esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 2 Feature Engineering Based on Circle Model
### Principle of Circle Modeling
\tag{10}\]  
Eliminating \(R_{x}\), the following formula can be obtained:  
\[\frac{R_{o}}{X_{o}}\frac{uU_{osr}-mU_{osj}}{U_{osr}^{2}+U_{osj}^{2}}-\frac{mU _{osr}+uU_{osj}}{U_{osr}+U_{osj}^{2}}+1=0 \tag{11}\]  
\[\Rightarrow\frac{R_{o}}{X_{o}}\left(uU_{osr}-mU_{osj}\right)-\left(mU_{osr}+ uU_{osj}\right)+U_{osr}^{2}+U_{osj}^{2}=0\]  
\[\Rightarrow U_{osr}^{2}+\left(\frac{R_{o}}{X_{o}}n-m\right)U_{osr}+U_{osr}^{2}- \left(\frac{R_{o}}{X_{o}}m+n\right)U_{osj}=0\]  
\[\Rightarrow\left[U_{osr}-\frac{1}{2}\left(m-\frac{R_{o}}{X_{o}}n\right) \right]^{2}+\left[U_{osj}-\frac{1}{2}\left(n+\frac{R_{o}}{X_{o}}m\right) \right]^{2}\]  
\[=\frac{1}{4}\left[\left(m-\frac{R_{o}}{X_{o}}n\right)^{2}+\left(n+\frac{R_{o}} {X_{o}}m\right)^{2}\right]\]  
In Formula (7), \(\dot{U}_{os}=\dot{U}o-\dot{U}_{s}H^{\prime}(j\omega)\). If \(\dot{\dot{U}}_{o}=U_{or}+jU_{oj}\) and \(\dot{\dot{U}}_{s}H^{\prime}(j\omega)=U_{or}^{\prime}+jU_{oj^{\prime}}^{\prime}\), the following equations can be obtained:  
\[\left\{ \begin{array}{l}U_{osr}=U_{or}-U_{or}^{\prime}\\ U_{osj}=U_{oj}-U_{oj}^{\prime}\end{array} \right. \tag{13}\]Replacing \(U_{osr}\) and \(U_{osj}\) in Formula (12) with Formula (13), we have:  
\[ \begin{array}{l}\left[\mathrm{U}_{\mathrm{or}}-U_{\sigma r}^{\prime}-\frac{1}{ 2}\big{(}m-\frac{R_{0}}{X_{0}}n\big{)}\right]^{2}+\left[U_{oj}-U_{oj}^{\prime} -\frac{1}{2}\big{(}n+\frac{R_{0}}{X_{0}}m\big{)}\right]^{2}\\ =\frac{1}{4}\Big{[}\big{(}m-\frac{R_{0}}{X_{0}}n\big{)}^{2}+\big{(}n+\frac{R_{0 }}{X_{0}}m\big{)}^{2}\Big{]}\end{array}  \tag{14}\]  
\[\Rightarrow\left(\mathrm{U}_{\mathrm{or}}-a\right)^{2}+\left(U_{\mathrm{oj}}- b\right)^{2}=r^{2} \tag{15}\]  
where,  
\[\left\{ \begin{array}{l}\mathrm{a}=U_{0r}^{\prime}+\frac{1}{2}\big{(}m-\frac {R_{0}}{X_{0}}n\big{)},\\ b=U_{0j}^{\prime}+\frac{1}{2}\big{(}n+\frac{R_{0}}{X_{0}}m\big{)},\\ r=\frac{1}{2}\sqrt{\left(m-\frac{R_{0}}{X_{0}}n\right)^{2}+\big{(}n+\frac{R_{0 }}{X_{0}}m\big{)}^{2}}\end{array} \right. \tag{16}\]  
Obviously, Formula (15) is a circle equation, with a and b being the \(U_{\sigma r}\)- and \(U_{oj}\)-coordinates of the center and r being the radius. Because \(R_{0}\), \(X_{0}\), \(m\), and \(n\) are independent from the value of \(x\), a and b must be independent from the value of \(x\), too. Therefore, Formula (15) always holds regardless of the fault type occurring to \(x\). Hence, the circle equation governing the relationship between \(U_{\sigma r}\) and \(U_{oj}\) is applicable to both hard and soft faults, and thus selected as the circuit fault model of this research. The parameters \(a\), \(b\), and \(r\) that govern the circle equation are characteristic parameters. If \(x\) is a dynamic element, the same conclusions can be reached. In addition, because Formula (15) is independent from the value of x to be modeled. The fault-free output voltage, \(\dot{U}_{0}=U_{\sigma r}+jU_{oj}\) must satisfy (15). Therefore, all characteristic curves pass through the fault-free point (\(U_{\sigma r}\),\(U_{oj}\)) [13].
## 8 The Effect of Alloying Elements on Electrical Steel
### Effect of Tin  
The impact of adding Sn on the core loss and texture of non-oriented electrical steel (NOES) was thoroughly investigated in [95]. In NOES, iron loss includes both hysteresis loss and eddy current loss, with hysteresis loss being a significant contributor to power loss. Efforts were made to improve material crystalline texture with the aim of reducing hysteresis loss and increasing magnetic flux density and permeability [76]. The study revealed that Sn plays a significant role in improving a material's crystalline texture.  
According to the study conducted in [96], grain size is influenced by the presence of Sn, which hinders grain growth. The addition of Sn leads to improvements in magnetic properties, resulting in reduced core loss and increased magnetic flux density. However, when more than 0.1 wt% Sn is added, the core loss starts to increase, which contrasts with the effect observed when less than 0.1 wt% Sn is added. The enhancement of magnetic properties due to the addition of Sn correlates with the improvement in crystalline texture facilitated by the addition of Sn [97; 98].  
Each element carries distinct advantages and drawbacks, playing a pivotal role in guiding the optimization of electrical steel for a wide range of applications, as illustrated in Table 2.
## 4 Empirical Analysis
### Causal Relationship Analysis Based on DEMATEL  
Seventeen field experts with at least two years of management and research experience in prefabricated buildings were invited, including government personnel (3), industry professionals (11), and some university experts (3). Using a Likert 5-point scale (0 = \"very unimportant\", 1 = \"unimportant\", 2 = \"no impact\", 3 = \"important\", 4 = \"very important\"),they evaluated the direct positive correlation between indicators \(X_{i}\) and \(X_{j}\) (\(i,j=1\), 2,..., 16). The survey lasted nearly three months, resulting in 13 valid questionnaires with an effective response rate of 76.47%. Due to space limitations, only the evaluation of \(X_{1}\) against \(X_{1}\) to \(X_{16}\) in the 13 valid questionnaires is used as an example, with the results shown in Table 3.  
Using the COWA operator to calculate the score of indicator \(X_{1}\) against \(X_{2}\), the 13 valid questionnaires were listed as the initial matrix \(A=(2,\,2,\,3,\,3,\,3,\,2,\,2,\,3,\,3,\,3,\,4,\,2)\). Matrix \(A\) was sorted in descending order to obtain matrix \(B=(4,\,3,\,3,\,3,\,3,\,3,\,3,\,3,\,2,\,2,\,2,\,2)\). The weight vector \(\psi\) was calculated using Equation (1) as \(\psi=(1/2^{12},\,12/2^{12},\,66/2^{12},\,220/2^{12},\,495/2^{12},\,792/2^{12}, \,924/2^{12},\,792/2^{12},\,495/2^{12},\,220/2^{12},\,66/2^{12},\,12/2^{12},\, 1/2^{12})\), and the direct relation matrix element \(t_{12}\) was calculated using Equation (2) as \(t_{12}=B\psi^{\mathrm{T}}=2.9302\). Similarly, the direct relation matrix of the safety performance indicators for PBC was calculated using the COWA operator, \(T_{16\times 16}\); see Table 4.  
The direct relation matrix in Table 4 was normalized using Equation (3) to obtain the normalized matrix \(G_{16\times 16}\). Based on Equation (4), the indirect relation matrix \(Y_{16\times 16}\) was constructed.
###### Abstract  
This paper presents an optimized long-wave (10-300 kHz) wideband direction-finding system for scientific research. The antenna unit of the system comprises one vertical electric field sensor and two horizontal magnetic field sensors oriented in the north-south and east-west directions, respectively. The overall design prioritizes compactness, engineering feasibility, and ease of deployment, enabling the effective reception of long-wave radio signals within the 10-300 kHz range. The magnetic field sensitivity reaches \(8\,fT/\sqrt{\mathrm{Hz}}@10\,\mathrm{kHz}\), while the electric field sensitivity achieves \(3.2\,\mathrm{\SIUnitSymbolMicro m}/\sqrt{\mathrm{Hz}}@10\,\mathrm{kHz}\). The overall sensitivity of the receiver is \(1\,\mathrm{\SIUnitSymbolMicro m}\) (300 Hz bandwidth, 10 dB signal-to-noise ratio). The synchronization accuracy of the system is within 10 ns. Theoretically, with a baseline length of 5 km and a signal incidence angle ranging from \(9.9\,\mathrm{\SIUnitSymbolMicro}\) to \(170.1\,\mathrm{\SIUnitSymbolMicro}\), the direction finding error is less than \(2\,\mathrm{\SIUnitSymbolMicro}\). Additionally, direction-finding methods for MSK and ASK modulated signals are analyzed. To evaluate the system's actual performance, initial measurements were conducted in Qingdao, Shandong.  
long-wave wideband direction-finding; electrical and electronic instruments and components; receiver; MSK modulation; ASK modulation +
Footnote †: journal: Applied sciences
# Improvement on Flux Weakening Control Strategy for Electric Vehicle Applications
## 7 Conclusions  
This paper proposes different improved control strategies for flux weakening operation of IPM synchronous machines. The basis of the proposed FW schemes are some of the most traditional methods proposed in literature, and rely on the saturation detection of current PI regulators or on voltage saturation detection.  
The performance of traditional FW control methods showed a good exploitation of the IPM motor when it accelerates, but because of the integral action of PI regulators used to calculate the current angle, some issues arise in deceleration operation if an abrupt torque transition occurs. This behavior has been proven both via simulations and experimental results.  
Two additional feed forward contributions denoted \(i_{sd-safe}\) and _anti-wind up_ were shown in this paper. The FF loops were added to the traditional control schemes of [16; 17] to overcome the aforementioned issues, improving their performance during deceleration operation at high speed.  
The \(i_{sd-safe}\) current acts on the \(i_{sd}^{*}\) setpoint in accordance with motor angular speed and BUS voltage. While, the voltage-based _anti-wind up_ keep away current regulators from saturation making full use of the battery voltage.  
Both optimized methods have been validated at first by Matlab/Simulink simulations and then by extensive experimental tests on a dedicated test bench. The motor under test was a 12-10 slot-pole combination IPM synchronous machine for a light electric vehicle.  
Experimental results showed that the FF actions improve the robustness and repetitiveness of the FW controls, in case of a sudden torque transition due to an abrupt braking action. In particular the \(i_{sd-safe}\) FF loop sets a suitable current angle value, avoiding unwanted excessive braking that can occurs at high speeds improving the driver comfort and safety while the voltage-based _anti-wind up_ allows to exploit constant Power region in a more efficient way.  
Figure 19: Comparison between the basic FF method and the proposed ones. FF (red line), enhanced outer voltage (blue line) and enhanced Jahns’ method (black line). Experimental results.  
**Author Contributions:** Conceptualization and methodology, G.F. and C.B.; experimental results G.F. and C.B.; validation, C.B. and G.F., investigation and data care, A.T.; writing--original draft preparation, C.B.; writing--review and editing and supervision, A.T. and C.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimizing Rural Highway Maintenance Scheme with Mathematical Programming
## 6 Conclusions  
This study presents a novel approach to optimizing rural highway maintenance using bi-level mathematical programming. By considering both institutional and user costs, the proposed model aims to minimize the total costs while ensuring minimal disruption to the traffic flow. The transformation of the bi-level model into a single-level MILP problem facilitates practical implementation using commercial solvers, making the model accessible for real-world applications.  
The numerical examples demonstrate the effectiveness of the proposed approach in various network scenarios, providing valuable insights into the development of optimal maintenance strategies. The sensitivity analysis further highlights the importance ofconsidering travel demand fluctuations and the scalability of the model for different network sizes.  
While the study presents valuable findings, further research is needed to explore the potential of non-synchronous maintenance schedules, dynamic traffic demands, and the integration of environmental and multi-modal transportation considerations. Overall, this study contributes to the field of rural highway maintenance by providing a comprehensive and practical framework for the optimization of maintenance strategies while minimizing the total costs.  
Conceptualization, F.S. and H.L.; methodology, F.S.; software, D.C.; formal analysis, D.C.; investigation, H.L.; resources, Z.W.; data curation, Z.W.; writing--original draft preparation, F.S.; writing--review and editing, M.J.; visualization, M.J.; supervision, H.L.; project administration, H.L.; funding acquisition, F.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Henan Provincial Transportation Technology Plan Project, grant number 2022-5-6.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Henan Transportation Development Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results and Discussion
The highest values of uncertainty given by the factors and their interactions on the measurement of Geometrical Product Specifications are indicated in bold in Table 5. From the results appearing in this table, it can be concluded that the standard uncertainties of the machine associated with the factors Stylus diameter, the Step width, and the Speed do influence some of the variables being evaluated. For example, the highest value of the standard uncertainty is associated with the Stylus diameter in the evaluation of angularity and diameter. This means that any change related to this factor, i.e., a change related to its random levels, would influence the uncertainty in a value that reaches the up to 8.73 \(\upmu\)m and 6.63 \(\upmu\)m, respectively. Similarly, the effect of the Step width on the angularity evaluation is represented by an uncertainty value of 2.38 \(\upmu\)m. If there is a limit or tolerance set for the angularity higher than 3 \(\upmu\)m, it would not be necessary to deal with the Step width set up; however, in this particular case, it is also important to take into consideration that the same Stylus diameter would be also bringing higher uncertainty for the analyzed variable. In the case of the Speed factor, it has the biggest influence on the measurement and evaluation of a form parameter (roundness). The other evaluated variables were not affected by this factor. This finding is especially significant and could lead to higher productivity in terms of the dimensions' evaluation because it allows using higher values of scanning speed than the ones in the Cookbook suggested for the measurement and evaluation of features. Figure 10: Interaction plot for parallelism between Stylus diameter and Speed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Parallelism** & **Angularity** & **Roundness** & **Diameter** & **Distance** \\ \hline Stylus diameter & 1.82 & **8.73** & 0.00 & **6.63** & 0.00 \\ Step width & **1.92** & **2.38** & 0.00 & 0.00 & 0.00 \\ Speed & 0.77 & 0.00 & **4.66** & 0.00 & 0.00 \\ Stylus diameter*Step width & 0.00 & 1.80 & 2.61 & 0.03 & 1.51 \\ Stylus diameter*Speed & 0.34 & 3.44 & 2.41 & 0.00 & **2.58** \\ Step width*Speed & 0.00 & 3.45 & 3.65 & 2.73 & 1.35 \\ Stylus diameter*Step width*Speed & 0.06 & 1.12 & 0.74 & 0.47 & 1.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Coordinate measuring machine (CMM) measurement uncertainty analysis (\(\upmu\)m). It is also important to notice that the lowest uncertainty values (italicized in the table) are those related to maximal accuracy.
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 3 Algorithm Principle Explanation  
### Explanation of Basic Models: CNN, LSTM, and Attention  
We developed a CNN-LSTM-Attention-FVIM multi-input single-output regression prediction network model that integrates CNN, LSTM, Attention, and FVIM mechanisms. The model converts the original input data into a form suitable for CNN processing. The spatial features in the data are extracted through the convolution layer. The size of the feature value is reduced through the pooling layer, and the important features are further extracted. The features extracted by the CNN are input into the LSTM network through the LSTM layer to capture the long-term dependencies in the time series data. Then, the attention mechanism is applied to the output of the LSTM through the Attention layer, the attention weight of each time step is calculated, and the weighted sum is calculated. By adjusting the key parameters such as the learning rate, hidden layer nodes, and regularization coefficient in the four-vector optimization algorithm, the model achieves the best prediction accuracy for this dataset. The main working mechanisms of the three basic models of CNN, LSTM, and Attention are as follows.
## 3 Design of Isl Network for IoT
### Frequency and Bandwidth  
For ground-to-satellite IoT/M2M communication, the ground devices normally require small data rates (\(\sim\)kbps), low power consumption for battery life up to several years, compact antenna design, and coarse to even omni-directional antenna pointing. These requirements narrow down the considerable frequency bands for Earth-to-satellite to VHF (0.03-0.3 GHz), UHF (0.3-1 GHz), L band (1-2 GHz), and S band (2-4 GHz).  
For the ISL to function as an IoT backbone, the bandwidth depends, besides the required data throughput, also on TX frequency, signal propagation, network topology, onboard resources (e.g., power, size, and pointing), number of simultaneous links, and suitable antennas. Table 5 lists the frequencies currently allocated for ISL and space-to-space communication [16]. While a wide range of frequencies, from VHF/UHF to Ka bands, are usable, most pico- and nano-satellite missions in the past have used the UHF band, due to its omni-directional pattern, robustness, and low power consumption. However, to meet the requirements for an IoT backbone (\(\sim\)Mbps rate and \(\sim\)1000 km range), the frequency selection will go towards the S band, or even the Xband. The S band offers a good trade-off between bandwidth, directionality (\(\approx\)10\({}^{\circ}\)), antenna (e.g., patch antenna on S/C), and transceiver size and power consumption.
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 4 Description of Experimental _I-V_ Curves Database
It is worth noting that only a part of the available NREL database is necessary for the ANN training phases; thus, the proposed approach can be applied in practical applications where enough _I-V_ curves are available for different irradiance and temperature conditions. More details on the used _I-V_ curve are given in the sections discussing the simulation and experimental cases. During the normal operation, only a few values of voltage and current (\(V_{pv},I_{pv}\)) around the MPP, irradiance (\(G\)), and PV panel temperature (\(T\)) measurements are used as the inputs to the neural network. They are chosen since they are already measured on photovoltaic installations. Therefore, it is possible to take advantage of such information for online monitoring of the PV source's state of health by means of the proposed ANN architecture.
# IPv6 Convergence for IoT Cyber-Physical Systems
## 3 IP-Convergence Model
### LoRaWAN  
For the purposes of our research and as a representative candidate of a technically challenging technology that does not natively support IP networking, we focus on LoRaWAN. LoRaWAN is a popular low-power wide-area wireless networking technology using a protocol defined by  
Figure 6: Compressed IPv6 header.  
the LoRa Alliance and formalised in the LoRaWAN specification [3; 17]. It uses a chirp-spread or frequency shift keying (FSK) modulation scheme and is primarily targeted to the industrial, scientific and medical (ISM) radio bands. It is designed to support medium-to-long-range communications between energy-constrained wireless nodes and gateways, which operate in conjunction with backend network and application servers. LoRaWAN has three modes of operation; Modes A, B and C. In Mode A, endpoint devices initiate upstream messages and may open receive windows one and two seconds following transmission for receipt of downstream acknowledgements and/or additional MAC instructions, for example, for adaptive data-rate control. In Mode B, endpoints may receive beacons from gateways in addition to the Mode A functionality. In Mode C operation, endpoint devices may continuously receive frames when not transmitting messages.  
In our experimentation, we focused on Mode A operation, but our proposed approach can easily be adapted for use in other modes. In fact, we argue that achieving IP networking in LoRaWAN Mode A is the more technically challenging option as the endpoint must initiate messaging first and can only receive downstream messages during one or both of the subsequent receive windows following transmission.
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 3 Transfer Learning
### Artificial Neural Network Models
#### 3.2.3 EfficientNet  
The EfficientNet architecture was created through a focus on prioritizing efficiency while maintaining state-of-the-art accuracy. Traditionally, convolutional neural networks are scaled up from a baseline model to improve the accuracy of detections/classifications; more training data and model layers generally produce more accurate predictions. EfficientNet uses compound scaling of the network's dimensions (width, depth, and resolution) to achieve high accuracy while striving to be the most efficient CNN [32].  
The EfficientDet network is an extension of the EfficientNet architecture that was created specifically for object detection applications; it uses the EfficientNet model architecture as a base network. This variant of EfficientNet focuses on compound scaling paired with a weighted bi-directional feature pyramid network (BiFPN) to connect subsequent layers of the model together for the most successful optimizations of model efficiency and accuracy [27].
## 3 Methods
### Cross-Calibration of NDVI and LST Products Derived from MODIS and Sentinel-2 Data  
The temporal homogeneity of NDVI- and LST-based indices is crucial to derive reliable anomalies that could serve as crop yield predictors. Therefore, the MODIS NDVI and LST time series were cross-calibrated with analogous Sentinel-3 products to form homogenous data records. The calibration method is built on the automatic selection of the optimal machine learning method amongst: Random Forest (RF), K-nearest Neighbor (kNN), Support Vector Machine (SVM), and Neural Network (NN), which yielded the largest modeling efficiency (EF) (formula given in Section 3.6) between re-calibrated MODIS time series and corresponding original Sentinel-3 time series. The training and validation of machine learning techniques were performed for the period 2018-2020 when both MODIS and Sentinel-3 satellites were operational. The validation followed the leave-one-year-out approach to choose the most accurate machine learning method, which has occurred to vary for different data sets. To model the differences between MODIS and Sentinel-3 indices, three explanatory variables were used. The first one was calendar time expressed as a day of the year. The second explanatory variable was thermal time expressed by the growing degree days (see Section 3.4 for details) indicating the amount of thermal energy accumulated at a given time and the amount of energy needed to reach a given stage of crop development. The third explanatory variable was the MODIS product (i.e., NDVI or LST) to be homogenized with the Sentinel-3 counterpart. Ultimately, the trained calibration models were applied to the MODIS NDVI and LST time series between the years 2000 and 2017 to extend the Sentinel-3 time series.