esa-sceva/satcom-chunk-collection huggingface dataset:
# Mesostructural Model for the Fatigue Analysis of Open-Cell Metal Foams
## 4 Application and Validation of the Proposed Model  
The proposed model is validated using experimental campaign data. The experimental campaign is conducted to generate fatigue life information using open-cell metal foam for amplitude strains ranging from 0.003 to 0.0125. The base material for the foam is 6061 aluminum alloy with 40 pores per inch (ppi), subjected to a fully reversed strain-controlled cycle. More information about the experimental campaign can be found elsewhere [18].  
Figure 4: Flow chart of the proposed model.  
Figure 5 shows the results obtained using the proposed model, considering a constant strain amplitude of 0.003. The figure shows the relation between the maximum tension and number of cycles to failure of the open-cell metal foam. According to the proposed model, the predicted maximum tension is 3.96 \(\times\) 10\({}^{-5}\) ksi (80% of 4.95 \(\times\) 10\({}^{-5}\) ksi), with a predicted fatigue life of 3600 cycles.  
Table 1 provides a summary of the experimental results. The data in Table 1 were used in the Weibull E-N model to generate the whole E-N field curves indicated in Figure 6. Figure 6 shows the percentile curves of \(p\) = 0.01, 0.05, 0.5, 0.95, and 0.99 with blue lines. The percentile curves represent the probability of foam failure. Additionally, the experimental data of Table 1 are indicated with red dots, and the predicted fatigue life using the proposed model with a green dot.  
According to Figure 6, for a strain amplitude of 0.003, the fatigue life ranges from 800 to 5000 cycles. For a probability of failure of 0.5 (\(p\) = 0.5), the fatigue life is close to 3600, which is the predicted fatigue life according to the proposed model. Therefore, the proposed model allows us to predict the fatigue life of the studied foam for a strain amplitude of 0.003. The large variation in the number of cycles to failure at this low strain amplitude is due to the inherent variability in the material's response. At such low strain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strain Amplitude** & \multicolumn{3}{c}{**Number of Cycles to Failure**} \\ \hline
0.003 & 4076 & 2706 & 1069 \\ \hline
0.004 & 589 & 786 & 690 \\ \hline
0.005 & 111 & 173 & 161 \\ \hline
0.0075 & 31 & 29 & 26 \\ \hline
0.01 & 11 & 13 & 11 \\ \hline
0.0125 & 6 & 7 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Strain amplitude vs. number of cycles to failure  
Figure 5: Proposed model prediction: maximum tension versus number of cycles to failure of the open-cell metal foam with a strain amplitude of 0.003.  
levels, small differences in the foam's microstructure can lead to significant differences in fatigue life. Additionally, minor variations in the experimental setup, such as in loading conditions or specimen preparation, can further contribute to this variability. This behavior is consistent with observations in similar studies on metal foams under low-amplitude cyclic loading.
## 3 SAgic-IoT Platform Description
### Layered IoT Architecture
#### 3.2.2 Communication Layer  
The communication layer transmits all data collected by sensor nodes to the gateway. This layer defines the communication protocol for data transmission. As sensors are stationary, the network topology does not change. Additionally, data transmission requires low latency, high reliability, and a high level of security to protect the collected data. The gateway node is powered by both ZigBee and Wi-Fi interfaces. This layer selects the best route to send the information by using several selection parameters, such as RSSI and the number of hops.
## 6 Simulation Validation
### Analysis of Experimental Results
#### 6.3.1 Adaptability to Traffic Conditions  
When facing different traffic conditions, the proposed planning method can be flexibly adjusted according to changes in driving parameters, planning lane-changing paths that meet the current traffic conditions.
## 2 Fundamental Principles
### Finite Spectrometer Resolution  
The finite spectrometer resolution determined by the Gaussian beam profile of the spot size incident on the optical detector pixel array also introduces a sensitivity decay \((R_{SR})\) as shown in Equation (15):  
\[R_{SR}=exp\left[-\frac{\omega^{2}}{2 ln2}\left(\frac{\pi z}{2\cdot z_{max}} \right)^{2}\right], \tag{15}\]  
where \(\omega=\frac{\delta k^{\prime}}{\Delta k}\) and \(\delta k^{\prime}\) is the spectrometer's spectral resolution dependent on the size of the beam spot in the focal plane of the detector pixel. Thus, the total signal decay in the system as a function of depth comes in the form of a convolution of the finite pixel size with the Gaussian spectral resolution. After applying a Fourier transform, this leads to a multiplication of both of the decay functions which gives the expression shown in Equation (16) for sensitivity reduction R, as a function of imaging depth \(z\)[48]:  
\[R(z)=sinc^{2}\left(\frac{\pi z}{2\cdot z_{max}}\right)exp\left[-\frac{\omega^ {2}}{2 ln2}\left(\frac{\pi z}{2\cdot z_{max}}\right)^{2}\right]. \tag{16}\]The depth-dependent sensitivity of the system presented in this work is shown in Figure 3a, the SNR sharply declines with depth and measurements near the Nyquist limit become difficult to reliably analyse as shown in Figure 3b.  
A normalisation procedure can be applied in the Fourier domain to compensate for fall-off by multiplying the frequency components of the signal by the inverse of the signal drop-off envelope, indicated by the red dashed line in Figure 3a. This improvement can be seen in Figure 3c across the range of operation, Figure 3d shows the same signal as in Figure 3b with the normalisation curve applied to it. However, care should be taken to optimise this normalisation curve as it will also amplify high frequency noise and dampen low frequency signal components.
# SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
## 6 Discussion  
### Comparison  
This subsection compares SPD-Safe with the related works presented in Section 2.2 (i.e., TrainIntegrity, CMLRVT, and SENSORAIL) in terms of features. Table 6 summarizes the comparison results.  
Figure 7: The railway on-route WSN graphical user interface.  
All the related smart railway systems identified adopt semantic representation and reasoning. The service-oriented approaches conform to the specific application aspects and, therefore, in all relevant systems the agents are uniquely responsible for specific operations. The conflicting patterns are also not examined in most of these designs, limiting their applicability to specific deployments.  
Furthermore, the three related systems do not use any management middleware for embedded devices. This approach is quite limiting in the IoT era, where high volumes of heterogeneous equipment have to be deployed and co-function. The systems also neglect the popular agent frameworks which, among others, provide efficient agent-related functionality and implement relevant standards. The reasoning operation is developed with general purpose programming languages, ignoring the advantages offered by the deductive rule-based techniques. Mechanisms for resolving conflicts, when implemented, are based either on epistemic or relational reasoning. More importantly, these related systems do not safeguard security, privacy, and dependability, and do not utilize any built-in protection technologies.  
Conversely, SPD-Safe is a solution focusing on the SPD management of IoT and CPS settings. The SPD modeling is based on well-structured metrics that analyze the various configuration options of a multi-layered system. The AI process adjusts the railway CPS and counters attacks at runtime. SPD-Safe integrates state-of-the-art technological building blocks and platforms for the implementation of reasoning, as well as the management of devices and agents. Epistemic and relational reasoning are incorporated for resolving conflicts. Furthermore, the proposed framework adopts standardized technologies, from semantic standards to communication protocols and authorization schemes.
## 2 Materials
### NDVI Data  
The GIMMS3g NDVI dataset [43] is analyzed for the period of January 1982 to December 2013. Data is provided in 1/12 degree resolution and is composited into 15-day periods using the Maximum Value Compositing (MVC) technique [44]. The data were obtained from the AVHRR instrument onboard the NOAA satellite series 7, 9, 11, 14, 16, 17, 18, and 19. These data have been calibrated for sensor shifts, and corrected to remove the effects of sensor degradation, satellite orbit drift, solar zenith angles, and other factors, such as the effects of stratospheric aerosol loadings from the El Chichon and Mount Pinatubo eruptions in April 1984 and June 1991 [18; 45]. This dataset has been widely used for detecting vegetation phenological and photosynthetic changes, and the current version has improved data quality especially in high latitudes with shorter growing season when compared to previous versions of the GIMMS NDVI datasets [46].
# Toward Explainable AutoEncoder-Based Diagnosis of Dynamical Systems
## 4 Gap Distance Metric  
### Diagnosis via Sub-Space Analysis  
This section describes how we compute diagnoses via sub-space projections. We first introduce our diagnosis task over a Hilbert space. A Hilbert space \(\mathcal{H}\) is a vector space with an inner product \(\langle\cdot,\cdot\rangle\). Given a vector \(\mu\in\mathcal{H}\), we compute a metric using the norm of \(\mu\), which is induced by the inner product, \(\parallel\mu\parallel=\mu\cdot\mu\).  
**Definition 2** (Diagnosis Task).: _We define a classification (e.g., diagnosis) problem in Hilbert space as follows: given a subspace \(\mathcal{J}\in\mathcal{H}\), check if \(\mu\in\mathcal{H}\) belongs to \(\mathcal{J}\)._  
We solve this task using two steps: (i) project \(\mu\) onto \(\mathcal{J}\); and then (ii) calculate the distance between \(\mu\) and its projection using the induced norm.  
This notion of inner products is generalized through the notion of kernels. Kernel methods [34; 35] have been used extensively in machine learning for classification, i.e., to capture the nonlinear complex patterns underlying data. Kernel methods include support vector machines (SVMs), kernel Fisher discriminant analysis (KFDA), and Gaussian process models. They have been successfully applied to a wide variety of machine learning problems [34; 35]. These methods map data points from the input space to the feature space, i.e., higher dimensional reproducing kernel Hilbert space (RKHS), such that relatively simple linear algorithms can deliver impressive performance.  
**Definition 3** (Kernels via feature maps).: _Let \(\Omega\) be a nonempty set. A feature map \(\phi\) is any function \(\phi:\Omega\to\mathcal{H}\), where \((\mathcal{H},\langle\cdot,\cdot\rangle_{\mathcal{H}})\) is any Hilbert space (the feature space). The function_  
\[\mathcal{K}(x,y):=\langle\phi(x),\phi(y)\rangle_{\mathcal{H}}\quad x,y\in\Omega,\]  
_is a positive definite kernel on \(\Omega\)._  
Denoting the \(d\)-dimensional input space \(\mathcal{X}\in\mathbb{R}^{d}\) (\(\mathbb{R}\) denotes the set of real numbers), the kernel function \(\mathcal{K}:\mathcal{X}\times\mathcal{X}\to\mathbb{R}\) induces an implicit mapping \(\phi\) into a higher dimensional RKHS \(\mathcal{H}\) in which even simple linear models inferred are highly effective compared to their nonlinear counterparts learned directly in the input space \(\mathcal{X}\).  
Instead of formulating an optimization criterion with a fixed kernel \(\mathcal{K}\), one can leave the kernel \(\mathcal{K}\) as a combination of a set of predefined kernels, which results in the problem of multiple kernel learning (MKL) [36]. MKL maps each sample to a multiple-kernel-induced feature space, and a linear classifier is learned in this space.  
So far, we do not know which kernel will produce the most discriminative AE in terms of diagnostics isolation. We now show how the notion of principal angle provides a metric for the necessary kernel function.
## 3 The Design of OCPSI Mode
### The Analysis of the Signal-to-Noise Ratio  
In addition, the signal-to-noise ratio (SNR) is also an important parameter for evaluating image quality. The general definition of image \(SNR\) is the ratio of the power spectrum of the signal to the noise, which can also be expressed as the ratio of the number of signal electrons to the number of noise electrons. It can be expressed as  
\[SNR=20 lg\left(\frac{S}{N}\right) \tag{39}\]  
where \(S\) is the number of signal electrons, and \(N\) is the number of noise electrons.  
Figure 19: A cross-sectional view of the image plane \(A^{\prime}\) point.  
The signal electron number is generally related to camera parameters and imaging environment and can be expressed as  
\[S=\frac{\pi\cdot L\cdot\tau\cdot T_{e}\cdot P}{4F^{2}} \tag{40}\]  
where \(L\) is the entrance pupil brightness, \(\tau\) is the transmittance of the optical system, \(T_{e}\) is the exposure time, \(P\) is the detector sensitivity, and \(F\) is the F number of the optical system.  
The noise electron number of CMOS is mainly composed of shot noise, dark current noise, and transient noise, which can be expressed as  
\[N=\sqrt{\sigma_{shot}^{2}+T_{e}\cdot\sigma_{dark}^{2}+\sigma_{temporal}^{2}} \tag{41}\]  
where \(\sigma_{shot}\) is shot noise, \(\sigma_{dark}\) is dark current noise, and \(\sigma_{temporal}\) is transient noise.  
Finally, the \(SNR\) can be expressed as  
\[SNR=20 lg\ (\frac{\pi\cdot L\cdot\tau\cdot T_{e}\cdot P}{4F^{2}\cdot\sqrt{ \sigma_{shot}^{2}+T_{e}\cdot\sigma_{dark}^{2}+\sigma_{temporal}^{2}}}) \tag{42}\]  
Under the same load, only entrance pupil brightness (\(L\)) and exposure time (\(T_{e}\)) are variables, and \(SNR\) is proportional to both. The entrance pupil brightness (\(L\)) is related to the roll angle under the same sun altitude and ground reflectivity. The larger the yaw angle, the smaller the entrance pupil brightness (\(L\)).
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 3 A PCB-Based High-Damping Deployable Solar Panel Module
### Design Description
Figure 3: Configurations of a highly damped deployable solar panel module’s demonstration model: (**a**) stowed and (**b**) deployed. four guide holes were made on the four edge corners of the PCB panel and stiffeners. The stiffener attachment process on the PCB panel is shown in Figure 4. First, prepare the materials and integration tools such as PCB panel, stiffeners, 3M966 tape, integration jig, isopropyl alcohol (IPA), torque wrench, knife, and cleaning cloth, then wipe the stiffeners and PCB panel by using a cloth moistened with IPA to remove dust contamination. The tape was cut according to the shape of the stiffener by a knife tip, put into the integration jig through the guide hole interfaces, and the process was repeated up to the specified number of stiffeners that had to be attached to the panel. The PCB panel should be placed on the last stiffener attached to the tape, and then the top guide jig has to clamp with M3 bolts fastened on the jig through a torque of 1.1 Nm to create a compression force on the specimen. As recommended by the adhesive tape datasheet [22], the adhesive bonding resin of the attached tapes must be cured for up to 72 h for secure attachment. The number of attached stiffeners was determined by the 3U CubeSat solar panel's experimental test results [18] while considering dynamic clearance while accommodating the P-POD. The thickness of the solar panel after the attachment of the stiffeners is 3.7 mm, which provides a lateral edge gap margin of 3.3 mm for a dynamic clearance on P-POD [11].
# A Nighttime and Daytime Single-Image Dehazing Method
## 5 Conclusions  
In this paper, we have proposed a dehazing method for the removal of haze from both nighttime and daytime images. For the ambient illumination estimation, we analyzed the visual properties of hazy images and preserved the illumination direction and shape of the original image using the mixed-filter method. In addition, for the transmission estimation, the proposed method solves the problem of light source diffusion and enhances the image's sense of depth and definition. Both the qualitative and quantitative experimental results demonstrate that [PERSON] is superior to the recent methods and provides a novel  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**O-HAZE**} & \multicolumn{3}{c}{**I-HAZE**} \\ \cline{2-6}  & **SSIM** & **PSNR** & **CIEDE** & **SSIM** & **PSNR** & **CIEDE** \\ \hline [PERSON] et al. & 0.710 & 16.510 & 31.420 & 0.726 & 14.948 & 28.058 \\ [PERSON] et al. & 0.727 & 17.379 & 25.351 & 0.746 & 14.345 & 26.156 \\ [PERSON] et al. & 0.619 & 17.103 & 24.220 & 0.708 & 15.391 & 26.316 \\ [PERSON] et al. & 0.693 & **17.576** & 20.813 & 0.743 & 15.468 & 24.855 \\ [PERSON] et al. & 0.722 & 16.544 & 24.027 & 0.762 & 15.713 & 23.132 \\ [PERSON] & **0.770** & 17.272 & **20.231** & **0.792** & **16.135** & **20.958** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparision Index Of O-Haze Additionally, I-HazeE.  
Figure 7: Comparison of O-HAZE and I-HAZE [9; 12; 14; 15; 21].  
approach for obtaining a better quality dehazing image on the basis of preserving the original image color. However, much more work needs to be undertaken in the future. MF Dehazer shares this limitation because it uses a filter window to estimate the local ambient illumination. If the size of the local dense haze in the image is larger than the window size, it may overestimate ambient illumination and generate haze residue. The method will be improved to overcome these disadvantages.  
Investigation, G.C., Y.X.; methodology, Y.X., Y.T.; validation, Y.X.; data curation, Y.X., G.C.; writing--original draft preparation, Y.T.; writing--review and editing, Y.X., G.C.; project administration, G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
### Comparison of the Wrapper Approach
Recently some studies applied clustering methods to attribute sets, to enhance the overall performance of a computational model and demonstrate it is more effective than conventional attribute selection approaches. [PERSON] et al. [83], successfully developed a hybrid model using hierarchical clustering on spectral features and then selecting an optimal attribute set by means of the mutual information method. [PERSON] and [PERSON] [84], worked on the same model as developed in [83], with the exception that the former model forced each cluster to select successive attributes. [PERSON] et al. [85], presented a Fast clustering-based attribute reduction method (FAST) which generated a more useful and independent set of attributes that enhanced the performance of machine learning algorithms. [PERSON] et al. [86], discussed the importance of the clustering approach in simplifying the computation in a machine learning task. Moreover, recent studies have concluded that the structure of attributes can be effectively interpreted, and noisy variables can be removed from data records by means of the clustering approach [87; 88]. In the previous section, it has been observed that the CFS filter method and the BFS wrapper method generated most optimal results. The proposed attribute evaluator makes use of BFS and CFS method for generating reduced attribute set. In this era of advanced technology, users face the hurdle of datasets especially if they comprise of large number of attributes and instances. Though decision tree is quite efficient in classification tasks, still it is susceptible to noisy datasets. Certain datasets are tedious to handle since sometime decision trees is dependent on various metrics such as topological configuration of decision tree itself and the type of dataset used. Cluster analysis is widely applied in various domains but is rarely been used with decision tree. In this proposed work of chronic disease diagnosis, clustering approach is integrated with decision tree classifier to enhance the accuracy of classification. A suitable clustering technique like K-Means algorithm can be used to filter the data samples of training set which can help in reducing noise from decision tree thereby elevating the classification accuracy rate.
# High Precision Indoor Positioning System for Mobile Asset Management and Safety +
### Type of Positioning  
This portion focuses on two types of positioning techniques which are positioning objects and positioning users. The first type consists of RFID or any other tools carried by the users in an introduced situating framework inside the structure and is dependent on the remote sensor network utilized in the framework, known as a Fixed Indoor Positioning System. In the second kind, clients convey the gear, for example RFID, and there is no framework introduced in the structure inside that given environment, and this is known as pedestrian positioning [8]. The need for four principles that help in building positioning systems includes: Trilateration, which defines the address of one unknown point with the help of the three known points; Triangulation, a method which computes the distance based on the enumeration of angles; Scene Analysis, which uses fingerprinting for positioning purposes [9]; and Proximity, a technique that is basically dependent upon frequency-based systems [10].
## 6 Conclusions  
This study employed a hybrid dataset consisting of a combination of \"CICIDS2017 Dataset\" and \"Store-and-forward dataset and utilized\" CNN-LSTM models to detect anomalies in key metrics such as \"flow duration\" and \"forward packets\". The detection of potential anomalies can be accomplished through establishing a baseline of typical flow durations and forward packet counts, against which any deviations from the established pattern can be identified. The examination of flow durations and the transmission of forward packets can yield valuable insights for network administrators in evaluating the overall performance and efficacy of a network. Unusually brief flow durations or an exceptionally large number of forward packets may serve as indicators of network congestion, performance degradation, or potential security incidents. Abnormally prolonged flow durations or an unusually large volume of forward packets may indicate potential intrusion attempts or suspicious activities that require further examination. Therefore, a system that has undergone proper training would possess the capability to proactively mitigate any potential challenges that may arise in such situations. The detection accuracy and performance analysis exhibited significant levels of proficiency in both datasets, namely CIDS2017 and the store-and-forward dataset. On average, the findings indicate that both scenarios achieved an overall accuracy of 99% and an F1-score of 98%, surpassing the performance of certain models. This suggests that through acknowledging these limitations and considering potential avenues for future research, the field of intrusion detection can further advance in developing network security solutions that are more precise, resilient, and feasible. The rise in the number of packets inside each flow is contingent upon the precise architecture of the hybrid detection technique (CNN + LSTM), the attributes of the network traffic, and the caliber of the training data employed. Increased packet diversity facilitates the model's acquisition of knowledge from a diverse range of packet headers resulting from distinct communication patterns, network circumstances, and potential alterations. Moreover, the augmentation in data volume facilitates the extraction of more comprehensive and precise characteristics from the packet headers using this technique, resulting in enhanced differentiation between regular and altered packets.  
This research received no external funding.  
Not applicable.