esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation Results and Analysis
### The Influence of the Ratio of the Inner and Outer Radius on Frequencies  
Figure 2 studies the change law of the first three order dimensionless natural frequencies of the nano annular plates under the influence of the ratio of the inner and outer radius. \(\tau=0.05\), \(\overline{\Omega}\)= 5, \(\Delta T=10\) K. It can be seen from the figure that the dimensionless natural frequencies gradually increase with the increase in the ratio of inner and outer radius, and the larger the ratio of the inner and outer radius, the greater the influence on the dimensionless natural frequencies. The boundary conditions play an important role in the natural frequencies of the nano annular plates. Compared with the C-C boundary condition, the equivalent stiffness of the nano-annular plates under the C-F boundary condition is weakened, so the natural frequencies decrease. Comparing the data in the figures, it can be found that the differences in the data results under the three plate theories are small.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Material** & \(P_{-1}\) & \(P_{0}\) & \(P_{1}\) & \(P_{2}\) & \(P_{3}\) \\ \hline \(E_{c}/\text{Pa}\) & \(\text{Si}_{3}\text{N}_{4}\) & 0 & \(348.43\times 10^{9}\) & \(-3.070\times 10^{-4}\) & \(2.160\times 10^{-7}\) & \(-8.946\times 10^{-11}\) \\ \(E_{m}/\text{Pa}\) & \(\text{SUS}304\) & 0 & \(201.04\times 10^{9}\) & \(3.079\times 10^{-4}\) & \(-6.534\times 10^{-7}\) & 0 \\ \(\alpha_{c}/\text{K}^{-1}\) & \(\text{Si}_{3}\text{N}_{4}\) & 0 & \(5.8723\times 10^{-6}\) & \(9.095\times 10^{-4}\) & 0 & 0 \\ \(\alpha_{m}/\text{K}^{-1}\) & \(\text{SUS}304\) & 0 & \(12.330\times 10^{-6}\) & \(8.086\times 10^{-4}\) & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 2: Material coefficients of ceramics and metals.  
Figure 2: _Cont._
# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 2 Motivation
### Preliminary Results  
Our research in [21] focused on personalizing ML implementations on the VFC platform. Using general guidelines for selecting ML classification algorithms based on the data structure and problem characteristics, eight different algorithms were tested, AdaBoost (ADA), Decision Trees (DTC), k-Neighbors (KNN), Logistic Regression (LR), Neural Networking (NN), Stochastic Gradient Descent (SGD), Random Forest (RF), and Support Vector Classifiers (SVCs). This selection includes algorithms that have the potential to perform well on the given problem but also provides different algorithms for classification. The results show that the performance evaluation of the selected algorithms, based on the complete set of participants, does not give the same results as the performance evaluation based on a single participant. First, the performance metrics for personalized models are significantly better than those for general models, and secondly, for personalized models, the highest accuracy is obtained by the DT and RF models, whereas for general models, the ADA, NN, SGD, and SVC models show better results.  
To illustrate these findings, some of the results presented in this article are reproduced in Figure 1. In this graph, the accuracy and F1-scores in the test sets are compared for the models generated using eight different classifiers. The first bars represent the accuracy and F1-score results for the models generated using data from all participants. The second bars are the results of the personalized models for each participant. In this case, for each participant, eight different models were fitted using a cross-validated grid search with the selected classifiers, resulting in eight models per participant. In all cases, a 30-70% split was used for the test and training sets. The precision and F1-score reflect the model's capacity to predict the probability that a participant will achieve the step goal at the end of the day based on the hour of the day and the activity recorded up to that hour for the participant.  
Due to the stochastic nature of some algorithms used, the results presented here differ slightly from those in the original paper. Furthermore, a more recent version of Scikit-Learn had to be used, which resulted in improved results for the k-Neighbors, Neural Networking, and Stochastic Gradient Descent classifiers. These differences do not alter the interpretation of the results, namely that not only does personalized modeling yield higher performance scores, but we also observe that the ranking of classifiers is significantly different. The results discussed in [21] also show that the optimal choice of the ML algorithm and the settings of the corresponding parameters differ from individual to individual, underlining the fact that a personalized approach should be considered when using data from several individuals, obtained with the use of wearables.
## 3 The Proposed Fault Diagnosis Methodology
### Alarm Data Preprocessing
#### 3.1.3 Building Dataset  
Some alarm sequences have their fault events recorded in maintenance records, while others do not. In this part, we will match the alarm sequences with their maintenance records and build the labeled alarm sequence dataset. The alarm sequences without fault events will constitute the unlabeled alarm sequence dataset.  
We use the end time of an alarm sequence and the start time of a maintenance record to match the alarm sequence and its maintenance record. The schematic diagram of the match criterion is shown in Figure 3. The end time of the \(i\)-th alarm sequence is expressed as \(t_{i}^{end}\), and the start time of the \(j\)-th subsequent maintenance record is expressed as \(t_{j}^{start}\)  
Figure 2: The flow chart of the proposed fault diagnosis methodology.  
\(t_{i}^{end}\) should be earlier than \(t_{j}^{start}\). The alarm sequence corresponding to the maintenance activity is the last one. Thus, the alarm sequences that matched with maintenance records constitute the labeled alarm sequence dataset, which is expressed as Dataset A. The other alarm sequences constitute the unlabeled alarm sequence dataset, expressed as Dataset B.
## 5 Hybrid Localization Algorithms
### Distributed Localization Algorithms
#### 5.2.4 Localization scheme with Directional Beacons (LDB)  
UDB is a two-dimensional localization. [PERSON] _et al._ extended UDB into Sparse 3D Underwater environment and proposed a new Localization scheme with Directional Beacons (LDB) [72]. The depth of a node can be directly measured by a cheap pressure sensor, LDB is designed to determine its 2D position at the fixed depth. When AUV patrols at fixed depth of water following a straight line and sends beacons continuously, node \(S\) in its communication range can receive a series of beacons, as shown in Figure 13(a). Unknown node S hears the beacons when AUV sends them at \(T_{1},\,T_{2},\,T_{3},\,T_{4},\,T_{5}\) and \(T_{6}\), which are defined as mobile anchor points. \(T_{1}\) and \(T_{6}\) are the first-heard and the last-heard beacon points. The unknown node does not need to store all those six beacons. Only the first-heard and the last-heard beacon points are needed to localize node \(S\). The position of \(S\) is thus calculated as:  
\[x=\frac{x_{1}+x_{6}}{2},y=y_{1}+\sqrt{R_{a}^{2}-\left(\frac{x_{1}-x_{6}}{2} \right)^{2}} \tag{7}\]  
However, in practice, AUV sends beacons with time intervals. Nodes in the same small area will share the same first-heard beacon point and last-heard beacon point. As shown in Figure 13(b), four circles centered at point \(T_{1},T_{2},T_{8}\),\(T_{9}\) form a small area. If there are more than one unknown nodes residing in it, the unknown nodes share the same first-heard beacon point \(T_{2}\) and last-heard beacon point \(T_{8}\) with node S. Thus, using Equation (9) to localize unknown nodes incurs large localization error. Then, LDB uses the small area's centroid to compute the positions of unknown nodes and estimate the upper bound of localization error.
## 2 Materials and Methods
### Electrochemical Measurements  
Cyclic voltammetry (CV) at typical scan rates (5, 10, 20, 50 and 100 mV/s), galvanostatic charge/discharge (GCD) at representative current densities (1, 2, 5, 10 and 20 A/g) and electrochemical impedance spectroscopy (EIS) in wide frequency range (100 mHz-100 kHz) were conducted on an electrochemical workstation (Zahner IM6). The configuration, potential window, electrolyte, counter electrode and reference electrode were set as three-electrode system, 0-0.65 V, 2 M KOH, Pt net and Ag/AgCl (3.5 M KCl), respectively.  
The specific capacitance of the active material is calculated based on a GCD test according to Equation (1) [29]:  
\[C= \frac{I\Delta t}{m\Delta V}, \tag{1}\]  
where: \(C\) (F/g) is the specific capacitance; \(I\) (A) is the discharge current; \(\Delta t\) (s) is the discharge time; \(\Delta V\) (V) is the voltage window; and \(m\) (g) is the mass of active material.
# Infrared Fault Classification Based on the Siamese Network
## 3 Methodology  
The Siamese network (SN) is a supervised small-sample learning algorithm [23]. Compared to traditional deep learning algorithms, SN can not only learn the features of samples but also learn the differences between similarities and differences between similar and nonsimilar samples [24]. Therefore, with a small number of samples, SN can obtain strong generalization ability and reduce overfitting. Therefore, SN is suitable for the field of solar power diagnosis problems.  
A typical Siamese network is shown in Figure 1. Two sample images form sample pairs as input to the Siamese network. When the two inputs are of the same class, their labels are 1; when the two inputs are of different classes, their labels are 0. The two samples are, respectively, assigned to the two main CNN models of the same structure, and their feature vectors are mapped. The weights of the two core CNN networks are shared. Then, the Euclidean distance between these two feature vectors is calculated, and the similarity between the two samples is obtained through the fully connected layer and sigmoid function, leading to classification results.  
Figure 1: Architecture of a typical Siamese network.  
In the classification of solar panel infrared images, considering the small dataset and high similarity, to prevent overfitting, obtains good classification results. Firstly, input \(C~{}\times~{}H~{}\times~{}W\) = 3 \(\times\) 24 \(\times\) 40 solar power images into the CNN, and output a 64 \(\times\) 3 \(\times\) 5 feature map. The CNN is composed of 3 \(\times\) 3 convolution, batch normalization (BN), and ReLU, followed by 3 \(\times\) 3 convolution, BN, ReLU, and Max-pooling. Then, the output is divided into two parts: the adaptive attention module and the feature fusion module, and a 128 \(\times\) 2 \(\times\) 3 feature map is obtained. The adaptive coordinate attention module through the separation and selection mechanism achieves the dynamic adjustment of the module experience field size of the input feature map, enhancing the multi-scale feature extraction ability. The feature fusion module can improve the performance of the network model from both the depth and width aspects, and improve the accuracy of model classification. The overall structure of the proposed ACA-FFM Siamese network is shown in Figure 2.
## 4 Methodology
### Candidate Points Extraction Based on Image Semantic Information
#### 4.1.1 Dynamic Objects Segmentation Based on CNNs  
We use CNNs to segment the input image and obtain the pixel-wise semantic information. In this paper, we choose Mask R-CNN [25], which is the state of the art in the field of image instance segmentation. It can obtain high quality pixel-wise semantic information and the instance label of each segmentation region. In the process of candidate points extraction, we only use the pixel-wise semantic information. In future work, the instance labels may be used for tracking different dynamic objects in the image. We adapt the TensorFlow version implemented by Matterport [28].  
According to the prior experience of human life, we score the possible dynamic degree of an object in the real scene from 0 point (static) to 10 point (dynamic), and set a threshold artificially. When the score exceeds the threshold, meaning that it is a dynamic object, otherwise we consider it as a static object. The idea is to segment objects that may be dynamic or movable (e.g., people, bicycles, cars, motorcycles, airplanes, buses, trains, trucks, boats, birds, cats, dogs, horses, sheep, cattle). We consider that for most scenes, dynamic objects that may appear are included in the above list. Figure 2 shows the approximate score of common objects.  
The input of Mask R-CNN is an RGB image with \(m\times n\times 3\) dimension, and the output of the network is a matrix with \(m\times n\times l\) dimension, where \(l\) represents the number of objects to be detected in the image. For each output channel, we will get a binary mask. By synthesizing the output of all channels into one, the pixel-wise semantic results containing all the dynamic objects in the input image are obtained. Figure 3 shows the segmentation results for indoor pedestrians.
## 1 Introduction
### Contribution  
The current study proposes a unique approach for conducting security analysis on edge devices, which involves the retrieval of logs from Android devices. The aim was to minimise the impact on system resources and battery consumption by transferring the computational workload off the device and executing the analysis at the edge.  
Chidroid is a software package that provides a comprehensive solution for log retrieval and data distribution from Android devices. This package leverages a protected policy-driven decomposition technique to enable the generation and collection of logs and thus contribute to the development of detection and analysis tools. This study proposes a modular toolchain, with Chidroid as the central component for generating, collecting, parsing, and distributing datasets. The proposed approach was designed to be reusable and scalable and can be extended to address additional classes of vulnerabilities. Furthermore, Chidroid opens up several possibilities for further applications, such as permission monitoring. The contribution of this research paper and the characteristics of Chidroid are summarised as follows:  
* Chidroid is an Android log retrieval and data distribution process that enables the generation and collection of logs from Android devices.
* The evaluation process was conducted in the healthcare domain and interconnected devices such as the IoT and health wearables.
* Chidroid enables the creation of datasets, allowing non-structured data to be parsed into semi-structured or structured data that can be used for machine learning and deep learning. The proposed approach can be used as a universal policy-based tool to identify and analyse security issues in all recent versions of Android ([[https://www.appbrain.com/stats/top-android-sdk-versions](https://www.appbrain.com/stats/top-android-sdk-versions)]([https://www.appbrain.com/stats/top-android-sdk-versions](https://www.appbrain.com/stats/top-android-sdk-versions)), accessed 20 February 2023), approximately 95% of all active Android devices.
* A modular toolchain is proposed, with Chidroid as the main component to generate, collect, and distribute datasets. The approach is reusable, can be extended to detect additional classes of vulnerabilities, and can be applied to permission monitoring.
* Chidroid can retrieve the set of priorities for the applications and can also set the time duration and privileges used by the software packages. The detection rules can report on permissions if an application misbehaves due to the usage or to maintain excessive system resources.  
Finally, the research paper provides practical examples of log retrieval from Android devices and details the ways in which these logs can be utilised to extract meaningful insights and behaviour patterns in device usage. A comprehensive examination of the feasibility and potential of this approach is also presented.
# An Efficient Method for Document Correction Based on Checkerboard Calibration Pattern
###### Abstract  
Portable digital devices such as PDAs and camera phones are the easiest and most widely used methods to preserve and collect information. Capturing a document image using this method always has warping issues, especially when capturing pages from a book and rolled-up documents. In this article, we propose an effective method to correct the warping of the captured document image. The proposed method uses a checkerboard calibration pattern to calculate the world and image points. A radial distortion algorithm is used to handle the warping problem based on the computed image and world points. The proposed method obtained an error rate of 3% using a document de-warping dataset (CBDAR 2007). The proposed method achieved a high level of quality compared with other previous methods. Our method fixes the problem of warping in document images acquired with different levels of complexity, such as poor lighting, low quality, and different layouts.  
de-warping document image; document correction; camera calibration; checkboard calibration pattern; radial distortion +
Footnote †: journal: Applied Sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-000  
In this study, we look at how to fix the warping problem in camera-captured document images. Optical character recognition (OCR) utilizes the image of the acquired document to accomplish tasks like text extraction and character recognition. The de-warping technique is responsible for the majority of OCR systems' accuracy [3].  
In this research, we present a document image de-warping approach based on a checkerboard calibration pattern. To compute world and image points, the first step is to apply well-defined checkerboard patterns. The world and picture points obtained in the first step are used in the second step to determine camera calibration parameters. Finally, the document image is de-warped using the derived camera-calibration parameters. The main contributions of this paper are as follows:  
* We propose a novel document-image de-warping method based on a checkerboard calibration pattern.
* Well-defined checkerboard patterns are employed to compute world and image points.
* Calculated camera calibration parameters are employed to de-warp the document image.  
The remainder of this work is arranged in the following manner. The state-of-the-art algorithms are discussed in Section 2. The proposed algorithm is discussed in Section 3. Section 4 analyzes and discusses the results of the proposed method. Finally, in Section 5, a conclusion is derived.
# Controller-Pilot Data Link Communication Security
## 6 Conclusions  
The importance of improving CPDLCs security and privacy stems from the need to create a secondary communication channel, trustworthy enough to alleviate an already congested communication VHF voice communication and enable ATC and its airspace capabilities for continued growth. Without an acceptable level of trustworthiness, CPDLC will work against its intended purpose by adding a greater workload in the already stressed environment of the air traffic control managing system.  
We investigated CPDLC communication functions and its level of security and privacy in order to identify the risks and possible security threats. We have concluded that CPDLC communications lacksthe appropriate level of cybersecurity. Improvements within a system are constrained by the aging structure of the foundation of the system, designed using legacy protocols. Implementation of any encryption methods therefore needs to have as small impact on the system's performance as possible while still providing an all-round security protection.  
A commercial solution, called Protected ACARS, claims to be able to solve the system's lack of security using the ECC method. We argued towards a solution based on open IETF and IEEE standards. IETF protocols downpan, COAP, and Diet HIP can be adopted for the design of the future aviation communication system. IEEE 802.15.9 best current practice also offers a set of Key Management Protocols for lightweight security in IPless CPDLC. We proposed utilizing the current flight plan and AIP information systems to provide a root of trust for authenticating CPDLC encryption.  
Identity-defined networking was proposed as a generic solution to be applied to the air traffic communication system as a whole, including CPLDC and all the other air-to-ground and ground communication means utilized by air traffic control units. It can be incrementally deployed without the need to change the existing hardware.  
In future studies, we plan to discuss the pros and cons of the suggested solutions with security experts within aviation, and proceed with testing and prototyping of the most promising ones to adjust them to the current needs of the Swedish and global aviation infrastructure.  
Conceptualization, A.G. and T.P.; Methodology, T.P.; Validation, A.G.; Formal Analysis, M.W.; Investigation, M.W.; Writing--Original Draft Preparation, M.W.; Writing--Review & Editing, T.P.; Supervision, T.P and A.G.; Project Administration, T.P.; Funding Acquisition, T.P. and A.G.  
This research is part of the CybSec project supported by the Swedish Transport Administration (Traifkverket) and in-kind participation of the Swedish ANSP Luftfartsverket. [PERSON] was supported by the Center for Industrial Informatics (CENIIT).  
We also thank our consultants from the Swedish ANSP Luftfartsverket, [PERSON] and [PERSON], for their help with the assessment of the results of the studies and sharing their opinion on the most promising directions for future work.  
The authors declare no conflicts of interest.