esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background Theory and Proposed Framework
### Feasibility of Predicting Bridge Responses Using the LSTM Network  
This section evaluates the feasibility of using the LSTM network as a surrogate model to predict the vehicle-induced structural responses of a railway bridge. There is a highly nonlinear relationship between the load input and the response output of the railway bridge structure, so the surrogate model can be used to simulate the relevant physical laws. If there is a mathematical model that can accurately represent this complex mapping relationship and the load input is mainly caused by the train load, then the vehicle-induced dynamic response prediction model proposed in this paper is feasible. Therefore, the feasibility of the model depends on the following three conditions: (1) The rationality of the designed LSTM network structure; (2) A specific one-to-one mapping relationship between train load excitation and bridge responses; (3) The train load is much larger than other types of loads on the bridge.  
Firstly, the structure-health-monitoring system includes comprehensive sensors to collect a large amount of data; hence, it can provide a good foundation for training the neural networks for bridge response prediction. At the same time, the use of big data for the fitting of nonlinear relationships is the advantage of deep neural networks. The activation function within the LSTM network introduces nonlinear elements to neurons, enabling the neural network to effectively approximate a wide array of nonlinear functions.  
Consequently, the LSTM network finds extensive utility in simulating nonlinear systems, thereby facilitating the fulfillment of Condition (1). Furthermore, when viewed through the lens of structural dynamics, the relationship between the excitation and response of a bridge is governed by the vibration equation. Typically, the intrinsic characteristics of a specific railway bridge structure, encompassing the geometric properties, material attributes, boundary conditions, and mechanical properties of railway tracks, remain relatively stable over a certain duration. This implies that the quality, damping, and stiffness of the bridge structure remain constant.  
It becomes evident that the physical model governing the dynamic response of the bridge remains constant, establishing a direct correspondence between the load input and the response output, thereby fulfilling Condition (2). Subsequently, the dynamic load exerted on medium- and small-span railway bridges primarily comprises train load, wind load, and additional loads. For such bridges, the wind load is typically negligible when compared to the train load, especially if special loads such as earthquakes, ship impacts, ice pressures, frost heaving forces, and long rail breaking forces are not under consideration. Consequently, Condition (3) can be approximately satisfied. In summary, constructing predictive models for vehicle-induced the dynamic responses of small- and medium-span railway bridges based on LSTM networks appears to be feasible.
# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
## 2 Related Work  
### Deep-Learning-Based Unimodal Medical Image Registration  
Many researchers have introduced deep learning into the field of medical image registration and have proposed many models based on deep learning. Initially, in the deep iterative approach, deep learning is used to learn the similarity metric of two images. As deep learning continues to develop in the field of medical image registration, the unsupervised registration framework with the spatial transformation network (STN) [21] after the convolutional neural network was proposed by [PERSON] et al. [14] first. Subsequently, a large amount of STN-based unsupervised deformable image registration frameworks emerged. For example, [PERSON] et al. [22] proposed VoxelMorph, which uses U-net [23] as the network backbone for unsupervised brain MR image registration. In a further development, [PERSON] et al. [24] introduced the transformer to the field of medical image registration, enabling the model to acquire local information while obtaining rich global information. The model eventually achieved good results. Many transformer-based models have subsequently emerged, such as the better-known TransMorph [25]. In the same year, [PERSON] et al. [26] introduced graph convolution to medical image registration again. The model aims to capture high-quality long-range dependencies and expand the receptive field. However, the above models are focused on unimodal registration and still need to be explored in the field of multimodal registration.
## 4 Experimental Analysis
### Experimental Analyses  
#### 4.3.1 Comparison of Different 2D Transformation Methods  
To verify the superiority of MTF as a preprocessing method, this section compares other commonly used 2D image transformation methods. Under the same original vibration signals, the corresponding datasets were generated using Gramian Angle Field (GADF/GASF) and recurrence plot (RP), respectively, and compared with the MTF method. They were inputted into the MARN model for training, respectively. Taking the balanced dataset \(\mathrm{D_{A}}\) as an example, 150 rounds of model iterations were performed for each set of models in order to ensure sufficient model convergence.  
Figure 10 shows example images of several transformation methods, and Figure 11 shows the training curve of the model. As can be seen in Figure 11, using MTF as the preprocessing method has the highest validation accuracy
## 3 RTS/CTS Scheme for IEEE 802.15.6
### Exposed Node Problem in IEEE 802.15.6  
The exposed node problem occurs when some nodes are prevented from transmitting data due to interference with the neighboring transmission. This problem is likely to occur in inter-WBAN communication or when multiple networks coexist with each other. Figure 2 explains one of the possible scenarios of an exposed node problem in IEEE 802.15.6 CSMA/CA based networks. Both H1 and H2 are not in the transmission range of each other, however, the transmitting node B and node E of two adjacent networks are in the same transmitting range. When node B is sending data to H1, node E may defer its transmission to H2. However, node E could transmit data to H2 since both nodes are available for transmission. Unlike the hidden node problem that increases the number of collisions, the exposed node problem increases the delay of nodes by preventing them from transmitting data.
## 4 Conclusions  
In this study, intended and unintended emission spectra were used to classify and identify different pieces of equipment. Inspired by the basic emission waveform theory (BEWT), the radiation emission data of equipment or systems were decomposed into three kinds of radiation emission components, which consisted of peak components, broadband components, and trend components in this study. The method discussed in this paper showed that different sources of radiated emission data could be classified or identified according to the similarity of these three kinds of radiation emission components. Different kinds of electronic equipment radiation emission spectral data were used to verify the proposed method. The classification and identification accuracy for different kinds of radiation emission spectrum data was about 99%, which confirms the effectiveness of this method.  
Figure 15: Dissimilarity values between all the test data: (**a**) peak components (\(S_{1}\)); (**b**) broadband components (\(S_{2}\)); (**c**) trend components (\(S_{3}\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class and Status of EUTs** & **Verified Data Volume** & **Misidentification** & **Unable to Identify** \\ \hline
1. Background & 30 & 0 & 1 \\ \hline
2. MacBook (Retina 12-inch, Early 2015) & 110 & 0 & 0 \\ \hline
3. HP Pavillion 15-p074 TX & 110 & 1 & 1 \\ \hline
4. ASUS-FZ50V & 110 & 0 & 1 \\ \hline
5. SONY Alpha 7 II (digital camera) & 110 & 0 & 2 \\ \hline
6. ThinkPad E480 & 220 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The identification results of all test spectrum data.  
**Author Contributions:** Conceptualization, D.S., A.C. and F.Z.; methodology, F.Z. and W.W.; validation, F.Z. and W.W.; investigation, F.Z. and W.W.; writing--original draft preparation, F.Z. and W.W.; writing--review and editing, D.S., A.C., D.Z. and F.Z.; visualization, F.Z.; supervision, D.S. and A.C.; funding acquisition, D.S. and A.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under grant number 61771032 and 61427803.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank [PERSON] and [PERSON] for their work during the experimental preparation stage. The authors thank [PERSON] for the language modification of the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# SbMBR Tree--A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining
## 2 Related Work
### Tree-Based Data Indexing Methods  
The tree index structure is usually used in data storage and query scenarios, such as distributed databases. They usually need to solve the problems of storage efficiency optimization and query efficiency optimization, so a lot of researchers have designed various tree index structures to meet the needs of different scenarios.  
R-tree is a height-balanced tree similar to B-tree. In 1984, [PERSON] introduced the R-tree index structure. The paper gave algorithms for searching, inserting, deleting, and updating, and demonstrated that the R-tree structure is useful for indexing spatial data [24].  
R*-tree is an improvement for R-tree. In 1990, [PERSON] introduced the R*-tree index structure. In this paper, the author designed a new R-tree variant, the R*-tree, which outperforms the known R-tree variants under all experiments [24; 25].  
The parallel R-tree was introduced by [PERSON] and [PERSON] in 1992 [26]. The parallel R-tree promotes the traditional R-tree so that it can support a multi-disk environment. Then, in 1994, they improved the R-tree structure with the Hilbert curve and introduced corresponding algorithms in detail [27]. Experiment results show that the Hilbert R-tree with a '2-to-3' split policy on real data gives up 28% savings over R*-tree [26].  
In 1996, [PERSON] and [PERSON] introduced similarity indexing problems and a new dynamic structure for similarity indexing called SS-tree [28]. Their tests show that the SS-tree is more suitable for approximate queries than the R*-tree.  
In 2020, [PERSON] and others proposed a new distributed spatial data indexing scheme to support Digital Earth initiatives whose name is DAPR-tree. Compared to traditional distributed indexing schemes, the DAPR-tree has a more balanced indexing structure [29; 30].  
The above tree index structure is mostly optimized for distributed systems, with a small portion optimized for approximate queries. Based on these existing structures, we hope to study a structure that can compress with different compression ratios and index these compressed data for range querying.
# A Structure for Accurately Determining the Mass and Center of Gravity of Rigid Bodies
###### Abstract  
Measuring the mass and Center of Gravity (CG) of rigid bodies with a multi-point weighing method is widely used nowadays. Traditional methods usually include two parts with a certain location, i.e., a fixed platform and a mobile platform. In this paper, a novel structure is proposed to adjust the mobile platform for eliminating side forces which may load on the load cells. In addition, closed-form equations are formulated to evaluate the performance of the structure, and transformation matrices are used to estimate the characteristics of the structure. Simulation results demonstrate that repeatability of the proposed structure is higher than the traditional one and there are no side forces. Moreover, the measurement results show that the relative error of mass was within 0.05%, and the error of CG was within \(\pm\)0.3 mm. The structure presented in this paper provides a foundation for practical applications.  
Keywords:mass; center of gravity; side force; load cell; mechanical structure; modeling +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Cell cytotoxicity  
#### Cell culture and treatment  
Human cancer cell lines of human colon adenocarcinoma (LoVo) and human ovarian adenocarcinoma (SKOV-3) were obtained from the American Type Culture Collection (ATCC). Human umbilical vein endothelial cells (HUVECs), a normal cell line, were used as a reference. Adherent cells were regularly maintained in DMEM: F12 medium, supplemented with 2 mM L Glutamine, 10% fetal bovine serum, 100 units/mL of penicillin, and 100 ug/mL of streptomycin. The cells were incubated at 37 \({}^{\circ}\)C in a humidified atmosphere with 5% CO\({}_{2}\). Once the cells reached approximately 60% confluence, they were treated with different concentrations of the compounds for various periods. After treatment, cells in the flasks were detached using a nonenzymatic PBS/1 mM EDTA solution, washed twice in PBS, and used for proliferation/cytotoxicity assays and apoptosis. Throughout the study, all untreated cells were designated as control cells.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 5 Frameworks for TE
### Classification Based on Energy Trading Structure
#### 5.1.2 Classification Based on Centralized Control  
With centralized control, prosumers may not be in full control of sharing, trading and usage of the energy they generate. Some authors believe that some form of coordination with control is required to minimize energy loss. Therefore, an energy sharing provider (ESP) [78] will be needed to manage prosumer transactions and ensure energy transfer. Hence, centralized control systems for energy exchange are discussed.  
The authors in [78] proposed an energy sharing model using price-based DR. In such systems, there exists an ESP platform in the zone that coordinates the energy sharing. Here, the prosumers do not interact directly with the utility provider and with each other, but interact through the ESP. Although the ESP provides some form of coordination and transaction management, a major drawback of this approach is the increase in electricity cost for each participating prosumer and utility company. This is because the ESP purchases electricity from the prosumers and/or utility provider with net power export, then sells to the prosumers and/or the utility provider with some margin. In addition, communication delay is also a disadvantage because of the dependence on the ESP. Furthermore, Ref. [29] described NOBEL (Neighbourhood Oriented Brokerage Electricity and monitoring system) 2013, an energy market to evaluate market-driven DR of electricity trading. The NOBEL market is based on a stock exchange model, except that each day is subdivided into trading periods with discrete time-slots.  
In [21], two actors were considered; traditional electricity users that depend solely on the utilities and those with some DER and/or energy storage capacities. Their operation is regulated through an independent central unit. The aim of the electricity users is to reduce their monetary expense. The authors of [14] developed a pricing mechanism based on game theory for smart MG (SMG). They studied the benefits of an open market where multiple suppliers and prosumers coexist and considered groups of distributed consumers with an aggregate demand that can be met by a group of suppliers through a broker. For such systems, Ref. [13] proposed cloud-based control to manage energy generation and demand profiles of the prosumers.  
An example of the centralized system is the federated power plant (FPP), also known as the virtual power plant (VPP). Throughout this paper, both terms are used interchangeably.
## 3 Principle and Processing Flow of Atmospheric Correction Algorithm
### Data Matching  
To perform atmospheric correction on the images captured by the Multispectral Camera (MSC) using the AOD and CWV simultaneously retrieved by the PSAC, it is necessary to first match the L2 data from the PSAC with the L1 data from the MSC. The PSAC L2 product includes cloud detection, AOD, and CWV, all accompanied by geolocation (latitude and longitude) and altitude information. The MSC L1 product consists of radiometrically calibrated data, including scientific data (TIFF images) and corresponding calibration coefficient files, RPB parameter files, and auxiliary metadata in XML format. Both the MSC L1 product and the PSAC L2 product are stored scene by scene. The four CCDs of the MSC are not stitched together, with each scene having a swath width of 200 km. In contrast, the PSAC L2 product has a swath width of 800 km per scene.  
Scene-by-scene matching is performed by extracting the imaging date and time from the XML metadata of the MSC L1 product and aligning it with the corresponding PSAC L2 product acquired at the same time. Since the MSC L1 data are not geometrically corrected, the geolocation of each pixel is derived by decoding the rational polynomial coefficients (RPCs). Using this pixel-level geolocation information, the atmospheric parameters retrieved by the PSAC are mapped onto the MSC image on a pixel-by-pixel basis.  
Given the resolution disparity between the two datasets, where the PSAC has a spatial resolution of 6 km while the MSC operates at 16 m, each PSAC pixel corresponds to a 375 \(\times\) 375 pixel block in the MSC image. This results in block-like structures in the mapped AOD and CWV, as shown in Figure 3a,c, where both axes represent pixel indices of the MSC image.  
While this mapping process effectively integrates PSAC atmospheric parameters into MSC images, it introduces block artifacts in the atmospheric correction results, especially in regions with abrupt changes in the AOD and CWV. To address this issue, quality checks are performed on the PSAC retrievals to exclude invalid data. Spatial smoothing is then applied using a linear interpolation method, which ensures a gradual transition between adjacent blocks. This approach effectively resolves block artifacts and enhances the visual and quantitative quality of the corrected MSC images.
## 1 Introduction
Unfortunately, the variation in pixels of the slant-range coordinates is very small, because of the short distance existing between the bridge and the water surface. In order to obtain robust measurements, it is necessary to design an efficient pixel dilation stage, to be performed before the tracking algorithm [21]. The absolute shifts are derived after phase unwrapping [22]. In this paper the proposed method is assessed by estimating water surface elevation for narrow-medium rivers (50-300 m) using temporal series of COSMO-SkyMed (CSK) data. To the best of the author's knowledge, this is the first attempt to use SAR images directly to estimate river water level. The remainder of the paper is organized as follows--details of the signal processing techniques are described in the following section. In Section 3 results of a set of simulated data are presented whereas in Section 4 the experimental results using real data (CSK) are reported. Section 5 provides a discussion about the experimental results with the help of performance indices while Section 6 concludes the paper.
## 2 Materials and Methods
### sEMG Data Processing  
sEMG processing starts with segmenting the signals using an overlapping sliding window with a length of 200 ms and a step of 50 ms. Each segment was filtered using a 4 th order Butterworth bandpass filter with a cutoff frequency of 20-450 Hz and a 50 Hz notch filter [5; 8; 10]. From the filtered data, we followed two approaches to extract the features, the first is by using the filtered features, and the second is by differentiating each segment to make the signal more stationary [21] using Equation (1). Features extracted from the first  
Figure 1: Anatomical (red) and tracking (white) markers.  
approach will be referred to as sEMG features, whereas the second approach features will be referred to as differentiated sEMG (DEMG) features.  
\[dx_{t}=(x_{t}-x_{t-1})*fs \tag{1}\]  
\(dx_{t}\) is the DEMG at the time \(t\), and \(fs\) is the sampling rate.  
Five time-domain features were chosen based on the first three groups outlined in [PERSON] et al.'s work [10]. The selected features consisted of the root mean square (RMS) (Equation (2)), mean absolute value (MAV) (Equation (3)), and waveform length (WL) (Equation (4)) from the energy information group. Zero-crossing count (ZC) (Equation (5)) from the signal frequency information group and the fourth-order autoregressive model (AR) coefficients (Equation (6)) from the prediction model group. As a result, eight features were extracted for each sensor utilized in the study. Part of the first trial was used to normalize the features using a MinMax filter ranging from 0 to 1.  
\[RMS=\sqrt{\frac{1}{N}\sum_{t=0}^{N}x_{t}^{2}} \tag{2}\]  
\[MAV=\frac{1}{N}\sum_{t=0}^{N}|x_{t}| \tag{3}\]  
\[WL=\sum_{t=0}^{N-1}|x_{t}-x_{t+1}| \tag{4}\]  
\[ZC=\sum_{t=0}^{N}[sgn(x_{t}\times x_{t+1})\cap|x_{t}-x_{t+1}|\geqslant 0];\]  
\[sgn(x)=\begin{cases}1&\text{if x}\geqslant 0\\ 0&\text{otherwise}\end{cases} \tag{5}\]  
\[x_{t}=\sum_{p=1}^{P}(a_{p}x_{t-p}+\epsilon) \tag{6}\]  
\(N\) is the number of samples, \(x_{t}\) is the sample value at time \(t\), \(P\) is the order of the AR coefficients (4 in the current study), \(a_{p}\) is the \(p\)th order AR coefficients, and \(\epsilon\) is the white noise.
# Experimental Study on the Strength and Durability for Slag Cement Mortar with Bentonite
## 4 Results and Discussion
### Microstructures of Slag Cement Mortar with Bentonite  
The typical structures of bentonite are presented in Figure 8, where the enlargement of the structure in the picture is 2000 times. Figure 9a,b depict typical structures of a hydrated slag cement mortar with bentonite, where the lumped bentonite(B) constituents can clearly be observed along with the hydrated products of slag cement such as the needle-shaped hydrated sulfate-aluminate silicate (ettringite, AFt) and C-S-H gel [30]. Figure 9a shows a uniform distribution of bentonite deposited in every position, meanwhile, more voids exist with the higher addition of bentonite in the mix, as presented in Figure 9b. Figure 9c,d show the microstructures of hydrated slag cement mortar with bentonite after performing the sulfate resistance test. In both pictures, some crystallized sodium sulfate(S) can be found among the C-S-H colloid. Moreover, some gypsum (G) plates, a compound of sulfate and calcium cation, are observed on the sample [31].  
Figure 8: Typical structure of bentonite material.  
Figure 7: Chloride permeability testing results for bentonite mortar.  
The lower water absorption and the higher compressive strength were obtained at an additional level of 0.25% bentonite in each w/b ratio. However, impermeability and strength of mortar decreased as bentonite replacement ratio increased over 0.25%. Due to the fact that particles of bentonite are much finer compared to most aggregates, they are uniformly arranged in the binder matrix of concrete and make the material denser. The bentonite particles, with high specific surface area, result in an high amount of water moistening of their substantial surface areas. This phenomenon limits the pozzolanic material hydration reaction, and then that diminishes the workability and impermeability of high bentonite content mixes. The pozzolanic reaction could also have caused the formation of extra calcium silicate hydrate products that decreased the voids volume. Therefore, the mixes incorporating suitable content of bentonite are able to improve the mechanical properties and impermeability, which is in line with the finding of SEM as well as that of a previous study [32].