esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of a New-Concept Secure File Server Solution
## 3 Research Methodology
### Methodology of the Performance Tests
#### 3.2.2 Defining the Data Set to Be Used for Measurement  
In order to make valid comparisons between the different server environments, we needed to ensure not only identical hardware and network infrastructure but also identical sample data. The data transfer tests were carried out on both 1 Gbit/s and 10 Gbit/s capacity direct copper-based cable connections, measuring both transmission directions (read/write).  
When designing the measurement plan, it was important to take into account that users typically work with different file sizes in different productive environments. In order to ensure that the compression procedures used by the file transfer protocols, various CIFS/SMB (Common Internet File System--CIFS, Server Message Block- SMB) versions and configurations in the test process, did not notably affect the results, the files used for testing were generated by using random bit patterns. For both the read and write tests, 1 GiB of data were transferred in the form of the required number of 500 KiB, 4 MiB, 256 MiB, and 1 GiB files, as follows:  
* \(1\times 1\) GiB
* \(4\times 256\) MiB
* \(256\times 4\) MiB
* \(2097\times 500\) kiB  
File operations were deliberately carried out by imitating normal user practice, using native command-line solutions (copy, del, cp, rm), as tools such as robocopy or rsync could have greatly affected the results of the tests--not to mention that the use of such tools would not reflect real-life scenarios. Each read and write test was followed by a short deletion process when the target directory was emptied. This was, of course, not an actual and safe wipe but a logical wipe and was not counted as part of the transfer time. The maximal throughput of the network connections was tested using Iperf3 (network bandwidth test application) prior to the transfer tests.
# Mechanical Properties of Sandwich Composites Reinforced by Nanoclas: An Overview
## 1 Introduction  
Sandwich composites are structures made of rigid outer layers--also known as skin--and an inner layer, known as the core. Several works describing the various materials used in sandwich composites are reported in literature. In terms of skins, for example, they are composed mainly of fiber-reinforced polymers (matrices like epoxy, PEEK, polyamide and fibers like glass, carbon or aramid fibers), metals alloys (such as aluminum, steel and titanium) and natural materials (such as wood, pine and other types of cellulose based materials). On the other hand, the core is essentially composed of non-rigid and low-density elastic materials, such as foam core, honeycomb structures or balsa wood [1; 2; 3; 4; 5; 6; 7; 8]. Building composite structures with these two types of materials make it possible to design structural components with good outer-layer mechanical properties, and simultaneously, good impact and vibration tolerances within the core layer, making them good options for aerospace, marine and wind power industries [9; 10].  
In order to improve the performance of sandwich composites, it is possible to add nano reinforcements to the base material. One such nano reinforcement often used is nanoclay (NC), which is a new generation of processed clay composed mainly of silicon dioxide (SiO\({}_{2}\)) and aluminum oxide (AlO\({}_{6}\)) in different proportions, depending on the type. There are three main types of nanoclas: kaolinite, with a proportion of 1:1 of SiO\({}_{2}\) and AlO\({}_{6}\), respectively; montmorillonite, with a 2:1 proportion; and chlorite, with a 2:2 proportion [11]. Montmorillonite is the nanoclay most used in this type of application because it offers a high aspect ratio and high swelling properties [11]. Nanoclas improvesandwich composite performance and can be mixed with resin or foam in the outer or inner layers, respectively, depending on the desired characteristic that needs to be improved.  
Considering that the first research work involving nanoclays in sandwich structures was published only in the late 2000s, this work aims to provide an overview of all studies available in the open literature, as well as the benefits discussed, in terms of their incorporation into the core or skin.
# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 4 Experimental Section
### Comparison with Previous Methods  
The proposed approach was compared with previous defocus map-based methods and recent end-to-end learning-based methods: Just Noticeable Blur estimation (JNB) [23], Edge-Based Defocus Blur estimation (EBDB) [24], DPDNet [1], [PERSON] et al.'s method [12], and IFAN [4]. Among these methods, the JNB and EBDB are both defocus map-based approaches. They start by estimating the defocus map and subsequently carry out non-blind deconvolution. [PERSON] et al.'s method attempts to learn direct mapping from the blurry input image to the clean image by utilizing the estimated defocus map to condition the mapping. DPDNet and IFAN belong to the end-to-end learning-based method that restores deblur images directly. A visual comparison is shown in Figure 7, indicating that WIG-Net restored clearer and sharper details.  
These networks involved in the experiment were trained using the custom-made dataset. Table 1 presents the quantitative comparison. The results indicate that under the same training conditions, our network exhibits significantly improved defocus deblurring performance compared to the current mainstream methods, with notable enhancements in both PSNR and SSIM metrics.  
Our network outperforms the other competing networks in defocus deblurring due to several key reasons. Firstly, the introduced wavelet transform enables lossless image decomposition, significantly reducing the loss of valuable information during processing. Secondly, IFA and GCN extend the network's receptive field, enhancing its ability to handle large-scale defocusing blur effectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **PSNR** & **SSIM** & **MAE** \\ \hline Input & 22.31 & 0.614 & 0.502 \\ [PERSON] et al. [23] & 22.39 & 0.620 & 0.504 \\ [PERSON] et al. [24] & 22.45 & 0.632 & 0.487 \\ [PERSON] et al. [1] & 22.73 & 0.687 & 0.464 \\ [PERSON] et al. [12] & 23.54 & 0.715 & 0.428 \\ [PERSON] et al. [4] & 23.64 & 0.723 & 0.419 \\ [PERSON] & 23.71 & 0.742 & 0.412 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison with previous defocus deblurring methods.
# The Enhanced-Earned Value Management (E-EVM) Model: A Proposal for the Aerospace Industry
## 5 Results
### Analysis of Results (Second Improvement)  
Following the former general procedure and running the algorithm described previously, results can be derived. The system provides a project status overview and addresses subprojects problems, which have worse outcomes.  
The dataset used in this model has two layers, primary and secondary inputs, as shown before in the Figure 1. The four primary inputs are the planned values (_PV_), the current costs (_AC_), the percentage of progress (_EV_), and the final budget at the end of the project (_BAC_) for each \(i\) subprojects. Secondary inputs are the average hourly rates (EUR/h) of each functional area, milestones, detailed planning, and WBS for each \(i\) subprojects. In this second layer, pre-processing analysis to convert _program and operational_ data into _computational_ data is needed, this being the main difficulty dealing with the dataset construction. It is beyond any doubt that this pre-processing task requires a very detailed knowledge of the project, and some project management skills focused on scope, chronogram, and cost management.  
Additionally, the E-EVM model is a cyclical model running lots of iterations. At the end, this case study was managing more than 1 million data which clearly shows the dimension of the dataset.  
Figure 4 simulates Cost Variance (_CV_) by identifying projects with current cost above and under the established limits.  
It is necessary to point out that, in this case, the upper and the lower limit have been defined for the value equivalent to 5000 h. Strictly speaking, this is a decision of the project manager and different criteria could be considered, among which we could indicate:  
* A percentage of the subproject with the highest associated cost.
* A penalty set for contracting with the end customer.
* Reference of a possible variable financial compensation assigned to the project leader.  
Table 4 only shows the subprojects where Cost Variance parameters are out of the as defined limits.  
Figure 4: Cost variance—complete graphical analysis. Source: Own elaboration.  
Figure 5 simulates Schedule Variance (\(SV\)), where the projects with a current schedule behind or ahead of the planned time have been identified.  
The upper and lower limits have also been established by the project manager by adopting a value equivalent to 10,000 h.  
Table 5 shows the parameters of the projects whose Schedule Variance values are out of the limits defined by the project manager.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 3 Solutions of the Formulated Problems
### Power Allocation with Fixed \(N_{\rm s}\) and \(I\)
Thus, the lower bound of \(p_{1}\) can be denoted as  
\[p_{1}^{lb}=\frac{f^{-1}(\mathcal{Q}^{-1}(\varepsilon_{\max}))\delta_{1}^{2}}{h _{1}}. \tag{27}\]  
From Theorem 3 and Constraint (12c), we know that \(p_{1}\leq P/2\). In addition, to guarantee the significance of \(\bar{\varepsilon}_{2},\log_{2}(1+\gamma_{2})\geq\frac{I}{N-N_{s}}\) should be satisfied. Therefore, we can derive that \(p_{1}\leq 2^{-\frac{I}{N-N_{s}}}P+\frac{\delta_{2}^{2}}{h_{2}}(2^{-\frac{I}{N- N_{s}}}-1)\). The upper bound of \(p_{1}\) can be denoted as  
\[p_{1}^{ub}=\min\left\{2^{-\frac{I}{N-N_{s}}}P+\frac{\delta_{2}^{2}}{h_{2}}(2^{ -\frac{I}{N-N_{s}}}-1),P/2\right\}. \tag{28}\]  
Theorem 4: _To maximize \(\Phi_{2}\), the equality of Constraint (12f) should be guaranteed._  
Proof: Suppose that the optimal power allocation that maximizes the effective throughput of the far secondary users is \(p_{1}^{*}\) and \(p_{2}^{*}\). The effective decoding error probability for the near secondary user can be denoted as \(\bar{\varepsilon}_{1}(p_{1}^{*})\), which satisfies \(\bar{\varepsilon}_{1}(p_{1}^{*})<\varepsilon_{\max}\). From (27), we have \(\bar{\varepsilon}_{1}(p_{1}^{lb})=\varepsilon_{\max}\). Since \(\bar{\varepsilon}_{1}(p_{1}^{lb})>\varepsilon_{1}(p_{1}^{lb})\), we have \(\bar{\varepsilon}_{1}(p_{1}^{lb})>\varepsilon_{\max}\). Based on the fact that \(\bar{\varepsilon}_{1}(p_{1}^{*})<\varepsilon_{\max}\) and \(\bar{\varepsilon}_{1}(p_{1}^{lb})>\varepsilon_{\max}\), there must exist a value \(\bar{p}_{1}\in(p_{1}^{lb},p_{1}^{*})\) that can make \(\bar{\varepsilon}_{1}(\bar{p}_{1})=\varepsilon_{\max}\). Then, following \(\bar{p}_{1}<p_{1}^{*}\) and Theorem 3, we have \(\bar{p}_{2}>p_{2}^{*}\). It is easily derived that \(\bar{\gamma}_{2}>\gamma_{2}^{*}\). According to Theorem 2, we can obtain \(\Phi_{2}(\bar{p}_{2})>\Phi_{2}(p_{2}^{*})\). This contradicts the assumption that \(p_{1}^{*}\) and \(p_{2}^{*}\) are the optimal values of power allocation. Therefore, \(\bar{\varepsilon}_{1}=\varepsilon_{\max}\) should be guaranteed in the optimization problem. Theorem 4 is proved. Based on Theorem 4, we can use a one-dimensional linear search algorithm to find the optimal value of \(p_{1}\) in the region \(p_{1}^{lb}\leq p_{1}\leq p_{1}^{ub}\). Then, we obtain the optimal value of \(p_{2}\) by \(p_{1}+p_{2}=P\).
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 2 Description and Diagnosis: Engineering Knowledge for Smart Data Collection  
The productivity of the machine in the case of freight train wheel reprofiling is limited by the presence of strong marks in the surface of the reshaped wheels and the premature breakage of mechanical elements due to the presence of strong vibrations. Proper diagnosis of the origin of the problem by domain experts is fundamental for successful design of the system. In this case, several cutting and impact tests were performed to identify the problem and select the proper sensors, broadbands, and admissible vibration limits. These tests were the key to defining the hardware of the smart system and the different vibration levels that were used to classify the status of the cutting process.
## 5 Results
### Classification Results
#### 5.3.4 Total Profile Deviation
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Feature Selection** & \begin{tabular}{l} **Number of** \\ **Features** \\ \end{tabular} & \begin{tabular}{l} **Accuracy** \\ **Bayes** \\ \end{tabular} & \
\begin{tabular}{l} **Accuracy** \\ **SVM** \\ \end{tabular} &
\begin{tabular}{l} **Accuracy** \\ **KNN** \\ \end{tabular} \\ \hline MANOVA RS + STFT & 2 & 0.76 & 0.76 & 0.76 \\ \hline MANOVA RS + CWT & 2 & 0.81 & 0.76 & 0.72 \\ \hline MANOVA RS + DWT & 2 & 0.71 & 0.76 & 0.72 \\ \hline MANOVA RS + WPT with 15 coeff. & 2 & 0.71 & 0.76 & 0.67 \\ \hline MANOVA RS + WPT with 128 coeff. & 2 & 0.67 & 0.76 & 0.67 \\ \hline ANOVA + rs RS + STFT & 60 & 0.79 & 0.78 & 0.79 \\ \hline ANOVA + rs rs + CWT & 60 & 0.71 & 0.72 & 0.76 \\ \hline ANOVA + rs rs + DWT & 60 & 0.76 & 0.16 & 0.36 \\ \hline ANOVA + rs rs + WPT with 15 coeff. & 60 & 0.67 & 0.72 & 0.78 \\ \hline ANOVA + rs rs + WPT with 128 coeff. & 60 & 0.72 & 0.81 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluations of the classification models for the classification of the profile total deviation. tool wear and significantly increase the size of the training data set. In this context, the use of neural networks is promising, which, in addition to a regression, also enables an image-based evaluation of the recorded acoustic signals in the frequency domain. There are already studies, on which to build on, which investigate the suitability of convolutional neural networks and deep belief network for the classification of faulty tool states by means of image-based acoustic data [37; 38].
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 1 Introduction
### Objectives  
The experimental implementation of a 5G NR FiWi system employing an analogue optical transportation network built on RoF technology with a connection length of 10 km is shown in this study with the motivation to address wired and wireless link reach for the considered scenarios. Apart from this, a linearization technique is evaluated to enhance the performance. In eMBB situations, we implement 2 \(\times\) 2 MIMO to increase the multiband 5G NR signal transmission range. As a proof of concept, the supercell scenario is accomplished at a 20 m distance using 3 GHz, while the femtocell scenario is realized at a distance of 20 m using the 20 GHz band. The following are the paper's key contributions:  
1. Combination of a 2 \(\times\) 2 MIMO and an RoF-based hybrid FiWi system, as well as testing with a 5G NR transceiver at 3 and 20 GHz to show the multiband propagation and extend the wired transmission range through a wireless link.
2. In a supercell situation, convolutional neural network (CNN)-based digital pre-distortion (DTD) is used to improve the performance.
3. In line with the C-RAN architecture, experimental assessment is made of multiband 5G NR transmissions using a 10 km analog FH.  
The remainder of the paper is divided into the following sections. The implementation of a MIMO 5G NR FiWi system is discussed in Section 2. Section 3 explains the experimental testbed followed by the results in Section 4, and the article is concluded in Section 5.
## 4 Effects of Different Mode Bond-Slip Fracture
### Research Review on Modeling of Bond-Slip Phenomena  
In the analysis of RC beam shear resistance conducted by [PERSON] and [PERSON] in 1967 [32], reinforcements and concrete were modeled separately. Crack propagation was considered using linkage elements, and the bonding between the reinforcements and concrete was taken into consideration through modeling.  
Subsequently, to enhance the simulation capabilities of link elements, researchers proposed a spring-based modeling method that employs multiple spring elements to connect two material surfaces to simulate the bond-slip behavior between materials [33].  
With the advancement of finite element techniques, researchers introduced more sophisticated modeling methods, such as interface elements. These specialized finite elements simulate the bond-slip behavior between material surfaces by utilizing nonlinear and multidirectional intrinsic structural relationships. They provide a more accurate representation of complex bond and slip phenomena and allowed automatic calculation of material parameters.  
Figure 22: Damage to Axial Reinforcements at Base of Columns by Analysis without Bond-slip (Case-2).  
The numerical models for bond stress-slip between reinforcement and concrete have been investigated in various ways, from the earliest single linear models to later bilinear and nonlinear models. Finally, the numerical model that accounts for a reduction in bond strength due to bond damage is the most prevalently employed approach in the field.  
[PERSON] proposed a bond-slip model for cyclic loading based on extensive experiments, which is still widely used today [4]. The numerical bond-slip model employed in this study is founded on [PERSON]'s model, which modifies the CEB-FIP 1990 Model of the European International Concrete Committee. This modification adopts the CEB-FIP 2010 Model [34] and incorporates a softening equation for bond strength due to reinforcement strain and an increasing equation for bond strength due to lateral restraint.
# Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer
## 2 Proposed Method
### Lung Segmentation
#### 2.2.1 Segmentation in Lung Window  
For image segmentation, there are several existing segmentation techniques, such as flood fill, thresholding, and watershed. The flood fill algorithm is not suitable for segmentation since it can miss some important areas, as with thresholding. The watershed algorithm is widely used in lung segmentation and is more effective; however, in most cases it requires seeding the region of interest.  
Extracting lungs from the lung window scan is indeed a tri-class pixel classification problem (Figure 1a). To achieve this pixel categorization, we use the multi-Otsu thresholding algorithm [34]. [PERSON]'s algorithm has been explored to solve many medical imaging  
Figure 1: Sample CT scan in lung window (**a**), mediastinal window (**b**).  
Figure 2: (**a**) Histogram of lung window shown in Figure 1a, (**b**) histogram of mediastinal window shown in Figure 1b.  
problems, e.g., [35; 36; 37]. It finds the optimal thresholds by maximizing the between-class variance with an exhaustive search [38]. The multi-Otsu method returns two thresholds for three class problems. In our case, the first threshold comes from the valley between the first and the second peaks, and the second threshold separates the second class of pixels from the third class of pixels. The thresholds computed in the lung window shown in Figure 1a using the multi-Otsu method are shown in Figure 3 using the red markers. The second threshold separates the lungs from the rest of the image. Figure 4 shows a few lung window samples from the test dataset and the segmented lungs using the proposed method.
## 3 The Principle of Arbitrary Baseline Algorithm
### The 2D Arbitrary Baseline Algorithm  
The elements A, B, C are placed in the same plane as shown in Figure 5. \((x_{i},y_{i})\) represents the element's coordinate (\(i\) = A, B, C). Z-axis coordinate is set to zero.  
The phase difference ambiguity exists here. Under this condition, the phase differences of the incident signal impinging on the antennas A, B, C are simplified as:  
\[\phi_{\rm AB}+2k_{1}\pi=\frac{2\pi}{\lambda}[(x_{\rm B}-x_{\rm A})\cdot\cos \beta\cdot\cos\alpha+(y_{\rm B}-y_{\rm A})\cdot\cos\beta\cdot\sin\alpha] \tag{7}\]  
\[\phi_{\rm AC}+2k_{2}\pi=\frac{2\pi}{\lambda}[(x_{C}-x_{\rm A})\cdot\cos\beta \cdot\cos\alpha+(y_{C}-y_{\rm A})\cdot\cos\beta\cdot\sin\alpha] \tag{8}\]  
\[\phi_{BC}+2k_{3}\pi=\frac{2\pi}{\lambda}[(x_{\rm B}-x_{C})\cdot\cos\beta\cdot \cos\alpha+(y_{\rm B}-y_{C})\cdot\cos\beta\cdot\sin\alpha] \tag{9}\]  
Figure 4: Model of arbitrary baseline algorithm.  
Figure 5: The position of antenna elements.  
where \(\rm k_{1},k_{2},k_{3}=0,\pm 1,\pm 2\cdot\cdot\cdot\cdot\tan\alpha\) is expressed as:  
\[\tan\alpha=\frac{(\phi_{\rm AB}+2{\rm k_{1}}\pi)\cdot(x_{\rm C}-x_{\rm A})-(\phi _{\rm AC}+2{\rm k_{2}}\pi)\cdot(x_{\rm B}-x_{\rm A})}{(\phi_{\rm AC}+2{\rm k_{ 2}}\pi)\cdot(y_{\rm B}-y_{\rm A})-(\phi_{\rm AB}+2{\rm k_{1}}\pi)\cdot(y_{\rm C }-y_{\rm A})} \tag{10}\]  
It can be seen from Equation (10) that there is a mirror ambiguity of the incident signal. _i.e._, if \(\tan\alpha>0\), the incident signal may come from both the first and the third quadrants; else if \(\tan\alpha<0\), the incident signal may come from the other two quadrants. Fortunately, the actual direction where the incident signal comes from can be distinguished. As shown in Figure 5, if \(\phi_{\rm AB}+2{\rm k_{1}}\pi>0\), the incident signal is from the first quadrant, otherwise the signal is from the third quadrant. So as the condition \(\tan\alpha<0\).  
A group solution is obtained by solving any two of Equations (7-9) containing the ambiguous value. Assume there are \(n\) elements in the space, so there are \(C_{3}^{n}\) combinations of antennas. In order to solve the phase ambiguity, several other combinations are considered. Because the real value exists in all combinations, which can be selected by comparing the ambiguous values contained in several combinations. Finally, the 2D-DOA of the incident signal is obtained.  
It can be seen from Figure 5, the antennas of arbitrary baseline algorithm are still placed in the same plane. The crucial reason for that is the angle information contained in the trigonometric function _i.e._, Equations (4-6). We have to search the angle in the same way as MUSIC algorithm, because the analytical solution does not exist. In high frequency, the computation is enormous when so many ambiguous values exist. Therefore, it is difficult to implement in practice.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 4 Experiment
### Ablation Study
#### 4.6.2 Patch Selection Strategy and Regression Methods  
In the ablation study, we evaluated the impact of varying patch numbers and regression methods on PSNR performance on the LOLv1 dataset. Figure 5 illustrates the PSNR performance of both the Baseline method and the proposed method under different numbers of random patches. Specifically, the Baseline method employs SSIM regression within each patch, while the proposed method utilizes hierarchical clustering regression.  
The results indicate that as the number of patches increases, the PSNR performance consistently improves, suggesting that increasing the patch number positively influences model performance. Additionally, under the same patch number conditions, the proposed method consistently achieves higher PSNR values compared to the Baseline method, indicating the superiority of hierarchical clustering regression in terms of reconstruction quality. These findings validate the rationality and effectiveness of the proposed patch selection and regression strategy, demonstrating its clear advantage in PSNR performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Methods** & **PSNR** & **SSIM** \\ \hline B & 24.029 & 0.782 \\ B + PWM & 27.431 & 0.894 \\ B + UC & 26.737 & 0.835 \\ All & 27.923 & 0.882 \\ \hline \end{tabular}
\end{table}
Table 4: Performance analysis of the proposed Patch-wise Diffusion and Uncertainty-Guided model: ‘B’ represents the diffusion model with Unet; ‘PWM’ denotes the patch-wise diffusion model; ‘UC’ represents the uncertainty-guided model; ‘All’ represents the use of both models. PSNR is measured in dB, SSIM is unitless (lower is better).  
Figure 5: The performance of our method in terms of PSNR (Peak Signal-to-Noise Ratio) under different path selections.