esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Backbone  
The base YOLOv5s model has approximately 7 M parameters, a computational cost of 15.9 GFLOPs, and a weight file size of 14.4 MB. The aim was to make the model lighter and more efficient by reducing these values in the context of speed and accuracy trade-offs. First, to reduce the computational complexity of the model, the CBS module in the backbone structure and neck structure of the YOLOv5s network was replaced with the Ghost convolution module. The backbone structure consists of deep convolutional layers to extract feature maps at hierarchically different scales from the input image. The GhostConv module replaces the standard convolution process with the Ghost convolution process, which provides more information with fewer parameters, resulting in deeper and more abstract representations without significant changes in accuracy. This results in lower computational cost, fewer parameters, and less memory consumption.  
The C3 module (C3_1), which follows the CBS convolution blocks in the neck structure, is replaced by the ECAC3 attention module. The addition of this attention mechanism improves the learning of which features of the model are more important on a channel-by-channel basis. It reduces the computational burden by capturing nonlinear defect information across channels with a one-dimensional convolution. Weights are dynamically adjusted to emphasise important features more. This supports deeper and more abstract representation learning. With the ECAC3 change, the overall performance and flexibility of the model is improved, supporting the development of a more sensitive and efficient defect detection algorithm.
## 4 Smart Money Corda Implementation
### Security Considerations  
While the Smart Money system builds upon the inherent security features of the R3 Corda platform, it is essential to address potential vulnerabilities in order to ensure a robust and trustworthy infrastructure. First, concerns about malicious users, criminals, or unfriendly governments participating in the network are mitigated by Corda's permissioned architecture. Participation is restricted to authenticated and authorized entities through stringent identity verification processes, including comprehensive know your customer (KYC) and anti-money laundering (AML) checks. Nevertheless, the Smart Money CBDC infrastructure would be built in accordance with established industry standards such as the ISO27001 [85], which provides a framework for an effective information security management system, and the National Institute of Standards and Technology (NIST) Cybersecurity Framework [86], which is a comprehensive set of guidelines and best practices to help organizations manage cybersecurity risks.  
Moreover, the protection of end users against the theft of cryptographic keys is paramount. Implementing robust key management practices has been widely adopted in the banking industry, such as the use of hardware security modules (HSMs), encrypted key storage solutions, and enforcing multi-factor authentication, and can significantly reduce the risk of key compromise. The Smart Money CBDC will rely on existing standard best practices of the UK banking system for the security of cryptographic keys, such as ISO/IEC 27001 [85], ISO/IEC 19790 [87], FIPS 140-2 [88], FIPS 140-3 [89], and PCI DSS [90], and will adhere to national regulations and guidance from bodies like the Financial Conduct Authority [91].  
Finally, the system must be resilient against well-known attack vectors like denial of service (DoS), man-in-the-middle (MiM), and side-channel attacks. Network-level security measures, including firewalls, intrusion detection systems, and rate-limiting mechanisms, can mitigate DoS attacks. Secure communication protocols with end-to-end encryption, such as TLS with mutual authentication, help prevent MiM attacks by ensuring data integrity and authenticity between parties. If hardware tokens are adopted for authorization and authentication of end users, side-channel attacks against point-of- sales (PoS) systems would be considered a security risk. To protect against side-channel attacks, especially those targeting cryptographic operations, the system should employ constant-time algorithms and avoid patterns that could leak sensitive information through timing or power analysis. Regular security assessments, code audits, and penetration testing are essential practices to identify and address vulnerabilities proactively, ensuring the ongoing security and integrity of the Smart Money system. Therefore, the Smart Money system employs the same cybersecurity practices, cryptographic protocols, and standards, indicated above, as the existing regulated banking system and can block known attack, thereby maintaining the same level of security.
## 2 Computational Intelligence
### Fuzzy Systems  
Fuzzy logic [16; 17] is a unique form of intelligence related to decision-making methodology. It is based on the extension of the concept of the classical binary set \(\{0,1\}\), in which the relation of \"belongs to\" \((\in)\) for a function \(I_{A}(x)\) is generalized so that instead, \(x\) takes infinite values in the closed interval \([0,\,1]\). In other words, it creates a new majority set \(A\), where the transition from the category of elements of \(X\) that belong to the fuzzy set \(\widetilde{A}\), to the type of elements of \(X\) that do not belong to \(A\) is not abrupt-unclear but gradual-unclear, as is usually the case in reality [16; 18]. In this sense, the characteristic two-valued function \(I_{A}(x)\) expresses a compact set \(A\) in the two-member domain \(\{0,1\}\). It is included in the concept of the participation function, \(\mu_{\widetilde{A}}(x)\), which expresses a fuzzy set at the extreme values of the infinite space \([0,\,1]\) and uses functions such as \(\mu_{\widetilde{A}}(x)\)[19]:  
1. Triangular: \[\mu_{A_{i}}(x)=\left\{\begin{array}{c}0,\,\,x<a\\ \frac{x-a}{b-a},a\leq x\leq b\\ \frac{c-x}{c-b},b\leq x\leq c\\ 0,\,\,c\leq x\end{array}\right.\]
2. Trapezoid: \[\mu_{A_{i}}(x)=\left\{\begin{array}{c}0,\,\,x<a\\ \frac{x-a}{b-a},a\leq x\leq b\\ 1,\,\,b\leq x\leq c\\ \frac{c-x}{c-b},c\leq x\leq d\\ 0,d\leq x\end{array}\right.\]
3. Gaussian: \[\mu_{A_{i}}(x)=exp\]  
Figure 1: Example of a network with convolutional layers ([[https://developersreach.com](https://developersreach.com)]([https://developersreach.com](https://developersreach.com)), accessed on 4 March 2023).  
Among the fuzzy sets [18; 20; 21] (sets whose elements have degrees of membership), it is possible to perform certain operations such as [16; 17; 22]:  
1. Fuzzy disjunction \[\mu_{\widetilde{A}\cup\quad\widetilde{B}}(x)=\mu_{\widetilde{A}}(x)\bigvee\mu_{ \widetilde{B}}(x)=max\big{[}\mu_{\widetilde{A}}(x),\mu_{\widetilde{B}}(x) \big{]}\forall x\in X\]
2. Fuzzy conjunction: \[\mu_{\widetilde{A}\cap\quad\widetilde{B}}(x)=\mu_{\widetilde{A}}(x)\bigwedge \mu_{\widetilde{B}}(x)=min\big{[}\mu_{\widetilde{A}}(x),\mu_{\widetilde{B}}(x )\big{]}\forall x\in X\]
3. Fuzzy product: \[\mu_{\widetilde{A}\cdot\widetilde{B}}(x)=\mu_{\widetilde{A}}(x)\cdot\mu_{ \widetilde{B}}(x)\forall x\in X\]
4. Fuzzy complement: \[\mu_{\widetilde{A}}=1\cdot\mu_{\widetilde{A}}(x)\]  
Fuzzy reasoning is called the process of deriving fuzzy conclusions, a process which is based on three fundamental concepts of the theory of fuzzy logic [19; 23] and specifically on fuzzy variables, inference rules and fuzzy relations, which can be combined through the process of composition with operations such as [18; 24; 25]:  
1. Fuzzy composition max-min: \[\mu_{R^{\circ}Q}(x,z)=\max_{y\in Y}\big{(}\min\big{(}\mu_{R}(x,y),\mu_{Q}(y,z) \big{)}\big{)}=\bigvee_{y\in Y}\big{(}\mu_{R}(x,y)\bigwedge\mu_{Q}(y,z)\big{)}\]
2. Fuzzy composition max-prod: \[\mu_{R^{\circ}Q}(x,z)=\max_{y\in Y}\big{(}\mu_{R}(x,y)\cdot\mu_{Q}(y,z)\big{)} =\bigvee_{y\in Y}\big{(}\mu_{R}(x,y)\cdot\mu_{Q}(y,z)\big{)}\]
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 2 Overview of Location Technologies  
The focus of the cellular-user location has changed over the generations from outdoors to indoors [1]. Thus, GNSS has had to adapt to the new requirements. However, other technologies and techniques have overcome satellite-ranging solutions for indoor positioning. Ranging-based or fingerprinting location have been studied [11; 12] to provide a high accuracy for indoors with technologies presented in Table 2, such as Bluetooth or WiFi. Some of these technologies have been discarded for this work for several reasons. First of all, the scope of this work is to study the real-time positioning with technologies that do not need the data collection phase in fingerprinting, such as geomagnetism [12]. Secondly, user location must be computed in the cloud for two main reasons: it will help future applications, such as driver-less cars in which the cloud runs the commands to the cars and must know their positions [13], and computing in the cloud also helps reducing energy consumption in the end-device [14]. Thus, inertial navigation system (INS) is excluded for this study because it is unfeasible to send the information of the accelerometer or gyroscope in real time (update rate \(\geq\) 100 Hz) with high energy efficiency. Thirdly, low-stability technologies, such as Bluetooth, might not allow to update in real-time, for the RSSI variations [15]. Moreover, Bluetooth provides an insufficient coverage for wide scenarios [16]. Hence, we decided to discard Bluetooth for this indoor positioning study. Henceforth, the technologies that are finally studied in depth for indoor positioning with high-precision performance are UWB and WiFi FTM. LTE is also studied as a back-up technology due to the wide-area coverage and deployed infrastructure. Table 2 presents an overview of the technologies mentioned in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Definition** \\ \hline
5G & Fifth generation \\ \hline AP & Access Points \\ \hline AR & Augmented Reality \\ \hline BLE & Bluetooth Low Energy \\ \hline FCC & Federal Communication Commission \\ \hline FTM & Fine-Time Measurement \\ \hline GNSS & Global Navigation Satellite Systems \\ \hline GPS & Global Satellite System \\ \hline IoT & Internet of Things \\ \hline L-BFGS & Limited-memory Broyden–Fletcher–Goldfarb–Shanno \\ \hline LoS & Line of Sight \\ \hline LTE & Long Term Evolution \\ \hline NLoS & Non Line of Sight \\ \hline MLE & Maximum Likelihood Estimator \\ \hline PANS & Positioning And Networking Stack \\ \hline RSSI & Received Signal Strength Indicator \\ \hline RTT & Round-Trip Time \\ \hline ToA & Time of Arrival \\ \hline UE & User Equipment \\ \hline UWB & Ultra-Wide Band \\ \hline WLS & Weighted Least-Square \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of acronyms.  
Hereinafter, a brief description of cellular-based radio, UWB and WiFi that will be used in this paper is completed.
# Turning Video Resource Management into Cloud Computing
## 3 The Video Resource Management System in HDFS-Based Cloud Computing
### HDFS-Based Architecture of Video Resource Management
#### 3.2.3 HDFS-Based Video Resource Clusters  
The feature of writing once and reading many times is a main advantage of HDFS clusters, and is very fit to applications needs of video resource management. A HDFS cluster is a distributed file system that is made from a large amount of computers with low costs in resources and costs. HDFS video resource clusters are used to store and manage the huge number of video resources, also a data source of streaming media servers.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 5 Test and Results
### Design of Test Based on MUSHRA
#### 5.1.1 Test Material  
(i) Several typical EBU test sequences were selected: piano, trombone, percussion, vocals, song of rock, multi sound source background and mixed voice, and so on.  
(ii) Contrast test objects: PCA-based codec signal that transmits two channels separately, PCA-based codec signal with traditional parametric stereo, PCA-based codec signal with improved parametric stereo, G719 codec signal with traditional parametric stereo [24], HE-AACv2 codec signal, anchor signal, and original signal. In the algorithm proposed in this paper, the relationship between the quality of the restored signal and bitrate is not linear, as Figure 17 shows, which uses a simple subjective test with different bitrate allocation; therefore, the test chooses a case in which the qualities of restored signal and bitrate are both acceptable.  
Figure 16: Test results for spatial sense.  
Figure 17: Relationship between quality and bitrate.  
The bits allocations of each module in PCA-based codec for stereo signal are shown in Table 7.  
(iii) In order to eliminate psychological effects, the order and the name of each test material in each group are random. The listener needs to select the original signal from the test signals and score 100 points, and the rest of the signals are scored by 0-100 according to overall quality, including sound quality and the spatial reduction degree.
## 3 Results  
### Analysis of Extraction Results by D-InSAR  
#### 3.1.1 Verification of Extraction Accuracy of D-InSAR Technology  
Initially, a total of 152 radar images captured by the ESA's Sentinel-1A satellite between 6 January 2017 and 23 December 2021 were subjected to two-track differential interferometry processing. The primary stages comprise merging image pairs, aligning images, estimating the baseline, generating interferograms, applying filters and calculating coherence, removing the leveled phase and terrain phase, unwrapping the phase, and geocoding. The outliers and incoherent regions of interference data in the study area are processed after the D-InSAR processing stage to obtain the surface deformation of the study area. To validate the precision of the D-InSAR technology's extraction, 50 elevation sites inside and above the effect range of the goal in the mining area were chosen for verification based on an examination of the real conditions, as depicted in Figure 3. Utilizing Real-Time Kinematic (RTK) technology, we conducted measurements to quantify the extent of surface deformation of the study area. The results of the proposed method are shown in Figure 4.
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 2 Materials and Methods
### Proposed Method
#### 2.2.1 Network Structure  
The overall structure of the two-stage pipeline is shown in Figure 1.  
Backbone consists of a ResNet18 [30] and a FPN [31]. ResNet performs convolution and pooling operations on the original image in four stages, using residual network [30]. FPN uses the strategy of upsampling to fuse the results from different stages and outputs features at different scales. A feature is a \(C\times H\times W\) tensor, where \(C\), \(H\) and \(W\) are channelnumber, height and width, respectively, and its scale is controlled by \(H\) and \(W\). RPN takes these feature as input and produces region proposals based on some pre-generated anchors. With RoIs and the features from backbone, detection head provides accurate bounding boxes with objects' classes information [9].  
Instead of using the whole features for keypoints detection as in PVNet [29], the regions specified by the predicted bounding boxes are utilized to select local features by RoAlAlign [9]. The keypoint head consists eight convolutional layers and one deconvolutional layer. Its structure is determined experimentally. The keypoint head generates raw direction maps with the local features. The final direction maps can be obtained by masking the raw direction maps with the prediction from mask head, which is designed based on Mask R-CNN [9].  
After aligning the direction maps with the origin image, keypoints in the image are recovered by pixel voting through RANSAC. With the predicted keypoints and the corresponding keypoints on the priori point cloud model [32], the 6D-Pose is calculated by solving PnP. In this study, EPnP [2] is selected as the PnP algorithm.
# A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions
## 3 Results and Discussion  
### Experimental Condition  
For estimator accuracy verification, a 4 WD production vehicle with in-vehicle network is prepared to complete experiments on a split road. The signals of the four wheels' speed, longitudinal acceleration and steering angle are obtained from the in-vehicle network. The basic parameters of this production vehicle are shown in Table 2.  
The accuracy is verified by experiments driving on the split road, which is covered by snow and asphalt; the road condition is shown in Figure 7.  
Conventional studies mainly use experiments on dry or wet roads, but we use a split road covered by snow and asphalt. An acceleration test on a split road is necessary to simulate the entire range of behaviour from no-slip to entire-slip. The purpose is to verify our estimator using real and poor conditions. Meanwhile, the adaptability of the estimator to different conditions is verified from no slip to four-wheel slip.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Parameter** & **Values** \\ \hline
1 & Vehicle weight & 2205 (kg) \\
2 & Wheelbase & 2866 (mm) \\
3 & Front wheelbase & 1594 (mm) \\
4 & Rear wheelbase & 1622 (mm) \\
5 & Tire radius & 245/45R19 \\
6 & Length of the car & 4717 (mm) \\
7 & Width of the car & 1891 (mm) \\
8 & Height of the car & 1689 (mm) \\
9 & Track & 1568 (mm) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic parameters.  
Figure 6: Final fuzzy logic algorithm.  
Figure 7: Snowy road driving experiment.
# A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography
## 3 Experiment and Analysis
### Experiment Description and GNSS Data Processing Strategy
[35] regarded the average of troposphere gradients within 12 h as the dry delay gradients and subtracted it from troposphere delay gradients. ZTD was converted to PWV using Equations (1) and (2), and SWV and virtual SWV were obtained using Equations (3) and (10), respectively.
# Examining Pipe-Borehole Wall Contact and Pullback Loads for Horizontal Directional Drilling
## 2 Analysis of Pipe-Borehole Wall Contact
### Classical Contact Models
#### 2.1.3 Liu Model  
The Winkler elastic foundation model is commonly used to describe the outer cylinder in cylinder contact problems [31; 32; 33]. Based on the Winkler elastic foundation model and the FEM, [PERSON] et al. [31] developed a method for calculating the maximum contact pressure as follows:  
\[q_{0}=\frac{E^{*}\delta}{2R_{2}}, \tag{11}\]  
where the compressive displacement \(\delta\) can be calculated by the following formula:  
\[P=\frac{1}{2}\pi\delta E^{*}\sqrt{\frac{\delta}{2(\Delta R+\delta)}}. \tag{12}\]  
The contact pressure distribution of the Liu model also aligns with that of the Hertz model (Equation (1)).  
Notably, these models are based on different theoretical assumptions and have different application scopes. The Hertz model assumes that the pressure distribution exponent \(n\) is \(1/2\). The shape and size of the bodies and how they are supported must be accounted for [33]. [PERSON] et al. [31] further examined the applicability of the Hertz model and found that the Hertz model is only applicable when there is a large clearance between cylinders and the external load is small. [PERSON]'s model is based on a geometric relationship (Equation (8)) of the contact area. When \(\Delta R\) is large, this relationship also has significant errors at the edge of the contact area (\(\beta=\varepsilon\)). [PERSON]'s model assumes that the displacement of the elastic foundation at the vertex of the wedge is half the compressive displacement; however, this ratio may change when the cylinder size or external load changes [34].  
Unlike common internal cylinder contact problems (such as friction pairs) in mechanical systems, the pipe-borehole wall contact in HDD has a large clearance (the order of magnitude is typically 10-10\({}^{2}\) mm), and the borehole wall is subjected to drilling fluid pressure. Therefore, it is necessary to find a method for determining the contact pressuredistribution and external load-displacement relationship suitable for HDD. This topic will be discussed in the following section.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 1 Introduction
In the antenna design process, optimization was made in terms of minimizing dimensions and minimizing weight, which will allow the use of the designed antenna in portable terminals and easy integration with electronic devices.
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## 3 Results  
The preliminary analysis of vessel traffic was based on a traffic density map. After the analysis, it was found that, in some cases, the main traffic flows were composed of two characteristic vessel traffic flows that could be selected and analysed in detail, as shown in Figure 3a. One flow consisted of vessels entering the port using the fairway marked by navigational aids; the second contained vessels passing outside the defined fairway. All regression models presented in this paper refer to the second stream, for inbound vessels only.  
No factor influencing the navigator's route selection was identified in studies. It can be inferred that it is random or due to weather conditions, which was not considered in the study. It can only be concluded that vessels with a draught greater than 10 m follow the main fairway, as shown in Figure 3b. It can be seen that vessels enter and leave virtually the entire length of the fairway. For this reason, the number of vessels at successive gates on the same lane varies.
## 2 Literature Review  
### Stock Price Prediction  
Diverse methodologies have been explored over time for stock price prediction. With advancements in machine learning and deep learning models, there has been a significant increase in research employing these artificial intelligence approaches for forecasting stock prices.  
[PERSON] (2021) investigated a method to enhance the performance of stock price prediction models by integrating LSTM deep learning models with various indicators, such as technical, macroeconomic, and market sentiment factors [15]. Through experimenting with 290 combinations, the study demonstrated that selecting the appropriate combination for each industry could improve the model's predictive capability. [PERSON] et al. (2019) combined CNN with the characteristics of the Thai stock market for data preprocessingand model training [16]. This approach yielded predictions for three stocks listed on the Thai stock exchange (BBL, CAPLL, PTT), and reported high accuracy in comparison to actual stock price data. [PERSON] and [PERSON] (2013) proposed a semi-supervised learning (SSL) model designed to capture the complex interrelationships between features, using network structures to address the interconnectedness and complexity of factors necessary for stock prediction [17]. This model exhibited superior AUC and ROI results compared to other predictive models. [PERSON] et al. (2023) tackled the limitations of traditional models in capturing rapid changes in stock price data by introducing an adaptive cross-entropy loss function [18]. This function assigns greater weights to samples with significant stock price volatility. Applying this method, they developed an LSTM-BN network and conducted predictions on the S&P500, CSI300, and SSE180 indices, ultimately showing that their model outperformed existing models in terms of returns. These studies exemplify the use of diverse techniques combined for predicting stock prices. [PERSON] and [PERSON] (2019) classified Korean stocks using the Global Industry Classification Standard (GICS) and integrated causal relationship information from financial news to predict stock movements [19].