esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 4 The Proposed Scheme
### The Re-Encryption Processes
#### 4.3.4 Data Retrieval by System Manager  
The system manager can retrieve the stored EHR. Because the manager decrypts the encrypted EHR using the second level of the PRE, the following process is performed. First, the manager decrypts the symmetric key and then decrypts the encrypted EHR using the symmetric key.  
The system manager requests the server to send the encrypted data \(\mathcal{C}\) and the encrypted key \(\mathcal{E}\). Let \(\mathcal{C}\) be \((D,\ E,\ F,\ s)\);  
The manager calculates \(F\oplus H_{2}(E^{\frac{1}{M_{4}M_{4}(pk_{i,2})+\frac{1}{2}}})\). If no problem arises, then the result is two concatenated values; the first is the symmetric key \(K\), and the second one is \(\omega\);3. The manager verifies whether \(E=\left(pk_{M,1}^{H_{2}(p\{k_{2}\}}\cdot pk_{M,2}\right)^{H_{1}(K,\omega)}\) is established. If it is not established, then the manager rejects \(\mathcal{C}\) and \(\mathcal{E}\), and terminates the process;
4. The manager decrypts \(\mathcal{C}\) with \(K\) using the AES decryption algorithm. Subsequently, the manager obtains the original data \(M\) and its signature \(S\);
5. The manager verifies the integrity of \(M\) via a verification procedure using the signature \(S\). If the verification process is completed successfully, then data retrieval is terminated.
# Data Access Based on a Guide Map of the Underwater Wireless Sensor Network
## 5 Performance Analysis of DAGM  
The location of the center ring node for metadata will affect the data access performance, as well as the energy consumption. The center ring nodes are selected according to balance and optimization between data query time and the energy consumption of the network. This section discusses the data storage and query time performance, and the network energy consumption.
# Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model
## 4 Experimental Results
### Robustness Comparison of Screen Shots at Different Angles  
In this experiment, we selected different perspective angles to take screenshots for the watermarked images. The relevant experimental data are shown in Table 5, and the graphical accuracy is shown in Figure 11b.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distance** & **20 cm** & **30 cm** & **40 cm** & **50 cm** \\ \hline Watermarked & & & \\ Image & & & \\  & &  
The experimental results show that the HiDDeN model has poor robustness to screenshot attacks under different perspective angles. The accuracy rate of the watermark information extraction is less than 60% for HiDDeN. The Stegastamp model also maintains good watermark extraction accuracy under different perspective angles, and its extraction accuracy of watermark information reaches about 80% of ScreenNet. Under different perspective angles, the accuracy of watermark extraction for the proposed ScreenNet model is slightly higher than the accuracy of Stegastamp and HiDDeN models. As ScreenNet applies the self-built moire with more obvious moire traces, the accuracy of watermark information extraction is improved. It can be noticed that the proposed ScreenNet algorithm is more robust to screenshot attacks.
# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 3 System Model
### Beam Synthesis Method under Low-Bit Phase Array
According to the equivalent substitution theory of Figure 4 in [28], the elevation and azimuth of the beam can be designed by the following formula:  
\[\sin\theta_{0}=\frac{\lambda}{d_{2}\times 2^{M}}{\sum_{i=1}^{b}\frac{1}{q_{i}}} \tag{7}\]  
\[\sin\theta=\frac{\varphi\lambda}{2\pi d} \tag{2}\]0  
\[\sin\theta=\frac{\varphi\lambda}{2\pi d} \tag{2}\]1 Equivalent uniform plane array (UPA) formed by 4 Sub-UAVs. In order to show the above process more vividly, it is proved by the following simulation. The simulation parameters are as follows: \(m=n=2,p=[5]\), \(q=[3,2]\), \(M=2\), frequency is 10 GHz, element space \(d=1\) cm. Based on (7) and (8), we can get \(\theta_{0}=6.15^{\circ}\), \(\phi_{0}=31.87^{\circ}\). The simulated coding scheme is shown in Figure 5a, and the two-dimensional beam is shown in Figure 5b. At this time, the simulated beam pointing of two-dimensional beam is \(5.63^{\circ}\) (elevation) and \(31.13^{\circ}\) (azimuth) which is basically in line with theoretical design. In this way, we use four phase values only to obtain relatively high beam synthesis accuracy. It is shown that the low-bit beam synthesis method can also be applied to the equivalent UPA.
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 2 Related Work
### Machine Learning Techniques Applied to Networking Issues  
The literature contains many examples of the application of machine learning techniques to networking issues. For example, in [20], a two-layer machine-learning detection model for detecting malware on the Android operating system is proposed. The structure of the model is based on ensemble learning using an RF with extra trees and a bagging technique. All features are considered when splitting a node, thereby reducing uncertainty and potential bias. With the aid of RF, this method outperforms techniques employed in previous work and is more accurate when classifying attack types on the same dataset. The Temporal Deep Q-Learning Network (tDQN) [21] represents a self-learning reinforcement-based model that prevents a surge in network traffic on IoT devices using SDN. It designs a model that utilizes DQN to account for dynamic network traffic and fluctuations and is pre-trained with an LSTM model to prevent over-fitting. This algorithm was tested in three scenarios, including mild, medium, and heavy traffic. It outperformed traditional networks regarding throughput, delay, jitter, packet delivery ratio, and packet loss. Additionally, it was shown to auto-balance traffic fluctuations. This approach has been tested on email, HTTP, FTP, and several other applications. Further, in [22], a method is proposed to measure the trustworthiness of driverless cars using deep neural network (DNN) models. Being  
Figure 4: Batch learning for replay memory (the blue blocks are randomly sampled experiences among all stored in the replay memory).  
Figure 5: The core DQN formula.  
feedforward networks, DNNs represent complex functions, such as traffic management, more efficiently. The study utilized traffic data, both real-time and historical, to calculate the trust scores of vehicles. The traffic data used in the study were generated through SUMO, with a map of Melbourne used to simulate real-world scenarios. The proposed method also detects compromised components such as cameras, radar, and LiDAR. However, it assumes that the historical data are correct and does not include correcting GPS positions.
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
## 6 Case Studies and Results  
The case studies were conducted on the IEEE RTS79 system [32], which has 24 buses and 38 branches. The total generating capacity of the test system was 3405 MW. The peak load was 2850 MW. Three wind farms were added, where the five 12 MW generating units on Bus 15, six 50 MW generating units on Bus 22 and the two 76 MW generators on Bus 2 were replaced by wind turbines. The penetration rate of wind power was 15%. The wind power data were from the Wind Integration National Dataset (WIND) Toolkit [33] in the New York area. With wind power generation and corresponding forecasts, the time horizon spanned from 1 January 2007 to 31 December 2012. The time resolution was 5 min. For the sake of comparative study, the CDF of the worst-case attack and the IPA followed the same Laplace distribution \(L\) (80,50).
# Ground-Borne Vibration Model in the near Field of Tunnel Blasting
## 4 Vibration near Blasting Area
### Analysis of Prediction Results of New Approach  
The following conclusions can be drawn from the monitoring data of blasting vibration and the vibration law obtained by fitting:  
(1) The blasting vibration law predicted by the new approach has the characteristics of less classification and strong integrity. The obtained model is simple and clear, with stronger practicability.  
(2) The value of \(\gamma_{2}\) in the formula is obviously higher than that of \(\gamma_{1}\), indeed even one order of magnitude higher than that of \(\gamma_{1}\). This shows that the influence of the free surface on the vibration velocity in the near field of small-spacing tunnel blasting is much greater than that of the distance to the blasting center and the charge quantity. It also indicates that rock clamping has a great impact on blasting vibration, which is why the peak behind the wave peak of the monitoring waveform is greater than that in front.  
(3) The correlation coefficient obtained by the vibration data fitting of the new approach is generally high, which shows that the obtained blasting vibration law is more consistent with the actual situation in the near field of blasting, it can more accurately reflect the real blasting situation, and its prediction result is more convincing.  
(4) Compared with the traditional Sadovsky formula, the new approach takes into account more influencing factors, the calculation process is still simple and clear, the amount of calculation to obtain the required result is less, and the correlation of the formula is high. Therefore, the obtained vibration law will be more consistent with reality, more objective, and can accurately predict the vibration speed before blasting and guide blasting construction.
# A Multicomponent Face Verification and Identification System
## 3 Results
### Face Identification and Verification Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Rank-1 (\%)** & **Rank-5 (\%)** & **Inference Time (ms)** \\ \hline Dlib (baseline) & 79.5 & 81.3 & 30 \\ ArcFace & 92 & 96.1 & 97 \\ MobileNetV2 & 85.2 & 89.6 & 20 \\ FaceNet & 91.8 & 95.8 & 52 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Face identification evaluation via Rank-1 and Rank-5 identification rates attained by each face recognition algorithm and the baseline model on the curated ID vs. Spot dataset. Figure 2: ROC curves and AUC scores attained by each face recognition algorithm and the baseline model on the curated ID vs. Spot dataset. In addition to the primary metrics evaluated, the implementation of FaceNet was rigorously tested to assess its FNR and FAR, which are crucial for understanding the algorithm's robustness in correctly identifying matching face pairs. Remarkably, the FaceNet implementation exhibited a nearly negligible FNR of 0.02 (2%) and FAR of 0.001 (0.1%). Such an exceedingly low FNR and FAR underscore the model's proficiency in accurately recognizing faces, minimizing the likelihood of overlooking true matches. Also, such low FNR and FAR further bolster FaceNet's standing as a highly reliable choice for the proposed system, ensuring that false negatives cases are significantly mitigated. Also, a more related analysis is provided in Figures 3 and 4. Figure 3 illustrates the trade-off between the FNR and the FAR in a logarithmic scale via the detection error tradeoff (DET) curve. Each point on the curve represents a different decision threshold, capturing how tightening the threshold to reduce FNR inevitably increased FAR and vice versa. Notably, at an operating point where FNR was 0.1%, the FAR rose to approximately 19%, underscoring how demanding a near-zero FNR came at the expense of a high FAR. Conversely, at a more stringent threshold aimed at FAR = 0.1%, the FNR reached just over 5%, reflecting a stricter acceptance criterion that lowered the false acceptance but increased the rejection of true matches. Figure 4 complements this perspective by plotting both FNR and FAR as functions of the similarity threshold on a log scale. As the threshold increases, we see a pronounced decrease in FAR (orange curve) but a corresponding uptick in FNR (blue curve), revealing a typical crossover point where the two error rates are comparable. The vertical dashed lines  
Figure 4: FNR and FAR as functions of the similarity threshold. Figure 3: Trade-off between the FNR and the FAR. highlight specific threshold values--one that achieves FNR \(\approx\) 0.1% and one that achieves FAR \(\approx\) 0.1%--visually confirming the steepness of the trade-off region.
## 5 Experiment  
### Experimental Data  
The proposed model was validated using three datasets. The first dataset is the Malimg dataset [33], which was also utilized in previous studies by [PERSON] and [PERSON] [34], [PERSON] et al. [35], [PERSON] et al. [36], and [PERSON] et al. [37]. The second dataset used is the Mal60 dataset [38], as studied by [PERSON] and [PERSON] [39]. The last dataset was VXHeaven's 2010 virus collection dataset [40].  
The Malimg dataset used in malware imaging research is categorized into 25 families. The dataset comprises a total of 9458 malware samples. However, for the purpose of this paper, which focuses on classification with a limited number of samples, only 20 samples were randomly selected from each family, resulting in a total of 500 samples in the composed dataset.  
The Mal60 dataset used in this research is classified into 60 families, each containing a total of 20 malware samples, resulting in a dataset size of 1200 samples. Notably, each family represents a unique category of malware. In some cases, different security companies may assign different names to the same family of malware based on their naming criteria. However, despite these naming differences, the code samples are classified under the same group as they exhibit similar malicious behavior or characteristics. Additionally, certain malware samples in Kaspersky and Bitlefender products may have diagnosis names that cannot be confirmed or are classified as heuristic diagnosis names. Specifically, two examples of such samples are identified as belonging to the Akdoor and Rifdoor families, targeting specific areas.  
The VXHeaven dataset comprises 270 k malware executable files, but it does not include malware family information. To determine the family of each sample in the VXHeaven dataset, we added family labels through Virustotal [41], a malware analysis site. When a suspected malware file is uploaded to Virustotal, the site analyzes it for family prediction using various antivirus engines. The malware family label is assigned based on the results obtained from the Microsoft Antivirus engine.  
A total of 70% of each dataset is used for training, and the remaining 30% for testing.
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
## 3 The Principle of Defect Size Recognition  
As discussed in Section 2, when the defect size approaches to the diffraction limit of the MSDI system, the gray distribution of the defect will change from bimodal to unimodal, which makes the defect size not to be recognized by the distance between two peaks. To solve this problem, we propose a defect size recognition method based on DRT. In order to obtain the defect size from the unimodal gray distribution, DRT needs much gray distribution of the known size defect to study the relationship between the defect gray distribution and the defect size. The gray distribution is also called the training data set of the DRT. Due to the limit of actual machining technique and machining accuracy, these tiny defects are too difficult to make. Thus, an electromagnetic simulation model of the MSDI system is built, and the far-field light intensity distribution of defects is acquired as the training data set of the DRT. Considering the simulation model cannot calculate the diffraction effect caused by the limit of the aperture to the scattered light, we measure the PSF of the MSDI system and use PSF to revise the light intensity distribution.
## 3 System Model
### Antenna Pattern  
Planar patch antennas [36] used in each antenna array are considered in this paper. To obtain the vertical and horizontal antenna gains, for each antenna array, four antenna parameters are considered: Figure 3a shows the vertical beam half power beam width (HPBW) \(\theta_{3 dB}\) and the horizontal beam HPBW \(\phi_{3 dB}\), Figure 3b shows the vertical tilt \(\theta_{tilt}\) and the horizontal tilt \(\phi_{tilt}\). In Figure 3b, \(\Delta\phi_{tilt}\) and \(\Delta\theta_{tilt}\) denote the changing of \(\phi_{tilt}\) and \(\theta_{tilt}\), respectively. From there, we could derive the expression of the horizontal or vertical antenna gains for an angle \(\Psi\) from the main beam direction as [37]:  
\[G_{h}(\Psi)=G_{v}(\Psi)\left\{\begin{array}{ll}-3(\Psi/\Psi_{b})^{2},&(0^{ \circ}\leq\Psi\leq\Psi_{1})\\ L_{N},&(\Psi_{1}<\Psi\leq\Psi_{2})\\ X-60\log_{10}(\Psi),&(\Psi_{2}<\Psi\leq\Psi_{3})\\ L_{F},&(\Psi_{3}<\Psi\leq 90^{\circ})\end{array}\right. \tag{1}\]  
where \(\Psi_{b}\) is one-half the 3 dB beamwidth in the plane of interest, \(\Psi_{1}=\Psi_{b}\sqrt{-L_{N}/3}\), \(\Psi_{2}=3.745\Psi_{b}\), \(X=L_{N}+60\log_{10}(\Psi_{2})\), and \(\Psi_{3}=10^{(X-L_{F})/60}\)[37], \(L_{N}\) denotes the near-in-side-lobe level, and \(L_{F}\) denotes the far-side-lobe level.  
Figure 1: A HAPS system model.  
Figure 2: Movement scenarios.  
Therefore, we could express the combined gain \(G\) as:  
\[G=\max(G_{v}+G_{h},L_{F})+G_{p}, \tag{2}\]  
where \(G_{v}\) and \(G_{h}\) are the vertical and horizontal antenna gains, respectively, and \(G_{p}\) denotes the maximum antenna gain as shown in Equation (3).  
\[G_{p}=10\log_{10}(\frac{B_{w}{}^{2}}{\theta_{3 dB}\theta_{3 dB}})+G, \tag{3}\]  
where \(B_{w}\) denotes the beamwidth. Figure 4 shows an example of an antenna pattern for vertical or horizontal polarization.
## 3 Methodology
### Data Preparation  
After collecting the raw data related to sent and received packets, a series of preprocessing steps were applied to extract novel time-domain features. These preprocessing steps were designed to improve the dataset with meaningful features that could be used for effective classification. The following time domain features were derived for each network traffic.
# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 2 Materials and Methods
### Acquisition System and Data Construction  
#### 2.2.1 Data Acquisition System and Condition  
The custom dataset was collected from the monorail transporter testing platform at South China Agricultural University (23\({}^{\circ}\)9\({}^{\prime}\)29.52\({}^{\prime\prime}\) N, 113\({}^{\circ}\)20\({}^{\prime}\)53.52\({}^{\prime\prime}\) E). During transportation, the monorail transporter loads and unloads agricultural materials anywhere around the mountain orchards, and a battery provides power for the system. An Intel RealSense D455, a powerful RGB-D camera with an infrared laser transmitter, was installed in front of the trailer and 50 cm higher than the front bar at an angle of 45\({}^{\circ}\) relative to the loading platform, as shown in Figure 3. Therefore, the RGB-D camera could capture complete images of the trailer. We set the resolution of the RGB and depth images to 1280 \(\times\) 720 pixels. We used the RGB images to train the object detection algorithm and used the depth images in the height measurement model to determine the load volume.
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## Appendix A
### Proof of Theorem 4  
The proof of Theorem 4 is drawing inspiration from Proposition 1 in [47].  
Proof.: According to Corollary 1, it can be easily observed that there exists \(\mu\geq 0\) such that inequality \(\lambda\Psi_{B}^{\text{GMC}}+\frac{\mu}{2}\|\beta\|_{2}^{2}\geq 0\) is convex.  
Now consider the augmented Lagrangian \(L(\beta,z,u)\) with regard to \(z\) as follows  
\[L(\beta,z,u) =\frac{1}{2n}\|y-X\beta\|_{2}^{2}+\lambda\Psi_{B}^{\text{GMC}}(z) +u^{\top}(z-\beta)+\frac{\rho}{2}\|z-\beta\|_{2}^{2}\] \[=\lambda\Psi_{B}^{\text{GMC}}(z)+\frac{\rho}{2}\|z\|_{2}^{2}-u^{ \top}z+\frac{1}{2n}\|y-X\beta\|_{2}^{2}-\rho u^{\top}\beta+\frac{\rho}{2}\| \beta\|_{2}^{2}. \tag{103}\]  
Note that \(\frac{1}{2n}\|y-X\beta\|_{2}^{2}-\rho u^{\top}\beta+\frac{\rho}{2}\|\beta\|_{ 2}^{2}\) is independent of \(z\). Given the choice of \(\rho\geq\mu\), \(L(\beta,z,u)\) is convex with respect to each of \(\beta,z\) and \(u\). Therefore, Algorithm 1 can converge to limit points \(\beta^{*},z^{*}\), and \(u^{*}\).  
The implication is that the dual residual \(\lim_{k\to\infty}s^{(k+1)}=\|\rho(z^{*}-z^{*})\|_{2}=0\). Regarding the primal residual, it can be observed from the \(u\) update step in line 6 of Algorithm 1 that for all \(k,t\geq 0\),  
\[u^{(k+t)}=u^{(k)}+\sum_{i=1}^{t}(\beta^{(k+i)}-z^{(k+i)}).\]  
For fixed \(t\) and as \(k\to\infty\), we have  
\[u^{*}=u^{*}+t(\beta^{*}-z^{*}),\]  
holds \(\forall t\geq 0\). Thus, \(\beta^{*}-z^{*}=0\), and therefore \(\lim_{k\to\infty}r^{(k)}=\|\beta^{*}-z^{*}\|_{2}=0\).
# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
## 1 Introduction
The system flow is illustrated in Figure 1.  
Evaluation results on two in-operation mass-transit systems from Chicago and Tainan demonstrate that BiasSpan can retrieve solutions in a Pareto-optimal sense over comparative methods between profit under queried constraints (_the expected passenger flow to be maximized, referring to the social welfare for the public_) and cost for construction as well as maintenance (_the cost of route to be minimized, referring to the sustainability for the government_), and runs in one logarithmic time less than comparative algorithms. In conclusion, the contribution for this paper is three-fold:  
* We formulate the nonmonotonic multicriteria constrained route planning problem that arranges new routes in an existing mass-transit system, which includes the nonmonotonic characteristic originated from the heterogeneous urban feature and heuristics developed from the neural-network-based inference module;
* We propose BiasSpan for the targeted constraint planning problem considering optimization criteria: (1) Maximizing PF of route (_refers to social welfare and revenue of fixed fares_), (2) Minimizing length of route (_refers to the cost for sustainability concerns_);  
Figure 1: System flow for solving the nonmonotonic multicriteria constrained route planning. * Evaluation using two existing public transportation systems with different urban characteristics and structures shows the adaptability of the proposed method, which performs well in comparison with peer methods from the literature.