esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 4 Practical Realization of the Proposed Biometric Protection Scheme for Facial Recognition  
### Implementation of a Non-Device-Centric Facial Authentication  
A OnePlusST smartphone was considered as the client device, which executes an Android App developed in Java. For the computation, authentication and database servers, an Intel Core i5-9400F was considered, which executes Python code in version 3.6.14 for the computation and authentication servers and MySQL for the database server to store the protected templates.  
The client device uses the front camera to capture user faces. Then, BlazeFace [36], a convolutional neural network, was applied to detect faces and crop the input image. Subsequently, FaceNet [18], a convolutional neural network to extract floating-point embeddings, was employed as a feature extractor. The load and inference of BlazeFace and FaceNet pretrained models [37] and [38], respectively, were supported by the library TensorFlowLite, which is suitable for implementations in mobile devices. With these pre-trained models, 128 floating-point feature elements, also referred to as embeddings, were obtained from 160 \(\times\) 160 bits face images. The floating-point embeddings were discretized by means of Linearly Separable Subcode (LSSC) [35], which was implemented to obtain a binary representation with the codes 000, 001, 011 and 111. The feature space was segmented into four intervals: \((-\infty,-0.1)\), \([-0.1,\ 0.0)\), \([0.0,\ 0.1)\) and \([0.1,+\infty)\), in order to improve the recognition performance. After discretization, each embedding or feature vector was made up of 384 bits.  
A shared library in C named as libClassicMcEliece was developed to include the _KeyGen_, _ENCODE_ and _DECODE_ functions from the Classic McEliece proposal [32] and the _Entrollment Feature Transformation_ and _Verification Feature Transformation_ functions from our proposal. On the client device, a Java Native Interface (JNI) was employed to allow for running C code in Java. In the computation, authentication and database servers, this functionality was achieved by the use of the libraries _ctypes_ and _numpy_.
## 2 Materials and Methods
### Physicochemical Analysis  
Moisture, fat, ash content, and NaCl were determined according to official methods [18]. Fat content analysis was performed following the AOAC method [19]. Protein content was determined according to the Kjeldahl method [20]. Non-protein nitrogen was determined as described by [21]. The proteolysis index was calculated from the ratio between the non-protein nitrogen and the total nitrogen determined by the Kjeldahl method [20]. Water activity (\(\mathrm{a_{w}}\)) was measured using a LabMaster \(\mathrm{a_{w}}\)-meter (Novasina, Switzerland) according to the manufacturer's instructions. For pH measurement, a suspension of 10 g of sample of smoked dry-cured ham was mixed with 90 mL distilled water, and pH was measured by pH-meter (benchtop sensION \(\mathrm{m}\) + MM374, Hach Company, Loveland, CO, USA). Thiobarbituric acid reactive substances (TBARS) were quantified to determine the degree of lipid oxidation according to the method described by [22] with slight modifications. Muscle samples (5 g) were dispersed in 25 mL of 5% TCA and then treated with 10 mg of BHT to prevent further oxidation. The mixture was homogenized on ice for 60 s (Ultra Turrax T18 basic, IKA Werke GmbH & Co. KG, Baden-Wurttenberg, Germany) and centrifuged (10 min, 12,000 rpm, 4 \({}^{\circ}\)C) (Rotina 380 R, Hettich LabTechnology, Tuttlingen, Germany). The supernatant was filtered through filter paper (Whatman N\({}^{\circ}\) 54, Whatman, Dassel, Germany). An aliquot of 4 mL was treated with 4 mL of 0.02 M TBA solution. The mixture was heated for 1 h at 100 \({}^{\circ}\)C in a block heater (Stuart SBH130D, Cole-Parmer Ltd., Stone, UK). The absorbance was measured at 532 nm (Specord 50 Plus, AnalytikJena, Jena, Germany) using 1,1,3,3-tetra-methoxy-propane (TMP) as standard. The results were expressed as mg malonaldehyde (MDA)/kg sample.
## 2 Characteristic Signal Selection and Wavelet Denoising Processing
### Wavelet Denoising
#### 2.2.2 Selection of the Wavelet Basis  
There is no uniform standard for selecting the best wavelet base. Usually, the best wavelet base is selected by experience according to the properties of the wavelet base function, the characteristics of the signal to be detected, and the purpose of signal processing. In the actual selection of the wavelet base, the best wavelet base can be selected by observing the shape of the signal and combining the calculation results. The best wavelet base is selected by analysis and comparison. It can be seen from the analysis that there are similar wavelet types with the shape of acoustic emission signal and vibration signal in the Daubechies wavelet coefficient. Therefore, this type of wavelet coefficient is used for further experiments. The experimental results of denoising with different wavelet bases are shown in Table 1.  
According to Table 1, when the wavelet base is selected from db1 to db8, the calculated signal-to-noise ratio gradually increases, and the mean square error gradually decreases. When the wavelet base is selected from db8 to db9, the mean square error no longer decreases, and the signal-to-noise ratio shows a downward trend. This result proves that db8 is the best wavelet base type.
## 3 Proposed Methodology
### Distribution of Pairwise Key  
Once the information about the neighbor is obtained from the network nodes, BS analyzes the exact network topology and generates the neighbor matrix. Then the DFS algorithm is applied by which multiple paths are identified from BS to each source node. Beforehand, for each neighbor pair, BS generates the secret key, a random number, termed as pairwise key with the help of the hash function given by Equations (4)-(6).  
\[k_{xy}=h\big{(}secret,ID_{x},ID_{y}\big{)} \tag{4}\]  
BS unicasts this key to the corresponding node as  
\[BS\to x:PAIR\_KEY|seq_{no}|ID_{bs}|CERT_{bs}|ID_{x}|ID_{y}|E(k_{xbs},k_{xy} \Big{|}E\Big{(}k_{ybs},k_{xy}\Big{)})| \tag{5}\]  
\[MAC(k_{xbs},PAIR_{KEY|seq_{no}|ID_{bs}|CERT_{bs}|ID_{y}|E(K_{xbs},K_{xy}|}|E \Big{(}K_{ybs},K_{xy}\Big{)})) \tag{6}\]  
The packet holds its type, sequence number, the ID of BS, neighbor and destination, certificate of BS, pairwise key and MAC of entire data. Every intermediate node that receives the packet performs the following operations:  
1. The certificate of BS is verified with the public key.
2. The sequence number as well as the node pair is checked in the receiver packet table. When no such value is found, sequence number, type of the packet and node pair along with packet rebroadcasting is stored or else dropped.
3. When the ID of the destination is identical to its ID, the pairwise key is encrypted, MAC is verified and the encrypted packet of neighbor with nonce as well as its ID encrypted using a pairwise key is sent, which is given by Equation (7).  
\[x\to y:CHALLENG\big{|}ID_{y}\big{|}E\Big{(}K_{yb},K_{xy}\Big{)}\Big{|}E(K_{xy},ID_{x}\Big{|}nonce) \tag{7}\]The packet at node \(y\) is decrypted using unique shared key \(K_{yb}\) and the pairwise key is generated followed by decrypting the next packet using the generated pairwise key then forwards the packet to \(x\), which is given by Equation (8).  
\[y\to x:CHALLENG\_REP|ID_{x}|E(k_{xy},ID_{y}\big{|}nonce\_1) \tag{8}\]  
Both the neighboring nodes verify one another by swapping the challenge packet as well as reducing overhead of resending pairwise key to \(y\) from BS. In end, each pair of nodes holds a pairwise key. When the CHALLENG_REP packet is not received at node \(x\) in the expected form, node \(x\) reports about the fake node to BS.
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
###### Abstract  
Vehicle-to-everything (V2X) communications enable vehicles to interact with each other and various components of the traffic system, forming the backbone of modern intelligent transportation systems. However, V2X communications are highly susceptible to cyberattacks, posing a threat to both safety and operational efficiency. This paper proposes a real-time anomaly detection framework that integrates the reconstruction capabilities of Variational Autoencoders (VAEs) with the feature extraction power of Convolutional Neural Networks (CNNs). Our model processes streaming data using a sliding window mechanism, ensuring prompt detection of anomalies in the dynamic V2X environment. Extensive experiments demonstrate that our method achieves high performance across diverse anomaly types, with precision, recall, and F1-scores reaching up to 0.91, 0.99, and 0.95, respectively, on challenging anomalies such as constant position offsets. The model consistently outperforms both a traditional autoencoder and a VAE with Long Short-Term Memory (LSTM) layers, particularly on complex anomalies like vehicle speed and position offsets. Additionally, our framework maintains a low inference time of approximately 0.0013 s, making it highly suitable for real-time deployment. Designed to adapt to evolving traffic patterns through periodic retraining, the proposed approach ensures long-term reliability and robustness. By delivering high performance, adaptability, and efficiency, our method provides a reliable way to detect and prevent cyberattacks, thereby making intelligent transportation systems safer and more dependable.  
V2X security; generative AI; VAE; CNN; anomaly detection; GAN +
Footnote †: journal: Applied sciences  
Article
# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
###### Abstract  
Spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) can estimate the geophysical parameters by receiving Earth's surface reflected signals. The CYclone Global Navigation Satellite System (CYGNSS) mission with eight microsatellites launched by NASA in December 2016, which provides an unprecedented opportunity to rapidly acquire ocean surface wind speed globally. In this paper, a refined spaceborne GNSS-R sea surface wind speed retrieval algorithm is presented and validated with the ground surface reference wind speed from numerical weather prediction (NWP) and cross-calibrated multi-platform ocean surface wind vector analysis product (CCMP), respectively. The results show that when the wind speed was less than 20 m/s, the RMS of the GNSS-R retrieved wind could achieve 1.84 m/s in the case where the NWP winds were used as the ground truth winds, while the result was better than the NWP-based retrieved wind speed with an RMS of 1.68 m/s when the CCMP winds were used. The two sets of inversion results were further evaluated by the buoy winds, and the uncertainties from the NWP-derived and CCMP-derived model prediction wind speed were 1.91 m/s and 1.87 m/s, respectively. The accuracy of inversed wind speeds for different GNSS pseudo-random noise (PRN) satellites and types was also analyzed and presented, which showed similar for different PRN satellites and different types of satellites.  
spaceborne GNSS-R; DDM; ocean surface wind speed; GMF; CYGNSS 2019 11 2747; doi:10.3390/rs11232747
## 1 Introduction
The LC-MS used in this setup employed a Perkin Elmer Sciex API 111 triple quadrupole mass spectrometer equipped with an atmospheric pressure ionization source as the detector for the Merck-Hitachi L-6200 system. The current methods for the liquid chromatographic detection of HNS predominantly utilize single-wavelength detection. These methods have long detection times of 20 min, typically generate numerous extraneous peaks, and primarily employ acetonitrile (ACN) as the mobile phase, which is more costly compared to methanol (MeOH). Additionally, the standard solutions of HNS are prepared using acetonitrile as the diluent instead of ultrapure water, which results in relatively higher costs associated with the detection and analysis of HNS. The overall expenses for analyzing HNS are consequently high, making these methods unsuitable for measuring HNS concentrations from 0.64 to 2.00 mg/L and above 20.00 mg/L. The Merck-Hitachi L-6200 high-performance liquid chromatograph, an older model, has been replaced by newer versions such as the Ultimate 3000 HPLC System (Shanghai Si instrument Biochemical Technology Co., Ltd. Shanghai, China). Modern HPLC systems typically offer greater sensitivity, separation efficiency, and automation capabilities. However, the LC-MS methods described earlier do not fully align with the rapid advancements in current instrumental technologies. Therefore, there is an urgent need to develop an efficient, precise, and cost-effective liquid chromatography-mass spectrometry method for HNS detection. In this study, a controlled variable method was employed to develop a rapid, simple, sensitive, accurate, and economical LC-MS analytical method for the qualitative and quantitative detection of HNS, aiming to address the high costs, low efficiency, and limited detection range associated with existing methods. This research was divided into three main parts. Firstly, the solvent and extraction agent for HNS were identified through full-wavelength UV scanning. Additionally, the optimal wavelength for HNS detection was determined using a high-performance liquid chromatography ultraviolet spectrophotometer. Secondly, the conditions for HNS detection such as mobile phase, flow rate, column temperature, injection volume, and detection time were sequentially determined using the controlled variable method. Finally, by integrating experimental results with prior LC-MS conditions, the optimal HPLC conditions for the Ultimate 3000 HPLC System were established. Additionally, specific mass spectrometry conditions were defined, suitable for the widely used electrospray ionization quadrupole electrostatic field orbitrap high-resolution mass spectrometry (ESI-Q Orbitrap HRMS) to detect HNS and its products. The feasibility of these conditions was validated through testing HNS degradation samples.
## 2 Related Work
[17] developed a simple and efficient data delivery schemes tailored for DFT-MSN, which has several unique characteristics such as sensor mobility, loose connectivity, fault tolerability, delay tolerability, and buffer limit with an optimized flooding scheme that minimizes transmission overhead in flooding. In [18] an optimum predefined mobility trajectory of the sink that was explored to balance the two performance metrics was given. The authors focused on a lattice-based analytical model to understand performance as system and mobility parameters are scaled. The authors of [19] proposed a new solution with adaptive location updates for mobile sinks to resolve rapid energy consumption and collision problems. The fantasy of this paper is that it only needs to broadcast its location information within a local area other than among the entire network. The authors of [20] investigated the benefits of a heterogeneous WSN architecture with rich mobile sensors and static sensors. Their algorithms show many significant effects on the computational network lifetime and performance. However, the shortcoming of this approach is that they assumed all the sensors in the network to be aware of the location of the mobile sensor. Our paper explores an optimal barrier coverage based sensor deployment for event driven WSNs where a dual-sink model was designed to evaluate the energy performance of not only static sensors, but Static Sinks (SS) and Mobile Sinks (MSs) simultaneously based on parameters such as sensor transmission range \(r\) and the velocity of the mobile sink _v etc._ Moreover, a MS mobility model was developed to enable SS and MSs to effectively collaborate, while achieving a set of spatiotemporal energy performance efficiency by using the knowledge of the cumulative density function (cdf), Poisson process and M/G/1 queue. The remainder of this paper is organized as follows: the next section presents the proposed efficient Dual Sink Algorithm for an event driven Wireless Sensor Network (eDSA) with optimized barrier coverage design. Section 4 generally illustrates the network model and detailed solutions for modeling the energy performance and Section 5 shows the simulation results. Finally, Section 6 concludes the paper.
## 2 Cases of SCWIDs
### Details of SCWID in Working Face 322 of Qidong Coal Mine

Table 1: Statistics of supports crushing and water inrush disasters (SCWIDs) in Qidong coal mines when mining under the UCA. standard requirements for mining under water, the height of water flowing fractured zone under medium hard roof was calculated to be about 27.3-38.5 m. The thickness of the protective layer was designed to be 6 times of the thickness of the coal seam. Therefore, the height of waterproof rock strata to be reserved was 42.3-53.5 m. The actual minimum height of waterproof rock strata to be reserved was 63 m, which was about 9.5 m higher than the maximum height required by the theoretical calculation. Theoretically, no water inrush accident should occur. One hundred and ten \(ZZ4400/17/35\) hydraulic supports were installed in the working face, MG250/600-WD1 coal cutter was used for coal mining, and natural caring method was used to manage the roof. From 5 November 2001 to 10:30 on 18 November 2001, the working face was advanced by 28.8 m; the main roof experienced the first weighting, most of the support safety valves of the working face were opened. The immediate roof caved sufficiently into the goat, and there was no water dripping phenomenon. Then the working face continued to advance, and no abnormal phenomenon was found. At 4:30 on 24 November, when the working face was advanced by 42 m, the roof underwent the weighting again and fracture, which caused No.50-60 supports to be crushed (see Figure 2); and the roof at the head of the machine appeared to show water seepage. At 5:56, the speed of water seepage at the seepage point increased to 10 m\({}^{3}\)/h, to 80 m\({}^{3}\)/h at 8:00, sharply to 1300 m\({}^{3}\)/h at 11:30, and at 4:20 a.m. on 25 November, the value reached 1520 m\({}^{3}\)/h and was still increasing. The water level outside the retaining wall reached 1.8 m and rose at the speed of 300 mm/h. The water level of the pump room reached 5.22 m. At this time, the pump room was flooded. Due to the excessive water volume, the increase speed was also fast, and the drainage capacity of the mine was limited. Finally, the mine was flooded. Fortunately, there were no casualties. It took only 24 hours from water seepage in goat to mine flooding. The water inrush accident seriously affected the normal production of the mine, resulting in economic losses as high as 36.4856 million Yuan. It took one year to repair the mine to resume the normal production.
## 6 Discussion
### Benefits
#### 6.1.8 Better Network Administration  
Network administration in a KDN scenario involves the management of diverse aspects such as users, mobility, spectrum, faults, energy, etc. As reviewed in Section 4, blockchain and cryptocurrency can be utilized together for intelligent user management [285]. Blockchains can be further utilized for secure and efficient handover in wireless communication network environments through full forward key separation [286]. Furthermore, the integration of blockchain has prevented re-authentication and repeated handover, resulting in low latency and overhead for handover [290]. Blockchain has been utilized for secure consensus and reliable resource offloading, where resources are offloaded appropriately based on consensus [299]. Blockchain consensus may also be incorporated into smart power networks for safe energy transactions [316]. Blockchain can be used along with machine learning to detect anomalies and identify network faults in a privacy-preserving manner [309].
## 2 Materials and Methods
### Methods
#### 2.3.4 Performance Evaluation Criterion  
Due to the significant class imbalance, that characterizes the MIT-BIH arrhythmia database, we utilized the the Area Under Curve (AUC) criterion as a performance metric for hyper-parameter selection and classification evaluation. We leveraged the AUC criterion during the benchmarking process to select the best classifier because it is a concise single-value metric for overall classification performance and can be easily interpreted and compared. In addition, the AUC is a robust metric for the case of imbalanced datasets, such as the MIT-BIH database.  
Figure 5: NG-RC-based classification. ‘\(X_{in}\)’: input data (280 samples for each heartbeat). ‘NVAR’: nonlinear vector autoregressive. ’PCA’:principal component analysis. Readout Ridge: Linear ridge regression. Readout MLP: Multilayer perceptron. ‘N, V, and S’: heartbeat categories.  
AUC is calculated by integrating the area under the receiver operating characteristic (ROC) curve over all possible thresholds. The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) at different thresholds, Figure 7. More information about AUC and ROC can be found in [55,65]. The convergence towards the upper-left quadrant signifies high performance characterized by both high sensitivity and specificity.  
The ROC curve reflects the sensitivity (TPR) and specificity (1-FPR) of the model's classification performance. The accuracy metric cannot be alone employed for evaluating the classification performance of a predictor trained on an imbalanced dataset since it results in a biased evaluation. However, when benchmarking with the literature, we utilized the overall accuracy (ACC) Equation (5), with sensitivity (SEN), Equation (6), positivity (PPV), Equation (7), and specificity (SPEC), Equation (8) metrics so that an unbiased evaluation could be performed. These metrics are commonly used in an imbalanced multi-class heartbeat classification problem.  
Figure 6: Architecture of the classifiers based on reservoir computing (NG-RC and RC). Each path in the schematic illustrates a scenario of classification. ‘LR’: linear RC states. ‘NR’: nonlinear RC states. ‘PCA’: principal component analysis. ‘MLP’: multilayer perception. ‘BD’: bidirectional. ‘\(X_{in}\)’: input data (280 samples for each heartbeat). ‘\(W_{in}\)’: input weights (initialized randomly and fixed). ‘NLRC’: nonlinear circular. ‘LRC’: linear circular. ‘NVAR’: nonlinear vector autoregressive.  
Figure 7: ROC curve. As the ROC curve approaches the upper left corner of the coordinate (1, 0), the AUC metric correspondingly increases. The blue and black curves are two ROC curves.  
\[ACC=\frac{TP+TN}{TN+TP+FN+FP}. \tag{5}\]  
\[SEN=\frac{TP}{TP+FN}. \tag{6}\]  
\[PPV=\frac{TP}{TP+FP}. \tag{7}\]  
\[SPEC=\frac{TN}{TN+TP}. \tag{8}\]