esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Deep Learning-Based Method for Wind Turbine Operation and Power Forecasting
### Deep Learning-Based Wind Power Forecasting  
The wind speed and power indicators collected through the wind turbine SCADA system are all time series data. Time series forecasting can estimate their future development based on indicators or events. At the same time, there are complex nonlinear relationships among other indicator data related to power. From the previously published research works, it has been realized that deep learning-based time series forecasting has higher forecasting accuracy than the traditional methods [47], so the deep learning-based time series forecasting (TSF) method has been widely utilized.  
The recurrent neural network (RNN)-based TSF method given in [48; 49] compactly summarizes the past information in the internal memory used for prediction, where the memory state is recursively updated with new inputs at each time step, as shown in Figure 1a below. Ref. [50] proposed a long and short-term memory-based recurrent neural network (LSTM-RNN) to predict the wind power from 1 to 24 h. Transformer relies on the attention mechanism to model the global dependency of input and output, and breaks the non-parallelization problem of RNN-based methods, so it is gradually replacing the RNN model in almost all sequence modeling tasks. Therefore, various Transformer-based TSF methods were presented in [51], as shown in Figure 1b. The multi-head self-attention mechanism is used to extract the spatial correlation between wind farms [52]. Models based on convolutional neural networks (CNNs), such as temporal convolutional networks (TCNs), are also used in time series forecasting (TSF) [53; 54]. The TCN uses a series of causal convolutional layers stacked to make full use of convolution. Parallel operation with efficient modeling of the dependency relationship between multiple sequence features is shown in Figure 1c. Long-term prediction of wind power with a mean absolute percentage error of 10% was carried out in [9] by using the temporal convolutional network (TCN).  
Ref. [23] proposed a new neural network structure named SCINet, as shown in Figure 1d, specifically designed for time series forecasting, which lifts causal convolutional layers and the forced numbers of network input and output to be the same constraints of TCNs, and achieved very good performance in TSF tasks. To the best of our knowledge, however, SCINet has not been applied to the field of wind power forecasting (WPF). At the same time, the binary tree structure of SCINet causes information blockage as the number of network levels increases. For this reason, we propose shuffle-and-fusion interaction networks (SFINet) to overcome this issue.  
Figure 1: Typical deep-learning network structure for processing time series tasks [23; 51; 53; 54].
## 3 Optimization on Reasonable Constructing State for Cable-Stayed Bridge with Steel Box Girder  
### Engineering Profile  
The main bridge of Xiangshan Port Bridge is a five-span continuous, semi-floating cable-stayed bridge featuring a steel box girder, double pylons, and double cable planes. The span arrangement is 82 m + 262 m + 688 m + 262 m + 82 m, as illustrated in Figure 4a. A schematic diagram of the standard cross-sectional dimensions of the main beam is given in Figure 4b. The pylons, which have a height of 226.5 m, are designed as diamond-shaped concrete structures with a concrete grade of C50. The streamlined steel box girder has a width of 26.4 m and a centerline height of 3.5 m, constructed from Q345D steel. The girder is divided into 11 types, comprising a total of 99 sections. The standard girder sections  
Figure 3: Optimization process of constructing state for cable-stayed bridge with steel box girder.  
are erected using the cantilever method with a deck derrick crane, while girder sections near the pylons, transition piers, auxiliary piers, and the closure segments of the side spans are installed using a large floating crane. The stay cables are made of parallel steel wires, with a tensile strength of 1670 MPa. In total, there are 176 cables arranged in a spatial fan-shaped configuration.
## Materials and Methods
### Image Processing
#### Speckle Processing  
Several established methods exist to determine areas of blood flow using OCT for biomedical imaging. One approach is complex differential variance (CDV), which intrinsically limits phase noise due to bulk motion [35]. Because of its outperformance of other methods based on angiographic analysis of collected SAR-OCT retinal images, CDV was selected as the angiographic method for this SAR-OCT device.  
The CDV signal for any given A-scan can be described as  
\[f_{CDV}(z)\ =\ \sqrt{1-\frac{\sum_{t=1}^{M-1}\left|\sum_{k=-L}^{L}w(k)R(z-k,t) R^{*}(z-k,t+1)\right|}{\sum_{t=1}^{M-1}\sum_{k=-L}^{L}w(k)\frac{1}{2}\left| \left[R(z-k,t)\right]^{2}+\left|R(z-k,t+1)\right|^{2}\right|}}, \tag{6}\]  
where \(M\) is the total number of repeating B-scans, \(L\) (more precisely, \(2L+1\)) is the length of the kernel, \(w\) is the depth window function, and \(R\) is the complex analytic OCT signal at time point \(t\) (\(R=A(z,t)e^{i\varphi(z,t)}\)). The coherent average is normalized by division with the incoherent average of the original A-scan data.  
Three sequential processes take CDV volumes as input to obtain high-fidelity angiograms: (1) filtering to remove bulk motion; (2) Frangi vesselness filter to improve SNR; and (3) adaptive thresholding for binarizing angiograms.  
Despite the CDV algorithm's robustness compared to other methods, the problem of bulk motion can still be observed such that vertical lines obscure vascular structures. These vertical lines are due to bulk movements of the mouse including heart beat and breathing. For filtering to remove bulk motion artifacts, these lines are first detected manually, with the user clicking to the left and right of a line in the superficial plexus image. The width of the line is then determined by a custom MATLAB script, and the entire artifact is replaced with values taken from the left and right of the obscuring line. The same replacement process is repeated automatically in the intermediate and deep plexuses.  
The second filtering process applies the Frangi vesselness filter [36] to the filtered image to isolate those areas of the image that are vessel-like. The Frangi filter, which uses Gaussian-like distributions with user-defined widths to search for vessel-like features, is a helpful tool for the retina because the sizes of the vessels are generally known, a priori. The Frangi filter takes as inputs the size distribution of vessels and searches for structures that are around that size. A range of three sigma values suited the vessel size distribution are selected. Additional Frangi parameters included \(\beta_{1}\) (0.5) and \(\beta_{2}\) (5). If not handled judiciously, Frangi filtering could introduce false vessel structures from noise.  
The final step was to binarize image such that more intensive vascular analysis could be performed. The image was binarized using a built-in MATLAB filter with adaptive thresholding. One note here is that adaptive filtering assumes the presence of a vessel.
# An Object Detection and Localization Method Based on Improved YOLOv5 for the Teleoperated Robot
## 2 Method
### Object Localization  
#### 2.2.1 Binocular Ranging Principle  
Accurately estimating the distance between the object and the teleoperated robot is fundamental to object localization. We developed it based on the binocular stereo vision principle. Through parallax, humans are able to estimate an object's distance. Based on this, the binocular stereo vision ranging estimates the distance to an object by the difference between the images captured by the left and right cameras. The geometric model of binocular camera ranging is shown in Figure 5.  
Where _Camera_\(L\) and _Camera_\(R\) are the optical centers of the left and right cameras, respectively, and the distance between them is called the baseline of the binocular camera, denoted by \(b\). \(P_{L}\) and \(P_{R}\) are the imaging points of the spatial point \(P\) on the left and right cameras at the same time, respectively. \(x_{L}\) and \(x_{R}\) are the distances between the imaging points \(P_{L}\) and \(P_{R}\) and the optical axes of the left and right cameras, respectively, \(f\) is the focal length. \(d\) is the distance between the spatial point \(p\) and the baseline of the binocular camera.  
The intrinsic and extrinsic parameters of the left and right cameras are the same, in theory. The imaging points \(P_{L}\) and \(P_{R}\) also differ only on the x-axis because the optical centers of the left and right cameras only have a positional deviation at the x-axis. According to the law of trigonometric similarity, the distance \(d\) can be solved through geometric relations, as shown in Equation (6).  
\[d=\frac{f*b}{x_{L}-x_{R}}=\frac{f*b}{u} \tag{6}\]  
where \(u\) is the difference between the x-axis coordinates of the imaging points \(P_{L}\) and \(P_{R}\), which is called parallax.  
The focal length \(f\) and the baseline distance \(b\) of the binocular camera can be obtained through camera calibration. Therefore, after calculating the parallax \(u\) of the target point in the left and right cameras, the distance between the target point and the camera can be calculated through Equation (6).
## 3 Results and Discussion
### Moment-Curvature Relations  
In the current study, the moment-curvature responses of the UHPC beams were evaluated for the three characterized phases (uncracked, cracked, and nonlinear phases (Figure 13a)) using the loading and strain measurements. The readings of the strain gauges were employed to determine the sectional strain distribution over the effective depth under the assumption of plane sections remaining planar after bending. Thus, the linear distribution of these strains was applied to evaluate the beam's curvature. Gauge measurements were employed as long as their values had reasonable consistency, while the values associated with crack propagation in concrete surfaces were ignored.  
Therefore, a general evaluation of representative moment-curvature curves for the UHPC beams is shown in Figure 13b. These curves demonstrate that all beams had an uncracked phase that extended to a curvature of about 7.5 \(\times\) 10\({}^{-6}\) rad, which occurred at about 10 kNm (at the first cracking moment). Beyond this curvature, the moment-curvature behavior can be assumed as linear. In this linear cracked phase, the curvature quickly increased for the unreinforced beam (A-type); however, the bar reinforcement helped control the curvature deformations of the UHPC (B-F type) beams. The maximum observed curvatures (about 100 \(\times\) 10\({}^{-6}\) rad) were observed for the beams with bar-effective depths of 125 and 115 mm. These findings suggested that increasing the bar-effective depth of the UHPC beam would likely enhance its curvature capacity.  
Figure 11: Load vs. strain responses of the B2 UHPC beam.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 4 Constructing the Multispectral Datasets
### Sentinel-2  
The Sentinel-2 data were selected because they were used to train the original Cloud-Scout [5] model. The Sentinel-2 Cloud Mask Catalogue [75] contains cloud masks for 513 Sentinel-2A Level-1C top-of-atmosphere (TOA) reflectance [7] data cubes (1024 \(\times\) 1024 pixels), collected from a variety of geographical regions. Each data cube, which includes 13 spectral bands, was resampled to a spatial resolution of 20 m (if not already at that resolution) using bilinear interpolation (the same method used by Sen2 Like [76]). Following [5], the data cubes were spatially divided into 2052 data (sub)cubes of 512 \(\times\) 512 pixels each.
# Computational Investigations and Control of Shock Interference +
## 2 Code Verification
### Results of Code Verification  
The results from the first code verification study are shown in Figure 4. In this figure, the results from the exact solution, which was obtained using the method of characteristics, and the results from the CFD solution of the same equation are shown at time = 0.25 s. Specifically, these plots show the comparisons between the numerical and exact solutions of density, axial velocity, pressure, and internal energy. A close comparison between these results is shown, demonstrating that the numerical methods are able to capture the formation and propagation of normal shock, expansion fan, and compression waves.  
The results from the second code verification study are shown in Figure 5. In this figure, the results from the exact solution and the CFD solutions of the same equation are shown at time = 0.035 s. These plots show the comparisons between the exact and numerical solutions using Koren TVD, van Albada, and van Leer flux limiters. Comparisons of density, axial velocity, pressure, and internal energy are shown. The results varied slightly between the different limiters, but they were equal in terms of comparison with the exact solution.  
Close comparisons between the numerical and exact solutions showed that the combined discretization and numerical method error due to mesh resolution is negligible.  
Figure 4: Code Verification: Comparison of numerical results with exact solution at \(t=0.25\) s.
# A Critical Review on Obesity: Herbal Approach, Bioactive Compounds, and Their Mechanism
## 2 Hormones Related to Obesity
### Acylation-stimulating Protein (ASP)  
Acylation-stimulating protein, a 76-amino acid peptide produced by adipocytes, stimulates the synthesis of triglycerides (TG) in adipose tissue by acting on its C5L2 receptor [45]. Patients with obesity, type 2 diabetes, and cardiovascular disease have higher plasma ASP levels than healthy people, whereas exercise or weight loss lowers the ASP levels. A disrupted adipose tissue metabolism and dyslipidemia, frequent in diabetes and cardiovascular disease, have also been linked to an ASP-resistant condition. It has been proposed that complement C3, an ASP precursor in adipose tissue, is activated to generate ASP [46]. Therefore, ASP could offer a target for regulating fat accumulation.
# Comparing End-to-End Machine Learning Methods for Spectra Classification
## 2 Related Work
### Traditional Machine Learning-Based Approach  
Numerous traditional machine learning methods have been developed for time series data or specifically spectra data classification [30]. These traditional methods are unsupervised learning or nonparametric algorithms that do not contain learnable parameters but are more procedural [31]. Time series data, especially time-resolved spectra data, commonly has a high dimensionality, and it is very common for the number of input variables (features) to be greater than the number of samples. These algorithms can effectively identify underlying patterns or key descriptors in spectra data [31]. For instance, principle component analysis (PCA) is usually used as a dimensionality reduction by using an orthogonal transformation to convert original data into a set uncorrelated variables of higher space; k-means clustering, is widely used for exploratory data analysis, applied to data clustering; random forest, which is an ensemble learning method based on multiple decision trees, always results in the convergence of generalization error [32], and can be used for classification and regression. The authors of [33] presented a novel method called nearest clusters based PLS-DA (NCPLS-DA) to analyze the classification of spectral data. The proposed method can effectively address the multimodality and nonlinearity issues. The work [30] proposed a framework for spectra data classification using the kernel extreme learning machine (KELM). They used PCA as spectra preprocessing and dimensionality reduction, and then applied the kernel extreme learning machine for classification. One of the most relevant studies to our work [34] presented the Phase-Mapper, based on convolutive nonnegative matrix factorization (NMF), which can effectively solve the multi-phase identification problem in the context of x-ray diffraction (XRD). Even though these traditional ML classification methods are widely recognized, there are still some questions. For example, they usually require the pretreatment of data and rely heavily on preprocessing such as the PCA, which makes end-to-end work difficult.
# Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System
## 3 Background  
We adopted the Ethereum blockchain framework [19] in the proposed scheme to construct an e-health system. Ethereum is closely related to other blockchain-based systems, such as Bitcoin, as a novel hybrid public blockchain. The benefit of adopting Ethereum is related to its efficient and versatile functionality, which allows developers to create various blockchain-based solutions such as e-healthcare.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Papers** & **Techniques/Algorithm Used** & **Blockchain Types** & **Performance Evaluation Metrics** & **Limitations** \\ \hline
[9] & Smart Contract &
\begin{tabular}{c} Permissioned \\ Blockchain \\ \end{tabular} & Memory Analysis & It is not applicable for large pictures \\ \hline
[10] & BFT Consensus Algorithms & Private Blockchain & \
\begin{tabular}{c} Communication Cost, \\ Computation Overhead \\ \end{tabular} &
\begin{tabular}{c} Communication \\ Overhead is high \\ \end{tabular} \\ \hline
[13] & Smart Contract &
\begin{tabular}{c} Ethereum \\ Blockchain \\ \end{tabular} & Blockchain Cost & It is not applicable for Larger Datasets \\ \hline
[14] & ABE & IPFS & Computation Cost Analysis &
\begin{tabular}{c} Lacking to provide \\ confidentiality \\ \end{tabular} \\ \hline
[15] & Smart Contract & IPFS & Computation Cost Analysis & It is lacking to offer \\  & & & & integrity \\ \hline
[16] & Smart Contract & IPFS & Computation Cost Analysis & It is applicable for smart devices \\ \hline
[17] & Smart Contract & IPFS & Computation Cost Analysis & Lacking Cloud Security \\ \hline
[18] & Smart Contract & IPFS & Gas Cost Analysis & Lacking in Smart devices \\ \hline
[22] & Smart Contract & Public Blockchain & Computation Cost Analysis &
\begin{tabular}{c} Lacking in Access \\ Control \\ \end{tabular} \\ \hline
[23] & Smart Contract & Public Blockchain &
\begin{tabular}{c} Registration Contract, Access \\ Control Contract Cost \\ \end{tabular} & Lacking to offer high \\ \hline
[19] & Smart Contract, Pairing-Based &
\begin{tabular}{c} Ethereum \\ Blockchain \\ \end{tabular} & Computation Cost Analysis & Lacking in Access \\  & & & Control \\ \hline
[20] & Edge-Cloud Environment & Amazon AWS & Communication Cost, Time Cost &
\begin{tabular}{c} Fail to provide insider \\ attacks \\ \end{tabular} \\ \hline
[12] & Micro Genetic Algorithms & Cloud Environment & Optimization Cost &
\begin{tabular}{c} Fail to offer \\ Confidentiality and \\ Optimizations \\ problems \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of existing research works.  
The Ethereum blockchain maintains data blocks and executes smart contracts. Every block can be verified and then added to a chain to create a chain of blocks through consensus protocols, such as proof of work (PoW). Each block consists of a hash (unique value), checked proof of legitimate timestamp operations, and a preceding block hash. The preceding block hash is used to avoid altering the blocks or adding a block between them.
## 3 Results and Discussion
### Morphology  
The cryo-fracture surface of the two composites, shown in Figure 4, shows the large differences between the morphology of both composites. HDPE30 showed large agglomerates of particles, which are heterogeneously distributed in the polymeric matrix. Contrarily, the zirconia powder is homogeneously distributed in AAHDPE30 without large agglomerates (Figure 4). The differences in the morphologies of both composites can be explained by their different adhesion with the zirconia powder as observed in the binding energy values obtained in the MD simulations at melt temperature (see Section 3.1) and in the interfacial tension values at room temperature (see Section 3.2). The poor adhesion between the non-polar HDPE chains and the zirconia's surface cannot overcome the attractive forces existing between the submicron particles of zirconia, which promote their agglomeration [60]. Furthermore, the hydrogen bonding between the hydroxyl groups in the surface of the powder (see Section 3.3) results in strong powder agglomerates [27; 28; 61; 62]. The use of grafted polymers as compatibilizers [27] has proven to be an effective solution in the reduction in the hydrogen bonding of the hydroxyl groups in silica nanoparticles. Thus, the combination of a high adhesion to the oxide surface and the hydrogen bonding of the acrylic acid with the hydroxyl groups on the powder surface are the responsible factors for the better powder dispersion in AAHDPE30 than in HDPE30.
# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
## 5 Hierarchical and Multi-Tier Communication and Data Management for the Industry 4.0
### Decentralized Data Distribution
Initial results show that the decentralized data management scheme of the proposed architecture can indeed enhance various target metrics when applied to various industrial IoT networking settings:  
* Average end-to-end latency. In [21; 38], we have provided an extensive experimental evaluation, both in a testbed and through simulations, and we demonstrated that the adoption of the proposed decentralized data management (i) guarantees that the access latency stays below the given threshold, and (ii) significantly outperforms traditional centralized and even distributed approaches, in terms of average data access latency guarantees. * Maximum end-to-end latency. In [39], we demonstrated that the proposed decentralized data management (i) guarantees data access latency below the given threshold, and (ii) performs well in terms of network lifetime with respect to a theoretically optimal solution. * Adaptiveness. In [40], based on the proposed decentralized data management, we provided several efficient algorithmic functions which locally reconfigure the paths of the data distribution process, when a communication link or a network node fails. We demonstrated through simulations increased performance gains of our method in terms of energy consumption and data delivery success rate.
# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 3 Dynamic Environment Target Identification and Localization (DETIL)
### Identification and Localization of Interest Targets  
Currently, there are many algorithms in target detection research that can accurately identify targets of interest in two-dimensional images and annotate their positions in the two-dimensional image coordinates. However, most of them rely on high-resolution images, pre-training, and substantial computational resources. Under conditions of low image resolution and limited computational power, the results of target detection algorithms may suffer from incorrect identification or coordinate offsets. Additionally, the cumulative error in the agent's own positioning can gradually increase over time, making the world coordinates of the interest targets obtained through coordinate transformation even less accurate. The DETIL algorithm addresses the problem of identifying and localizing interest targets under such stringent conditions, offering a low computational cost solution.