esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Contribution of This Paper  
The proposed system in this article presents a hybrid approach for detecting intrusive UAVs, combining two complementary detection techniques to ensure effective and rapid detection. The employed techniques are the detection of radio signals produced between the UAV and its controller using autoencoders that can identify anomalies in the analyzed frequency bands, and visual detection through a camera system with YOLOv10, which can locate the UAV in 3D and track it using an autonomous PTZ camera. In summary, the main contributions of this work are as follows:  
1. Development of a detection system using autoencoders based on radio signals that can operate in various frequency bands up to 100 MHz of bandwidth, with these bands being selectable to monitor those most likely to be used by intrusive UAVs. Moreover, the autoencoders do not require a database with UAV signals; they only need samples of the frequency bands to be analyzed when there are no UAVs present for training, which simplifies the creation of the database as it does not require data labeling or samples with an operating UAV.  
Development of a scalable system with fixed cameras plus an autonomous PTZ camera, where all cameras perform detection with the YOLOv10 algorithm trained on a dataset of over 60,000 images obtained from various online sources.
3. Implementation of a triangulation algorithm with fixed cameras for 3D localization of the UAV to provide the exact position to the PTZ camera, in addition to a tracking algorithm through which the PTZ camera continuously follows the UAV once detected, keeping it always at the center of the camera's image to prevent it from being lost.
4. Implementation of the complete system in which both the SDR module and the camera module are in constant communication to indicate when either of them detects a UAV. There is also communication using the Contact ID and SIA protocol with an external alarm-receiving center, which is responsible for deciding the course of action when an intruding UAV is detected.
## 3 Results
### Estimation of Emission Factors for the Electricity Grids in the CI Alternative  
EUROSTAT (EU Commission, DG Energy, Unit A4, 2020) [25] information about gross electricity generation per type of generation was used for the electricity grid of France and continental Spain, while the required information for the islands was taken from the Canary Islands' Energy yearbook [29]. This information, reflected in Table 4, along with the relationship between PM\({}_{2.5}\) and PM\({}_{10}\) offered by E-PRTT (see Table 2) permitted us to obtain the emission factors of these pollutants, as reflected in Table 5.  
Finally, the pollutant emissions of the electric power generating plants (main activity-NACE: 35.1 (Electric power generation, transmission, and distribution)) not just for the countries (France and Spain) but also for the islands, has been taken from the European Pollutant Release and Transfer Register -E-PRTR- (Regulation (EC) No 166/2006). This procedure is valid for all pollutants except for CH\({}_{4}\) emissions on the islands [29], due to the absence of information in E-PRTR [27] in this regard.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Hard** & **Brown** & **Oil and** & **Optimal** & **Natural Gas and** & **Solid Biofuels and** & **Renewable Wastes** & **Nonek** & **Main Activity** \\  & **Coal** & **Coal** & **Pertroleum** & **Manufactured Gas** & **S**olid & **Biofuels and** & **Renewable Wastes** & **Nonek** & **Nuclear** & **Electricity Only** \\  & **Coal** & **Products** & **Manufactured Gas** & **Renewable Wastes** & & & & **Plants [TWh]** \\ \hline
**SPAIN** & 15.44 & 0.93 & 5.72 & 23.68 & 1.86 & 31.31 & 21.06 & 239.59 \\
**FRANCE** & 2.28 & 0.00 & 1.24 & 7.64 & 1.01 & 16.84 & 70.98 & 530.76 \\
**GRAN** & & & & & & & & \\
**CANARIA** & 0.00 & 0.00 & 91.86 & 0.00 & 0.00 & 8.14 & 0.00 & 3.48 \\
**ISLAND** & & & & & & & & \\
**TENERIFE** & & & & & & & & \\
**ISLAND** & & & & & & & & \\ \hline \hline \end{tabular} Data source: [25; 29].  
\end{table}
Table 4: Gross electricity generation by fuel in 2017 (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pollutants (EUR/kg)**} & \multicolumn{2}{c}{**France**} & \multicolumn{2}{c}{**Spain**} \\ \cline{2-5}  & **Urban** & **Metropolitan** & **Urban** & **Metropolitan** \\ \hline
**NO\({}_{\mathbf{x}}\)** & 28.56 & 28.56 & 8.64 & 8.64 \\
**SO\({}_{\mathbf{2}}\)** & 14.59 & 14.59 & 6.91 & 6.91 \\
**PM\({}_{\mathbf{2.5}}\)** & 137.55 & 427.35 & 113.79 & 353.57 \\
**PM\({}_{\mathbf{10}}\)** & 6.19 & 6.19 & 10.36 & 10.36 \\
**CO\({}_{\mathbf{2}}\)** & 0.10 & 0.15 & 0.10 & 0.10 \\
**CH\({}_{\mathbf{4}}\)** & 2.62 & 2.62 & 2.54 & 2.54 \\ \hline \hline \end{tabular} Data source: [15; 16; 17].  
\end{table}
Table 3: The unitary cost of air pollutants for France and Spain in 2017.  
In light of the foregoing, Table 5 shows the estimated emission factors for the European grids considered in the application cases: Spain, France, Gran Canaria island and Tenerife island (\(EFG_{uk}\); \(\forall u\in\)U \(\wedge\)\(\forall k\in\)K).
# Functionalization and Modification of Bitumen by Silica Nanoparticles
## 2 Experimental
### SNPs Characterization  
Sonication used to disperse nanoparticles during the synthesis step was achieved by using a UP200S ultrasonic Hielscher processor equipped with a sonotrode of 40 mm. Thermogravimetric analysis was performed on a PerkinElmer PYRIS 6 TGA thermogravimetric analyzer. Fourier transform infrared spectroscopy (FT-IR) was recorded on a PerkinElmer Spectrum 100 spectrometer. Rheological measurements were carried out using an SR5000 rheometer (Rheometrics, Piscataway, NJ, USA) controlled by shear stress and equipped with plate geometry (2 mm gap, 25 mm diameter). The temperature was controlled by a Peltier system (\(\pm 0.1\) \({}^{\circ}\)C). The rheological behavior at different temperatures was investigated using a time cure test at 1 Hz with a ramp rate of 1 \({}^{\circ}\)C/min within the linear viscoelastic region. [16] Scanning electron microscopy (SEM) was undertaken using an environmental SEM (ESEM; FEI Quanta 400; Hillsboro, OR, USA).
###### Abstract  
Due to the lack of sensitivity of visual acuity (VA) measurement to quantify differences in visual performance between progressive power lenses (PPLs), in this study, we propose and evaluate an eye-tracking-based method to assess visual performance when wearing PPLs. A wearable eye-tracker system (Tobii-Pro Glasses 3) recorded the pupil position of 27 PPL users at near and distance vision during a VA test while wearing three PPL designs: a PPL for general use (PPL-Balance), a PPL optimized for near vision (PPL-Near), and a PPL optimized for distance vision (PPL-Distance). The participants were asked to recognize eye charts at both near and distance vision using centered and oblique gaze directions with each PPL design. The results showed no statistically significant differences between PPLs for VA. However, significant differences in eye-tracking parameters were observed between PPLs. Furthermore, PPL-Distance had a lower test duration, complete fixation time, and number of fixations at distance evaluation. PPL-Near has a lower test duration, complete fixation time, and number of fixations for near vision. In conclusion, the quality of vision with PPLs can be better characterized by incorporating eye movement parameters than the traditional evaluation method.  
high contrast visual acuity; progressive power lenses; eye-tracking; eye fixations Article
## 4 Discussion  
[PERSON] et al. found that the calyx position is the optimal sampling position for detecting orange TSS based on VIS/NIR transmitted spectrometer [29]. This research further proved the calyx position is also the optimal sampling position for detecting orange TSS utilizing the VIS/NIR reflection spectrometer method.  
The detection ability of orange TSS based on multi-source information fusion is better than using a single detection method (for either VIS/NIR reflection spectrometer or E-nose) confirming other reports [30]. However, to achieve the optimal detection ability using multi-source information fusion, it is important to retain useful information while eliminating feature information with low correlation. Thus, this research further provided a feature extraction method for VIS/NIR reflection spectrometer and E-nose, respectively, in orange TSS detection based on fusion data.  
In this research, BPNN has a better ability than PLSR for orange TSS detection. That BPNN has better detection ability than PLSR has previously been shown for other parameters as well [31; 32]. The optimal prediction function for PLSR involved minimizing the quadratic sum of error. This not only solves linear problems, but also some simple non-linear problems. BPNN is a nonlinear classification method that can achieve arbitrary nonlinear mapping from input to output, and displays good performance when applied to functional mapping, functional approximation and self-adaption. Thus, BPNN is better than PLSR for nonlinear problems, such as orange TSS detection. However, the model structure of PLSR is much simpler than BPNN, thus, PLSR is the optimal option if the detection ability is similar.  
With the development of integrated technology, portable handheld reflection spectrometers and E-noses have been produced, such as the PAL-HIKARi 3 reflection spectrometer (ATAGO co. Ltd., Tokyo, Japan) with a size and weight of 6.1 \(\times\) 6.4 \(\times\) 11.5 cm and 0.053 kg and the Cyranose 230 E-nose (Cyrano Sciences inc., Pasadena, CA, USA) with a size and weight of 10 \(\times\) 22 \(\times\) 5 cm and 0.9 kg. These research results provide reference to the data analysis section.
## 5 Discussion
### Newly Identified Subsidence Spots in Yunlin by InSAR
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subsidence Rates (mm/a)** & **Leveling (km2)** & **InSAR (km2)** \\ \hline
30–40 & 221.3325 & 215.5625 \\
40–50 & 100.9125 & 98.0775 \\
50–60 & 37.9300 & 61.325 \\
60–70 & 9.2275 & 11.9975 \\ over 70 & 0.9575 & 1.1875 \\ Significant subsidence area & 370.36 & 388.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Areas with subsidence rates >30 mm/a from leveling and InSAR in 2017.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 3 Related Work
### State-of-Art of Edge-Cloud Computing  
Authors in [11] validated edge computing's efficiency and resourcefulness. Edge systems are carefully surveyed, and a comparative cloud computing system research is presented. The results demonstrate that edge computing systems perform better than cloud computing systems after studying various network aspects of the system. In comparison to cloud computing standards, the current edge computing and fog computing standards provide stable and enhanced quality of service for IoT applications. [PERSON] et al. in [34] examined a joint communication and computation user collaboration for mobile edge computing to enhance the power consumption for mobile edge computing (MEC). Consider a basic three-node MEC framework consisting of a helper node, an access point node, and a user node connected to an MEC server for demonstration purposes. A nearby helper node is enabled to contribute its computation and communication resources to increase the user's computing performance. Numerical findings show the value of the collaboration in computing and communication offered by the users. Another study by [PERSON] et al. [35]  
Figure 3: Deep learning in edge computing.  
performed a full review of edge computer state-of-art research initiatives. This model integrates computing, caching, and communication resources. The edge computing is initially outlined, including description, design, and benefits. A complete examination of computing, caching and communication concerns is then provided at the network's edge. The proposed framework architecture is composed of mobile edge, fog computing, and cloud. [PERSON] et al. [36] provided a summary of significant advances in research for edge computing. Modelling methodologies for key edge computing (EC) components, such as computing tasks, communications, mobile computing, and the EC servers, were initially summarised. Models of essential elements of EC systems such as computation tasks, communications, and the computation of mobile devices and MEC servers were initially summarised. This facilitates characterising the latency and energy performance of EC systems. A research perspective is also presented, which contains a set of prospective MEC research guidelines. Finally, they discussed current industry standardisation initiatives, as well as numerous common application scenarios. The surveys addressed [11; 34; 36] highlight the potential of edge computing in data analytics and the relevance of edge computing in IoT for dealing with the number of linked devices that is rapidly increasing. As our work contributes to the use of edge computing for data analytics by merging edge and cloud computing for the delivery of deep learning applications, different applications for both IoT and edge cloud computing are reviewed below.
## 3 Results and Discussion
### Interpretation of SIP Signatures
The obtained models revealed a better coefficient of correlation (R\({}^{2}\) = 0.72) for Equation (7) than for Equation (8), as observed in Table 6, and can be written as:  
\[\varnothing=25\text{-M}^{1.8} \tag{7}\]  
\[\varnothing=108.7\text{-M}_{\text{n}}^{0.5} \tag{8}\]  
Assuming that the plots presented in Figure 8 represent an experimental model linking the electrical properties (i.e., M and M\({}_{\text{n}}\)) and the graphite content for heterogeneous samples, we believe that Equations (7) and (8) may be applicable for all SIP measurements collected at the Zettlitz site. In this regard, we assume that the plugs provide a representative sample for the real materials, yet we require the data collected in ground samples, where the graphite content was measured through X-ray fluorescence spectroscopy and LECO analysis. Such methods for direct quantification of graphite content are not possible on larger samples (i.e., the plugs) or in-situ.
###### Abstract  
Bacteriorhodopsin (bR) is a photoactive protein that has gained increasing importance as a tool for optical memory storage due to its remarkable photochemical and thermal stability. The two stable photostates (bR and Q) obtained during the bR photocycle are appropriate to designate the binary bit 0 and 1, respectively. Such devices, however, have limited success due to a low quantum yield of the Q state. Many studies have used genetic and chemical modification as optimization strategies to increase the yield of the Q state. Nonetheless, this compromises the overall photochemical stability of bR. This paper introduces a unique way of stabilizing the conformations of bacteriorhodopsin and, thereby, the bR and Q photostates through adsorption onto graphene. All-atom molecular dynamics (MD) simulations with NAMD and CHARMM force fields have been used here to understand the interactive events at the interface of the retinal chromophore within bR and a single-layer graphene sheet. Based on the stable RMSD (\(\sim\)4.5 A), secondary structure, interactive van der Waals energies (\(\sim\)3000 kcal/mol) and electrostatic energies (\(\sim\)2000 kcal/mol), it is found that the adsorption of bR onto graphene can stabilize its photochemical behavior. Furthermore, the optimal adsorption distance for bR is found to be \(\sim\)4.25 A from the surface of graphene, which is regulated by a number of interfacial water molecules and their hydrogen bonds. The conformations of the key amino acids around the retinal chromophore that are responsible for the proton transport are also found to be dependent on the adsorption of bR onto graphene. The quantity and lifetime of the salt bridges also indicate that more salt bridges were formed in the absence of graphene, whereas more were broken in the presence of it due to conformational changes. Finally, the analysis on the retinal dihedrals (C11 = C12-C13 = C14, C12-C13 = C14-C15, C13 = C14-C15 = NZ and C14-C15 = NZ-CE) show that bacteriorhodopsin in the presence of graphene exhibits increased stability and larger dihedral energy values.  
bacteriorhodopsin; graphene; photocycle; retinal; molecular dynamics +
Footnote †: journal: Applied Sciences  
1
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 3 Evaluation Method
### High-Performance Computing Benchmark
#### 3.3.5 Convolution_2D and Correlation_2D Benchmark  
Convolution_2D and Correlation_2D are used in combination with 2D images and form the foundation for implementing convolutional neural networks (CNNs), which can be used for visual-based navigation and image processing. In image processing tasks, the convolutional layer performs convolution operations on the input image by sliding a small matrix called a convolution kernel or filter, extracting specific feature information [26].  
In this experiment, convolution and 2D correlation operations are implemented on the GPU using the CUDA Deep Neural Network (cuDNN) library provided by NVIDIA. cuDNN is a GPU acceleration library specifically designed for deep convolutional neural networks. By calling functions provided by cuDNN, such as cudmConvolutionForward(), and cudmPoolingForward(), and passing the corresponding handles, input data, weights, and output data descriptors, the corresponding convolution operations can be executed.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 3 Implementation
### Aggregation Verification and Signature  
\(FN_{w}\) will receive multiple charging and discharging data reports uploaded by the local CP. If these signcryption messages are verified individually, it will significantly impact the response time of charging and discharging, which is unsuitable for large-scale EV charging and discharging scenarios. Additionally, if \(FN_{w}\) uploads these data reports directly to the CSO without aggregation, a significant communication and computation overhead will result. Therefore, in this scheme, \(FN_{w}\) chooses to perform an aggregated verification on these data reports, re-sign the aggregated signcryption messages, generate regional data reports, and then upload them to the CSO. This approach effectively utilizes the computational resources of the FN, reduces the computational burden on the CSO, and enhances the service quality of the V2G network. The specific process is as follows:  
Firstly, after receiving the data report \(P_{i}\) uploaded by \(CP_{i}\), \(FN_{w}\) performs initial verification by checking the validity of the timestamp \(TS_{i}\) in the data report and verifying the validity of the \(EV_{i}\)'s pseudonym through the pseudonym timestamp \(T_{i}\). If the verification fails, the data report \(P_{i}\) uploaded by \(CP_{i}\) is discarded.  
Secondly, after successfully performing the initial verification on all \(n\) data reports \(P_{i}\) (\(i=1,2,\ldots,n\)), \(FN_{w}\) proceeds to conduct aggregated verification according to the following formula. Upon successful verification, the aggregated signcryption message \(\varphi_{w}=(R_{1},R_{2}\ldots R_{n},C_{1},C_{2}\ldots C_{n},S)\) is obtained.  
\[\left\{\begin{array}{c}h_{1i}=H_{1}(PID_{i},X_{i},Y_{i})\\ h_{2i}=H_{2}(ID_{CP_{i}},X_{c},Y_{c},R_{i},C_{i},TS_{i})\\ S=\sum_{i=1}^{n}s_{i}\\ SP=\sum\limits_{n=1}^{n}Y_{i}+P_{pub}\sum\limits_{n=1}^{n}h_{1i}+\sum\limits_{ n=1}^{n}X_{i}h_{2i}\end{array}\right. \tag{3}\]  
Thirdly, \(FN_{w}\) generates a signature \(sig_{w}\) using its private key based on the aggregated signcryption message \(\varphi_{w}\) according to the following formula.  
\[\left\{\begin{array}{c}h_{2w}=H_{2}(ID_{FN_{w}},X_{c},Y_{c},\varphi_{w},TS_{ w})\\ sig_{w}=d_{w}+x_{w}h_{2w}\end{array}\right. \tag{4}\]  
Finally, \(FN_{w}\) generates the data report \(T_{w}=\{PID_{i},ID_{CP_{i}},ID_{FN_{w}},\varphi_{w},sig_{w},\ \ PK_{w},\ TS_{w}\}\) for its local region and uploads it to the CSO, where \(TS_{w}\) represents the current timestamp of the FN.
# Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset
## 3 Literature Review of Related Works
### Existing Works Using Self-Generated Network-Traffic-Based Datasets
The findings of the experiments showed that the attacks can be detected early on. In the work of [47], the authors proposed an approach to detect network- and application-based flooding attacks based on a fuzzy-logic-based protection mechanism. Moreover, the authors used a self-generated network-traffic-based dataset to evaluate the work. In the experiment, the algorithm learned by using training data, and then rules were created based on the potential traffic patterns of the CCE so that the system maydeduce the traffic class based on the knowledge gained. The authors adopted fuzzy rules based on predetermined traffic features that differ considerably between an attack and a normal traffic pattern. Nevertheless, for every data center, the features may be altered depending on specific requirements, and rules can be defined accordingly. As a result, defense methods should be updated and adjusted regularly. In the work of [48], the authors proposed an approach that works similarly to a service broker within an SOA model. The proposed approach is named filtering tree. It converts the user request in XML tree form and uses the virtual cloud defenders to protect against these attacks. The CCE users create their requests in XML, submit them over HTTP, and create system interfaces by using REST methods such as Microsoft Azure or Amazon EC2. This study aims to offer security to the Open Application programming interface (API) against XML-, HTTP-, or REST-based attacks. In their study [49], the authors proposed an approach to protect the CCE from X-DoS/H-DoS attacks. Their approach uses Cloud TraceBack (CTB) to trace and detect these attacks. Additionally, they trained a Cloud Protector by using a back-propagation algorithm for feed-forward neural networks to detect and filter the attack traffic. The authors used a real attack traffic dataset generated by the StuPot project 2009 [50] (the page of the dataset is not available) for training. In their experiment, to generate the dataset, they set a timer for 1 s and updated the targeted website. They collected the HTTP traffic by using tcpdump and Wireshark (Tshark). The authors generated a huge labeled corpus that included evidence of 10 different attacks against HTTP and QUIC services in their study [51]. They performed a full review of the security of the HTTP/2 and HTTP/3 protocols, detailing numerous HTTP/2 threats and their relevance to HTTP/3. Furthermore, they examined the efficiency of various attacks and created the \"H23Q\" dataset, which was particularly designed to measure the security of these protocols. The study also looked at the use of machine learning techniques to analyze the dataset. The results of the research were provided, demonstrating the performance of several classifiers in terms of the AUC, precision, recall, F1 score, and accuracy. The Bagging model performed the best, with an AUC of 77.60% and an F1 score of 68.77%.