esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Methods  
### Support Vector Machines  
This numerically optimized and generalized method was developed by _[PERSON]_, _[PERSON]_, _and [PERSON]_ in the 1990s [49], while the basic algorithm behind Support Vector Machines (_SVMs_) was introduced by _[PERSON] and [PERSON]_ in the early 1960s [51]. With regard to its application to classification tasks, the main idea is to introduce hyperplanes using a so-called kernel trick, which maps points in a nonlinear way onto a higher-dimensional space so that the margin between the points and hyperplanes is maximized, increasing their separability. SVM's suitability for human activity recognition classification tasks, as well as its great potential, has been confirmed by numerous authors.  
In [40], a bistatic UWB radar system working at 4.3 GHz was used to obtain datasets of time-based signatures of human interactions with the radar signal. These were used to train an SVM based on the one-vs.-one method to classify seven activities performed by eight subjects: walking, running, rotating, punching, crawling, standing still, and a transition between standing and sitting. The data were significantly reduced by 98.7% using PCA, where 30 main coefficients were selected. The classification accuracy reached only 89.88% due to difficulties resulting from confusion between certain activities containing similar micro-Doppler signatures.  
In a recent study, [PERSON], [PERSON], and [PERSON] [42] studied radar-based human activity recognition using sub-6 GHz and mmWave FMCW radar systems. Three-dimensional features consisting of the minimum, maximum, and mean of the matrix, \(\Sigma\), derived from range-time-power signatures were extracted using SVD. These were used to train a medium Gaussian SVM, which was applied to classify three different activities (walking, sitting, and falling). With an average classification accuracy of 89.8% for the mmWave radar system and 95.7% for the sub-6 GHz radar, it was shown that radar systems with higher resolutions do not necessarily lead to better classification.
## 4 Existing Underwater System Models
### Underwater Noise Estimation Model  
Underwater communication is influenced by various factors including shipping noise \"N\({}_{\mathrm{SH}}\)\", thermal noise \"N\({}_{\mathrm{TH}}\)\", waves noise \"N\({}_{\mathrm{WV}}\)\" and turbulence noise \"N\({}_{\mathrm{TR}}\)\". These types of noises are characterized by Gaussian statics (GS), and power spectral density (PSD) is defined by [9; 20]. The total noise \"N\({}_{\mathrm{TT}}\)\" can be expressed as  
\[\mathrm{N_{TT}=N_{TR}+N_{SH}+N_{TH}+N_{WV}} \tag{7}\]  
\[10\mathrm{logN_{TR}(f)=17-30 logf} \tag{8}\]  
\[10\mathrm{logN_{SH}(f)=40-60\mathrm{log}(f+0.03)-20(SH-0.5)+26\mathrm{log}\;f} \tag{9}\]  
\[10\mathrm{logN_{TH}(f)=-15+20\mathrm{log}f} \tag{10}\]  
\[10\mathrm{logN_{WV}(f)=50+7.5\sqrt{WV+20\mathrm{log}f-40\mathrm{log}(f+0.4)}} \tag{11}\]  
The shipping factor \"SH\" ranges from 0 to 1, whereas the wind velocity \"WV\" ranges from 0 to 10 m/s [36; 41; 42]. Empirical evidence suggests that there is a positive relationshipbetween noise levels and factors such as shipping, carrier frequency, and wind velocity. The signal-to-noise ratio (SNR) of this system can be calculated by  
\[\text{SNR}=\frac{\text{P}}{\text{A}(\text{d},\text{f})\cdot\text{N}_{\text{TT}}} \tag{12}\]  
where \"P\" denotes the transmission power in (watts) and \(\text{N}_{\text{TT}}\) In (W/Hz) is the noise power spectral density (PSD). Shannon-Hartley theorem can be applied to compute the underwater channel capacity (CC) [43] in bits/sec.  
\[\text{CC}(\text{d},\text{f})=\text{B}\text{log}_{2}(1+\text{SNR}(\text{d}, \text{f})) \tag{13}\]  
where \"B\" represents the bandwidth of the channel in (Hz). The channel capacity \"CC\" is inversely proportional to the channel noise. The higher the noise between two nodes, the lower the link quality (LQ).
# Probabilistic Analysis of Fatigue Behavior of Single Lap Riveted Joints
## 5 Conclusions  
A high number of fatigue tests have performed on single riveted lap joint specimens by considering three different levels of stress with stress ratio \(R=0.05\). Herein, results about fatigue life and critical crack length were shown. As expected, both fatigue life and critical crack size decrease as the level of stress increases.  
Moreover, it was interesting to observe that two modes of cracks occurred: through the hole and in proximity of the hole. Bearing pressure at the rivet-hole interface forces the crack to propagate through the hole. As the fatigue stress increases, the bearing pressure increases and the crack tends to propagate through the hole. Indeed, under the fatigue load with a stress level of 90 MPa, less than 30% of specimens showed cracks propagating through the hole. At the stress level of 120 MPa, the percentage increases until reaching 36.3%, while at the stress level of 160 MPa all the specimens failed across the hole.  
Finally, aimed to use experimental data for probabilistic methods, a statistical analysis was performed by using the Anderson-Darling test in order to investigate if datasets fit a distribution function.  
About fatigue life, it was observed that even if the dataset achieved for the tests at the level stress of 90 MPa does not fit any distribution, both datasets at 120 MPa and 160 MPa level fit normal distributions.  
About critical crack size, the test demonstrated that dataset at 90 MPa fits a Weibull distribution while it is possible to affirm that both the datasets at 120 MPa and 160 MPa fit normal distributions.  
Figure 13: Critical crack size at 160 MPa: probability plots for normal (**a**), log-normal (**b**), extreme value (**c**) and Weibull (**d**) distributions.  
**Author Contributions**: E.A.: investigation, methodology; A.G.: writing--original draft, data curation; A.D.L.: resources, writing--review and editing; R.S.: supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 6 Micromegas Readout of TPCs
### PandaX-III: Micromegas Readout TPC for Neutrinoless Double-Beta Decay Searches  
Micromegas TPC can also be used in the search for neutrinoless double-beta decay events. Such experiments aim to show the Majorana nature of the neutrinos, by searching for rare double-beta decays of specific isotopes, like the \({}^{136}\)Xe one, in which no anti-neutrino are emitted. In such events, the whole energy of the decay would be carried out by the two electrons, signing an annihilation of the two anti-neutrinos.  
The PandaX-III experiment [87; 88; 89] plans to study those events with large 1.5-m, 200 kg, gaseous \({}^{136}\)Xe TPC modules at 10 bar pressure (Figure 20). The electron trajectories  
Figure 19: Perspective of the detector with the passive and active shield showing the 4\(\pi\) sr coverage.  
Figure 18: Evolution of the Micromegas background levels in different periods of the CAST data taking. The last point on the right represents the background level obtained in LSC under special shielding conditions. The background level targeted for BabyIAXO is at least \(10^{-7}\) c/keV/cm\({}^{2}\)/s.  
in the TPC will be measured as well as the deposited energy using Microbulk Micromegas detectors. Such detectors have an excellent energy resolution, expecting to reach 1% at 2.5 MeV, which is the double-beta decay energy for \({}^{136}\)Xe.  
Microbulks also have very low radio-active contamination, thus, reducing the impact of the gamma background compared to similar double-beta decay experiments. The electron reconstructed trajectories, based on the Micromegas readout data, give the possibility to discriminate topologically between double-beta decay and gamma background events, allowing an expected reduction of the impact of the background by a factor up to 100. The PandaX-III experiment will be installed in the Jinping underground laboratory (province of Sichuan, China). It is expected to be commissioned at the end of 2021 with one TPC module, with a final goal of five modules installed within five years, representing 1 ton of \({}^{136}\)Xe.
## 4 The Predicted Stiffness  
### Equivalent Stiffness  
Based on the mid-span deflection of the T-beam during the destructive test process, the equivalent stiffness calculated from Formula (1) under concentrated force is derived using the material mechanics formula. The bending stiffness reduction coefficient is calculated by Formula (2). The measured equivalent stiffness on a cross section is finally calculated as shown in Table 1.  
\[B_{S}=\frac{Fbx}{6\omega L}(L^{2}-x^{2}-b^{2}) \tag{1}\]  
where \(B\)s is the equivalent stiffness of a simply supported beam, \(F\) is external concentrated force loading value, \(L\) is the calculation span of simply supported beams, \(\omega\) is the deflection, \(x\) is the distance from the stiffness calculation position to the fulcrum, and \(b\) is the distance from the loading position to the fulcrum.  
The stiffness reduction coefficient is defined as follows.  
\[\beta=B_{i}/B_{0}\;\;i=1,2,3,\dots \tag{2}\]  
where \(\beta\) is the stiffness reduction coefficient, \(B_{i}\) is the stiffness on a cross-section after damage, and \(B_{0}\) is the initial stiffness on a cross-section.  
Referring to the data in Table 3, it can be seen that the beam is in a complete linear elastic state during the initial loading stage, and the equivalent stiffness is relatively large. The equivalent stiffness hardly changes with the increase in the cyclic load level. After the third cyclic loading (1030 kN), the equivalent stiffness begins to decrease, while cracks begin to be formed at the bottom of the beam. When the cyclic load reaches 1110 kN, the equivalent stiffness of the T-beam decreases by 31% compared to the initial value. It decreases sharply during a load between 1110 kN and 1300 kN. After the cyclic load is increased to 1300 kN, the decreasing rate of the equivalent stiffness relatively slows down. The equivalent stiffness will continuously decrease as the load increases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Load (kN)**} & \multirow{2}{*}{**Deflection (mm)**} & \multicolumn{2}{c}{**Stiffness**} & \multirow{2}{*}{**Reduction Coefficient**} \\  & & **(1 \(\times\) 10\({}^{16}\) N\(\cdot\)mm\({}^{2}\))** & \\ \hline
630 & \(-\)20.82 & 7.33 & 1.00 \\
870 & \(-\)28.18 & 7.47 & 1.02 \\
1030 & \(-\)36.62 & 6.81 & 0.93 \\
1110 & \(-\)53.03 & 5.07 & 0.69 \\
1190 & \(-\)78.52 & 3.67 & 0.50 \\
1300 & \(-\)90.29 & 3.49 & 0.48 \\
1500 & \(-\)122.95 & 2.95 & 0.40 \\
1580 & \(-\)134.63 & 2.84 & 0.39 \\
1700 & \(-\)167.24 & 2.58 & 0.35 \\
1900 & \(-\)183.85 & 2.50 & 0.34 \\
2100 & \(-\)212.54 & 2.39 & 0.33 \\
2220 & \(-\)243.47 & 2.21 & 0.30 \\
2300 & \(-\)269.44 & 2.07 & 0.28 \\
2500 & \(-\)401.33 & 1.51 & 0.21 \\
2540 & \(-\)452.24 & 1.36 & 0.19 \\
2550 & \(-\)473.28 & 1.30 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The equivalent stiffness and its reduction coefficient at the mid-span position.
###### Abstract  
Solar radiation significantly affects terrestrial gross primary productivity (GPP). However, the relationship between GPP and solar radiation is nonlinear because it is affected by diffuse radiation. Solar radiation has undergone a shift from darker to brighter values over the past 30 years in China. However, the effects on GPP of variation in solar radiation because of changes in diffuse radiation are unclear. In this study, national global radiation in conjunction with other meteorological data and remotely sensed data were used as input into a two-leaf light use efficiency model (TL-LUE) that simulated GPP separately for sunlit and shaded leaves for the period from 1981 to 2012. The results showed that the nationwide annual global radiation experienced a significant reduction (2.18 MJ m\({}^{-2}\) y\({}^{-1}\); \(p<0.05\)) from 1981 to 2012, decreasing by 1.3% over this 32-year interval. However, the nationwide annual diffuse radiation increased significantly (\(p<0.05\)). The reduction in global radiation from 1981 to 2012 decreased the average annual GPP of terrestrial ecosystems in China by 0.09 Pg C y\({}^{-1}\), whereas the gain in diffuse radiation from 1981 to 2012 increased the average annual GPP in China by about 50%. Therefore, the increase in canopy light use efficiency under higher diffuse radiation only partially offsets the loss of GPP caused by lower global radiation.  
GPP; diffuse radiation; global radiation; TL-LUE
## 7 Discussion of the Filtering Impact on Security and Privacy
### Influence of Filtering on STRIDE Impacts
Unlike all other approaches, the scenario-specific adjustments lead to different influence levels for data collected by the server (_SCD_) between the _Essential Server_ and the _Unessential server_ in scenario \(S_{a}\). These adjustments also lead to higher ratings regarding the _Private Smart Home Device_ on the threat impacts of obtaining credentials or tokens used for authentication of the user (_OCR_), as well as obtaining (_DCD_) or logging (_LLM_) messages as compared to the _Accessible Smart Home Device_. Those examples show that the consideration of different types of devices and servers changes not only the influence on attack vectors but also the influence on threat impacts. Another _high_ influence was found for changing the control routines on the _Smart Hub_ (_CCR_) and a _medium_ one for obtaining control over the _Smart Home Device_ indirectly by controlling the _Smart Hub_ (_IDC(SH)_). Despite those rather positive points, for many entries, _no or only low_ influence was identified. Noticeably, the results show, that _no or only low_ influence exists on _Repudiation_ threat impacts.
# Effect of Axial Modification on the Meshing Performance of Involute Developid Gear Pair
## 5 Conclusions  
In this paper, a mathematical model of the beveloid gear under consideration of modification is presented. The segmented parabolic axial direction modification is selected to establish the paralleled beveloid gear pair model. Furthermore, the impact of modification and varying combinations of modification coefficients on the meshing performance of the beveloid gear pair is investigated.  
1. As the degree of modification is increased, the contact area of the tooth flank of the beveloid gears is observed to decrease. This results in a shift of the maximum equivalent stress from the edge to the middle position on the tooth flank. Subsequently, the maximum equivalent stress is seen to decrease and then tend towards a level equilibrium.
2. As the length of the modification increases, the contact area is progressively concentrated towards the centre of the tooth flank. Concurrently, the distribution range of the tooth root stress is also gradually enlarged, while the maximum equivalent stress of the tooth flank shows a decreasing trend.
3. The implementation of a toothwise segmented parabolic modification can serve to diminish the edge load of a beveloid gear pair, mitigate the occurrence of transmission error within the gear pair, augment the bearing capacity of the gear pair, attenuate the degree of vibration, enhance the smoothness of transmission, extend the service life, and exert a beneficial influence on the dynamic meshing performance of the gear pair.  
Figure 19: Comprehensive equivalent stress of tooth root. (**a**) Pinion; (**b**) gear.  
**Author Contributions:** [PERSON] conceived and designed the study and applied for the funding which financially supported the study. [PERSON] conducted data extraction, performed the analyses, and wrote the article. [PERSON] provided their comments and suggestions during discussions and by writing, especially on the data extraction method and paper formatting. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China No. 52265008; the Lanzhou Youth Talent Science and Technology Innovation Project 2024-QN-108; the Key Research and Development Plan of Gansu Province (Grant No. 24 YFA021); the Youth Science Foundation of Gansu Province, No. 23 JRRA751; the Education Department of Gansu Province: University Teachers Innovation Fund No. 2023A-021; and the Wenzhou Municipal Basic Scientific Research Project of China No. 2022G0145.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are not available due to restrictions privacy or ethical, confidentiality.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Fault Localization Using TrustRank Algorithm
###### Abstract  
Spectrum-based fault localization (SBFL), a widely recognized technique in automated fault localization, has limited effectiveness due to its disregard for the internal information of the program under test suites. To overcome this limitation, a novel TrustRank-based fault localization (TRFL) technique is introduced. TRFL enhances traditional SBFL by incorporating internal data dependencies of the program under the test suite, thereby providing a more comprehensive analysis. It constructs a node-weighted program execution network and employs the TrustRank algorithm to analyze network centrality and re-rank program entities based on their suspiciousness. Furthermore, a bidirectional TrustRank algorithm (Bi-TRFL) is extended that takes into account the influence relationship between network nodes for more accurate fault localization. When applied to large-scale datasets with real faults, such as Defects4d, TRFL, and Bi-TRFL, it significantly outperforms traditional SBFL methods in fault localization. They demonstrate up to 40% and 13% improvement in Top-1 and Top-5 rankings, respectively, proving their robustness and efficiency with minimal sensitivity to related parameters.  
fault localization; program spectra; complex networks; data dependency; TrustRank algorithm +
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Molecular Profile of Skin Cancer
## 4 Gene Mutations in NMSC
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gene/Gene Product** & **Type** & **Incidence (\%)** & **Type of Skin Cancer** & **Comments** & **Therapeutic Modality** \\ \hline \multirow{6}{*}{P53 ([PERSON] et al., 2020)} & & & & - identified even in & \\  & & & & & - preceding p53 proteinie, a & \\  & & & & & - encodes p53 proteinie, a & \\  & & & & & - well-known tumor & \\  & & & & & - suppressor & \\  & & & & & - causes the cell cycle to stop & \\  & & & & & - in the presence of DNA & \\ \hline \multirow{6}{*}{P16 ([PERSON] et al., 2004)} & & & & & - frequently inactivated in & \\  & & & & & human cancers, consists of & \\  & & & & & two overlapping genes that & \\  & & & & & encode two unrelated & \\  & & & & & proteins, p16 Mika and & \\  & & & & & p14 REF, functioning as cell & \\  & & & & & cycle inhibitors & \\ \hline PTCHI ([PERSON] et al., 2014; [PERSON] et al., 2018) & tumor suppressor gene & mutations PICH in 90 & & -overwensed in BCC & \\  & & & & & - induces GL 1 & PTCHI drug efflux \\  & & & & & promoter-driven luciferase & \\  & & & & & activation in keratinocytes & \\ \hline \multirow{6}{*}{RAS ([PERSON] et al., 2010)} & & & & & - the molecular mechanism is & \\  & & & & & consistent with the & \\  & & & & & & \\ \cline{1-1}  & & & & & paradoxical activation of & \\ \cline{1-1}  & & & & & MAPK signaling and leads to & \\ \cline{1-1}  & & & & & - asclearted growth of these & \\ \hline XPC ([PERSON] et al., 2013) & DNA repair gene & 10-90 (more prevalent in Africa) & Xeroderma pigmentosum & - early during skin &
\begin{tabular}{c} Megamucleases, \\ zinc-finger nucleases or TALE \\ nucleases \\ \end{tabular} \\ \hline MCIR ([PERSON] et al., 2015) & DNA repair gene & 24-67 for European origin) & BCC & - important role in normal & \\  & & (66-67 for European origin) & SCC & pigmentation & BMS-470539 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Gene mutations in non-melanoma skin cancer. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Gene/Gene Product** & **Type** & **Indidence (\%)** & **Type of Skin Cancer** & **Comments** & **Therapeutic Modality** \\ \hline  & & & & - frequently overexpressed & \\  & & & & - increased expression & \\  & & & & & following mutations at any & \\  & & & & level of the HH signaling & \\  & & & & & pathway (PTCH, SMG, \\ GLII and GL2 ([PERSON] et al., 2017) & transcription factor & 17 & RCC & SUFU) & \\  & & & Melanoma & - GLI transcription factors & \\  & & & & regulate angiogenesis & \\  & & & & - GLI activity is positively & \\  & & & & influenced by KRAS, TGF, and and negatively by & \\  & & & & & 95,3 PCA, PKC, PKC & \\ \hline  & & & & - TP 53 inactivation is & \\  & & & & detected in 50\% of human & \\ TPS3 ([PERSON] et al., 2017) & tumor suppressor gene & 50 & BCC, CSC & cancers & \\  & & & & - inactivation of TP 53 gene & \\  & & & & the second most common & \\  & & & & event associated with BCC & \\  & & & & pathogenesis & \\ \hline SMO ([PERSON] et al., 2020) & proto-oncogene & 10-20 & BCC & - coupling to G protein Gen