esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Method
### Time-Frequency Compression Fusion
#### 2.1.4 Time-Frequency Compression Fusion  
The two STFT reconstruction methods determine the applicable redistribution methods according to the above analysis. The traditional STFT is reconstructed by integrating along the time axis, showing that it is suitable for multiple synchronous compression transform time redistribution. The improved short-time Fourier transform reconstruction method integrates along the frequency axis, which is suitable for frequency redistribution multiple synchronous compression transform. At the same time, different redistribution methods apply to other signal components. Time redistribution compresses horizontally on a time-frequency image and applies to SCRVF. Frequency redistribution compresses vertically on a time-frequency image and is more suitable for SCSVF. The two methods can learn from each other and enhance their application value.  
Figure 2: Time–frequency analysis and comparison of simulation signals: (**a**) time–domain waveform of simulation signal \(x_{e}(t)\): (**b–f**) are STFT, TMSST and MSST time–frequency images of simulation signals, respectively; (**d,e**) are enlarged images of regions 1 and 2 in (**c**), respectively; (**g,h**) are respectively enlarged images of regions 1 and 2 in (**f**).  
Therefore, we propose a time-frequency compression fusion method to fuse the information of two time-frequency images obtained by TMSST and MSST, respectively. Since the scale range of the time-frequency coefficients of the two time-frequency images is not consistent, the time-frequency coefficients are normalized before fusion.  
\[TFI^{{}^{\prime}}(t,f)=\frac{TFI(t,f)-TFI_{min}}{TFI_{max}-TFI_{min}} \tag{13}\]  
where \(f=\omega/2\pi\), \(TFI_{min}\) and \(TFI_{max}\) are the minimum and maximum values of all time-frequency coefficients in the time-frequency image, respectively, and \(TFI^{{}^{\prime}}(t,f)\) is the result after normalization. The fusion method is named time-frequency compression fusion (TFCF). TFCF superposes and splices two time-frequency images to form dual-channel images, which can fully play the characteristics of multi-channel feature fusion of the convolutional kernel in the convolutional neural network. It is suitable for deep learning diagnosis methods.
## 3 Results  
### EOS Spatial Distribution and Variation Characteristics  
The obtained EOS presented high spatial heterogeneity across the grassland of the Qinghai-Tibetan Plateau during 1982-2015 (Figure 2). The EOS results extracted using the HANTS and Polyfit methods were not consistent, but their spatial distribution trends were similar (Figure 2a,b). The mean multi-year EOS began on the 291 th day of the year (end of September) and spanned nearly one month from the southeast to the northwest (Figures 2 and 3a). The EOS started early (around the 277 th day) in subregion IX, which has the highest elevation and lowest \(\rm{AGDD_{0}}\) values, and started late (around the 300 th day) in subregions II and III, which are characterized by relatively warm-humid conditions. In the central Qinghai-Tibetan Plateau (subregion V), the EOS occurred on the 295 th day. In subregion I, the EOS was early in the west and late in the east with a mean EOS on the 292 th day. The spatial heterogeneity variations were significantly controlled by the MI (EOS = 16.55 \(\times\) MI + 287.28, \(\rm{R^{2}_{adj}}\) = 0.20 \({}^{\ast\ast}\)), with an early EOS in the drought subregions (IV, VII, VIII, and IX) and late EOS in the relatively humid subregions (II, III, and VI). The EOS spatial heterogeneity was essentially insensitive to \(\rm{AGDD_{0}}\).  
The mean EOS on the Qinghai-Tibetan Plateau exhibited a slow delayed trend with an average rate of 0.08 days/year. The EOS results extracted using the HANTS and Polyfit methods presented similar patterns (Figure 2d,e). Using these two fitting methods, 60.2% of the study area presented delay trends (27.8% area is significant), while 39.8% of the study area presented advance trends (13.4% area is significant). The EOS trends differed between the nine subregions during 1982-2015 (Figure 3b), showing a delay in the northwest and an advance in the southeast. Subregions I and IX showed significantly delayed trends with more than 0.20 days/year. The EOS of subregion II, with a main land use type of wetland, was also delayed by a rate of 0.12 days/year. The EOS in subregion VIII, which is characterized by alpine, cold, and dry climatic conditions, presented a negative trend with the fastest variation rate (\(-\)0.12 days/year) compared with the other subregions. The EOS of subregion IV showed an advanced trend in the north but delayed trend in the south, with a mean EOS trend of 0.02 days/year. The EOS in subregions III and V showed slight advanced trends of \(-\)0.02 and \(-\)0.01 days/year, respectively. Subregions VI and VII both presented a slightly delayed trend with an average of approximately 0.04 days/year.
# Microorganism Detection in Activated Sludge Microscopic Images Using Improved YOLO
## 2 Materials and Methods
### Data Augmentation  
Data preprocessing is a fundamental part of computer vision model development. Implementing preprocessing techniques such as data augmentation, data normalization, and resizing raw data is essential for improving model performance. In models employing deep learning, the size of the dataset significantly impacts the model's performance. In the dataset that we collected, there were variations in the quantities of different object classes, with filamentous bacteria and rotifers being less prevalent. Therefore, data augmentation was necessary to overcome this problem.  
There are various methods for data augmentation. One of these techniques, DC-GAN, has shown promising results in generating realistic images. In this study, we applied DC-GAN to augment the less-prevalent classes in the dataset. DC-GAN consists primarily of two parts: the generator and the discriminator [18]. The generator plays a crucial role in DC-GAN. It takes random noises as input and gradually transforms them into synthetic images through a series of transposed convolution layers. Typically, the generator consists of multiple transposed convolution layers, each of which progressively increases the size of the input feature maps, resulting in the generation of more detailed images. The discriminator is another vital part of DC-GAN. It takes input images and extracts features using a series of convolutional layers. Its primary function is to classify the input images, determining whether they are real or synthetic. During training, it updates the weights and biases of both the generator and discriminator by backpropagation. This optimization process aims to make the generator generate more realistic images while enhancing the discriminator's ability to accurately distinguish between them [18]. Figure 2 shows the workflow of DC-GAN.  
In the data augmentation, we fed both the synthetic images generated by the generator and the real images into the discriminator. Through iterative adversarial training, the generator continually improved its ability to generate synthetic images, while the discriminator enhanced its capability to discriminate between real and synthetic images. As training progressed, the performance of both the generator and discriminator gradually improved, eventually reaching a balance where the discriminator could no longer distinguish between real and synthetic images [18]. Finally, the images generated by the generator could be used to balance the class distribution within the dataset.  
The dataset could be used in training after annotation. We utilized annotation tools to label the five objects in the dataset, as shown in Figure 3. The completed annotations were saved as text files, with each microscopic image corresponding to one annotation file. These annotation files contained the category IDs within the images, along with the location and size of the bounding box.  
Figure 2: The workflow of DC-GAN.
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
## 2 Basic Theory of Random Field
### Discretization of Random Field
#### 2.1.2 2D Random Field  
\(H(x,y)\) represents a 2D uniform random field, and the rectangular elements are used to divide the distribution area of the random field, as shown in Figure 3. \(A_{i}\) denotes a rectangular element with centroid coordinates of \((x_{i},y_{i})\) as well as the lengths of \(l_{xi}\) and \(l_{yi}\) in two directions (\(x\) and \(y\)), respectively. The local average of \(H(x,y)\) defined on this element is expressed as follows:  
\[H_{i}=H_{i}(x_{i},y_{i})=\frac{1}{l_{xi}l_{yi}}\int_{x_{i}-(l_{yi}/2)}^{x_{i}+ (l_{yi}/2)}\int_{y_{i}-(l_{yi}/2)}^{x_{i}+(l_{yi}/2)}H(x,y)\mathrm{d}x\mathrm{ d}y, \tag{3}\]  
Figure 1: Discretization of a 1D random field.  
where \(H_{j}\) represents the local average of \(H(x,y)\) defined on the rectangular element, \(A_{j}\), with the lengths of \(l_{xyj}\) and \(l_{yj}\) in the direction of \(x\) and \(y\), respectively. The covariance between \(H_{j}\) and \(H_{i}\) is expressed as follows:  
\[Cov(H_{i},H_{j})=\frac{\sigma^{2}}{4l_{xi}l_{yi}l_{xj}l_{yj}}\sum_{k=0}^{3} \sum_{l=0}^{3}\left(-1\right)^{k+l}(l_{xt}l_{yl})^{2}\Gamma^{2}(l_{xk},l_{yl}), \tag{4}\]  
where \(l_{xk}\), \(l_{yl}\) (\(k\), \(l\) = 0, 1, 2, 3) describe the relative position between \(A_{i}\) and \(A_{j}\), as shown in Figure 3.  
The discretization of a random field can be different from that of a structural finite element model. Generally, a random field element can contain several finite elements [30]. When the mesh is determined, the distribution characteristics of the random field on each element are represented by their local average on the element. Thus, a set of random variables is obtained, and their statistical properties are reflected by mean \(E\) (\(H_{i}\)), _Var_ (\(H_{i}\)), and _Cov_ (\(H_{i}\), \(H_{j}\)).
# High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
## 4 Discussion
### Limitations of Orbit Correction Based on DEM-Simulated Image Coregistration  
The SRTM used to be a reference for estimating geolocation errors in SAR images owing to its high horizontal accuracy, and a common method was to register SAR images and DEM-simulated amplitude images. We tested the effectiveness of this method using the ISCE (Figure 17).  
We projected the SRTM onto the SAR image coordinate system using the Topo module and refer to the result as z data, which the Simamplitude module can further utilize to generate a simulated amplitude image with the same horizontal accuracy as the SRTM. Finally, the Ampcor module calculated the range shift between the two images.  
Given the substantial differences between the SAR image and the simulated image, the Ampcor result generally has a much lower signal-to-noise ratio (SNR) than the results from the two real SAR images. The SNR corresponding to the peak of the probability density is 1.3 for the TerraSAR-X image (Figure 18a) and 2.9 for the Tianhui-2 reference image (Figure 18b), with most of them weaker than 5.0, which is a common threshold for SAR image coregistration. Therefore, we do not believe that this method applies to high-resolution SAR geolocation.
# Adaptive Metrics for Adaptive Samples
## 2 Methods
Then, for all \(\alpha\geq 0\), \(A^{L}_{X}(\alpha)\subseteq A^{L}_{\widehat{X}}(\alpha+\delta)\) and \(A^{L}_{\widehat{X}}(\alpha)\subseteq A^{L}_{X}(\alpha+\delta)\). Proof: Let \(y\in A^{L}_{X}(\alpha)\) be any point. By the definition of \(A^{L}_{X}\), we have \(f^{L}_{X}(y)\leq\alpha\). Therefore, there exists \(x\in X\) such that \(\mathrm{d}^{L}(x,y)\leq\alpha\). The Hausdorff assumption that \(\mathrm{d}^{L}_{H}(\widehat{X},X)\leq\delta\) implies that for all \(x\in X\), we have \(f^{L}_{\widehat{X}}(x)\leq\delta\). By Lemma 1, \(f^{L}_{\widehat{X}}(y)\leq f^{L}_{\widehat{X}}(x)+\mathrm{d}^{L}(x,y)\leq \delta+\alpha\), implying \(y\in A^{L}_{\widehat{X}}(\alpha+\delta)\). The second inclusion is proven by a symmetric argument. The following is the definition of an adaptive sample we will use throughout. For the special case when \(X\) is a manifold and \(L\) is its medial axis, it corresponds to the \(\varepsilon\)-sample used in surface reconstruction. Definition 4: Given a compact set \(L\subset\mathbb{R}^{d}\) and compact sets \(X,\widehat{X}\subset\mathbb{R}^{d}\setminus L\) such that \(\widehat{X}\subseteq X\), we say that \(\widehat{X}\) is an \(\varepsilon\)-sample of \(X\), for \(\varepsilon\in[0,1)\), if for all \(x\in X\), there exists \(p\in\widehat{X}\) such that \(\|x-p\|\leq\varepsilon f_{L}(x)\). This definition is closely related to that of the approximate \(\alpha\)-offsets, because if \(\widehat{X}\) is an \(\varepsilon\)-sample of \(X\), then for all \(x\in X\), \(\mathrm{ball}(x,\varepsilon f_{L}(x))\cap\widehat{X}\
eq\emptyset\).
## 4 Results and Discussion  
### Broadband Generation  
A broadband THz radiation spectrum has been obtained by sub-ps single bunches. In particular the Figure 3a shows the CTR spectrum for a 500 pC, 180 fs electron beam measured sampling with different band pass filters. As previously discussed, the drop below 0.3 THz is due to the effect of finite screen size combined with the fall of responsivity of pyroelectric detector. At high frequency instead the longitudinal extension of the bunch limits the generation just above 5 THz. The integrated energy per pulse reaches up to 35 \(\upmu\)J measured by pyroelectric detector. The Figure 3b shows the CTR spatial distribution at the corresponding frequencies, as collected and focalized by APM2. As one can see, generated CTR shows the peculiar annular distribution of radially polarized light. The spot size strongly reduces increasing the frequency due to the reduction of the source size, whose radius depends on the wavelength. In particular at 1 THz the FWHM diameter is \(\sim\)1.5 mm that correspond to a THz fluence of \(\sim\)1 mJ/cm\({}^{2}\).  
As one observes from Equation (3), the emitted THz energy depends on the square of the bunch charge Q. Figure 4 shows the measured CTR spectra for different bunch charges, keeping the bunch length constant, as indicated by the high frequency cut-off. The detector response, the z-cut quartz window transmission and the single particle TR spectrum for a finite size target have been taken into account to retrieve the CTR spectra depicted in Figure 4. The generated THz energy is consistent with a quadratic Q\({}^{2}\) dependence as reported in the inset of Figure 4.  
Figure 4 shows also the capability of our detecting system to measure very low charge beams, few tens of pC and less when the air-drying system is turned off. By decreasing the charge of a factor \(\sim\)45 (from 500 pC down to 11 pC), the measured pulse energy reduced from 40 \(\upmu\)J to 20 \(\upmu\)J. The low frequency components (below 0.7 THz) are not shown in Figure 4 because they are strongly affected the transmission function of Michelson interferometer (in particular Fabry-Perot interferences in Mylar BS), both diffraction limited size and drop of efficiency of pyroelectric detector. For this, such effects in particular in the low THz frequency region are difficult to evaluate and compensate.
## 4 Results and Discussion
### Discussion
#### 4.5.2 The Influence of Iteration Numbers  
The SAA algorithm is a type of approximate method. The result is not the solution of the true problem. Changes in iteration numbers result in changes in the accuracy of the results. Thus, experiments are done to study the impact of the iteration numbers. In the experiments, SLs and HLs are involved in the optimization, and the risk level is kept as 0.2. The results are shown below.  
Tables 9 and 10 show that with \(S=5\) and \(M\) increasing from 5 to 10, the gap between the upper bound and optimal value of wind power utilization decreases from 0.61% to 0.42%, and the gap of the system operation cost decreases from 0.34% to 0.21%. With \(M=5\) and \(S\) increasing from 5 to 10, the gap between the upper bound and optimal value of wind power utilization decreases from 0.61% to 0.31%, and the gap of the system operation cost decreases from 0.34% to 0.17%. The decrease in the gaps indicates that the solution became more precise; with accumulating iterations, the proposed SAA algorithm converges to the true values. What's more, an increase in \(S\) can lead to a greater decrease of the gap than an increase in \(M\).
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 3 Construction of the Digital Twin Monitoring System
### Construction of the Main Modules of the Digital Twin Monitoring System
#### 3.1.5 Data Processing Module  
The data processing module is the core component of the entire mixed-reality human-computer interaction platform, responsible for the calculation and analysis of the data flow within the entire program. The byte stream composed of the information segments will be decomposed into independent data fields after being processed and segmented by the network communication module, and these fields will be stored in a temporary data pool for other modules to call. The data processing module will perform secondary processing of this information and convert it into the format required by the program. Take the information segment composed of the twin model of the roadheader's cutting motion as an example. The data processing module will store the control right indicator in a temporary variable to record the client where the control right of the roadheader is located in the current system. However, there are a large number of action parameters and status parameters during the transmission process, and the data format does not meet the requirements of the TCP protocol. Therefore, the original data will be encoded, such as converting from floating-point numbers (float) to bytes (byte). This can reduce the volume of each information segment while retaining sufficient information, thereby improving the transmission efficiency. The data processing module will then perform secondary processing on this information and convert it into the format required by the program. In addition, the data processing module can use the function extension interface to connect to the algorithm programs of other functions and export the sensor information of the roadheader to be calculated outside the program to obtain the results required by the user.  
During the functional development process of the digital twin monitoring system based on mixed-reality technology, the addition of subsequent new functions will also be considered. To avoid the inconvenience caused by modifying the original program itself, function extension interfaces will be reserved in advance during the development of the program.
## 2 Characteristics of UWSNs Transmission
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Features**} & **UWSNs** & **TWSNs** & **References** \\ \hline Localization & GPS Non-Supportive & GPS Supportive & [17] \\ Stability of Links & Unstable & Stable & [23] \\ Transmission Range & Up To 2 km & 10–100 M & [27] \\ Transmission Speed & 1.5 \(\times\) 10\({}^{3}\) m/s & 3 \(\times\) 10\({}^{8}\) m/s & [30] \\ Energy Consumption & High & High & [33] \\ Data Rate & Low Data Rate & High Data Rate & [34] \\ Bandwidth & Limited & Limited & [48] \\ Bit Per Second Rates & Low & High & [49] \\ Transmission Delays & Extended and Flexible & Small and Steady Transmission Delays & [49] \\ Noise & High-Influence & Low-Influence & [52] \\ Collective Association Technique & Acoustic Signals & Radio Signals & [53] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main differences among the characteristics of TWSNs and UWSNs.
## 3 Data and Method
### Experimental Settings
#### 3.3.2 Testbed  
For a fair assessment of the proposed model, several models of varying types were chosen as baseline models for comparison. These include the following. Random Forest [24]: an ensemble learning method that combines the outputs of multiple decision trees, offering high accuracy and robustness. Support Vector Machine (SVM) [25]: the Support Vector Machine is a powerful model widely used for classification and regression problems. Time-series Neural Network [26]: a Transformer-based time series analysis model that employs the self-attention mechanism to capture long-term dependencies. Temporal Transformer [27]: designed specifically for time series data, this Transformer model integrates traditional time series analysis techniques. Seq2 Seq Transformer [28]: a model utilizing the Transformer architecture for sequence-to-sequence prediction.  
These baseline models were selected for comparison because they represent the current mainstream techniques in time series prediction. Random Forest and SVM stand as classical machine learning models, while the Transformer-based models represent the latest deep learning methods. Such a selection ensures the fairness and comprehensiveness of the experimental results.
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 4 Method
### Adaptive Density Peaks Selection
#### 4.1.2 Observation  
Nodes on the decision graph exhibit several distinctive features:  
1. Cluster Centers: Nodes characterized as potential cluster centers typically possess a higher local density and a lower dependency similarity, consistently appearing in the lower right quadrant of the decision graph.
2. Non-cluster Nodes: Conversely, nodes that are not cluster centers often display high values of both \(\rho\) and \(\delta\) or low values of \(\rho\) and high values of \(\delta\), typically found in the upper right or upper left quadrants.  
Observations indicate that cluster centers typically exhibit higher local density (\(\rho\)) and lower dependency similarity (\(\delta\)), while non-cluster centers display the opposite char  
Figure 2: Overview of ADSCAN.  
acteristics, with lower local density and higher dependency similarity. This distribution characteristic provides a critical insight: it is feasible to distinguish between cluster centers and non-cluster centers by setting appropriate thresholds. Specifically, determining suitable thresholds for \(\rho\) and \(\delta\) can effectively identify potential cluster centers. The selection of these thresholds not only impacts the accuracy of clustering but also directly influences the efficiency of the clustering process. Next, we will explore in detail how to automatically determine these thresholds through the algorithm, optimizing the identification of cluster centers and reducing the dependency on manual intervention. This step is a key component of the adaptive density peak identification in our proposed structural graph clustering algorithm.  
By observing the local density graph in Figure 2(a) and the dependency similarity graph in Figure 2(b) of nodes, it is apparent that certain nodes exhibit significantly higher local density and significantly lower dependency similarity, albeit in smaller numbers. The presentation of extreme values in local density and dependency similarity characteristics is crucial here. The generalized extreme value distribution is used to model extreme values in samples, particularly suitable for extreme value theory. Thus, employing statistical methods to determine thresholds for local density and dependency similarity is appropriate. Therefore, the decision graph is divided into two sections by these thresholds, with cluster centers located in the lower right quadrant and other nodes in the opposite section. Therefore, cluster centers can be automatically identified.