esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes
## 3 Control System Configuration
### Control System with Active Front Steering Saturation
To mitigate this, the auxiliary saturation angle \(\delta_{sat}\) is used to limit the steering angle \(\delta_{f}\), thereby reducing the magnitude \(v\) in (33) and effectively suppressing the disturbance \(\mathbf{B}_{v}(V_{0})_{\zeta}(t)\) in (35). Similarly, the matching condition part \(\mathbf{B}_{m}\xi\) in (35) represents a disturbance in the direction of \(\mathbf{B}_{m}\) with an uncertain magnitude \(\xi\). This magnitude can be managed by the yaw moment \(M_{z}\) and is expected to be on the order of \(10^{3}\) N-m (the same order with the yaw moment \(M_{z}\)). The significant difference in scales between the magnitudes of \(\zeta\) and \(\xi\) in (35) presents challenges when managing the bounds on disturbance uncertainty. To address this issue, this study considers the following constraint  
\[\left(\zeta/\rho_{\zeta}\right)^{2}+\left(\xi/\rho_{\xi}\right)^{2}\leq 1 \tag{36}\]  
to normalize the combined effect of the uncertainties \((\xi,\xi)\), where \(\rho_{\zeta}>0\) and \(\rho_{\xi}~{}>~{}0\), assumed to be given, are on the orders of \(10^{-2}\) and \(10^{3}\), respectively. By using the representation (35) and the restriction (36), the error dynamical Equation (32) can be rewritten as  
\[\dot{\mathbf{e}}_{v}=\mathbf{A}_{v}(V_{0})\mathbf{e}_{v}+\mathbf{B}_{m}M_{z}+ \mathbf{B}_{w}(V_{0})\mathbf{w},~{}V_{0}\in[V_{\text{min}},V_{\text{max}}] \tag{37a}\] \[\mathbf{w}^{T}\mathbf{Hw}\leq 1,~{}\mathbf{H}=diag(\rho_{\zeta}^{-2}, \rho_{\xi}^{-2}) \tag{37b}\]  
where \(\mathbf{B}_{w}(V_{0})=\left[\mathbf{B}_{v}(V_{0})\mathbf{B}_{m}\right]\) and \(\mathbf{w}=\left[\xi~{}\xi\right]^{T}\). Unlike the previous study in Reference [7] that utilized structured uncertainty to address variations in the cornering stiffness, our approach employs an uncertainty reconfiguration that deals with overall disturbances through the type of structured uncertainty (35). This approach reduces the complexity of the control design.
## 2 Methodology
### Bayesian Optimization Methods
#### 2.2.3 Update the Gaussian Distribution of \(L(p_{s})\)  
The new Gaussian distribution as shown in Equation (4) could be determined after adding the new observation points.  
\[\left\{\begin{array}{l}\left[\begin{array}{c}[L(p_{1}),L(p_{2}),\cdots,L (p_{r})]\\ L(p_{best})\end{array}\right]\,\sim N\Bigg{(}0,\left[\begin{array}{cc}K&k^{ \prime}\\ (k^{\prime})^{\text{T}}&k(p_{best},p_{best})\end{array}\right]\Bigg{)}\\ k^{\prime}=[k(p_{best},p_{1}),k(p_{best},p_{2}),\cdots,k(p_{best},p_{r})]\end{array}\right. \tag{4}\]  
The new distribution \(P(F(p_{best})|D_{r},p_{best})\) of \(L(p_{r}+1)\) can then be calculated by Equation (5):  
\[\left\{\begin{array}{l}p(L(p_{best}|D_{r},p_{best}))\,\sim N(\mu,\sigma^{2}) \\ \mu=(k^{\prime})^{\text{T}}K^{-1}[L(p_{1}),L(p_{2}),\cdots,L(p_{r})]\\ \sigma^{2}=k(p_{best},p_{best})-(k^{\prime})^{\text{T}}K^{-1}k^{\prime}\end{array}\right. \tag{5}\]  
where: \(\mu\), and \(\sigma\) are the mean and standard deviation, respectively.  
The model estimates the The certainty coefficient (\(R^{2}\)) between the calculated water level and the actual water level at the water level observation point in this model was used to determine the goodness of the model fit. The closer the \(R^{2}\) is to 1, the better the calculated water level fits the actual water level and the more accurate the predicted hydraulic conductivity value. The loss function of the Bayesian optimization model above is \(R^{2}\) and the optimization objective is to find a combination of hydraulic conductivity that maximizes \(R^{2}\). \(R^{2}\) can be calculated by using Equation (6):  
\[R^{2}=1-\frac{\sum(y_{i}-\hat{y_{i}})^{2}}{\sum(y_{i}-\overline{y})^{2}} \tag{6}\]  
where: \(\hat{y_{i}}\) is the calculated value of the water level at the \(i\)-th observation point; \(y_{i}\) is the measured value of the water level at the \(i\)-th observation point; and \(\overline{y}\) is the average value of all measured water levels.
## 2 Construction of Intelligent Automatic Blasting Vibration Monitoring System  
### Configuration of the Data Acquisition Device  
To realize the timely and effective collection of massive blasting vibration data, the new generation of network vibration meter TC-6850 developed by CST is selected, as shown in Figure 1. TC-6850 uses the most advanced intelligent sensors. The sensor and the main engine are integrated to make the whole volume small. Besides, it has a built-in WIFI/4G/3G communication module, forming a LAN for wireless control of multiple instruments on site.  
The TC-6850 is powered by solar energy or directly from the nearby port. In the case of no vibration, the instrument is in the standby state. Moreover, when the vibration exceeds the trigger value, the data will be automatically recorded and transmitted. The automatic feature of TC-6850 realizes unattended blasting vibration data acquisition. Table 1 describes the parameters of the TC-6850.
## 2 Related Work  
### Applications of UAV Remote-Sensing Technology in Transportation Systems  
Unmanned aerial vehicles (UAVs) are increasingly being utilized in urban management to enhance the efficiency of various tasks, such as traffic congestion analysis, vehicle tracking [18], and safety evaluations [19]. Equipped with high-resolution cameras and advanced image-processing algorithms, UAVs can capture detailed aerial imagery and real-time data. This capability is crucial for monitoring traffic flow, detecting vehicles, and managing incidents [20]. By providing a bird's eye view and being deployable in various locations, UAVs enable urban planners to gather comprehensive data on infrastructure usage and traffic patterns [8]. This real-time data collection is essential for effective traffic management and safety evaluations. Additionally, UAVs are used to monitor traffic density, estimate traffic flow parameters, and detect vehicle collisions, thus enhancing overall urban traffic management [21]. This technology empowers urban planners to address traffic congestion, improve road infrastructure, and respond effectively to emergencies [14; 22; 23].
## 1 Introduction
### Sources of Silicon of Mineral Origin Used in Functional Foods and Food Supplements
#### 1.2.5 Re-Evaluation of Silicon Dioxide (Silica, E551) as a Food Additive
There are few signs of SAS being absorbed after human consumption; however, silicon dioxide has been found in liver and spleen tissue. Overall, there is no evidence of the acute oral toxicity of SAS. No adverse clinical effects have been observed with doses up to 20,000 mg of SAS per kg. In subchronic toxicity studies, no side effects were observed in rats fed with a diet containing SAS at a level of 4000 mg/kg/day for 13 weeks. There were no effects on food consumption, food efficiency, and weight gain. Hematology, urinalysis, clinical chemistry, and macroscopical and microscopical pathology also did not reveal any abnormal findings [67; 68]. Similarly, no adverse effects were detected in female rats fed a diet containing silica gel at doses up to 8980 mg/kg/day for 6 months [69]. In a further subchronic toxicity study in rats fed a diet with up to 3500 mg/kg/day of fumed silica for 90 days, the low toxicity of the applied doses was confirmed [70]. Other studies used lower doses that were not sufficient to assess repeat-dose toxicity [71; 72]. The in-vitro and in-vivo studies of SAS used as a food additive do not indicate its genotoxic potential. Synthetic amorphous silica, characterized by various physicochemical properties, is allowed for use based on the EU specifications for silicon dioxide (E551). SAS does not appear to induce genotoxicity when used as a food additive, and there is no evidence of carcinogenic effects in long-term studies, with the highest tested doses at 7500 and 2500 mg silica gel/kg/day, respectively, in mice and rats. However, due to the lack of long-term studies on silicon dioxide containing nanoparticles, the EFSA was unable to transfer the results from the available silicon dioxide studies due to the lack of data for the nanoparticle sizes that may be present in E551. The EFSA considered that the current specifications for E551 are not sufficient to adequately characterize silicon dioxide that possibly contains nanoparticles. These specifications should include the characteristics of nanoscale particles. ADI for silicon dioxide has not been confirmed due to insufficient data. Silicon dioxide seems to be poorly absorbed; however, Si, considered to be in the form of silicon dioxide, was found in some tissues. Overall, there are no signs of adverse effects, and SAS does not raise any concern for genotoxicity or carcinogenicity when used as a food additive [48].
## 2 Problem Description
### Benchmark Instances  
The benchmark instances for DPFSP that will be used for validating the efficiency of the approaches described in this work are publicly available; see the Data Availability Statement at the end of this manuscript. These benchmark instances were first introduced in [1] and contained two datasets: one small and one large. The small dataset consists of 420 small-sized instances, with five instances for each combination of \(n=\{4,6,8,10,12,16\}\), \(m=\{2,3,4,5\}\), and \(F=\{2,3,4\}\), where \(n\) is the number of jobs, \(m\) is the number of machines, and \(F\) is the number of factories. Likewise, the large dataset is composed of 720 problem instances, derived via the classic benchmark instances of Tailard [4], by augmenting them with six values for the number of factories \(F=\{2,3,4,5,6,7\}\). In particular, there are 540 problem instances, with 10 instances for each combination of \(F=\{2,3,4,5,6,7\}\times n=\{20,50,100\}\times m=\{5,10,20\}\), 120 problem instances for \(F=\{2,3,4,5,6,7\}\times n=\{200\}\times m=\{10,20\}\times 10\) instances, and 60 problem instances for \(F=\{2,3,4,5,6,7\}\times n=\{500\}\times m=\{20\}\times 10\) instances. The processing times of each job at each machine are randomly selected from a uniform distribution in [99; 1].  
An example of the file format used in input files is shown in Figure 1 and it refers to the first problem instance involving 10 jobs, two machines, and two factories of the small instance (i.e., file I_2_10\(2\)1.txt). On the first row of the file, there are the number of jobs and the number of machines (i.e., 10 and 2, respectively), while on the second row, there is the number of factories (i.e., two). Then, at subsequent rows in the red rectangle, there are the processing times at factory 0 for each one of the 10 jobs, while in the green rectangle, there are the processing times of the jobs at factory 1.  
Figure 1: Example of a problem instance of the small dataset: I_2_10\(2\)1.txt.  
An optimal solution for this problem instance achieving a makespan of 345 is shown in Figure 2. The permutation of jobs for the machines of factory 0 is \(\mathrm{j0,j3,j8,j7,}\) and \(\mathrm{j5,}\) and the permutation of jobs for the machines of factory 1 is \(\mathrm{j4,j6,j2,j9,}\) and \(\mathrm{j1}\). Note that the order of visiting machines for each job in both factories is first machine 0 and then machine 1 (i.e., flow-shop constraint).
###### Abstract  
Power consumption of Global Positioning System (GPS) acquisition is a great challenge for energy-constrained applications. In this work, a metric named _acquisition mean computation overhead_ is proposed to measure the energy cost. A novel multi-peak double-dwell (MP/DD) acquisition method for GPS weak signal is proposed. It adopts multi-peak correlation results as candidates in the first acquisition dwell and selects the largest one as the final acquisition result in the second acquisition dwell. Theoretical analysis and numerical simulation are presented in detail. Meanwhile, the very-large-scale integration (VLSI) implementation of coarse- and fine-grained acquisition engines applied to the proposed method is done. The detection probability and acquisition mean computation overhead are simulated using the Monte-Carlo method, and its mean acquisition power is tested with an actual chip. We fabricated the GPS signal acquisition engine with a 40 nm complementary metal oxide semiconductor (CMOS) process. The simulation results demonstrate that detection probability is promoted from 18% to 67% when signal power is equal to 23 dB/Hz. The acquisition mean computation overhead is reduced greatly by 64%. Measurement results show that the energy consumption of this design is only 21.5% of the conventional double-dwell/maximum (DD/MAX) method.  
GPS acquisition; multi-peak; double-dwell; energy-constrained applications; very-large-scale integration +
Footnote †: journal: Electronics Electronics
# Practical Application of Mesh Opportunistic Networks
## 3 Materials and Methods
### Range of the Devices  
Before the experiment, the range and performance of the OppNet devices in the mesh were tested in real conditions to compare them with the manufacturer's proposed nominal range of 6.4 km (Km) from device to device with a single effective node. That nominal distance assumes that one of the devices is in high, clear terrain. To achieve this, two goTenna devices were given to professional journalists who, previously in another location, covered acts of an electoral campaign. The interpersonal communications were satisfactory at the meetings and at the venues where the electoral acts were held, where there were high concentrations of people. However, during testing the coverage did not exceed 3.5 km. The connection is very sensitive to obstacles, decreasing to 300 m inside some installations or buildings. In the context in which this study was carried out, it was considered that working with a distance of 1 km between the terminals would be sufficiently reliable and stable to locate the teams and maintain conversations.
# Towards a Digital Twin for Open-Frame Underwater Vehicles Using Evolutionary Algorithms
## 2 Related Work
[28] experimentally identifies hydrodynamic parameters for the LAURS open-frame AUV in one and three degrees of freedom, and ref. [29] uses experimental inertial measurement unit (IMU) values from an underwater vehicle to compare with results obtained through the genetic algorithm-based identification of hydrodynamic coefficients. Both refs. [30; 31] evaluate hydrodynamic coefficients of an AUV, validating them through CFD simulations and experimental tests using a PMM. Furthermore, ref. [32] conducts towing tank and free-floating experiments to determine hydrodynamic coefficients of a ROV in 2 degrees of freedom. The study of damping and added mass parameters usually accompanies the dynamic vehicle model. For instance, ref. [33] uses COMSOL Multiphysics to estimate added masses and damping parameters in the 6 degrees of freedom. However, comprehensive studies simultaneously addressing dynamic modeling, propulsion modeling, coefficients, and added masses are common, as seen in [26; 34; 35]. A comprehensive review is presented in [36]. In this paper, several important steps toward the development of a complete digital twin for an AUV are described. Specifically, Section 3 presents a comprehensive methodology for keeping the digital model up to date by combining information from traditional tests and measurements with the support of evolutionary algorithms to characterize the dynamic behavior of an AUV. An application of this methodology, using a BlueROV2 and a genetic algorithm, is presented in Section 4. The model is then validated through real-world tests, and the differences between model predictions and experimental results are analyzed and discussed in Section 5. Finally, Section 6 presents the conclusions drawn from the work.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 4 Application of Digital Twins to PHM  
### Research Status  
Since its first appearance in 2002, the concept of DTs has evolved. Given the complexity of the concept, a wide variety of definitions can be found in the literature. [PERSON] et al. state that the concept of a digital twin is two identical physical and virtual spaces that are mirrored to analyze what is happening at various stages of an object's life cycle [18]. [PERSON] and [PERSON] elaborate that a DT encompasses all useful physical and functional information. Both authors agree that it is not only data but also algorithms that describe behavior and determine operations in production [19]. In recent years, [PERSON] et al. added some features to digital twins to define DTs, i.e., the self-evolution of DTs, meaning something living that constantly changes, improves, and evolves while maintaining a comparison between the physical and virtual spaces, and interaction and fusion are the two critical aspects of DTs, so, based on the described features, a systematic \"thinking approach\" can help to detect DTs.  
From the literature we have retrieved, it is clear that DTs have become increasingly popular in fault diagnosis and intelligent maintenance in recent years, and this phenomenon should be attributed to the growing maturity of various aspects of technology, such as the Internet of Things, big data, and the efficiency of machine learning algorithms.  
This section focuses on the relationship between digital twins and PHM and the current status of research in this area carried out by many scholars and organizations.  
The first to apply digital twins to device PHM was [PERSON] et al. [20]. [PERSON] et al. proposed a PHM system based on advanced digital twin (DT) technology. They evaluated the safety status of the FAST cable network and predicted the fatigue life of the components in the network through finite element analysis of the DT model [21]. [PERSON] proposed a digital-twin-based PHM system architecture for high-speed rolling stock by constructing a digital twin model of high-speed rail physical entities, fusing real-time data on physical and virtual devices and related twin data and providing theoretical guidance for the engineering of the PHM system [22]. Based on the working principle of a rutting machine, [PERSON] combines its common failure manifestations with digital twin technology and proposes a PHM framework for a rutting machine based on a digital twin [23]. [PERSON] et al. reviewed the research progress on digital-twin-driven fault diagnosis for rocket control systems by addressing the problems in fault diagnosis and health management in the aerospace field and classifying NASA's digital twin goals [24]. Similarly, [PERSON] et al. investigated on-board health monitoring and predictive maintenance for civil aircraft engines and utilized digital twin technology to achieve aircraft safety monitoring and fault warning for civil aircraft [25]. A definition of digital twins' maturity is shown in Figure 6.
## 2 Study Area and Data  
### Study Area  
The abundance of solar energy resources is the primary factor affecting the large-scale development and utilization of solar energy, and it is also one of the most important factors for evaluating the potential of solar energy. Annual global solar radiation is the main evaluation index used to measure the abundance of solar energy resources in a certain area. The annual global solar radiation in China can be roughly divided into east and west sections by drawing a straight line between Xilinhot in Inner Mongolia and Tengchong in Yunnan. The annual global solar radiation in eastern China is weaker than that in western China as a whole. Figure 1 shows the distribution of annual global solar radiation in different regions of China after classification by 500 MJ/m\({}^{2}\)[37]. According to GB/T 31155-2014 [38], China's national standard for solar energy resource grade, annual global solar radiation is divided into four grades: A, B, C, and D. See Table 1 for the specific classification standards. The rational utilization of solar energy resources in the utilizable area is of great importance. Therefore, Harbin (125\({}^{\circ}\)42\({}^{\prime}\) E-130\({}^{\circ}\)10\({}^{\prime}\) E, 44\({}^{\circ}\)04\({}^{\prime}\) N-46\({}^{\circ}\)40\({}^{\prime}\) N), Shenyang (122\({}^{\circ}\)43\({}^{\prime}\) E-123\({}^{\circ}\)31\({}^{\prime}\) E, 41\({}^{\circ}\)07\({}^{\prime}\) N-42\({}^{\circ}\)45\({}^{\prime}\) N), Beijing (115\({}^{\circ}\)25\({}^{\prime}\) E-117\({}^{\circ}\)30\({}^{\prime}\) E, 39\({}^{\circ}\)26\({}^{\prime}\) N-41\({}^{\circ}\)03\({}^{\prime}\) N), Wuhan (113\({}^{\circ}\)41\({}^{\prime}\) E-115\({}^{\circ}\)05\({}^{\prime}\) E, 29\({}^{\circ}\)58\({}^{\prime}\) N-31\({}^{\circ}\)22\({}^{\prime}\) N), Shanghai (120\({}^{\circ}\)42\({}^{\prime}\) E-122\({}^{\circ}\)12\({}^{\prime}\) E, 30\({}^{\circ}\)40\({}^{\prime}\) N-31\({}^{\circ}\)53\({}^{\prime}\) N), and Guangzhou (112\({}^{\circ}\)57\({}^{\prime}\) E-114\({}^{\circ}\)03\({}^{\prime}\) E, 44\({}^{\circ}\)04\({}^{\prime}\) N-46\({}^{\circ}\)40\({}^{\prime}\) N), which are all located near the available areas, were selected as the research objects to carry out a study on the distribution law of solar energy.  
\begin{table}
\begin{tabular}{c c c} \hline
**Grade** & **Threshold (MJ/m\({}^{2}\))** & **Rank Symbol** \\ \hline Rich area & \(G\geq\) 6300 & A \\ Relatively rich area & \(5040\leq G<6300\) & B \\ Available area & \(3780\leq G<5040\) & C \\ Poor area & \(G<3780\) & D \\ \hline \end{tabular}
\end{table}
Table 1: Grade of annual global solar radiation [38].
# Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
###### Abstract  
Highway bridges are crucial civil constructions for the transport infrastructure, which require proper attention from the corresponding institutions of each country and constant financing for their adequate maintenance; this is important because different types of damage can be generated within these structures, caused by natural disasters, among other sources, and the heavy loads they transport every day. Therefore, the development of simple, efficient, and low-cost methods is of vital importance, allowing us to identify damage in a timely manner and avoid bridges collapsing. As reported in a previous work, the wavelet energy accumulation method (WEAM) and its corresponding application in the Rio Papaloapan Bridge (RPB) represented an important advance within the field. Despite identifying damage in bridges with precision and at a low cost, there are several aspects to improve in that method. Therefore, in this work, that method was improved, eliminating several steps, and meaningfully reducing the computational burden by implementing an algorithm based on the Shannon entropy, thus giving way to the new entropy wavelet-based method (EWM). This new method was applied directly with regard to the real-life RPB, in both its healthy and damaged conditions. Also, its corresponding numerical model based on the finite element method in its healthy condition and different damage scenarios were carried out. The results indicate that the new EWM retains the advantages of WEAM, and it allows for damage identification to be completed more efficiently, increasing the precision by approximately 0.11%, and significantly reducing the computing time required to obtain results by 5.67 times.  
damage identification; efficiency increment; entropy; highway bridges; vibration signals; wavelet +
Footnote †: journal: Applied Sciences  
Article