esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 3 Computation and Comparison  
In order to verify the feasibility and effectiveness of the proposed GNSS parallel ultra-rapid orbit determination approach, multiple sets of experiments are carried out. The precision loss of the parallel POD, the improvement of computational efficiency, and the orbit accuracy after using the parallel approach are analyzed in detail.  
Two sets of tracking stations from IGS/MGEX networks around the world are selected to analyze the accuracy and efficiency of multi-GNSS parallel ultra-rapid POD. The networks are shown in Figure 2. Relevant data from day of year (DOY) 182 to 189, 2018 are selected for the processing. The approach is implemented by Fortran and intel MPI/OpenMP parallel library. The experiments are carried out on a Linux computer cluster.  
Figure 1: GNSS parallel precision orbit determination flowchart.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 2 Theoretical Foundation
### Prediction Model
#### 2.2.1 Autoregressive Moving Average Model  
The ARMA model is designed for the variables for which past observations imply a linear function with the future value. ARMA is first used to predict the IMF series. Because the response of IMF \(Y_{lt}\) at \(t\) is related to the response of its previous time \(Y_{t-1}\), \(Y_{t-2}\), \(\ldots\), \(Y_{t-p}\), it is also affected by \(q\) random errors, namely \(\epsilon_{t-1},\epsilon_{t-2},\ldots,\epsilon_{t-q}\). For the ARMA\((p,q)\) model,  
\[\hat{Y}_{lt}=\beta_{1}Y_{t-1}+\beta_{2}Y_{t-2}+\ldots+\beta_{p}Y_{t-p}+ \epsilon_{t}-\theta_{1}\epsilon_{t-1}-\theta_{2}\epsilon_{t-2}+\ldots+ \theta_{q}\epsilon_{t-q}, \tag{5}\]  
provides high accuracy prediction, where \(\beta_{p}(p=1,2,\ldots)\) and \(\theta_{q}(q=1,2,\ldots)\) are the auto-regression (AR) and moving average (MA) coefficients, respectively. \(p\), \(q\) are the orders of the ARMA model, which are identified based on [PERSON]'s information criterion and Bayesian information criterion in this paper. When \(q=0\), the model is reduced to an AR \((p)\) model. If \(p=0\), the model is reduced to a MA \((q)\) model. Note that \(E(\epsilon_{t})=0\), \(Var(\epsilon_{t})=\sigma_{\epsilon}^{2}\).
# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
###### Abstract
In summary, the core considerations in designing a networking protocol for large-scale mobile UV communication networks lie in the following: overcoming the impact of low communication rates and multiple relaying on throughput in UV communication; reasonably allocating channel resources to improve resource utilization; and mitigating the adverse effects of mobile scenarios on network performance. We have designed a networking protocol applicable to large-scale mobile UV communication networks (LSM-UVCN). The main features of this protocol are as follows:  
1. In view of the bandwidth limitation of UV communication, a novel neighbor discovery algorithm is designed based on a lossless competition mechanism. The proposed neighbor discovery algorithm can generate a network connection matrix with extremely low control overhead, which covers the connection relationships of all nodes in the network. 2. A channel access mechanism of \"competition-first-then-transmission\" is designed. Based on the network connection matrix, the protocol establishes multiple transmission paths synchronously through multi-round competition, and prevents interference between adjacent transmission paths by setting interference isolation nodes, so as to realize spatial division multiplexing and further alleviate the problem of low throughput in ultraviolet communication networks. 3. To address the issue of random changes in network topology in mobile networks, the proposed protocol, based on a neighbor discovery algorithm with low control overhead, can effectively reduce the adverse impact of frequent network topology changes on network performance by periodically updating the connection matrix. The subsequent chapters of this article are organized as follows: In Section 2, the proposed neighbor discovery algorithm, networking protocol, and mobility model are introduced in detail. Section 3 conducts an analysis of the key parameters influencing network performance and derives the calculation formulas for throughput and data loss rate. In Section 4, the Monte Carlo method is employed to perform simulation analyses of network performance under different parameter settings and motion conditions. Finally,Section 5 summarizes the entire article and discusses the directions for future research and development.
## 4 Conclusions  
We studied a method for measuring the dark current value of the LWIR QWIP by measuring the focal-plane device temperature, and a dark current noise correction method was also introduced. First, the response model of the LWIR QWIP detection system was introduced, and the dark current model was analyzed in conjunction with the theory. Then, the composition of this detection system was introduced, including the design details of the method closely integrated in the paper. Then, dark current measurement experiments and dark current noise correction experiments were described. Finally, the effect of dark current noise correction was examined, and the total time noise was reduced by 57.69% after correction with this method, which is significant. This method is only applicable to LWIR QWIP detection systems. Because the dark currents of various types of detectors are generated by different mechanisms and depend on different degrees of cooling. The extension of this method to other bands and types of detectors is subject to further validation and improvement. This study included a rigorous theoretical analysis, closely integrated with engineering applications, which will be beneficial to the development of LWIR QWIP remote sensing detection technology.  
Conceptualization, H.D. and Z.G.; methodology, H.D.; software, D.L. and Y.Z.; validation, H.D., Y.W. and Z.G.; formal analysis, H.D.; investigation, H.D.; resources, Y.Z. and J.X.; data curation, Z.G. and H.D.; writing--original draft preparation, H.D.; writing--review and editing, D.S. and J.X.; visualization, D.L.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Program of National Natural Science Foundation of China, grant number 42192582, and the National Key Research and Development Program of China, grant number 2016 YFB0500400.  
Not applicable.  
The authors declare no conflict of interest.
# MoCoformer: Quantifying Temporal Irregularities in Solar Wind for Long-Term Sequence Prediction
## 4 Results  
### Datasets  
The dataset utilized in this study was provided by the Space Environment Center of the National Aeronautics and Space Administration (NASA) and comprise a multitude of parameters pertaining to the solar wind and the Earth's magnetic field. Data on solar wind from the omni dataset were chosen, and correlation analyses were carried out between the IMF and Field Magnitude data for the time frame spanning from 1 January 2006 to 31 December 2015. The omni-Field-Magnitude dataset is more concerned with changes in the Earth's magnetic field, which is important for geophysical research and navigation. In contrast, the omni-IMF dataset is more closely related to solar activity and can be used to predict the effects of solar activity on the Earth, such as geomagnetic storms.  
The elements included in the omni-Field-Magnitude dataset are DST Index, Bulk flow longitude, Flow Pressure, MAC, Proton density, IMF, and Proton temperature. A visual presentation of the dataset is illustrated in Figure 4. By analyzing the variations in mean magnetic field strength, scientists can gain insight into the behavior of the solar wind's magnetic field, including the occurrence of magnetic storms, the presence of magnetic structures, and the effect of solar activity on Earth's magnetosphere. The features exhibit significant fluctuations and extreme instability, including significant temporal irregularities.  
Figure 4: Visualization view of omni-Field-Magnitude dataset (**left**) and omni-IMF dataset (**right**). To make the images more harmonious, the Proton temperature is scaled down.  
The elements included in the omni-IMF dataset are Plasma beta, Field Magnitude, MAC, Proton density, Alfven mach number, Kp*10, and Proton temperature. A visual presentation of the dataset is illustrated in Figure 4. The omni-IMF dataset represents a valuable resource for the study of the behavior and properties of the solar wind, with a particular focus on the interplanetary magnetic field. The dataset provides an opportunity to explore the complex relationship between the solar wind and Earth's magnetic environment, contributing to the advancement of understanding of space weather and its impact on Earth. It can be observed that these features exhibit significant volatility, and their time series show clear instability and strong temporal irregularities.  
To more effectively illustrate the irregularity of the solar wind sequence, it is compared to common time series, as illustrated in Figure 5. The Electricity dataset contains hourly electricity consumption data from 321 customers from 2012 to 2014. The Exchange_rate dataset collects daily exchange rate data from 1990 to 2016 for eight countries. The ETT dataset contains load characteristic data for seven types of oil and power transformers from July 2016 to July 2018. A comparison of these datasets reveals that the solar wind sequence exhibits a higher temporal irregularity than other common sequences.
## 2 Results and Discussion  
### Optimization of SPME Conditions  
Unlike exhaustive extraction through hydro-distillation, the volatile profiles of extracts obtained using SPME deeply depend on fiber type, sampling temperature, and sampling time [10]. In order to evaluate the feasibility of SPME conditions, volatile profiles of a CP sample (S5) were obtained through SPME, and compared with those that flowed from hydro-distillation. The hydro-distillation process was conducted in accordance with the procedure described in the Chinese Pharmacopoeia [15]. The volatile profile of S5 extracted through hydro-distillation is shown in Figure 1a. Monoterpenes were found to be the predominant compounds, including D-limonene, \(\gamma\)-terpinene, \(\alpha\)-pinene, \(\beta\)-pinene, etc. The volatile profiles extracted through HS-SPME were basically consistent with that of the hydro-distillation method. Furthermore, when the sampling temperature was 50 \({}^{\circ}\)C, the components with higher boiling points received a relatively high response (shown in Figure 1b,c). As extraction time increased, the percentage of monoterpenes (components with lower boiling points) decreased, and the proportion of components with higher boiling points increased (shown in Figure 1d). The percentage peak areas of the two main monoterpenes (D-limonene and \(\gamma\)-terpinene) and the two sesquiterpenes (D-germacrene and \(\alpha\)-farnesene), obtained using various extraction methods and parameters, are presented in Table 1. As shown in previous studies [16; 17], SPME can be used for quantitation in a non-equilibrium state. In order to obtain more comprehensive information about the volatile compounds of CRP, SPME conditions were selected so as to obtain a relatively high response for components with higher boiling points, which were proportionally low in CRP. The reproducibility of the method was evaluated through the determination of five replicates of a specified sample. The relative standard deviation (RSD) was found to oscillate between 6% and 14% (generally close to or lower than 10%). Acquired results showed that the conditions for extraction and GC-MS analysis were robust and reliable.
# Fault Location and Restoration of Microgrids via Particle Swarm Optimization
## 1 Introduction
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, which is usually inspired by nature such as biological systems. SI has been used for a wide range of engineering applications, including power systems, for its simple rules and ability to solve any type of problem. [PERSON] et al. [21] have discussed the advantages and disadvantages of the genetic algorithm (GA), artificial bee colony (ABC), etc., for fault location estimation. [PERSON] et al. [22] proposed a combined biogeography-based optimization with a population competition algorithm method to plan a substation location. [PERSON] et al. [23] used the coyote optimization algorithm to estimate the parameters of transformers. [PERSON] et al. [24] used the evolutionary algorithms to optimize the deployment of electric vehicle-charging stations. MGs provide feasible and reliable power restoration after an upstream system outage, such as transmission line, main transformer, and feeder failures. In this paper, the network connection matrices are used to quickly form the system topology before and after the occurrence of a fault, and the voltage error matrix is computed to search for the fault section by using PSO instead of brute force. After the allocation of the fault section, the multi-objective function is also implemented by PSO for optimal restoration with its constraints. Consequently, an integrated fault location and restoration approach for MGs is proposed to effectively solve the FDIR problem in MGs. This paper is divided into six sections. Section 1 introduces the background and objective of the study. Section 2 describes the flow chart of the proposed approach. Section 3 explains the fault location algorithm and solution procedure. Section 4 presents the power restoration algorithm and solution procedure. Section 5 discusses the simulation results. Section 6 presents the conclusion.
## 3 Results and Discussions
### The Cascade Calculation of the Single Material Type
In order to further verify the correctness of the slice analysis method, it is extended to the visible and near infrared bands. The adopted structure is shown in Figure 1a-c, \(P_{x}=P_{y}=0.4\) um, \(R=0.3\) um. Others remain unchanged. Here, as shown in Figure 4, cascading calculations are performed on a single substrate. In common with in the mid infrared band, the results of direct simulation and cascade calculation are in good agreement. Figure 2: Cascade response when only transparent substrate is contained on 60–100 THz band, wherein the black curve is the case with a thickness of 0.1 um, that is, the cascade benchmark. (**a**) Al\({}_{2}\)O\({}_{3}\); (**b**) SiO\({}_{2}\); (**c**) Si. In view of the single metal cascade in the mid infrared band, only gold is used as the research object here. Unlike transparent substrates, there are many free electrons in metals. When the response band is small (i.e., the frequency is higher), a small part of free electrons will participate in the coupling with photons, especially in the visible light band. It is worth noting that when the metal thickness gradually increases, the coupling strength will gradually weaken. Therefore, as shown in Figure 5, with the increase of the number of cascaded layers, the results of direct simulation and the results of cascade calculation are more consistent. Figure 4: Cascade response when only transparent substrate is contained on 120–750 THz band. The black curve is the case with a thickness of 0.1 um, that is, the cascade benchmark. (a) Al\({}_{2}\)O\({}_{3}\); (b) SiO\({}_{2}\); (c) Si. Figure 3: Cascade response when only metallic film is contained on 60–100 THz band, wherein the black curve is the case with a thickness of 0.1 um, that is, the cascade benchmark. (a) Au; (b) Ag; (c) Al.
# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
###### Abstract  
Uneven roads pose significant challenges to vehicle stability, passenger comfort, and safety, especially in snowy and mountainous regions. These problems are often complex and challenging to resolve with traditional detection and stabilization methods. This paper presents a dual-method approach to improving vehicle stability by identifying road irregularities and dynamically adjusting the balance. The proposed solution combines YOLOv8 for real-time road anomaly detection with a GY-521 sensor to track the speed of servo motors, facilitating immediate stabilization. YOLOv8 achieves a peak precision of 0.99 at a confidence threshold of 1.0 rate in surface recognition, surpassing conventional sensor-based detection. The vehicle design is divided into two sections: an upper passenger seating area and a lower section that contains the engine and wheels. The GY-521 sensor is strategically placed to monitor road conditions, while the servomotor stabilizes the upper section, ensuring passenger comfort and reducing the risk of accidents. This setup maintains stability even on uneven terrain. Furthermore, the proposed solution significantly reduces collision risk, vehicle wear, and maintenance costs while improving operational efficiency. Its compatibility with various vehicles and capabilities makes it an excellent candidate for enhancing road safety and driving experience in challenging environments. In addition, this work marks a crucial step towards a safer, more sustainable, and more comfortable transportation system.  
unicent road detection; GY-521 sensor; YOLOv8; vehicle stability; IoT solutions; passenger comfort; accident prevention
## Materials
### Data Collection
#### 2.2.1 Satellite Imagery Collection  
Three datasets are exploited for the assessment of the LCZ mapping performance. These consist of Landsat 8, Sentinel-2, and RapidEye satellite imagery available for Milan for the Year 2016.  
* Landsat 8: Landsat 8 satellite collects images of the Earth every 16 days in 11 different spectral bands, through two main sensors, an optical sensor (the Operational Land Imager, OLI), and a thermal one (the Thermal Infrared Sensor, TIRS). With the aim of generating LCZ maps exclusively from optical data, thermal, panchromatic, aerosol, and cirrus bands were not considered in this study. Hence, only six bands were considered, which have a spatial resolution of 30 m. Landsat 8 data is freely available for download from GloVis, EarthExplorer, and also LandsatLook Viewer. In this study, data was downloaded through the EarthExplorer platform [31].
* Sentinel-2: The Sentinel-2 satellites acquire, approximately every 5 days, multispectral imagery with 13 bands, but only 10 bands are useful for classification purposes with a spatial resolution of 10 m and 20 m. Sentinel-2 imagery is distributed under a fully open license through the Copernicus Open Access Hub [32] interface. Data access through a dedicated Application Programming Interface (API) is also available.
* RapidEye: The RapidEye satellite constellation provides multispectral images every 5.5 days at nadir. Data are available at 5 m spatial resolution in 5 different spectral bands: Blue, green, red, red-edge, and NIR. RapidEye imagery is distributed under commercial fee by, among the others, Planet Labs Inc. through its Planet Explorer platform [33] API. Free of charge access to the RapidEye archive data API is kindly provided for research groups, with the download limit of 10,000 Km\({}^{2}\) per month.  
According to the satellite revisiting time and weather conditions, two different images were downloaded for each satellite: The first representing a non-vegetative period (from October to March) and the second representing a vegetative period (from April to September). The two periods were defined according to seasonal features. Non-vegetative includes autumn and winter seasons, whereas vegetative refers to spring and summer seasons. Table 1 summarizes the acquired images for Landsat 8, Sentinel-2, and RapidEye. Descriptions of the reference synoptic conditions at the dates of acquisition of the satellites are reported in Table 2.
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
## 4 An RB RAIM Based on a Robust MM Estimation
### Robust MM Estimation  
When using an MM estimator, the main idea is to have a high breakdown point method that is aggressive toward the elimination of observations, often eliminating more observations that necessary, making it less efficient for obtaining an initial estimate for scale and state. The scale factor is then held constant during consecutive iterations of the M estimator starting with the final robust state parameters.  
Figure 1: Flow chart of a residual-based (RB) receiver autonomous integrity monitoring (RAIM) detector based on a robust estimator.  
In this paper, the employed high breakdown point estimation is based on the LTS estimator and the considered refining M estimation is based on the Huber score function. On this basis, a fast subset selection method was designed to optimize the calculation amount of the LTS estimator to improve the practicability of the MM estimator for more available observations.  
The flow chart of the RB detector based on the proposed MM estimator is shown in Figure 2.
## 6 State of Art in Control and Automation in Distribution Networks
Fault Locator (FLOC): It employs statuses of fault locators that communicate with DMS to locate fault branches in a network and then employs terminal impedance-based method approaches to precisely localize the faulty line [98; 107]. Fault Isolation (FISO): This is a method of isolating a fault by opening circuit breakers. It isolates a small portion of the distribution network by opening switches upstream and downstream of the fault [108]. Service Restoration (SRE): Dispatches power to load nodes affected due to FISO via alternative feeders [105]. Load forecasting: This is a key component of the distribution management system that enables the system operator to assess the short-, medium-, and long-term expected load on the network, which then facilitates network planning and network infrastructure development. Short-term load forecasting is considered to have a duration of up to one day, medium-term load forecasting is considered to have a duration of up to one year, and long-term forecasting is considered to have a longer duration of up to ten years. With the growing integration of distributed generation in the distribution network, generation forecasting is also becoming a key functionality of the DMS. This involves the analysis of weather patterns, seasonality, and other data to estimate the output that can be expected from distributed generation, especially the weather-dependent intermittent renewable generation (e.g., photovoltaic and wind power generation). Effective load forecasting contributes greatly to reliable, economical, and efficient network operation [109]. Figure 5: Architecture and key functionalities of a DMS [101]. The transition from passive to active distribution networks necessitates the development of advanced distribution management system functionalities that can handle the growing complexity of distribution network operation in the presence of a variety of active distributed resources, such as distributed generation, distributed energy storage, demand-side management, and demand response. The following subsection briefly discusses control and automation at the medium-voltage level of the distribution network.