esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Trajectory Prediction  
Recurrent neural networks (RNNs) are widely used to analyze the time-series data structure. The long short-term memory (LSTM) model is a variant of RNN that learns long-term dependent information, and the main difference between the long short-term memory network and the traditional recurrent neural network is its added gate structure, which is input gate, forgetting gate, and output gate [33]. The relevant formula for the LSTM is:  
\[f_{t}=\sigma\Big{(}W_{f}h_{t-1}+U_{f}x_{t}+b_{f}\Big{)} \tag{15}\]  
\[i_{t}=\sigma(W_{i}h_{t-1}+U_{i}x_{t}+b_{i}) \tag{16}\]  
\[C_{t}^{\prime}=\tanh(W_{C}h_{t-1}+U_{c}x_{t}+b_{c}) \tag{17}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times C_{t}^{\prime} \tag{18}\]  
\[O_{t}=\sigma(W_{O}h_{t-1}+U_{O}x_{t}+b_{O}) \tag{19}\]  
\[h_{t}=O_{t}\times\tanh(C_{t}) \tag{20}\]  
where \(f_{t}\) denotes the forgetting gate, \(i_{t}\) denotes the input gate, \(C_{t}^{\prime}\) denotes the state update, \(O_{t}\) denotes the output gate, and \(h_{t}\) denotes the final output value. \(W_{f}\), \(U_{f}\), \(W_{i}\), \(U_{i}\), \(W_{c}\), \(U_{c}\), \(W_{o}\), \(U_{o}\) denotes the weight coefficient; \(b_{f}\), \(b_{i}\), \(b_{c}\), \(b_{o}\) denotes the bias; and \(\sigma=\frac{1}{1+e^{-2}}\) is the activation function.  
First, three LSTM algorithm models were trained using data from different driving intentions, with the historical 4-step length and current heading angles as inputs and the future 1-step heading angle \([\theta_{1},\theta_{2},\theta_{3}]\) as output. Then the probability \([\omega_{1},\omega_{2},\omega_{3}]\) of three lane change patterns is determined by combining the FCM algorithm, the future heading angle of the three LSTMs deviation model is obtained with multimodel fusion, and the future vehicle position is calculated with Equations (22) and (23) based on the current speed of the vehicle.  
\[\theta_{p}=\theta_{1}\times\omega_{1}+\theta_{2}\times\omega_{2}+\theta_{3} \times\omega_{3} \tag{21}\]  
\[\Delta x=v_{p}\times\sin\theta_{p} \tag{22}\]\[\Delta y=v_{p}\times\cos\theta_{p} \tag{23}\]
## 4 JFML-IoT-Based Energy Management System
### System Operation  
The proposed JFML-IoT-Based Energy Management System operates through a sequence of coordinated steps that involve data acquisition, communication, inference, and actuation. The system is designed to respond dynamically to varying environmental conditions using a fuzzy logic controller executed on embedded devices. The operational workflow is as follows:  
1. **Data Acquisition**: Arduino-compatible sensors (e.g., DHT22 for temperature and humidity, BH1750 for luminance, MH-Z19 for CO2, and PIR sensors for occupancy) periodically collect environmental data. These devices are configured to sample data at intervals optimized for energy management, typically ranging from 30 s to 5 min, depending on the variable being measured. For instance, occupancy and lighting conditions are sampled every 30 s, while temperature, humidity, and CO2 levels are sampled every 2 min, balancing responsiveness and resource efficiency.
2. **Data Transmission**: Each sensor node publishes its readings to dedicated MQTT topics, using lightweight JSON messages. These topics follow a structured naming convention (e.g., building/room1/temperature, building/room2/occupancy) to ensure scalability and integration with external platforms.
3. **Fuzzy Inference Execution**: The MQTT broker forwards incoming sensor data to the processing unit, which parses the values and updates the input variables of the fuzzy rule-based system (FRBS). The inference engine, implemented using the JFML-IoT library, evaluates the current rule base--defined in FML format according to IEEE Std 1855-2016--and calculates output control values for lighting, ventilation, and HVAC systems.  
Figure 1: Modular architecture of the proposed energy management system, organized into four functional layers: perception, communication, processing, and application. The diagram illustrates data flow and communication mechanisms (e.g., MQTT, REST, WebSocket) across layers and networks (LAN/WAN).  
4. **Actuation**: The resulting output values are published to MQTT control topics (e.g., building/room1/lighting/command). Actuator devices subscribed to these topics (e.g., relay switches, dimmable drivers, or IR-based controllers) interpret the fuzzy output and execute the required actions. For example, the lighting system may dim to a medium level if luminance is moderate and occupancy is detected.
5. **Monitoring and Feedback**: Control decisions and sensor updates are logged and can be visualized via an application-layer dashboard. This enables facility managers to monitor system behavior in real time and review historical patterns for further optimization.  
This distributed operational logic ensures low-latency, local decision-making, while maintaining global visibility and configurability through standard IoT communication protocols. The integration of JFML-IoT provides a transparent and interpretable control mechanism, particularly suitable for energy-sensitive environments.
# Exploring the Determinants of Service Quality of Cloud E-Learning System for Active System Usage
## 2 Theoretical Foundation of Framework
### Reliability  
SERVQUAL [30] is a widely used customer-driven instrument for research. It is used to know the preferences and expectations of the services received by the customer. Many researchers used this scale in different industries, from retail to hospitality and consulting. It is using in assessing the service delivery of E-Learning quality as well [31]. It has five different dimensions which are assumed to reflect the quality of service. Reliability is one of the essential dimensions of SERVQUAL.  
Reliability is the capacity to carry out the promised operation consistently and reliably [31]. It is the ability to survey the services offered with reliability and accuracy. It is one of the five dimensions presented in the SERVQUAL model survey of quality models of e-learning systems [32]. Reliability is good working efficiency of the overall structure and function of the E-Learning system or website and dealing with the technical issues of the interface of the E-Learning users; it also includes accuracy of the system as well as providing all the services as promised by the system [17]. An increase in Reliability will motivate the user to use the system. Based on these research studies, it is observed the E-Learning system Reliability will increase satisfaction and influence to increase its usage among the university students. Thus, the following hypothesis is proposed:  
**Hypothesis 1 (H1).**_Reliability of the system influences the actual system usage._
# Effects of Phase Shift Errors in Recurrence Plot for Rotating Machinery Fault Diagnosis
###### Abstract  
For fault diagnosis and predictive maintenance of rotating machinery, the phase errors generated by the integration processing of a vibration signal are an essential investigation subject. Phase errors affect the solution of mechanical systems with multiple vibration sources and also the information transmitted through the vibration that is used for fault diagnosis. This work proposes the use of phase plane, recurrence plot (RP), and cross recurrence plot (CRP) to evaluate phase shift error effects on the solution of multiple asynchronous and simple periodic functions, and on the smoothing of a Gaussian peak with white noise. Noisy peaks were smoothed twice with the triangular method and with a different number of points. The analysis of the asynchronous periodic functions and the smoothing indicated that a small phase shift changes the phase plane and the RP pattern. These changes can affect not only the accuracy of machinery fault diagnosis but also prediction for the application of timely maintenance actions.  
recurrence plot; time series integration; machinery fault diagnosis; non-linear vibration Article
# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
## 2 Materials and Methods  
### Spectra Collection and Processing  
The focus of this study is on Yongquan tangerines, which are primarily grown in Yongquan, Linhai, Taizhou, and Zhejiang, China. A total of 150 tangerines were purchased from the same farmer in November 2022, with the requirement that the tangerines be of similar size and that no more than 20 tangerines be picked from the same tree, to ensure the diversity of the samples. During the experiment, some tangerines were damaged, so the number of valid samples is 118. The tangerines were gently wiped with a paper towel, numbered, and stored at room temperature (around 23 \({}^{\circ}\)C). The 118 samples were measured in batches, and for each batch, the collection of spectra and the determination of quality parameters were completed on the same day. The spectra measurement system was set up consisting of the QE Pro spectrometer (Ocean Optics, Dunedin, FL, USA), two fibers, the Halogen light source (HLG-150W), and a personal computer. The spectral resolution of the QE Pro is 0.798 nm, and the integration time was set as 0.2 s. The dark noise correction and nonlinear correction were enabled in the OceanView software, and the average sliding width was set as 2, to reduce the effect of noises associated with the whole system. The wavelength is in the Vis-NIR range, specifically between 348.311 nm and 1137.377 nm, containing 1044 wavelengths. The transmittance spectra were acquired as depicted in Figure 1, where for each tangerine sample, the spectra of 12 locations were collected, including four around the equatorial position, four around the top area, and four around the bottom area. Therefore, a total of 118 \(\times\) 12 measurements were conducted. The emission of the light source HLG-150W is depicted in Figure 2.  
Figure 1: Schematic of the set-up for measuring the transmittance spectra of tangerines.  
Figure 2: The emission of the light source.  
The raw spectra are converted to absorbance values according to Equation (1):  
\[A=-\log_{10}(T)=-\log_{10}\left(\frac{I}{I_{0}}\right) \tag{1}\]  
where \(I_{0}\) represents the background spectra taken without the sample present, \(I\) represents the amount of light that reaches the detector, and consequently, and \(I/I_{0}\) is the fraction of the incident light that penetrated the sample and detected [24]. The two ends of Vis-NIR spectra are eliminated where the signal-to-noise ratio is low and spectra between 550 nm and 1100 nm are retained, thus the shape of the spectra data is 118 samples, 12 locations, 701 wavelengths. The absorbance of 118 tangerines is depicted in Figure 3.
## 2 Theory and Methodology
### Development of a Reconstruction Model for Seepage Pressure Anomaly Data  
The proposed online detection, identification, imputation, and correction method. Substantively, online detection and identification are first performed based on the Bernoulli probability model which autonomously establishes outlier determination criteria without manual intervention, while simultaneously representing the occurrence timing, location, and magnitude of multi-type anomalies across different measurement channels. Identified anomalous data and removal: The MissForest imputation method is finally applied to reconstruct missing values, yielding corrected seepage pressure data for each monitoring point. The workflow of the proposed seepage pressure multi-type anomaly reconstruction model is illustrated in Figure 2.
## 3 Results
### Performance Metrics and CR-ICC
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Team Level**} & \\ \hline
**Predictor** & **Estimator** & **SE** & **Z** & **\(p\)-Value** & **OR** & \multicolumn{2}{c}{**95\% CI**} \\ \cline{5-8}  & & & & & **Inf** & **Sup** \\ \hline Constant & 0.093 & 0.349 & 0.268 & 0.789 & 1.098 & 0.554 & 2.178 \\ CR-ICC & \(-\)0.1925 & 0.0523 & \(-\)3.684 & \(<\)0.001 & 0.825 & 0.825 & 0.914 \\ \hline Constant & \(-\)0.0092 & 0.408 & \(-\)0.022 & 0.982 & 0.991 & 0.445 & 2.204 \\ Pts & \(-\)0.661 & 0.164 & \(-\)4.034 & \(<\)0.001 & 0.516 & 0.374 & 0.712 \\ \hline Constant & \(-\)0.352 & 0.397 & \(-\)0.088 & 0.929 & 0.965 & 0.443 & 2.10 \\ Eff A & \(-\)17.107 & 4.12 & \(-\)4.144 & \(<\)0.001 & \(3.72\times 10^{-8}\) & \(1.14\times 10^{-11}\) & \(1.21\times 10^{-4}\) \\ \hline Constant & \(-\)0.203 & 0.266 & \(-\)0.761 & 0.447 & 0.816 & 0.484 & 1.38 \\ Eff R & \(-\)0.710 & 1.269 & \(-\)0.559 & 0.576 & 0.492 & 0.040 & 5.92 \\ \hline Constant & 0.139 & 0.309 & 0.449 & 0.654 & 1.15 & 0.626 & 2.11 \\ Balance & 0.089 & 0.023 & \(-\)4.227 & \(<\)0.001 & 1.09 & 1.04 & 1.15 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \hline
**Predictor** & **Estimator** & **SE** & **Z** & **\(p\)-Value** & **OR** & \multicolumn{2}{c}{**95\% CI**} \\ \cline{5-8}  & & & & **Inf** & **Sup** \\ \hline Constant & \(-\)0.206 & 0.0838 & \(-\)2.46 & 0.014 & 0.814 & 0.690 & 0.959 \\ CR-ICC & 0.102 & 0.0202 & 5.07 & \(<\)0.001 & 1.108 & 1.065 & 1.152 \\ \hline Constant & \(-\)0.224 & 0.0967 & \(-\)2.32 & 0.020 & 0.799 & 0.661 & 0.966 \\ Pts & 0.110 & 0.0287 & 3.83 & \(<\)0.001 & 1.116 & 1.055 & 1.180 \\ \hline Constant & \(-\)0.1341 & 0.0789 & \(-\)1.70 & 0.089 & 0.874 & 0.749 & 1.02 \\ Eff A & 0.0090 & 0.0020 & 4.52 & \(<\)0.001 & 1.009 & 1.005 & 1.01 \\ \hline Constant & 0.0303 & 0.0876 & 0.345 & 0.730 & 1.03 & 0.868 & 1.22 \\ Eff R & \(3.37\times 10^{-4}\) & 0.0019 & 0.169 & 0.866 & 1.00 & 0.996 & 1.00 \\ \hline Constant & 0.018 & 0.069 & 0.262 & 0.793 & 1.02 & 0.889 & 1.17 \\ Balance & 0.160 & 0.030 & 5.341 & \(<\)0.001 & 1.17 & 1.107 & 1.25 \\ \hline \hline \end{tabular} Note. Estimators represent the log odds of “Win set = False” vs. “Win set = True”; SE—standard error; Z—Wald value; \(p\)-value—\(p\)-value of the Wald test; OR—odds ratio; IC 95%—confidence intervals for the odds ratio; significance (bilateral): \(p<0.05\).  
\end{table} 
Table 1: Team level vs. player level. Model coefficients and other traditional variables regarding set win.
###### Abstract  
This article presents a discrete event simulation-based approach for assessing the operating performance of the Finnish-Swedish Winter Navigation System (FSWNS) under different operating scenarios. Different operating scenarios are specified in terms of ice conditions, the volume of maritime traffic, number of icebreakers (IBs), and regulations such as the Energy Efficiency Design Index (EEDI). Considered performance indicators include transport capacity, number of instances of icebreaker (IB) assistance, and IB waiting times. The approach is validated against real-world data on maritime traffic in the Bothinian Bay. In terms of the number of ship arrivals per port, indicating the transport capacity of the FSWNS, the simulation agrees well with the data. In terms of the number of instances of IB assistance and IB waiting times per port, the standard deviations between the mean of 35 independent simulation runs and the data are 13% and 18%, respectively. A sensitivity analysis indicates that the simulated number of instances of IB assistance and IB waiting times is particularly sensitive to assumptions concerning the presence of brash ice channels. Case studies indicate that, unless the number of IBs is increased, the EEDI regulations may result in a significant increase in both the number of instances of IB assistance and the cumulated IB waiting times.  
winter navigation; arctic shipping; ice class ships; icebreakers; maritime transport systems; energy efficiency design index (EEDI); discrete-event simulation +
Footnote †: journal: Applied  
article
## 4 Results and Discussion
### Model Validation and Experimental Results
#### 4.1.4 Experimental Results and Pressure Variations  
Figure 13 shows the variation trends of gas mass flowrate and volumetric efficiency for a DSDC under different experimental conditions. As shown in Figure 13a, the experimental results indicated that the gas mass flowrate (\(Q_{mg}\)) of the DSDC increased with elevated rotational speed (\(n\)), while it decreased with higher second-stage discharge pressure (\(p_{dg2}\)). The maximum gas flowrate of 508.0 kg/h was achieved under the case #3 experimental  
Figure 11: Validation of the stress simulation results. (**a**) Comparison of the simulated and experimental results for a SEP-type profile cavity. (**b**) Comparison of the FEM and theoretical stress simulation results for a DAP-type profile cavity.  
Figure 12: ANN fitting process and validation. (**a**) Comparative analysis of ANN fitting and FEM simulation results for the first-stage swept volume (\(V_{m}\)). (**b**) ANN iterative process for the first-stage swept volume. (**c**) Comparative analysis of ANN fitting and FEM simulation results for the second-stage swept volume. (**d**) ANN iterative process for the second-stage swept volume.  
condition. Figure 13b shows the volumetric efficiency variations for both first-stage and second-stage diaphragm compressors across different experimental conditions. The results revealed that the volumetric efficiency of both stages decreased with increasing rotational speed and second-stage discharge pressure. The highest volumetric efficiencies were observed under the case #1 condition, reaching 74.02% for the first stage DC and 87.63% for the second stage DC. Furthermore, based on the volumetric efficiency results, experimental oil overflow pressure results, gas pressure results, and the pressure variation model, the variations in oil and gas pressures of the DSDC could be derived, as detailed in Figure 14. It was observed that the crank angle for the gas expansion stage increased with decreasing volumetric efficiency. Specifically, a comparison between case #1 and case #3 operating conditions revealed that the crank angle during the gas expansion stage increased by approximately 10\({}^{\circ}\) for both the first-stage and second-stage diaphragm compressors.
## 2 Background  
### Search and Rescue (SAR)  
About 432 disasters were reported in the past year (2021), affecting a hundred million people, resulting in 10,492 fatalities, and causing 252.1 billion USD in economic losses globally. This overall impact and death rate is consistent for the last 20 years [15]. As a result, it is critical to implement cutting-edge (robotic and cognitive) technologies to handle the problems associated with disasters. According to a survey by Mordor Intelligence, the SAR robotics sector is growing at a 20% Compound Annual Growth Rate (CAGR) [16]. Moreover, as the cost of robots has decreased over the past ten years, it has been noted that robots are now 80% less expensive than they were in 1990 [17]. With the use of low-cost and better technology, the first responders and rescue teams can access more comprehensive information to find and rescue people. On the other hand, using technology without the right rules and guidelines throughout development and implementation can lead to issues and even pose a significant threat.  
The World Health Organisation (WHO), the Council of Europe, and other organisations have provided certain ethical guidelines to the rescue teams, however, the majority of these recommendations are constrained and human-centric [13; 18; 19]. In addition, there are a few fundamental rules for robotics that deal with ethics directly. For instance, some articles and perspective studies covered ethical concerns in robotic SAR [20] and service operations (healthcare, and entertainment) [21; 22]. Using robots in SAR is undoubtedly challenging when it entails manipulation and intervention because the affected people at the disaster site may be impacted by these structural changes. Furthermore, because robots lack feelings (i.e., pain, emotion, empathy, etc.) in contrast to human rescuers traditional (human-centric) ethical principles cannot always be upheld in complex decision-making scenarios.
## 2 Investigations Focusing on Overall Accumulated-Bed Characteristics
### Investigations with Mixed Solid Particles
#### 2.2.3 Modeling Studies  
To extend the model's applicability for mixed particle situations, based on the 132 sets of experiments (119 for single-sized and single-shaped particles; 13 for mixed-sized particles), Equation (1) was modified as follows [28]:  
\[\frac{H_{\text{b}}}{d_{\text{n}}}=k_{2}\left(\frac{d_{\text{p}}}{d_{\text{n}}} \right)^{a_{2}}\left(\frac{\Delta\rho}{\rho_{\text{p}}}\right)^{b_{2}}\left( \frac{(1-\varepsilon)V_{\text{p}}}{d_{\text{n}}^{3}}\right)^{c_{2}}\left(\frac {\rho_{\text{p}}v_{\text{i}}d_{\text{n}}}{\mu_{\text{f}}}\right)^{d_{2}}\left( \frac{D_{\text{in}}}{d_{\text{n}}}\right)^{c_{2}}\left(\frac{V_{\text{T}}}{v_{ \text{i}\text{p}}}\right)^{f_{2}}\left(\frac{A_{\text{p}}}{d_{\text{n}}^{2}} \right)^{g_{2}} \tag{4}\]  
In Equation (4), instead of particle sphericity \(\phi\), the particle surface area \(A_{\text{p}}\) was related to \(\phi\), and \(d_{\text{p}}\) was used. Additionally, the values of the empirical constant indices are given in Table 2. Figure 10 illuminates the accuracy of the predicted \(H_{\text{b}}/d_{\text{n}}\) results in accordance with Equation (4). Owing to this model, a good agreement between the experimental and predicted particle bed heights was achieved. In addition, for mixed-sized cases, higher mound heights can be reproduced, as observed in experiments through this model. Compared to the model proposed by [PERSON] et al. (i.e., Equation (1)), this model can more reasonably predict the particle bed heights for non-spherical cases and mixed-sized cases [28].  
In summary, by applying the model developed by [PERSON] et al. [28], the particle bed height can be predicted well in both single-sized and mixed-sized cases. Nevertheless, it is worthwhile to emphasize again that it is not adequate to estimate the debris bed coolability solely via bed height because the decay heat removal capacity for the debris bed is also highly associated with the bed shape [45; 46; 77]. Therefore, to obtain a deeper and more comprehensive understanding of DBF behavior and an important factor representing bed characteristics, bed shapes are also required considerations in the modeling studies and predictions of various realistic conditions.
## 2 Power Internet and High Voltage Transmission Line Condition Monitoring Intelligent Sensor Design
### Power IoT Architecture and Component Technologies
#### 2.1.3 Wireless Communication Technology  
Communication technology is divided into wired communication and wireless communication; the main difference is in the medium of signal transmission. Wireless communication technology is one of the more advanced modern communication technologies, which is mainly used to transmit information in free space by means of electromagnetic wave signals to achieve long-distance signal transmission. The technology mainly involves microwave as well as satellite communication, etc. Its signal needs to be transformed at the base station of the transmission process. The early wireless transmission technology is easily affected by external interference factors, resulting in unstable transmission signals. With the continuous innovation of science and technology, the new wireless communication technology ensures normal communication and promotes the overall development of the wireless communication business.  
The distance of communication can be categorized as short-range wireless communication or long-range wireless communication; each has its own advantages and characteristics. There are four kinds of wireless communication technologies commonly used in the Internet of Things, namely ZigBee low-speed short-range transmission communication technology, GPRS technology, 5G communication technology, and NB-IoT technology.