esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
## 5 Experiment  
The dual-telecentric system designed in this study has been applied to a large-sized stepped shaft diameter measurement experimental system. This measurement system is composed of a symmetrical dual-telecentric dual-CCD system, a CCD signal processing system, and an upper-computer display system. The configuration of the large-sized stepped shaft diameter measurement system, incorporating the dual-telecentric system, is shown in Figure 8.  
The system was tested using standard parts with diameters of 652 mm and 750 mm. The measurements were conducted using the large-sized stepped shaft diameter measurement experimental system without our imaging system installed, the same system with our imaging system installed, and a Keyence measuring instrument for comparison. The measurement results are presented in Figure 9.  
Figure 8: Experimental system for measuring the diameter of large-sized stepped shafts.  
Figure 7: Results of the second tolerance analysis.  
In Figure 9, 'A' represents the large-sized stepped shaft diameter measurement experimental system without the installation of our imaging system, 'B' represents the Keyence measuring instrument, and 'C' represents the large-sized stepped shaft diameter measurement experimental system with our imaging system installed. From Figure 9a, it can be observed that in the measurement of a standard component with a diameter of 652 mm, the maximum diameter measured by the large-sized stepped shaft diameter measurement experimental system without our imaging system is 652.055 mm, and the minimum diameter is 652.047 mm. The Keyence measuring instrument measures a maximum diameter of 652.041 mm and a minimum diameter of 652.30 mm. The maximum error of the large-sized stepped shaft diameter measurement experimental system with our system installed is 652.032 mm, and the minimum error is 652.024 mm. From Figure 9b, it can be observed that in the measurement of a standard component with a diameter of 750 mm, the maximum diameter measured by the large-sized stepped shaft diameter measurement experimental system without our imaging system is 750.055 mm, and the minimum diameter is 750.050 mm. The maximum diameter measured by the Keyence measuring instrument is 750.043 mm, and the minimum diameter is 750.044 mm. The maximum error of the large-sized stepped shaft diameter measurement experimental system with our system installed is 750.036 mm, and the minimum error is 750.028 mm. Through comparison, it can be seen that the installation of our imaging system significantly reduces the errors in the measurement of the stepped shaft diameter in the experimental system compared to the system without our imaging system installed. Furthermore, the errors are also reduced when compared to the Keyence measuring instrument.
## 6 Conclusions  
This paper presents a matrix-based energy flow model for the electricity-heat-gas MESs. Specifically, the injection of alternative gas sources and the effect of gas compressibility factor on the DGN state are considered to improve the universality and accuracy of the DGN model. Then, the deviation flow and directional factor are introduced to fundamentally prevent the potential ill conditioning of the gas composition tracking equations. After that, the Jacobian matrices with gradient descent direction are derived to further ensure the convergence of the EFC process solved by the NR method. Finally, adopting the NR-based unified EFC solution strategy, the effectiveness of the proposed method is validated through numerical simulations, and the main conclusions can be drawn as follows:  
* The proposed MES model is accurate, with an error of less than \(1\times 10^{-8}\) compared to the traditional MES model. Owing to the precisely derived Jacobian matrices, the convergence steps of the proposed model are less than 0.5 times that of the traditional method.
* Ignoring the change in the gas compressibility factor will lead to inaccurate estimation of the DGN state, especially the node pressure.
* In the framework of the classic NR solution method, the convergence domains of the proposed modified MES models are significantly expanded under different initial values, gas quality conditions, and load levels.
* The matrix-based formulations for MES models and Jacobian matrices are proven to be computationally efficient, whose time consumption is only one-third of that of the traditional method under the same conditions.  
During the case study analysis, we found that the proposed model still has local non-convergence space under the classic NR iteration format. Therefore, improved NR algorithms can be investigated in the future to further enhance the convergence of the MES model. In addition, the initial value optimization method for NR iteration can also help to further improve the convergence rate of the proposed model.  
Conceptualization, J.W. and J.Z.; methodology, J.W.; software, F.M.; investigation, S.W., R.X. and K.L.; writing--original draft preparation, J.W.; writing--review and editing, F.M.; visualization, S.W.; supervision, J.Z.; funding acquisition, F.M. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Key Research and Development Program of Jiangsu Province (Grant No. BE2020027), the National Key Research and Development Program of China (Grant No. 2022 YFE0140600), and the China Scholarship Council (Grant No. 202306090135).  
Not applicable.
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 4 The Proposed NPIVGAE
### Algorithm and Complexity Analysis  
The flow of our algorithm is demonstrated in Algorithm 1. First, we initialize the network parameters and the block-block correlation matrix \(\mathbf{\Pi}\). For each iteration, the mean and variance of the latent variables \(\mathbf{Z}\) are obtained through step 4, and the noninformative prior of \(\mathbf{Z}\) is obtained according to \(p(\mathbf{Z})\infty\frac{1}{\sigma^{2}}\). Then sample \(\mathbf{Z}\) according to (7). Finally, we calculate the objective function and use gradient descent to update the parameters \(\mathbf{\mu}\), \(\mathbf{\sigma}\), and \(\mathbf{\Pi}\).  
In the following, we analyzed the time complexity of our model. In each iteration, it takes \(O(dn)\) to calculate the \(d\)-dimensional vectors \(\mathbf{\mu}\), \(\mathbf{\sigma}\), and \(\mathbf{Z}\), so the total time complexity is \(O(Edn)\), where \(d\ll n\) and \(E\) represents the number of iterations.  
```
0: The adjacency matrix \(\mathbf{A}\) and the attribute matrix \(\mathbf{X}\). Hyperparameters: Number of iterations \(E\), latent dimension d.
0: Latent variables \(\mathbf{Z}\).
1:Initialize neural networks' parameters and \(\mathbf{II}\).
2:for\(e=1\) to \(E\)do
3:for\(i=1\) to \(n\)do
4: Obtain the mean \(\mathbf{\mu}_{i}\) and variance \(\mathbf{\sigma}_{i}\) according to (4) and (5), respectively.
5: Get the noninformative prior of \(\mathbf{z}_{i}\) using \(p(\mathbf{z}_{i})\propto\frac{1}{\sigma_{i}^{2}}\).
6: Sample to get the latent variable \(\mathbf{z}_{i}\) through (7).
7:endfor
8: Calculate the loss function \(\mathcal{L}\) according to (11).
9: Update parameters \(\mathbf{\mu}\), \(\mathbf{\sigma}\), and \(\mathbf{II}\) with back propagation.
10:endfor
11:return\(\mathbf{Z}\).
```  
**Algorithm 1** NPIVGAE.
## 2 Materials and Methods
### Measurements  
In this study, a general reclining wheelchair without a back-sliding device (Saber GT; Karma, Chiayi, Taiwan) and a reclining wheelchair with a double-sliding device (Smart Wheelchair; KOREC, Incheon, Korea) were compared (Figure 2).  
Figure 3 shows the system model and operating method of the proposed two-slide recline system. The fixed-slide system of the first stage has a link structure connecting the backrest and the seat that operates in relation to the recline-operation angles. The second-stage slide system was constructed by combining a linear guide and a gas spring to support and return the backrest position. This recline system allows the second slide to move in conjunction with the first slide motion, which is in accordance with the recline-operation angle to prevent body-slip between the user and the backrest. The first- and second-slide ranges are approximately 150 mm and 90 mm, respectively. As the second slide moves the required range according to the body type and sitting conditions of the user, it can accommodate most types of users and nearly eliminate body-slip in various positions.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Age (y)** & **Height** & **Weight** & **BMI** & **Level of** & **AIS** & **Total Length** & **Wheelchair** \\  & **(m)** & **(kg)** & **(kg/m\({}^{2}\))** & **Injury** & **AIS** & **of Wheelchair** & **Use Per Day** \\ \hline Patient \#1 & 55 & 1.65 & 64.2 & 23.6 & L1 & B & 15.2 & \textgreater{}11 \\ \hline Patient \#2 & 48 & 1.63 & 65.4 & 24.6 & L1 & B & 15.4 & \textgreater{}11 \\ \hline Patient \#3 & 52 & 1.70 & 64.1 & 22.2 & T11 & A & 20.5 & \textgreater{}8 \\ \hline Patient \#4 & 52 & 1.75 & 66.0 & 21.6 & T11 & A & 8.2 & \textgreater{}7 \\ \hline Patient \#5 & 60 & 1.75 & 85.2 & 29.5 & T7 & A & 14.7 & \textgreater{}8 \\ \hline Patient \#6 & 59 & 1.77 & 75.9 & 24.2 & T11 & A & 13.5 & \textgreater{}10 \\ \hline Patient \#7 & 52 & 1.72 & 69.9 & 23.6 & T11 & A & 13.2 & \textgreater{}6 \\ \hline Patient \#8 & 55 & 1.65 & 74.5 & 27.4 & T11 & A & 11.9 & \textgreater{}6 \\ \hline Patient \#9 & 47 & 1.70 & 80.2 & 27.8 & L1 & A & 7.8 & \textgreater{}6 \\ \hline Patient \#10 & 66 & 1.63 & 50.2 & 18.9 & T12 & A & 15.0 & \textgreater{}6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participants’ general characteristics (N = 10).
## 2 Materials and Methods
### Dataset
TON_IoT provides data in multiple formats, including CSV, TXT, and PCAP, and contains a variety of attack scenarios such as DoS, DDoS, ransomware, reconnaissance, XSS, backdoor, injection, and password attacks. The dataset includes over 21 million samples in its network stream subset alone, as well as smaller subsets for telemetry and operating system logs. The network features (42 in total) capture statistical patterns, flow attributes, and protocol behaviors. What distinguishes TON_IoT is its versatility: it supports supervised, unsupervised, and multi-task learning settings across diverse data modalities. The dataset enables fine-grained anomaly detection, cross-platform behavioral modeling, and time-aware detection strategies. Its heterogeneous composition makes it ideal for evaluating hybrid models, such as Transformer-GAN-AE, that benefit from simultaneous generative learning, feature encoding, and temporal attention across diverse input domains. Prior to feeding the datasets into our proposed architecture, several preprocessing steps were applied to ensure consistency, quality, and compatibility across all input sources. First, redundant, incomplete, and corrupted records were removed to eliminate noise and reduce inconsistencies that could impair model performance. Following this, categorical attributes--if present--were encoded into numerical representations using label encoding or one-hot encoding, depending on the feature semantics. This step was essential for standardizing input across heterogeneous fields, particularly in datasets such as TON_IoT, which include mixed data modalities. After encoding, all numerical features were normalized using min-max scaling to ensure they fall within a uniform range \([0,1]\), which facilitates smoother convergence during training and prevents dominant features from overshadowing smaller-scale variables. To address class imbalance, which is prevalent across all three datasets, we applied a combination of undersampling for dominant classes and oversampling for minority attack categories using SMOTE and random sampling strategies. These steps helped stabilize learning, especially during the adversarial training phase of GANs. Finally, for sequence-based modules such as the Transformer, the datasets were segmented into fixed-length time windows to capture temporal patterns and preserve the ordering of events. Each window was transformed into an input sequence with consistent dimensions, suitable for parallel processing through the encoder. The prepared datasets were then split into training, validation, and test subsets, with stratified sampling to maintain class distributions. These preprocessing efforts ensured a clean, balanced, and model-compatible input structure for robust performance evaluation of our hybrid intrusion detection architecture.
## 1 Introduction
In this respect, it was recently appraised on account of both experimental and theoretical studies that various interconnected phenomena could be at work at metal-support interface, which in turn exert a profound influence on the catalytic activity, including the following [49; 50; 51; 52; 59; 61; 14; 25; 14; 25; 15; 16; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]:  
1. Electronic perturbations linked to bonding interactions between TMs and ceria nanoparticles
2. Facilitation of oxygen vacancies' formation resulting in enhanced reducibility and oxygen exchange kinetics
3. High intrinsic activity of interfacial sites  
However, it is well-known today--thanks to the huge progress in cutting-edge characterization techniques--that the individual characteristics of catalyst's counterparts can notably affect not only their own activity but also the interaction between them with great consequences in catalysis. More specifically, by adjusting the geometrical and electronic features of the different counterparts through suitable synthetic and promotional routes highly active materials are obtained [12; 13; 14; 15; 16; 22; 24; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. Table 1 depicts at a glance, the main physicochemical modifications that can be induced by adjusting each of the aforementioned parameters. The recent advances in relation to the role of shape, size and electronic modulation on inherent reactivity and interfacial interactions were recently reviewed on the basis of CuO\({}_{x}\)/CeO\({}_{2}\) oxides [13]. It became evident that by appropriately adjusting the aforementioned parameters, highly active composites can be obtained with a comparable or even superior performance as compared to NMs-based composites. The present critical review focuses on the particular role of ceria shape on the physicochemical characteristics and the catalytic performance of bare CeO\({}_{2}\), as well as of TMs/CeO\({}_{2}\) composites, with particular emphasis on our recent efforts in the field. The synthesis procedure of bare CeO\({}_{2}\) nanoparticles of well-defined morphology, i.e., nanocubes (NC), nanorods (NR), and nanopolyhedra (NP), as well as of the corresponding ceria-based TMs (Fe, Co, Ni, and Cu) is initially described. A thorough characterization study by applying various characterization techniques is next presented for single CeO\({}_{2}\) and TMs/CeO\({}_{2}\) samples so as to disclose the effect of ceria shape on the intrinsic properties and interfacial phenomena. Finally, the implications of ceria shape engineering will be exemplified on the basis of CO oxidation, N\({}_{2}\)O decomposition, and CO\({}_{2}\) hydrogenation reactions over TMs/ceria catalysts.
## 3 Results
### Measurement of SOAEs  
Following DPOAE data collection, 21 of the 26 newborns were quiet enough to record spontaneous otoacoustic emissions (SOAEs) in the same ear where DPOAEs had been recorded. SOAEs were recorded and repeated without probe removal. To be considered an SOAE, the level had to be at least 3 dB greater than the surrounding points at both lower and higher frequencies, three co-authors had to agree that the SOAE was not noise, and identified SOAEs had to be present in both trials. Noise in the data varied among newborns making identification of SOAEs difficult in some, which is why three co-authors had to agree that the SOAEs identified did not appear random. While the minimum starting point was a level with an SNR of 3 dB in the spectrum, more often than not the SNR values were greater.  
Data from one newborn (male) were too noisy to interpret and two newborns (one male and one female) had data that were quiet enough to analyze, but no SOAEs were identified. Thus, SOAEs are present in 18 newborns (86%), ten females and eight males. The number of SOAEs per ear examined range from 1-10 with an average of 4.94 SOAEs per newborn. A total of 47 and 54 SOAEs were identified in the females and males, respectively. SOAEs are present at frequencies ranging from 1.05-12.25 kHz, with 68% between 1-4 kHz, 18% between 4-8 kHz, and 14% above 8 kHz. SOAE levels range from \(-\)18.76 to 27.05 dB SPL. SOAEs from one newborn are shown in Figure 5.  
Figure 4: Average DPOAE levels in newborns across frequency using either a red (red symbols) or yellow (gray symbols) eartip and FPL (**left panel**) or SPL (**right panel**) for calibration. Solid lines in the top panels represent trial 1 and long-dashed lines in the bottom panels represent trial 2 data. Short-dashed lines in all panels denote the average noise floor for each condition. The number of data points for each frequency are indicated at the top or bottom of the standard error bars in each plot.
## 4 The Proposed Model
### Virtual Relational Knowledge Graph (VRKG) Construction
#### 4.1.2 Knowledge Graph Reconstruction  
After computing virtual relation embeddings, we replace the original triples \((h,r,t)\in\mathcal{T}\) with virtualized triples \((h,\bar{\mathbf{r}},t)\in\hat{\mathcal{T}}\). To formalize this, we define a projection operator \(\phi:\mathcal{R}\rightarrow\mathbb{R}^{d}\), where  
\[\phi(r_{p})=\bar{\mathbf{r}}_{p}. \tag{8}\]  
The reconstructed knowledge graph becomes  
\[\mathcal{G}=\big{(}\mathcal{E},\hat{\mathcal{R}},\hat{\mathcal{T}}\big{)}, \quad\hat{\mathcal{T}}=\{(h,\phi(r),t)\mid(h,r,t)\in\mathcal{T}\}. \tag{9}\]  
This transformation improves embedding quality and facilitates better generalization over sparse relations by reducing high-variance gradients from tail edges.
## 6 Experimental Results and Discussion
### Waveform Test on AISRT  
In this experiment, after signal amplification, the analog image signals were firstly loaded onto the LED driver and then transmitted through optical signals. After the relay transmission by two relay nodes, these optical signals were detected by the PIN photodiode of the final receiver. The digital signals were finally restored to images in the PC after photoelectric conversion by the photodiode and power adjustment by the attenuator. Figure 5 illustrated the waveform test on the AISRT in the water pipe. The purple wave in Figure 5a,b,c indicated the waveform of the video/image signal received by the first relay node, second relay node, and final receiver respectively. The green wave in each figure demonstrated the waveform of the original video/image signal sent by the transmitter. Generally, the analog video/image signal contained two components the field synchronization signal and image data. The signal could only be detected if the field synchronization signal was correctly judged. In Figure 5a,b, these field synchronization signals of first and second relay nodes could basically maintain synchronization with the original image signal from the transmitter, whereas sometimes the severe distortion of these signals could be observed just as depicted in Figure 5c. At this time, the distortion could induce the failure to recover from the video/image signal. Furthermore, the voltage peak-to-peak amplitude value of the original image signal was 1 V, however, this value decreased drastically during the transmission in the 3 m water channel. In the final receiver, the value reached an averagely 325 mV caused by the channel attenuation. This was also the reason why the image  
Figure 4: The structure of image data frame.  
of the final receiver is dim. As conclusion, the short-range (\(<\)1 m) video/image transmission could be basically achieved by the AISRT method, nevertheless, for the longer distance, due to the severe distortion, this method could not be applied even using signal relaying.
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
## 2 Methodology
### SRT Technique
Furthermore, the _phase shift_ method [47] was implemented to construct the dispersion image for each record. This method uses a _Fast Fourier Transformation_ to convert the acquired seismic records from the time domain to the frequency domain, while it also calculates the _phase velocities_ of the Rayleigh waves. Each multichannel seismic record was converted to the frequency domain, and their corresponding dispersion curves were generated by picking the maximum energy amplitude at the frequency range of 4.5-50.0 Hz. Afterwards, with the contribution of the _WaveEq_ module, a bandpass filter was applied to the dispersion curves to remove the noisy picks. To invert the dispersion curve, a theoretical initial velocity model was created with the depth conversion result method, which was incorporated by the software. This method estimates the _Vs_ values by multiplying the phase velocities with a factor equal to 1.1, while it uses the one-third wavelength approximation in order to estimate the depth. The minimum and maximum phase velocity values are defined by the highest and lowest frequency peaks in the dispersion curves, correspondingly. Subsequently, the least-squares method was used to run the inversion process. The initial model was modified five times for the calculated dispersion curve to best match the observed data. Finally, the 1D shear-wave velocity model was calculated, which corresponded to the midpoint of the seismic line. The _Vs_ value refers to the time-depth averaged shear-wave velocity of the top 30 m of a layered subsurface, and it is calculated by the following equation [57]:  
\[V_{s30}=\frac{30}{\sum_{i=1}^{N}\frac{h_{i}}{V_{i}}} \tag{1}\]  
where \(V_{i}\) and \(h_{i}\) are the shear-wave velocity and thickness of the _ith_ layer, in a total number of N layers, of the top 30.0 m of the propagation medium. According to the European Committee for Standardization Eurocode 8 [57], the _Vs_30 is used for the classification of the ground type, as shown in Table 1.
###### Abstract  
Under the same principle, laser radar could be more sensitive to the micro-Doppler (m-D) effect due to its wave length, as the characteristic of multi-resolution, S transform could reduce the influence of the micro-Doppler component and enhance the imaging effect. This paper presents a method for micro-Doppler feature extraction in Inverse Synthetic Aperture Imaging Laser Radar (ISAIL) imaging. It is accessible and comprehensive, applying Random Sample Consensus (RANSAC) for the separation and reconstruction of micro-Doppler and rigid body signals. Experiments show that the method can effectively remove the micro-Doppler information and obtain a clear target distance-instantaneous Doppler image.  
Keywords:time-frequency analysis; S-transformation; inverse synthetic aperture imaging laser radar; micro-Doppler; RANSAC +
Footnote †: journal: Applied Sciences