esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
In the static case, one might assume that a secondary incident has arisen if it occurs within 15 min of the primary event and within 1 mile upstream [28]. This rule might be applied even though the primary incident impact area is longer. That is, incidents occurring more than a mile upstream are not classified as secondary. Dynamic thresholds overcome some of these limitations [23; 24; 29; 30]. [PERSON] and [PERSON] [29] created a \"simulation-based secondary incident filtering\" method (SPSIF). They implemented a regression model to identify the boundaries of the primary incident impact area. All subsequent incidents within that area were classified as secondary. [PERSON] and [PERSON] [31] used queuing analysis to study single-pair events (one primary and one secondary incident) and large-scale events (one primary and many secondary incidents). Their objective was to determine the \"back of queue\" location. [PERSON] et al. [32] proposed the use of historical virtual sensor measurements to identify secondary crashes. They used a Representative Speed Contour Map (RSCM) with percentile speed of historical incident-free virtual sensor speed measurements of each spatiotemporal cell. Moreover, [PERSON] [13] used private-sector speed data provided from INRIX TMC data for the first analysis of secondary crash occurrence to integrate incident timelines. However, the study still needs to identify the precise primary incidents. The work presented here differs from previous work in four important ways. 1. Each crash is linked with an incident timeline, which is a necessary precursor to investigating the relationship between reliability and safety. Previous studies have conducted a statistical test with traffic volume and accident frequency. 2. A novel methodology is proposed to classify crashes in recurrent congestion as well as crashes in non-recurrent congestion. 3. The \"recurrent\" bottleneck identification approach is used to identify recurrent bottleneck locations and their impact areas. 4. The methodology does not require any information on primary incidents (crashes and non-crash incidents) to identify crashes in non-recurrent congestion.
# Computational Analysis of Parallel Techniques for Nonlinear Biomedical Engineering Problems
## 3 Computational Efficiency and Numerical Results
```
1:Polynomial coefficients \(p\), initial root guesses roots_guesses, tolerance tol, maximum iterations \(max\_iter\)
2:Computed roots, residual errors, convergence percentages, and resource usage metrics
3:Initialize:
4:\(n\leftarrow\) degree of \(p\)
5:\(iter\gets 0\)
6:Start CPU timer and record total available memory
7:Initialize arrays: residual errors, CPU time, memory usage, convergence percentage
8:while\(iter<max\_iter\)do
9:\(iter\gets iter+1\)
10: Compute \(poly\_val\gets p(roots\_guesses)\) and \(poly\_der\gets p^{\prime}(roots\_guesses)\)
11:for\(i=1\) to \(n\)do
12: Compute Newton correction: \[\delta\leftarrow\frac{poly\_val[i]}{poly\_der[i]}\]
13: Compute Parallel correction: \[sum\_inv\_diff\leftarrow\sum_{j\
eq i}\frac{1}{roots\_guesses[i]-roots\_guesses[j]}\]
14: Update intermediate root: \[y[i]\leftarrowroots\_guesses[i]-\frac{1}{1/\delta-sum\_inv\_diff}\]
15: Compute final correction terms \(m_{1}\) and \(m_{2}\): \[m_{1},m_{2}\leftarrow\) using product of differences for corrections
16: Update root: \[roots\_guesses[i]\gets y[i]-\frac{m_{1}\cdot m_{2}}{1/\delta-sum\_inv\_diff}\]
17:endfor
18: Compute residual error: \[err\leftarrow|p(roots\_guesses)|\]
19: Record maximum residual error
20: Compute convergence percentage: \[converged\leftarrow\frac{\text{number of roots with }err<tol}{n}\times 100\]
21: Record CPU time and memory usage percentage
22:if\(\max(err)<tol\)then
23: Terminate loop
24:endif
25:endwhile
26:Output:
27: Display computed roots and iteration statistics
28: Plot residual errors per root, CPU time, and memory usage vs iterations
```  
**Algorithm 1** Parallel scheme for solving nonlinear equations
## 4 A Brief Discussion of the Literature Review Assessment
### Artificial Neural Networks  
Through a data-driven approach, recent research in GWL modelling using ANNs has increased accuracy and reliability in predictions. Various studies indicate that ANN methods, such as FGANN, RNN, and ELM, perform exceptionally well. Several investigations demonstrate that ANNs exhibit higher accuracy than traditional statistical models like ARIMA due to their high capability to identify complex patterns and nonlinear relationships between input and output variables. Multiple studies have shown that the type and number of input variables significantly impact the model's accuracy. For example, [18] demonstrated that combining water quality data with GWL significantly improves prediction accuracy. Similarly, [8] illustrated that using monthly evaporation and precipitation data as inputs for ANNs leads to a more accurate prediction of GWL. Different algorithms have been utilised to train ANNs, such as LM, BR, and BP [17]. These results highlight the crucial role of selecting the appropriate algorithm for training ANNs in improving prediction accuracy. Furthermore, hybrid methods, such as combining ANNs with ARIMA and optimising algorithms, have also improved prediction accuracy. For example, [13] demonstrated that learning machine methods are the best way to predict GWL, compared to more conventional ones. Incorporating geographical and hydrological data can help improve model accuracy. For instance, research by [12] demonstrated that using these data as ANN inputs improves daily GWL prediction accuracy. [21] successfully used the FFANN for hourly GWL prediction. A study by [86] also demonstrated that the BP-ANN performs better than the Integrated Time Series (ITS) in predicting GWL. Similarly, in research by [87], the RNN achieved higher accuracy in daily GWL prediction than the SVR method.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 2 Smart Mobility Development  
Smart Mobility is a general concept for the integration of different modes of transport and infrastructure. The concept of Smart Mobility has been a topic discussed in recent years, not only in scientific circles, but also at city level. It brings benefits that will enable cities to manage transport in an efficient and sustainable way.  
Smart Mobility is a new vision of traffic management. It brings expectations in relation to addressing the issue of current transport as well as transport in the near future. It is seen as a new way of thinking and perceiving how people are moving and has evolved over the years from the convergence of the digital revolution with transport [13]. With the advent of modern technologies such as fast data connectivity, reliable sensor systems or powerful mobile devices, the concept of intelligent mobility with the advent of automated and autonomous vehicles is becoming an increasingly common part of transport systems.  
The continual increase in urbanization puts pressure on the construction of additional road infrastructure. The problem of the growing number of passenger cars has so far been addressed by adding new roads to meet the requirements [13]. The construction of additional roads in cities should ease the unmanageable traffic situation. However, it does not provide a sufficient solution to the problem as this method reaches its limits due to existing urban infrastructure. Cities can increase the efficiency of traffic intensity management by setting up appropriate solutions and implementing them into current transport infrastructure.
## 5 Discussion
### About the Analysis of the Modal Damping Ratio  
#### 5.5.1 Study of the Modal Damping Ratio Values  
In contrast to what was indicated in the previous section, the values of the modal damping ratio show greater variability between some events and others, as [PERSON] suggested for other bridges [15]. Therefore, with few tests, it is difficult to specify this variable, recommending a study over time. Likewise, it is interesting to reference these values with those of other structures of similar typology.
###### Abstract  
Autonomous navigation and orbit determination are key problems of asteroid exploration missions. Inter-satellite range link is a type of measurement widely used in the orbit determination of Earth satellites, but not so widely used in missions around small bodies. In our study, we assume that highly accurate inter-satellite range data can be obtained around small bodies between the chief spacecraft and some low-cost deputies, and study the feasibility of simultaneous autonomous orbit determination of the spacecraft and the gravity-field recovery without the support from ground stations. After the feasibility analysis, two modified methods are proposed. Both methods demonstrate obvious improvements in both the convergence region and the accuracy. One remark is that the inter-satellite range data can be also used together with various observation data from ground stations to enhance the accuracy of the determined orbits and the gravity field.  
autonomous orbit determination; gravity field; asteroid; inter-satellite range data; optimization Article  
Faculty of Astronomy and Space Science, Nanjing University, Nanjing 210023, China  
1
## 5 Conclusions  
We propose a parameter updating method for an antenna FE model founded on the RBF surrogate model, which aims at the BUS deformation's difference between a large-aperture radio telescope antenna and an FE model. Under a 35\({}^{\circ}\) elevation angle, the antenna FE model is optimized and updated by combining the primary reflector surface deformation data for a 25 m aperture radio telescope antenna under gravity load. Firstly, according to the design data, the 25 m antenna original FE model is established, and the parameter sensitivity analysis is carried out. The elastic modulus parameters of different BUS bars with high sensitivity to the antenna primary reflector surface deformation are selected as the optimization variables. The relative displacement error RMS between the antenna's nodes and the corresponding nodes in the original FE model is taken as the optimization target, and the antenna FE model is optimized and updated by a genetic algorithm. Due to the complex structure of the antenna, it is very time-consuming to use the FE model for analysis and calculation. Therefore, in the modified parameter sensitivity analysis and optimization part, the RBF surrogate model is used instead of the FE model for calculation. We compare the calculation time of the FE model and the RBF surrogate model and find that the calculation efficiency is improved by about 99%, significantly saving the FE model updating process's calculation cost. The RMS's error between the antenna and the FE model is reduced by 36.8%, which preliminarily shows that the method is feasible. To further verify the method feasibility, compared with the photogrammetry data under 10\({}^{\circ}\) and 70\({}^{\circ}\) working conditions, the error of the RMS between the antenna and the updated antenna FE model primary surface is reduced by 21.6% and 8.1%, respectively, which again shows that the updating method is feasible.  
Methodology, S.W. and B.X.; investigation, S.W.; validation, P.L. and S.L.; writing--review and editing, B.X., W.W. and J.Z.; writing--original draft, S.W.; data processing, Y.Z. and H.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No.12363011, 52275270, 52275269), the Tianchi Talents Program of Xinjiang, the Natural Science Foundation of Xinjiang Uygur Autonomous Region, the National Key Basic Research Program of China (No. 2021 YFC2203501), and the Xinjiang Postdoctoral Foundation. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest.
# A Systematic Review and Future Development of Automotive Classis Control Technology
## 3 Automotive Braking by Wire System
### Key Technologies  
(1) Optimal distribution strategy of braking force  
The purpose of braking force distribution is to calculate the braking forces of four wheel brakes based on the vehicle load, actual road conditions, and operating conditions at the moment when the driver presses the brake pedal, in order to ensure the safety and stability of the vehicle while obtaining the shortest stopping distance [46; 47].  
The relationship between the pedal displacement, pedal force, and brake pressure in the traditional hydraulic brake system can be inferred from the relationship between the pedal force and brake deceleration in the EMB system, and a relationship curve can be established.  
Due to factors such as tire and road adhesion coefficient, the maximum ground braking force of the wheels should not exceed the adhesion force [48]. When the local braking force exceeds the adhesion force, the wheels may experience lock-up and slip, and there may be phenomena such as front wheel lock-up or rear wheel lock-up.  
When a car brakes, the tires not only roll but also slide, and the magnitude of the slip rate is related to the coefficient of adhesion to a certain extent; In an ideal state, there is a linear relationship between the braking force of the front and rear brakes when a car brakes, but there may be some deviation in actual situations.  
(2) System safety and fault tolerance  
The application of EMB systems has always been greatly affected by safety and reliability, as the failure of any electronic signal may bring catastrophic consequences. Therefore, system safety and fault tolerance technology are particularly important, including software redundancy and hardware redundancy [49; 50].  
Considering the real-time performance and cost of the EMB system, the dynamic redundancy of hot backup is the best choice. Double redundancy design of the brake pedal module, ECU module, communication network, and power module can effectively improve the reliability of the system [51].  
The EMB system brake pedal module adopts a dual redundant structure, with two identical machines working simultaneously. The work results are compared, and output is based on the predetermined self-diagnosis strategy and fault judgment strategy.
## 5 LPWAN Security Analysis
### Confidentiality

Table 1: Some prominent low power wide area network (LPWAN) technologies and their technical features [15; 32; 37; 47; 54]. their conservation. A typical example of the MITM attack is the popular eavesdropping attack. Eavesdropping attack refers to a situation whereby intruder passively listen to the network communication over compromised communication links to capture or gain access to private information ranging from access codes to passwords. On the other hand, compromised key attack refers to a situation whereby attackers use stolen keys to gain admission into a network. The stolen accessed key is referred to as \"compromised key\". Compromised key allows intruders to decrypt, modify or alter data that are being sent, and to access communications [58]. With regards to LPWAN confidentiality menaces, various studies in the literature have discussed the confidentiality challenges, effects and most significantly, proposed varieties of solutions. Describing the issue of key management in LoRaWAN, the authors in [59] explained that ideally, the network server is responsible for creating session keys (i.e., network key and the application key) in LoRaWAN v 1.0, and the authors identified unauthorized access into the network server as a serious threat. The authors proposed a public key infrastructure (PKI) scheme as a trusted third-party solution to the key crisis problem. Despite the success achieved from the proposed solution, the authors acknowledged that the procedure can be computationally inept, complex, and time-consuming as the communication overhead is increased due to the newly-added third party. Similarly, the authors in [60] proposed a LoRaWAN architecture enhancement solution using several AES-128 encryption keys at the network layer and application layer for data authentication and privacy. The authors deployed a reputation system that utilizes proxy nodes to reduce the complex computing in the constrained node side thereby providing interoperability and adequate security of message exchanges between end nodes. In another compromised key attack security study involving LoRaWAN, the authors in [61] proposed an Ephemeral Diffie-Hellman Over COSE (EDHOC) based lightweight and authentication approach that uses a cryptographic material for updating LoRaWAN session keys at the application layer. The authors in [21] also proposed a dual key-based activation scheme for mitigating LoRaWAN key update security problems. The approach involves utilizing network key and application key in performing initial join procedure and the second join procedure for a real-world testbed. For additional security, the author proposed the generation of each session key independently at each layer and there are no additional third-party entities involved. For the strengthening of session key derivation, the authors in [62] proposed a Rabbit stream cipher-based Key Derivation Function (KDF) scheme for updating the root key. The two-step KDF offers high computing efficacy and it provides suitable randomness of generated keys.
## 1 Introduction
Moreover, the key wavelengths of classification of wheat classes were identified by a stepwise discriminant analysis [12]. [PERSON] et al. (2015) used a hyperspectral imaging system at the spectral range of 400-1000 nm to identify 4 varieties of waxy corn with consideration of the placement of germ. Spectral information, texture features were extracted from each corn. PLS-DA and SVM models were built using full spectra and image features of selected important wavelengths. The results indicated that hyperspectral imaging can be applied to differentiate maize seed varieties and test seed purity effectively [13]. Deep learning, a very hot topic in artificial intelligence (AI), has gained attentions from different fields. Convolutional neural networks (CNNs) are one of the most popular deep learning models. A typical CNN includes convolutional layers for feature extraction, pooling layers for feature compression and fully-connected layers for classification. Multiple convolutional layers and pooling layers can be chained to learn features of increasing levels of abstraction. CNNs have achieved state-of-the-art performance in various tasks, especially in computer vision. In the field of hyperspectral image analysis, CNNs have been employed for classification of two-dimensional image and three-dimensional hyperspectral remote sensing data. Since CNNs are generally used for two-dimensional and three-dimensional data classification, trails using CNNs to handle one-dimensional spectral data are challenging. Researchers have tried to develop CNNs for spectral data analysis. [PERSON] et al. (2017) proposed a CNN architecture for vibrational spectroscopic data analysis [14]. [PERSON] et al. (2017) proposed a CNN architecture for Raman spectra recognition [15]. One drawback of CNN is the number of training samples needed. For non-image spectroscopy, collecting large number of spectra is time-consuming. A hyperspectral image of multiple objects can be obtained in one scan. After image segmentation, spectra of each object can be extracted. This enables fast collection of samples for CNN training. However, the sample collection procedure is still not free. A study on the influence of training samples on CNNs would help in the trade-off between model performance and sample collection cost. The objective of this study was to identify rice seed varieties using hyperspectral imaging combined with convolutional neural network. The specific objectives were to: (1) develop deep learning models using full spectra to identify seed variety at two spectral ranges; (2) to compare the results of deep learning methods with SVM and KNN; (3) to evaluate the influence of the number of training samples on KNN, SVM and CNN.
# Effect of Harmonics on Ferroresonance in Low Voltage Power Factor Correction System--A Case Study
###### Abstract  
This paper presents a case study of three-phase ferroresonance in a low-voltage power factor correction system and investigates the influence of harmonic distortion on the occurrence of ferroresonance. Ferroresonance is an extremely dangerous and rare phenomenon that causes overvoltages and overcurrents in the system and degrades the power quality. The study is carried out on real field measurements in an industrial plant where ferroresonance occurs in the power factor correction (PFC) system at the detuned reactor. The three-phase ferroresonance analysed in this paper is an extremely rare phenomenon that has never been reported in this type of configuration. The measurement results have shown that in this type of configuration the high harmonic distortion is a necessary condition for ferroresonance to occur. In such conditions, switching on the capacitor stage triggers the ferroresonance with quasi-periodic oscillations supported by the medium voltage grid. The main contribution is the analysis of the three-phase ferroresonance in the detuned PFC system and the influence of the harmonics on the occurrence of the ferroresonance in such a case. The possible solutions to this problem and recommendations to avoid this phenomenon are discussed.  
ferroresonance; harmonic distortion; power quality; power factor correction; detuned reactor +
Footnote †: journal: Applied Physics Letters  
article
###### Abstract  
The thermal performance and environmental impact of agricultural greenhouses (GH) connected to earth-to-air heat exchanger (EAHE) systems depend on the ambient temperature, soil temperature, EAHE system, and greenhouse specifications. The impact of an EAHE system on the temperature and humidity of a GH microclimate, as well as its effects on CO\({}_{2}\) emissions and heating energy consumption, are determined experimentally. Two scaled-down models of agricultural GHs (2 \(\times\) 1.4 \(\times\) 1.4 m\({}^{3}\)) were developed. Each GH was equipped with a heater. A spiral EAHE system was integrated into only one of the GHs. The temperature differences in the microclimate range from 3.5 \({}^{\circ}\)C to 7.5 \({}^{\circ}\)C, with the microclimates of GH + EAHE and GH being quite similar. In summary, the EAHE system helped to reduce the hourly energy consumption of the heating system by more than 40%. It also reduced emissions to the environment by more than 100 g (CO\({}_{2}\))/hour. The EAHE coefficient of performance (COP) for the cooling mode has a higher average value than that for the heating mode. The closed-loop performed better in cooling mode, while the open-loop performed better in heating mode. When the difference between the set temperature in the heater and the air outlet temperature of the EAHE system is smaller, the heater performs better in reducing energy consumption and CO\({}_{2}\) emissions of the heater. The COP\({}_{\rm heating}\) range is between 0 and 3.4 and the COP\({}_{\rm cooling}\) range is between 0.5 and 7.3. The energy consumption ranges between 0 and 1.41 kWh and the CO\({}_{2}\) emissions are between 0 and 359.55 g. Thus, using EAHE in agricultural greenhouses improves thermal performance and reduces environmental impact, providing an overall benefit in terms of energy consumption and environmental sustainability.  
greenhouse; earth-to-air heat exchanger; CO\({}_{2}\) emissions; energy consumption; environmental impact; sustainability +
Footnote †: journal: Applied Sciences