esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Blind Schnorr Signatures--Results and Discussion
### The Remaining Threat--Rejection Sampling Attack  
Let us note that a malicious device \(\mathcal{D}\) has an opportunity to partially disclose its identity in a blind signature due to the rejection sampling technique, and no watchdog can prevent it. For this purpose, \(\mathcal{D}\) must use a hidden key \(K_{A}\) of an adversary. The details are presented in Figure 13.  
Figure 13: Partial identity disclosure with rejection sampling: \(I_{\mathcal{D}}\) is a \(k\)-bit identifier of \(\mathcal{D}\), \(K_{A}\) is a secret key shared with an adversary, \(k\) is a (very) small integer.  
The idea of the attack is to adjust \(R^{\prime}\) so that \(\mathsf{H}(R^{\prime},K_{A})\) has prefix bits identical with \(I_{\mathcal{D}}\). Since H is a pseudorandom function, the only way to find such \(R^{\prime}\) is by random sampling the values of \(R^{\prime}\). So, in the case of failure detected at Step 5, we simply backtrack to Step 3. Note that the parameter \(k\) must be small (e.g., \(k=4\)) to skip backtracking in a reasonable time. So, only a few bits of information on the identity of \(\mathcal{D}\) can be leaked.  
The reconstruction of \(I_{\mathcal{D}}\) from a signature \((\mathcal{c},\mathcal{s}^{\prime})\) is immediate, as \(R^{\prime}\) is recalculated during the signature verification process as \(g^{\mathcal{s}^{\prime}}\cdot\mathsf{pk}_{S}^{-\mathcal{c}^{\prime}}\). Then, the adversary knowing \(K_{A}\) can compute \(\mathsf{H}(R^{\prime},K_{A})\) and truncate it to \(I_{\mathcal{D}}\). On the other hand, an Auditor \(\mathcal{J}\) cannot derive any information on \(\mathsf{H}(R^{\prime},K_{A})\) without \(K_{A}\), if H is a correlated-input secure hash function [33].  
Note that no watchdog can prevent this attack without introducing additional message exchange and modifying the blind-signature creation protocol. The reason is that \(R^{\prime}\) is determined on the side of \(\mathcal{D}\) immediately after choosing \(\alpha\), \(\beta\) and remains unchanged until signature verification, where it is used as an argument of H.  
Despite the short length of \(I_{\mathcal{D}}\) (and lack of uniqueness in most scenarios), the presented attack must be considered as a serious risk in some situations. For example, if blind signatures are used for e-voting in a small committee, there is a serious risk of vote deanonymization by the Adversary holding \(K_{A}\).
## 2 The Framework
### Rationale and Summary
#### 2.1.1 Legal  
There is a need to customize the framework in a functional scenario and country, given that laws are different in different areas of application. In this case, the authors customized the legal aspect for the healthcare sector providers in Germany. The internals of these aspects were presented by authors [28]. The specific questionnaire defined included this set of questions (all questions are presented in their final form):  
1. What types of data are influenced by the subcontracting approach? Significant data, such as health, social, or private clinical data?
2. What types of legitimate outlines can be used?
3. What are the possibilities if subcontracting takes place: Either of data being available, confidential, or integrable?
4. What are the necessities to protect the data? Deciding on suitable security architecture, encryption and cryptography of data, authorization management, managing potential risks, management of the occurrences of security issues, measuring and planning of contingency, and other concerns regarding the data protection.
5. Whether the legal and data protection necessities are met by the provider?
6. Whether the supplier has the required information security management system (ISMS)?
7. Measuring the extent to which the ISMS is assessed with respect to the suitability of the technical and organizational level and how are the outcomes are documented?
8. Whether the supplier has the required up-to-date and international certificates (for example, ISO 27001)?
9. Whether the cloud computing service is formulated neatly and obviously or not?
10. Whether the control rights of the organization using the cloud along with the required obligations for the supplier of the cloud service are specified or not?
11. Are there some rules supporting the probable operations and the retrieval of data if the cloud service provider commits any bankruptcy?
12. Whether any explicit and appropriate service-level agreement are available? If yes, whether they ensure the requirement of availability and dependability, deadlines for restoration and response, computing power, and support specifics?
13. In case of disastrous failures, are there any particular business community management (BCM) guidelines?
14. Whether controls are carried out regularly? Additionally, whether there exists any evaluations of the agreed upon technical and organizational measures?
15. Whether the security aspects are being assessed regularly and whether they are up-to-date and belong to the recent state of the art?
16. Whether the international data communication is influenced by the adjustments?
17. Whether sanctions are foretasted?
18. Whether any access points exist for state agencies (e.g., National Security Agency, NSA)?
19. Whether there exist any specific constraints?
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 4 Experimental Comparison
### Undisturbed Scenario
#### 4.1.2 Comparative Analysis of Experiments  
The experimental results are analyzed for 2D and Z-direction errors, and correlation error analysis plots and CDF plots are constructed. As shown in Figure 9. Their specific data are shown in Tables 1 and 2.  
Tightly coupled Kalman filtering improves localization performance by (0.133644 \(-\) 0.138227)/0.138227 = 0.0332 compared with loosely coupled Kalman filtering.  
Based on the experimental results, it can be observed that the tightly coupled system exhibits a noticeable improvement in fusion navigation effectiveness after utilizing raw observation information. Combining the network architectures of loosely coupled and tightly coupled systems, their advantages and disadvantages can be summarized as follows:  
The main advantage of the loosely coupled approach lies in its simple and easy-to-implement structure. In a loosely coupled system, each sensor (such as IMU and GNSS) works independently and outputs navigation information, which is then used to correct errors in the Kalman filter by taking the difference of this information as input. This approach preserves the original GNSS results without requiring modifications to GNSS hardware. However, the drawback of the loosely coupled approach is that when the number of visible satellites in the environment is small, GNSS navigation information cannot be obtained, leading to a decrease in the performance of the Kalman filter and thus affecting the positioning accuracy of the entire system.  
In contrast, the tightly coupled approach is relatively more complex than the loosely coupled approach. It requires the processing of raw GNSS data (such as pseudorange and pseudorange rate) and comparing them with the corresponding data outputted by INS, with the difference being used as input for the Kalman filter. The main advantage of this approach is that it addresses the problems associated with observation input in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Value** & **Variance** & **Standard Deviation** \\ \hline Statistics of 2D error & 0.138227 & 0.004441 & 0.066644 \\ \hline The statistics of the \(Z\)-direction error & 0.017386 & 0.018335 & 0.135407 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of 2D and Z-direction errors for loosely coupled closed-loop systems.  
Figure 8: Experimental results of the loosely coupled closed-loop system and tightly coupled system.  
a combined system with only one Kalman filter. However, the drawback of the tightly coupled approach lies in its relatively complex implementation structure and inability to independently output GNSS navigation results.
# Three-Dimensional Wide-Band Electromagnetic Forward Modelling Using Potential Technique
###### Abstract  
The efficacy of Krylov subspace solvers is strongly dependent on the preconditioner applied to solve the large sparse linear systems of equation for electromagnetic problems. In this study, we present a three-dimensional (3-D) plane wave electromagnetic forward simulation over a broadband frequency range. The [PERSON]'s equation is solved in a secondary formulation of the Lorentz gauge coupled-potential technique. A finite-volume scheme is employed for discretizing the system of equations on a structured rectilinear mesh. We employed a block incomplete lower-upper factorization (ILU) preconditioner that is suitable for our potential formulation to enhance the computing time and convergence of the systems of equation by comparing with other preconditioners. Furthermore, we observe their effect on the iterative solvers such as the quasi-minimum residual and bi-conjugate gradient stabilizer. Several applications were used to validate and test the effectiveness of our method. Our scheme shows good agreement with the analytical solution. Notably, from the marine hydrocarbon and the crustal model, the utilisation of the bi-conjugate gradient stabilizer with block ILU preconditioner is the most appropriate. Thus, our approach can be incorporated to optimize the inversion process.  
finite volume method; numerical simulation; potentials; iterative solver +
Footnote †: journal: Applied Sciences
# A 37-40 GHz 6-Bits Switched-Filter Phase Shifter Using 150 nm GaN HEMT
## 1 Introduction
The all-pass network (APN) type showcases exceptional bandwidth performance in lower-frequency designs. The passive vector sum modulator (PVSPS) offers superior phase resolution. Nevertheless, its intricate structure and high insertion loss, attributed to the utilization of an X-attenuator, introduce significant drawbacks. Switched filter-type PS (STPS) has no limitations in phase shift range, is suitable for high-frequency applications, and features a simple structure. There is a concern regarding chip area because each phase shifting stage includes inductors. Unlike CMOS, compound processes such as GaN have a higher Q factor due to wider spacing between substrates and lower transmission loss. These characteristics facilitate the design and utilization of inductors [23; 24; 25]. GaN processes are also suitable for the implementation of switch-filter type phase shifters due to the simplified structural design methods required due to constraints on available metal layers. In this paper, we present a phase shifter designed for high-power phased array systems operating in the n260 (37-40 GHz) frequency band in line with the 5G NR standard. The designed phase shifter employs a structure and process suitable for high-power systems and consists of a 6-bit STPS (Switched-Type Phase Shifter) configuration. It has been finely tuned to minimize insertion loss and phase error. The phase shifter was fabricated and measured using a 150 nm GaN HEMT process.
###### Abstract  
To enhance the overall communication rate and capacity of mobile networks, a real-time application (RTA)-classification vertical handover (Ac-VHO) scheme is presented for indoor visible light communication (VLC) heterogeneous networks. The scheme considers user mobility, optical link instability, and the variability of real-time applications. It incorporates VLC line-of-sight (LOS) blocking and MT entry into co-channel interference (CCI) areas as triggers, while also providing differentiated delay tolerance intervals and optimized handover dwell time calculations for various RTAs, including video, audio (VoIP), and web/text. A simulation and comparative analysis demonstrate the superior performance of Ac-VHO in terms of signaling cost, and its average rate compared to other methods (hybrid application-aware VHO, dynamic time-dwelling VHO, and static time-dwelling VHO). In addition, it maintains a consistent and relatively high overall quality of user experience (QoE) for different RTA types.  
visible light communication; heterogeneous network; vertical handover; time dwelling Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], R. A Real-Time Application-Classification Vertical Handover Scheme for Coexisting Visible Light Communication and Radio Frequency Indoor Networks on Mobile Terminals  
1,2, 2961. [[https://doi.org/10.3390/electronics12132961](https://doi.org/10.3390/electronics12132961)]([https://doi.org/10.3390/electronics12132961](https://doi.org/10.3390/electronics12132961))  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 5 Results and Discussion
### Analysis of the Combined Fault Model  
#### 5.3.1 Double Faults  
In the comparison of the double fault model, the double fault kNN models for time-frequency domain data analysis all yielded better classification results than the time-domain model. Compared to the single fault kNN model, the accuracy rate maintained a high accuracy rate of over 93.5% as shown in Table 7 The accuracy rate of the double fault kNN model for the time-domain data analysis was around 70-75%, which indicates a reduction in the accuracy rate of nearly 20% as shown in Table 8. It can also be found that the accuracy of the loosening of the motor screw and the propeller fault still had low accuracy, whereas regarding the running time, the double fault kNN model of the time-domain data analysis still had a faster solution speed.
## 3 Results and Discussion
### Normalization Analysis of Stress-Strain Curves
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Soils**} & \multicolumn{6}{c}{**Stress Path**} \\ \cline{3-8}  & & **A\(-\)0** & **B\(-\)1** & **B\(-\)2** & **B\(-\)3** & **C\(-\)1** & **C\(-\)2** & **C\(-\)3** \\ \hline \multirow{3}{*}{_a_ (kPa\({}^{-1}\))} & Silt & 0.0017 & 0.0035 & 0.004 & 0.0043 & 0.0017 & 0.0018 & 0.0018 \\  & Silty sand & 0.0018 & 0.0038 & 0.0039 & 0.004 & 0.0018 & 0.0019 & 0.0019 \\  & Mucky silty clay & 0.0072 & 0.0353 & 0.0359 & 0.0360 & 0.0075 & 0.0077 & 0.0079 \\ \hline \multirow{3}{*}{_b_ (MPa\({}^{-1}\))} & Silt & 0.023 & 0.073 & 0.089 & 0.09 & 0.023 & 0.028 & 0.03 \\  & Silty sand & 0.013 & 0.048 & 0.049 & 0.051 & 0.013 & 0.014 & 0.015 \\  & Mucky silty clay & 0.014 & 0.078 & 0.077 & 0.077 & 0.011 & 0.010 & 0.009 \\ \hline \multirow{3}{*}{_E\({}_{0}\)_ (MPa)} & Silt & 43.48 & 13.70 & 11.24 & 11.11 & 43.48 & 35.71 & 33.33 \\  & Silty sand & 76.92 & 20.83 & 20.41 & 19.61 & 76.92 & 71.43 & 66.67 \\ \cline{1-1}  & Mucky silty clay & 71.43 & 12.82 & 12.99 & 12.99 & 90.91 & 100.00 & 111.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Normalized parameters under different stress paths. Figure 8: Normalized stress–strain curves under different stress paths. (**a**) Groups A and C; (**b**) Group B.  
Figure 9: Normalized initial elastic modulus–unloading intensity relationship under different stress paths.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 4 Autonomous Positioning and Grasping
### SLAM-Based Autonomous Positioning and Navigation System
#### 4.1.4 Back-End Optimization  
The loop closure detection module [36] determines whether a loop closure has occurred in the system by calculating the Euclidean distance between the current frame and previous keyframes. The keyframes that meet the criteria are stored in a subset (i.e., subkeyframes). For each point in the subkeyframes, a corresponding point is searched in the current frame to form a distance constraint. We then use the ICP (iterative closest point) algorithm [38] to compute the optimal rotation and translation matrices that satisfy the constraints. The transformation parameters are solved by constructing an error function using the matched points, aiming to minimize the error, as shown in Equation (6):  
\[f(R,T)=\frac{1}{N}\sum_{i=1}^{N}\left\|q_{i}-(R_{p_{i}}+T)\right\|^{2} \tag{6}\]  
where \(p_{i}\) is a point in the original point cloud \(p_{q}\); \(q_{i}\) is the point in the historical point cloud that has the minimum Euclidean distance with \(p_{i}\); and \(N\) represents the number of points in the subset of keyframes.  
By using the obtained transformation matrices \(R\) and \(T\), we update the point cloud in the subset of keyframes. We then continue to construct the error function until the result is smaller than the set threshold or the iteration reaches the specified limit.
# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 4 Experimental Results and Analysis
### Parameter Impact and Selection  
#### 4.1.1 Impact and Selection of Datasets and Models  
We used the Fashion-MNIST, Cifar10, Cifar100, and Animal10 datasets as benchmarks for the model, and Animal10 and Fish9 [38] as performance tests. Fashion-MNIST is an alternative to MNIST, with 60,000 grayscale images of 28 \(\times\) 28 resolution, and is widely used to benchmark DL models; Cifar10 is a pervasive object recognition dataset with the same number of 32 \(\times\) 32 RGB color images. Cifar100 is a 100-class dataset for pervasive object recognition. Animal10 has 26,000 images of 10 species of animals, with a size of approximately 300 \(\times\) 200; its labels are reviewed manually, and some wrong labels are included to simulate real situations. Fish9 has 9000 images of nine fish species, of size 2832 \(\times\) 2128. Animal10 and Fish9 images both have size 224 \(\times\) 224 in this work, to facilitate model training and testing. Table 1 displays information on the processed image dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Image Size (Input)** & **Number** \\ \hline Fashion-MNIST & 28 \(\times\) 28 & 60,000 \\ Cifar10 & 32 \(\times\) 32 & 60,000 \\ Cifar100 & 32 \(\times\) 32 & 60,000 \\ Animal10 & 224 \(\times\) 224 & 26,000 \\ Fish9 & 224 \(\times\) 224 & 9000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image dataset parameters.  
We conducted classification comparison experiments on image datasets using four models: CNN + SVM, CASVM, CNN + softmax, and CNN + CA + softmax. All models were trained 100 times, and the training sample size was N/128.  
Since there are two sizes of the dataset used in this paper, 32 \(\times\) 32 and 224 \(\times\) 224, two CNN structures were improved for the small-size dataset. In the improved LeNet-based model CNN1, the number of convolutional kernels is increased, the original 5 \(\times\) 5 kernels are changed to 3 \(\times\) 3, dropout is added, and CA is embedded in the final convolutional layer. Based on AlexNet, the improved model CNN2 reduces the number of convolutional kernels, embeds CA in the last convolutional layer, and reduces the parameters of the fully connected layer. CNN structures used for our experiments include AlexNet, ResNet18, ResNet152, and VGG19. Table 2 shows the number of parameters (millions float), amount of computation (billions float), and model size (MB) of each CNN model.
# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
###### Abstract  
We introduce a novel multipath data transport approach at the transport layer referred to as _'Deep Deterministic Policy Gradient for Multipath Performance-oriented Congestion Control'_ (DDPG-MPCC), which leverages deep reinforcement learning to enhance congestion management in multipath networks. Our method combines DDPG with online convex optimization to optimize fairness and performance in simultaneously challenging multipath internet congestion control scenarios. Through experiments by developing kernel implementation, we show how DDPG-MPCC performs compared to the state-of-the-art solutions.  
multipath TCP; congestion control; experience-driven approach; performance-oriented congestion control; deep learning +
Footnote †: journal:  
0000-0001-4507-227][PERSON] 1,*
## 4 The Proposed STCA Protocol
### STCA with Variable Frame Length  
The present section describes the flow of STCA with the variable frame length algorithm, and Figure 7 shows the flowchart of the system.  
During initial system startup, the satellite equally allocates sub-channel quantities to each aerial access control node. The aerial node receiving this channel resource allocation will broadcast the service transmission channel indices, contention access channel indices, and the current frame's accessible micro-timeslot count \(K_{i}\) to its controlled piece of SUE. Upon receiving these indices, the piece of SUE randomly generates multiple copies, selects multiple timeslots from the access frame, and transmits these copies to the aerial access node.  
The aerial control node then performs Successive Interference Cancellation, calculates the number of potentially accessible pieces of SUE for the next frame based on the current copy probability distribution, adjusts the access frame length, and feeds back the access results along with the next frame's accessible micro-timeslot count to the piece of SUE. SUE that successfully gained access transmits data to the satellite via service transmission channels according to the feedback, while failed SUE awaits the next frame of the contention access channel to reattempt access.  
The ACN is configured with \(K_{i}\) micro-timeslots in the \(i\)-th frame. For SUE, there are \(M_{i}\) pieces of SUE with data packets to transmit in the \(i\)-th frame. For a single micro-timeslot, the probability that only one piece of SUE accesses the slot is expressed as  
\[P_{\text{idle}}=M_{i}\left[\sum_{l=1}^{N_{C}}\left(r_{l}\frac{(K_{i}-1)}{(K_{i} ^{\prime})}\right)\right]^{M_{i}}. \tag{18}\]  
The estimated number of successfully accessed minislots for the aerial access control node in the \(i\)-th frame can be obtained by multiplying the above result by \(K_{i}\), as follows:  
\[n_{\text{idle}}=K_{i}P_{\text{idle}}=f(M_{i}). \tag{19}\]  
To compute \(M_{i}\), we need to use the inverse function of the above equation, which is given by  
\[M_{i}=f^{-1}(n_{\text{idle}}). \tag{20}\]  
An ACN can determine the status of each minislot at the end of every frame, thereby enabling it to estimate the number of pieces of SUE that will have data packets to transmit in the next frame:  
\[M_{i+1}=(M_{i}-M_{\text{succ}})+(M-M_{i}+M_{\text{succ}})\Big{(}1-e^{-K_{i}T_ {C}M_{i}}\Big{)}. \tag{21}\]  
Figure 7: System flowchart of STCA with variable frame length.  
The \(\lambda_{i}\) values are all estimated from the previous frame and then used in the current frame's calculations. The \(\lambda_{i}\) for the current frame can be estimated iteratively using the following formula:  
\[\lambda_{i}=-\alpha\frac{\ln\Bigl{(}\frac{M_{i}-M}{M_{i-1}-M-M_{\text{Max}}^{ \text{tr}}}\Bigr{)}}{K_{i}T_{C}}+(1-\alpha)\lambda_{i-1}. \tag{22}\]