esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Model Results and Validation
#### 3.2.1 Model Results--Zone 3  
For Zone 3, the regression algorithms were evaluated using LOOCV. Table 5 summarizes their performance alongside a simple baseline model (which always predicts the mean duration):  
Among all methods, KNN delivers the greatest improvement over the baseline, reducing MAE by 0.18 h and RMSE by 0.34 h while raising R\({}^{2}\) to \(\approx\)0.60, suggesting that local, non-linear relationships, captured through a simple structure such as KNN with a small neighborhood size, are the best fit for the current characteristics of the dataset. Figures 11 and 12 present the actual vs. predicted and residual plots, respectively. Because RMSE penalizes larger errors more heavily than MAE, the fact that RMSE decreased almost  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition** & **Soot Level** & **Number of Data Points** \\ \hline First Training & 3 & 45 \\ First Training & 4 & 32 \\ Production Test & 3 & 27 \\ Production Test & 4 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset Overview.  
Figure 10: Fuel Intensity Index (Prev Delta) and target variable for Zone 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **MAE** & **RMSE** & **Parameters** \\ \hline Baseline & 0.0000 & 0.6998 & 0.9188 & \\ Linear Regression & 0.3395 & 0.6508 & 0.7467 & \{\} \\ Ridge Regression & 0.3397 & 0.6505 & 0.7466 & \{\textbackslash{}pha\textbackslash{}: 0.01\} \\ Decision Tree & 0.4705 & 0.5853 & 0.6685 & \textbackslash{} ‘max\_depth’: 2, ‘min\_samples\_split’: 2\} \\ KNN & 0.5970 & 0.5233 & 0.5833 & \textbackslash{} ‘n\_neighbors’: 3, ‘weights’: ‘uniform’)  
\begin{tabular}{c c c c} \hline \hline \end{tabular}
\end{table}
Table 5: Performance metrics of regression models for predicting duration in Zone 3.  
twice as much implies that KNN is particularly effective at mitigating extreme deviations. This means the model not only improves average accuracy but also substantially curbs the most significant prediction errors, yielding a more reliable distribution of residuals. Regarding the rest of the models, the Decision Tree model outperformed the purely linear approaches, and its optimal configuration was very shallow. When deeper trees or smaller min_samples_split values were tested during grid search, overfitting became apparent: training accuracy improved, but test performance deteriorated sharply. Linear Regression and Ridge Regression performed nearly identically, confirming that a simple linear relationship is insufficient to capture the underlying dynamics. As a result, KNN was selected as the primary model for Zone 3.
## 4 Engineering Application
First, motor speed is 1797 r/min, sampling length is 4096, and sampling frequency is 12 kHz. The rolling element fault characteristic frequency is, theoretically, \(f_{BSF}=141.17\) Hz. For the original vibration signal, Figure 9a shows its time-domain waveform and power spectrum. Figure 9b is the envelope graph of the bandpass filtering and envelope processing of it, and the calculated _SNR_ of roller failure frequency is \(-19.67\) dB. Then, the MSR algorithm is used to process the envelope signal, \(a=0.12\) and \(b=2.85\) are optimal parameters, the frequency compression scale is \(R=1000\), output _SNR_ is \(-17.4597\) dB. Figure 9c is the output result. Although the high-frequency noise is controlled, the noise component of low-frequency is amplified instead. Finally, the output result of the CMSR system is shown in Figure 9d. The fault characteristic frequency of the rolling element is particularly prominent in the whole spectrum, and other noise frequencies are almost non-existent in the amplitude spectrum. The system parameters at this time are \(a=-0.31\), \(b=1.8\), \(r=-1.76\), \(R=592.5\). The output _SNR_ = \(-4.34\) dB. The result of the rolling element fault signal analysis again shows the feasibility of the CMSR method. Figure 7: Time-domain waveform and amplitude spectrum of SR output signal. Figure 8: Inner race failure analysis. According to the theoretical value and the obtained value for inner race and the rolling element of motor bearings, the calculation error is shown in Table 2.  
It can be seen from Table 2 that the obtained values of the inner race and the rolling element of motor bearings are 162.6 Hz and 140.6 Hz, respectively. Compared with their \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objects** & **Theoretical Value** & **Obtained Value** & **Error** \\ \hline Inner race & 162.2 Hz & 162.6 Hz & 0.4 Hz \\ Rolling element & 141.1 Hz & 140.6 Hz & 0.5 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculation error between the theoretical value and the obtained value. Figure 9: Rolling element signal analysis. theoretical values, the calculation errors between the theoretical values and the obtained values are 0.4 Hz and 0.5 Hz, respectively. The obtained values are infinitely close to the theoretical values, which show that the proposed CMSR system can effectively detect the faults of motor bearings. Therefore, the proposed CMSR system is a weak signal feature extraction method for fault detection in engineering rotating machinery.
## 4 Results
### Electrical Anisotropy  
#### 4.2.1 Impedance Phase Split  
In cases where a dipping boundary from the surface to depth could create a phase split, convergence towards the same values of phase split values should be observed for MT sites close to that dipping interface. The lack of this convergence for long periods along entire 2D profile may exclude this hypothesis. Consequently, the anisotropy hypothesis may be preferred for the long periods.  
Furthermore, if a consistency of phase split along a 2D profile and with multiple stations is observed as shown in Figure 6, the presence of anisotropy is an interpretation that is preferred to the lateral variation of conductivity [28; 39]. This can be explained considering the idea that if the lateral variation is the cause, the phase split should decrease or disappear for the MT sites far away from any lateral resistivity gradient. This is not what is observed in Figure 6, whereby the phase split remains for a range of periods and with most of MT sites along entire 2D profile. It is thus reasonable to attribute this to the electrical anisotropy of geological structures at the corresponding depth. Additionally, the period range (long periods) where the degree of phase split is large corresponds to the period range where an approximate constant geoelectrical strike is well defined for all dimensionality analysis methods described in Section 3.2. This preferred direction of regional electric fields is roughly north-south and remains almost constant for the entire range of periods where the phase split is manifested. The same pattern was demonstrated by [39] to indicate the presence of dyke in anisotropic crust. It is obvious that at the center of the rift the degree of phase difference is more pronounced and increases with depth.  
Figure 6: Phase difference between the two orthogonal components (TE and TM) phases. The dashed black triangles indicate the locations of MT stations at various periods. Values of phase difference greater than 10\({}^{\circ}\) are considered high. Delaunay triangulation method is used.
## 4 Case Study
### The Energy Consumption Forecasting
Thus, the information introduced in the matrix depends on the intensity of activity (small, medium, and high) considering the working time (reduced, normal, and long) and energy consumption associated with each equipment and installation and the number of working cycles, if some operations can be repeated to various time intervals. Figure 15 and Table 9 present the results obtained after testing of the method for a week (  
It can be observed that the percentage errors are in the range [1.95, 3.80], and the mean absolute percentage error calculated on the analysed period (a week) is 0.24%. These values can be considered very satisfactory for the Decision-Maker in the situation in which the evolution of the activities associated with the technological process is dynamic and uncertainties regarding the execution of specific operations, such as tinsmith and mechanical repairs for each programmed car, can occur. Details about the energy consumption forecasting for the day with the medium intensity of the activity, highlighted with bold in Table 9, are presented in Table 10, where the grey cells belong to the Decision-Making Matrix (DMM), and the signification of the rules associated with the expert system being:  
The specific (for each equipment and installation) and total energy consumptions forecasted for Tuesday are highlighted with bold. The results obtained for the other days are presented in Tables A3-A6, Appendix A. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Equipment or Installation** & **Operating Modes** & **Working Time** & **Operating Cycles** & **W\({}_{t}\) (kWh)** \\ \hline
1 &
\begin{tabular}{c} Dyeing installation—drying operation/R1 \\ \end{tabular} & On & Reduced & 2 & 13.44 \\
3 &
\begin{tabular}{c} Air compressor \\ \end{tabular} & On & Long & 1 & **14.04** \\
4 &
\begin{tabular}{c} Welding machine \\ \end{tabular} & On & Reduced & 1 & 0.27 \\
5 &
\begin{tabular}{c} Lighting system—tinsmithing department \\ \end{tabular} & On & Long & 1 & **4.86** \\
6 &
\begin{tabular}{c} Lighting system—regular department \\ \end{tabular} & On & Long & 1 & **2.88** \\
7 &
\begin{tabular}{c} Lighting system—dyeing installation \\ \end{tabular} & On & Reduced & 1 & **1.1** \\
8 &
\begin{tabular}{c} Reception office \\ \end{tabular} & On & Long & 1 & 2.7 \\
9 &
\begin{tabular}{c} Other electric equipment \\ \end{tabular} & On & Reduced & 1 & **1** \\
10 &
\begin{tabular}{c} Vehicle elevator \\ \end{tabular} & On & Reduced & 3 & **0.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The Decision Matrix and forecasted energy consumption for Tuesday. Figure 15: The comparison between the real (red) and forecasted (blue) energy consumptions.
# Online Track Vertex Reconstruction Method Based on an Artificial Neural Network for MPGD
## 1 Introduction  
The micro-pattern gaseous detector (MPGD) was originally developed for high-energy physics experiments [1], and has several advantages such as high spatial resolution, fast response time, and a large sensitive area. Since then, the development of large scientific devices in high-energy physics has increased the performance requirements of the particle detectors they are paired with. The structure of the MPGD has been continuously optimized to obtain a higher spatial resolution and counting rate. Micromegas and gas electrons multiplier (GEM) detectors appeared and were equipped with larger and more complex readout devices [2; 3; 4; 5]. This results in a large amount of data from one single event, which means that data acquisition and analysis of the detector is challenging.  
In MPGD-based experiments, the pulsed signal is often sampled by an analog-to-digital converter (ADC) with a high frequency such as 40 MHz, and then uploaded to a computer for analysis. There are often bottlenecks in this process due to the huge amount of data transfer and storage, which is caused by the pulse shape sampling of the signal, especially in the large-area and high-precision case. The large number of readout electrodes lends itself well to parallel computation, whereas the traditional track vertex reconstruction algorithms run by personal computers (PCs) are mostly serial. The consumption of computational resources can be large. The signal generated by the incident particle does not always have a linear correlation with the position of the incident particle [6], especially when the incidental angle is large. Furthermore, this relationship is also affected by the detector structure and readout device, which makes it more difficult to use an analytical function to describe these relationships. In this work, our aim was to develop a new scheme of tracking vertex reconstruction to overcome the disadvantages mentioned above and to implement an online analysis with the precision to obtain a position resolution of 0.35 mm. An artificial neural network (ANN) can be used as a mature algorithm to solve difficult problems in the field of nuclear detection and other challenging projects [7]. [PERSON] et al. used this algorithm to successfully extract physical information from the signal of the liquid argon time projection chamber [8]. [PERSON] et al. used deep neural networks to reconstruct energy and position information for EXO-200 [9]. We used this algorithm for MPGD track vertexreconstruction and implemented the algorithm on a Field-Programmable Gate Array (FPGA) chip to obtain online processing data.  
In this paper, we used a Geant4 software toolkit to implement a fast neutron simulation of MPGD [10], to train a reduced-dimensional network and a feedforward neural network (FNN) from the resulting dataset to reconstruct the track vertexes [11], to compare the reconstruction performance of this track vertex reconstruction algorithm with that of the centroid method [12], and then, to demonstrate the feasibility of the algorithm at the FPGA level.
# A New Method of Applying Data Engine Technology to Realize Neural Network Control
## 3 Research on DRNN Hybrid Controller Based on DE
### Summary of DRNN Hybrid Controller Based on DE Theory  
In view of the current investigation of neural network controller in industrial application, neural network algorithms run in the upper computer are often processed in non-real-time databases, and the real-time data collected by the control station also needs to be transferred to the upper computer, which affects the real-time performance of system control to a certain extent. So, a common neural network hybrid controller implementation method is proposed in this paper.  
Step 1--Constructing the topology of controller algorithm by using data unit. According to the structure of the designed DRNN neural network and the learning methods of the network, the topology of the DRNN network algorithm is constructed by the data unit of the configuration software IAPlogic, and the time series judgment and analysis of the calculation order of each unit module are carried out, and then the variable data calculation and signal transfer process of the control algorithm are determined.  
Step 2--Establish a mapping relationship between the data unit and the generic middleware. The function of the general middleware is to establish a connection between the computing unit module and the target platform, which is composed of a set of memory-based database, transaction scheduler, an algorithm executing program, and a middleware program. The algorithm information of the DRNN neural network topology is sent to the general middleware, the program which reflects the data unit of the network node is structured and digitally encoded according to a certain mapping relationship, and the mapping relationship between them will be established. The factors that should be considered in digital coding include real-time data, data type, identification of data in the system, control logic of the calculation unit module and calculation timing between modules, communication address, and so on.  
Step 3--Establish a mapping relationship between general middleware and target platform. Analyze the programming specification of the target platform instruction system and establish a set of mapping relationship is between the structured digital coding system of the common middleware and the target platform instruction system.  
Step 4--Parsing the mapping relationship between general middleware and target platform and generating the code supported by the target platform. Using the middleware program to analyze the mapping relationship between the digital code of the general middleware control algorithm and the program code of the target platform, the encoding algorithm is transformed into executable code supported by different platform computing hardware and its instruction system by using the program code automatic generation method. Figure 7 depicts the architecture of our abstract platform based on DE Theory.
## 3 SENDI: System for Detecting and Forecasting Natural Disasters Based on IoT
### Protocol Stack and Routing  
The protocol stack used by SENDI is shown in Figure 2, where there is an IEEE 802.15.4 standard, that defines the physical layer and the media access control layer of the OSI model for LR-WPAN (Lowdata-Rate Wireless Personal Networks), and Ipv6 in the network layer. The UDP protocol was used on the transport layer because it does not require connection, and is a simple protocol, which generates less overhead in communication; in addition it does not block communication if the target node is not working. On top of the stack, there is the natural disaster monitoring and forecasting application.  
As the routing protocol, RFC 6550 defines the RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) for LLNs (Low-Power and Lossy Networks). LLNs are networks which have restrictions with regard to processing power, energy and/or node memory. The nodes act as routers in these networks and usually only support low data rates, as well as having other specific features such as point-to-multipoint or multipoint-to-point communication standards. Some characteristics of these networks include high number of nodes with hardware and energy limitations, reaching thousands of nodes, all interconnected by lossy links and supporting various traffic patterns [16]. These particular features mean that the routing in these kinds of networks can be distinguished from others and raise their own challenges and solutions [17], as well as clearly describing the properties found in the REDE system.  
The RPL protocol was initially based on widely used routing protocols and research prototypes in WSN area, being later extended and re-designed for IPv6. The main implementations of RPL found in the literature are ContikiOS and TinyOS, with TelosB being the most widely used hardware. [16] also comments that, although it already has some years of development and research, the RPL protocol still needs to be tested more deeply in real-world applications and compared with other alternative routing protocol [18].  
Unlike wired networks, where predefined topologies can be found because of the point-to-point, LLNs do not usually have this property which means that they have to discover the links that exist between the nodes and select them carefully. Thus, the RPL arranges the network topology as a set of either a single or several Destination Oriented DAGs (DODAG), that form a Directed Acyclic Graph (DAG), which is a DODAG formed by the sink node (root). Figure 3 shows an example of the configuration of an RPL instance where the R1, R2 and R3 nodes are the roots of their DODAG and are, for example, interlinked with the remaining nodes in the Internet or another network. The nodes that have a direct path to the root, such as nodes A and B, are considered to be Rank 2. All the nodes which are unable to see the root directly but are able to see the Rank 2 nodes are regarded as Rank 3, such as node C from the other DODAG, and so on.  
Figure 3: Example of an RPL instance.  
Figure 2: Protocol stack used in the simulation implemented in ns-3.
## 3 Results and Discussion  
### Analysis of the Evaporation Duct Characteristics  
The Taiwan Strait is the confluent area between the SCS and the East China Sea, and is also one of the interaction areas of the Kuroshio Current [17]. Moreover, as one of the major sources of the Tsushima Warm Current, the TSWC exists all year round [17]. It has an overwhelming influence on the heat balance in this area, especially in summer [18]. Intense evaporation over the oceans can be maintained throughout the year, while the SST reaches nearly 30 \({}^{\circ}\)C in summer and 20 \({}^{\circ}\)C in winter, which is conducive to increasing the occurrence of the evaporation duct.  
Based on NCAR datasets in 2020 and the NPS model, the average EDH in the Taiwan Strait during July 2020 is shown in Figure 3. Affected by the TSWC, the yellow area representing high EDH is distributed around the Taiwan Strait, which forms a distinct contrast with any other southern and eastern coastal area in China. The maximum height was close to 35 m, while most of the calculated region was concentrated around 10 m.  
Monthly and hourly statistical results of the EDH at a typical position (23.4\({}^{\circ}\) N, 120.0\({}^{\circ}\) E) of the Taiwan Strait are shown in Figures 4 and 5, respectively. To analyze the pattern of the EDH variation with the alternation of day and night, we used local time (Beijing time) instead of Universal Time Coordinated (UTC). From 9 to 14 o'clock, the percentage of the EDH exceeding 10 m was significantly higher  
Figure 3: The average EDH in the Taiwan Strait during July 2020.  
the proportion reached over 90%. In addition, the analyzed position could maintain high evaporation intensity as night approached, which constructed favorable communication conditions. The lowest EDHs were clustered at sunrise and sunset, affected by the transition between stable (when the air-sea temperature difference (ASTD) was greater than 0) and unstable conditions (when the ASTD was less than 0). In terms of seasonal characteristics, the EDH was significantly increased in summer. The average EDH reached 29 m in July, while the percentage of the EDH exceeding 10 m was close to 100%.  
Figure 6 displays the statistical results of the EDH in different periods and seasons with a violin chart.  
For different periods, the distribution of the EDH was mainly clustered in the range of 10 m-25 m from 9 to 14 o'clock, while a large number of the EDH were lower than 10 m in other periods. Influenced by the unstable condition, partial height increased to nearly 40 m during this period.  
Furthermore, the distribution of the EDH in summer also showed distinctive seasonal characteristics from 9 to 14 o'clock; the maximum EDH exceeded 32 m, while it was lower than 28 m in other seasons.  
Figure 4: Temporal distribution of the EDH in the Taiwan Strait during 2020.  
Figure 5: Percentage of the EDH exceeding 10 m in the Taiwan Strait during 2020.