esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 3 Proposed Quality Control Method
### Quality Control Process Design  
A block diagram representation of the quality control design process is shown in Figure 3. The first stage of the algorithm is feature extraction. The input to this stage is the Fully Developed Seas (FDS) winds over a reference wind speed region of 0-25 m/s. The CYGNSS Level 2 wind retrievals are of two kinds--the FDS and Young Seas Limited Fetch (YSLF) winds. The FDS winds are low to moderate winds (up to 25 m/s) over fully developed waves in the ocean. This forms the major proportion of the total measurements. The YSLF winds are hurricane force winds measured over the tropical cyclones that have varying wave age and fetch conditions. The filter proposed in this work is developed specifically for FDS winds as this dataset encompasses the majority of the measurements and have a well behaved nature relative to its counterpart. The feature extraction stage extracts the different diagnostic variables listed in Table 1 for every sample point. Next is the training stage. One Neural Network (NN) classifier is trained for each of the wind speed space over the individual training datasets described above. The last stage is the validation and testing stage where the skill of the filter is assessed. The performance assessment of this Neural Network filter is discussed in detail in Section 4.  
Apart from the Neural Network filter, other standard supervised outlier detection techniques such as Logistic Regression, Decision Trees, Naive Bayes and K-NN are also considered and their confusion matrices are listed in Table 2 (bottom). In the confusion matrix, the rows represent the true classes, the columns represent predicted classes, and the percentage of samples are mentioned in each of the boxes. Outliers are represented as class '0' and good samples are represented as class '1'. Among the various classifiers experimented with, the K-NN and the NN have a similar performance. In general, NN is preferred over K-NN because of the heavy computational memory requirement of K-NN as compared to the memory requirement for training the NN coefficients. This can be seen in terms of the time requirement for training each of the classifier, shown in Table 2 (top). It can be seen that K-NN requires the most time, followed by the NN.  
Figure 3: Steps involved in the new quality control algorithm for CYGNSS data. The algorithm has 3 major stages—Feature Extraction, ML training and Validation/testing.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 7 Conclusions  
In this paper, we presented the link-level and system-level simulators for SC and MC-NOMA which are believed to be promising candidates for 5G. This is the initial version of the simulator series for NOMA-based systems. The performance of the simulator was evaluated in terms of BER, throughput, spectral efficiency and receiver design. The results provided by our simulator are valid and fully aligned with the results reported in the literature. Future work includes MIMO-based scenarios and MUD techniques other than SIC. In addition, mobility will be added to improve the efficiency of the simulator. Moreover, coded MC-NOMA transceiver design with LP was analyzed in the presence of AWGN, impulse noise, and intercell interference based on the BER performance. Moreover, it is shown that MC-NOMA performs better with the FEC and LP against channel impairments.  
Conceptualization, A.K., M.A.U. and M.R.U.; methodology, A.K.; software, A.K.; validation, A.K., M.A.U., M.R.U., M.A. and S.-Y.S.; resources, S.-Y.S.; writing--original draft preparation, A.K., M.A.U. and M.R.U.; writing--review and editing, M.A.; supervision, S.-Y.S.; project administration, S.-Y.S.; funding acquisition, S.-Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government. (MSIT) (No.2019R1A2C1089542).  
The authors declare no conflict of interest. The funder had no role in the design of the study; collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 18: BER comparison of un-coded MC-NOMA and coded MC-NOMA with and without WHT in the presence of AWGN and impulse noise.
## 4 Project Cases
### Data Processing and Results Analysis  
Radar imaging of the study area uses Sentinel 1A data, which cover the entire study area. Considering the surface vegetation above the working surface and the large deformation gradient leading to decoherence, a total of six images with a time baseline of 12 days from 4 November 2017 to 5 January 2018 were selected for the experiment. The main parameters of Sentinel imaging are shown in Table 2.  
Figure 7: 1613 working face overview.  
The subsidence value information at the edges was extracted as the parameter data. The subsidence values between 4 November 2017 and 28 November 2017 were derived from points Q1-Q27, and the subsidence values between 10 December 2017 and 3 January 2018 were derived from points T1-T26 (see Figures 8 and 9). The dynamic prediction parameters were \(q=0.91\), \(P=0.75\), \(b=0.31\), \(\theta=86.81^{\circ}\), \(S_{1}=41.89\)\(S_{2  
of the fitting was 3.64 mm. The subsidence values monitored by InSAR were basically consistent with the values predicted by InSAR-DIB.  
Based on the obtained dynamic prediction parameters, the comparison of the surface subsidence values of the dip line during the mining (level monitoring) of the 1613 (1) working face is shown in Figure 12. The fitting error of the four periods was between 80.2 and 112.5 mm. The prediction accuracy met the needs of mining subsidence, realizing the transformation of short-term D-InSAR monitoring of the mining area surface to three-dimensional deformation dynamic monitoring and further verifying the reliability and scientific validity of the three-dimensional deformation dynamic monitoring method of mining subsidence based on D-InSAR technology.
## 2 Materials and Methods
### Characterization
#### 2.2.2 Thermal Gravimetric Analysis (TGA)  
The samples' thermal stability and degradation were analyzed by TGA on a STAR\({}^{\mathrm{e}}\) 2 System by METTLER TOLEDO in the temperature range between 25 and 600 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C/min and sample masses between 15 and 20 mg. The uncertainty of the instrument's balance is reported to be \(\pm\)0.1 \(\mathrm{\SIUnitSymbolMicro g}\). For each sample, a blank measurement of the empty crucible was performed with the same method as described above for correct baseline subtraction. The starting degradation temperature was typically defined as the earliest temperature for which mass losses \(\geq\)5% were observed. Similarly, the end temperature degradation was marked as the earliest temperature for which the mass loss was \(\geq\)99%.
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 3 Preliminaries
### Datasets for Validation
#### 3.4.2 TexYarn Dataset for Weaving  
Before the weaving process, the necessary yarns undergo a sequence of control and production stages until they reach the looms. During the incoming material control phase, the yarn can either be accepted, rejected, or conditionally approved. If accepted, the yarn may be subject to a series of production stages (for example, fixing) in order to meet the requisite fabric specifications. Incoming material control test parameters and production process parameters vary based on the type of yarn required by the plant. With these parameters, the aim is to automatically predict which yarn may break during the weaving process. This will help to formulate the decision to either reject or approve the lot prior to its entry into the weaving looms. The dataset features are described below:  
* The required tests are applied to yarn lots, which are obtained from the supplier during the quality control phase, and only yarns with values within specified ranges are allowed into the production area. The test parameters include Boiling Shrinkage, Breaking Load, Strength, Denier, and Elongation.
* Boiling Shrinkage: accepted values are between 1.40 and 67.00.
* Breaking Load: accepted values are between 124.19 and 2329.20.
* Strength: accepted values are between 1.40 and 4.85.
* Denier: accepted values are between 30 and 673.
* Elongation: accepted values are between 14.00 and 221.84.
* Production parameters include the process parameters applied to the yarn lots following the incoming control process, such as Waiting Duration and Temperature.
* Waiting Duration: applied values are between 30 and 50.
* Temperature: applied values are between 80 and 122. The goal of this dataset is to predict whether the yarn will break during the weaving process based on these seven features. The proposed decision support system aims to enhance productivity by preventing yarns which are liable to break from being used in the looms.
## 3 A Prototype System for Pedestrian Positioning
### Pedometer  
When a person walks, the body undulates in unison with the steps. The step-detection algorithm examines the acceleration of the body only in the vertical direction (parallel to the gravity axis). The gravity axis is obtained from the algorithm presented in Section 3.3. The acceleration signal also reflects knee movements, body swings, sensor jerks and so forth. A 3 Hz 4-order low-pass Butterworth filter was applied to eliminate undesired constituents. The step detection algorithm is presented in Figure 4. The minimum \(A_{\mathrm{min}}\) and maximum \(A_{\mathrm{max}}\) acceleration in the filtered signal are detected. The difference between the peaks is raised to the empirical power of \(0.25\). The result is multiplied by the scale factor \(K\), depending on the individual. Equation (13) expresses an estimated step length \(d\). Related work can be found in [10, 13, 34]. Step length accuracy measurements are between 1% to 10% depending on an individual and gait style.  
\[d=(A_{\mathrm{max}}-A_{\mathrm{min}})^{0.25}\cdot K \tag{13}\]
## 5 Simulation and Experimental Results
### Second Simulation Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **BA-H** & **DE-H** & **PSO-H** & **ICA-H** & **IWO-H** \\ \hline mean & 0.00125 & **3.848 \(\times\)10\({}^{-4}\)** & 0.09077 & 3.921 \(\times\)10\({}^{-3}\) & 0.011339 \\ std & 0.00761 & **0.00125** & 0.04460 & 0.00144 & 0.00541 \\ best & 3.809 \(\times\)10\({}^{-11}\) & 3.706 \(\times\)10\({}^{-6}\) & 0.01205 & **6.844 \(\times\)10\({}^{-16}\)** & 0.00190 \\ worst & 0.07373 & **9.217 \(\times\)10\({}^{-3}\)** & 0.21037 & 0.01013 & 0.02502 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical comparison of the performance of hybrid algorithms in solving the inverse kinematics of the Baxter robot with an unreachable desired end-effector orientation. Bold text indicates superior position error values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **BA-H** & **DE-H** & **PSO-H** & **ICA-H** & **IWO-H** \\ \hline mean & 0.80584 & **0.62786** & 0.99834 & 1.2006 & 0.64396 \\ std & 0.1702 & **0.05801** & 0.32869 & 0.35645 & 0.11902 \\ best & 0.61119 & 0.59024 & **0.37152** & 0.62033 & 0.54221 \\ worst & 1.2591 & **1.0353** & 1.8244 & 1.9267 & 1.1768 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistical comparison of the performance of hybrid algorithms in solving the inverse kinematics of the Baxter robot with an unreachable desired end-effector orientation. Bold text indicates lower orientation error values.
# The Use of Non-Destructive Testing (NDT) to Detect Bed Joint Reinforcement in AAC Masonry
###### Abstract  
Detecting non-metallic reinforcement made of FRP (Fibre Reinforced Polymers) can be problematic, particularly at the stage of work inspection and constructional evaluation. In contrast to steel reinforcement, detecting non-metallic reinforcement is difficult using NDT (Non-Destructive Testing) techniques. These difficulties mainly arise from considerably lower density, radiation resistance or electromagnetic impedance and cross-section of rebars when compared to steel reinforcement. Specific problems with the reinforcement detection are experienced in masonry structures, in which reinforcement is laid in bed joints. Measurements are made on a masonry face in the plane perpendicular to the reinforcement plane, and not the parallel one compared to reinforced concrete structures. Thus, the interpretation of results obtained from NDT can be complicated due to many physical phenomena occurring during tests, methods of presenting measurements and their accuracy. This paper compares different testing techniques used to detect non-metallic reinforcement in the masonry wall made of autoclaved aerated concrete (AAC). For the purpose of the tests, fibreglass and basalt meshes, traditional steel trusses and steel wire meshes were placed in bed joints of the masonry wall. An ultrasonic tomography and GPR (Ground-Penetrating Radar) scanner operating within a broad range of frequencies were used for the tests. We also used the electromagnetic device to detect metal meshes. As expected, the tests confirmed problems with detecting the non-metallic reinforcement. Only the radar method was effective in detecting the non-metallic method, whereas other methods failed. The electromagnetic method detected only the steel reinforcement in the masonry.  
rebar location; FRP reinforcement; NDT methods; GPR testing; ultrasonic testing; electromagnetic testing; bed joint reinforced masonry; autoclaved aerated concrete (AAC) +
Footnote †: journal: Applied Sciences
## 2 Data and Methods  
### Methodological Rationale  
To address the research objectives, we evaluated multiple methodologies commonly employed in literature reviews, including a systematic literature review (SLR), content analysis, and bibliometric analysis. While SLRs emphasize qualitative synthesis through predefined protocols, they often struggle to map large-scale trends or collaboration patterns quantitatively [22]. Content analysis, though effective for thematic categorization, lacks tools to visualize interdisciplinary linkages or temporal evolution. In contrast, bibliometric analysis leverages quantitative metrics to objectively identify research frontiers, collaboration dynamics, and knowledge clusters within a field. This method is uniquely suited to our goal of mapping global trends and quantifying interdisciplinary integration in construction robotics for the following three reasons: (1) Scalability: Bibliometrics allows for the efficient processing of large datasets to uncover patterns invisible to manual reviews, such as the dominance of specific institutions or sudden shifts in research focus. (2) Dynamic visualization: Tools such as CiteSpace enable time-sliced network analysis, highlighting how clusters like \"human-robot collaboration\" evolved from theoretical proposals to applied frameworks. (3) Reproducibility: Algorithm-driven approaches reduce subjective bias, aligning with our aim to provide an objective, data-driven synthesis [23].
# Passive Earth Pressure and Soil Arch Shape: A Two-Dimensional Analysis
## 3 Analysis of Soil Arching Under Passive Conditions  
In passive earth pressure analysis, the soil arch shape is commonly defined by the trajectory of the major principal stress. The horizontal layer method incorporating soil arching effects often assumes this trajectory to follow circular, parabolic, or catenary forms. In contrast, the present study does not impose a predefined shape. Instead, the trajectory of the major principal stress is derived directly from the computed stress field within the sliding wedge. The resulting curve is then compared with commonly assumed circular and parabolic arch shapes.
# Wideband Circularly Polarized Antenna Based on a Non-Uniform Metasurface
## Antenna Design
### Design Procedure  
Based on the abovementioned investigation, the antenna design procedure can be summarized as follows:  
* Step 1: Design the truncated corner square patch with operation in the low-frequency band. The size \(W_{p}\) will have a strong effect on the operating frequency, and it is initially defined by Equation (1). Meanwhile, the AR value is tuned by the corner truncation parameter, \(d\).
* Step 2: Design the unit cell with operation in the high-frequency band. The operating frequency can be predicted based on the dispersion diagram. The number of unit cells (\(N\)) is chosen as a trade-off between the antenna's overall size and the gain. A higher gain can be achieved with a greater number of cells, but the antenna's size is consequently increased.
* Step 3: Design the antenna with a uniform MS, tuning the patch size and unit cell size to allow operation at the desired band. The AR value is mainly determined by the truncation parameter \(d\).
* Step 4: Design the antenna with a non-uniform MS. In this step, the size of the four center unit cells is slightly changed in comparison with the optimal value achieved in Step 3. Meanwhile, the size of the outer unit cells is significantly tuned for a higher CP operating band. The matching performance and the AR value are optimized by tuning the dimensions of the stub and the corner truncation of the patch.
## 4 Results
### Data on Quantifiable Indicators  
**Data format:** All indicators are presented as mean (M) and standard error (SE). The degrees of freedom for the \(t\)-test are all 48 (both the control group and the experimental group  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Classification of Indicators** & **Specific Indicators** & **Measurement Method** \\ \hline user engagement & Subjective Perception Interviews of Flow States & User Interview Transcript ‘Motivation for Continuous Creation’ \\ \hline immersion & Virtual Environment Presence Interview & Analyzing sensory details (visual/auditory associations) in user descriptions from interviews \\ \hline Creativity & Plot originality & Analysis of the interviews included ‘Breaking out of trope-based narratives’ and ‘The subtlety of combining VR scenes with text’ \\ \hline Emotional and Social & Teamwork identity and emotional resonance & Analyzing ‘new connection making’ and ‘sense of recognition’ in interviews, and collecting cases of emotional resonance of the episode in interviews \\ \hline \multirow{2}{*}{Open Feedback and Creative Experience Narratives} & Analyze interviews describing ‘most impressive collaboration scenarios’ and analyze responses for social interaction keywords (e.g., ‘collaboration’ ‘empathy’) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Non-quantifiable indicators (qualitative).  
have 24 samples). **Significance indication:**\(p\) < 0.001 indicates a highly significant difference, \(p\) < 0.05 indicates a significant difference, and \(p\) > 0.05 indicates no significant difference.  
1. **User Engagement Indicator**  
User Engagement visualization and data are shown in Figure 9 and Table 4.
## 2 Statement of RSU Deployment in Expressway
### Roadside RSU Perception Analysis under Cooperative Vehicle Infrastructure Perception  
The cooperative vehicle infrastructure network is mainly composed of a roadside unit, vehicle unit, and communication network; the communication network mainly includes V2V, V2I, and V2X. In the cooperative vehicle infrastructure network, V2V and V2I are the keys to information transmission [17; 25] while the roadside RSU is the hub of information interaction and its deployment effect will directly affect the quality of the cooperative vehicle infrastructure network. When only considering V2I, dense roadside units need to be deployed, thus causing an increase in deployment cost; there is also the problem of information interference between multiple roadside RSU, resulting in redundancy of vehicle information.  
At present, the academic research on roadside RSU deployment has not considered the impact of permeability, that is, the ratio of intelligent vehicles to the total number of vehicles. The traditional model for roadside RSU deployment is generally considered through two factors, such as communication coverage and deployment cost, without taking into account the deployment impact of intelligent connected vehicles as information transmission units. When the roadside RSU interacts with the surrounding intelligent connected vehicles, the intelligent connected vehicles within its coverage can transmit information normally while the intelligent connected vehicles outside the coverage can realize V2I information interaction through the transmission of intermediate vehicles which expands the scope of information transmission, as shown in Figure 1. In addition, due to the influence of equipment performance and external environment interference, when the communication distance between devices is too large under the existing wireless communication technology, the information cannot be transmitted effectively in time. Therefore, when considering roadside RSU deployment, it is necessary to consider the suitable deployment spacing which can maximize the information transmission. In addition, it is beneficial for autonomous vehicles to drive according to rules since the expressway scene is relatively simple. Therefore, most of the smart road pilot projects are based on the expressway scene, as is the case in this paper.  
Figure 1: Information transmission process of cooperative vehicle infrastructure system.
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 2 Materials and Methods
### Graph Convolution Module  
As described in Section 2.1 regarding the principle of adaptive graph generation, the function of the graph convolution module is to capture the spatial features of nodes with known node structure information. [PERSON] et al. proposed a first-order approximation algorithm that smoothens node spatial information by aggregation and transformation of adjacent information of nodes and the algorithm-defined graph convolution layer as in Formula (9) [19; 20].  
\[Z=\widetilde{A}XW \tag{9}\]  
where \(\widetilde{A}\in R_{N\times N}\) represents the adaptive adjacency matrix or transition matrix, \(X\in R_{N\times D}\) represents the input time series, \(Z\in R_{N\times M}\) represents the output prediction time series, \(W\in R_{D\times M}\) represents the model parameter matrix, \(D\) represents the data input dimension, and \(M\) represents the number of layers of the graph convolution module.  
The graph convolution layer can also extract node space features based on the local structure information of the graph. [PERSON] et al. proposed a spatio-temporal model containing a diffusion convolution layer by modeling the diffusion process of graph signals and proved the effectiveness of a diffusion convolution layer in predicting road traffic flow sequences [21]. According to the form of Formula (9), the diffusion convolution layer is defined as in Formula (10) [21].  
\[Z=\sum_{c=0}^{c}P^{c}XW_{k} \tag{10}\]  
where \(c\in R\) represents a finite step, and \(P^{c}\) represents the power series of the transition matrix \(\widetilde{A}\). Because SST time series data belong to undirected graphs, we propose the graph convolution layer formula as shown in Formula (11) by combining the time dependence and hidden dependence of adaptive graph learning layer in this paper.  
\[Z=\sum_{c=0}^{c}\widetilde{A}^{c}XW_{c} \tag{11}\]  
where \(\widetilde{A}\) is the adaptive adjacency matrix, \(X\in R_{N\times D}\) represents the input history time series, \(Z\in R_{N\times M}\) represents the output prediction time series, and \(W\in R_{D\times M}\) represents the model parameter matrix. In this paper, graph convolution belongs to a space-based method. Although the graph signal and node feature matrix are used interchangeably in this paper for consistency, the graph convolution represented by the above formula is still interpretable and can aggregate transform feature information from different neighborhoods.