esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 5 Results
### Performance Analysis of Proposed Method  
#### 5.6.1 Analysis on CSECIC-IDS2018 Dataset  
The CSECIC-IDS2018 and CIC-IDS 2017 datasets were assessed using various anomaly detection methods. Key metrics such as precision, recall, specificity, accuracy, F-measure, and false negative rate (FNR) were used in the evaluation. Table 6 presents the analysis of these metrics for different methods applied to the CSECIC-IDS2018 dataset. This is followed by a detailed explanation of our proposed intrusion detection approach, which involves OCSVM + dAE + DBSCAN.
## 3 The RIS-Aided UAV Secure Transmission
### Phase Shift Optimization  
With the given \(\mathbf{w}[n]\) and \(\mathbf{s}_{u}[n]\), the problem \(P1\) can be represented as RIS's phase shift optimization problem as  
\[\max_{\varphi} f(\varphi), \tag{18}\] \[s.t. \left|\varphi_{n}{}^{2}\right|\leq 1,\forall n=1,\ldots,N,\]  
where  
\[f(\varphi)=\log_{2}(1+\frac{\left|\mathbf{h}^{H}[n]\mathbf{\Phi}[n]\mathbf{ W}[n]\mathbf{w_{DA}}[n]\right|^{2}}{\sigma_{u}^{2}}). \tag{19}\]  
It can observe that \(f(\varphi)\) is a continuously differentiable function, and the constraint set of \(\Phi\) just forms a complex circle manifold. Therefore, the steady-state solution of \(f(\varphi)\) can be obtained by the Riemannian conjugate gradient (RCG) algorithm. This algorithm is widely used in the design of the hybrid precoding problem, and it shows good performance in the RIS-aided MISO system. It usually consists of three key steps [34]:  
1. Calculation of Riemannian gradient: Riemannian gradient is the orthogonal projection of \(f\) of Euclidean gradient on the complex field as \[\text{grad}f(\varphi)=f(\varphi)-\text{Re}\{\
abla f(\varphi)\circ\Phi^{*}\} \circ\Phi.\]
2. Tangent vector of search direction: \[\mathbf{d}=-\text{grad}f_{\mathbb{C}}+\tau_{\mathbb{I}}\mathcal{T}(\overline{ \mathbf{d}}).\] \(\mathcal{T}(\cdot)\) is the vector transfer function, which is defined as \[\mathcal{T}(\mathbf{d})=\overline{\mathbf{d}}-\text{Re}\{\mathbf{d}\circ\Phi^{* }\}\circ\Phi.\]
3. The tangent vector is projected onto a complex circular manifold: \[\Phi_{n}\leftarrow\frac{\left(\Phi+\tau_{2}\mathbf{d}\right)_{n}}{\left|\left( \Phi+\tau_{2}\mathbf{d}\right)_{n}\right|}.\]  
\(\tau_{2}\) is the size of the Armijo step [35] (We use the Armijo rule for descent in Riemannian manifolds, and provides a detailed description).
# Spherical-Model-Based SLAM on Full-View Images for Indoor Environments
## 4 Method Overview
### Map Point Projection and Pose Update  
To project map points into the image plane; it is first transformed from the world coordinate to the current frame coordinate C.  
\[p_{jC}=R_{CW}p_{jW}+T_{CW} \tag{6}\]  
where \(p_{jC}\) is the \(j\)th map point in the current frame coordinate, \(p_{jW}\) is the \(j\)th map point in the world coordinate, and \(R_{CW}\) and \(T_{CW}\) represent the current camera pose from the world coordinate.  
Then, we project the map point \(p_{jC}\) onto the unit sphere, as \((m_{jx},m_{jy},m_{jz})\) shown in Equation (5). Then, we detect all the feature points in current frame by SPHORB. Next, we perform a fixed-range search around the unit coordinate \(\left(m_{jx},m_{jy},m_{jz}\right)\). Since the map point \(p_{jC}\) has a spherical descriptor, we can compare it with candidate feature points to find the best match point \(p_{jC}^{\prime}(m_{jx},m_{jy},m_{jz})\).  
Given a pair of matched feature points, \(\left(m_{jx},m_{jy},m_{jz}\right)\) and \(\left(m_{jx},m_{jy},m_{jz}\right)\), the angle between the pair of matched feature points can be computed as  
\[e_{j}=\arccos(\frac{m_{jx}\times m_{jx}+m_{jy}\times m_{jy}+m_{jz}\times m_{jz }}{\sqrt{m_{jx}{}^{2}+m_{jy}{}^{2}+m_{jz}{}^{2}}\cdot\sqrt{m_{jx}{}^{2}+m_{jy }{}^{2}+m_{jz}{}^{2}}}) \tag{7}\]  
Since the spherical point is unit,  
Figure 4: The flow of our full-view SLAM system.  
\[e_{j}=\arccos\left(m_{jx}\times\hat{m_{jx}}+m_{jy}\times\hat{m_{jy}}+m_{jz}\times \hat{m_{jz}}\right) \tag{8}\]  
Substituting Equations (5) and (6) into Equation (8), we obtain a function about rotation \(R_{\text{CW}}\) and translation \(T_{\text{CW}}\).  
Since for a successfully-matched feature point and true rotation \(R_{\text{CW}}\) and translation \(T_{\text{CW}}\), \(e_{j}=0\), we can compute rotation \(R_{\text{CW}}\) and translation \(T_{\text{CW}}\) by minimizing the following objective function given \(n\) matched feature points.  
\[\sum_{j=1}^{n}e_{j} \tag{9}\]
## Applications
### Categories Based on System Architecture  
#### 3.2.1 Centralized Federated Learning  
In Centralized Federated Learning, a central server coordinates the training process. Clients train local models on their data and send updates to the central server, which aggregates these updates to form a global model. This architecture simplifies the orchestration and aggregation process but still relies on a central point for coordination.  
**Applications**  
* Enterprise environments: Where a central server can manage and coordinate model updates efficiently.
* Academic collaborations: Coordinated research projects where data are sensitive but require central oversight.
## 5 Conclusions  
Considering the experimental design used in this study, it can be concluded that:* Flexible resins showed lower values for microhardnesssen when thermally aged, but this effect did not occur with the acrylic resin. In contrast, thermal aging did not change the flexural strength of any of the resins. Overall, thermal aging only affected the microhardness of the flexible resins and had no effect on the flexural strength of any of the resins.
* The type of resin has no influence on the microhardness of the resins after thermal aging. In contrast, the acrylic resin retained a higher flexural strength compared to the flexible resins, which proves that the type of resin influences the flexural strength of the resins after aging.
* Since chemical disinfection with Corega Oxygen Bio-Active significantly reduced the microhardness of Deflex Classic SR and Corega Whitening reduced the flexural strength of Deflex Supra SF, it can be concluded that the chemical disinfection protocols influence the microhardness and flexural strength of the resins.
* It was found that the effects of disinfection methods on microhardness and flexural strength depend on the type of resin.  
The implications of this study for clinical advice on denture cleaners must include that wearers of removable dentures based on ProBase Hot resin can disinfect them with all tested disinfectant solutions. Flexible dentures based on Deflex Classic SR acrylic should not be disinfected with Corega Oxygen Bio-Active as it has been shown to be particularly harmful. Flexible dentures based on Deflex Supra SF, on the other hand, should not be disinfected with Corega Whitening.  
Acrylic resins have also been shown to be more stable against physiological and chemical influences, which suggests that these resins last longer and retain their mechanical properties. In this context, flexible resins should be avoided in patients with poor hygiene habits.  
Conceptualization, C.B.N. and V.S.; methodology, C.B.N.; software, I.N.; validation, C.B.N., V.S. and V.A.; formal analysis, N.R.d.S.; investigation, I.N.; resources, N.R.d.S.; data curation, V.A.; writing--original draft preparation, I.N.; writing--review and editing, V.A. and C.B.N.; visualization, I.N.; supervision, C.B.N.; project administration, C.B.N.; funding acquisition, C.B.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Application Layer Protocol Identification Method Based on ResNet
## 2 Related Work
### [PERSON] [12] proposed a DPI method integrating machine learning techniques for protocol recognition in Software Defined Networks (SDN). This method comprises two stages, early detection and deep inspection, for detecting both encrypted and unencrypted traffic. For unencrypted traffic, a logistic regression binary classifier and an adaptive packet sampling mechanism are utilized for detection. Significant features are extracted for encrypted traffic to train a decision tree classifier. Experimental results demonstrate that this method significantly improves recognition accuracy at an acceptable cost.  
[PERSON] [13] presents an improved method for Deep Packet Inspection (DPI) based on regular expressions. The method effectively reduces the number of transition edges by using character ranges to represent multiple consecutive characters. The specific optimizations are as follows: (1) replacing multiple consecutive characters with character ranges significantly reduces the number of transition edges. (2) Range searching: to address the issue of discontinuous character ranges, we propose methods for merging adjacent character ranges and querying character ranges. (3) By constructing a range tree, we can quickly find the corresponding character range for each input character with a time complexity of O(log(m)). (4) Overlapping range segmentation: to handle the scenario in NFA where multiple valid transition edges point to different states, we propose an algorithm for splitting overlapping ranges, ensuring that the target state set for each new range is correct.  
From the above two papers, it can be seen that the main directions for optimizing DPI technology are feature selection, computational efficiency, and resource usage. The accuracy of recognition is particularly influenced by the features.
# Development and Pilot-Test of Blockchain-Based MyHealthData Platform
## 4 Discussion
### MyData with Dynamic Consent  
MyData ultimately aims for transparent and reliable data sharing. It is necessary to prepare technical tools for obtaining consents from data subjects based on various scenarios given the impact of the European Union General Data Protection Regulations' (GDPR) regulations on explicit consent of data subjects [39]. Therefore, it can be said that the user's consent is at the core of utilizing MyData. The MyData model uses dynamic consent, to make people easier to understand and use [5,40]. Data subjects must express positive intentions for the processing of their personal information through actions, and the content of consent must be specified individually to secure user control and transparency, thus limiting the use of multiple purposes with one consent. Users can optionally consent to the use of MyData. In the MyHealthData platform in this study, users were able to selectively consent to provision of personal information. Given that MyData expands to various service items, in addition to medical institutions, it will be necessary to consider how data subjects will use dynamic consent in various fields such as insurance and pharmaceuticals in detail.
# A Machine Learning Approach for the Classification of Refrigerant Gases
## 2 Related Work  
The environmental and health impacts of refrigerants are critical areas of ongoing research, given their complex implications for both global warming and local health risks. In the study [13], a detailed trade-off analysis between global impact potential and local risk was performed, showcasing significant challenges when substituting refrigerants like R-22 with R-410a, which, although reducing ozone depletion, inadvertently increases the global warming potential. This emphasizes the nuanced decisions required in selecting optimal refrigerants that meet environmental compliance and sustainability goals.  
Further research into the Japanese household air conditioning sector [14] highlighted the need for comprehensive policies that consider both the direct and indirect environmental impacts of refrigerants, suggesting a transition towards low GWP alternatives like R-32 to mitigate adverse effects effectively.  
Furthermore, Ref. [15] provides insights into the variations in energy efficiency and operational patterns of residential air conditioning systems. This supports the implementation of continuous monitoring and adaptive control systems to optimize HVAC performance and minimize ecological footprints.  
Advancements in machine learning have enabled the prediction of the thermophysical properties of refrigerants, aiding in the selection and design of more efficient refrigeration systems with potentially reduced environmental impacts, as discussed in [16].  
Additionally, developments in sensor technology, such as those detailed in [17] enhance the accuracy of refrigerant management systems, contributing to better maintenance practices and reduced leakage rates. Infrared thermography has been utilized to analyze refrigerant distribution within heat exchangers, offering significant improvements in understanding refrigerant behavior, essential for designing more efficient and less environmentally damaging refrigeration systems, as explored in [18].  
The impact of F-gas regulations in Europe on market trends for various refrigerants underscores how regulatory measures are reshaping the refrigerant landscape, pushing towards low GWP refrigerants and phasing out harmful substances, as analyzed in [19].  
Together, these studies form a comprehensive view of the current challenges and considerations in managing refrigerants, underlining the critical need for ongoing research and technological adaptation to mitigate the environmental impact of refrigerant gases effectively. This collective body of work also highlights the importance of policy frameworks that integrate life cycle analyses and risk assessments, supporting the development of sustainable refrigeration technologies.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 4 Experiments and Evaluation
### Experiment Environment  
The deep learning framework of this paper adopts Pytorch1.8, based on the operating system of Ubuntu18.04; the Python version is 3.8, the integrated development environment is PyCharm professional version, and the Vitis AI version is Vitis AI 2.5. Two NVIDIA A100s are used to train the network model and pruning. The hardware platform CPU is AMD R7-4800H, the GPU is NVIDIA RTX 2060 (6G), and the FPGA is Xilinx KV260. During the training and pruning process, the batch size is set to 32, the input image size is 416 \(\times\) 416, the initial learning rate is 0.001, the optimizer is SGD, and the cosine annealing strategy is used to dynamically adjust the learning rate.
## 3 Materials and Methods
### Proton Transfer Reaction-Time-of-Flight-Mass Spectrometry (PTR-ToF-MS) Analysis  
Headspace analysis was conducted on samples following the setup and instrumental conditions outlined by [PERSON] et al. [26]. Each sample consisted of 10 g of dried spice and underwent analysis three times, with the average value calculated. VOC analysis was performed using a PTR-ToF-MS (8000 model, Ionicon Analytik GmbH, Innsbruck, Austria) with H\({}_{3}\)O\({}^{+}\) as the reagent ion and within a mass range of \(m/z\) 20-220. In brief, each sample was individually sealed within an airtight jar and subjected to a 120 s analysis at a flow rate of 50 sccm. To mitigate the risk of contamination, the jars were thoroughly cleaned with purified air from a Zero Air Generator (Peak Scientific, Glasgow, UK) before starting the measurements. In the same way, the PTR inlet was consistently refreshed to ensure optimal conditions during the measurements. Moreover, noise thresholds were applied to refine the results. Specifically, peaks not significantly distinct from blank samples, signals associated with isotope mass peaks, peaks related to water chemistry (such as hydronium ions and water clusters), and other interfering ions (e.g., oxygen, nitrogen monoxide) were eliminated. Additionally, signals corresponding to ions present in trace amounts (<0.5 ppbv) were excluded.
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
## 2 System Description
\tag{11}\]Combining Equations (9) and (11) yields:  
\[\mathbf{L}_{IG}^{b_{c}}=\mathbf{L}_{OG}^{b_{c}}-\mathbf{C}_{b}^{b_{c}}\mathbf{L}_{OI}^{b}. \tag{12}\]  
This means that the dynamic lever arm \(\mathbf{L}_{IG}^{b_{c}}\) can be decomposed into two constant lever arms \(\mathbf{L}_{OG}^{b_{c}}\) and \(\mathbf{L}_{OI}^{b}\), because of the fact that the ISP rotating center \(\mathbf{O}_{O}\) is fixed to both the \(b\)-frame and the \(b_{c}\)-frame. From the geometric location relationship shown in Figure 1, we can deduce the same results as Equation (12). Therefore, if the two constant lever arms \(\mathbf{L}_{OG}^{b_{c}}\) and \(\mathbf{L}_{OI}^{b}\) are precisely measured in advance, the dynamic lever arm \(\mathbf{L}_{IG}^{b_{c}}\) can be compensated as Equation (12). However, as mentioned before, manual measurement is complex, tedious, and inefficient. To avoid these drawbacks and enhance the efficiency of surveying, we propose a method to automatically estimate the lever arm and to accomplish the integrated navigation simultaneously.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 2 Related Work
The MAC protocols using this technique include Reverse Opportunistic Packet Appending (ROPA) [5], Hybrid Sender- and Receiver-initiated (HSR) [6], Bidirectional-Concurrent MAC (BiC-MAC) [7], Delay-aware Opportunistic Transmission Scheduling (DOTS) [8], Receiver Initiated Packet Train (RIPT) [9], and Decoupled and Suppressed Handshaking (DSH-MAC) [10]. These protocols improve the transmission efficiency effectively; however, they still lack the ability to support multiple handshakes and concurrent transmissions for multiple pairs of senders and receivers in distributed networks. In the hybrid MAC protocols, different medium access techniques are employed and can switch to each other on demands. Because the hybrid MAC protocols can take advantage of different medium access techniques, they usually produce better performance, so have become attractive research topics in underwater acoustic networks. Many hybrid MAC protocols have been proposed, which include the hybrid of contention-free protocols like the Hybrid Spatial Reuse Time-Division Multiple-Access (HSR-TDMA) [19], which combines the TDMA and CDMA techniques; the hybrid of contention-based protocols like the Underwater Practical MAC (UPMAC) [20], where ALOHA- and receiver-based protocols are used for low load mode and high load mode, respectively; and the hybrid of contention-free and contention-based protocols like Data-Collection-Oriented MAC (DCO-MAC) [21], where a contention-based MAC protocol is used in the sub-network with a light traffic load, whereas a reservation-based MAC protocol is used in the sub-network with a heavy traffic load.
# Seismic Resilience Evolution of Shield Tunnel with Structure Degradation
## 4 Tunnel Structure Response Characteristics
### Radial Deformation of Tunnel  
To eliminate the boundary effect, the radial deformation of the middle ring structure of the five-ring shield tunnel is selected for analysis; the results are shown in Figure 9. For the convenience of observation, the radial displacement without loss of preload is set to zero here. Figure 9a shows the radial deformation of the shield tunnel segment before seismic loads, which refers to the impact of varying degrees of performance loss on the radial deformation of the shield tunnel.  
It can be observed from Figure 9a that the loss of preload leads to initial radial deformation in the shield tunnel before the earthquake, and the radial deformation increases with the increasing degree of preload loss. It can be seen from Figure 9b-d that the variation in radial deformation caused by the initial radial deformation due to preload is not significant under the action of seismic waves with accelerations of 0.1 g and 0.3 g. This implies that under relatively small seismic wave actions, most of the tunnel's radial deformation can be restored to the state before seismic loading. However, it can be seen  
Figure 8: Lining horizontal convergence of shield tunnel under load action. (**a**) Different peak acceleration; (**b**) Different degrees of loss of preload.  
from Figure 9e-g that there is residual deformation after the seismic action when the seismic wave is 0.5 g, especially when there is a significant loss of preload. The residual deformation becomes more pronounced with an increasing degree of preload loss.
###### Abstract  
This study aims to assess the spatial distribution and health risk potential of nitrate (NO3\({}^{-}\)) contamination in groundwater resources of the Harran Plain, a semi-arid agricultural region in Southeastern Turkishye. Groundwater samples were collected from 20 locations during pre- and post-irrigation periods and analyzed for a range of hydrochemical parameters. A probabilistic risk assessment framework, based on the U.S. Environmental Protection Agency (USEPA) guidelines, was employed to evaluate non-carcinogenic health risks across different demographic groups. The integration of Geographic Information Systems (GIS), multivariate statistical analyses, and Monte Carlo simulation enabled a comprehensive evaluation of exposure scenarios and contributing factors. This research contributes to the scientific understanding of groundwater vulnerability in intensively farmed areas, provides a decision-support framework for water quality management, and emphasizes the importance of protecting sensitive populations in nitrate-affected regions.  
Keywords:nitrate contamination; Monte Carlo simulation; noncarcinogenic risk; groundwater pollution; spatial distribution +
Footnote †: journal: Applied Sciences
# Handover Management for Drones in Future Mobile Networks--A Survey
## 2 Drone Networks
### Drones in 4G/5G Networks  
Current Fourth Generation (4G) and 5G New Radio (NR) technologies used in autonomous vehicles for Vehicle-to-Everything (V2X) communication may be suitable for drones' communication. The 5G NR can connect autonomous vehicles and infrastructures via side links [41], enabling Non-Line-of-Sight (NLoS) visibility and predictability for further traffic control and autonomous driving improvements. Since wireless networks are designed especially for ground mobile users, the usage of 4G and 5G NR may provide network connections such as UAV-to-UAV (U2U) (Drone-to-Drone) and UAV-to-Infrastructure (U2I); however, these do not guarantee full network coverage. Drones can also be used as BSs to provide 4G and 5G services in remote environments with limited coverage caused by natural disasters [42]. Existing 4G and 5G NR terrestrial networks are fixed at a specific location and can support ground users or vehicles traveling along predetermined routes. Fourth and Fifth Generation-NR systems can be utilized to provide communication for  
Figure 3: Drone system architecture.  
ultra-low-altitude drone networks using U2U and U2I modes. However, they may have coverage and other mobility issues, as discussed in the challenges section.