esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Data Pre-Processing  
The raw Myogym dataset comprises intricate time-series data from various sensors embedded within the Myo armband wearable gadgets the study participants wore. These sensors encompass a 3-axis accelerometer and gyroscope adept at capturing motion dynamics. Moreover, eight channels of sEMG recordings are utilized to gauge muscle activations. However, the collected data streams require appropriate pre-processing and refinement steps to convert them into suitable input formats for developing and applying deep learning models in subsequent analyses.  
Initially, denoising algorithms are employed to identify and eliminate irregularities that could result from sensor recording errors or wireless transmission issues. If addressed, these anomalies could positively affect the accuracy of classification. To ensure consistent significance ranking among various sensor inputs, normalization algorithms are utilized to adjust the cleaned signals to a uniform range. Subsequently, the continuous recordings are segmented into overlapping windows of a predetermined size to extract contextual segments suitable for processing by deep neural networks.  
The main goal of this systematic data preparation procedure is to enhance the overall quality, compatibility, and utility of the Myogym dataset, enabling efficient training of sensor-based models for human activity recognition. Through a combination of denoising, normalization, and segmentation, dependable time-series tensor representations are generated. These representations are adept at capturing unique features that distinguish different types of gym exercises.
###### Abstract  
The article describes an innovative method for detecting conveyor belt damage using a strain gauge system and deep learning techniques. The strain gauge system records measurement data from conveyor belts. A major challenge encountered during the research was the insufficient amount of measurement data for effectively training deep neural networks. To address this issue, the authors implemented a hybrid data augmentation method that combines generative artificial intelligence techniques and signal analysis. The TimeGAN model, based on Generative Adversarial Networks (GANs), was used to augment data from undamaged belts. Meanwhile, the superposition of one-dimensional observation sequences was applied to generate data representing damages by combining signals from randomly selected undamaged runs with strain gauge system responses to damage, effectively increasing the number of samples while accurately replicating defect conditions. For damage diagnosis, a Long Short-Term Memory Network with an attention mechanism (LSTM-AM) was employed, enabling anomaly detection in strain gauge signals. The application of the LSTM-AM algorithm allows for real-time monitoring of conveyor operation and facilitates precise localization and estimation of damage size through data synchronization.  
Keywords:anomaly detection; strain gauges system; conveyor defects; deep neural network; data augmentation; LSTM network; attention mechanism +
Footnote †: journal: Applied sciences
## 4 Results
### Determining the Required Stations within Each Individual Group  
Table 6 shows the weather stations needed for measuring every individual parameter in each group upon summarizing the similarity analysis described in the previous two sub-sections. It revealed that every weather station would be essential for the following parameters of interest, i.e., (i) AT for all groups except G3; (ii) SR, PR, and WSD for all groups; (iii) RH for G7; and (iv) BP for G1. Furthermore, we found that three parameters, namely RH, BP, and SD showed some degree of redundancy. Despite this, we would recommend not to get rid of them as they might be essential in the event of malfunctioning of a same parameter-specific instrument from another station.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Station**} & \multicolumn{8}{c}{**Measurement at Different Wind Measurement Heights**} \\ \cline{3-11}  & & \multicolumn{2}{c}{**2 m**} & \multicolumn{2}{c}{**10 m**} & \multicolumn{2}{c}{**16 m**} & \multicolumn{2}{c}{**21 m**} & \multicolumn{2}{c}{**29 m**} \\ \cline{3-11}  & & **n** & **PS (\%)** & **n** & **PS (\%)** & **n** & **PS (\%)** & **n** & **PS (\%)** & **n** & **PS (\%)** \\ \hline G1 & C1 vs. C4 & - & - & 2076 & 11.46 & - & - & - & - & - & - & - \\ \hline G2 & JPS16 vs. JE316 & 28,699 & 4.09 & - & - & - & - & - & - & - & - \\ \hline \multirow{6}{*}{G3} & JP 104 vs. JE306 & 36,112 & 3.73 & - & - & - & - & - & - & - & - \\ \cline{2-11}  & JP 107 vs. JE306 & 27,888 & 5.47 & - & - & - & - & - & - & - & - \\ \cline{2-11}  & JP 311 vs. JE306 & 26,850 & 2.75 & - & - & - & - & - & - & - & - \\ \cline{2-11}  & JP 104 vs. JP107 & 31,121 & 10.22 & - & - & 32,835 & 15.13 & 32,842 & 19.16 & 32,834 & 14.51 \\ \cline{2-11}  & JP 104 vs. JP311 & 29,855 & 6.94 & - & - & 32,394 & 9.00 & 32,402 & 12.35 & 32,540 & 8.03 \\ \cline{2-11}  & JP 107 vs. JP311 & 33,622 & 10.60 & - & - & 37,775 & 11.00 & 37,851 & 10.96 & 37,983 & 10.91 \\ \hline G4 & JP201 vs. JP213 & 30,376 & 10.69 & - & - & 31,758 & 13.36 & 33,156 & 13.55 & 32,910 & 12.89 \\ \hline G5 & R2 vs. JE306 & 16,846 & 4.93 & & - & - & - & - & - & - \\ \hline G6 & C2 vs. C4 & - & - & 2060 & 13.64 & - & - & - & - & - & - \\ \hline G7 & JE308 vs. JE312 & 20,576 & 10.72 & - & - & - & - & - & - & - \\ \hline \end{tabular}
\end{table}
Table 5: Analysis of similarity for available data of wind speed and direction for all seven groups. Here, ‘-’ indicates that measurements were not available (or comparable) for the pair.
## 4 Discussion
But even a tuning range of about 156 nm is to date only achievable with external, grating-tuned cavities that are orders of magnitude physically larger than the demonstrated device. The wavelength at 0 mA tuning current (being in the midst of the stop band of the investigated device) can be tuned by the cavity length during manufacturing and fine-tuned during experiments by the base temperature, allowing full use of the maximum tunability. The designed MEMS structures are well suited for implementation in VCSELs where tuning ranges between 40 nm and 103 nm have yet been demonstrated [16; 30; 31]. We remark that the tuning range of MEMS-VCSELS is usually limited by VCSEL gain and not by the filter bandwidth. One of the smallest cavity size demonstrated in this manuscript is of the order of 650 nm, being several times smaller than MEMS-tuned cavities investigated in vertical cavity diode lasers (VCSELs). We, thus, do not expect any mode hops over the complete tuning range. This is supported by the fact that no new modes arise at the lower tuning end of the CTL when the current is increased until the rupture limit of the cavity. The tuning continues towards wavelengths higher than 1630 nm as it was already expected from the simulation of Figure 1b, the reflectivity profile of Figure 3, and the theoretical reflectivity mentioned in the Section 3 calculated by the matrix method. Other authors have achieved 112 nm with DBR-based FP-filters in terms of wavelength tuning by employing similar kind of dielectric materials [27]. Other more complex and bulk MEMS designs in the near-infrared region can tune from 1479 to 1590 nm as in [32] in this case avoiding the mechanical disturbances that suspended multilayer MEMS suffer, yet still considerably less than the extrapolated tuning range of 156 nm reported in this manuscript.
# Force Analysis of Circular Diaphragm Wall Based on Circular Cylindrical Shell Theory
## 3 Force Analysis of Circular Diaphrag Wall
### Horizontal Supporting Force of the Cap Beam  
The cap beam has the effect of horizontal support at the top of the circular diaphragm wall, which is considered to be the normal uniform distribution force \(q_{\rm M}\) along the circumference of the top surface of the circular diaphragm wall, as shown in Figure 4. \(q_{\rm M}\) can be calculated by using Equation (1) below. In Equation (1), \(K_{\rm M}\) represents the supporting stiffness of the cap beam; \(w(0)\) represents the horizontal displacement at the top (\(\alpha=0\)) of the circular diaphragm wall, caused by the excavation of the soil layer with \(\Delta h\) in thickness in every condition; \(E_{\rm M}\) represents the Young's modulus of the cap beam material; \(A_{\rm M}\) represents the effective section area of the cap beam; and \(R_{\rm M}\) represents the central radius of the cap beam.  
\[q_{\rm M}=K_{\rm M}\cdot w(0)=\frac{E_{\rm M}A_{\rm M}}{R_{\rm M}^{2}}\cdot w(0) \tag{1}\]
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 1 Introduction
Clustering is a key technology for organizing networks and forming routing protocols; an efficient cluster routing protocol can effectively avoid network congestion, improve network stability, and enhance the effectiveness of data transmission. As large-scale greenhouse systems, plant factories cover extensive areas, making the construction of a stable and reliable large-scale network a critical challenge. One of the most important requirements in designing routing protocols is to avoid excessive retransmission of data over low-quality links [6]. However, many current studies on network coverage optimization and routing protocols often overlook the evaluation of wireless link communication quality [10]. In most cases, links are assumed to be ideal during the design or simulation of routing protocols [30]. Moreover, constructing a large-scale and reliable wireless sensor network tailored for plant factory scenarios is essential to ensure the stable operation of an environmental monitoring system. This reliability directly affects the accuracy of environmental monitoring, which is crucial for maintaining optimal conditions for plant growth. However, research on improving the reliability of wireless sensor networks in plant factory scenarios is still relatively scarce. Therefore, this study focuses on developing a wireless sensor network system strategy to improve network reliability in plant factories with the following objectives:  
1. Developing a nonlinear link quality prediction model suitable for plant factory scenarios based on function fitting methods;
2. Designing a reliable network coverage optimization scheme by integrating the particle swarm optimization (PSO) algorithm with the link quality prediction model;
3. Proposing a reliable clustering routing protocol by combining the network coverage optimization scheme with the K-means algorithm.
## 5 Case Study: Autonomous Management of Battery Recharging
### Description of the Simulation Framework  
The interface of our \"Airport Baggage Handling Simulation\" application, in Figure 6, is designed to provide a comprehensive overview of the autonomous management and operation of AIVs for baggage handling. This intuitive and structured interface allows us to monitor and analyze the performance of AIVs. The variables used in our case study simulation, such as the number of baggage, AIV speed, circuit size, and the number of operational nodes, are summarized in Table 1. The circulation scenario is detailed with a distance-oriented graph presented in Figure 7. The interface is divided into several sections, each displaying critical information about the simulation status and AIV performance.  
The different sections of the interface are as follows:  
* **Energy-level display**: Each AIV's energy level is represented by a horizontal bar, which visually indicates the remaining battery life. For instance, _AIV3_'s bar is red, signifying a critical battery level, while the other AIVs have yellow bars, indicating varying levels of charge.
* **Charge-level information**: Below the energy-level display, this section provides detailed statistics on the recharging activities:  
Figure 5: Simulator agent architecture: dynamic elements in red, static in green, and not related to the environment in purple.
# A High Latitude Model for the E Layer Dominated Ionosphere
## 3 Modeling Approach  
As indicated in the introduction, our ELDI model consists of four submodels, each representing a particular model observable. Every submodel is expressed by a separate equation identical for both hemispheres. However, the equation coefficients differ between the Northern and Southern Hemispheres. In the next section, we describe how the equation for a single model observable is put together and how we computed its coefficients.
## 6 Conclusions  
As the demand for IoT solutions grows, especially in areas such as Smart Grid applications and the energy sector, the number of resource-constrained or even battery-powered devices connecting to next-generation cellular networks also increases. By improving the achievable BER for these receivers through the use of a deep learning architecture, we aim to facilitate resource efficiency improvements for these devices by being able to reduce the transmit power requirements while maintaining or improving their BER performance. In this paper, we presented a deep learning approach to channel estimation, equalization, demodulation, and error correction decoding for OFDM-based receivers. CNN models were evaluated for 200 k, 800 k, and 1.2 M parameters, showing improvement with each model over a traditional receiver. A GNN architecture with 20 k and 80 k parameters was trained and evaluated against standard LDPC BP and Min-Sum algorithms, showing similar performance to that of standard algorithms. Finally, by combining both deep learning components into the proposed architecture, we could demonstrate the capabilities and benefits of an end-to-end deep learning-driven approach to OFDM receiver operations, showing not only the feasibility of this approach but also its tremendous benefits in the form of a much lower BER than a standard OFDM receiver for comparable transmit power usage. These results show significant promise for the future work of implementing a complete OFDM receiver through deep learning, with additional model optimizations to further improve resource efficiency. From this work, we observe the benefits this can provide to critical infrastructure sectors, such as the energy grid, by making device connectivity more robust and more energy efficient at the same time.  
**Author Contributions:** Investigation, M.B., M.H. and H.S.; writing--original draft preparation, M.B. and M.H.; writing--review and editing, M.B., M.H. and H.S.; supervision, H.S. and M.H.; project administration, H.S. and M.H.; funding acquisition, H.S. and M.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by the University of Nebraska-Lincoln's Nebraska Center for Energy Sciences Research (NCESR) under Cycle 16 Grant# 20-706.  
**Data Availability Statement:** The data can be shared upon request. The data are not publicly available due to being a part of an ongoing study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Water Quality Evaluation and Prediction Based on a Combined Model
## 2 Literature Review  
### Research Status of Water Quality Evaluation  
At present, multivariate statistical methods are widely used in water quality evaluation and analysis abroad. [PERSON] et al. (2005) applied multivariate statistical methods to the water quality evaluation of eight monitoring sites of the Gomti River in India from 1999 to 2001, demonstrating the advantages of multivariate statistical methods in processing and evaluating a large number of complex water quality datasets and obtaining effective water quality evaluation results [8]. [PERSON] et al. (2007) collected a total of 14,976 data points measuring 12 water quality indicators at 13 different monitoring points from 1995 to 2002 using multivariate statistical tools for spatiotemporal variable analysis of a large set of complex water quality data of the Fuji River. The water quality status of the 13 observation points was divided into three categories by stratified cluster analysis: mild, moderate and severe pollution [9]. [PERSON] et al. (2011) used the monthly data of 23 indicators from 16 different monitoring points in southwest Kowloon, Hong Kong, from 2000 to 2007, employing hierarchical cluster analysis to divide the 12 months into two periods and the 16 monitoring points into three categories. Discriminant analysis provides analysis results from both spatial and temporal aspects. Among the 23 indicators, 4 are the main factors affecting the temporal distribution, and 8 indicators are the main factors affecting the spatial distribution [10]. [PERSON] (2015) used principal component analysis, cluster analysis and factor analysis to study the water quality of the Kaduna River and analyze the potential pollution factors of the river [11]. The multivariate statistical method is a classical method for water quality assessment and management, but it cannot provide comprehensive information about water quality. On this basis, in this study, the selected neural network constantly updates and iterates the model parameters, adjusts the weights and thresholds to the state that can output the optimal results and applies the trained model to the evaluation task.
# Special Issue \"Body Sensors Networks for E-Health Applications\"
## 3 Articles Related to the Design and Characterization of Antennas In BSN
The dipole structure of the antenna facilitated the correction of the deviations observed in the tuning of the antenna when it was introduced into the phantom. An analysis of the radiation characteristics of the antenna inside an anechoic chamber showed an omni-directional behavior, close to that of the isotropic antenna, which is ideal for the intrabody use of the endoscopic capsule. Furthermore, compared to other antennas in the literature, the antenna bandwidth was greater.
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 4 Forecasting Download and Upload Traffic
### Evaluation Metrics  
The first step is to choose the parameters for the models. For the SARIMA model, we choose the parameters \(p,d,q,P,D,Q\) using the Akaike information criterion (AIC) [46] in order to minimize it. Specifically, we use the following equation for AIC:  
\[\text{AIC}=2k-2\log(L)=2(p+q+P+Q)-2\log(L),\]  
where \(k\) is the number of estimated parameters in the model, and \(L\) is the maximized value of the likelihood function for the model. For the Holt-Winters model, the equations depend on the types of trend and seasonality, namely, additive and multiplicative. We perform brute force checks to determine the appropriate equations.  
The second step is to compare different models. We use typical evaluation metrics [52; 53] such as mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared logarithmic error (MSLE). Table 8 provides a summary of these metrics. In our notation, \(x_{t}\) represents the time series data and \(y_{t}\) denotes the forecasted value of \(x_{t}\).  
The dataset was normalized and divided into a training dataset of 13 days and testing datasets of 2 days, which is approximately 13%. We used Python for programming and its modules statsmodels and pmdarima.
## 3 Numerical Validation and Results  
### Validation of the Numerical Model  
In order to verify the accuracy of the proposed numerical model, its results were extracted and compared with long-term monitoring data. The comparison results are presented in Figure 11. It should be noted that the determination of concrete's elastic modulus at different service times followed the theory described in Section 2.5.2. Additionally, due to a lack of field test data at various service times, it was assumed that prestress loss rates increased linearly over time, and the final prestress loss rates of all tendons are listed in Table 4. It can be observed that the gap in the mid-span deflection between the measured and predicted data was narrow, with a maximum error of \(-2.8\%\). It can be concluded that the gap between the model predictions and the measurements became wider after reinforcement; even so, the maximum error was still less than \(-5\%\). It can be seen that the proposed FE model in the current paper provided a good agreement with the field test results, validating that the accuracy of the refined simulation method could be used to predict the structural performance of CRPR bridges.  
In the following sections, a meticulous investigation is carried out on the impact of concrete cracks, concrete creep age, joint damage and prestress loss on the overall stiffness of Bridge A. In order to ensure the generalizability of the findings for general CPCR bridge cases, the models without reinforcement measures were the most appropriate to select as the base models for analysis. To ensure comparability, other parameters were taken as design data when conducting sensitivity analysis on specific variables.
## 3 Results
### Impermeable Surface Accuracy Verification  
#### 3.2.1 Spatial Accuracy of Impervious Surfaces  
Based on the impervious surface results obtained from the Luojia1-01, NPP-VIIRS, and Flint data, a total of 150 verification samples were randomly selected in each of the aforementioned six different geomorphological zones. These samples were compared with the actual impervious surface vector data. The impervious surface within each sample was vectorized using ArcGIS to obtain the true scale of each impervious surface and compared with the impervious surface results extracted from the three types of nighttime lighting image data. Figure 6 compares the spatial accuracy of the impervious surfaces obtained from the Luojia1-01, NPP-VIIRS, and Flint data. Overall, the impervious surface extraction accuracy was poor for all three data types. In addition, the use of a single nighttime light data source was not suitable for obtaining impervious surface information in large scale topographically fragmented areas. For the Karst plateau, impervious surfaces were extracted with the highest accuracy from the VIIRS/DNB data, while the urban impervious surfaces were extracted with a considerably higher accuracy from the Luojia1-01 data than the NPP-VIIRS and Flint data for the remaining five landscape types. This high accuracy is indicated by high R\({}^{2}\) values and low RMSE and SE values.  
Figure 5: Density distribution of impervious surfaces extracted from different nighttime lighting data sources.  
The above results suggest that the Luojia1-01 data are more suitable for extracting impervious surfaces in topographically fragmented areas than the NPP-VIIRS and Flint data. The SEs of all three data sources are greater than zero for all landscape types, except for the impervious surface extracted from the Luojia1-01 data in Karst plateau. This indicates that the use of Luojia1-01 data causes underestimation in the Karst plateau and overestimation in the rest of the density range, while the use of both NPP-VIIRS and Flint data leads to overestimation in all landscape types. This phenomenon is mainly caused by the low resolution of the NPP-VIIRS and Flint data and the outward spillover of the data itself, which results in overestimation of the impervious surface.