esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Uncertainty Budget Evaluation  
The maximum uncertainty, related to detector positioning over the whole PDD curve, was estimated to be in the range 2-4% for all the beam energies and field sizes. This finding was related to the product of the used scanning step size of 0.4 mm for the evaluated dose gradient of about 4% mm\({}^{-1}\) for the higher beam energy and 9% mm\({}^{-1}\) for the lower beam energy, regardless of the field size.  
For all the profile curves, the maximum uncertainty related to detector positioning was estimated to be within 0.4% in the centre area and 3% in the maximum slope area, for all beam energies and field sizes, given the step size of 0.4 mm. Moreover, another component of the uncertainties of about 1% must be introduced to take into account that the reference detector was not used for relative dosimetry.  
For OF determination, the positioning accuracy resulted to be \(\pm\)0.2 mm, and therefore, the uncertainty related to detector positioning was lower than 0.1%. This finding takes into account the method used to identify the centre field in the present study [15]. The accuracy of the electrometer was \(\pm\)0.2%, according to the manufacturer. The measurement readout uncertainties were calculated as one standard deviation of the mean of repeated measurements and resulted lower than 0.5% for solid-state detectors and 0.2% for the ionization chamber. The reproducibility of the OF values, obtained in different measurement sessions, was about 1% (1 SD) for the solid-state dosimeters and about 0.5% (1 SD) for the ionization chamber. By combining the components with Gaussian error propagation, the total uncertainties for the measurements were estimated to be about 1.5% for the solid-state dosimeters and 1% for the ionization chamber.
## 3 Numerical Simulation  
### Numerical Model  
In this study, the finite element software ETABS (V17, CSI Computers and Structures, Inc, Walnut Creek, CA., USA and 2017) was used to construct a seven-story benchmark structure in numerical simulation [13]. The geometrical properties of this model are outlined as follows:  
* The model was a steel structure with yield strength of \(F_{y}\) of 0.24525 Pa.
* The height of each story was 1.06 m, and the column cross-sectional area was \(0.025\,\times\,0.15\) m\({}^{2}\).
* L-shaped steel angles measuring \(0.065\,\times\,0.065\,\times\,0.006\) m\({}^{3}\) were used to brace the structure in the Y-axis.
* The beam was a steel plate with a cross-sectional area of \(0.10\,\times\,0.07\) m\({}^{2}\).
* The area of each story was \(1.32\,\times\,0.92\) m\({}^{2}\), and the floor lengths on the x- and y-axes were 1.32 and 0.92 m, respectively. An additional 500 kg was placed on each floor to simulate real structural behavior.  
Time history analysis used white noise to simulate ambient vibrations. In ETABS, a white noise of 1 MW power was set as the acceleration input along the \(y\)-axis with a wide bandwidth of 100 Hz. The sampling rate was 200 Hz, and the total time was set to 5 min. After the database was created by conducting the time history analysis for each damage cases, velocity response data were extracted from the center of each floor along the \(y\)-axis. The ETABS model and damage scenario are presented in Figure 3a,b. The modal frequencies were used as a criterion for modelling, and the details of the modal frequencies are presented in Table 1.  
Figure 3: (**a**) The finite element model. (**b**) The damaged scenario (the bracings was removed from the red circle).
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## 4 Data Transmission Performance of SRSS/ILMO  
As shown in Figure 1, the proposed SRSS/ILMO has two data transmission modes, including remote monitoring with satellite and underwater acoustic communication, and ship-borne monitoring with underwater acoustic communication. Based on the above application of submarine engineering geological observation, the performance of the two data transmission modes is analyzed. In the application, the Beidou satellite communication module and the AquaSeNt underwater acoustic communication module are used. The Beidou communication data rate is 77 bytes/min, and the underwater acoustic communication data rate is 2 kbps. The one-way underwater acoustic communication distance is 1500 m. Therefore, in the application of remote real-time observation including satellite communication, the data transmission bottleneck of the system is satellite communication. The data transmission performance of the system including Beidou satellite communication is shown in Table 2. When conducting parallel resistivity observation, the system can achieve the maximum data transmission capacity of 33,272 bytes per day. In ship-borne applications, the SRSS/ILMO can only use underwater acoustic communication to enhance the data transmission capability. Table 3 shows the data transmission performance only using underwater acoustic communication. For the bottleneck problem of the satellite communication mode, higher performance satellites can be adopted, such as the Iridium satellite and Beidou satellite with multiple cards. If the water surface relay platform is stable enough, directional satellite communication with higher performance can be adopted.  
Figure 11: The data frame format of SRSS/ILMO.  
Figure 12: The 3.5 characters interval mechanism.
### Modelling Light Use Efficiency  
#### 2.5.1 Predictor Variables  
Several satellite bands, vegetation indices, and agroclimatic variables were used as predictors for LUE. Although vegetation indices vary with crop maturity [16], we included both days after sowing (DAS) and day length as predictor variables to capture potential within-season variation in LUE. Using DAS as a predictor overcame the limitation of using day of year which may be site, rather than crop type, specific. The inclusion of these predictors also enabled the relative importance of seasonality and vegetation indices to be investigated.  
Satellite-derived data were masked according to the analysis boundary as per Section 2.4.1. The details of each predictor variable used in the random forest regression models are summarised in Table 3.
## 2 Materials and Methods
### Improved YOLOv5s Model
#### 2.6.1 Optimizing the Backbone Network  
The attention modules commonly used in object detection today are CBAM and the Squeeze-and-Excitation Network (SENet) [43]. Compared with SENet, CBAM has a spatial attention mechanism. In addition to considering the channel features of the target, it also focuses on the location information of the target [27]. In the remote sensing images obtained by satellites, there is low contrast between the target, the background, and complex scenes, Therefore, in order to enhance the feature expression ability of the model and enrich the information of smoke feature in the feature map, we introduced CBAM after the backbone network.  
As shown in Figure 10, CBAM is structured as two independent modules, CAM and SAM. The input feature map \(H\) is passed through CAM. Each channel undergoes both max pooling and average pooling. After a multi-layer perceptron, the resultant intermediate vector is added element-wise and sigmoid-activated to produce a channel attention \(M_{c}\)[27]. \(H\) is activated by \(M_{c}\) to obtain the characteristic figure \(H^{\prime}\). The input to SAM is \(H^{\prime}\). This module performs average and maximum pooling in the channel dimension and obtains the spatial attention \(M_{s}\) after a convolution operation and sigmoid activation. After \(H^{\prime}\) is activated by \(M_{s}\), the final feature map \(H^{\prime\prime}\) is obtained:  
\[\left\{\begin{array}{l}H^{\prime}=M_{c}(H)\otimes H,\\ H^{\prime\prime}=M_{s}(H^{\prime})\otimes H^{\prime},\end{array}\right. \tag{1}\]  
where \(\otimes\) indicates element-wise multiplication.  
Figure 9: FPN + PAN structure diagram.  
The features at different scales are extracted by SPP, which is the last unit of the backbone network, and input into the CBAM module, which focuses on the channel features and location information, as these play a decisive role in the final prediction. CBAM emphasizes important smoke features and suppresses general features, which enhances the backbone network's expression of features and improves the model's prediction accuracy.
## 4 Proposed Approach to Assure Anonymity and Privacy in E-Voting Using Blockchain Technology
### Discussions  
Current schemes and protocols do not meet the reliability criteria since they do not adequately meet the security, privacy, and anonymity characteristics. The BSJC and Anti-Quantum systems, for example, fail to meet voter expectations for accuracy, correctness, scalability, and variability. The OVN, DATE, BES, and BEA schemes, on the other hand, do not meet the requirements for correctness, integrity, and scalability. Our scheme manages to balance the qualities of privacy and anonymity by using two Blockchains (DKB and EVB). Integrity, precision, and correctness are also obtained, in addition to anonymity and privacy. This is accomplished by using a cryptographic nonce and a hash of the voter's private key, which allows the voter to verify their vote and ensure that their vote is correctly counted. Future researchers should consider the component of the vote separation from the voter and the part of anonymization that occurs in step six of the scheme, as presented in Figure 2.
## 5 Methodology and Metrics  
To evaluate the effectiveness of our framework and its impact on executive functioning in students with special educational needs, we will employ the Behavior Rating Inventory of Executive Function--BRIEF-2 [39] as the primary benchmark. BRIEF-2 is a validated tool designed to assess executive functioning behaviors across multiple domains, including working memory, task completion, planning, and emotional regulation. This instrument is particularly suitable for capturing the nuanced executive function challenges faced by children and adolescents in educational settings. This is supported by a formal agreement with a specialized school for children with special needs. Its multidimensional approach will allow us to make profiles of students' self-regulatory strengths and weaknesses, gaining insights aligned closely with the goals of our framework. The inclusion of subscales such as behavior regulation, emotion regulation, and cognitive regulation will let us quantitatively measure improvements in areas targeted by the XR platform.  
For the evaluation, we will use structured experiments, incorporating control groups and longitudinal studies, to validate the framework's effectiveness.  
Control groups: Participants will be divided into two groups, those using the XR-enhanced learning platform and those engaging with traditional learning methods. By comparing outcomes such as task completion rates, learning success, and executive function improvements between these groups, we aim to isolate and quantify the contribution of the XR framework.  
- Longitudinal studies: We will measure changes over time in key metrics, such as working memory performance and task completion consistency, to assess the framework's sustainability. Data collection will occur at predefined intervals, allowing us to analyze trends in learning retention and skill development over extended periods.  
A list of specific metrics which will be used for future implementation and testing of our system, categorized by functionality, can be seen in Table 1.  
Future iterations of this research will involve the collection of interaction and performance data from students with special educational needs. These data will be gathered in collaboration with schools and therapy centers, ensuring diversity in user demographics and learning profiles.  
To promote transparency and reproducibility, the datasets that we will collect during future evaluations will be anonymized and made available through open data platforms, abiding by ethical guidelines and privacy regulations.
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 3 Proposed Method
### Graph Perception Module  
The filter in a graph convolution can be represented by a polynomial of order \(K\), and the parameter K is also referred to as the kernel size of the graph convolution. When utilizing Chebyshev polynomials to approximate the filter, its order remains fixed. This implies that the receptive field of the graph convolution layer is also fixed, limiting its ability to learn multi-domain information. To address this limitation and aggregate information from different receptive fields, a multi-perceptual graph convolution neural network (MPGCN) is designed.  
The specific structure of the MPGCN is illustrated in Figure 5. The MPGCN comprises three GConv layers with varying receptive fields designed to capture the characteristics of the instance graph. The key idea of the MPGCN is to collect information from different receptive fields and combine features through the concat layer, aiming to create a more robust feature representation. It consists of three parallel graph convolution layers with different receptive fields (k = 1, k = 2, k = 3), which can perceive the information of different numbers of neighbors.  
Figure 4: Process of the graph generation.  
Features from these neighborhoods are then computed to fuse the output features into enhanced feature representations. For a graph with node feature \(X=[\hat{x}_{1},\hat{x}_{2},...,\hat{x}_{n}]\), its convolution with the GConv layer can be defined as follows:  
\[H=\big{[}A^{K_{1}-1}XW^{0},A^{K_{2}-1}XW^{1},\dots,A^{K_{l}-1}XW^{l}\big{]} \tag{12}\]  
where \(K_{1}\), \(K_{2}\), and \(K_{l}\) represent the size of the receiving field, \(A\) is the constructed adjacency matrix, \(X\) is the node characteristic matrix, and \(W_{0}\), \(W_{1}\), and \(W_{l}\) denote the weight matrix of each receptive field. Therefore, the feature representation learned by the MPGCN for the input graph can be defined as follows:  
\[\begin{split} H^{0}=\text{GConv}(\text{AXW}^{0})\\ H^{1}=\text{GConv}(\text{AH}^{0}W^{1})\\ H^{2}=\text{GConv}(\text{AH}^{1}W^{2})\\ H=\text{concat}(H^{0},H^{1},H^{2})\end{split} \tag{13}\]  
where \(H^{0}\), \(H^{1}\), and \(H^{2}\) are the learned features from the first, second, and third GConv, and \(W\) is the trainable weight matrix.
## 5 IO Data Management and Data Sharing
### Distributed SQL Databases with RPC
```
//CreateanANNtrainer varsql=DB.sql('ws://host:port:key'); //Createac checkpointtablesfordatastatesnapshots varcptable='checkpoint-'+myGroupId+'-'+myWorkerId if(!options.checkpoint){ sql.create(cptable, { time:'number',iter:'number',err:'number',  model:'blob'}) varmodel=ML.learner(ML.ML.ANN,{options}) }else{ varrow=sql.query(cptable,'*','rowid='+options.checkpoint) varmodel=ML.deserialize(row[0].model); } while(i<maxIter||err>errThr){ varresult=ML.train(model,data,{options}); if(i>0&&(i%4)==0)sql.insert(cptable, {time:time(),iter:i,err:result.error,  model:ML.serialize(model)});  i++;err=result.error; } send(ML.serializes(model)) //deletecheckpointtable sql.drop(cptable)
```
Example 6. Worker check-pointing of the data state, enabling recovery.
# Analysis of the Effect of Pulse Length and Magnetic Field Strength on Nonlinear Thomson Scattering
## 3 Results
### Electron Motion and Corresponding Radiation Properties 4  
The parameters of Figure 5 are consistent with those above, with the difference being that the radiated power of Figure 5 is normalized by the maximum value of the radiated power in each set of corresponding magnetic field strengths B\({}_{0}\). The normalization is conducted via a linear transformation, consistent with the above, corresponding to the parameter C = 7.  
Looking at Figure 5, we can see that as the magnetic field strength increases, the radiated power emitted by electrons with smaller pulse lengths differs from that emitted by electrons with larger pulse lengths by several orders of magnitude to a small difference.  
This is because when the pulse length and magnetic field strength is small, the contact time between the laser field and the electron is short, not providing sufficient energy, so the magnetic field can save less energy. When the contact time between the laser field and the electrons increases, the magnetic field is able to save the energy; the energy is therefore larger, so the difference in the radiated energy of the electrons at this time is not large.  
We also find that when both the magnetic field and the pulse length are exceptionally large, a situation occurs wherein the radiated power of the final spiral cylindrical portion of the electron is much less than the maximum radiated power due to the fact that with a larger pulse length, the electron consumes more time and is subjected to more pronounced deceleration when it is removed from the action of the laser field, which is significantly increased by the presence of the magnetic field, so that at times when both the pulse length and the magnetic field are both larger, this occurs.  
Comparing the plots in group (a) of Figures 4 and 5, we can see that when the pulse length is small, the color at the maximum radiated power in Figure 4 is dark blue, while  
Figure 5: Recombination diagram of the electron’s trajectory and the maximum radiated power normalized to the same magnetic field.  
the color at the maximum radiated power in Figure 5 is light blue. This indicates that the increase in magnetic field has a more significant increase in radiated power.
# A Novel Method to Measure the Powder Consumption of Dental Air-Polishing Devices
## 2 Materials and Methods  
### Powder Consumption Measurement  
The measurement method in this study was based on light dispersion, which occurs when a powder passes through a light beam (Figure 1). Every particle crossing the incoming light beam will disperse light, and the intensity of the dispersed light is proportional to the number of particles in this beam. As bigger particles disperse more light than smaller ones, it was determined that the intensity of light is more directly proportional to the volume of the particles, rather than the number of particles. As a consequence, the measurement relieson the relationship of the light intensity with the amount of powder [16]. This relationship was experimentally calibrated with the total powder consumption for every test.  
The laser source used in this study was a 532 nm laser with a 30\({}^{\circ}\) line generator that created a light sheet sufficient to illuminate all of the powder (PGL-V-H-532-200 mW, CNI Lasers, Changchun, China). The powder was sprayed without water in order to avoid light dispersion from the water itself. Indeed, the water was generally added at the nozzle end only, and so did not interact with the powder delivery of the device. In order to avoid dust deposition on the optical part, the powder was sprayed into a hollow cylinder 32 mm in diameter and 110 mm in length, with a side opening for the laser to cross the cylinder. At the end of this cylinder, an air evacuator was added to direct all of the powder towards the tube exit. The light measurement was obtained with a spectrometer (Avantes AvaSpec-UL2048 CL-EVO-RS, s/n 1904311U1, Avantes, Apeldoorn, The Netherlands). This allowed for a specific measurement of the light's integral signal between 530 and 535 nm. The measurement time varied according to the different conditions, and ranged between 0.1 and 5 milliseconds. These measurements were then averaged over the output measurement time, which was set to 0.1 s. The spectrometer measured the light emitted in the hollow cylinder through the use of an optical fiber fixed perpendicular to the laser beam. The total quantity of powder passing through the system was recorded by weighing the powder chamber of the device before and after the test using a precision scale (Metller Toledo XP10002S, Greifensee, Switzerland).  
This method was based on the proportionality of the light intensity (integral to the light intensity over time) with the quantity of powder. The calibration factor was determined via the integration of the total light measured divided by the total amount of powder used during the test. Therefore, the instantaneous powder consumption was simply given as the multiplication of the instantaneous light integral with the calibration factor. Using this method, a powder flowrate with a resolution of 0.1 s or less could be easily obtained.
## 5 Experimental Results and Analysis
### Indicators for Model Evaluation
#### 5.1.1 Evaluation of SVM Model  
Table 22 demonstrates that the average Accuracy was 81.14%, the average Precision was 74.12, the average Recall was 63.08%, and the average F1 score was 41.86%. The average Loss value was 6.513, the maximum Loss value was 7.346, the minimum Loss value was 5.658, and the standard deviation of Loss value std was 0.269.
## 5 Conclusions  
Considering that the MS signal contains a large amount of random noise, an EMD_CS_ST denoising method was proposed, and the advantages and disadvantages of the EMD_CS_ST, DWT, EEMD, and VMD_DWT are compared and analyzed through a numerical simulation experiment. The denoising analysis of MS signals obtained from the underground cavern of Shuangjiangkou hydropower station was carried out. The following conclusions are drawn.  
The numerical simulation experiment demonstrates the NF phenomenon of the compressed sensing denoising method in the process of signal reconstruction with low SNR. Although the theory of CS has a certain ability to reduce noise in the denoising of MS signals, the effect is weak.  
Figure 11: The waveform before and after denoising of the MS signal, and their time-frequency spectrum.  
The CS denoising method is not suitable for direct application to the denoising processing of MS signals. The EMD_CS_ST method can better compensate for the deficiency of CS in MS signal denoising and greatly weaken the NF phenomenon caused by CS.  
Simulation experiments were conducted to compare the effects of four kinds of MS signal denoising methods such as EMD_CS_ST, DWT, EEMD, and VMD_DWT. The FFT time spectrum of MS signals before and after denoising were compared and analyzed. The feasibility of the EMD_CS_ST method is demonstrated qualitatively.  
By introducing the \(SNR\), the \(SD\), and the \(CC\), the rationality and effectiveness of the EMD_CS_ST method were proved quantitatively. The actual three MS signals were denoised, and the effect was remarkable.  
The CS technology itself has matured, and we can apply EMD_CS_ST to data collection, storage, and noise reduction in the later stage to achieve integrated data processing of the three, which can greatly reduce the space occupied by data.  
[PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] processed and analyzed the signal data. [PERSON] conceived the research and provided some suggestions for the research method and revised the article completely. All authors have read and agreed to the published version of the manuscript.  
The National Key R&D Program of China (Nos. 2018 YFC1505004 and 2017 YFC1501100) and the National Natural Science Foundation of China (No. 51679158).  
Thanks are given to colleagues at the Shuangjiangkou hydropower station for their valuable contributions to the project. Finally, the authors would like to thank the editors and reviewers for their valuable comments and constructive suggestions.  
The authors declare that they have no conflict of interest.