esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Selection and Description of Studies
Figure 1 (the PRISMA flow diagram) shows the process of collecting suitable studies based on the predefined criteria. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Sample** & **Aim** & **Sample of Variables** & **Results** \\ \hline \hline \multirow{6}{*}{[PERSON] et al., 2008 [31]} & N = 17 & \begin{tabular}{c} Examine the effects of RS \\ versus HIT on \\ performance and \\ postexercise \\ parasympathetic \\ reactivation. \\ \end{tabular} & \begin{tabular}{c} Mean and peak HR, \\ 30–15 IFT, CMJ, 10 m \\ sprint, RSA, \\ postexercise HR \\ recovery \\ \end{tabular} & \begin{tabular}{c} HIT was more effective than \\ RS training at improving \\ postexercise parasympathetic \\ verification \\ performance. \\ \end{tabular} \\ \cline{1-1}  & N = 59 & \begin{tabular}{c} Capture evidence \\ supporting the accuracy of \\ 30–15 IFT for \\ individualizing interval \\ training. \\ \end{tabular} & \begin{tabular}{c} Vo\({}_{2}\)max, MAS, UM-TT, \\ 20 m 20 mSRT, 110 m \\ sprint, CMJ, HR \\ recovery \\ \end{tabular} & \begin{tabular}{c} For interval training \\ programming, MRS appears to \\ be an accurate reference speed. \\ \end{tabular} \\ \cline{1-1}  & N = 33 & \begin{tabular}{c} 10 m sprint, CMJ, RSA, \\ handball throw velocity, \\ VIFT (30–15 IFT), iEI \\ \end{tabular} & \begin{tabular}{c} Both HIT and HBT were found \\ to be effective training modes. \\ \end{tabular} \\ \cline{1-1}  & \begin{tabular}{c} N = 15 \\ (32 male; 27 female) \\ \end{tabular} & \begin{tabular}{c} Assess the reliability of \\ short-term resting HR \\ variability HRV and \\ postexercise \\ parasympathetic \\ reactivation. \\ \end{tabular} & \
\begin{tabular}{c} HRV and HR indices \\ at rest and submaximal \\ and supramaximal \\ and supramaximal \\ exercise \\ \end{tabular} &
\begin{tabular}{c} Discrepancy in CVs of \\ vagal–related heart rate indices. \\ Supramaximal exercise was \\ shown to worsen the reliability \\ if HRV-spectral indices \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 4: Research studies where IFT 30–15 was used for training programming. \begin{table}
\begin{tabular}{c c c c} \hline \hline
A total of 21 studies met the inclusion criteria. All studies were conducted between 2009 and 2020. Most research was conducted on a sample of soccer players (4), rugby players (3), handball players (3), players from diverse team sports (5), basketball (2), hockey (1), futusal (1), wheelchair rugby (1) and netball (1). There was a total of 433 participants. Studies where IFT 30-15 was used to verify validity, reliability and aerobic capacity assessment included 342 participants, and the number of participants per study ranged from 9 to 63. Moreover, 246 participants were male and 96 were female. As far as studies in which IFT
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 3 Results and Discussion
### Evaluation Metrics  
Metrics are used to evaluate the performance of all test algorithms in terms of endmember extraction, abundance estimation and noise robustness.  
SAD estimates the shape similarity between the spectra of the real endmember **z** and the extracted endmember **z**\({}^{*}\) by calculating the angle of them. The smaller the value of SAD, the more similar the two spectra.  
\[\text{SAD}=\text{cos}^{-1}\bigg{(}\frac{\textbf{z}^{\textbf{T}}\textbf{z}^{*} }{\|\textbf{z}\|\cdot\|\textbf{z}^{*}\|}\bigg{)}. \tag{14}\]  
The root mean square error (RMSE) calculates the error between the real abundance vector **m** and the estimated abundance vector **m**\({}^{*}\) for each endmember. The fully constrained least squares (FCLS) algorithm is utilized to obtain the abundance map in this work [34]. A smaller RMSE means a higher unmixing accuracy with the extracted endmembers.  
\[\text{RMSE}=\sqrt{\frac{\|\textbf{m }-\textbf{m}^{*}\|_{2}^{2}}{\text{n}}}. \tag{15}\]
## 4 Experimental Verification  
To verify the accuracy of the Model, the predicted results were compared with telemetry data, which were obtained during the bidirectional stable laser communication of an L-ISL based on on-orbit commercial satellites.
# Design for Additive Manufacturing and for Machining in the Automotive Field
## 1 Introduction
In order to allow for the correct functioning of finished parts, the surface roughness is reduced by means of surface finishing, being polishing, sand blasting and shot peening, some common techniques that can be used to the aim [19]. If required, post-processing treatments are completed by machining operations. On account of the extreme complexity of geometries that are typical of LPBF, conventional clamping systems and tool paths are often inadequate, so the concurrent product/process development within DfAM should also consider Design for Finishing requirements [20; 21]. The overall acceptability of components after the described sequence of operations is usually verified through dimensional controls, which are necessary to check the dimensional accuracy as a result of layer-wise build-up, thermal stress relief and finishing [22]. The last step of the validation process very often relies on non-destructive testing methods (NDT). One of the most versatile techniques for high-end applications is X-ray computed tomography (XCT), which is able to detect most of the defects such as pores, cracks and areas where the melting is lacking [23]. However, it is expensive if compared to other techniques capable of detecting only superficial defects [24], as for example, dye penetrant testing or the relatively new proposal of thermography. The minimization of lead-time, time-to-market (TTM) and production cost is a basic goal of industrial manufacturing technology. With LPBF, lead-time and, therefore, TTM are sensibly shorter if compared to conventional technologies such as casting or forging. However, it is important to stress that the aforementioned post-processing operations and controls unavoidably increase the actual production time [25]. The ongoing establishment of LPBF in new fields of application for small to medium production batches requires an evaluation of its time- and cost-effectiveness as compared to traditional manufacturing methods, as for example, computer numerical control (CNC) machining [1; 26; 27]. The present paper applies the DfAM approach to a bracket for an electric motor as a case study and proposes a comparison in terms of production cost and time using CNC as a benchmark. Although there are several case studies in the literature regarding the topological optimization of metal parts obtained by LPBF [28], this work aims at showcasing the holistic approach by which design and manufacturing considerations can be integrated into the development of products. Moreover, in the era of industrialization of additive processes,quantifying their time- and cost-competitiveness versus traditional manufacturing options can be of benefit for industrial applications.
# Two-Machine Job-Shop Scheduling Problem to Minimize the Makespan with Uncertain Job Durations
## 5 Properties of the Optimal Pairs of Job Permutations
### Sufficient Conditions for an Optimal Pair of Job Permutations
: _Let \(S^{\prime}_{1,2}\subseteq S_{1,2}\) be a set of permutations from the dominant set for the problem \(F2|l_{ij}\leq p_{ij}\leq u_{ij}|C_{max}\) with job set \(\mathcal{J}_{1,2}\), and let \(S^{\prime}_{2,1}\subseteq S_{2,1}\) be a set of permutations from the dominant set for the problem \(F2|l_{ij}\leq p_{ij}\leq u_{ij}|C_{max}\) with job set \(\mathcal{J}_{2,1}\). Then, \(<S^{\prime}_{1,2},S^{\prime}_{2,1}>\subseteq S\) is a dominant set for the problem \(J2|l_{ij}\leq p_{ij}\leq u_{ij},n_{i}\leq 2|C_{max}\) with job set \(\mathcal{J}=\mathcal{J}_{1}\cup\mathcal{J}_{2}\cup\mathcal{J}_{1,2}\cup \mathcal{J}_{2,1}\)._  
**Theorem 9**. : _Let a pair of identical permutations \((\pi_{1,2},\pi_{1,2})\) determine a single-element \(J\)-solution for the problem \(F2|l_{ij}\leq p_{ij}\leq u_{ij}|C_{max}\) with job set \(\mathcal{J}_{1,2}\), and let a pair of identical permutations \((\pi_{2,1},\pi_{2,1})\) determine a single-element \(J\)-solution for the problem \(F2|l_{ij}\leq p_{ij}\leq u_{ij}|C_{max}\) with job set \(\mathcal{J}_{2,1}\). Then, the pairs of permutations \(\{(\pi_{1,2},\pi_{1},\pi_{2,1})\text{and}(\pi_{1,2},\pi_{2},\pi_{2,1})\}\) are a single-element dominant set \(DS(T)\) for the problem \(J2|l_{ij}\leq p_{ij}\leq u_{ij},n_{i}\leq 2|C_{max}\) with job set \(\mathcal{J}=\mathcal{J}_{1}\cup\mathcal{J}_{2}\cup\mathcal{J}_{1,2}\cup \mathcal{J}_{2,1}\)._  
The following claim follows directly from Theorem 9. **Corollary 5**. : _If the conditions of Theorem 9 hold, then there exists a single pair of job permutations, which is an optimal pair of job permutations for the problem \(J2|p,n_{i}\leq 2|C_{max}\) with job set \(\mathcal{J}\) and any scenario \(p\in T\)._  
Theorem 9 implies also the following corollary proven in Appendix A. **Corollary 6**. : _If the conditions of Theorem 9 hold, then there exists a single pair of job permutations which is a \(J\)-solution for the problem \(J2|l_{ij}\leq p_{ij}\leq u_{ij},n_{i}\leq 2|C_{max}\) with job set \(\mathcal{J}=\mathcal{J}_{1}\cup\mathcal{J}_{2}\cup\mathcal{J}_{1,2}\cup \mathcal{J}_{2,1}\)._  
Note that the criterion for a single-element \(J\)-solution for the problem \(F2|l_{ij}\leq p_{ij}\leq u_{ij}|C_{max}\) is given in Theorem 2.
## 3 Results and Discussions
### Validation of Long-Range Sound Propagations with Upward Refraction under Wind Condition  
Similar as shown in the previous test case, the ground acoustic impedance is also infinity. However, an upward refraction case is considered which is caused by a moving atmosphere. The wind speed recorded at 10 m height above the ground is \(-\)4.165 m/s, and the surface roughness length is 0.1 m. In a moving medium, the sound speed can be computed using the equation below  
\[c_{eff}(z)=c_{0}+b\ln(\frac{z}{z_{0}}+1) \tag{15}\]  
where \(c_{eff}\) is the profile of effective sound speed along the vertical direction, \(c_{0}\) = 340 m/s is the sound speed and \(z_{0}\) = 0.1 m is the roughness height. Knowing the wind speed at 10 m height and  
Figure 5: Sound transmission loss over 200 m at various frequencies (rigid ground and downward refraction).  
Figure 6: Comparisons of the RSPL in 1/3-octave bands at a distance of 200 m.  
the roughness length, it is easy to show that \(b\approx-1\). Such a sound speed profile represents a slightly upward atmospheric refraction. In Figure 7, except at very low frequencies, the RSPL decays very fast along the propagation path. Figure 8 shows a very good agreement with the reference data which indicates the capability of the current method to handle noise propagations through moving media.
## 2 System Description  
The microgrid serves a cluster of 1000 residential buildings in North Texas. The electricity demand in the region is very high in the summer--because of the air-conditioning needs of the population--and significantly less in the other seasons. The hourly electric energy demand of the cluster of buildings was simulated from available data on residences in the area for an entire year [18]. Figure 1 depicts this hourly demand in the microgrid for two typical days, in January and July.  
It is observed in Figure 1 that the peak daily demand in the summer is approximately seven times higher than that in the winter. It was calculated that the total electric energy consumed during the day in July is 4.5 times higher than that of the day in January. The air-conditioning demand in the area is the principal reason for these differences. Winters in the region are mild, and any residential heating needs are supplied by natural gas, which is extracted from the local Barnett shale field and is abundant in the area.  
A number of wind turbines, which are placed at a distance of 1-2 km from the residences to avoid noise issues, as well as arrays of solar cells, supply the electric power needs for this community. Since energy storage is necessary for the uninterrupted supply of electric power to the cluster of buildings, hydrogen has been chosen for storage [19]. There are two reasons for the choice of hydrogen as the storage medium. First, the region does not have the geological features that would enable the installation of pumped-hydro storage (PHS) systems and compressed air energy storage (CAES) systems. Additionally, most battery types exhibit internal current drift and are unable to store energy through seasons, e.g., store energy in the winter and spring when winds are strong to be used in the summer when the electricity demand is high [8]. A second reason is that hydrogen has very high specific energy for storage (119 M)/kg), is clean, and does not degrade [8; 20; 21]. With hydrogen as the energy storage medium, when the demand for electric energy exceeds the supply, a system of hydrogen fuel cells provides the energy deficit.  
Figure 2 shows the schematic diagram for the proposed energy system. Photovoltaic (PV) cells and wind turbines supply all the needed energy for the cluster of buildings. Controllers and maximum power point trackers (MPPT) ensure that maximum electric power is produced by the PV cells. The generated power is directed to the cluster of buildings to meet the demand. If the generated power is higher than the present demand, the excess energy is directed to electrolysis systems that feed the hydrogen storage system. When the power supply is less than the demand, the power deficit is provided by the fuel cells, which convert the stored hydrogen to electricity. If the cluster of buildings operates with alternating current (ac), voltage inverters convert the direct current (dc) generated in the fuel cells and the PV cells to ac.  
Figure 1: Hourly electric energy demand in the microgrid on two typical days in the summer and winter.
# Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images
## 2 Materials and Methods
### Methodology
#### 2.2.1 Feature Extraction  
Extending the Autoencoder, a CAE enables a dimensionality reduction while preserving the original spatial representation, which is an absolutely critical characteristic when analyzing multi-dimensional data [29]. The overall structure of a CAE comprises two main phases: (1) in the encoding phase, a convolutional encoder transforms the input data into a lower-dimensional feature space, followed by a decoding phase (2), where the compressed representation passes through decoding layers to achieve the original input image reconstruction. Having the input as the target, the CAE learns the best set of features that enables the input reconstruction while eliminating the need for labeled data. The application of this dimensionality reduction technique lies in the idea that the knowledge achieved while training to reconstruct the input data might provide a useful weight initialization if one intends to use the encoder part for a different task in a transfer learning approach.  
In the proposed CAE architecture, represented in Figure 3, the encoding phase is composed of four convolutional layers with 3 \(\times\) 3 kernels, and an increasing number of filters as the network goes deeper. All the convolutional layers are followed by a Rectified Linear Unit (ReLU) activation function and a _max-pooling_ layer to reduce the output feature map by half. Giving an input tensor of size \(C\times H\times W\), passing through the encoding layers results in a feature map of 256 filters with size \(\frac{H}{8}\times\frac{W}{8}\). To reconstruct the original input, three _max-unpooling_ layers were employed to double the input size before the first three convolution blocks. The last convolutional layer is followed by a sigmoid activation, ensuring that all output pixels belong to the \([0,1]\) range of values.  
Table 1 depicts the considered values in the hyper-parameter search.  
Figure 3: Proposed CAE architecture for lung nodule unsupervised feature learning by optimizing input reconstructions.
## 6 Papain Use as an Exfoliant Agent in Cosmetic Applications
### Bromelain, Ficin, and Papain Biochemical Pathways of Action and Relative Properties
As of debridement, by hydrolyzing the devitalized tissue and breaking down necrotic tissues, bromelain facilitates the removal of damaged tissue from the wound site in cases of burn treatment and other skin injuries [35,42,133,146]. Moreover, it enhances cell proliferation by facilitating the replacement of impaired tissue with new cells [42,46,57]. Bromelain's proteolytic activity aids in breaking down the proteins involved in edema formation, reducing the accumulation of fluid in tissues, and stimulating fibroblast. This activity leads to decreased bruising and swelling, improved circulation, and boosted collagen deposition and epithelialization [42,57]. Ficin is also known for its similar potent proteolytic, wound healing, and skin regeneration actions, making it useful for reducing post-injury edema and swelling, as well as infection risk [140,147]. Similarly, papain accelerates wound healing and skin repair, removes necrotic tissue, stimulates collagen synthesis, and reduces edema and swelling; thus, it is beneficial against burn treatment, scar healing, and pressure ulcers (Figure 8) [148,149,150]. Bromelain, ficin, and papain proteolytic enzymes have also exhibited antithrombotic, anti-skin cancer, and anti-tumor potential. As with bromelain, properly inhibiting the action of NF-\(\kappa\)B, COX-2, and PGE2, may represent a potent anti-skin carcinoma and anti-tumorigenesis targeted cure [35]. In the same context, bromelain led to the suppression of proliferation of cell lines like those of the human epidermoid carcinoma-A431 and melanoma-A375, induced basal cell carcinoma destruction, and halted their anchorage-independent growth [151]. _F. carica_ latex-derived ficin possesses polyphenic constituents acting as potent antioxidants, and is employed to treat skin aberrations based on its anti-proliferative impact [152], while it has also a notable capability of inhibiting tumor formation [153]. Finally, papain has displayed significant antitumor properties. Papain suppresses atopic skin inflammation in atopic dermatitis (AD)-suffering mice and human keratinocytes (HaCaT cell lines) [26]. Also, papain has shown anti-proliferative effects on tight junction proteins, as observed in human primary keratinocytes (C57 BL/6 cell lines) and toll-like receptor (TLR) 4-deficient mice, where it affected skin barrier function by elevating transepidermal water loss (TEWL), degrading tight junction proteins, and promoting vasodilation [154]. All biochemical cascades and effects of bromelain, ficin, and papain are depicted in Figure 8.
## 4 Results and Discussion
### FEM-Based Verification of the Proposed Analytical Model
(iv) \(\mathrm{Q_{tg,v}=2079576.482\ W\cdot m^{-3},\ h_{cS}=10.913\ W\cdot m^{-2}\cdot K ^{-1},\ h_{cT}=11.608\ W\cdot m^{-2}\cdot K^{-1},\) and \(\mathrm{h_{cB}=5.189\ W\cdot m^{-2}\cdot K^{-1}--for the case of vibration class C. Values of \(\mathrm{k_{t}}\), \(\varepsilon\) and \(\mathrm{T_{a}}\) are given in the previous sections. According to Figure 3, it is evident that the temperature difference \(\mathrm{T_{s}-T_{a}}\) corresponds with that of the experiments performed in [21]. In addition, FE mesh independence tests are performed in accordance with the procedure described in [31]. The results of these mesh independence tests are shown in Table 3. These tests showed that the mesh density does not affect the accuracy of the results obtained for the computational domain of Figure 3a consisting of only one block, that is, one material. Results of other numerical simulations, except for the four temperature distributions in Figure 3, are not given here in order to reduce the content of this study. According to Figure 3, the maximum and minimum bus bar temperatures are approximately equal to the continuously permissible temperature \(\mathrm{T_{cp}=}343.157\) K = 70 \({}^{\circ}\)C. The differences between these temperatures are less than \(\pm 0.1\)\({}^{\circ}\)C. Moreover, the accuracy of each simulation carried out using the updated version of the BUSBAR.m program was also verified in the same manner. It was determined that the maximum and minimum bus bar temperatures obtained using the BUSBAR.m program and COMSOL 4.3 differ from the temperature \(\mathrm{T_{cp}=}70\)\({}^{\circ}\)C by approximately \(\pm 0.2\)\({}^{\circ}\)C. Finally, it can be considered that the accuracy and effectiveness of the proposed steady-state thermal model have been successfully verified by means of the corresponding FEM-based simulations in COMSOL 4.3. Figure 3: Temperature distribution over (**a**) the meshed part of a two-dimensional representation of the rectangular bus bar 0.0004 m \(\times\) 0.025 m, obtained for vibration amplitudes of (**b**) X = 0 mm, (**c**) X = 1 mm, (**d**) X = 1.6 mm, and (**e**) X = 3 mm at a vibration frequency of 29 Hz.
## 2 Proposed Fault Diagnosis Method
### Signal Preprocessing Based on EEMD  
[PERSON] and [PERSON] [9] proposed the EEMD method, in which multiple averaged noise is added to different scales of the signal. Based on the statistical randomness of the white noise, the signal components were automatically mapped to the scale plane related to the background white noise. After multiple averages, the noise was approximated to an IMF. The calculation method is as follows:  
1. Let the raw signal be \(x(t)\), and let \(N\) be the number of aggregates. Let \(j=1\).
2. Add Gaussian white noise with amplitude coefficient \(k\) to \(x(t)\). Generate a new signal \(x_{j}(t)\): \[x_{j}(t)=x(t)+k\cdot n_{j}(t)\] (1)
3. Decompose \(x_{j}(t)\) into a series of IMFs using the EMD method.
4. When \(m<N\), repeat steps (2) and (3), but the newly added Gaussian white noise needs to be different from the previous noise. Let \(j=j+1\).
5. After the above \(N\) decompositions, generate several groups of IMFs. Their mean values are: \[\overline{c}_{i}=\sum_{j=1}^{N}c_{i,j}\ /N\] (2) where \(N\) denotes aggregates numbers and \(c_{i,j}\) is the \(i\)th IMF from the \(j\)th decomposition. The final IMF is the \(N\) decomposition of each of the above IMFs.  
Figure 1: Flow chart of the proposed diagnosis method.  
When using EEMD to decompose a complex signal, it is prone to producing false IMFs that have no physical meaning. Especially in the low-frequency part of the later decomposition stage, there are more false components, and the signal interferes with the extraction of useful feature components. In this respect, [PERSON] et al. [35] proposed an SD threshold method and a termination condition based on zero and an extreme point, and [PERSON] and [PERSON] [36] proposed an amplitude ratio judgment method. EEMD was improved using the above methods, and the low-frequency pseudo-components generated in the later decomposition can be eliminated to a certain extent, but the noise interference components or the pseudo-components of the intermediate frequency generated by decomposition still exist. Therefore, in our research, to reduce the influence of these false components, IMF component extraction based on characteristic frequency correlation is adopted.