esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Optimization for Outer Rotor Low-Speed Permanent Magnet Motor
## 6 Experimental Tests  
According to the multi-objective optimization design, a 22 kW, 380 V, 56 rpm LSPMM prototype is manufactured. The prototype adopts a PM with a thickness of 14 mm and a pole arc coefficient of 0.89. The stator adopts a round copper wire with double-layer winding.  
For the experimental evaluation of the overall performance of the LSPMM prototype, an experimental test platform is built, as shown in Figure 17. In the test platform, the prototype is driven by a variable frequency power converter. To obtain the voltage and current values, the voltage and current sensors are employed.  
The power factor of the prototype can be directly measured by the power analyzer. The load power can be obtained from the test platform, as shown in Figure 17. The load is connected to an existing DC motor, and thus the load power is equal to the output power of the DC motor. The efficiency of the DC motor can be obtained from the manufacturer, and the input power of the DC motor can be obtained by calculation under the premise that the efficiency and output power of the DC motor are known. The prototype and the DC motor are connected by a belt, so the input power of the DC motor is equal to the output power of the prototype. In addition, the input power of the prototype can be obtained through the control system in the test platform. Therefore, the efficiency of the prototype can be obtained by the obtained input and output power of the prototype.  
At the rated speed, the voltage of the prototype measured by the test platform is 378 V, which is in good agreement. Under the rated load, the  
Figure 17: Prototype and experimental platform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Initial** & **Optimization** \\ \hline Efficiency (\%) & 92.62 & 94.66 \\ Thermal load (A\({}^{2}\)/mm\({}^{3}\)) & 233.7 & 157.6 \\ Power factor & 0.910 & 0.982 \\ Core loss (W) & 639.6 & 1247.6 \\ Copper loss (W) & 15,572.8 & 10,708.1 \\ Torque (Nm) & 34,110.7 & 34,117.8 \\ No-load back EMF (V) & 215.2 & 216.2 \\ Current density (A/mm\({}^{2}\)) & 4.074 & 3.690 \\ Torque ripple (\%) & 7.2 & 6.8 \\ Air gap flux density (T) & 0.84 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of initial and optimized performance.  
0.98, which is consistent with the calculation results of 0.982. In addition, the efficiency of the prototype is 94.2%, which is slightly lower than the calculated result of 94.66%. To evaluate the machine temperature, the winding temperature is measured by Pt100 resistance temperature detectors in the end windings. Table 6 lists the measured and calculated electromagnetic and temperature performances at the rated load. As shown, the measured temperature is only slightly higher than the calculated result. The experimental test of the prototype well proves the effectiveness of the optimization.
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 2 Acquisition System Influencing Factors
### Laser Beam Jitter  
In the telescope-pointing scheme, the scanning of the entire UC is achieved through the SC's own motion and the rotation of the telescope for signal acquisition. The SC's movement is accomplished under the control of the drag-free and attitude control system, which relies on the micro-propulsion thruster actuation system for power. Attitude measurement during motion is conducted using STRs. Due to the requirement for ultra-high precision in GW detection, we can consider the entire SC to be a rigid body. Deviations of the beam from the intended trajectory arise from two sources. The first is attitude control errors coupled to the outgoing beam, which are determined by the thrust noise from the micro-propulsion thruster and attitude positioning error from the STRs. The second source is telescope motion error, which could be viewed as a long-term drift that is significantly smaller than the platform jitter and can be neglected [21,22].  
According to [18,23], we can assume that the power spectral density (PSD) of the laser beam coupled with the is as shown below:  
\[S(f)=\frac{3.15\ \mathrm{\mu rad^{2}/Hz}}{1+\left(f/f_{r}\right)^{2}}, \tag{12}\]  
where \(f_{r}\) is the roll-off frequency. The PSD of platform jitter is shown in Figure 4.
## 5 Experiment
### Experimental Setup
#### 5.1.2 Comparative Methods  
To validate the novelty of our method, we compared the proposed method (PM) with its version without the SM kernel in Equation (10) (PM w/o SM kernel) and without the prior in Equation (18) (PM w/o prior). Furthermore, we compared our method with the following comparative methods.  
1. **PCA**[3] is a classical method for dimensionality reduction and linearly derives its embedding. We compared our method with PCA as a benchmark method. Note that GP-LVM-based methods typically use PCA as the initial values of their latent variables.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Samples** & **Dimensions** & **Categories** & **Type** & **Features** \\ \hline MNIST & 20,000 & 784 & 10 & Image & Pixels \\ COIL-20 & 720 & 16,384 & 10 & Image & Pixels \\ DBPedia & 20,000 & 100 & 14 & Text & FastText \\ FMNIST & 20,000 & 784 & 10 & Image & Pixels \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the datasets used in this study.  
**LE**[34] is a classical graph-based approach and derives its low-dimensional embedding as the eigenvectors of the graph Laplacian. We used LE as the benchmark in the graph-based approaches.
* **t-SNE**[10; 11] is a long-standing graph-based data visualization technique that derives the low-dimensional representation by minimizing the KL divergence between the observed and low-dimensional spaces. We used t-SNE as the baseline method of the graph-based dimensionality reduction approaches.
* **Bayesian GP-LVM (BGP-LVM)**[16; 25] is a GP-LVM that introduces the Bayesian inference into the latent variables. We used BGP-LVM as the baseline method and visualized the mean vectors as a low-dimensional representation. We used the RBF kernel in Equation (6) and standard normal density as the prior distribution following the original work [16].
* **Potential of heat diffusion of affinity-based transition embedding (PHATE)**[41] is a recently proposed graph-based approach and derives the neighborhood graph on the basis of the diffusion operation [56], enabling the global preservation of data. We used PHATE as a state-of-the-art method among the methods aimed at preserving global structures.
# Fuzzy Synthetic Evaluation of the Long-Term Health of Tunnel Structures
## 2 Determination of the Evaluation Methods and Ideas for the Tunnel Structural Health
### The Classification of Health Levels for a Tunnel Structure  
The structural health status of a tunnel is a very abstract concept. It is necessary to divide the health status into several measurable safety levels. At present, the main methods are the three-class classification method, the four-class classification method, the five-class classification method, and the 10-class classification method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial** & **Mileage of** & \begin{tabular}{c} **Typical** \\ **Monitoring** \\ **Section** \\ \end{tabular} & \begin{tabular}{c} **Serial** \\ **Number** \\ \end{tabular} & \
\begin{tabular}{c} **Mileage of** \\ **the Segment** \\ \end{tabular} &
\begin{tabular}{c} **Typical** \\ **Monitoring** \\ **Section** \\ \end{tabular} \\ \hline \(1\) & ZK94+760–ZK95+400 & ZK94+900 & 11 & K95+304–K95+850 & K95+350 \\
2 & ZK95+400–ZK96+350 & ZK95+609 & 12 & K95+850–K96+650 & K96+000 \\
3 & ZK96+350–ZK97+073 & ZK96+370 & 13 & K96+650–K97+650 & K96+790 \\
4 & ZK97+730–ZK97+550 & ZK97+093 & 14 & K97+650–K98+469 & K98+000 \\
5 & ZK97+550–ZK98+600 & ZK97+655 & 15 & K98+469–K99+100 & K98+490 \\
6 & ZK98+600–ZK99+400 & ZK98+895 & 16 & K99+100–K99+969 & K99+240 \\
7 & ZK99+400–ZK100+160 & ZK99+521 & 17 & K99+960–K100+719 & K100+000 \\
8 & ZK100+160–ZK100+900 & ZK100+220 & 18 & K100–79-K101+150 & K100+740 \\
9 & ZK100+900–ZK101+700 & ZK101+050 & 19 & K101+150–K102+300 & K101+535 \\
10 & ZK101+700–ZK102+200 & ZK101+900 & 20 & K102+300–K102+365 & K102+340 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Segment classification and typical monitoring sections in Cang Ling Tunnel.  
Figure 6: Embedding figures of the reserved cavity, pressure cell, and strain gauge in the field. (a) Pressure cell; (b) Strain gauge; (c) Reserved cavity.  
Of these classification methods, the three-class classification method is relatively simple. It is mainly used for the evaluation of the results of the tunnel inspection (the daily inspection, the regular inspection, and the special inspection). The five-class classification method is based on the four-class classification method. However, the classification of the 10-class classification method is too detailed. In the health evaluation of the highway tunnel structure, some conditions are not necessary. Therefore, on the basis of the existing tunnel health level classification method, considering the wide application of the four-class classification method, the health status of the structure of the Cang Ling Tunnel is divided into four levels. There are: \(V=\{\
u_{1},\
u_{2},\
u_{3},\
u_{4}\}\) = {I,II,III,IV}. Among them, \(\
u_{1}\) means the structure is safe, \(\
u_{2}\) means the structure is basically safe, \(\
u_{3}\) means the structure is potentially unsafe, and \(\
u_{4}\) means the structure is unsafe. The meanings of each level are shown in Table 2.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Decision Tree  
The decision tree is applied to the dataset based on the crop quantity produced. Here, the crop quantity shall be the feature to compare for high-rated production, and the outcome shall be the suggested crop that is expected to have better production. A decision tree will present the flow of decisions to reach the final node.  
The DT is a popular classification method that is used to train the model using simple decision rules. It always starts with the root node, and by comparing the root with the record's attribute, the branching begins. The decisions can be based on a categorical or continuous target variable. In this tree, each node is treated as a test case for some attribute. Then, the CART method shall be applied here for analysis. The value of the attribute shall be obtained by finding entropy, information gain, gain ratios, Gini index, reduction of variance, and chi-square. The standard equations to measure the above metrics are entropy shown in Equation (2), information gain in Equation (3), and Gini index in Equation (4).  
\[E(S)=\sum_{i=1}^{c}-P_{i}log_{2}P_{i} \tag{2}\]  
where \(E(S)\) is entropy, \(P_{i}\) is the probability of a random variable \(S\), and \(c\) is the categories that \(S\) can take in Equation (2).  
Information gain (\(G\)) is measured based on the following:  
\[G(T,X)=E(T)-E(T,X) \tag{3}\]  
where \(X\) is the attribute, \(T\) is the dataset, \(E(T)\) is the entropy of the dataset, and \(E(T,X)\) is the entropy of \(T\) after a split based on \(X\), as shown in Equation (3).  
The cost function Gini index shall be used to calculate the splits in the dataset. It shall be used with the CART to identify the spit points in the dataset. In Equation (4), the Gini index \(G_{i}\), \(c\) is the count of classes in the crop dataset, and \(P_{i}\) is the probability of a particular crop at class \(i\).  
\[G_{i}=1-\sum_{i=1}^{c}(P_{i})^{2} \tag{4}\]
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 5 Conclusions  
The continuous increase in IT/OT data traffic in smart substations gives rise to the problem that the transmission delay and jitter of time-sensitive streams also experience an increase. In this paper, within the network architecture of a TSN-based smart substation automation system, a traffic scheduling scheme utilizing TSN has been proposed. This scheme not only introduces TSN switches into the structure of a three-layer two-network intelligent substation, but also incorporates an end-side device combining Qbv, Qbu technology and the ETF Qdisc of Linux, which provides low latency and low jitter services for some time-sensitive data streams on the basis of realizing hardware-timed message transmission capability. The results show that the proposed scheme can significantly reduce transmission delay to 60 us and stable delay jitter to 140 us without losing packets. Compared to traditional best-effort transmission, this scheme is able to provide a bounded delay for time-sensitive streams, thereby significantly improving the reliability of data transmission. Additionally, it presents a reference for enhancing the QoS of the smart substation automation system.  
For future research, efforts can be concentrated on real-time optimization and compatibility as well as interoperability. On one hand, further algorithm optimization can be pursued to achieve lower latency and more stable jitter, thereby enhancing the system's real-time performance. On the other hand, addressing compatibility issues between TSN devices from different providers is necessary to ensure seamless interoperability within the system.  
Conceptualization, X.M. and B.C.; Methodology, X.M. and B.C.; Software, C.L. ([PERSON] 1) and L.P.; Investigation, J.W. and Z.C.; Data curation, J.X. and C.L. ([PERSON] 2); Validation, C.L. ([PERSON] 2); Writing--original draft, X.M. and C.L. ([PERSON] 1); Writing--review & editing, Z.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Method of Optical Diagnostics of Grain Seeds Infected with Fusariumum
## 4 Discussion
Photoluminescence is excited by two radiation sources (LEDs) in the short-wave 232 nm and medium-wave 424 nm/485 nm ranges. 3. Luminescence is recorded by two photodetectors (photodiodes) with operating ranges: the first is 290-380 nm; the second is 450-600 nm or 520-650 nm. 4. The electrical signal (the obtained ratio \(\Phi_{1}/\Phi_{2}\)) from photodiodes is amplified by an amplifier, converted into digital form and fed to the microcontroller. 5. The ratio of photoluminescence fluxes and the degree of infection, taking into account a priori information, are calculated using the microcontroller. 6. The obtained result is sent to the output indicator device. Taking into account the obtained data, the degree of seed infection is determined. For out seeds, it is possible to determine the degree of infection by Equation (15), but it is also possible to use the flow ratio, for example, \(\Phi_{424}/\Phi_{485}\), to unify the methodology. The method developed by the authors for diagnosing the infection of seeds with fusarium has limitations: seeds must be of conditioned humidity and have low contamination (no more than 5%).
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 2 Fuzzy Methodologies
### Fuzzy Inductive Reasoning (FIR)  
The conceptualization of the FIR methodology arises from the General System Problem Solving (GSPS) approach proposed by [PERSON] [22]. FIR offers a model-based approach to predicting either univariate or multi-variate time series [23; 24; 25; 26]. Visual-FIR is a tool based on the FIR methodology (runs under Matlab environment), that offers a new perspective on the modelling and simulation of complex systems.  
FIR is composed of four main processes: fuzzification, qualitative model identification, fuzzy forecasting, and defuzzification. The fuzzification process converts quantitative data stemming from the system into fuzzy data. The qualitative model identification process is responsible for finding causal and temporal relations between variables (feature selection process) and is used to obtain the best model that represents the system. Once the FIR model is available, the prediction system can take place using the FIR inference engine. This process is called fuzzy forecasting. The FIR inference engine is a specialization of the KNN algorithm, commonly used in the pattern recognition field. Defuzzification is the inverse process of fuzzification, it allows converting the qualitative predicted output into quantitative values that can then be used as inputs to an external quantitative model.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 2 Mobile Communications Security Issues
### 4G-LTE Security  
Avoiding the weaknesses in the UMTS security mechanism such as man-in-the-middle attacks, rogue base station attacks, and deny of service (DoS) attacks, The LTE security architecture makes better and more attractive the previous generations [11; 12; 13]. The expectations from the next generation mobile communication systems are to provide more security functionality than the UMTS systems in order To achieve a mutual authentication between the user equipment (UE) and the mobility management entity (MME) through the evolved-universal terrestrial radio access network (E-UTRAN). The mutual authentication between the UE and the evolved packet core (EPC) is the most important security feature in the LTE security architecture. The AKA procedure is used by the LTE system to achieve the mutual authentication between the UE and the EPC and generate a ciphering key (CK) and an integrity key (IK), that are used to derive different session keys for the encryption and the integrity protection. Several different AKA procedures are implemented in the LTE security architecture when the UEs access to the EPC via distinct access networks because of the support of non-3 GPP access [14]. The 3 GPP classifies the security architecture of the LTE system into five different functional domains as follows [15].  
* **Network access security:** use of universal subscriber identity module (USIM) to provide secure access for a user to the evolved packet system (EPS). Includes mutual authentication and privacy features.
* **Network domain security:** refers to features that allow for secure communications between Evolved Packet System/Evolved Packet Core (EPS/EPC) nodes in order to protect against attacks on the network.
* **User domain security:** securing access to the terminal, e.g., screen lock password, or personal identification number (PIN) to enable USIM usage.
* **Application domain security:** security features used by applications, e.g., hypertext transfer protocol (HTTP).
* **Visibility and configurability of security:** features to allow a user to know whether a security feature is in operation or not, and user-configured control over whether use of a service depends on enabled security features.
## 3 Optimal Multichannel ANC for Multiple Noise Scenarios  
In practice, the characteristics of the noise in open window scenarios could change over time. The optimal filter that is derived from one condition usually performs poorly in other conditions. Whereas adaptive filters can be employed to update the control filters in real-time to track the changes, the requirement of the error microphones raises privacy concerns, increases computation cost, and manifests as a physical obstruction in a domestic environment. Hence, the goal here is to design an optimal fixed filter method that could potentially handle multiple noise scenarios. In the ANC through open windows, the traffic noise arriving at the opening from nearby roads tends to exhibit a plane wave characteristic [25; 26]. In the consideration of traffic noise impinging at different floors of a high-rise building, the angles of noise incidence are fixated at each floor [10]. When more urban noise sources, such as from trains and/or aircraft, are considered, noise impinging at the window aperture becomes more complex.
# Hyperspectral Python: HypPy
## 2 Design and Implementation  
In this section we aim to show that HypPy has a set of features, which sets it apart from other packages and makes it a versatile and flexible tool for processing hyperspectral data. HypPy has the following features:  
* HypPy supports three levels of interfacing: a graphical user interface (GUI), a command-line interface (CLI), and a programming interface (API). The user interfaces of HypPy can be accessed from one top-level menu.
* The top-level menu is configurable and can be adapted for different purposes.
* HypPy has viewers for hyperspectral images and spectral libraries. These viewers have simple interfaces yet offer sufficient functionality for exploring the quality and information content of the data.
* HypPy offers a read and write capability for ENVI data files.
* Hyperspectral images tend to be huge, yet HypPy is memory-friendly.
* HypPy supports ENVI's three native image formats: BSQ, BIL, and BIP. Internally, HypPy translates these to a BIP format, relieving the programmer of the hassle of having to deal with three different formats.
* HypPy offers functionality for working with wavelengths instead of band numbers. Band numbers have no physical meaning, whereas wavelengths do.
* Raw image data may not have bands in the order of increasing wavelengths. Therefore, HypPy is able to sort wavelengths in the image on-the-fly.
* HypPy is able to use the bad band list (bbl) and skip bands that are marked as bad.
* HypPy can process ENVI spectral libraries as if they were ENVI images.
* If necessary, spectra are resampled on-the-fly. This eliminates the need to spectrally resample before combining spectra from different sources.
* HypPy is free and open-source software, which makes it an ideal tool for education. Students can pick up the package for free and use it at home as well.  
In the following sections, the points mentioned above will be discussed in more detail.