esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### EVs of Different LULC Types  
The ESVs were further calculated for all LULC types at different scales (Figure 5). The ESVs of RES and ESA are different for different LULC types at different scales, and the ESVs rankings corresponding to the LULC types differ between the two LULC products. The ESVs ranking of ESA for all LULC types is GL > WO > WA > CL > WL > UL; the ESVs of GL can reach more than 4200 billion yuan, representing the maximum, while that of UL is less than 70 billion yuan, signifying the lowest ESVs. For RES, the ESVs ranking for all LULC types is WO > CL > GL > WA > WL > UL; the ESVs of WO exceeds 4100 billion yuan, while that of UL is less than 2 billion yuan. In addition, the overall ESVs calculated by different LULC product data also differ (Table 1). The ESVs calculated by ESA is about 8000 billion yuan, and the ESVs calculated by RES is about 5700 billion yuan. Compared with the difference among the ESVs of ESA, the difference among the ESVs of RES is approximately 28%, and this difference tends to decrease with increasing scale. In general, the ESVs vary when calculated by the LULC data of different products or even by the LULC data of the same product at different scales.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scale (m)**} & \multicolumn{2}{c}{**ESVs (Billion Yuan)**} & \multirow{2}{*}{**Difference (\%)**} \\ \cline{2-3}  & **ESA** & & **RES** \\ \hline
300 & 7962.59 & 5647.11 & 29.08 \\
600 & 8001.19 & 5697.90 & 28.79 \\
900 & 8002.90 & 5727.62 & 28.43 \\
1200 & 8018.20 & 5747.61 & 28.32 \\
1500 & 8022.77 & 5759.58 & 28.21 \\
1800 & 8033.11 & 5771.83 & 28.15 \\
2100 & 8041.12 & 5782.11 & 28.09 \\
2400 & 8011.67 & 5789.69 & 27.73 \\
2700 & 8037.35 & 5799.36 & 27.84 \\
3000 & 8042.39 & 5802.37 & 27.85 \\
3300 & 8024.51 & 5806.49 & 27.64 \\
3600 & 8020.77 & 5814.64 & 27.51 \\
3900 & 8031.54 & 5812.77 & 27.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall ESVs of ESA and RES at different scales and their differences.  
Figure 4: Dynamic degrees of LULC in RES (**a**) and ESA (**b**).
## 3 Identifying Promising Vacant Technologies  
### Overall Research Framework  
The overall process of our proposed approach consists of the following three steps: the identification of vacant technology fields, technical analysis of vacant technology fields, and investigation into emerging and vacant technology fields. Each step consists of substeps to achieve the goal, as shown in Figure 1.  
The first step is an attempt to develop a GTM-based patent map based on the patent documents by analyzing the IPC vector and locating the patents on a two-dimensional map to identify vacant technology fields that contain relatively few patents compared to other areas. For the second step, we explore the potential vacant technology fields by identifying potentiality with a trend analysis. The final step derives a technology development strategy through IPC information and significant keywords. Technical analyses of the vacant technology field include criticality and trend analyses. In the criticality analysis, patents are visualized based on the number of applications, enabling us to identify which technical fields are technically valuable through TAI and TII indicators and trends. Trend analysis can be performed based on patent application trends. Finally, we can identify the relationship between IPC and the significant keywords. A more detailed explanation of the process is provided below.
# Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM
## 5 Conclusions  
The government has made some efforts of Jakarta to control COVID-19 cases, such as appeals for quarantine and vaccination. This effort is carried out to reduce the number of cases of COVID-19 and the number of deaths caused by COVID-19. Therefore, it is necessary to forecast daily positive cases and deaths caused by COVID-19 in Jakarta for the future. Based on the findings obtained from this study, daily positive and death cases of COVID-19 are predicted to increase from 4 April 2022 to 10 April 2022, but it is not significant.  
After more than two years of a worldwide COVID-19 pandemic, it is hoped that COVID-19 will become an endemic disease. The forecast results of this study show that it is very likely that COVID-19 is no longer a pandemic but has transformed into an endemic in Jakarta. The results of modeling support this carried out using NNAR (24,9,1), which shows that COVID-19 cases are predicted to increase, but it tends to be sloping. Other than that, forecasting results for the death cases due to COVID-19 using LSTM are expected to experience a very small increase, only one or two death cases each day.  
This condition can be achieved with cooperation between the government and the community in dealing with COVID-19, specifically in implementing social restrictions policy (PPKM) and vaccination. Therefore, the government and the community must continue to collaborate to reduce the spread of COVID-19.  
Moreover, further study is needed to get a better accuracy of the estimation results.  
Conceptualization, R.S.P., T.T., R.E.C., F.G., F.A.P., M.N.A. and B.N.R.; formal analysis, R.S.P., T.T., R.E.C., F.A.P., M.N.A. and B.N.R.; investigation, R.S.P., T.T., R.E.C., F.G., F.A.P., M.N.A. and B.N.R.; resources, R.S.P., T.T., R.E.C., F.G., F.A.P., M.N.A. and B.N.R.; data curation, R.S.P., T.T., R.E.C., F.G., F.A.P. and M.N.A.; writing--original draft preparation, R.S.P., T.T., R.E.C., F.G., F.A.P. and M.N.A.; writing--review and editing, R.S.P., T.T., R.E.C., F.G., F.A.P. and M.N.A.; visualization, R.S.P., T.T., R.E.C., F.G., F.A.P. and M.N.A.; supervision, R.S.P., T.T., R.E.C. and B.N.R.; project administration, R.S.P., T.T., R.E.C. and B.N.R.; funding acquisition, R.S.P., T.T., R.E.C. and B.N.R. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Department of Statistics, Padjadjaran University, and Rector Universitas Padjadjaran, who supported this research with Academic Leadership Grant (ALG) 2022.  
Not applicable.  
The Jakarta responsive COVID-19 web portal can be accessed via corona.jakarta.go.id (accessed on 3 April 2022). Supplementary material: [[https://github.com/[PERSON]/APPSCLSTM/](https://github.com/[PERSON]/APPSCLSTM/)]([https://github.com/[PERSON]/APPSCLSTM/](https://github.com/[PERSON]/APPSCLSTM/)).  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 4 User Finite State Machine for Proposed Scheme
### State Transition Rules  
In this FSM model, we describe state transition rules to explain the scenario of state machines. The \(i\)-th user follows the state transition rules at every subframe (1 ms).  
**State transition rules:**  
**c1. \(\gamma_{i}>Q_{\text{in},k_{s}}\).**  
**c2.** event A3 entering state.  
**c3. \(\gamma_{i}<Q_{\text{out},k_{s}}\).**  
**c4.** T310 expiry, \(T310(i)=T310_{\text{set}}(l_{s})\).**  
**c5.** TTT expiry, \(TTT(i)=TTT_{\text{set}}(l_{s})\).**  
**c6.** \(TTT(i)=TTT_{\text{set}}(l_{s})\) and \(\gamma_{i}>Q_{\text{in},k_{s}}\).**  
**c7.** \(N_{r}(i)<N_{\text{set}}(l_{s})\).**  
**c8.** \(N_{r}(i)=N_{\text{set}}(l_{s})\).**  
**c9.** triggered beam switching, satisfied (22).**  
**c10.** triggered handover, satisfied (21).**  
**c11.** \(T_{p,i}=T_{p}\).**  
**c12.** \(\gamma_{i}<\gamma_{\text{th}}\).**  
**c13.** \(\gamma_{i}>\gamma_{\text{th}}\).**  
**c14.** reselect source beam.**  
**c15.** reselect target beam,**  
**where** \(Q_{\text{in},k_{s}}\) **is a threshold value to determine if the channel recovery is successful.** \(Q_{\text{out},k_{s}}\) **is a threshold value to determine if a channel status is too poor quality to maintain communication.** \(T310_{\text{set}}(l_{s})\) **is a timer for waiting for the recovery. Once** \(i\)**-th user recognizes the channel state is out-of-sync, such as data pending or no ACK,** \(T310(i)\) **is initiated.** \(TTT_{\text{set}}(l_{s})\) **is a time-to-trigger (**\(TTT\)**) of serving beam** \(l_{s}\)**. Once the** \(i\)**-th user goes to the \"event A3 entering state\",** \(TTT(i)\) **is initiated from 0. After** \(TTT(i)\) **increases and becomes the same value as** \(TTT_{\text{set}}(l_{s})\)**, the user can send an MR for notifying the \"event is triggered\" to the serving cell.** \(N_{\text{set}}(l_{s})\) **is the maximum-number of MR for a user in the serving beam,** \(l_{s}\)**. MR can be executed multiple times without any beam switching nor handover; nonetheless, the number of sent MRs at the** \(i\)**-th user,** \(N_{r}(i)\)**, cannot exceed** \(N_{\text{set}}(l_{s})\)**.** \(T_{p}\) **is the handover preparation time, and** \(\gamma_{\text{th}}\) **is the SINR of guaranteeing the success of handover or beam switching via random access of the target.  
Figure 3: User finite state machine for proposed scheme.
# Identification of Crude Distillation Unit: A Comparison between Neural Network and Koopman Operator
## 1 Introduction
(2018) explored five machine learning models for optimizing product yields based on varying feed properties and operating conditions. These models included decision tree regression, support vector regression, ANN, random forest regression, and extreme gradient boosting (XGBoost), with XGBoost demonstrating superior performance. While ANNs have proven to be suitable for modeling a CDU, they rely on static mapping of outputs from inputs using data; this limits our understanding of the physical or chemical mechanisms governing the CDU. Linear predictors can be utilized for nonlinear systems to effectively capture and model nonlinear behavior while also identifying the dynamic response of a linear system. From a control engineering perspective, this approach provides valuable insights into the process by analyzing its time-domain characteristics ([PERSON] et al., 2018). In this context, [PERSON] (2017) introduced his operator, which describes the evolution of measurements in Hamiltonian systems over time. [PERSON] and his collaborators (Mexico and others, 2018; [PERSON] and others, 2018) expanded these concepts to include nonconservative systems, extending its utility to a wide range of applications, solar panels (Mexico and others, 2018), power systems (Mexico and others, 2018), robotics ([PERSON] et al., 2016), autonomous driving ([PERSON] et al., 2016), biology ([PERSON] et al., 2016), and traffic flow ([PERSON] et al., 2016), to list a few. The quality of a model is usually evaluated by its ability to generalize to new, unseen data. In regression tasks, if the model is properly selected in terms of structure and hyperparameters, and overfitting is avoided, the problem can be considered conceptually solved. However, when dealing with real data from advanced process control units, inputs to the model may fall outside the training domain due to factors like model-plant mismatches, poor tuning, or stringent constraints in the closed-loop system. In such situations, it is essential for the regression model to make acceptable predictions or, at the very least, not fail. This subject is the topic of this paper. In this work, a comparative analysis of modeling a CDU under different experimental conditions is conducted. This includes the Koopman operator in both linear (KL) and bilinear (KB) forms, as well as a NARX\(-\)NN model. The performance of these models is tested in real-world settings, such as gain and delay mismatches, nonlinearities, and disturbances. Bayesian optimization is used for hyperparameter tuning to ensure a fair comparison. The remainder of the paper is structured as follows: Section 2 provides preliminaries of the methodologies used. Section 3 covers the process description and data generation. The results and discussion are presented in Section 4. Finally, the conclusion and suggestions for future work are outlined in Section 5.
# Pose-Driven Body Shape Prediction Algorithm Based on the Conditional GAN
## 4 Results
The image quality of the real-world images was lower than that of the training data because they were resized to 256 \(\times\) 256. Furthermore, caps and shoes were present in the real-world images, which were not included in the training data, making accurate reconstruction of these areas difficult. Finally, artifacts were identified in the real-world outputs that were not present in the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **RMSE** & **PSNR** & **SSIM** \\ \hline Pix2 Pix (baseline model) & 0.0971 & 20.7997 & 0.9393 \\ Pix2 Body (our model) & 0.0888 & 21.7656 & 0.9487 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Front full-body images—quantitative comparison. previous qualitative results, indicating insufficient background removal. Nonetheless, despite the low accuracy observed in the generalization test, the results demonstrate that Pix2 Body is capable of generating body shapes from actual full-body photographs of clothed individuals, rather than from template-like synthetic body shapes. Moreover, if self-portraits are used, it may be possible to infer body shapes from both the front and side views, suggesting the scalability of this approach for healthcare applications such as body measurement. Since this study predicts human body shape through 2D image translation, we evaluated performance using image-based metrics such as SSIM, PSNR, and RMSE on the synthetic SHADER dataset specifically constructed for this research. To contextualize our approach with respect to major prior works in 3D body reconstruction, Table 4 presents a comparative summary of modeling strategies, benchmark datasets, and reported evaluation metrics. Among these, SSIM, PSNR, and normal consistency are similarity-based metrics, where higher values indicate better performance. In contrast, RMSE, MPJPE, PVE, P2S, and chamfer distance are error-based metrics, where lower values represent better accuracy. These distinctions help clarify the comparative positioning of Pix2 Body relative to the existing methods. Table 4 highlights three key structural differences that preclude direct metric-based comparison: (1) differing evaluation criteria (2D vs. 3D metrics), (2) different datasets, and (3) fundamentally distinct modeling paradigms. Given these factors, direct performance ranking based on numerical metrics is neither feasible nor academically meaningful. Rather, this comparison clarifies Pix2 Body's distinct design goals, emphasizing visual plausibility, inference efficiency, and deployment practicality within a lightweight 2D framework. This  
Figure 11: Example results on real photograph data. alternative is especially valuable for real-world applications requiring fast and intuitive shape estimation without reliance on full 3D supervision.
# Low-Profile High-Efficiency Transmitarray Antenna for Beamforming Applications
## 4 Conclusions  
A 2D partial H-plane waveguide slot array antenna integrated with an FSS is proposed and designed to achieve a beamforming TA in this paper. The designed TA has a high taper efficiency by the multi-array sources and the compactness of the partial H-plane waveguide. Moreover, the proposed TA can inherently have a high spillover efficiency because the FSS cell is located just above the radiating slot, which can prevent the leakage of power. The FSS is designed by a square patch array based on a metallo-dielectric FSS and is formed by three dielectric layers with equally spaced capacitive printed patches. To verify its feasibility, three types of FSSs to operate a beamforming angle of \(-40^{\circ}\), \(-20^{\circ}\), \(0^{\circ}\), \(+20^{\circ}\), and \(+40^{\circ}\) are designed at 12 GHz. The F/D and aperture efficiency of the proposed antenna are 0.016 and 69.2%, respectively. From the measured results, the proposed TA is confirmed to have good beamforming characteristics and high aperture efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Ref. [16]** & **Ref. [25]** & **Ref. [26]** & **Ref. [27]** & **Ref. [28]** & **This Work** \\ \hline Operation frequency (GHz) & 31 & 10.3 & 18 & 14.25 & 20 & 12 \\ \hline F/D & 1.27 & 0.49 & 7.1 & 1.4 & 1.37 & 0.013 \\ \hline \multicolumn{5}{c}{Aperture efficiency (\%)} & 61.3 & 21.8 & 51.4 & 60.3 & 53.5 & 69.2 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of the performances between the previously published TAs and the proposed antenna.  
Figure 8: Full-wave simulated and measured far-field radiation patterns (\(x\)–\(z\) plane).  
Figure 7: Photographs of the fabricated beamforming 8 \(\times\) 10 partial H-plane waveguide slot array antennas using an FSS. (**a**) \(0^{\circ}\), (**b**) \(+20^{\circ}\), and (**c**) \(+40^{\circ}\).  
**Author Contributions:** Conceptualization, J.-G.L. and J.-H.L.; methodology, J.-G.L.; software, J.-G.L.; validation, J.-G.L.; formal analysis, J.-G.L.; investigation, J.-G.L.; writing--original draft preparation, J.-G.L.; writing--review and editing, J.-G.L. and J.-H.L.; visualization, J.-G.L.; supervision, J.-H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2015R1A6A1A03031833) and by the MSIT (Ministry of Science and ICT), Korea, under the Innovative Human Resource Development for Local Intellectualization support program (IITP-2023-RS-2022-00156361) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 MIMO Radar Simulation Platform
### Signal Receiving and Processing Module
The calculation error will be reduced if the FFT number is large enough, thus the final solution of the Doppler frequency shift is more accurate, but the corresponding amount of calculation required will become larger. The number of points can be changed according to the requirements of parameter accuracy, and the number must be an integer power of 2. In order to balance the calculation and system accuracy, the number of FFT in this platform was 32 for the simulation. If the target was not detected, no significant peaks are found in the received waveform (Figure 8). The distance between the target and the radar can be obtained by (22). \[D=\frac{c\Delta t}{2}=\frac{cn}{2f_{s}} \tag{22}\]  
The Doppler frequency shift is also related to the pulse repetition frequency, which determines the upper limit that the system can measure. In different application situations, the pulse repetition frequency needs to be carefully selected to satisfy different target speeds. After the signal processing, if the target was detected, as is shown in Figure 9, there is a peak in amplitude and its position corresponds to the horizontal coordinate of sampling points, which means the position of the target. The blind speed phenomenon exists in the pulse radar mechanism. If the range is too small, the target may not be able to detect the result correctly when the target speed is greater. In order to solve this problem, pulse compression (PC) can be used to compress the received waveform into narrow pulses to improve the radar range resolution. In the simulation system, the problem can be solved by accessing a pulse compression element. The structure is shown in Figure 10. This method can effectively improve the measurement performance of the MIMO radar. It is worth noting that in the single RF link in MIMO radar systems, the two output signals are modulated by two sets of orthogonal bases, so the receiver also needs to establish a group of filters corresponding to the different orthogonal bases, which are controlled by SPDT switch. After the two groups of signals pass through different filters, we will obtain the speed and position information and the weighted average value as the final results. The traditional MIMO radar with the same \(2\times 2\) element structure cannot form groups of orthogonal bases even though there is no need for the switches, which has its own advantages and disadvantages. There is no doubt that, in the miniaturization of radar, single link RF MIMO radar is more suitable than traditional MIMO radar. Figure 8: The target was not detected after the pulse detection (PD) process. If the target was not detected, no significant peaks are found in the received waveform. Figure 9: The target was detected after the PD process. If the target was detected, there will be significant peaks in the received waveform, as is shown in the figure.
## 1 Introduction
The initial and main requirement was the unlimited range of communication between the drone and the ground segment (including the pilot GCS and the data processing center), accounting for telemetry and live video to monitor transport  
Figure 1: General objectives to address with the 4G communication framework. Note that this framework is extensive to many applications, although in this study we apply it to traffic monitoring. tion infrastructures and traffic, namely StratoTrans project [18]. This requirement involves overpassing the local radio-link communications between the GCS and the drone Flight Controller (FC). The objective is tackled by using the 4G network, which allows to link the drone with the GCS and with any device connected to internet wherever it is located, being the latter the main interest of the presented framework. The purpose of the StratoTrans project is to run CV algorithms over the video acquired to extract object identification using convolutional networks [19; 20], tracking trajectories [21; 22], and velocity estimation [23; 24]. Therefore, the drone acquired video is sent to a data server to be processed and finally it is visualized in the operator's headquarters computer or in a user's mobile device.
## 3 AIS and Small-Satellite Constellation Configurations
We assume that the satellite and the ship have the same position at the three times mentioned above. In fact, it is possible to satisfy the coverage of the voxels in the reconstruction areas if there are enough small satellites carrying AIS receivers at different moments, or if we design a suitable constellation of small-satellite orbits to pass through our area at all three times. We can also assume different constellations of small satellites at different moments to achieve coverage of the area to be reconstructed. The distribution of small satellites and AIS-installed ships are shown in Figure 5.  
Figure 4: Satellite orbits of the four small-satellites constellation and position one of the AISs installed on ships. Figure 5: Position of small satellites and AIS-installed on ships. The distribution area of AIS-carrying ships is set to be over the sea, and it is assumed to be a naval congested area on the ship channel; therefore, there is a good opportunity for CIT. There are two kinds of AIS transponders installed on different ships: Class A transponders, with a broadcast period of 2 to 10 s, and Class B transponders, with a broadcast period of 30 s or 3 min for different ship traveling speeds [30]. A Class A transponder is chosen in this study, and the signal sampling interval is 10 s with a duration of 3 min in the simulation, during which the ionosphere is regarded as unchanged. Under this assumption, within the reconstruction area we set, we can obtain 2375 effective signal rays through the area, and 89.6% of the voxels can be transversed by the small-satellite constellation-AIS signal rays.