esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method  
Usually, it is necessary to train the deep learning model with enough fault samples, which is difficult to implement in practical detection. Since the wire rope works properly most of the time, it is easy to acquire normal samples, while it is relatively difficult to obtain fault data. Therefore, it is difficult to achieve accurate recognition from the limited sample size of fault data.  
In this paper, a new method is proposed for quantitative identification of broken wires based on GANs. The framework of the proposed method is illustrated in Figure 3. The specific process of fault diagnosis is displayed as follows:  
1. The broken wire signals of the steel wire rope are collected by a probe using the magnetic flux leakage (MFL) method.
2. In order to make the broken wire signals suitable for model training, these original damage signals are transformed into time-frequency images through continuous wavelet transform (CWT).
3. This image data are divided into a training set and a testing set. In order to meet the requirements of deep learning model training, the training set is used to generate more fault samples based on GANs. These generated data and original data are combined and input into the CNN model for training.
4. The trained CNN model is used to classify the testing set to achieve broken wire diagnosis of the wire rope.  
For the purpose of data generation, a deep convolutional generative adversarial networks (DCGANs) are selected in this paper because of its advantage in generating high-quality images [18]. The structure of DCGANs can be seen in Figure 4. The training process of GANs is very unstable, which often leads to meaningless output of the generator [23]. Compared with GANs, some structural changes were made in DCGANs to improve the quality of samples. The CNN is used in the discriminator and generator of the  
Figure 3: The framework of the proposed method.  
DCGANs to replace the multi-layer perceptron in the GANs. At the same time, to make the whole network differentiable, the pooling layer in the CNN is removed. In DCGANs, deconvolution is used in the generator and the convolution architecture is adopted in the discriminator.  
As shown in Figure 5, a CNN is used in this paper for fault classification [9]. This CNN consists of three convolutional layers with the convolution kernel size of 11 \(\times\) 11. Between the convolutional layers, the max pooling layer is deployed for feature dimensionality reduction. Following the convolution layers and pooling layers, there is a fully connected layer to realize classification of T-type defects.
###### Abstract  
Artificial ground freezing technology is the most important construction method of complex water-bearing soft clay rock. The thermodynamic properties of soft clay rock are important evidence for the design and construction of space resources development, and the variable hydrothermal parameter can directly affect the uncertain thermodynamic properties of soft clay rock. In this work, an array of field experiments on the soft clay rock are carried out, and the anisotropic spatial variations of hydrothermal parameters of soft clay rock are obtained. The statistical variability characteristics of variable hydrothermal parameters are estimated. A stochastic coupling model of soft clay rock with heat conduction and porous flow is proposed, and the uncertain thermodynamic properties of soft clay rock are computed by the self-compiled program. Model validation with the experimental and numerical temperatures is also presented. According to the relationship between anisotropic spatial variations and statistical variability characteristics for the different random field correlation models, the effects of the autocorrelation function, coefficient of variation, and autocorrelation distance of variable hydrothermal parameters on the uncertain thermodynamic properties of soft clay rock are analyzed. The results show that the proposed stochastic analysis model for the thermal characteristics of soft clay rock, considering the spatial variability of frozen soil layers, is scientifically reasonable. The maximum standard deviation of average thickness is 2.33 m, and the maximum average temperature is 2.25 \({}^{\circ}\)C. For the autocorrelation function, the most significant impact comes from DBIN. For the coefficient of variation, the most significant impact comes from thermal conductivity. Different variations of hydrothermal parameters have different effects on the standard deviation of soft clay rock temperature. The biggest influence is the thermal conductivity, while the lowest influence is the specific heat capacity.  
soft clay rock; uncertain thermodynamic properties; stochastic analysis method; spatial variability; influencing factor +
Footnote †: journal: Applied sciences  
1 SINOPEC Key Laboratory of Geology and Resources in Deep Stratum, Beijing 102206, China 12 SINOPEC Petroleum Exploration and Production Research Institute, Beijing 102206, China 2  
The Natural Resources and Planning Bureau of Yiling District, Yichang 443100, China 1  
State Key Laboratory for Geomechanics and Deep Underground Engineering, School of Mechanics and Civil Engineering, China University of Mining and Technology, Yuzhou 221116, China 4
## 3 Results and Discussion
### Application of Electronic Nose to the Odor of Fish Meal of Different Qualities
#### 3.1.2 Response of Electronic Nose to Fish Meal of Different Qualities  
Figure 2 was the fingerprint of fish meal of different qualities obtained by considering the relation steady-state response average value, as an example. As shown in the figure, the fingerprint of fish  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Sensor** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** & **S10** \\ \hline RSD/\% & 2.72 & 1.05 & 3.15 & 4.65 & 2.43 & 3.53 & 1.91 & 2.24 & 1.26 & 2.27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Repeatability of response values of E-nose 10 sensors.  
meal of different qualities differed to some extent, which provided the basis for the classification of fish meal quality in the future. Therefore, in subsequent research, the impact of each sensor's characteristic values on the classification of fish meal samples of different qualities must be analyzed to compare the contribution of different characteristic values.  
Based on gas chromatography-mass spectrometry, more than 30 compounds, including aldehydes, alcohols, ketones, nitrogen containing, oxygen containing or sulphur-containing compounds, esters, and alkanes were identified in fish meal. These volatile organic compounds (VOCs) contain both reducing and oxidizing agents that increase/decrease the electrons from crystal surface of MOS (Metal oxide semiconductor), leading to the decrease/increase in resistance of gas sensor. Therefore, a little  
Figure 2: Fingerprints of different fish meal samples with the relation steady-state response average value as an example (samples numbered 1 (**a**); samples numbered 2 (**b**); samples numbered 3 (**c**); samples numbered 4 (**d**); samples numbered 5 (**e**); samples numbered 6 (**f**)).  
decrease in sensor responses may cause from suppression of oxidizing gas emission. In addition, the TGS 2620 and TGS 2602 show high sensitivity in sensor responses with different fish meal samples. In fact, TGS 2620 and TGS 2602 exhibit high sensitivity to alcohols. In case of the fish meal, alcohols were found to be one of the most dominant volatiles in the fish meal. It suggests that alcohol odors may play an important role in detection of fish meal freshness based on single gas sensor. With the increase in storage levels, the response of MQ137 gradually increased, indicating that the concentration of ammonia and amine compounds increased gradually with the increase of storage time. In sample 6, the response of MQ137 increased rapidly, indicating severe corrupt samples had higher concentrations of ammonia and amines which were found in GC-MS assays. To support our above proposed sensing mechanism, the volatile compounds of fish meal were analyzed by using GC/MS. All volatile compounds of fish meal are listed in Table 4. One can be observed from Table 4 that no H\({}_{2}\)S was found from GC-MS experiment but MQ136 showed a response to fish meal (see Figure 2). This comes from cross sensitivities of MQ136 to many VOCs such as ethanol, ammonia, and toluene.
# Haze Removal Based on Refined Transmission Map for Aerial Image Matching
## 2 Physical-Based Image Dehazing and Matching
### Correction of Transmission Map Using Hidden Markov Random Field
\[V_{c}\left(x_{i},x_{j}\right)=\ \frac{1}{2}\Big{(}1-I_{x_{i}x_{j}}\Big{)} \tag{19}\]  
From the above equations, we obtain Equation (20) to smooth the edges of the block artifacts, as follows:  
\[x_{i}^{(k+1)}= \begin{array}{c}argmax\\ l\in L\end{array} \left[U(y_{i}|l)+\sum_{j\in N_{i},z_{j}=0}V_{c}\Big{(}I|x_{j} ^{(k)}\Big{)}\right] \tag{20}\]  
where \(\mathbf{z}\) is the assumption of the binary edge map (if \(z_{i}\ =1\), the edge is not deteriorated; otherwise, the edge is deteriorated) and \(V_{c}\) is the clique potential. Algorithm 1 explains each step to obtain a refined transmission map using the HMRF-EM method. ```
Input: Hazy Image (\(\mathbf{y}\)) Output: Dehazed image (\(\mathbf{x}\))
1: Calculate the dark channel prior (DCP) using the Equation (2)
2: Use the dark channel prior to estimate atmospheric light. 3: Calculate the DCP estimated transmission map by the Equation (3). 4: Compute the refined estimated matting transmission map by using the HRMF-EM method by Equation (20). 6: Calculate the new image by recovering the scene radiance. ```  
**Algorithm 1**Procedure for estimating a refined transmission map with HMRF-EM method and dehazed image by recovering the scene radiance.
## 4 Experiments and Result Analysis
### Experimental Environment Settings  
In this study, all the experiments were implemented in Python 3.10. The proposed network model was implemented in the PyTorch 2.3.1 framework, trained on Anaconda3, PyCharm 2020.1, and performed on hardware environment of AMD Ryzen 9 and NVIDIA GeForce RTX A6000. The detailed experimental settings are listed in Table 2.
## 2 Results and Discussion  
### Preparation of Polymer-Supported Azolium Salt PS-L3 Using Merrifield Resin  
This study began with the synthesis of polymer-supported benzimidazolium chloride from commercially available Merrifield resin (1.3 mmol Cl/g, cross-linked with 1% DVB, 200-400 mesh). The hydroxyapatite-functionalizedazole **1** was derived from optically active (\(S\))-leucinol [46]. A mixture of **1** and chlorophyll polystyrene resin **2** (**1/2** = 10/1 molar ratio) in DMF was stirred at 70 \({}^{\circ}\)C for 5 days (Table 1). After the coupling reaction, the mixture was filtered through a membrane filter (pore size: 0.2 \(\upmu\)m), and the resulting solid resin was washed with MeOH and dried, yielding the polystyrene-supported benzimidazolium chloride PS-**L3**. Its azolium loading was determined based on the nitrogen content obtained from an elemental analysis (Table 1). The nitrogen content introduced into the polymer  
Figure 2: Asymmetric silane reduction reaction of ketones catalyzed by an NHC–Ir complex. References [42; 43; 44; 45; 46; 47].  
was 3.55 wt%, indicating an azolium loading of 0.84 mmol/g (entry 1, run 1). The coupling reaction of **1** with **2** is reproducible, as confirmed by three independent trials (entry 1, runs 1-3), yielding consistent results in both the reaction and elemental analysis. The reaction conditions (70 \({}^{\circ}\)C for 5 days) follow those of a known procedure [41]. When **1** reacted with **2** at 70 \({}^{\circ}\)C for 1 day, a PS-**L3** with a lower azolium content was obtained (entry 2).  
Additionally, PS-**L3** was characterized using IR spectroscopy (Figure 3). For comparison, the spectrum of Merrifield resin **2** is also included in Figure 3 to highlight the differences between PS-**L3** and **2**.  
The characteristic methylene (CH\({}_{2}\)) group of the benzyl chloride moiety in Merrifield resin **2** was observed at 1265 cm\({}^{-1}\)[39; 40], but this band is absent in the spectra of PS-**L3**. The medium-intensity band at 1556 cm\({}^{-1}\) in PS-**L3** is attributed to the out-of-phase N-C-N stretching of the anchored benzimidazolium. A new absorption band at 1676 cm\({}^{-1}\) appeared due to the C=O bond stretching vibration from the side arm of the azolium ring in PS-**L3**. The broad bands around 3300 cm\({}^{-1}\) are associated with the hydroxy group, amide group, and tertiary nitrogen group, respectively. These spectral features confirm a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entry** & \multicolumn{3}{c}{**Elemental Analysis of PS-L3**} & **Azolium Content 1** \\ \hline
1  & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preparation of PS-**L3** and reproducibility of the experimental procedure.  
Figure 3: IR spectra of Merrifield resin **2** (top) and PS-**L3** (bottom).  
successful substitution reaction between **1** and **2**, resulting in the anchored NHC ligand precursors on the polystyrene resin.
###### Abstract
As for the auditory environment, a sound is presented when an object collides with the reconstructed environment, which enables the real-world environment to be reproduced in more detail. As a result, the VR play area can be extended to the entire building, and a larger, more realistic experiment can be performed in the virtual space. In this paper, we propose a dynamic generation system of impact sounds, related to dynamically reconstructed objects, as an implementation of an auditory environment in building-scale VR. In the proposed method, the material of the target object is first estimated based on a component image of the object. The estimation model is trained using an existing dataset, in which the image of the object and the impact sound are associated with each other, and then the estimation model is transferred to the model, according to the actual usage environment of the system. In this way, we can estimate the material of the object that matches the environment assumed by the user. We generate multiple component images for the object image, acquired from the real space, by performing component segmentation with processing, based on pixel information and clustering. Next, a generative model, that outputs a spectrogram image of the impact sound with the material label output by the material estimation model, is constructed using generative adversarial networks (GANs) [2]. With the architecture we propose, we can obtain better results than the existing methods by comparing the generated images. The generated spectrogram image is converted into sound data by the Griffin-Lim algorithm [3]. By constructing a series of systems, from the acquisition of the component image of such an object to the generation of the impact sound, it is possible to present the user with the impact sound for the real-world object reflected in the virtual environment. In addition, by using GANs, we can present various impact sounds that are not limited to the sound data in the dataset. Our main contributions are three-fold: (1) a dataset of spectrogram images, based on the existing \"Greatest Hits\" dataset, whose creation method can be applied in the future, when a new dataset of sounds is constructed, (2) a method for generating impact sounds from object images in real time using GANs, and (3) the results of our evaluation experiment of the proposed method, conducted with subjects.
## 3 TCM Protocol
### The Topology Maintenance Phase  
After clustering, the network will be in the topology maintenance phase. The sensor nodes will establish a safe communication link with a cluster head, and then send the collected data to the cluster head. The mobile sinks will collect the data fused by cluster heads along with the planet route. Because of the mobility of the nodes, the topology may change. The three main reasons are as follows: the first is the nodes' movement within the cluster, the second is the nodes' movement between the different clusters, and the last is that some nodes will occasionally join in or die. Malicious nodes can also forge legal nodes, and obtain sensitive network information. In order to avoid this happening, we must perform identity authentication for the nodes after their dynamic changes. Besides considering the security factor, the energy consumption and efficient transmission of data are also important factors which should be seriously considered.  
Before the nodes' deployment, the server creates a couple of keys for each node, namely the public key \(P\) and private key \(S\), and they satisfy the equation \(P=S*\ G\), where \(G\) is the base point  
Figure 3: The clustering flowchart.  
on the elliptic curve. Each sensor node stores a unique identity \(ID_{N_{i}}\), a couple of keys (\(S_{N_{i}}\),\(P_{N_{i}}\)), digital signature (\(W_{N_{i}}\), \(w_{N_{i}}\)), binary symmetric function \(F\), and the hash function \(H\). The mobile sink restores the only identity \(ID_{MS}\) of the whole network, a couple of keys (\(S_{MS}\),\(P_{MS}\)), digital signature (\(W_{MS}\), \(w_{MS}\)) and the hash function \(H\). The expression of the digital signature (\(W_{i}\), \(w_{i}\)) is shown as follows:  
\[\left\{\begin{array}{l}W_{i}=r_{i}\times G=(x_{w_{i}},y_{w_{i}})\\ w_{i}=r_{i}{}^{-1}(H(ID_{i}\parallel T_{i})+S\times x_{w_{i}})(mod~{}n)\end{array}\right. \tag{5}\]  
where \(r_{i}\) is a random number. \(G\) is the base point on the elliptic curve. \(H\) is the hash function, and \(S\) is the private key.  
If node \(i\) sends a verification request for the digital signature (\(W_{i}\), \(w_{i}\)) within time \(T\), we can prove the legitimacy of node \(i\) only if we prove the following equation \(N_{i}=W_{i}\) is true. The process is shown as follows:  
\[\begin{array}{l}N_{i}=w_{i}^{-1}H(ID_{i}\parallel T)G+w_{i}^{-1}x_{w_{i}}P \\ =w_{i}^{-1}(H(ID_{i}\parallel T)+x_{w_{i}}S)G\\ =w_{i}^{-1}r_{i}r_{i}{}^{-1}(H(ID_{i}\parallel T)+x_{w_{i}}S)G\\ =w_{i}^{-1}r_{i}\left\{r_{i}{}^{-1}(H(ID_{i}\parallel T)+x_{w_{i}}S)\right\}G \\ =w_{i}^{-1}r_{i}w_{i}G\\ =W_{i}\end{array} \tag{6}\]
## 6 Numerical Example  
We illustrated the efficiency, accuracy, and applicability of our procedure by comparing the approximate solutions given by Equations (93) and (94), with the results obtained numerically by means of a fourth-order Runge-Kutta method. For the coefficients of Equations (77) and (78) given in Appendix B, we had the dimensionless parameters: \(m_{2}=0.0215\); \(m_{3}=0.00054\); \(a_{1}=2.161\); \(a_{2}=0.0341\); \(a_{3}=-0.000024\); \(a_{4}=0.53\); \(b_{3}=0.014\); \(b_{4}=0.4\); \(b_{5}=0.433\); \(b_{6}=0.0052\); and \(b_{7}=0.338\).  
The convergence-control parameters and the frequencies were:\[\begin{array}{c}\mathrm{C_{1}}=-2.52517\times 10^{-5},\mathrm{C_{2}}=-5.812576 \times 10^{-5},\mathrm{C_{3}}=1.424351\times 10^{-5},\mathrm{C_{4}}=-2.681332\times 10^{-4},\mathrm{C_{5}}=\\ 5.487307\times 10^{-5},\mathrm{C_{6}}=-1.8129083\times 10^{-5},\mathrm{C_{7}}=-1.754587 0\times 10^{-7},\mathrm{C_{8}}=-1.067565\times 10^{-5},\ \mathrm{C_{9}}=\\ 1.67156\times 10^{-6},\ \omega_{1}=1.471,\ \omega_{2}=0.727\end{array} \tag{95}\]  
To validate the present results obtained with OAFM, in Figure 2, we illustrated a comparison between analytical approximate solutions (93) and (94) and the numerical solution.  
In another case, considering an increased value of the parameter \(b_{5}\) from \(b_{5}=0.433\) to \(b_{5}=0.52\), we obtained the convergence-control parameters: \(\mathrm{C_{1}}=-0.0000252435\), \(\mathrm{C_{2}}=-0.0000581228\), \(\mathrm{C_{3}}=0.0000142428\), \(\mathrm{C_{4}}=-0.000268119\), \(\mathrm{C_{5}}=0.0000548702\), \(\mathrm{C_{6}}=-0.0000177365\), \(\mathrm{C_{7}}=-9.70021\times 10^{-8}\), \(\mathrm{C_{8}}=-0.0000102798\), and \(\mathrm{C_{9}}=1.59806\times 10^{-6}\), and the obtained approximate solutions are presented in Figure 3, in comparison with numerical integration results.  
It is easy to observe that our approximate solutions of CNT were nearly identical to the numerical integration results, which proved the efficiency of our technique. The maximum errors between solutions (93) and (94) and the numerical results were 0.113% and 0.093%, respectively.
## Appendix A Measurable Spaces and Related Concepts
** The subsets that belong to the \(\sigma\)-algebra \(\mathcal{F}\) of subsets in a probability space represent the possible events we want to deal with. Why do we bother with a \(\sigma\)-algebra rather than simply allowing all subsets as possible events? We can certainly do it if \(\Omega\) is finite. In the infinite case, however, we need to be careful. For example, if \(\Omega=\mathbf{R}\), then provably there exists no measure on _all_ subsets that satisfies the axioms of a probability measure; there are always non-measurable sets, even though they tend to be contrived (the proof requires the Axiom of Choice). **Random variable. ** A _random variable_, in the most general setting, is a measurable function from a probability space to a measurable space. Let us illustrate it with an example. Let \(\Omega\) be the set of all infinite bit sequences, containing infinitely many 1-bits. Each such infinite string is a possible outcome of an experiment. For each such string let us assign a real number in \([0,1]\), which is obtained by viewing the bit string as the binary expansion of the number, after a leading 0 and the decimal point (it will be a 1-1 mapping, due to requiring infinitely many 1-bits). For a set of strings, let the probability measure of the set be some standard measure of the size of the corresponding set of real numbers, such as the Lebesgue measure. Then the \(\sigma\)-algebra \(\mathcal{F}\) of events is the family of those string sets that map into Lebesgue measurable subsets of \([0,1]\). (This does not contain all subsets, as there are non-measurable sets, albeit contrived ones.) To define a random variable, let us chose the set of all non-negative integers as the target measurable space, allowing all subsets in its \(\sigma\)-algebra. Let a random variable defined by the function that maps a bit string into the integer that tells how many 1-bits are among the first 100 bits of the string. It is not hard to see that this satisfies the general definition of a measurable function. Therefore, it indeed correctly defines a random variable.
## 4 Results and Discussions
### Evaluation of the Monitoring Messaging Methods
#### 4.1.2 The Message Build and Parsing Times  
The time required to build and parse monitoring messages was measured for 1,000,000 messages with different numbers of fields (F) to compare different messaging methods. Both operations were performed using Python bindings. The testing was conducted on a machine equipped with an AMD Ryzen 7 PRO 4750U processor and 16 GB of RAM, running Ubuntu 22.04 in WSL2. The results presented in Figure 16 show that the PROTO methods provide significantly shorter build and parsing times for messages of all sizes (number of fields) when compared to the PMT methods. The time difference between the PROTO and PMT methods decreases with the number of fields. The PROTO-ANY and PROTO-BLOB methods exhibit similar message-building and parsing times, with the PROTO-ANY method requiring less time in both cases.