esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 4 Results
### Result of Change Detection  
The SAR and optical images are preprocessed and paired. The CVA algorithm is applied to quickly generate the difference map between the images, and the segmentation threshold provided by the OTSU algorithm is used to extract the change information. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Confusion Matrix** & **Positive** & **Negative** \\ \hline Positive & 913 & 97 \\ Negative & 87 & 903 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix of test data.  
Figure 8: Comparison of farmland extraction results: (**a**) optical images; (**b**) the result of ESA; (**c**) the result of ESRI; (**d**) the result of DW; (**e**) the result of this paper.  
extracted data are then vectorized to produce the preliminary change patches. Extensive experimentation has shown that the optimal segmentation threshold for the collaborative use of optical and SAR data in CVA is achieved by adding 50 to the reference value calculated by OTSU. Subsequently, the extracted change patches are verified one by one using Sentinel-2 optical images. Given the 10 m spatial resolution of Sentinel data, an area filtering threshold of 5 mu (approximately 33 pixels) is applied to ensure accurate identification of change patches. The final statistical results are presented in Table 5.  
The results demonstrate that the integration of optical and SAR data effectively extracts three typical categories of cropland non-agriculturalization. The detection accuracy exceeds 70% for all years, with an overall average accuracy of 73.65%, satisfying the requirements for remote sensing monitoring of cropland non-agriculturalization [29; 47].
## 6 Results
### Classification of Synthetic 7D Datasets
#### 6.3.2 Cloud Detection over Ice/Snow Surfaces  
Cloud discrimination over ice/snow surfaces during daytime is more challenging than over water surfaces due to the low optical and thermal contrasts between the surface and clouds (see Section 2.2). This is particularly apparent in the case of the MYD35 product (Figure 8), which, over ice/snow, is 21% less accurate than over water (ACC = 0.671 vs. ACC = 0.881), and the Cohen's kappa is 56% lower (ACC = 0.727, \(\kappa\) = 0.317). Amongst the selected classifiers, only the Naive Bayes method produced worse results than the MYD35 product (ACC = 0.659, \(\kappa\) = 0.296). As compared to the VEOR algorithm (ACC = 0.809, \(\kappa\) = 0.611), the QDA (ACC = 0.742, \(\kappa\) = 0.467) and decision tree (ACC = 0.764, \(\kappa\) = 0.526) methods were less accurate, the nearest neighbor (ACC = 0.809, \(\kappa\) = 0.614) and linear SVM (ACC = 0.808, \(\kappa\) = 0.613) classifiers were equally accurate, while the AdaBoost (ACC = 0.816, \(\kappa\) = 0.629), random forest (ACC = 0.818, \(\kappa\) = 0.632), Gaussian process (ACC = 0.822, \(\kappa\) = 0.639), RBF SVM (ACC = 0.886, \(\kappa\) = 0.745) and neural net (ACC = 0.890, \(\kappa\) = 0.752) algorithms were more accurate. Regarding the optimal feature combination for cloud discrimination over ice/snow (see Appendix A), the internal VEOR feature selection procedure selected the following features (ordered by decreasing significance): B7_2130 nm, B18_936 nm, B26_1375 nm, B3_469 nm. In this respect, MODIS reflectance at 2130 nm was selected by all of the classifiers apart from the Naive Bayes classifier, while the reflectance at 936 nm was selected by the nearest neighbor, neural network, RBF SVM, random forest and AdaBoost methods. Other features that were often selected were: B29_8550 nm_B28_7325 nm_diff (5 times), B27_6715 nm (4 times), B32_12020 nm (3 times), B28_7325 nm (3 times). As opposed to the cloud discrimination over water surfaces, only the linear SVM classifier required angular information (i.e., Sun zenith angle) to discriminate cloud cover over ice/snow surfaces.  
Figure 7: Comparison of classifier performance for cloud discrimination over water surfaces using MODIS spectral measurements collocated with the CALIOP/CPR reference cloud mask. MYD35 denotes AQUA MODIS collection 6 cloud mask product.
## 3 Candidate Universal Decoders for URLLC
### Guessing Random Additive Noise Decoding (GRAND)  
GRAND is a noise guessing decoding algorithm that initially generates the most likely test error patterns (TEPs) (_e_) based on channel-induced noise. The error patterns are then applied to the hard demodulated received signal (_y_), and checked whether the resulting string is a member of the codebook. It makes use of ordered statistics of noise for decoding, and the codebook is only used for the look-up of the potential decoded codeword [26]. Incontrast to other decoding algorithms, which find errors in the received vector by utilizing the code's structure, GRAND uses hard-decision decoding to identify the most likely error pattern \(e\) or noise sequence \(z\). The error patterns are rearranged from most probable to least probable, and the remaining sequence in the codebook is checked to see if it is still valid. An \((N,K)\) linear block code with \(K\) information bits characterizes a codebook denoted by \(C\). The codewords in \(C\) have length N and rate R, with \(2^{K}=2^{NR}\). The generator matrix for the code is given as \(G_{K\times N}\) and the parity-check matrix as \(H_{(N-K)\times N}\). A codeword \(u\in C\) if and only if  
\[Hu^{T}=0 \tag{3}\]  
The encoded signal \(x\) is transmitted through a channel, and the signal is obtained as \(y=x\oplus z\) at the receiver, where \(z\) is the added noise and \(\oplus\) denotes a modulo-2 operation. For the detected word \(\hat{y}=x\oplus e\), the syndrome, \(s\) is calculated as  
\[s=H\hat{y}^{T}=H(x\oplus e)^{T}=He^{T} \tag{4}\]  
which is the \(\mathbf{0}\) vector only if \(e\) is 0 or e is a valid codeword. GRAND identifies the error pattern based on the syndrome. Since GRAND's decoding complexity decreases with increasing code rate and is suitable for short codewords with a high code rate, it is appropriate for low-latency communications. The Algorithm 2 of the GRAND decoder is developed based on the study of [26].  
```
1 Variable declaration
2\(y^{n}\); channel output of length \(n\)
3\(z^{n,i}\); \(i\)th noise sequence
4\(G(z^{n})\): list of noise sequences in descending order of likelihood, i.e., \(G(z^{n,i})\leq G(z^{n,i})\) for \(z^{n,i}\geq z^{n,i}\)
5\(H\): parity-check matrix
6\(C^{n}\): codebook
7 Step I
8 Input:\(y\), \(G(z^{n})\)
9 Output:\(x^{n}\)
10 for\(i=1\) to \(n\)
11 set \(G(z^{n})\) = \(i\) as the most likely noise sequence
12 endfor
13 Compute \(x^{n}=y^{n}\ominus z^{n}\)
14 Step II
15 Input:\(x^{n}\), \(H\)
16 Output:\(\hat{x}^{n}\)
17 if\(H(x^{n})^{T}=(0^{n})^{T}\)then
18\(\hat{x}^{n}\in C^{n}\leftarrow\chi^{n}\)
19 elseif\(x_{n}=y_{n}\ominus z_{n}\
otin C^{n}\)do\(i=i+1\)
20 go to line 10
21 endif
```  
**Algorithm 2**Guessing random additive noise decoding (GRAND)
## 5 Conclusions  
This paper proposed a comprehensive framework to assess the UUSR quality for shield tunnel construction in a relatively small-scale area covered by thick soil layers. The assessment index system incorporated a total of seven indicators based on the knowledge of designers with extensive experience in tunnel design, and can reflect the development difficulty well. A 3D geological model attributed with the data of assessment indicators was created based on geotechnical test data and the geostatistical method (Kriging). The AHP-EWM weighting method adopted can process both subjective and objective indicators. The continuous characteristics of the ICMM model successfully achieved the high-resolution 3D visualization of assessment results.  
The results of the demonstration example in the Jiangbei New District of Nanjing, China, revealed a range of \(QI\) values from 0.28 to 0.85. The relatively high-quality space is located at depths ranging from 10 to 30 m in most areas, while the low-quality space is found at depths of 5 to 10 m and below 60 m. Particularly in some areas, the soft soil (2) and its contact zone with silty sand (3) at a depth of 20 to 30 m contributes to the lowest \(QI\) values. Furthermore, the application to a planned subway section shows that a low-quality area within a depth range of 20-30 m is mainly composed of soft soil and its contact zone with silt sand, which is unfavorable for shield tunneling construction.  
Conceptualization, Y.Y. and L.W.; methodology, Y.Y. and D.L.; software, R.W.; validation, Y.Y., D.L., R.W., L.W. and J.S.; formal analysis, Y.Y.; investigation, Y.Y. and R.W.; resources, L.W.; data curation, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, L.W.; visualization, R.W.; supervision, L.W. and J.S.; project administration, L.W.; funding acquisition, Y.Y. and L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42071256, and the Scientific Research Project of Jiangsu Bureau of Geology and Mineral Exploration, grant number 2019 KY01.  
Not applicable.  
The data supporting the conclusions of this article will be made available upon request and with the permission of the Scientific Research Project of Jiangsu Bureau of Geology and Mineral Exploration.  
**Acknowledgments:** The authors would like to thank Senior Engineer [PERSON] from Shanghai Municipal Engineering Design Institute, Senior Engineer [PERSON] from China Coal Technology and Engineering Group Nanjing Design and Research Institute, Engineer [PERSON] from Shanghai Tunnel Engineering Co., Ltd., Senior Engineer [PERSON] from PowerChina Huadong Engineering Co., Ltd., and [PERSON] from Jiangsu Transportation Research Institute Co., Ltd. for their advice in assessment indicators selection and weights assignment.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Paint Removal with Pulsed Laser: Theory Simulation and Mechanism Analysis
## 3 Results and Discussion
### Morphology Analysis of Collected Particles during the Cleaning Process  
A micrograph of particles collected during the laser cleaning process is shown in Figure 13. Figure 13c is a magnified image of the boxed area in Figure 13b. The collected particles were of different sizes (micrometer, submicron, and nanometer). The nanoparticles were irregular in shape and had uneven edges, as shown in Figure 13a. The circular area, as shown in Figure 13b, shows obvious mechanical tear marks and a lamellar structure. The elliptical region from Figure 13b shows a transparent and pleated paint sheet with thickness of about several nanometers. This special structure was due to the cracking that occurred inside the paint layer during the laser cleaning process. As the delamination progressed, the paint layer was finally stripped off the paint base. Figure 13c shows a network structure at the edge of the particle, formed by the incomplete condensation of vaporous material. Spherical nanoparticles (average size of 31.36 nm) of uniform shape are shown in Figure 13d. Particle distribution is shown in Figure 12f: the relatively large particles were about 146 nm, the particles in the network structure were 2 nm in size or smaller, and particles in the 2-16.4 nm range were found to be the most abundant.  
Figure 12: Scanning electron micrograph of the laser cleaning surface and residual paint layer fracture cross-section: (**a**) the untreated surface, (**b**) the cleaning surface, (**c,d**) the fracture section.  
The percentage of C element in the uncleaned paint layer was found to be 68.17 at%, and fraction of C element in spherical particles was 85.01 at%, indicating that spherical particles were generated due to bond cleavage of the polymer. The paint layer absorbed a large amount of laser energy, resulting in a breakage of chemical bonds such as C-C, C-H, and C-O from the molecular chain. A large amount of C atoms was continuously generated and assembled in the high-energy environment. Combustion also took place during the process and a large number of C atoms formed uniform nanoparticles because they came into contact with cold air and were assimilated into droplets during the ascending process. This process indicated a chemical bond breaking combustion mechanism during laser paint removal, which is consistent with the analysis in Figure 5.
# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
###### Abstract  
In nuclide identification, traditional methods based on nuclide library comparisons rely on the identification of characteristic peaks, often overlooking the full spectrum information, which leads to cumbersome operations and low efficiency. In recent years, machine learning and deep learning techniques have been introduced into the field of nuclide recognition to improve identification efficiency; however, most existing methods fail to effectively extract deep features from the data. To address this issue, this paper proposes a method that integrates the Gram Angular Summation Field (GASF) algorithm with a Deep Residual Network (ResNet) for processing nuclide energy spectrum data. First, the GASF algorithm is used to transform one-dimensional spectral data into two-dimensional images, thereby fully extracting spatial features from the data. Then, these two-dimensional images are input into the ResNet model, where features are automatically extracted through multiple convolutional layers. Finally, the Softmax layer is used for nuclide classification. Experimental results demonstrate that the proposed method can effectively improve both the accuracy and efficiency of nuclide identification; the recognition accuracy on the simulated data reaches 99.5%, and, when tested with actual measurement data containing unknown radionuclides, the model still achieves a high accuracy of 92.6%. This study shows that the combination of deep learning and signal processing techniques can significantly improve the accuracy and application scope of nuclide identification, offering substantial practical value.  
ResNet; GASF; neural network; nuclide identification 2025  
15218  
3
## 2 Description of the Power Converter System Solution for a Cogenerator with an Induction Machine
### Topology of the Generator AC-DC Power Converter  
The most commonly used two-level three-leg topology was selected for the construction of the PC1 generator converter. The induction generator works with a symmetrical load; therefore, a three-leg topology was used. Figure 4 shows the selected topology of a three-leg DC-AC converter with an LCL filter and two contacts, ST1 and ST2. For a three-leg split capacitor converter, the V\({}_{\mathrm{dc}}\) voltage must be greater than twice the phase voltage amplitude. This means that V\({}_{\mathrm{dc}}\) > 2\(\surd\) 230 V = 650.5 V. In practice, this voltage is set to approximately 700 V.  
IGBT power transistors, in the form of MB150 BC120 IGBT half-bridges, were used in the prototype of the generator power converter. The transistor parameters were: maximum voltage 1200 V, maximum current 150 A.
## 3 Tracking Algorithm
### Single Model Algorithm  
Equation (3) shows the process of position and velocity in continuous time. Then the basic process equation can be written by making Equation (3) discretized. To develop the single model tracking algorithm based on the aerodynamic model, we need to approximate the process of the two control variables from discrete time \(k-1\) to \(k\). Furthermore, the augmented equations regarding the control variables can be written as  
\[\left\{\begin{array}{l}\alpha_{k}=\alpha_{k-1}+Tw_{k-1,\alpha}\\ \
u_{k}=\
u_{k-1}+Tw_{k-1,\
u}\end{array}\right. \tag{9}\]  
where \(T\) is the time interval. We consider that the changes of the AOA and the bank angle at discrete time \(k\) and \((k-1)\) are caused by white noise \(w_{k-1,\alpha}\) and \(w_{k-1,\
u}\), respectively.  
After getting the process and measurement equations, according to CKF we can get the predicated state \(\hat{X}_{k}\) at discrete time \(k\) using the state \(\hat{X}_{k-1}\) at discrete time \(k-1\) and the new measurement \(Y_{k}\).
## 5 Major Equipment and Technologies
### Sensor Devices  
Among all the devices used in advanced agriculture today, sensor devices are highly critical and perform a crucial role in collecting crop status and other information. Sensor devices can be utilized separately or in the group as needed. Moreover, sensor devices are used for advanced farming because of their robust technology. Next, the discussion is based on the working process and purpose of the main sensor types and their benefits. The major sensor devices used in advanced agriculture are listed in Table 4.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Wireless Sensors** & **Employment** & **References** \\ \hline \multirow{4}{*}{Telematics Sensors} & Telematics sensors are the leading equipment used for communication, an agricultural-based toolkit, which is the most accurate and precise communication tool. This application is mainly used to gather information from remote areas that are not accessible easily, report the information of the machine’s working status, collect information about areas, locations, and assist in locating travel routes. These programs help farming managers automatically store and record information correlated with agriculture. \\ \cline{2-3}  & Remote sensing tools are used to capture and store geographical information and several environmental and climatic parameters. Moreover, it helps in managing, manipulating, displaying, and analyzing geographical and spatial information. These sensors help assess several factors, such as forecasting, monitoring, yield assessment, crop evaluation, land degradation, and pest management (e.g., using LiDAR, satellite, UAVs). For example, the Argos sensor can be used for processing, disseminating, and collecting global-based data and is compatible with smartphone platforms. \\ \cline{2-3}  & \multicolumn{1}{p{113.8 pt}}{} & \\ \cline{2-3}  & \multicolumn{1}{p{113.
# EMF Assessment Utilizing Low-Cost Mobile Applications
## 2 Materials and Methods
### Measurement Equipment  
The measurement equipment included three identical Xiaomi 12 Pro 5G smartphones, each connected to a different mobile operator with mobile data enabled, and the EME Spy Evolution exposimeter (Figure 2a).  
For data collection, the smartphones utilized the G-NetTrack Pro application (Sofia, Bulgaria, version 30.1) [11], a mobile app capable of capturing and recording certain Key Performance Indicators (KPIs) such as signal strength (e.g., RSRP), signal quality (e.g., Reference Signal Received Power, RSRQ), downlink bitrate, and other parameters like timestamp, latitude, and longitude. The full list of parameters can be found in [12]. The app records data from the main cell and up to 17 neighboring cells and does not require device rooting. Notably, a neighbor cell may refer to a different 4G band within the same Base Station (BS) rather than a different BS. The app's sampling rate was 1 s, resulting in  
Figure 1: The selected 10-km routes in the city of Thessaloniki.  
approximately 2500 measurements per route repetition. The exported file also included a timestamp and geographic coordinates for each measurement.  
The EME Spy Evolution exposimeter (Plouzane, France) can isotropically capture and record electromagnetic radiation with a measurement uncertainty of \(\pm\)1.5 dB (for frequencies below 4 GHz) in specific bands selected by the user [13]. The chosen bands for our scenario included all downlink (DL) bands of mobile networks (791-821, 925-960, 1805-1880, 2110-2170, 2570-2620, 2620-2690, 3300-3800 MHz) for the first route, resulting in a minimum sampling period of 4 s and a total number of about 600 measurements per route repetition. For the second route, all mobile network bands, including uplink (UL), were included, resulting in a sampling rate of 6 s and a total of about 400 measurements per route repetition. The exported file included a timestamp.  
Equipment placement involved two bags: a bag with three compartments positioned below the scooter's handlebars containing the three mobile phones and a second bag placed at the front of the driver holding the exposimeter (Figure 2b). The instrument placement was consistent for both routes and all route repetitions.
## 3 Real Application Case
### Compared with Other Prediction Models
Table 3 reports the prediction errors of PM2.5 concentration using the four models. The R\({}^{2}\) of the MGWODBN model is 0.884, lower than the 0.708 of the GABP model, the 0.758 of the DESVM model \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Hidden** \\ **Nodes** \\ \end{tabular} & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Momentum** \\ **Coefficient** \\ \end{tabular} & \begin{tabular}{c} **MAE** \\ **(ug/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **MSE** \\ **(ug\({}^{2}\)/m\({}^{6}\))** \\ \end{tabular} & \
\begin{tabular}{c} **R\({}^{2}\)** \\ \end{tabular} &
\begin{tabular}{c} **Optimization** \\ **Time (s)** \\ \end{tabular} \\ \hline PSODBN & 324 & 340 & 0.896 & 0.959 & 18.437 & 463.176 & 0.844 & 726.362 \\ DEDBN & 5 & 61 & 0.569 & 0.273 & 18.568 & 476.006 & 0.857 & 623.746 \\ GWODBN & 8 & 33 & 0.106 & 0.645 & 18.162 & 442.553 & 0.879 & 286.254 \\ MGWODBN & 6 & 5 & 0.077 & 0.807 & 17.604 & 410.266 & 0.884 & 293.367 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structures of deep belief network (DBN) based on the optimization algorithms. MAE: mean absolute error; MSE: mean square error; PSODBN: PSO optimized DBN; DEDBN: DE optimized DBN; GWODBN: GWO optimized DBN; MGWODBN: MGWO optimized DBN. and the 0.863 of the random forest model. This shows that the MGWODBN model can more essentially reflect the relationship between the influencing factors and the PM2.5 concentration. In addition, the MAE and MSE of the MGWODBN model are 17.604 \(\upmu\)g/m\({}^{3}\) and 410.266 \(\upmu\)g\({}^{2}\)/m\({}^{6}\) respectively, lower than that of the other three models. These results indicate that the MGWODBN model outperforms the GABP, DESVM, and random forest models.
# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
## 3 Experiments and Results
### HKPU Database  
The Hong Kong Polytechnic University (HKPU) finger-vein image database [3] includes 3132 images with a resolution of 513 \(\times\) 256 pixels. All images are collected from 156 subjects using an open and contactless imaging device. The first 105 subjects provided 2520 finger images (105 subjects \(\times\) 2 fingers \(\times\) 6 images \(\times\) 2 sessions) in two separate sessions with a minimum interval of one month and a maximum of over six months, with an average of 66.8 days. In each session, each subject provided 2 fingers (index finger and middle finger) and each finger provided 6 image samples. The remaining 51 subjects only provided image data in one session. To verify our approach, the 2520 finger images captured in two sessions are employed in our experiment because it is closer to a practical captured environment. A pre-processing method [3] is employed to extract the region of interest (ROI) image and carry out translation and orientation alignment. In addition, the image background is cropped because it contributes matching errors and computation cost. As a result, all images are normalized to 39 \(\times\) 146.