esa-sceva/satcom-chunk-collection huggingface dataset:
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 2 Method
### Data Envelopment Analysis (DEA) and Inverse DEA for Efficiency Analysis  
In this study, the directional Data Envelopment Analysis (DEA) was used to assess the performance of selected Multinational Oil Companies operating in Nigeria, classifying them as either efficient or inefficient. The directional DEA is primarily preferred because it considers the direction in which the DMUs are moving in terms of inputs and outputs. Unlike traditional DEA, which assesses efficiency without considering the direction of change in inputs and outputs, directional DEA considers whether a DMU is moving towards or away from the production frontier by classifying it into good and bad outputs. In this research, the bad output from the petroleum industry is the gas flare. In the petroleum industry, it is not just about being efficient, but also about continuous improvement in efficiency.  
Terminology  
The terminologies used for DEA models in different studies are almost similar.  
The general nomenclatures used are listed below:  
* number of inefficient decision-making units (DMUs);
* number of inputs of each DMU;
* number of good outputs of each DMU;
* number of bad outputs of each DMU.  
Data Parameters  
\(x_{ij}\)  
ith input of DMUj (\(j=1\ldots n\));  
\(y^{g}_{rj}\)  
rhd good output of DMUj (\(j=1\ldots n\));  
\(y^{g}_{pj}\)  
rhd bad output of DMUj (\(j=1\ldots n\));  
\(\propto_{k}\)  
inefficiency score of DMUk (\(k=1\ldots n\)).  
Figure 4: Methodology model.  
To obtain the efficiency score of the DMU-k (k = 1..., _n_), the following directional distance DEA proposed by [53] was used to classify both good and bad outputs; this will be used as the base model. The rationale for using this proposed model by [53] is its success in waste reduction, as shown in Equation (1)  
\[\begin{array}{c}\alpha_{k}^{*}=max\propto\\ \hskip 142.26378 pts.t.\\ \sum_{j=1}^{n}\lambda_{j}^{\alpha}x_{ij}\leq x_{ik}\quad i=1,\ldots,\ m\\ \sum_{j=1}^{n}\lambda_{j}y_{j}^{g}=(1+\alpha)y_{rk}^{g}\quad r=1,\ \ldots,\ s\\ \sum_{j=1}^{n}\lambda_{j}y_{j}^{b}=(1-\alpha)y_{pk}^{b}\quad p=1,\ \ldots,\ q\\ \sum_{j=1}^{n}\lambda_{j}=1\\ \lambda_{j}\geq 0\quad j=1,\ \ldots,\ n\end{array} \tag{1}\]  
where \(\alpha_{k}^{*}\) is referred to as the inefficiency score  
**Assumptions**  
From Equation (1) above, assuming there are \(n\) numbers of DMUs that will use \(m\) of the good inputs (_xij_) to produce \(s\) of the good outputs (_yij_) and \(q\) of the bad outputs (_yij_), the aim of Equation (1) is to reduce the undesirable bad outputs under evaluation, which in this case is the flare gas.  
As a general guideline in efficiency evaluation, DMUk is said to be efficient if its inefficiency score \(\alpha_{k}\) = 0.  
To obtain the efficiency score of each of the DMU-k, and using the formula according to [53] as depicted in Equation (2),  
\[\begin{array}{c}\text{Efficiency score}=\frac{\left(1-\ \alpha_{k}^{*} \right)}{\left(1+\ \alpha_{k}^{*}\right)}\end{array} \tag{2}\]
## 4 Case Study: Conceptual UAM Flight Routes and Establishment of Holding Areas  
### Analysis of Airspace Environment in Seoul-GyungIn Area  
The target area of this study is the Seoul and GyungIn area. These areas were selected for the case study because the airspace structures such as prohibited areas, restricted areas, and danger areas are complex and there are control zones of two hub airports (Incheon International Airport and Gimpo International Airport). In addition, as 50% of the total population of Korea lives in the Seoul and GyungIn area, the possibility of using the service is high [16]. On the other hand, it is an optimal area for research and analysis for UAM flight routes and holding areas because there are various ground obstacles and noise-sensitive areas.  
First, in terms of airspaces, the northern area of Seoul is surrounded by prohibited areas P-73A and P-73B, while R-75, a restricted area, surrounds the whole of Seoul in a polygonal shape. Since R-75 is set up to 10,000 ft AMSL (above mean sea level), prior approval is required for aircraft to enter Seoul. In addition, danger areas up to 1900 ft AGL (above ground level) have been established in Mokdong, Yeoksam, and Guui, etc. On the other hand, Incheon International Airport, Gimpo International Airport, and Seoul Air Base have a 5 NM (natural miles) radius control zone [1]. Figure 10 shows the various airspaces and noise-sensitive areas (marked in yellow) in the Seoul and GyungIn area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Item Classification**} & \multicolumn{2}{c}{**Specifications**} \\ \hline \multirow{4}{*}{General characteristics} & Length & 11.66 m \\ \cline{2-3}  & Height & 3.28 m \\ \cline{2-3}  & Empty weight & 683 kg \\ \cline{2-3}  & Gross weight & 1134 kg \\ \cline{2-3}  & Main rotor diameter & 33 ft (10 m) \\ \hline \multirow{4}{*}{Performance} & Maximum speed & 240 km/h (130 knots) \\ \cline{2-3}  & Cruise speed & 202 km/h (109 knots) \\ \cline{2-3}  & Range & 560 km \\ \cline{2-3}  & Altitude restrictions & 14,000 ft (4300 m) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Robinson R44 specifications (Raven II) [15].  
Figure 9: Bank angle vs. turning radius (**left**) and bank angle vs. turn rate (**right**) of R44.  
In Seoul and its suburbs, VFR (visual flight rules) routes have been established for visual flight operations by helicopters and general aviation aircraft such as Cessna 172S. These flight routes enable safe flight operations by avoiding ground obstacles and specific airspaces. Each flight route has visual reporting points, and pilots are required to report each time they pass these points as they fly along these routes. Major VFR routes include the P-73 VFR route that surrounds the edge of Seoul, and the Han River Corridor (Yongsan Corridor, Nodeusecom Corridor) established along the Han River. In addition, Gimpo Airport VFR routes for arrival and departure from the airport have been established on the outskirts of Seoul [1]. Figure 11 shows the VFR routes in the Seoul and GyungIn area.
# Monocular Real Time Full Resolution Depth Estimation Arrangement with a Tunable Lens
## 2 Materials and Methods
### Setup  
#### 2.1.1 General Overview  
To achieve the hardware part of our vision system, several components were selected (Figure 1): a variable focus lens, a high speed camera, a synchronization module and a processing system.  
A liquid lens was chosen as a variable focus lens. Due to the capability of fast movement, the fastest in the market is capable of moving at least 156 fps with high precision and repeatability. The camera captures Full HD (1920 \(\times\) 1080) with a high frame rate (greater than 156), and supports the lens mount (C-mount). As a synchronization module, we use a microprocessor to synchronize the lens and the camera with high accuracy, to this purpose, an FPGA was selected, since the FPGAs allow to generate multiple clocks without losing cycles. Finally, the processing system must support reading all the camera frames and needs a GPU that executes the parallel algorithm.  
Our algorithm is capable of extracting the distances from the captured frames, estimating distances with high accuracy, and dealing with the low frequencies problem by using the input color information, a common issue in DFF algorithms [22].
# Low Cost AIP Design in 5G Flexible Antenna Phase Array System Application
## 2 Patch Antenna Design  
In this study, the micro strip patch antennas constructed the array system. The patch antenna is a kind of a resonant antenna that is like a resonant cavity. One important parameter of a resonant cavity is its quality factor (\(Q_{0}\)), which is defined as shown in Equation (1) [22].  
\[Q_{0}\equiv\omega\frac{2W_{e}}{P_{l}} \tag{1}\]  
where \(\omega\) is the frequency, \(W_{e}\) is the stored energy in the resonant cavity, and \(P_{l}\) is the power loss of the resonant cavity. There are three kinds of losses in the resonant antenna namely radiation loss (\(P_{rad}\)), dielectric loss (\(P_{d}\)), and conducted loss (\(P_{c}\)). The formula is shown in Equation (2).  
\[\frac{1}{Q_{0}}=\frac{P_{md}}{2\omega W_{e}}+\frac{P_{d}}{2\omega W_{e}}+\frac {P_{c}}{2\omega W_{e}}=\frac{1}{Q_{md}}+\frac{1}{Q_{d}}+\frac{1}{Q_{c}} \tag{2}\]The antenna efficiency can be enhanced when the dielectric loss is reduced. This is due to the antenna efficiency (\(\xi\)), as shown in Equation (3) that is proportional to \(Q_{0}\) when the conducted loss is fixed in the critical coupled condition.  
\[\xi=\frac{P_{rad}}{P_{l}}\propto\frac{Q_{0}}{Q_{rad}} \tag{3}\]  
This study used an air-filled cavity structure to design the patch antenna on a standard FR4 substrate [23]. This design constructed a metal patch that was located on the FR4 substrate with the open air cavity. The reference ground used the copper layer on the carrier board as illustrated in Figure 1. This design can reduce the dielectric loss and enhance the patch antenna performance with better radiation efficiency. The dielectric constant of air was 1.0006 and the loss tangent of air was 0, which can enhance the patch antenna performance with better radiation efficiency. The top and cross section views are shown in Figure 2.  
The return loss and radiation efficiency of the patch antenna are presented in Figure 3. Figure 3 illustrates that the return loss of the patch antenna with air cavity is better than 10 dB at 26.5-29.5 GHz. Figure 3 presents the radiation efficiency of the two types of antenna where the radiation efficiency of the patch antenna with air cavity was 92% while the radiation efficiency of patch antenna without air cavity was 66.25% at 28 GHz. The radiation efficiency was enhanced by 25.75% at 28 GHz. The maximum radiation efficiency of the patch antenna with air cavity was 93.28% while the maximum radiation efficiency of patch antenna without air cavity was 77.25%. The radiation efficiency was  
Figure 1: The structure of the patch antenna with air cavity.  
Figure 2: (**a**) Top view and (**b**) cross section view of the patch antenna.
## 3 Result Analysis
### Relationships between Occupant-related Variables and Thermal Sensations  
In this study, two occupant-related variables were selected as features. One is side face skin temperature and the other is clothing surface temperature. Before developing the thermal models, the relationship between skin temperature and thermal sensation, as well as the relationship between clothing surface temperature and thermal sensation were investigated. Figure 6a-d show the relationships based on the data collected with the two subjects. As a result, for female subject, the correlation coefficient between skin temperature and thermal sensation is 0.747 while that between clothing surface temperature and thermal sensation is 0.768. In addition, for male subject, the correlation coefficient between skin temperature and thermal sensation is 0.688 while that between clothing surface temperature and thermal sensation is 0.763. Therefore, clothing surface temperature has a higher correlation with thermal sensation than skin temperature for both subjects.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Very Cold** & **Cold** & **Cool** & **Neutral** & **Warm** & **Hot** & **Very Hot** \\  & **(-3)** & **(-2)** & **(-1)** & **(0)** & **(1)** & **(2)** & **(3)** \\ \hline Female & 3 & 16 & 29 & 168 & 73 & 58 & 15 \\ Male & 7 & 13 & 24 & 188 & 100 & 58 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Data distribution over different sensations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Negative** & **Predicted Positive** \\ \hline
**Actual Negative** & True negative & False positive \\
**Actual Positive** & False negative & True positive \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix.
# Optimal Interplanetary Transfer of Solar Wind Ion Focusing Thruster-Based Spacecraft
## 2 Mathematical Model  
The performance of a SWIFT-based spacecraft is here investigated assuming that the mission consists of a heliocentric transfer trajectory between two circular and coplanar orbits. Such a two-dimensional spacecraft motion may be conveniently described with the aid of a heliocentric polar reference frame \(\mathcal{T}(\mathcal{O};r,\theta)\), whose origin \(\mathcal{O}\) coincides with the Sun's center of mass; \(r\) is the Sun-spacecraft distance, and \(\theta\) is the spacecraft polar angle measured counterclockwise from a fixed direction, which coincides with the Sun-spacecraft line at the initial time \(t=t_{0}\triangleq 0\); see Figure 1.  
The fundamental unit vectors of \(\mathcal{T}\) are denoted by \(\{\hat{\mathbf{t}}_{r},\hat{\mathbf{t}}_{\theta}\}\), where \(\hat{\mathbf{t}}_{r}\) is the outward radial unit vector, and \(\hat{\mathbf{t}}_{\theta}\) is the circumferential unit vector, so that the spacecraft position (\(\mathbf{r}\)) and velocity (\(\mathbf{v}\)) vectors can be written in \(\mathcal{T}\) as  
\[\mathbf{r}=r\,\hat{\mathbf{t}}_{r}\quad,\quad\mathbf{v}=v_{r}\,\hat{\mathbf{t}}_{r}+v_{\theta} \,\hat{\mathbf{t}}_{\theta}, \tag{1}\]  
where \(v_{r}\) (or \(v_{\theta}\)) is the radial (or circumferential) component of \(\mathbf{v}\). In the polar reference frame, the SWIFT-induced propulsive acceleration is written as a function of the radial (\(a_{r}\)) and circumferential (\(a_{\theta}\)) components as  
\[\mathbf{a}=a_{r}\,\hat{\mathbf{t}}_{r}+a_{\theta}\,\hat{\mathbf{t}}_{\theta}, \tag{2}\]  
so that the spacecraft equations of motion are  
\[\dot{r} =v_{r} \tag{3}\] \[\dot{\theta} =\frac{v_{\theta}}{r}\] (4) \[\dot{v}_{r} =\frac{v_{\theta}^{2}}{r}-\frac{\mu_{\odot}}{r^{2}}+a_{r}\] (5) \[\dot{v}_{\theta} =-\frac{v_{r}\,v_{\theta}}{r}+a_{\theta}, \tag{6}\]  
where the dot symbol denotes a derivative with respect to time \(t\), and \(\mu_{\odot}\) is the Sun's gravitational parameter. The resultant (spacecraft) heliocentric trajectory requires a suitable model describing the SWIFT-induced propulsive acceleration components as a function of  
Figure 1: Sketch of the polar reference frame and SWIFT-induced thrust contributions.  
the solar wind properties and of the propulsion system design parameters. Such a model is illustrated in the next section.
###### Abstract  
While it is recognized that some medical workers could receive significantly higher radiation doses to their hands than the routinely monitored personal doses, accurate retrospective dosimetry of their hand exposure is still challenging. To solve this issue, a combination of electron spin resonance (ESR) measurement and fingernails is worth to be investigated. However, the application of fingernail ESR dosimetry requires establishing an effective protocol to eliminate the background signal (BKG) which changes due to mechanical stress and other unclear factors, so that the radiation doses would be precisely evaluated from the radiation-induced signals (RIS) only. Thus, the authors investigated possible applications of antioxidants to remove or reduce the BKG in fingernails. In the present study, the effectiveness of chemical treatment using the dithiothreitol (DTT) reducing reagent was examined in irradiated and unirradiated fingernails. Chemically and non-chemically treated fingernails were subsequently exposed to 20 Gy of \({}^{137}\)Cs \(\gamma\)-rays and the time changes of the BKG and RIS were confirmed in two different storage conditions: vacuum chamber and freezer. The results show that the non-chemically treated fingernails displayed significant intra-individual variations in the peak-to-peak intensities of both BKG and RIS. RIS from chemically and non-chemically treated samples showed correlations after freezer storage; signals were more stable than the samples stored in the vacuum chamber. Moreover, while the BKG of non-chemically treated samples demonstrated higher levels than those chemically treated, the intra-individual variations were further reduced by the DTT treatment. Our results imply that the use of an antioxidant for hand washing of medical workers prior to starting their work could be effective in reducing the pre-existing free radicals in their fingernails. This also suggests a practical application of hand exposure monitoring using fingernails as a part of radiological emergency preparedness in occupations where radiation or radionuclides are used. Research for finding safer and easier-to-handle antioxidants is to be focused on in future studies.  
medical worker; radiological accident; emergency response; retrospective dosimetry; ESR; EPR; fingernails; antioxidant
## 3 Simulation Methods
### Computational Models
#### 3.1.2 SCA/C-S-H Model  
For cement mortar adhered to RCA surfaces, C-S-H accounts for about 60-70% of the hydration products and is the main component and strength source of the hydration products [53; 54]. In this study, a C-S-H model was established based on the method proposed by [PERSON] et al., as shown in Figure 6. The specific steps are as follows: (1) Tobormorite 11 was used as the initial model. After removing the water molecules of the initial model, a \(4\times 3\times 1\) supercell simulation was performed, and the monoclinic structure was changed to an orthorhombic one (Figure 6a); (2) To get the silicate chains to satisfy Q1 = 11.63%, Q2 = 67.44%, Q3 = 20.93%, and Ca/Si = 1.67, some SiO\({}_{2}\) groups and Si\({}_{2}\)O\({}_{5}\) groups were randomly removed from the silicate chains (Figure 6b); (3) The adsorption of water molecules to saturation was achieved using the grand canonical Monte Carlo (GCMC) method (Figure 6c); (4) To achieve equilibrium of model, the relaxation was continued under the NPT system (Figure 7d). Finally, the dimensions of the C-S-H model are 22.32 A \(\times\) 22.17 A \(\times\) 22.77 A. The density (2.45 g/cm\({}^{3}\)) of the constructed model is close to the results obtained from previous simulations or experiments, indicating that the model is reasonably valid [55; 56].  
Figure 7 illustrates the establishment of the SCA/C-S-H model. Firstly, the C-S-H model 2 \(\times\) 2 \(\times\) 1 supercell simulation was performed. Then, based on the SCA and cement mortar binding mechanism, the hydrolysis products of SCA were connected to the non-bridging oxygen atoms of the C-S-H matrix through Si-O-Si chemical bonding. The dimensions of the SCA/C-S-H model are 44.64 A \(\times\) 44.34 A \(\times\) 24.86 A.  
Figure 6: The construction of the C-S-H model (**a**) Orthogonal transformed model; (**b**) Remove some SiO\({}_{2}\) groups and dimer structures; (**c**) Absorb water molecules through the GCMC simulation; (**d**) Relax the model until equilibrium.
# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
## 2 Materials and Methods
### Cycle-Consistent Generative Adversarial Network
#### 2.2.2 Cycle Consistency and Identity Loss  
To ensure the entire framework with a heightened level of precision and authenticity compared to images in the target domains, we implemented an adversarial loss function, the cycle consistency loss. This function plays a crucial role in the training process, driving the networks to produce images that are not only realistic but also indistinguishable from those naturally occurring within the target domain, ensuring coherence between synthesis in both directions (from the MR to the CT domain and vice versa). This loss guarantees that, if we take an image from the MR domain, translate it to the CT domain, and then translate the resulting image back to the MR domain, we should obtain an image similar to the original. This property is crucial for preventing the model from generating incoherent translations.  
The cycle consistency loss (\(L_{cycle}\)) is mathematically defined as the difference between the original image and the image that is synthesized and then synthesized back to the original domain. This is denoted as follows:  
\[L_{cycle}(G,F)=\|F(G(I_{MR}))-I_{MR}\|_{1}+\|G(F(I_{CT}))-I_{CT}\|_{1}, \tag{1}\]  
where \(G\) and \(F\) are the functions implemented by the G and F generative components of the CycleGAN, respectively, \(I_{MR}\) is an image from the MR domain, \(I_{CT}\) is an image from the CT domain, and \(\|\|_{1}\) is the L1 norm. This loss ensures that synthesized images are consistent with their original counterparts.
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
###### Abstract  
Radio-frequency (RF) energy harvesting (EH) is emerging as a reliable and constantly available free energy source. The primary factor determining whether this energy can be utilized is how efficiently it can be collected. In this work, an RF EH system is presented. More particularly, we designed a dual-band RF to DC rectifier circuit at sub-6 GHz in the 5G bands, able to supply low-power sensors and microcontrollers used in agriculture, the military, or health services. The system operates at 3.5 GHz and 5 GHz in the 5G cellular network's frequency band FR1. Numerical results reveal that the system provides maximum power conversion efficiency (PCE) equal to 53% when the output load (sensor or microcontroller) is 1.74 k\(\Omega\) and the input power is 12 dBm.  
Internet of Things; radio frequency energy harvesting; dual-band rectifier; wireless power transfer; impedance matching network; voltage multiplier; power conversion efficiency; 5G; voltage doubler; FR1
# Performance Analysis of Ground Target Detection Utilizing Beidou Satellite Reflected Signals
## 2 Detection Method
### Phase Difference Analysis of Reflected Signal
\(\xi_{1}\) is the signal reflected twice between the ground and the target, which is represented by \(r_{s}\) in Figure 2.4 \(\xi_{2}\) is a part of the lost signal in Figure 3, which is shown in the blue part. The phase difference information of the reflected signal in the presence of the target can be expressed as  
Figure 2.5  
Because of the uncertainty of \(\xi_{1}\) and \(\xi_{2}\), the phase difference information cannot be calculated accurately, and the stable state cannot be achieved. In order to express the existence and non-existence of a target in a unified way, the phase difference information can also be expressed as  
Figure 2.6  
Figure 2.7 Ground target detection using BeiDou satellite reflected signals. \(s_{1}\), \(s_{2}\) and \(s_{3}\) are the direct signals from the BeiDou satellite; \(r_{target}\) is the satellite signal reflected by the target; and \(r_{ground}\) is the signal reflected by the ground. The blue part indicates that the BeiDou satellite signal cannot reach the ground surface covered by the target, and \(r_{s}\) represents the signal of the BeiDou satellite after secondary reflection between the ground and the target. where \(\beta\) is the signal attenuation coefficient. When a target appears, the stability of the tracking loop is affected, and the phase difference between the local replica and the received signal is close to 90\({}^{\circ}\). When \(\beta^{-1}\) is close to 0, it means that there is no target, and the phase difference information can be calculated normally. Assuming that the carrier-to-noise ratio of the reflected signal is represented as \((C/N_{0})_{target}\), when there is no target, it is \((C/N_{0})_{ground}\). If the target is located in the first Fresnel diffraction region of the satellite when the reflector antenna is in the azimuth of the satellite, the reflected signal from the satellite will decrease because of the existence of the target. In this case, the following relationship between these two parameters can be expressed as  
Figure 2.8  
When the direction of the reflector antenna deviates from the azimuth of the satellite, the change in the target position complicates the reflection of the satellite signal and changes the strength of the parameters in Equation (9). In this case, the power of the reflected signal will be enhanced at a certain time when the target is moving. Additionally, the following relationship between these two parameters can be expressed:  
Figure 2.9  
Here, Equations (12) and (13) describe changes in the intensity of the reflected signal when the target moves, which are verified in the second experiment presented in this paper.