esa-sceva/satcom-chunk-collection huggingface dataset:
# Maternal-Foetal Effects of Exposure to Bisphenol A: Outcomes and Long-Term Consequences
## 4 Characterisation of Bisphenol A (BPA)
Even though there is still much to be understood, human research indicates that maternal exposure to BPA may have an impact on several foetal and obstetric outcomes, such as an increased risk of miscarriage, changes at delivery (premature birth or longer gestation), metabolic dysfunction, changes in the newborn's somatometric parameters, changes in the baby's birth weight and may increase the risk of pre-eclampsia (PE) [3]. Consequently, daily exposure to BPA has become a major public health problem for all these reasons, leading to the need to find solutions, either by reducing the acceptable amounts of BPA in their products or by replacing BPA with substitutes such as Bisphenol S (BPS) and Bisphenol F (BPF), among others. Regarding the amounts of BPA allowed for the manufacture of their end products, the European Food Safety Authority (EFSA) recently reduced the tolerable daily intake value from 4 \(\upmu\)g/kg body weight/day set in 2015 to 0.2 ng/kg body weight/day in April 2023, highlighting concerns about the BPA effects, but may not be enough [8]. The estimated daily intake of BPA for people worldwide is 30.76 ng/kg body weight/day; however, pregnant women had a significantly higher daily intake of 42.03 ng/kg body weight/day, which is now considerably higher than the previously reported acceptable daily intake value [21]. Nevertheless, because BPA has non-monotonic effects (typical U-shaped response or its inverse), the effects of exposure are not always dose-dependent, where lower doses can be more dangerous than higher doses, even if they are considered safe, so the acceptable daily intake rate is not entirely secure and reliable, especially for the susceptible population, i.e., pregnant women and the foetus [29]. Despite various regulations and updates, these are still insufficient to protect the population from exposure to BPA. On the other hand, BPA substitutes are rapidly appearing on the market as a necessity for replacement in various utensils such as feeding bottles, soothers and thermal papers, among others. However, these BPA analogues, including BPS, BPF and BPAF, with similar structure and properties, do not appear to be free of adverse effects and should therefore be carefully considered as an alternative [30; 31; 32]. Therefore, given the characteristics of BPA as an EDC and its continuous exposure to humans, and more specifically to susceptible individuals such as pregnant women and their foetuses, the endocrine-disrupting effects of BPA in the maternal and foetal period will be described in the following section.
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 1 Introduction
### Related Works  
Currently, there are many control methods for the path tracking of articulated vehicles. Feedback linearization control and optimal control are the most common of these.  
The history of path tracking for articulated vehicles based on feedback linearization control is very long. [PERSON] et al. designed controllers in this way earlier [6; 7; 8]. [PERSON] et al. proposed a similar controller in 1997 [9]. Based on the classic kinematics model of articulated vehicles, [PERSON] et al. also designed a controller using feedback linearization control [10]. Until 2019, there are still new controllers based on feedback linearization [11; 12; 13; 14; 15].  
Feedback linearization control is a mature traditional control method, however its shortcomings are also obvious. The parameters of feedback linearization controllers are usually fixed, however the reference path is complex and diverse, so consequently feedback linearization controllers often perform poorly when tracking complex reference paths. [PERSON] et al. have proven that the performance of feedback linearization control is not as good as that of model predictive control (MPC) [16; 17; 18].  
MPC is a kind of optimal control method. Linear quadratic regulation (LQR) is the other optimal control method that is often used for path tracking in articulated vehicles [19; 20; 21; 22; 23]. However, [PERSON] et al. have also demonstrated that MPC performs better than LQR. In addition, other researchers have also found that MPC performs better than LQR in the path tracking of front-wheel steering vehicles [24; 25]. The reason why MPC performs better is that this control method can handle system constraints well and can add the feedforward information of the reference path.  
However, in previous studies, we found that MPC is not perfect. Like feedback linearization and LQR, [PERSON] et al.'s switching MPC and our nonlinear MPC also cannot adjust the velocity in a wide range [5]. However, the accuracy of the path tracking control can be greatly improved by reducing the velocity during steering. Therefore, we believe that it is necessary to propose a new controller for the path tracking of articulated vehicles.
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 4 Methodology Based on Cooperative Game Model for Platoon
### \(\tau\) Value and Shapley Value for the Cooperative Game  
\(\tau\) value is defined within the quasi-balanced game, which can be obtain from the upper vector \(M\) and lower vector \(m\)[30].  
The upper vector \(M\) (\(N\), \(v\)) denotes the marginal contribution and the biggest desired profit in grand coalition. It can be obtained from Equation (12).  
\[M_{i}^{K}(v)=v(K\cup i)-v(K) \tag{12}\]  
The lower vector m denotes the minimum right profit, otherwise, it is not individual rational for \(i\) to join in the coalition. The vector \(m\) could be calculated by Equation (13).  
\[m_{i}=J_{ai}+J_{vi} \tag{13}\]  
**Definition 4**.: _The game theory is quasi-balanced if_  
1. \(m(v)\leq M(N,v)\)__
2. \(\sum\limits_{i=1}^{n}m(v)\leq v(N)\leq\sum\limits_{i=1}^{n}M_{i}(N,v)\)__  
For the first condition, it can be easily proved. Besides, the aforementioned definition will lead to the conclusion as shown in Equation (14).  
\[v(N)=\sum\limits_{i=1}^{n}M_{i}(N,v) \tag{14}\]  
For all games where \(v\in G^{N}\), the \(\tau\) value could be obtain from Equation (15).  
\[\tau(v)=\alpha m(v)+(1-\alpha)M(N,v) \tag{15}\]  
where \(\alpha\) is a coefficient belong to [0, 1], and is uniquely decided by Equation (16).  
\[\sum\limits_{i\in N}\tau_{i}(v)=v(N) \tag{16}\]According to the Equation (16), the \(\tau\) value of game v is calculating with Equation (17).  
\[\left\{\begin{array}{l}\tau_{1}=\alpha m_{1}+(1-\alpha)M_{1}=J_{a1}+J_{v1}\\ \tau_{2}=(1-\alpha)J_{c1}+J_{a2}+J_{v2}\\ \tau_{3}=(1-\alpha)J_{c2}+J_{a3}+J_{v3}\\ \ldots\\ \tau_{n}=(1-\alpha)J_{cn-1}+J_{m}+J_{on}\end{array}\right.\rightarrow\sum \limits_{i=1}^{n}\tau_{i}=\sum\limits_{i=1}^{n}J_{ai}+\sum\limits_{i=1}^{n}J_{ vi}+(1-\alpha)\sum\limits_{i=1}^{n-1}J_{ci}\\ \ldots\\ \tau_{n}=(1-\alpha)J_{cn-1}+J_{m}+J_{on}\end{array}\right. \tag{17}\]  
where \(\alpha\in[0,1]\).  
Combined with Equation (13), we can know that \(\alpha=0\). This result reveals that every participant in platoon will get their maximum available profit with \(\tau\) value which can satisfy the individual rational and group rational.  
The order of the platoon is fixed while each vehicle is numbered according to the location which cannot be randomly arranged. Let the sequence of the platoon consist of a leading vehicle and \(n\) following vehicle as \(N=\{0,1,2,\ldots,i,\ldots,n-1,n\}\). The Shapley value of the participant is equal to the average of the marginal contribution in different coalition [31].  
The weigh coefficient given to the coalition \(S\) is the probability that the participant who is in front of the participant happens to be a member of \(S\) in a random order. However, the particular sequence of the platoon has decided the number of the random orders and the essence of the Shapley value is the mathematical expectation of marginal contribution. In this game, the Shapley value is equal to \(\tau\) value.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 2 UAVs Overview
### UAV Networks and Communications
#### 2.3.5 Swarms  
With the advancement of UAV systems, their usage is increasing. This also means the number of applications is increasing. When one looks at a singular UAV, the tasks that they can do are outstanding. A significant problem with a singular UAV is that they are generally slow and not very time efficient on their own. To increase efficiency, more UAVs can be implemented.  
In general, a group or fleet of UAVs that work together to achieve a holistic goal is known as a swarm of UAVs. In addition to swarm, different terms are employed for multiple UAV systems based on the number of UAVs deployed. [PERSON]. [32] provided three such classifications--teams, formations, and swarms. A 'team' is said to have less than ten UAVs working individually in a cooperative or competitive manner. 'Formations' have about tens or more of UAVs that usually interact cooperatively. 'Swarms' refer to a relatively larger UAV group consisting of dispensable units, in which the local interaction can be either competitive or cooperative. Ref. [33] proposed a similar classification, where multiple teams are termed a 'quadron,' and all squadrons collectively form a 'group' of UAVs.  
Figure 4 depicts the pictorial representation of a UAV swarm controlled by an integrated control system. There are a variety of applications that multiple UAVs are helpful for. Video surveillance, photogrammetry, providing cellular networks, traffic monitoring, Simultaneous Localization and Mapping (SLAM), and search and rescue are just some of the applications of multiple UAVs [33].
## 2 FMCW TMRD System  
The FMCW TMRD consists of several main parts: antenna, transmitter, receiver and digital signal processing (DSP) module. Its functional block diagram is depicted in Figure 1 in which Tx and Rx denote radar transmission signal and received vehicle return, respectively. The transmitter transmits FMCWs to vehicles on multiple road lanes, and the receiver receives reflected signals. The DSP module converts a received target signal into a FFT, and estimates the range/or velocity information of the target. The range from the TMRD to the target (i.e., the position of the FFT main lobe of the returned target signal) can be estimated by using the frequency analysis for the reflected target signal. This in turn estimates the lane on which the vehicle is located since the range between each lane and the TMRD is fixed and known. In addition, we can program and deploy our algorithms to the DSP module for vehicle flow detection.  
The FMCW TMRD is small in size and light in weight so that it can be conveniently mounted on a fixture. Unlike the forward-looking automotive radar sensing in [16], the sideway-looking vehicle detection of the TMRD in a multiple-lane environment is shown in Figure 2.  
The operation of the FMCW TMRD system follows the linear FMCW radar principle in [9; 10]. Assume a FMCW radar beam is transmitted and reflected on a vehicle. The receiver receives the return vehicle signal after a delay time \(\tau\). Then the distance \(d\) between the vehicle and the TMRD is obtained as  
\[d=\frac{\tau\cdot c}{2} \tag{1}\]  
where \(c\) denotes the speed of the light. The frequency versus time characteristics for the linear FMCW is shown in Figure 3. Based on the similarity of two triangles in Figure 3, we directly obtain  
\[\frac{\tau}{T}=\frac{f_{\tau}}{f_{T}} \tag{2}\]  
i.e.,  
\[\tau=\frac{f_{\tau}\cdot T}{f_{T}} \tag{3}\]  
Figure 1: Functional structure of the frequency-modulated continuous waveform (FMCW) traffic microwave radar detector (TMRD) system.  
Figure 2: Sideway-looking traffic sensing of the FMCW TMRD.  
where \(f_{T}\) is the total frequency difference during the signal period \(T\), and \(f_{\tau}\) is the beat frequency for the time delay \(\tau\). Putting Equation (3) into Equation (1) yields  
\[d=f_{\tau}\cdot\frac{T\cdot c}{2\cdot f_{T}} \tag{4}\]  
Equation (4) implies that the distance between the target and the TMRD is proportionally related to the beat frequency, which can be extracted by the DSP module. Hence, as the distance gets larger, so does the beat frequency. Accordingly, the FFTs of returned signals in the frequency-magnitude axis can be depicted in the distance-magnitude axis. Illustrations can be foundin Section 5.
# Experimental Investigation in Drag Coefficient of Cubes
## 4 Results and Discussion  
### Behavior of Uniform Cube Drag Coefficient  
The free settling experiment using the uniform cube was performed first. The Reynolds number of particles ranged between 4.75 \(\times\) 10\({}^{-1}\) and 5.7998 \(\times\) 10\({}^{3}\) during the experiment. The experimental Reynolds number ranges across the Creeping flow regime where the flow inertia effects are negligible and the flow is laminar; the Transition regime where the flow changes from laminar to turbulent; and the Newton regime where the drag coefficient is roughly constant. Similarly to the former investigations [15], the motion of the uniform cube with noticeable oscillations appears when the particle Reynolds number is larger than a certain value in the Transition regime. In the current investigation, when the Reynolds number is larger than 10, the particle oscillation arises. The experimental drag coefficient of the uniform cube, as well as [PERSON]'s standard correlation of the sphere as a function of the Reynolds number, are exhibited in Figure 4. As can be seen, the drag coefficient of the cube is always higher than that of the sphere in the experimental Reynolds range, especially at the range of \(Re>2.58\)\(\times\) 10\({}^{2}\). This indicates that the drag coefficient of the cube has its own characteristics which are different from that of the sphere. In addition, the discrete degree of the measured drag coefficient of the cube becomes larger and more complex when the Reynolds number is larger than 10\({}^{2}\), since the settling orientation angle of the cube varies at this Reynolds number range. Therefore, to more accurately characterize the drag coefficient of cubes, further investigation into the effect of orientation angle on cube-shaped particles is necessary.  
Figure 3: Comparison of experimental sphere drag coefficient and standard correlation of sphere brought forward by [PERSON] [9].
## 3 Results and Discussion
### Gas Pipeline  
The results of the acoustic emission evaluation of the high-pressure gas pipeline were analyzed by grouping using non-hierarchical methods (k-means) and by comparison to the reference signals (Table 3). The results showed reduced thickness of the pipe wall due to pitting and deformations and perforations of the structure due to changing working and corrosion conditions.  
Analysis of absolute energy parameter as a function of time, correlated with parameters such as duration, amplitude, rise time, and AE counts, shows that destructive processes exhibit low intensity across a small area of the gas pipeline (Figure 14).  
It should be emphasized that the destructive processes do not proceed continuously, which indicates the local character of these phenomena. The next step in the analysis is to locate places generating destructive processes.  
Linear location of destructive process sources shows the area of increased intensity of AE signals extending between 55.79 m and 59.66 m, which corresponds to the pipes laid under the woodland path (Figure 15). Further analysis of destructive process location on the examined section of the pipeline indicated two points, at 57 m and 59.5 m.  
Figure 14: AE signal energy as a function of time for the gas pipeline with the numbers of signal classes marked.  
Figure 15: Exact location of destructive process sources.  
Analysis of the amplitude versus time graph for AE signals recorded in the area beyond the indicated points shows that no signals indicating destructive processes were recorded in that area.
## Data and Methods  
### Research Location  
Denpasar (Bali, Indonesia) is located at 08\({}^{\circ}\)35\({}^{\prime}\)31\({}^{\prime\prime}\)-08\({}^{\circ}\)44\({}^{\prime}\)49\({}^{\prime\prime}\) south latitude and 115\({}^{\circ}\)10\({}^{\prime}\)23\({}^{\prime\prime}\)-115\({}^{\circ}\)16\({}^{\prime}\)27\({}^{\prime\prime}\) east longitude. Denpasar city has four districts: North Denpasar, South Denpasar, West Denpasar, and East Denpasar. In 1995, Denpasar had an area of 120.47 km\({}^{2}\), which increased to 124.68 km\({}^{2}\) because of land reclamation at Serangan Island and Benoa port in 1996, which is located in South Denpasar. A map of Denpasar city, including boundaries before and after reclamation, is presented in Figure 1. Denpasar is located between two neighbourhood regencies, namely, the Badung Regency to the north and west, the Gianyar Regency to the east, and the Badung Strait area to the south. The three districts, including Denpasar, have the highest tourism activity in Bali. In Denpasar, the centre of tourism activity is in the southeastern part, namely, the Sanur region.  
The population of Denpasar reached 335,196 people in 1992 and increased to 880,600 people in 2015, an increase of more than 250%. Topographically, the study area has a flat relief with zero elevation in the south and southeast, a highest elevation of 89 m a.s.l. in the north, and an average elevation of 23 m a.s.l. In general, the rainfall patterns of the Denpasar area are influenced by monsoons, with the maximum amount of precipitation occurring during the peak of the wet season from December to February and decreasing to a minimum during the valley of the dry season from June to August [44; 45]. Moreover, the annual average temperature of Denpasar is 27.6 \({}^{\circ}\)C, the average annual rainfall is 1803.62 mm year\({}^{-1}\)[44], and settlement was the dominant land use in Denpasar city in 2006 followed by rice field areas [46].  
Figure 1: The research location: Denpasar, Bali Province, Indonesia. Dark grey regions in southern Denpasar are Serangan Island and Benoa port after reclamation, and these areas are used for Denpasar boundaries in 2003 and 2014. The dotted line inside Denpasar is the district boundaries.
## 4 Detection Process of Degraded Insulators Based on Time-Domain Waveform of Space Electric Field
In this paper, an optimization strategy of spatio-temporal information fusion is proposed, which transforms the corresponding relationship between the electric field intensity of the sampling point and the time of the sampling point into the corresponding relationship between the electric field intensity of the sampling point and the coordinates of the sampling point and then obtains the electric field information of the corresponding spatial coordinates and determines the effectiveness of the sampling point according to whether the coordinate error corresponding to the electric field intensity meets the detection requirements. Figure 14: Spatiotemporal information fusion strategy. Figure 13: Test diagrams and schematic diagrams of sensitive insulators. The specific implementation process of the spatio-temporal information fusion optimization strategy is as follows:  
1. The position of the center of the electric field sensor is calculated and set as the target point by using the UAV RTK main antenna position, the structure, and installation position of the optical electric field sensor and other information;
2. When the spatial electric field data of the insulator associated with time starts to be recorded, a positioning request is triggered to record the position information of the target point associated with time;
3. The clock difference between the local time of the electric field detection device and the time of the UAV system is calculated by using the underlying processing program of PSDK to realize the synchronization of the electric field detection device and the time of the UAV system;
4. The time request position of the UAV system where the locating event occurred is used to obtain the position coordinates corresponding to the measured spatial electric field information;
5. The relative distance between the position coordinates corresponding to the spatial electric field information and the route position coordinates are compared, and the electric field data of the coordinate points whose relative distance is greater than 5 cm are eliminated. Then the spatial electric field distribution curve of the target path is generated. Through the above pre-detection method based on sensitive insulator criteria and the optimization strategy for piece-by-piece detection under complex working conditions, not only can the efficiency of insulator detection be greatly improved, but also the measurement error under actual working conditions can be effectively reduced, which has a certain guiding significance for actual field detection.
## 3 Case Study and Results
### Management Storage  
The storage management system was implemented to minimize the global energy cost. The models used established that the battery must not be charged during peak hours. It should not be charged during flat hours, and there are no restrictions for valley hours. To consider the prediction of future consumption from the inputs presented, a lag 2 was considered, i.e., 1 h. In addition, an error of 5% of the inputs was considered in the lagged periods to simulate the imprecision of the prediction system. Furthermore, with the purpose of protecting the battery and maximizing its life cycle, a depth of discharge of 90% was considered. Figure 6 shows the evolution of the state of charge of the battery from the management storage system proposed with the data from the SNN model with k-fold. This figure shows both the Train/Dev and the Test divisions. The results for both partitions are similar, not showing a priori irregularities in the prediction model.  
As can be seen in Figure 6, the battery was charged in the valley hours and discharges in the flat and peak hours. The greatest discharge occurred in the hours in which the demand is greatest, corresponding mainly to the central hours of the day, establishing both peak hours and a small period of flat hours. Battery discharge began at 8 a.m., when the building workday begins. From 9 p.m. the battery maintained its state of charge until the valley hours are reached, since the flat hours were established and there was no consumption in the building.  
Once the evolution of the battery's state of charge was determined, Figure 7 shows the variation that the storage management system produced in the instantaneous energy costs. In the same way as before, both the Train/Dev and the Test divisions are shown, not existing strange results in the predictions.  
Figure 6: Battery charge period in a working day in the selected zone of the building. (**a**) Train/Dev sample division; (**b**) Test sample division.  
The period of electrical consumption that was established in the building generated costs in the hours with high energy cost, i.e., peak hours and flat hours. For this reason, as can be seen in Figure 7, the building's habitual consumption costs were established in these hours. On the contrary, with the energy storage system, the building's electrical consumption was covered by the discharge of the battery. Thus, the costs in the battery system were set at the lowest energy cost, i.e., valley hours, the costs in peak hours and flat hours being null. In this way, an economic benefit was generated. The differences between both situations are presented in Table 5.  
As previously determined, the battery allowed us to alleviate the demand of the electrical network in the periods in which the price of electricity was higher and to consume in periods with low price. As can be seen in Table 5, the total electricity price in the study period, twelve months divided into two periods, of the building's usual consumption was higher than that generated with the energy monitoring system proposed.