \(z\).
# Force of Intermaximillary Late Elastics from Different Suppliers: A Comparative In Vitro Study
## 1 Introduction  
Intermaximillary elastics have become an integral part of treatment with fixed orthodontic appliances, aligners, and a combined orthodontic-surgical approach. When indicated appropriately and with good patient compliance, they can reduce the duration and increase the efficacy of ongoing orthodontic treatment. Elastics can be used along with an orthodontic appliance within one dental arch, in an intermaximillary manner between the upper and lower dental arches, or can be attached to intraoral as well as extraoral auxiliary appliances [1, 2]. According to the material used, elastics can be divided into latex elastics and nonlatics; they can be further divided based on their size and thickness [3]. The manufacturers declare that the desired force should be achieved when each elastic is stretched to three times its diameter [4, 5, 6]. The clinician chooses an adequate size and thickness of the elastic to achieve the optimal force and desired therapeutic effects.  
It is important that each of the elastics used meet the criteria and produce the desired force for as long as possible while worn by the patient. Elastics are known to undergo force degradation that gradually decreases during wear [7, 8]. Therefore, clinicians typically advise patients to replace their elastics every 8 h, or after 12 or 24 h; however, clear guidelines for the timing of replacing elastics are lacking.  
Previous studies dealing with elastics have evaluated in detail force degradation in vitro and/or in vivo; however, they largely utilized a small sample of elastics of theorder of dozens of elastics evaluated from each brand [5; 7; 9] and used elastics of one batch. The results may have been biased due to the use of a relatively small number of elastics from a single package.  
The most widely used size of elastics in clinical practice is 3/16\" Medium latex elastics (4.8 mm in diameter), which when stretched to three times their size should correspond to a force of 4.5 oz \(\approx\) 127.6 g (1.3 N). They have also been shown to be the most reliable in terms of applied force with respect to the declared force, and in terms of force degradation [10]. In addition, [PERSON] et al. [11] reported that 3/16\" Medium elastics were more homogeneous and showed less variation within the sample in comparison with their light and heavy counterparts. The present paper reports the values of the initial force of 3/16\" Medium elastics from five different manufacturers and five different packages of various batches; the degradation of force after 2, 8, 24, and 48 h; and the nature of the course of this force degradation.