esa-sceva/satcom-chunk-collection huggingface dataset:
# A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion
## 4 Experimental Setup
### Network Configuration  
The size-controlled AFGAN model is designed based on the measured data set. The input to the generator has a uniform distribution with a 100-dimensional noise vector z, and z is projected onto a four-dimensional convolutional representation with eight fractional-strided convolution layers. The convolution kernel size is 5 \(\times\) 1. For each layer, the convolution kernel numbers \(\times\) the signal lengths are 2048 \(\times\) 8, 1024 \(\times\) 16, 512 \(\times\) 32, 256 \(\times\) 64, 128 \(\times\) 128, 64 \(\times\) 512, 32 \(\times\) 1024, 16 \(\times\) 1024, and finally 1024 \(\times\) 1-dimensional single-channel signals are output through these characterization transformations. The output layer in the generator uses the Tanh function, and the other layers use the ReLU activation functionn [20].  
The discriminator was also a fully convolutional network, building the reverse process of the generator. The input signal is a 1024 \(\times\) 1-dimensional single-channel vector. There are eight strided convolution layers. The channel number doubles and the signal length halves (except in the second layer, whose signal length remains the same as the first layer) per layer compared with the previous layer. The final output is a scalar, which denotes the probability of the generated sample belonging to the real training data. The Leaky ReLU activation functionn [20] is used in all convolution layers in the discriminator.
# A Review of the Evolution Characteristics and Argillization of Clay Interbeds in Rockslides
## 4 Observation Technique and Analysis Method of Clay Interbed
### Analysis Method of Clay Interbed
#### 4.2.2 Statistic Methods  
There are many parameters involved in the argillization process, and the reactions occur simultaneously. However, statistics are well-suited for dealing with multiple variables, and internal correlation can be analyzed using a large amount of data, which play a significant role in mechanism research.  
[PERSON], [PERSON] [69] conducted a statistical analysis using [PERSON]'s correlation coefficient method and an r-type cluster tree. The ion concentration, which could reflect the degree of reaction of specific clay minerals, was obtained. Three groups of ions were classified as follows: Na\({}^{+}\), Mg\({}^{2+}\) and Cl\({}^{-}\); K\({}^{+}\) and Ca\({}^{2+}\); F\({}^{-}\). This classification in statistical analysis showed a close correlation between Mg\({}^{2+}\) and Na\({}^{+}\) ions. Montmorillonite, calcite, and fluorite were the sources of Na\({}^{+}\), Ca\({}^{2+}\), and F\({}^{-}\), respectively. It was concluded that K\({}^{+}\) could be used as a standard to evaluate the hydrolysis and ion exchange between illite and montmorillonite under the influence of a strong acid because only a small fraction of K\({}^{+}\) ions could be separated by dissolution. Furthermore, by analyzing the comprehensive data, it was found that clay minerals exhibited a strong fixation and adsorption effect on K\({}^{+}\).
## 5 Experiments with Frame Aggregations
### Packet Success Rate  
To demonstrate the effect of the IEEE 802.11 ax standard, we let a wireless node (sender) move away from the other node (receiver). The sender moved at a speed of 6 m/s during a simulation time of 12 s while sending CBR traffic of 150 Mbps to the receiver. The receiver remained at a fixed location. The initial distance between the sender and receiver was set to 5 m. Thus, the distance between the two nodes changed from 5 to 77 m during the simulation time. Both wireless nodes are quipped with one antenna each. Thus, \(1\times 1\) SISO mode was used by the sender. The sliding window time was set to 300 ms considering the simulated network environment.  
Figure 4(a) shows the experimental results of the packet success rates according to the SINRs of the received packets when the sender adopted one of the 12 MCS levels during the simulations. The experimental results in Figure 4(a) clearly show that the packet success  
rates of the packets decreased when the SINRs of received packets deteriorated regardless of the used MCS levels. Figure 5b shows the changes in the packet success rates according to the distances between the sender and receiver. The results in Figure 5b also show that the packet success rates decreased when the distance between the sender and receiver increased independently of the used MCS levels.  
The experiment results indicated that, by using low MCS levels, the packets are more likely to be successfully delivered to the receiver than when using high MCS levels when two nodes are far from each other. In addition, by comparing the experiment results in Figure 1a in Section 4.1 with those in Figure 5a in Section 5.1, we observe that the sender with the IEEE 802.11 ax standard could utilize higher SINRs. This is because of the improved performance by the IEEE 802.11 ax standard.
## 4 Conclusions  
In this paper, the changes in electron motion states and radiation distributions under the influence of chirped parameters and pulse width cross parameters are studied. The effect of pulse widths on chirp parameters is investigated. It is found that the compound  
Figure 5: Comparison of the frequency spectrum of the peak electron radiated power under three pulse widths. The chirp parameters \(c_{0}\) from (**left**) to (**right**), respectively, are \(-0.05,-0.03,-0.01,\,0,\,0.01,\,0.03,\) and \(0.05\).  
effect of chirp parameters and a pulse width has a significant effect on t = electron trajectory and radiation characteristics, and the influence of chirp parameter changes under the same pulse width is explored. The asymmetry of the chirping effect is proposed, and the mechanism of the chirping effect is explored, which provides a theoretical basis for the parameter formulation of chirped pulse and electron interactions.  
The conclusions of this paper hold significant practical application value, as they provide analytical theories for the special trajectory electrons observed in actual nonlinear Thomson scattering experiments. Additionally, this study elucidates the pulse laser parameters and underlying mechanisms responsible for the generation of such electrons, offering relevant theoretical parameters for precise electron trajectory control.  
Conceptualization, J.L. and Y.T.; Methodology, J.L. and Z.W.; Validation, J.L.; Formal analysis, J.L. and Q.Z.; Investigation, J.L., J.X., Z.W., Q.Z. and J.Y.; Resources, Y.T.; Data curation, J.L. and J.X.; Writing--original draft, J.L.; Writing--review & editing, J.L., Q.Z., J.Y. and Y.T.; Visualization, J.X.; Project administration, Y.T. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the National Natural Sciences Foundation of China under grants No. 10947170/A05 and No. 11104291, the Natural Science Fund for Colleges and Universities in Jiangsu Province under grant No. 10 KJB140006, and the Natural Sciences Foundation of Shanghai under grant No. 11 ZR1441300.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Influence of MOSFET Parasitic Capacitance on the Operation of Interleaved ZVS Boost Converters
## 1 Introduction
The analyzed converter topology in two-phase variation is presented in Figure 1. Connecting capacitors \(C_{1}\) and \(C_{2}\) in parallel to transistors T\({}_{1}\) and T\({}_{2}\) cause resonance to occur between them and inductors \(L_{1}\) and \(L_{2}\), which results in the capacitors being discharged during transistors' turn off, thus allowing turn-on in zero-voltage conditions. Gate signals of the transistors are phase shifted by 180\({}^{\circ}\) to decrease input current ripple, similarly to two-phase interleaved DC/DC converters. Waveforms of the chosen currents and voltages are shown in Figure 2.  
Figure 1: Examined converter topology.
# Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks
## 3 Feature Map Caching Method
### Feature Vector Extraction  
Feature maps are a set of features obtained by convolving a trained kernel on each image. In addition, images that are classified into the same class have similar features. This is because the  
Figure 1: Proposed cached CNN structure. The yellow line indicates the flow in which the input image is classified by the CNN. The red line indicates the flow in which input image is classified by the cache.  
intensity of each feature extracted from the images is similar. To generate a feature vector, this intensity can be expressed as energy. Therefore, for a feature map \(F_{i}(x,y)\) of size \(P\times Q\), we define the energy \(E\) as follows:  
\[E(i)=\sum_{x}\sum_{y}\lvert F_{i}(x,y)\rvert \tag{1}\]  
where \(i\) is the index of the feature map of each convolution layer.  
From this, each feature map \(F_{i}(x,y)\) is described by a mean \(\mu_{i}\), which represents the intensity of the individual pixels, and a standard deviation \(\sigma_{i}\), which indicates the extent to which each pixel differs from adjacent pixels.  
\[\mu_{i}=\frac{E(i)}{PQ} \tag{2}\]  
\[\sigma_{i}=\sqrt{\frac{\sum_{x}\sum_{y}(\lvert F_{i}(x,y)\rvert-\mu_{i})^{2}} {PQ}} \tag{3}\]  
A feature vector \(V\) using the mean and standard deviation of feature maps for each convolution layer is generated as follows:  
\[V=(\mu_{0},\ \sigma_{0},\mu_{1},\sigma_{1},\mu_{2},\sigma_{2},\ldots,\mu_{i-1}, \sigma_{i-1}) \tag{4}\]  
The feature vector extracted above applies to both the step of writing the image classified from the CNN to the cache and of classifying the image using the cache. However, when writing classified images to a cache, it is undesirable to write feature vectors for all input images because the proposed method is used as a cache. Therefore, to have only one feature vector in one class for each convolution layer, images classified into the same class should have only one feature vector per convolution layer. That is, a representative feature vector corresponding to each class must be generated. Therefore, each time a new feature vector \(V_{new}\) is cached in the same class, the mean feature vector \(V_{cachd}\) is updated as shown in Equation (5):  
\[V_{cachd}=\frac{(V_{cached}\times n)+V_{new}}{n+1} \tag{5}\]  
where \(n\) denotes the number of feature vectors accumulated for one class.  
For this calculation, the cache also records the number of cumulative feature vectors for each class. Figure 2 shows this feature vector extraction process.
## 2 Materials and Methods
### LC/MS Analysis  
The LC/MS method employed in this study was based on a reported study [18]. The dry lipids were reconstituted with pure methanol. In order to remove any particles, the sample solution was filtered (filter pore size: 0.20 \(\upmu\)m) and then transferred into an LC/MS insert vial. The LC/MS analysis was conducted employing a highly sensitive Q ExactiveTM Hybrid Quadrupole-OrbitrapTM mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) attached to an ultra-high performance liquid chromatography unit (Ultimate 3000 system, Thermo Fisher Scientific). Ions of the lipid species were generated using an electrospray ionization (ESI) source.  
A 5 \(\upmu\)L sample was injected by an autosampler, and the lipids were separated in a hydrophobic column (Acclaim 120 C18, 150 mm \(\times\) 2.1 mm, 3 \(\upmu\)m, Thermo Scientific, Waltham, MA, USA). Throughout the analysis, the LC column and the autosampler chamber were kept at 50 \({}^{\circ}\)C and 10 \({}^{\circ}\)C, respectively. The mobile phase (A and B) was delivered at a flow rate of 300 \(\upmu\)L/min for elution. Mobile phase A was prepared as follows: water- acetonitrile-methanol (2:1:1_v/v/v_), 0.1% formic acid, and 5 mM ammonium formate. Mobile phase B consisted of acetonitrile-isopropanol (1:9_v/v_), 0.1% formic acid, and 5 mM ammonium formate. The elution conditions were programmed as follows: gradients starting at 20% B, linearly increased to 100% B in 50 min, and isocratic at 100% B for 10 min. At 60.1 min, the gradient returned to the initial condition (20% B) and was maintained at 20% B until 70 min in order to equilibrate the column. Blank (pure methanol) was run before each sample injection.  
The MS conditions were optimized as follows: capillary temperature, 250 \({}^{\circ}\)C; probe heater temperature, 350 \({}^{\circ}\)C; spray voltage, 3.5 kV (for positive mode) and 2.5 kV (for negative mode); S-lens RF level, 50; auxiliary gas flow, 15 (au); sweep gas flow, 0 (au); and sheath gas flow, 50 (au). Data were acquired in both full MS and data-dependent MS/MS (dd-MS2) modes using Xcalibur v3.0 Software (Thermo Scientific). The settings for full MS mode were as follows: automatic gain control (AGC) target, 1 \(\times\) 10\({}^{6}\); \(m/z\) range, 220-2000; mass resolving power, 70,000 (FWHM, at \(m/z\) 200); and maximum sample injection time (IT), 100 ms. The following conditions were used for dd-MS2: mass resolving power of 17,500 (FWHM, at \(m/z\) 200); AGC target of 1 \(\times\) 10\({}^{5}\); maximum IT of 80 ms; loop count of 5; TopN of 5; isolation window of 2.0 \(m/z\); normalized collision energy (NCE) of 30.0 eV;dynamic exclusion of 15.0 s; and stepped NCE of 15.0% for positive mode and 35.0% for negative mode.  
The raw data were directly subjected to LipidSearch(tm) software version 4.2.13 (Mitsui Knowledge Industry, Tokyo, Japan) for lipid annotation and semi-quantification. The parameter settings are provided in Table S1. The processed data were exported to MS Excel (2021) in order to perform statistical analysis and to create necessary charts or plots.
## 3 Proposed Approach
### Contrastive Loss and Training  
The training process for the MD focuses on optimizing the model's parameters to compute embeddings for both subword-level contextual representations and morphological analyses. In order to effectively learn the parameters for both correct and incorrect analyses (for each tag in the analysis, we use the same dimensional dense vector for the root, POS, and morpheme tags, and we do not distinguish them separately here), we introduce a contrastive loss function, which reduces the distance between the correct analysis and contextual embeddings, while maintaining a margin between correct and incorrect embeddings. The goal is to reduce the distance between the contextual embedding and the correct analysis embedding, while increasing the separation between the contextual embedding and the incorrect analysis embeddings.  
This process is set up as a metric-based approach, where the objective is to minimize the distance between the correct analysis and the context, while maximizing the margin between the correct and incorrect analyses from multiple candidates.  
In order to illustrate how the contrastive loss works in the proposed approach, we take the dot product as the distance metric in the following notation, and for other distance metrics, the process should be similar and the gradient will be calculated with the corresponding formulae of the chosen distance metrics. For the contrastive loss, we calculate the distance (in the case of the dot product) between the context embedding \(c_{i}=E_{PLMSi1}\) and the morphological analysis embedding \(E_{a}(a_{ik})\), treating it as the similarity score.  
\[p_{ik}=softmax(\epsilon_{i}\cdot E_{a}(a_{ik})) \tag{14}\]  
where \(p_{ik}\) is the similarity score between the context and the \(k\)-th analysis embedding. By taking the negative of the dot product, we ensure that higher similarity (larger value of dot product) results in a smaller distance, and lower similarity (smaller value of dot product) results in a greater distance.  
The contrastive loss for MD includes two parts:  
(i) Positive loss: minimizes the distance (maximize similarity) for the correct morphological analysis \(a_{i}^{*}\) for word \(w_{i}\)  
\[\mathcal{L}_{\mathrm{positive}}=-\log(p_{i,a_{i}^{*}}) \tag{15}\]  
where \(p_{i,a_{i}^{*}}\) is the probability for the correct morphological analysis \(a_{i}^{*}\) for word \(w_{i}\).  
(ii) Negative loss: ensures that the sum of probabilities for incorrect analyses is below a margin \(\delta\).  
\[\mathcal{L}_{\mathrm{negative}}=\max\left(0,\delta-\sum_{k\
eq a_{i}^{*}}p_{ik}\right) \tag{16}\]where \(\sum_{k\
eq a_{i}^{*}}p_{ik}\) is the sum of probabilities for all incorrect analyses for the \(i\)-th word, and \(\delta\) is the margin.  
The total loss for the entire training set, averaged over \(N\) words, can be defined as follows:  
\[\mathcal{L}=\frac{1}{N}\sum_{i=1}^{N}\left(-\log(p_{i,a_{i}^{*}})+\text{max} \left(0,\delta-\sum_{k\
eq a_{i}^{*}}p_{ik}\right)\right) \tag{17}\]
## 3 Results and Discussion
### Two-Phase Flow Results
#### 3.2.1 Velocity Fields
An unstable flow phenomenon with a velocity distribution of 0.7-1.5 was found in an area with a wide gap between slugs. The reason for this phenomenon could be that even at the center where the external magnetic force is strong, if the gap between slugs is narrow, the slug velocity exhibits more action on the magnetic particles than the external magnetic force does, drawing the magnetic particles closer. In a wide gap between slugs, the slugs have a partial effect, and the magnetic particle shows a nonuniform velocity field. Figure 14 shows graphs comparing the theoretical, single-phase nonmagnetic particle, single-phase magnetic particle, and two-phase experimental velocities near the wall and at the center. Near the wall, the velocity of the slug flow is higher than the other velocities. The velocity between under the slug and the wall is higher than that between the wall and the slug. A lower velocity was measured at the center due to the external magnetic force effect. At the narrow gap between the slugs, a high velocity of approximately 1.7 was measured, but at the wide gap, a low velocity of approximately 0.9 was measured. Figure 12: Velocity fields of the slug flow near the wall. The slug is located (**a**) above (**b**) beside. Figure 13: Velocity fields of slug flow at the center. The slug is located with a (**a**) narrow (**b**) wide gap.
# Distribution of Magnetic Flux Density under Stress and Its Application in Nondestructive Testing
###### Abstract
This paper investigates and assesses a carbon steel plate subjected to elastic deformation under different loading stresses. The fact that MMM can be used under a low field makes the choice of a highly sensitive and reliable sensor crucial for the inspection; hence, the use of a TMR sensor here. The characteristics of the magnetic memory effect on the surface and bulk of the SA 106 sample are analyzed on the basis of the magnetic flux density measurement along the X, Y, and Z directions, and, on the other hand, through the modeling of the stress distribution. As MMM offers a new arena to assess residual stress and strain status, and based on previous research [30; 31; 32; 33; 34; 35; 36; 37; 38], the variations of magnetic signals during the stress process are not addressed in detail. This implies that it is not clear which components of the stress were specifically measured. Most studies concentrate on the variation of tangential or normal component signals, and some missed detection may ensue if the detection results are based only on this signal. There are also relatively few studies that have been performed that consider the memory effect of metal under compressive loading. Using a tridirectionally measurement will make the results more proficient and accurate. The distribution and intensity of stresses on the plate were analyzed with the Ansys software to determine the most effective direction. Moreover, a correlation was established by comparing the FEM stress analysis and the experimental triaxial flux measurement data. It was determined whether the measured data had a close relationship with the compressive stress and shear stress components, and the most effective direction was identified. The error range and the features of the quantitative evaluation were examined. The relational expression was verified by the results. The findings are optimistic for a possible introduction in the industry for 3D stress and strain analysis, including the nondestructive testing and evaluation.
# Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification
## 2 Materials and Methods
### Data Set
Relying on the assumption that the average temperature surrounding a passenger is more closely related to their thermal comfort compared to the other mentioned parameters, we decided to keep the average temperature \(T_{a}\) and discard the other values with higher VIF than 5. Even though \(RH\) has a high correlation to \(T_{a}\) (\(-0.87\)), see Figure 3, we decided to keep it as its VIF is below 5 and domain knowledge has shown that the humidity plays an important role in the thermal sensation. The resulting VIF values are found in Table 4, where it can be seen that all values lie beneath the 5 VIF threshold. A pairplot of the resulting dataset is represented in Figure 4.  
In order to compare the PMV-PPD model with the ML models, three different parameter sets were employed. The parameter set \(S_{1}\) was used to provide a fair comparison to the PMV-PPD model [10; 23; 24]. To investigate the flexibility of the employed ML classifiers, two further parameter sets \(S_{2}\) and \(S_{3}\) were also created. \(S_{2}\) aims to describe the predictive capabilities of the ML models using measurements that are easy to obtain, whereas \(S_{3}\) uses all the parameters that were shown to be relevant after the EDA. * Parameter set \(S_{1}\). This parameter set only includes the parameters required for modeling using the PMV-PPD model [10; 23; 24]. This set was chosen to ensure comparability between the PMV-PPD model and the ML models, and is defined as follows: \[S_{1}=[T_{a},RH,V_{a},T_{r},I_{cl}]\]
* Parameter set \(S_{2}\). This parameter set includes only the air temperature and humidity, which are easy to obtain within a measurement campaign: \[S_{2}=[T_{a},RH]\]
* Parameter set \(S_{3}\). This parameter set is the result of the EDA. Different tests regarding the encoding of \(age\) and \(BMI\) were carried out. Particularly one-hot encoding and ordinal encoding. The results showed that the best accuracy was achieved by one-hot encoding the \(sex\) parameter into \(Male\) and \(Female\), and by leaving the \(age\) and \(BMI\) at their original numerical values. For further details, see Supplementary Materials. \[S_{3}=[T_{a},RH,V_{a},I_{cl},age,sex,BMI]\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **VIF (Before)** & **VIF (After)** \\ \hline Air temperature & \(T_{a}\) & 63.45 & 4.93 \\ Relative air humidity & \(RH\) & 4.42 & 4.02 \\ Air velocity & \(V_{a}\) & 1.21 & 1.16 \\ Mean radiant & \(T_{r}\) & 60.78 & – \\ Temperature & \(T_{a,out}\) & 11.63 & – \\ Age & \(age\) & 1.07 & 1.07 \\ Sex & \(sex\) & 1.06 & 1.06 \\ Clothing insulation & \(I_{cl}\) & 1.64 & 1.57 \\ Body mass index & \(BMI\) & 1.14 & 1.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variance inflation factors (VIF) before and after feature engineering.