esa-sceva/satcom-chunk-collection huggingface dataset:
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 4 Analysis of Influencing Factors of Cable Damage Identification
### Influence of Bending Stiffness of Beam Body  
#### 4.3.1 Location Identification  
With the extension of the service time, the bending stiffness (EI) of the cable-stayed bridge may degrade as a whole. The stiffness' degradation is simulated by the change in the elastic modulus of the beam (the moment of inertia of the section remains unchanged). Assume that the stiffness of the main beam decreases by 40%. Damage conditions are preset for verification, as shown in Table 1 (double cable). The identification results are shown in Figure 17.  
According to Figure 17, in the case of double-cable damage, the damage cables are M16 and M7. It can be seen that the stiffness' degradation does not affect the accuracy of damage cable location identification. However, the wavelet coefficient corresponding to the position of the damage cable increases compared with before, which is caused by the increase in the deflection difference of the beam body after the stiffness degradation of the main beam.
# A Dynamic Model Updating Method with Thermal Effects Based on Improved Support Vector Regression
## 5 The Numerical Examples
### The Thermophysical Parameters Identification of Wing Structure
#### 5.3.2 Structural Dynamic Modal Updating  
A structural dynamic model with temperature dependence is presented when the identified temperature distribution is loaded. Assuming the elastic modulus is the only source of model error, the influences of other parameters are not considered. The range of the identified elastic modulus with temperature dependence is shown in Figure 9.  
Due to the large size of the model and the complexity of the structure, it would be time-consuming to use a direct finite element model for modal frequency analysis considering pre-stress, so a surrogate model is used instead of a finite element model for calculations, the number of sample points is 2500.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Node Number**} & **Real Temperature** & **Initial** & \multirow{2}{*}{**Deviation**} & **Identified** & \multirow{2}{*}{**Deviation**} \\  & **(\({}^{\circ}\)C)** & **Temperature (\({}^{\circ}\)C)** & & **Temperature (\({}^{\circ}\)C)** & \\ \hline
118 & 52.15 & 48.42 & 7.15\% & 52.17 & 0.038\% \\
565 & 77.64 & 71.18 & 8.32\% & 77.71 & 0.090\% \\
1108 & 117.35 & 107.06 & 8.77\% & 117.63 & 0.239\% \\
1928 & 142.49 & 130.25 & 8.59\% & 142.59 & 0.070\% \\
2542 & 168.74 & 155.50 & 7.85\% & 168.78 & 0.024\% \\
3006 & 201.83 & 188.99 & 6.36\% & 201.76 & 0.035\% \\
3633 & 223.14 & 211.60 & 5.17\% & 223.03 & 0.049\% \\
4108 & 254.30 & 246.09 & 3.23\% & 254.18 & 0.047\% \\
4522 & 280.70 & 276.75 & 1.41\% & 280.64 & 0.021\% \\
4605 & 252.63 & 244.20 & 3.34\% & 252.51 & 0.048\% \\
4624 & 160.11 & 147.07 & 8.14\% & 160.17 & 0.037\% \\
4715 & 133.59 & 121.94 & 8.72\% & 133.74 & 0.112\% \\
4722 & 192.10 & 178.96 & 6.84\% & 192.07 & 0.016\% \\
4740 & 270.89 & 265.20 & 2.10\% & 270.80 & 0.033\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: The comparison of real temperature, initial temperature and identified temperature.  
Figure 9: Mode shapes of the first four orders.  
The natural frequency calculated with the given elastic modulus is taken as the experimentally measured datum. The comparison of the first four orders of natural frequencies about the real natural frequency, initial natural frequency and updated natural frequency is shown in Table 11, and the first four orders of mode shapes are shown in Figure 9.  
In Table 12 and Figure 9, it is obvious that the deviation between the real natural frequency and initial natural frequency of the first three bending modes and the first tensional mode is \(6-8\%\), and the deviation between the real natural frequency and updated natural frequency is only \(1\%\). This illustrates that a thermal structural dynamic model with high precision can be obtained by thermophysical parameter identification and the dynamic modal updating method proposed in this paper.
# A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis
## 2 Materials and Methods
### Datasets  
In this study, four leukemia datasets were used to train, validate, test, and evaluate the proposed model. These four datasets were downloaded from the Kaggle websites [26; 27; 28; 29]. Each dataset has its number of images, types of leukemia, and the size of all images. The first dataset was downloaded from [26] and contains 3256 images from 89 patients. All images are of ALL. The size of this set is 116 MB. The second dataset was downloaded from [27] with 6512 images and a size of approximately 210 MB. This set contains ALL only. The third dataset was downloaded from [28] with a size of 909 MB and it includes 15,135 images of ALL. These images were collected from 118 patients. The last dataset was obtained from [29] and it contains 3242 images from 89 patients. The size of this set is 2 GB. These four datasets were divided into three groups: training, validation, and testing. The training group represents 70%, while the validation and testing groups represent 30%. Table 2 offers a description of the utilized datasets. Table 3 shows the number of assigned images from each dataset to training, validation, and testing groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Publication Year** & **Deployed Technology** & **Advantages** & **Limitations** \\ \hline
[1] & 2023 & GNA, GSA, and PCA & Categorization of all four subtypes of leukemia & Using one small dataset and time-consuming due to the utilized number of operations \\ \hline
[3] & 2023 & Histogram threshold segmentation classifier & Using color and brightness variations to detect leukemia & Requires multiple classifiers to reach high accuracy and classify two subtypes only \\ \hline
[4] & 2022 & CNN and hibernation of two CNN blocks & The number of images in the utilized dataset was sufficient & The achieved accuracy was less than 90\% and discovered two subtypes only \\ \hline
[6] & 2022 & VGG-16 CNN & Reached nearly 98\% accuracy & Classified two subtypes and the number of utilized images was insufficient \\ \hline
[8] & 2022 & Region-based CNN & Achieved 97.3\% accuracy and utilized a sufficient number of images & The evaluated performance quantities reached results between 93\% and 97\% \\ \hline
[9] & 2022 & CNN & Achieved 100\% accuracy & Utilizing a single dataset and categorizing a single type: ALL \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of some developed methods.
## 2 Materials and Methods
### Dosimetric Characterization
#### 2.2.6 Signal Stability  
A group of samples was identified, annealed, encapsulated and labeled. The samples were irradiated to a test dose of 10 Gy under standard irradiation conditions using a Cs-137 gamma cell. Signal stability was estimated under two different storage conditions, i.e., a light-tight environment and visible light environment at room temperature. Thermal fadingwas estimated by storing the samples in a light-tight environment at room temperature. Optical fading was estimated under two different storage conditions, i.e., under direct sunlight and under fluorescent light. Thermoluminescence measurements were obtained using an optimal heating rate.
# A Privacy-Preserving Routing Protocol Using Mix Networks in Opportunistic Networks
## 3 Anonymous Routing Using a Mix Network  
Our proposal is based on the use of a mixnet in OppNets. The main idea is to be able to use a relatively simple mixnet schema without requiring complex cryptographic mechanisms. This approach is not only is feasible in OppNet scenarios, but can also set the basis for interesting solutions to anonymous communications in dynamic networks.
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
## 4 Numerical Simulation
### All of the Satellites Are Scheduled to Serve for a Single Target (ASST)  
The availability of the ISL varies with the altitude and can be determined based on the statistics of each grid. The availability of an arbitrary grid point is the average of the visible satellites throughout the simulation time. The best location is the maximum availability of all the grids at an altitude, the worst location is the minimum availability at an altitude, and the global average location is the average availability at an altitude.  
The availability of ASST is shown in Figure 4. Due to the impact of the Earth obstructing angle, the number of visible satellites increases at first and then decreases with altitude. The maximum number of visible satellites is 24 at an altitude of approximately 16,000 km, and the differences between the best location, the worst location, and the average location are rarely within 30,000 km, which reveals that the coverage of the ISL signals is uniform. Moreover, any location within 72,000 km reaches four-fold coverage. In other words, the navigation constellation SSV is significantly expanded by the ISLs, and this can effectively complement the inadequate coverage for the HEO/GEO SSV using the navigation signal. A prediction can be made based on the trends shown in the curves that the availability above 72,000 km will continue to drop resulting in the received power being too weak to acquire, although this is not the topic of this paper. Note that the ISL SSV is larger than the navigational SSV because of its wide scanning angle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Comment** \\ \hline Grid Points & 1800 & \(6^{\circ}\times 6^{\circ}\) grids \\ Duration & 48 h & Length of simulation time \\ Step size & 1 min & Computed every minute \\ Scanning angle & \(\pm 55^{\circ}\) & Maximum angle of the antenna \\ Beamwidth & \(6^{\circ}\) & Width of the spot beam \\ Altitude & (2000–72,000) km & Within twice the geosynchronous orbit \\ BeiDou-MEO & 24 & \(24/3/1\) walker constellation \\ Visible constraint & Geometry & Line of sight except Earth obstructing \\ Start epoch & March 2016 & Simulation epoch is arbitrary \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the numerical simulation.  
Figure 4: Availability as a function of altitude.
# Unsteady Aerodynamic Design of a Flapping Wing Combined with a Bionic Wavy Leading Edge
## 4 Numerical Results
### Sensitivity Analysis of Design Parameters  
In order to further analyze the influence of design parameters of wavy leading edge flapping wing on its aerodynamic performance, the sensitivities of time averaged lift coefficient and thrust coefficient to wavelength and amplitude are calculated, respectively, as shown in Table 7. At the same amplitude, the larger the wavelength of the wavy leading edge flapping wing, the smaller the time averaged lift coefficient and the larger the time averaged thrust coefficient. At the same wavelength, the larger the amplitude, the larger the time averaged lift coefficient and the smaller the time averaged thrust coefficient.  
To obtain the maximum lift coefficient while losing the least thrust, the minimum wavelength and the smallest amplitude should be selected. That is, in the configuration calculated in this paper, the configuration with wavelength \(\overline{W}~{}=~{}0.01\) and amplitude \(\overline{A}~{}=~{}0.05\) is optimal. Compared with the straight wing, the time averaged lift coefficient is increased by 32.86%, and the time averaged thrust coefficient is reduced by 14.28%.
# Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
## 2 Proposed Method
### Training and Classification  
The structure of CNN is represented in Figure 5. The proposed CNN had six convolutional layers and two fully connected (FC) layers. Each convolutional layer consisted of a one-dimensional (1-D) convolutional layer, a batch-normalization layer, and a rectified linear unit (ReLU). All convolutional layers were connected to each other using a max pooling layer with a down-sampling factor of 2. The input size of each convolutional layer was half of the input size of the previous convolutional layer, except for the first layer. The FC layers and softmax role classification were the last layers [21]. For the training process, multiclass categorical cross-entropy was used as the loss function, and the Adam optimization algorithm was used for backpropagation [22].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Bearing Characteristic Frequency** & **Sideband (\(f_{side}\))** \\ \hline BPFO & No sideband \\ BPFI & Shaft speed \\ BSF & Fundamental train frequency 1  \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specification of the target bearing, FAG NJ206-E-TVP2.  
Figure 4: The extraction process of bearing characteristic components.  
Figure 5: Convolutional neural network (CNN) structure for bearing fault diagnosis.
## 4 Discussion
[51] which investigated the reliability of clinical examinations such as mouth opening and deflection during mouth opening to detect TMJ arthritis. The study concluded that a reduced mouth opening capacity might not always be a very reliable criterion for diagnosing TMJ arthritis. However, the authors stated further studies are needed to evaluate if this parameter could be considered as a reliable criterion for monitoring the pathology evolution in the same subject [51]. For the maximum opening deviation parameter, statistical significance was found, in accordance with what was stated in the literature by [PERSON] et al. [51]. Mouth opening limitations and deflections may be related to dysfunctions of the TMJ. In addition, another study by [PERSON] et al. [52] confirmed that chin deviation, TMJ crepitus and reduction in mouth opening are associated with a high risk of TMJ involvement and are more frequent when the damage of the TMJ is more severe. The authors suggested combining pain-related variables with function-related variables to achieve a higher diagnostic performance in diagnosing a TMJ involvement in JIA patients [52; 53]. Since a statistically significant difference between the kinesigraphic parameters of the two groups under investigation was found, they might help clinicians to find small and early joint abnormalities due to their sensibility which greatly surpasses that of clinical methods [54; 55]. For instance, the prevalence of jaw deviation towards the right side observed in the JIA group might explain why the left temporal muscle is not statistically significant in muscle palpation analysis. Jaw deviation is directed to the side who showed more condylar erosion; thus, it can be supposed that TMJ erosions were worse on the right side than the left side: condylar erosion on the right side forces muscles of the right side to work harder to bring the head of the condyle closer to the mandibular fossa. On the other hand, muscles on the left side have less workload and this could explain why they did not show statistically significant results on muscle palpation analysis. sEMG, kinesigraphy and muscle palpation data showed a greater alteration of the stomatognathic system. This could be explained considering that sEMG and muscle palpation are muscular pain indicators while kinesigraphy evaluates joint structure alterations on jaw movements. As stated by [PERSON] [47], muscular suffering is an earlier predictive sign of TMJ disorders while joint damage occurs later. In addition, kinesigraphy analysis might reveal signs of more advanced involvement of the TMJ in JIA, while sEMG could detect TMJ damage in an earlier stage. This suggests the importance of muscle evaluation in growing patients to detect an early involvement of the joint structures. Therefore, it is possible to emphasize the relevance to recognize clinical symptoms indicating TMJ involvement in an early stage to avoid JIA progressive lesions. The association of clinical muscle palpation, instrumental evaluation by kinesigraphy and radiological abnormalities may lead to early detection of imminent condylar erosion.
## 2 Orthogonal Vector Approach for Dynamic Directional Modulation  
Let us consider a conventional array in free space with \(N\) omnidirectional antennas distributed along the x-axis (Figure 1). A standard constellation of \(M\) symbols will be transmitted towards the observation angle \(\phi_{0}\) of the XY plane (\(\theta=90^{\circ}\)). In a conventional modulation system, the radiation pattern used in the transmission of symbol \(d_{m}\) is given by  
\[s_{m\_conv}(\phi)=\sum_{n=1}^{N}B_{mn\_conv}\,AEP_{n}(\phi)=\mathbf{AEP}(\phi )^{T}\cdot\mathbf{B}_{m\_conv}\quad m\in\{1\dots M\}. \tag{1}\]  
where column vector \(\mathbf{AEP}(\phi)\) contains the active element pattern [33] of each antenna in the array for angle \(\phi\), \({}^{T}\) is the transpose operator and \(\mathbf{B}_{m\_conv}\) is a column vector with the antenna weights defined as  
\[\mathbf{B}_{m\_conv}=d_{m}\mathbf{AEP}(\phi_{0})^{*}\quad m\in\{1\dots M\} \tag{2}\]  
with \({}^{*}\) denoting the conjugate operator.  
For convenience, and without loss of generality, symbols \(d_{m}\) are normalized such as  
\[\frac{1}{M}\sum_{m=1}^{M}\|\mathbf{B}_{m\_conv}\|^{2}=1 \tag{3}\]  
where \(\|\cdot\|\) is the norm of a vector.  
Figure 1: Array of \(N\) omnidirectional antennas along the x-axis.  
In DDM, each symbol \(d_{m}\) is transmitted using \(P\) different radiation patterns. For that purpose, antenna weights \(\textbf{B}_{mp\_DDM}\) are obtained adding a vector of artificial noise \(\textbf{W}_{mp}\) to \(\textbf{B}_{m\_conv}\)[19]  
\[\begin{split} s_{mp\_DDM}(\phi)=\textbf{AEP}(\phi)^{T}\cdot \textbf{B}_{mp\_DDM}=\textbf{AEP}(\phi)^{T}\cdot(\textbf{B}_{m\_conv}+\textbf {W}_{mp})\\ m\in\{1\dots M\}\quad p\in\{1\dots P\}.\end{split} \tag{4}\]  
According to the orthogonal vector approach [19], vectors \(\textbf{W}_{mp}\) are chosen randomly to satisfy  
\[s_{mp\_DDM}(\phi_{0})=s_{m\_conv}(\phi_{0})\quad m\in\{1\dots M\},\quad p\in\{ 1\dots P\}; \tag{5}\]  
therefore, vectors \(\textbf{W}_{mp}\) must belong to the null space of \(\textbf{AEP}(\phi_{0})\) (artificial orthogonal noise)  
\[\textbf{AEP}(\phi_{0})^{T}\cdot\textbf{W}_{mp}=0\quad m\in\{1\dots M\},\quad p \in\{1\dots P\}. \tag{6}\]  
As a consequence, a noisy constellation is transmitted along the observation angles different to \(\phi_{0}\). It must be noted that SDM is obtained for DDM with \(P=1\); in this case, a distorted version of the constellation is obtained.  
From the comparison of (1) and (4), it can be concluded that the implementation of DDM requires the addition of extra power to the transmission, so the power efficiency of DDM (\(PE_{DDM}\)) is defined as [9]  
\[PE_{DDM}=\frac{\frac{1}{M}\sum_{m=1}^{M}\|\textbf{B}_{m\_conv}\|^{2}}{\frac{1}{ MP}\sum_{p=1}^{P}\sum_{m=1}^{M}\|\textbf{B}_{m\_conv}+\textbf{W}_{mp}\|^{2}}, \tag{7}\]  
and taking into consideration (3) and (6), it becomes  
\[PE_{DDM}=\frac{1}{1+\frac{1}{MP}\sum_{p=1}^{P}\sum_{m=1}^{M}\|\textbf{W}_{mp} \|^{2}}. \tag{8}\]
# Weaknesses in ENT Battery Design
## 1 Introduction  
In many applications in different areas such as Statistics (Refs. [1; 2; 3], among others), Computational Simulation (for example, [4; 5; 6]) or Cryptography (for example, [7; 8; 9]), among others, it is necessary to work with random (or pseudo-random) sequences(A sequence of numbers is a finite ordered set \(\{x_{i}\}_{i=0}^{n-1}\), such that \(x_{i}\in C\) for all \(i:\,0\leq i\leq n-1\), where \(C\) is a finite set of integers. Usually, \(C=\{0,1\}\) or \(C=\{k\in\mathbb{Z}:0\leq k\leq 255\}\). Notation by juxtaposition is allowed: \(x=x_{0}\ldots x_{n-1}\)[10]. A sequence of numbers \(x=x_{0}\ldots x_{n-1}\) (where \(x_{i}\in C=\{0,\ldots,k-1\}\)) is random if it corresponds to a simple random sample of \(n\) independent and identically distributed random variables (i.i.d.), with a discrete Uniform distribution \(\mathcal{U}\{0,k-1\}\). Note that if \(C=\{0,1\}\), \(\mathcal{U}\{0,1\}=\) Bernoulli(0.5) [10]). It is of vital importance that these sequences are reliable. Imagine, for example, in a cryptographic key security application, any vulnerability in the sequence used could have catastrophic results such as the possibility of the key being discovered and the system being attacked. This is why it is necessary to check the sequences before considering their further use. The most powerful tools that can be considered for this purpose are hypothesis tests. These tests measure from different perspectives whether a sequence verifies the desired properties of uniformity and randomness. Generally, these tests are grouped in sets called batteries or suites. These batteries or suites provide the user with the possibility to perform the sequence checks in an organized and rather mechanical way. A battery or suite must fulfill some essential properties, the tests that compose them must be carefully chosen so that there are no dependent tests (which would slow down the use of the battery and would not provide more information about the sequence); furthermore, the tests that are incorporated into a battery must have a solid mathematical basis and must efficiently verify whether or not a sequence fulfills a certain property. On the other hand, a battery must have a wide coverage. There are several batteries or suites in the literature such as Diehard [11],Dieharder [12], NIST SP 800-22 [10], Cryp-X [13] or ENT [14], among others (for a more complete vision of this issue, you can see [15]).  
This paper will focus on the ENT battery. As will be seen throughout this paper, this battery, which is widely used due to its simplicity and ease of implementation, has some vulnerabilities that make it unsuitable for certain applications such as cryptographic security.
## 4 Current Status and Identified Needs
### Level 1 TOA Radiometry and Geometry
#### 4.1.4 Viciarious Calibration with Deep Convective Clouds  
Deep Convective Clouds (DCCs) are vertically extended and extremely opaque clouds (optical thickness of the order of 100). They exhibit bright and almost white spectra from the visible to the near infrared with nearly isotropic reflectivity. They are therefore considered the ideal invariant Earth target for vicarious calibration, owing to the very high signal-to-noise ratio [82].  
DCCs have a number of advantages over PICS, since they are very close to be Lambertian, except for large viewing angles, when Bidirectional Reflectance Distribution Function (BRDF) correction should be applied [83]. Moreover, DCCs are naturally located at very high altitudes, which strongly simplifies the atmospheric correction procedure, thus reducing the uncertainties associated with PICS. Finally, DCCs are more evenly distributed globally in the tropics, which is particularly important for geostationary satellites, as many of these satellites cannot view PICS under favourable conditions.  
DCCs have been used for inter-band calibration for more than two decades, mainly for coarse resolution satellites (see pioneering work from [PERSON] et al. [84]), and they were also successfully applied to S2 and S3 missions (see recent work by [PERSON] et al. [85; 86]). The relevant DCC calibration protocols and the used modelling approaches are well consolidated and mature enough, allowing the assessment of inter-band radiometric accuracy at the level of 1-2% and also to study long-term ageing effects. Some remaining challenges include the refinement of BRDF correction approaches for large viewing angles to remove underlying seasonal variability [83]. The GSICS group is currently working to address this remaining challenge and consolidate the DCC protocol for calibration.