esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization and Prediction of Air Transport Delays in China
## 3 Data and Methods
### Delay Data Set Description  
The delay data set used in this study has been kindly provided by the Aviation Data Communication Corporation ([[http://www.adcc.com.cn](http://www.adcc.com.cn)]([http://www.adcc.com.cn](http://www.adcc.com.cn))), including information for all flights crossing the Chinese airspace in the 30-months period from 1 May 2016 to 31 October 2018. For each flight the information provided includes, among others:  
* ICAO (International Civil Aviation Organization) code of scheduled departure/arrival airport;
* ICAO code of actual departure/arrival airport;
* Unixtime (time in seconds since 1 January 1970) for scheduled departure/arrival time;
* Unixtime (time in seconds since 1 January 1970) for actual departure/arrival time.  
In this study, we focus on the arrival delay for domestic flights, calculated as the difference between the actual and scheduled arrival time; a positive number indicates that the flight arrived later than scheduled. A few instances in which the scheduled and actual arrival airports do not coincide have been discarded. Possible explanations for such flights are flight diversion or data inconsistencies. Moreover, we removed all flights with at least one airport not being located in China. After this data cleansing step, a total of 11 million domestic flights have been analyzed. These flights cover the air transportation activity between 277 Chinese airports, as shown in Figure 1. The majority of airports are located in the Eastern part of China, given the higher population density. Figure 2 further summarizes the top-ranked airport pairs, in terms of number of flights and delay statistics. Note that the routes with the highest cumulative delays (bottom left panel) form a connected structure around the top airports; on the other hand, routes with the largest delays per flight (bottom right panel) are disconnected and mainly involve secondary airports.  
Figure 1: Chinese air transportation infrastructure. All 277 Chinese airports are represented by blue dots. The top 20 Chinese airports according to the total number of passengers are highlighted and labeled with their four-character ICAO codes.  
Figure 2: Highlighting of the top-ranked airport pairs in the data set according to different criteria: Largest number of flights (upper left), largest number of delayed flights (upper right), largest sum of delays (lower left) and largest median of delays (lower right).
# Plasmonic and Dielectric Metasurfaces: Design, Fabrication and Applications
## 1 Introduction
Inserts are the representation of the parameter space for permittivity \(\varepsilon\) and permeability \(\mu\) and the typical examples of the applications of metamaterials. the responses of plasmonic and dielectric metasurfaces are all relevant to the characteristics of the unit structure, such as dimensions and materials. One can manipulate the electromagnetic field of the light wave scattered by the metasurfaces through designing the dimension parameters of each unit structure in the metasurfaces [59]. To produce the designed metasurfaces, many attractive previous works have reported the fabrication techniques, such as electron-beam lithography [60; 61; 62; 63; 64; 65], focused-ion beam [66; 67], interference lithography [68; 69; 70; 71; 72] and nanoimprint lithography [73; 74; 75; 76; 77]. Electron-beam lithography is a standard method that can fabricate the metasurface with high nanoscale resolution. Focused-ion beam is a more appropriate method for rapid prototyping. Interference lithography can provide a solution for realizing large-scaled metasurface fabrication. Nanoimprint lithography is a more promising technique for combining many advantages of the former, such as high-resolution, large-scaled production and low processing cost [73]. The unique properties and the development of fabrication techniques have allowed metasurfaces to be applied in various fields, such as flat optics [33; 34; 35; 78; 79; 100; 101], nonlinear effects [101; 102; 103; 104; 105; 106; 107], photonic Hall effects [108; 109; 110], electromagnetically-induced transparency [111], cloaking [112; 113; 114; 115; 116; 117], etc., as shown in Figure 2. For instance, the applications of metasurfaces in the field of flat optics mainly include anomalous reflection/refraction [81; 34; 82], wave plates [79; 80], flats lens/axicons [83; 84; 85], mirrors [88], hologram [90; 91; 92; 93; 94; 95], filters [96], optical vortex generation [98; 99; 34; 85], polarization beam splitter [100], etc. The applications of metasurfaces in the field of nonlinear effects mainly include second/third harmonic generation [102; 103; 104; 105] and enhanced absorbing [106; 107]. In this review article, we go over our recent progress in plasmonic and dielectric metasurface-assisted nanophotonic devices and their design, fabrication and applications, including the metasurface-based broadband and selective generation of orbital angular momentum (OAM) carrying vector beams [118; 119], N-fold OAM multicasting using a V-shaped antenna array [120], metasurfaces on a conventional optical fiber facet for linearly-polarized mode (LP\({}_{11}\)) generation [121], graphene split-ring metasurface-assisted terahertz coherent perfect absorption [122], OAM beam generation using a nanophotonic dielectric metasurface array [123] and Bessel beam generation and OAM multicasting using dielectric metasurface array [124]. Figure 2: Schematic illustration of the classification, fabrication and applications of metasurfaces.
## 2 Materials and Methods
### Close-Agreement between DYU Air Box and Commercialized Measurement Instruments  
In order to evaluate the in situ sensing accuracy of the proposed DYU Air Box, the agreement between the acquired data and measurement ones of commercialized instruments including a TES-5322A IAQ meter (TES Electrical Electronic Corp., Taipei, Taiwan), a TES-5110 particle counter, a TES-1370H CO\({}_{2}\) m, a TES-1337B light intensity meter, and a TES-1364 temperature and humidity meter [27] was analyzed. Taking the measurement results of the above measurement instruments as references, the relative difference between the proposed DYU Air Box and the corresponding instruments is defined as  
\[e_{j}^{n}=x_{j}^{n}-\hat{x}_{j}^{n} \tag{1}\]  
where \(x_{j}^{n}\) and \(\hat{x}_{j}^{n}\) are the \(j\)th measurement values of the proposed DYU Air Box and the corresponding measurement instruments, \(j=I_{\text{AOI}},L_{\text{AOI}},\text{PM}_{1,0}\), \(\text{PM}_{2.5},\text{CO}_{2}\), \(\lambda\), \(T\), RH. Furthermore, the performance indexes of mean absolute error (MAE), mean absolute percentage error (MAPE) [28], and root mean square error (RMSE) for close-agreement analysis are defined as  
\[\text{MAE}=\frac{1}{N}\sum_{n=1}^{N}\left|x_{j}^{n}-\hat{x}_{j}^{n}\right| \tag{2}\]  
\[\text{MAPE}=\frac{100\%}{N}\sum_{n=1}^{N}\frac{\left|x_{j}^{n}-\hat{x}_{j}^{ n}\right|}{\hat{x}_{j}^{n}} \tag{3}\]  
and  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{n=1}^{N}\left(x_{j}^{n}-\hat{x}_{j}^{n} \right)^{2}} \tag{4}\]  
where \(N\) is the observation number. The results of statistical analysis of the agreement between the proposed DYU Air Box and the above commercially available measurement instruments will be presented later.
###### Abstract  
The use of intraoral scanners in the field of dentistry is increasing. In orthodontics, the process of tooth segmentation and rearrangement provides the orthodontist with insights into the possibilities and limitations of treatment. Although, full-arch scan data, acquired using intraoral scanners, have high dimensional accuracy, they have some limitations. Intraoral scanners use a stereo-vision system, which has difficulties scanning narrow interdental spaces. These areas, with a lack of accurate scan data, are called areas of occlusion. Owing to such occlusions, intraoral scanners often fail to acquire data, making the tooth segmentation process challenging. To solve the above problem, this study proposes a method of reconstructing occluded areas using a generative adversarial network (GAN). First, areas of occlusion are eliminated, and the scanned data are sectioned along the horizontal plane. Next, images are trained using the GAN. Finally, the reconstructed two-dimensional (2D) images are stacked to a three-dimensional (3D) image and merged with the data where the occlusion areas have been removed. Using this method, we obtained an average improvement of 0.004 mm in the tooth segmentation, as verified by the experimental results.  
tooth segmentation; generative adversarial networks; intraoral scanners; reconstruction; image completion; dental scan data; occlusion areas 2020 1  
the orthodontist insight into the possibilities and limitations of treatment, creating a diagnostic setup model from a dental plaster model is time-consuming and labor intensive.  
Since the advent of intraoral scanners, instead of using the dental plaster model, intraoral scan data can be manipulated to achieve a digital tooth setup. After acquiring the full arch scan data of the patient, the teeth are segmented and repositioned to the desired position using three-dimensional (3D) CAD software. This is more efficient because it eliminates the process of making a plaster model, and it has been reported to be as effective and accurate as a conventional method [2; 3].
## 5 Boom Configuration Optimization
### Results
#### 5.3.3 Decision Matrix for Boom Configuration Optimization  
In Figure 7, the DM for both algorithms, before and after the analysis of the obtained results, are given. Before optimization, the ES was expected to perform better in part due to the ability to generate design alternatives and better convergence behavior. After the optimization, it showed that the heuristic had a better than expected performance in \"Convergence to optimum value\" and the ES was worse than expected in \"Low Computational Time\", yielding a reversal of the algorithms ranking. In addition, for the ES the use of Random Forest led to an improvement with respect to \"Ease of Use\" and \"Algorithm customization\", but the respective models can no longer be interpreted. Hence, \"Comprehensibility\" is lowered. The complete DM for both cases are provided in the Supplementary Material. The given DM is the consensus view of three experts.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Obj.Function Value**} & \multicolumn{3}{c}{**Analysis Tool Calls**} & \multicolumn{3}{c}{**Stress**} & \multicolumn{3}{c}{**Fatigue**} & \multicolumn{3}{c}{**Buckling**} \\ \hline Case & Ref. & Heu. & ES & Heu. & ES & Ref. & Heu. & ES & Ref. & Heu. & ES & Ref. & Heu. & ES \\ \hline
1 & 28,117 & 25428 & **22,907** & 13 & 500 + 160 & 0.57 & 0.57 & 0.61 & 0.58 & 0.58 & 0.59 & 0.99 & 0.50 & 0.55 \\
2 & 25,184 & 17,347 & **16,163** & 6 & 500 + 153 & 0.39 & 0.43 & 0.46 & 0.51 & 0.54 & 0.55 & 0.29 & 0.51 & 0.61 \\
3 & 19,482 & 15,451 & **14,184** & 6 & 500 + 165 & 0.39 & 0.39 & 0.47 & 0.56 & 0.56 & 0.64 & 0.32 & 0.39 & 0.54 \\
4 & 12,749 & **7986** & 9367 & 3 & 500 + 155 & 0.25 & 0.32 & 0.30 & 0.26 & 0.32 & 0.35 & 0.16 & 0.43 & 0.45 \\
5 & 28,355 & 23,322 & **19,050** & 8 & 500 + 150 & 0.47 & 0.53 & 0.64 & 0.59 & 0.66 & 0.77 & 0.54 & 0.43 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the boom configuration optimization.
## 3 Constellation Construction
### Construction of the Ground System  
The ground system comprises four subsystems: satellite operation and control, data retrieval, data processing, and data management and distribution. Its primary function is to manage satellite operations, receive satellite data, process data, and distribute information. Satellite operational monitoring and control are conducted by the satellite mission ground monitoring and control center. This subsystem remotely controls satellites and receives telemetry data transmitted by the satellites. The data receiving subsystem, managed by remote sensing satellite ground stations in Beijing, Sanya, and Kashi, offers real-time data receiving services covering China and approximately 70% of the Asian landmass. The data processing subsystem processes raw data into Levels 0 and 1 after radiometric and geometric corrections. The data management and distribution subsystems handle metadata management, image data management, data service, and data portal nodes, based on four cloud servers for remote sensing data sharing services.
# Identification of Location and Camera Parameters for Public Live Streaming Web Cameras
## 4 Formulation of the Problem and Its Solution Algorithm
### Auxiliary Steps
#### 4.2.2 Coordinate Frame Associated with the Plane \(\Pi\)  
We can go from \(F_{c}\) to a coordinate system associated with the plane \(\Pi\). We apply it for illustrations, but it can be helpful for other purposes. We can regard the traffic in \(Q_{im}\) as anything that rises above the plane \(\Pi\). We denote by \(F_{\pi}\) the coordinate system connected to \(\Pi\). and use the normal to the plane \(\Pi\) at the coordinates \(F_{c}\) from (32) as the axis \(Z_{\pi}\).  
\[\vec{\mathbf{n}}_{\pi}=\begin{bmatrix}p_{x}\\ p_{y}\\ 1\end{bmatrix},\vec{\mathbf{e}}_{z\pi}=\vec{\mathbf{n}}_{\pi}/|\vec{\mathbf{n}}_ {\pi}|. \tag{43}\]  
We choose in the \(\Pi\) plane the direction of another axis (e.g., \(Y_{\pi}\)), and the third axis is determined automatically. Let the \(Y\) axis be pointed by the \(g_{4}-g_{2}\) vector (that is, along the direction of traffic movement in the \(Q\) area), then  
\[\begin{array}{ll}\vec{\mathbf{e}}_{yp}=&(\xi(g_{4})-\xi(g_{2}))/|\xi(g_{4}) -\xi(g_{2})|\\ \vec{\mathbf{e}}_{xp}=&\vec{\mathbf{e}}_{yp}\times\vec{\mathbf{e}}_{zp}\end{array}. \tag{44}\]  
Select the origin of the coordinate system \(F_{\pi}\) as follows  
\[o=\xi(g_{2}). \tag{45}\]The rotation matrices from \(F_{p}\) to \(F_{c}\) and their inverses look like the following:  
\[R_{\pi 2c}=\begin{bmatrix}\mathbf{\tilde{e}}_{x\pi},\mathbf{\tilde{e}}_{y\pi},\mathbf{\tilde{e}} _{z\pi}\end{bmatrix}=\begin{bmatrix}e_{x}^{\pi\pi}&e_{x}^{\eta\pi}&e_{x}^{\pi \pi}\\ e_{y}^{\eta\pi}&e_{y}^{\eta\pi}&e_{y}^{\eta\pi}\\ e_{z}^{\eta\pi}&e_{z}^{\eta\pi}&e_{z}^{\eta\pi}\end{bmatrix},\;R_{c2\pi}=R_{ \pi 2c}^{T}. \tag{46}\]  
Use the following expression for the translation of coordinates of \(P_{c}\) given in frame \(F_{c}\) to \(P_{\pi}\) given in frame \(F_{\pi}\):  
\[P_{\pi}=R_{c2\pi}(P_{c}-o). \tag{47}\]
# Airborne and Dermal Collection Methods of Gunshot Residue for Toxicity Studies
## 1 Introduction
Additionally, one study identified the presence of carcinogenic polycyclic aromatic hydrocarbons (PAHs) in lead-free GSR ammunition [41]. When considering inhalation or other exposure routes to GSR, the health effects are less investigated with studies primarily focused on blood Pb levels in firearm users [23; 24; 25]. One hypothesized mechanism for health effects following outdoor PM\({}_{2.5}\) exposures is oxidative stress. Oxidative stress is the imbalance of antioxidants and free radicals that leads to the accumulation of reactive oxygen species (ROS) and leads to inflammation in cells and tissues [42]. Oxidative potential is a proxy for oxidative stress as it measures the ability of particulate matter to oxidize various molecules to produce ROS; however, it is understudied for particulates collected from firearm discharge. In this study, the dithiothreitol (DTT) assay was used to measure the oxidative potential of the GSR samples. Here, we test GSR sample collection and analysis methods to determine optimal procedures for assessing the exposure to GSR particles through airborne and dermal contact. Different size fractions of airborne particles and hand swabs from a shooter were collected and analyzed following one or three shots from a 0.22 caliber revolver. Particle morphology, chemical composition, and oxidative potential were determined for all samples. In contrast to previous work, this study investigates the ability to detect and analyze GSR \(\leq\) 2.5 microns, a fraction of particular relevance to human health. This work outlines potential collection and analysis methods for airborne and dermal exposure to GSR in an outdoor setting. We begin to propose procedures to fill the substantial gaps in GSR research that exist in environmental and human health exposure assessments.
# One-Layer vs. Two-Layer SOM in the Context of Outlier Identification: A Simulation Study
## 2 The SOM Algorithm  
We now present a short review of the original SOM algorithm.  
The SOM is a neural network that allows us to project a high-dimensional vector space onto a low-dimensional topology (typically two) integrated by a set of different nodes or neurons displayed as a grid. This nonlinear projection results in a \"feature map\" of the high-dimensional space, which may be used to detect and analyze characteristics that allow for the identification of patterns or groups. SOMs have been applied to a number of fields, e.g., document retrieval, financial data analysis, forensic analyses, and engineering applications. Machine learning includes two categories, supervised and unsupervised learning. In the former, there is a division of the variables between inputs and outputs, and the purpose of the analysis is to estimate the input-output relationship and make predictions. In the latter, all variables are on equal footing, and one searches for a better understanding of their multivariate structure, possibly also involving dimensional reduction. With (unsupervised) SOMs, we are capable of identifying features and structures in high-dimensional data. SOMs show a self-organizing behavior with the capacity to detect hidden characteristics within nonlabeled groups when enough map nodes (also called neurons) are used. It also has the perspective of dimensional reduction that seeks to optimize topological conservation [1; 17; 18], that is that the relative locations of the points in the original space are reflected in some way on the map. One of those ways is distance preservation. Thus, it can be used to identify similar objects once the map has been trained.  
As mentioned above, the SOM produces a nonlinear mapping of the high-dimensional space onto a reduced dimension one. Several versions of the original algorithm [1; 7] have been proposed. Suppose \(x(t)\) denotes the input vectors and the weight vector of the \(k\)-th neuron (node) (\(k=1...K\)). The \(K\) neurons are arranged in a bidimensional network, which induces a set of corresponding weight vectors. A discrete time (training epoch) index (\(t=1...\)) is introduced so that \(x(t)\) is presented to the network at time \(t\), and \(w_{k}(t)\), which represents the state of the net at that moment, are updated. Before training the map, the weight vectors should be initialized, which is generally performed by taking K vectors, typically by random sampling within the initial data.  
Once the map has been trained, any new observation will be assigned to the map node with the lowest euclidean distance between the weight and observation (obviously, in the original space).
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 2 Construction of Longitudinal PID Controller for a Vehicle
### Longitudinal Dynamics Modeling of the Vehicle  
This study only considers the driving of the autonomous vehicle on a flat road surface; thus, the gradient resistance is zero. The force analysis diagram of the longitudinal model of the autonomous vehicle is shown in Figure 1.  
In longitudinal motion, the vehicle is primarily influenced by the following four forces:  
1. Driving Force \(F_{d}\): The driving force is provided by the engine or motor and transmitted to the wheels through the drivetrain. \[F_{d}=F_{d}+F_{d},\] (1)  
Figure 1: Force analysis diagram of longitudinal motion of an autonomous vehicle.  
\(F_{d}\) is the driving force on the front wheels, and \(F_{dr}\) is the driving force on the rear wheels. \[F_{d}\leq\mu F_{i}\] (2) where \(\mu\) is the road adhesion coefficient, and \(F_{i}\) is the vertical load on the wheels. 2. Aerodynamic Drag \(F_{a}\): This is proportional to the square of the vehicle's speed, and typically expressed as \[F_{a}=\frac{1}{2}\rho C_{d}A_{d}v^{2}\] (3) where \(\rho\) is the air density, \(C_{d}\) is the drag coefficient, and \(A_{d}\) is the frontal area of the vehicle. 3. Rolling Resistance \(F_{r}\): The friction force between the tires and the road, expressed as \[F_{r}=C_{r}mg\] (4) where \(C_{r}\) is the rolling resistance coefficient and \(m\) is the mass of the vehicle. 4. Braking Force \(F_{b}\): Generated by the braking system, controlling the deceleration of the vehicle. \[F_{b}=F_{bf}+F_{br}\] (5) \(F_{bf}\) is the front-wheel braking force, and \(F_{br}\) is the rear-wheel braking force. \[F_{b}\leq\mu F_{i}\] (6) where \(\mu\) is the road adhesion coefficient, and \(F_{i}\) is the vertical load on the wheels. The longitudinal dynamics model of the autonomous vehicle is expressed as \[m\ddot{v}=\min(F_{d},\mu F_{i})-\left(\frac{1}{2}\rho C_{d}A_{d}v^{2}+C_{r}mg \right)-\min(\mu F_{i},F_{b}).\] (7)
# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
###### Abstract
In order to apply our problem in the actual case, real-time data, such as the waste quantities of each type at each station, leads to an increase in the waste in the corresponding bin. However, a compartment in a station can be visited more than once on the same working day to avoid overflowing containers. In this article, we consider the dynamic vehicle routing problem for separate waste collection in each smart bin, using information and communication technologies in the concept of smart cities. To achieve this goal, we propose a meta-heuristic method for efficient planning of separate waste collection dynamic routes with the presence of a sensor at each compartment, which must be triggered once the container has reached a predetermined maximum threshold. We compare the meta-heuristic to an actual status given by a waste collection company. There are several studies have treated the topic of waste management problems but only a few studies thought about selective waste collection as well as waste collection problems in smart cities using the smart bin. Waste sorting is important not only to reduce the impact it has on the environment but also for health issues that can arise from improperly disposed of waste and toxins. Waste sorting is also an economically beneficial prospect because it makes recycling much easier. We search to combine both problems in order to avoid environmental and social issues as well as minimize costs; this method consists of selective waste collection using smart bins containing sensors indicating the real status of filling. as other objectives, we ensure to avoid altered cities caused by overflows bins. We have organized the article as follows: In Section 2, we present a state-of-the-art on smart waste management, selective waste collection, and the dynamic vehicle routing problem. Section 3 details the proposed system model and defines the Dynamic Vehicle Routing Problem of separate waste management using intelligent sensors. The formulation of the mathematical model is presented in Section 4. Section 5 describes a real case study and shows the results of the application of our approach. The conclusion of the article and future directions are presented in Section 6.