esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Emissions Inventories and Processing  
Anthropogenic emissions for California were from the ARB 2020 emissions inventory (EI) [22]. These emissions are available in a \"pre-merged\" format, which means they  
Figure 1: Study area: (**a**) Geographic extents and modeling domain parameters; (**b**) South Coast Air Basin study region with monitor locations and SoCAB counties.  
are stored by individual source sectors (e.g., on-road, aircraft), which facilitates applying different COVID-19 scaling factors to the different emissions source sectors. Biogenic emissions were based on the Model of Emissions of Gases and Aerosols From Nature (MEGAN) v3.1 biogenic emissions model [23] with ARB's adjusted urban leaf area index (LAI) using 12/4-km WRF meteorological data. Fire emissions were based on the Fire INventory from NCAR (FINN) [24]. Emissions for the Mexico region in the 4 km domain were from the South Coast Air Quality Management Plan (2016 AQMP) EI [25]. Figure 2 displays the anthropogenic by-sector emissions breakdown for the SoCAB for the ARB 2020 EI. Any sector with less than 1% contribution to the total is not displayed. The period-average is for the period 1 June-31 July 2020.  
We developed model-ready emissions for three CMAQ scenarios:  
1. 2019 Base Case;
2. 2020 BAU Case;
3. 2020 COVID Case.  
The 2019 Base emission were obtained by scaling the ARB 2020 EI. We inspected the 2019 and 2020 California Emissions Projection Analysis Model (CEPAMS) [26] summertime grown and controlled emissions for the South Coast Air Basin by source sector and found that only on-road was a substantially contributing source (i.e., \(>\)3%), with a substantial (i.e., \(>\pm\) 5%) year-to-year change for any pollutant [26]. Therefore, we kept all sectors besides on-road at the same emissions rate in 2019 as in 2020 and adjusted the on-road sector only. The CEPAM on-road 2019 adjustment factors are as follows: NOx = 1.11, TOG = 1.07, and CO = 1.10. These factors were applied uniformly over the 4-km domain to the on-road emissions for each hour.  
2020 BAU emissions were taken directly from the ARB 2020 EI without any scaling adjustment because these emissions we compiled prior to the pandemic and do not account for pandemic effects. The pre-merged categories that comprise this inventory are:  
* Aircraft;
* Area source;
* Consumer products;
* On-road;
* Point (point source format);
* Fertilizer;
* Livestock;
* Lvst (a2p);
* Ogv Area;
* Ogv InIn (point source format);  
Figure 2: Four-county South Coast Air Basin 2020 BAU emissions by sector: (**a**) NOx emissions; (**b**) TOG emissions.  
* Ogv military;
* Paved;
* Residential Wood Combustion;
* Unpaved.  
OGV is ocean-going vessels, and Lvst is a second livestock category. Off-road emissions (e.g., construction equipment) are included in the area source category.
# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 3 Methods and Design
### Trajectory Prediction of a Car
#### 3.3.3 The Training of the Car Trajectory Prediction Model  
In the training process, we required certain model training data. We used the data set from Section 3.3.1. Next, the training data and train label could be constructed. Our aim was to train the model to predict the future trajectory of the car. Points 1 to 35 were used to predict the next 35 points. Figure 18 shows the method used to obtain the training data and labels.  
Thus, we set points 1 to 35 as our first training data, and points 36 to 70 as our training labels. The training data had two columns, X and Y, which were the x coordinates and y coordinates of the car location in the road image. We decided to train the model to predict x and y coordinates separately. Thus, the training label contained only x coordinates or y coordinates. It is worth nothing that training labeling was significant in the training process. The training label can decide the model used to predict which features. For instance, if we want the model to predict future x coordinates of the car, the only thing we need to do  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Details** & **Output Size** \\ \hline \multicolumn{3}{c}{LSTM(num\_units = 256,} \\
1 & input\_shape = (35,2), & (None, 256) \\
2 & return\_sequences = False) & (None, 256) \\
3 & Dense(num\_units = 256) & (None, 256) \\
4 & Activation(’linear’) & (None, 128) \\
5 & Dense(num\_units = 128) & (None, 128) \\
6 & Activation(’linear’) & (None, 35) \\  & Dense(num\_units = 35) & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The parameters of the car trajectory prediction model structure.  
Figure 17: Car trajectory prediction model structure.  
Figure 18: The process of our training data and training labels.  
is set the training label as x coordinates of the car. Furthermore, the model will learn to predict future x coordinates of the car. Thus, if we set the training label as y coordinates of the car, the model will learn to predict future y coordinates of the car.  
After obtaining the training data, training label, and our car trajectory prediction model, we had to set up the training parameters. We set the epochs as 100 times and batch size as 5. Loss function was mean square error (MSE), and the optimizer was Adam. The essential parameters of the car trajectory prediction model are shown in Table 7.
## 1 Introduction
Consequently, InGaN QWs grown along the crystallographic c-axis exhibit an internal piezoelectric field in the MV/cm range [16]. Spontaneous electric and superimposed piezoelectric fields affect the material band gap (\(E_{g}\)) through the quantum confined Stark effect (QCSE) [39; 40; 41] and thus the optical properties and emission efficiency of the devices [42; 43]. The strong piezoelectric field causes a current-dependent red-shift in the emission and significantly contributes to the notoriously low efficiency of green InGaN LEDs. Under controllable piezoelectricity conditions, the blue-green nitride heterostructures and QWs can be grown along crystallographic directions where the piezoelectric field is small or zero. In fact, the piezoelectric field can be cancelled out for certain oblique crystal directions, as for example in case of non- and semipolar GaN crystal orientations. For GaN optoelectronics, an entirely new approach towards different rapidly expanding activities in the area of nonpolar and semipolar nitrides has latterly been initiated, with more and more groups worldwide joining this new facet of nitride semiconductor research [30]. Direct predetermination of the material band gap and the device efficiency by selecting the semiconductor or metal element fraction is not possible. Therefore, a closer look at the nature of the inherent electric fields generated by the incorporation of a controlled heterostructure inside the intrinsic region of a PIN-LED is expected to decipher the optical response and efficiency and reveal the phenomena behind them. Accordingly, in the present work, the electro-optical response of model PIN-diodes based on group-III nitride double heterostructure (DHS) quantum wells (QWs) is studied by electroreflectance (ER) and electroluminescence (EL) spectroscopy and associated, **for calibration purposes**, with the internal electric fields. In addition, the strength and orientation of fields extracted by ER and EL modulation techniques are compared to field properties determined by capacitance-voltage measurements (CVM) on the model diodes in [44; 45; 46]. The materials used are exclusively wurtzite III-nitrides: AlN, InN, GaN, and ternary combinations of the binary compounds grown in the direction of the c-plane normal. Accurate determination of emission energies and efficiencies in dependence of bias-voltage and frequency are acquired for the correct interpretation of the electro-optical device properties, which is a prerequisite for the implementation of III nitride materials based devices with favorable design parameters and sufficient output.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 3 Fundamentals of Reinforcement Learning (RL)
### Reinforcement Learning
* **Uncertainty:** Another challenge for RL is that the agent may face uncertainty when interacting with the environment and updating its state and reward, whereas the purpose of RL is to learn a policy that leads to a high value over time.
## 4 Thermographic Techniques for Breast Cancer Detection
### Dynamic Infrared Thermography
#### 4.2.4 Literature Survey on Breast Cancer Diagnosis  
The scientific research mentioned in Section 4 is summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Study**} & **Thermal** & \multirow{2}{*}{\begin{tabular}{c} **Sample Size and** \\ **Inaging** \\ **Technique** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Camera** \\ **Lesion Type** \\ \end{tabular} } & \multicolumn{2}{c}{**Cooling/Heating**} & \multirow{2}{*}{**Findings/Results**} \\  & **Imaging** & & & & **Acclimatization** \\  & **Technique** & & & **Information** & \\ \hline \multirow{3}{*}{[PERSON] et al. [17]} & Infrared & \multirow{3}{*}{326 patients.} & Camera & & & \\  & Thermography. & & & & & \\  & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [46]} & Infrared & \multirow{3}{*}{728 breast cancer patients.} & Camera & Cold air flow & Reached diagnostic \\  & Thermography. & & patients. & & provided. & \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [48]} & Steady-state & \multirow{3}{*}{50 healthy women.} & Encapsulated liquid & \multirow{3}{*}{15–20 min} & Investigated cyclic \\  & Thermography. & & & crystal SINOTEST & acclimatization. & temperature of \\  & & & & MSP8. & normal breast \\  & & & & & thermograms. \\ \hline \multirow{3}{*}{[PERSON] et al. [49]} & Steady-state & \multirow{3}{*}{35 healthy women.} & HYIR I-1206 & \multirow{3}{*}{5–10 min} & Demonstrated \\  & Thermography. & & & (384 \(\times\) 288 resolution, & acclimatization. & thermal symmetry in \\  & & & & sensitivity of 0.08 \({}^{\circ}\)C). & \\ \hline \multirow{3}{*}{[PERSON] et al. [50]} & Steady-state & \multirow{3}{*}{276 women with previous diagnoses.} & ATIR-M301 camera & \multirow{3}{*}{
\begin{tabular}{c} 15 min \\ (320 \(\times\) 240 FFA, temp. \\ resolution \(<\) 0.1 \({}^{\circ}\)C). \\ \end{tabular} } \\  & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Summary of scientific researches for breast cancer diagnosis using IR thermography.
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 2 Related Work
### Feature Extraction and Region-Proposal-Based Algorithms
The various algorithms to improve performance degradation due to noise and lighting changes have been suggested. However, the models need to be updated through mathematical modeling techniques such as machine learning or deep learning to recognize the location and the shape of an object accurately whenever there are some significant changes in the external environment. Thus, we propose a new algorithm to improve these problems, which is robust to the noise around objects, shadows, light reflections, and illumination interferences because it can separate the brightness information and the color information in the HSV color model. In addition, it does not require user intervention and prior knowledge, and it responds immediately to some changes in the external environment. Our work focuses on object localization in complex and ever-changing manufacturing environments.
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 4 Experimental Verifications of the Methodology
### Detection of Single Damage  
The experiment to detect single damage is performed on an aluminum alloy 6061 beam specimen with a single crack. The open crack is introduced into the specimen via wire-electrode cutting, measuring 4 mm in length and 4 mm in depth. The specimen's dimensions and the crack's location are displayed in Figure 20a, and an image of the used specimen is shown in Figure 20b.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**1 st Frequency/Hz** & FEM & 33.96 \\ Laser & 33.44 \\ Vision & 33.36 \\ \hline
**2 nd Frequency/Hz** & FEM & 213.32 \\ Laser & 212.45 \\ Vision & 212.55 \\ \hline
**3 rd Frequency/Hz** & FEM & 597.48 \\ Laser & 596.46 \\ Vision & 596.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of frequencies obtained from the vision-based system, laser vibrometer, and FEM.  
Figure 19: The comparison of mode shapes: (**a**) the high-spatial-resolution mode shapes obtained by vision-based method, (**b**) the MAC results between the vision-based method and FEM.  
In this experiment, to achieve high-spatial-resolution mode shapes, we select 1000 pixels along the beam's length for measurement, effectively deploying 1000 virtual sensors that do not impart mass loading effects on the beam. The first three mode shapes are obtained and depicted in Figure 21a-c. Despite the presence of damage in the structure, the mode shapes remain relatively smooth overall. This makes it challenging to detect damage through direct observation of the mode shapes.  
The results of the proposed MS-LIE at different scales are shown in Figure 22a,c,e, with the Gaussian multi-scale space constructed using a scale range of 2-10 and a step size of 0.1. The corresponding enlarged heatmaps at larger scales are demonstrated in Figure 22b,d,f. At smaller scales, although the damage feature is more sensitive, the presence of strong noise causes the confusion of damage-induced singularities with noise-induced ones, which complicates the assessment. In the area of larger scales, the damage feature is more distinguishable, albeit at the cost of reduced localization resolution. To solve the scale selection dilemma, we use the proposed data fusion algorithm to fuse the damage features across multi-scale space, maximizing the use of damage evidence and eliminating the effects of measurement noise. The final damage localization results obtained from the first, second, and third mode shapes are demonstrated in Figure 23a-c. It is observable that the proposed method pinpoints single damage accurately.  
Figure 21: The high-spatial-resolution mode shapes obtained via vision: (**a**) 1 st mode shape, (**b**) 2 nd mode shape, (**c**) 3 rd mode shape.  
Figure 20: The specimen with single damage: (**a**) the model of the cracked beam, (**b**) the image of the specimen.  
Figure 22: The MS-LIE results of single damage: (**a**,**c**,**e**) the MS-LIE heat map obtained from the 1 st, 2 nd and 3 rd mode shapes, (**b**,**d**,**f**) the corresponding enlarged heatmaps at larger scales.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 4 Conclusions  
A novel compact monopole concept based on a modified semi-circular Koch fractal patch antenna is measured and investigated. The FSS is used to address the reduced value of the gain due to the fractal operations and size miniaturization (36.67%). The advantage of the designed FSS is that it is simple, compact, economical (fabricated on low-cost FR-4), and easy to fabricate, and the size of the FSS is smaller than the existing antenna's size, which is very rarely considered in the literature. The introduced FSS enhances the overall gain of the antenna from 9.37 dBi to 14.02 dBi and produces improved radiation characteristics. The proposed semi-circular Koch fractal patch antenna is suitable for 5G sub-6 GHz FR-1 n77 (3.3-3.8 GHz), n78 (3.3-4.2 GHz), and n79 (4.4-4.99 GHz), but it also results in the second band ranging from 6.99 GHz to 8.81 GHz and the third band from 10.22 to 10.92 GHz. Therefore, this antenna is also suitable for use in partial C-band operations. The antenna's tuning and gain enhancement are achieved by adjusting the existing air gap between the antenna and FSS. The linearity plot of the frequency tuning with the air gap is present in the text. To validate the fabricated antenna's results, it is measured using a VNA N9916A and an anechoic chamber. The measured reflection coefficients, gains, and radiation patterns are found to be in excellent agreement with the simulated reflection coefficients, antenna gains, and radiation patterns. This validates the antenna results. The measured antenna's first resonating frequency matches the design frequency of 3.5 GHz. In the future, the same antenna could be modified using the Minkowski and other fractal techniques. Further, the FSS can be modified with metamaterials with electromagnetic band gap (EBG) structures
## 7 Performance Evaluation
### Numerical Results and Discussion
#### 7.2.1 Overlapping vs. WCD Evaluations

Table 3: GCL implementation in one-sided overlapping scenarios with higher- and lower-priority windows. Figure 14: The worst-case end-to-end delay (WCD) vs. lower-priority overlapping with different sizes of the lower-priority frame (\(I_{k^{+}}^{max}\)): (**a**) opening-edge overlapping case and (**b**) closing-edge overlapping case. OR at the end of the open-window interval, using Equations (1) and (18). In the guard band interval, large lower-priority frame sizes increase the latency with a percentage greater than smaller sizes, as shown in Figure 14a, because the maximum waiting time increases proportionally, as can be noticed from Equations (26) and (27). For closing-edge overlapping, as shown in Figure 14b, the effect of lower-priority overlapping appears after passing the guard band interval, which equals 16% OR with \(\left(f_{4}^{h,max}=400\text{ bytes}\right)\). After the guard band interval, the latency increases until passing the lower-priority frame's length, and then it remains constant, as depicted in Figure 14b. Cases 3 and 4: In these cases, the influence of the higher-priority overlapping at the opening edge and closing edge is evaluated in Figure 15a,b, respectively, with sizes of \(Q_{4}^{h}\) frames as 350, 400, and 500 bytes. Comparing the results in Figure 14a,b with those in Figure 15a,b shows that higher-priority overlapping has a higher impact on latency performance than lower-priority overlapping. In addition, Figure 15a,b proves that the \(Q_{4}^{h}\) frame experiences higher WCD bounds under opening-edge overlapping than under closing-edge overlapping. This happens because closing-edge overlapping does not affect the guard band interval, where the service is not guaranteed (block interval). As proof for this, it can be observed that both overlapping situations, i.e., opening and closing, have the same behavior with a horizontal shift equal to the guard band duration. For example, with a 500-byte frame size, the latency starts to increase at almost 40% overlapping in the closing-edge case and 20% overlapping (40-20% (guard band duration)) in the opening-edge case. Moreover, the size of the \(Q_{4}^{h}\) frame plays a significant role in the overall WCD performance, especially at large ORs. For instance, the \(Q_{4}^{h}\) frame with sizes of 350, 400, and 500 bytes experiences 289.3, 465.5, and 782 us WCDs, respectively, under 40% OR at the opening edge.
## 3 Methods
### Applications and Preprocessing  
Using the variable Tiaozini tidal flats as an example, based on the DEM using the waterline method and the HJ-1 image (21 March 2014), a BP neural network model was constructed (Figure 5). The input layer of this model has seven node parameters in three categories: reflectance (B1, B2, B3, B4), geographic coordinates (X, Y), and distance (D) from the waterlines. The structure of the ANN model includes double-hidden layers with 15 nodes in each layer. Seven-hundred points evenly distributed in the Tiaozini area were selected and separated into two parts as a training (600) and testing (100) dataset. To determine the network architecture (7-15-15-1), model training and testing were conducted until an acceptable error was reached. This model was also applied to simulate the topography for two other dates based on the HJ-1 images (11 February 2012 and 11 July 2013). The measured data were used to verify the accuracy of the ANN model. A flowchart illustrating the methodology used in this study is shown in Figure 5.  
Figure 4: ANN architecture used in elevation estimations. P: input vector; a: output vector; Wn: weight matrix; b n: offset values matrix; T: logsig transfer function, a = 1/(1 + e - n); \(F\): linear function, a = n.  
Figure 3: Comparison chart of the accuracy for different structures. (a) Comparison chart of R\({}^{2}\) between single-hidden and double-hidden layers with nodes (from 3 to 22); (b) Comparison chart of RMSE between single-hidden and double-hidden layers with nodes (from 3 to 22).
## 3 System Profile and Overall Scheme
### Communication Structure and Characteristics  
The high-speed train operating environmental monitoring system based on the wireless sensor network is simply called HSR-N, which is used for the completion, replenishment, or replacement of the transmission high-speed train operating environmental monitoring system. The proposed wireless sensor network can be roughly considered a linear network.  
In HSR-N, its monitoring areas are wide and varied, and different monitoring targets have great differences. Therefore, relevant parameters and network technologies need to be designed for specific monitoring targets. With limited network energy, HSR-N needs an energy-efficient transmission protocol that meets its requirements.
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 3 Design
### System Entities  
Our solution considers IoT-based services, e.g., a smart building management system, where IoT _endpoints_ can be grouped together. An IoT endpoint can be an IoT device or gateway.  
IoT endpoints are owned by an endpoint _owner_. Each IoT endpoint acts as a CoAP _server_ that \"hosts\" a number of IoT resources. A CoAP _client_ can access simultaneously \"similar\" resources using CoAP group communication.  
A group is administrated by a _group administrator_ and involves multiple IoT _endpoints_. Moreover, a group is associated with a _did:self_ DID denoted by \(Group_{DID}\) and a Fully Qualified Domain Name (FQDN). The DID document of a \(Group_{DID}\) includes the _assertion_ verification method: the public key defined in this method can be used for verifying VCs issued by the group administrator. A group may include endpoints from multiple owners.  
Group administrators are responsible for controlling which owners can can be part of a group.  
The FQDN of each group is associated with an IP multicast address: all CoAP servers are assumed to have joined the appropriate IP multicast groups. The semantics of a group FQDN are deployment specific; e.g., group FQDNs can be location specific such as \"building1.floor1\"; then, a CoAP client may read the temperature measurements of all sensors deployed in the \"first floor of building 1\" using a CoAP group URI such as \"coap://building1.floor1/temperature\".  
CoAP clients are pre-configured with the required group FQDNs (or they are configured with appropriate discovery mechanisms). Finally, it is assumed that the DNS resolution process is secured (e.g., using DNSSEC).