esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Gaseous Compounds  
Complete destruction of benzene, toluene, and naphthalene introduced into the studied DBD reactor may provide the hydrogen equivalent of 0.54%, 0.031% and 0.29%, respectively. Altogether, it provides up to 0.861% of additional hydrogen. This figure corresponds with an increase of H\({}_{2}\) concentration in the case of the reactor with the Rh catalyst (Figure 7a). As expected, application of the Ni catalyst leads to a lower increase in hydrogen concentration, whereas the absence of a catalyst results in insignificant hydrogen production. A similar relationship was observed in the concentration of methane (Figure 7b). Generally, it can be concluded that hydrogen and methane introduced into the reactor as components of the gaseous phase are not transformed in the nanosecond DBD plasma. A similar phenomenon was observed by [PERSON] et al. [9] in a plasma torch reactor.  
The concentrations of CO\({}_{2}\) and CO also depend on the presence and type of catalyst, and they follow an order: Rh catalyst \(>\) Ni catalyst \(>\) no catalyst (Figure 8a,b). In contrast to the H-containing components of the biomass producer gas simulator, carbon dioxide decomposes significantly in the nanosecond DBD plasma. The CO\({}_{2}\) concentration decreases almost linearly with the discharge energy causing an adequate increase in CO concentration. This phenomenon has been observed by many researchers in diverse non-thermal plasma sources, such as DBD [44; 45], gliding arc [46; 47] and microwave discharge [48; 49]. It was found that high energy electrons in these electrical discharges induce vibrational excitation of CO\({}_{2}\) molecules, which is a highly efficient channel for CO\({}_{2}\) decomposition [50]. This is also the reason for the decrease in energy efficiency of each plasma driven hydrocarbon reforming in the presence of CO\({}_{2}\).
## 3 Results
### Topological Optimization  
To better inform the placement of fibers in the TFP preform, the topological optimization platform in solidworks was used.  
A simplified topology study tool was used to provide a rough visual estimation as to how the stresses distribute along the surface. The results allowed for a diagrammatic visualization of where the bulk of the material would be required for a given load configuration. Different amounts of material reduction were investigated for the same load case and edge condition. A selection of these studies can be seen in Figure 8.  
Figure 7: Final prototype: (**a**) view of the TFP Pattern; (**b**) view of the internal CFW syntax.  
For the topology study, an 8 mm thick single curved surface, modelled in Rhino3D, was prepared and imported accordingly. The specified surface thickness was imposed by the fabrication limitations of the Tajima embroidery machine used at the fabrication stage. The filament winding syntax was omitted from the simulation. As the exact mechanical properties of the used combination of flax roving and resin were difficult to pinpoint and were not available at the specific moment of conducting the fabrication experiment, the simulation was run using the built-in properties of glass fiber, providing that the desired stiffness criteria could be reached when lower mechanical strength of flax fiber in comparison to glass fiber was compensated by using sufficiently higher volume fractions [24].  
An evenly distributed load of 800 N was applied to its horizontal seating surface. Winding pins were defined as a fabrication constraint, which forced the software to preserve the geometry during optimization.  
The simulation was run using the best mass-to-stiffness goal criteria, for which various percentages, ranging from 30% to 70%, were studied. No distinct outcome was favored. Instead, the simulations, all of which delivered similar results (such as in the associated figure), were used comparatively to identify the crucial areas transferring higher forces and thus requiring more material to be deployed.  
Regions in yellow indicate places where material was needed most and those in blue indicate where material was needed the least. Throughout the iterations, it was possible to notice the emergence of a Y-shape distribution of material on the legs, which suggested that these areas had the biggest fiber cross-sections, i.e., structural layers. It was also possible to conclude that the seat area needed to remain dense. Other areas of the initial input surface were excluded, generating a new edge condition for the final design. Finally, this simulation did not provide the definitive visual pattern of the surface, but rather guided the TFP surface design.
## 2 Background
### Natural Language Processing (NLP) Application for Mental Health Issues  
With the prevalent use of social media among individuals with mental health disorders, these platforms present an opportunity to enhance public health research and improve the provision of mental health services. NLP and ML are increasingly applied to detect mental health issues at an early stage, revolutionizing the potential for timely and effective intervention and alleviating the burden of mental disorders. The confluence of ML and NLP with social media data has proven invaluable for identifying behavioral patterns indicative of mental health disorders [37; 38]. This synergy has propelled proactive mental healthcare forward, facilitating early diagnosis and treatment [39]. Numerous studies have successfully employed classification algorithms, such as support vector machines and random forest, to identify conditions like depression, stress, and suicidal ideation from social media content, examining the use of ML and NLP in social media datasets to extract profound insights [40; 41].  
The period from 2012 to 2021 has seen a consistent increase in the application of NLP for mental health detection, with deep learning techniques becoming particularly prominent [42]. The majority of these studies have leveraged the power of supervised learning, utilizing a variety of models, including support vector machines [43], decision trees [44], and complex ensemble methods like AdaBoost [45], to accurately label data and predict mental health outcomes.  
Advancements in deep learning have inaugurated a new era of NLP applications in mental health research [46]. Innovative models using embeddings such as GloVe and Word2 Vec have been crafted to probe the linguistic intricacies of social media discourse more deeply [47]. Deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with long short-term memory (LSTM) units have overcome some limitations inherent in traditional RNNs, enhancing the models' ability to capture long-term dependencies [48; 49].  
In summary, the deployment of NLP in mental health research has established these technologies as essential tools within the mental healthcare sector. Their application extends beyond diagnosis to encompass support and treatment, marking them as crucial in reducing the prevalence of mental health issues [50].
# Optimum Geometries for Wireless Power Transfer Between an Oblong Coil and a Rectangular Coil
## 2 Wireless Power Transfer  
### A Simple Circuit Model for Wireless Power Transfer  
When submitted to sinusoidal oscillations of a frequency \(f_{0}\) that is low enough so that the radiated power is negligible, the magnetic coupling can be modeled in terms of phasors by ideal linear circuit components, as shown in Figure 1.  
Figure 1: Two non-radiating magnetically coupled coils (**a**) and their simplified circuit model (**b**).  
In this model, \(\omega=2\pi f_{0}\), and \(M\) is the mutual inductance between the two coils, with self-inductances \(L_{1}\) and \(L_{2}\). \(R_{1}\) and \(R_{2}\) are the resistances of the primary and secondary coil windings and depend, among other factors, on both skin and proximity effects. For any fixed frequency of operation, \(f_{0}\), these parameters should be recalculated so that the model can still conserve its linearity. In this manner, in a sinusoidal regimen, with no radiation assumed for the magnetic coupling, the power transferred from Coil 1 to Coil 2 is given by the phasor \(\dot{S}_{1,2}\):  
\[\dot{S}_{1,2}=\dot{V}_{M_{1}}\dot{I}_{1}^{*}=j\omega M\dot{I}_{1}^{*}\dot{I}_{ 2}, \tag{1}\]  
where for \(i\in\{1,2\}\), \(\dot{V}_{M_{1}}\) and \(\dot{I}_{1}\) are the phasors representing, respectively, the voltage across the dependent voltage source and the current flowing on side \(i\) of the magnetic coupling.  
Under the hypothesis of a compensated secondary, where the effect of the impedance of the self-inductance \(L_{2}\) is essentially canceled, and a linear resistive load \(R_{\mathrm{L}}\), \(\dot{I}_{2}\) will be proportional to the induced voltage on the secondary coil, \(j\omega M\dot{I}_{1}\), so the transferred power \(\dot{S}_{1,2}\) will be expressed by  
\[\dot{S}_{1,2}=\dot{V}_{M_{1}}\dot{I}_{1}^{*}=\left(j\omega M\dot{I}_{1}^{*} \right)\left(j\omega\omega M\dot{I}_{1}\right)=\alpha(\omega M)^{2}\dot{I}_{1 }\dot{I}_{1}^{*}=\alpha\left|\omega M\dot{I}_{1}\right|^{2} \tag{2}\]  
where \(\alpha>0\).  
So, as expected, the transferred power will be proportional to the square of the modulus of the voltage induced on the secondary coil by the primary current \(\dot{I}_{1}\), which will be proportional to the product of the frequency of operation, the mutual inductance between the coils, and the primary current itself.  
In this way, under the same circuit conditions of the secondary coil and oversimplifying the real magnetic links to the simple model of Figure 1, in order to increase the transferred power, it is necessary to increase either the frequency of oscillation, \(f_{0}\), the mutual inductance, \(M\), or the amplitude of the primary current, \(\dot{I}_{1}\). For a fixed frequency \(f_{0}\), and fixed a maximum primary current \(\dot{I}_{1}\) (a physical limitation of the primary coil or the circuit driving it), the maximum available power transfer will be obtained when \(M\) is also maximized.
# GNSS Signal Distortion Estimation: A Comparative Analysis of L5 Signal from GPS II and GPS III
###### Abstract  
The nonlinear navigation payload transmission channel introduces errors at a system level, resulting in a distortion of the received signal and affecting differential positioning users. This work models the transmission channel of the navigation payload and uses the joint estimation algorithm to calculate the digital distortion and analog channel response of the transmission channel from the received signal. On the above basis, the algorithm and model are verified by GPS-L5 measured data, and the channel characteristics of GPS II and GPS III are deeply analyzed. The results show that the existing GPS II satellites have a digital distortion, which is negligible in GPS III satellites. Regarding the analog channel response, GPS III has better amplitude and group delay consistency than GPS II. Lastly, the ranging bias caused by the transmission channel of the navigation payload is provided to the user for reference, and GPS III is improved by approximately 0.02 m compared with GPS II. This study provides support for adjusting the satellite signal pre-distortion and enriches subsequent GNSS signal quality assessment work; it can also be used as a reference for differential positioning users.  
GPS III; GPS II; signal distortion; digital distortion; analog distortion +
Footnote †: journal: Applied  
_Article_  
GNSS Signal Distortion Estimation: A Comparative Analysis of L5 Signal from GPS II and GPS III. _Applied_  
**Citation:** [PERSON]; [PERSON]; [PERSON].  
CNS Signal Distortion Estimation: A Comparative Analysis of L5 Signal from GPS II and GPS III. _Appl. Sci._  
**2022, 12, 3791. https:////doi.org/10.3390/app12083791**  
Academic Editor: [PERSON]  
Received: 5 March 2022  
Accepted: 7 April 2022  
Published: 8 April 2022  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Copyright:** © 2022 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/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Not All Seeds Are Important: Fuzzing Guided by Untouched Edges
## 6 Evaluation
### RQ3: Scalability  
To assess the scalability of the proposed approach, we integrated our method into the MOpt fuzzer, naming the modified tool \"MOpt-u.\" Table 5 provides a comparison of edge coverage between UntouchFuzz, AFL, MOpt-u, and the original MOpt fuzzer. The results in Table 5 demonstrate that UntouchFuzz and MOpt-u outperform the original, unmodified fuzzers in terms of edge coverage across all 12 benchmark programs. This further substantiates the capability of the untouched edge-guided mechanism to enhance the performance of the original fuzzers.  
\begin{table}
\begin{tabular}{l r r r r r} \hline  & **Default** & **RarePath** & **NewPath** & **SamePrefix** & **UntouchedEdge** \\ \hline
**Fuzzer** & **AFL** & **AFLFast** & **EcoFuzz** & **Alphuzz** & **UntouchFuzz** \\ \hline eviv2 & 102 & 96 & 59 & 99 & **105** \\ pdfotext & 159 & 166 & 25 & 100 & **176** \\ tcpdump & 515 & 488 & 435 & **571** & 519 \\ mp3 gain & 65 & 66 & **68** & 61 & 67 \\ mp42 aac & 22 & 26 & **28** & 24 & 24 \\ tiffcp & 381 & 406 & 318 & 336 & **418** \\ readelf & 3 & 2 & 1 & 2 & **6** \\ nm-new & **165** & 156 & 160 & 92 & 158 \\ xmlint & 17 & **24** & 15 & 19 & 19 \\ bsdtar & 37 & 37 & 31 & 12 & **39** \\ mujs & 20 & 19 & 19 & 10 & **25** \\ \hline Total & 1486 & 1486 & 1159 & 1326 & **1556** \\  & \((-4.17\%)\) & \((-4.17\%)\) & \((-34.25\%)\) & \((-17.35\%)\) & \\ \hline \end{tabular}
\end{table}
Table 4: Results after crash deduplication.  
\begin{table}
\begin{tabular}{l r r r r r} \hline  & **Default** & **RarePath** & **NewPath** & **SamePrefix** & **UntouchedEdge** \\ \hline
**Fuzzer** & **AFL** & **AFLFast** & **EcoFuzz** & **Alphuzz** & **UntouchFuzz** \\ \hline eviv2 & 503 & 476 & 301 & 529 & **546** \\ pdfotext & 3725 & 3424 & 122 & 2310 & **3937** \\ tcpdump & 1917 & 2008 & 1738 & **2090** & 2088 \\ mp3 gain & **1230** & 1219 & 986 & 1128 & 1226 \\ mp42 aac & **502** & 480 & 262 & 243 & 467 \\ tiffcp & 3495 & 3553 & 2317 & 3235 & **3642** \\ readelf & 19 & 2 & 1 & 5 & **83** \\ nm-new & 1550 & 1450 & 1435 & 641 & **1662** \\ xmlint & 4331 & **4377** & 3313 & 4063 & 4251 \\ bsdtar & 341 & 559 & 978 & 76 & **603** \\ mujs & 471 & **524** & 71 & 423 & 469 \\ \hline Total & 18,084 & 18,072 & 11,524 & 14,743 & **18,974** \\  & \((-4.92\%)\) & \((-4.99\%)\) & \((-64.65\%)\) & \((-28.70\%)\) & \\ \hline \end{tabular}
\end{table}
Table 3: Comparison on unique crashes.
# Stereo Digital Image Correlation in MATLAB
## 3 Implementation
### Stereo Matching Implementation
#### 3.3.1 StereoMatch Function  
StereoMatch, detailed in Table 9, loads the FIS1 and FIS2 as F and G, respectively, and performs Gaussian filtering on these using MATLAB's imgaussfilt function. Lines 4 and 6 can be changed to use alternative filtering methods. Bi-cubic b-spline interpolation coefficients are determined for the deformed image using MATLAB's griddedInterpolant function. In line 7'spline' can be replaced with 'linear' or 'cubic' for bi-linear or bi-cubic polynomial interpolation respectively. Moreover, line 7 can be replaced with an alternative interpolation method such as MATLAB's spapi function. Line 9 of SubCorr may need to be updated if alternative interpolation methods are employed.  
Lines 11 to 16 cycle through each subset of the FIS1, for which FeatureMatch in line 9 determined SFP initial estimates, performing correlation using subroutines SubShapeExtract and SubCorr. These displacement SFPs are used to determine the reference subset positions in the second image series.  
Subset pairs which achieve a ZNCC coefficient below 0.6, or which fall outside the bounds of the FIS2, are assumed to have failed stereo matching. These subsets are determined in lines 21-22 and their subset positions in both image series are set to NaN (not a number) in lines 23-26 such that they are not analyzed during temporal matching or displacement transformation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Line Numbers** & **Task Performed** \\ \hline Line 2 & Define SubExtract function to extract square subset data; \\ Line 3–6 & Load the FIS1 and FIS2 and perform Gaussian filtering on them; \\ Line 7 & Compute interpolation coefficients of G using MATLAB’s \\  & griddedInterpolant function; \\ Line 9 & Call subroutine FeatureMatch to determine SFP initial estimates; \\ Line 10 & Initialise temporary storage variables used to save correlation \\  & information during the for loop; \\ Line 11 & for subset number q = 1 to number of subsets, **do** \\ Line 12 & if FeatureMatch determined SFP initial estimates for subset q, **do** \\ Line 13 & Call subroutine SubShapeExtract; \\ Line 14 & Call subroutine SubCorr; \\ Line 15 & **end if** \\ Line 16 & **end for** \\ Line 17 & Save correlation information to RD.Stereo variable; \\ Line 18 & **for** image number d = 1 to d = n, **do** \\ Line 19 & Compute the subset positions in the FIS2 using Equation (32); \\ Line 20 & **end for** \\ Line 21–22 & Determine subsets which fail stereo matching and subsets which pass; \\ Line 23–26 & Set the subset position of subsets which fail stereo matching to NaNs \\  & throughout both image series; \\ Line 27 & Display information for stereo matching; \\ \hline \hline \end{tabular}
\end{table}
Table 9: StereoMatch algorithm summary.  
StereoMatch returns ResultData with the subset positions in the FIS2 assigned to ProcData2(d).Xos; the correlation results for stereo matching (P, C, Iter, StopVal) are assigned to Stereo.
## 5 Classification Performed by Machine Learning Algorithms
### Convolutional Neural Network
#### 5.3.2 Convolutional Neural Network Topology  
Due to a relatively small database employed in this study, it was decided to design the convolutional neural network based on the well-proven AlexNet architecture [46]. The layout of the proposed network is depicted in Figure 5. Out of the 23 layers used in the network, the first 14 are responsible for the spectrogram processing using the convolutional filters, whereas the remaining layers (15-23) constitute its fully connected part. The dimensions of the tensors processed by the network are indicated by the numbers in brackets, positioned at the interconnections between the adjacent layers.  
Five convolutional layers were employed in the network (layers 2, 5, 8, 11, and 14). They all had the same kernel size of \(3\times 3\). The size of the kernel was selected according to our pilot test results [14]. In order to reduce the resolution of the processed spectrograms, four max pooling layers were employed (layers 4, 7, 10, and 13). Batch normalization was exploited after the first four convolutional layers to accelerate the learning procedure (layers 3, 6, 9, and 12). While the original resolution of the spectrograms used at the network input was gradually reduced from \(150\times 349\) to \(5\times 5\) pixels at the output of the last convolutional layer (layer14), the number of convolutional filters progressively increased in each consecutive convolutional layer, amounting to 32, 64, 128, 256, and 512, respectively. The above strategy, involving the increasing of a number of filters in subsequent convolutional layers, combined with a progressive reduction in the image resolution, is commonly applied in convolutional networks [46].  
The two-dimensional spectrograms were converted into vectors in layer 15 and subsequently processed by the full connected dense layers consisting of 512, 128, and 16 nodes, respectively. Four dropout layers, with the dropout rate being equal to 0.5, were employed to prevent the network from overfitting (layers 16, 18, 20, and 22). The last layer was responsible for providing the results of the classification. A rectified linear unit was used in all the convolutional and dense layers, whereas a Softmax function was incorporated in the output layer.
## 3 Measurement Results and Discussion
### Experiments under Winter Conditions  
#### 3.2.1 Measurement Location and Environmental Conditions  
As an experiment to compare the difference between summer and winter seasons, we conducted an experiment on a sunny day in February at the Imperial Palace, the same place where we conducted the investigation in summer. To simplify the comparison with cloudy weather, the Imperial Palace, where the sky is not blocked by buildings or plants, was chosen. The conditions at the time of measurement were measured in the same way as in summer. The meteorological data are shown below in Table 2.
## 3 Proposed System Model
### Workflow of Proposed System  
In the proposed blockchain-based VN, smart vehicles are registered using crypto IDs provided by IVTP. A list of all the registered vehicles is stored in the proposed blockchain network. Ordinary vehicles communicate with edge node vehicles for required services. The edge node vehicles then communicate with RSUs to entertain the request of ordinary vehicles and respond accordingly. All communications in the blockchain network are done in an encrypted form to ensure privacy. In the proposed model, encryption is done using AES128 technique. The trust values of vehicles are calculated via IVTP, which further determine the behavior of the smart vehicles. The information provided by the vehicles to RSUs is saved using IPFS, which stores the provided information in a distributed hash table and generates corresponding hashes. These hashes are then stored in the blockchain network. For making the system efficient in terms of resource utilization, PoA consensus mechanism is used instead of PoW. Cache server is used at the edge node layer, which stores the frequently used services. Whenever an ordinary vehicle sends request for a service, the edge node vehicle checks its cache server. If the required service is present at the cache server, then the required service is transferred by edge node vehicle itself. Else, the service request is forwarded to RSU, which then provides the required service. If the required service is not present at RSU, then the vehicle is replied with a message _\"invalid service\"_. With the use of the cache server, the overall communication time of a system is optimized. Incentives are awarded to edge nodes for efficient sharing of validated services. However, there are pre-defined service charges against each service, which are to be paid by the ordinary nodes. All the financial transactions are stored in blockchain.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 4 Calculation Procedure of Main Circuit Parameters
### Basic Factors
#### 4.1.4 Operating Conditions  
The calculation of the operating conditions needs to consider factors such as the operating configuration, the power transmission direction, the DC voltage level, the DC circuit resistance and the AC system voltage levels at both sides. The typical operating conditions are shown in Table 1.