esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
In this section, the analytical model of the proposed approach is established in terms of required interference transmission power (\(P_{I}\)), achievable data rate, packet delivery ratio, packet loss rate, and average end-to-end delay in the context of a vehicular network. Based on the proposed approach, the communication between V2X could be established reliably and efficiently.
# Comparing Link Budget Requirements for Future Space-Based Interferometers
## 3 Simulations of Phase Tracking Performance
### Simulated Experiments
These results suggest that, in the future, the retro-reflector design should be explored with relaxed optical power requirements relative to the transponder configuration. The arm response offers benefits over the dual-stabilized configuration in a fixed power range. When the unity gain frequency is greater than the corner frequency of the arm response, 5 kHz for the 200 km arm case in Figure 2, the stabilized and arm response laser frequency noise levels are approximately the same. As the unity gain frequency decreases, the tracking error becomes increasingly dominated by untracked components of the gravity signal. As such, the three arm lengths explored converge to a gravity signal-limited performance at low optical powers. It should be noted that while the error contribution of the gravity signal begins to limit the phase tracking performance, sufficient suppression can still be achieved, such that the error contribution in the science measurement band is below the requirement of 10 nm/\(\sqrt{\mathrm{Hz}}\)[6]. In the region between these two cases, the arm response case offers superior tracking performance compared to dual-stabilized sources.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 5 Application Case: Method Refinement (Myeongnyang Site)
### Results and Discussion  
In the sonar images obtained at the Myeongnyang site, the locations of the reflector were displayed in red in contrast to the surroundings. The data indicated that the reflector strongly reflected the sound waves. The reflector was identified in every sonar image, facilitating the production of the mosaic image, and a complete sonar image of the entire survey area was produced (Figure 10). The mosaic image shows the trench that should run southward gradually skewing westward. This discrepancy between the perception of the direction during the diving survey and the actual direction was likely due to the poor underwater visibility. Moreover, it demonstrates that SS imaging contributes to a more accurate understanding of the survey site, even in adverse underwater environments.
# Data Analysis of the TK-1G Sounding Rocket Installed with a Satellite Navigation System
## 4 Data Processing and Analysis  
The data transferred from the rocket sonde mainly include time, latitude and longitude, height, three-dimensional velocity vectors (north direction, east direction and vertical direction), star number, star location, star carrier-to-noise ratio, three-dimensional position dilution of precision (PDOP), atmospheric pressure, and atmospheric temperature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Height (km)**} & **Receivable** & **Actual Valid Data** & **Actual Data** & **Supplementary Data** & **The Data Validity** \\  & **Data Packet** & **Packet Number** & **Validity Rate** & **Packet Number from** & **Rate of Station 1** \\  & **Number** & **from Station 1** & **Station 1** & **Station 2 and 3** & **after Data Addition** \\ \hline
60–50 & 103 & 87 & 84.5\% & 9 & 93.2\% \\
50–40 & 199 & 176 & 88.4\% & 13 & 94.9\% \\
40–30 & 455 & 415 & 91.2\% & 31 & 98.0\% \\
30–20 & 1205 & 1139 & 94.5\% & 26 & 96.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of the date validity rate.
## 2 Preliminaries  
### Markov Decision Process  
Broadly speaking, Reinforcement Learning (RL) addresses sequential decision-making issues by seeking decision sequences that maximize the expected cumulative reward. The Markov decision process (MDP) provides a mathematical framework to model the motion control problem of the robot arm as a reinforcement learning problem. The reinforcement learning algorithm learns the optimal strategy through continuous interaction (trial and error), allowing the robot arm to make autonomous decisions. In RL tasks, MDPs are typically chosen as the mathematical models to describe decision problems, which are represented as tuples \((S,A,R,\rho,\gamma)\), where \(S\) and \(A\) denote finite continuous state and action spaces, respectively; the reward function \(R(R_{t}=r_{t}\mid S_{t}=s_{t},A_{t}=a_{t}):S\times A\) specifies the reward received from the environment upon transitioning between states; the transition probability \(\rho(S_{t+1}=s_{t+1}\mid S_{t}=s_{t},A_{t}=a_{t}):S\times A\to P(S_{t+1})\) defines the probability distribution of moving to state \(s^{\prime}\) after taking action \(a\) in state \(S\); and the discount factor \(\gamma\) adjusts the significance of future rewards. The agent's behavior is defined by the policy \(\pi(a\mid s):S\to P(a)\), which maps states to probability distributions across actions.
## 3 Result Analysis and Discussion
### Spectral Efficiency and Data Rate Analysis  
Filter-OFDM offers substantial benefits in enhancing the system's spectral efficiency to face the demands of high transmission rates. Based on [38; 39], the filter design used in F-OFDM effectively minimizes out-of-band emissions, enabling a very small gap between sub-bands and thus promoting efficient spectrum utilization. In the proposed Flip-F-OFDM technique, the absence of Hermitian symmetry (HS) allows all carriers \((N)\) to be used for data transmission, resulting in an efficient data rate [12]. Conversely, in conventional Flip-F-OFDM, the imposition of HS reduces the data transmission rate to \((N/2)\), as only half of the available carriers are utilized for data transfer [19].  
Otherwise, in DCO-OFDM, the data transmission is reduced to \((N/2)\) due to the IFFT Hermitian symmetry. In ACO-OFDM, the data transmission is reduced to \((N/4)\) because it employs HS and only uses odd subcarriers. Furthermore, although Flip-OFDM employs even and odd subcarriers, it requires two subframes to recreate the dipolar signal and decode the data. As a result, Flip-OFDM consumes double the number of samples as ACO-OFDM to convey double the data symbols. Thus, the transmission rate for both ACO-OFDM and Flip-OFDM is congruent [40]. Unlike Flip-OFDM and ACO-OFDM, HSF Flip-OFDM preserves a similar data rate as the proposed method since it does not require the imposition of HS [42; 44]. Table 1 summarizes the transmitted data rate, transmitted data rate ratio, and spectral efficiency for the previously mentioned techniques.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Transmitted Rate** & **Transmitted Rate Ratio** & **Spectral Efficiency** \\ \hline Proposed Flip F-OFDM & N & 100\% & \(\frac{\log_{2}M}{1+(2/N)}\) \\ Conventional Flip F-OFDM & N/2 & 50\% & \(\frac{(\log_{2}M)/2}{1+(2/N)}\) \\ HSF Flip-OFDM [44] & N & 100\% & \(\frac{\log_{2}M}{1+(2/N)}\) \\ Flip-OFDM [19; 43] & N/4 & 25\% & \(\frac{(\log_{2}M)/2}{1+(2/N)}\) \\ ACO-OFDM [19; 37; 43] & N/4 & 25\% & \(\frac{(\log_{2}M)/2}{1+(2/N)}\) \\ DCO-OFDM [14; 19; 43] & N/2 & 50\% & \(\frac{\log_{2}M}{1+(2/N)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The transmitted data rate, transmitted data rate, and spectral efficiency for the proposed Flip-F-OFDM, Conventional Flip-F-OFDM, HSF Flip-OFDM, Flip-OFDM, ACO-OFDM, and DCO-OFDM.
# Multi-Attention Recurrent Neural Network for Multi-Step Prediction of ChlorophyllI Concentration
## 3 Baseline Methods and Results
### Results
The factors with large weights are more important for the prediction of chlorophyll concentration. We observe that the influence of factors on prediction tasks varies, and this influence changes over time. It can be concluded that MaRNN not only distinguishes the importance of each variable in prediction, but also captures the dynamic changes in the importance of variables. As shown in Figure 3, SDO, DO, Win_u, and Win_v all obtained high weights for both \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Horizon = 1**} & \multicolumn{2}{c}{**Horizon = 3**} & \multicolumn{2}{c}{**Horizon = 6**} & \multicolumn{2}{c}{**Horizon = 12**} & \multicolumn{2}{c}{**Horizon = 24**} \\ \cline{2-11}  & **MAE** & **MAPE** & **MAE** & **MAPE** & **MAE** & **MAPE** & **MAE** & **MAPE** & **MAE** & **MAPE** \\ \hline MTSMF & 0.4995 & 0.4071 & 0.6984 & 0.6113 & 0.7355 & 0.7370 & 0.7917 & 0.7479 & 0.9056 & 1.0001 \\ DSTP-RNN & 0.4676 & 0.3690 & 0.5809 & 0.5052 & 0.6629 & 0.5563 & **0.6838** & 0.5996 & 0.7532 & 0.6644 \\ CGA-LSTM & 0.4773 & **0.3559** & 0.5839 & 0.4797 & 0.6893 & 0.5305 & 0.7322 & 0.5913 & 0.7287 & 0.7164 \\ DyAt-Nets & 0.4655 & 0.3793 & 0.6217 & 0.5978 & 0.7185 & 0.7014 & 0.7282 & 0.6232 & 0.8411 & 0.7783 \\ HCA-GRU & 0.4828 & 0.3698 & 0.5864 & 0.5091 & 0.6827 & 0.5935 & 0.7182 & 0.5867 & 0.7446 & 0.6866 \\ MaRNN & **0.4475** & 0.3636 & **0.5494** & **0.4056** & **0.6554** & **0.5258** & 0.6893 & **0.5872** & **0.6945** & 0.6608 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of different methods compared in TAB_Data for different predictive horizons. Figure 2: Performance comparisons (MAE) among different methods and different datasets when \(\tau=24\).
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 2 Materials and Methods
### Data Preparation  
The dataset of 74,000 data points, generated from Aspen HYSYS simulations as described in Section 2.1, was prepared to ensure compatibility with the XGBoost, RF, DT, and KNN models for predicting the minimum methanol quantity required. The preparation process involved several key steps to enhance model training and testing efficacy. Initially, the dataset was randomly partitioned into a training set (80%; 59,200 data points) and a testing set (20%; 14,800 data points), following standard ML practices to evaluate model generalization [17]. This split balanced sufficient training data with robust validation, mirroring approaches in similar predictive studies [18].  
To address potential scale disparities among the features of flow rate (10,000-500,000 kg/h), temperature (\(-\)15-25 \({}^{\circ}\)C), pressure (5-100 bar), gas specific gravity (0.55-0.85), and water content (0.0007-0.004 mole %), data normalization was applied using min-max scaling at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Unit** & **Minimum** & **Maximum** \\ \hline Temperature & \({}^{\circ}\)C & \(-\)15 & 25 \\ Pressure & bar & 5 & 100 \\ Flow & Kg/h & 10 K & 500 K \\ Water content & Mole\% & 0.0007 & 0.004 \\ Specific gravity & - & 0.55 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature ranges used to generate training data.  
a [0, 1] range [16]. This step ensured equitable feature influence, particularly critical for distance-based models like KNN. Outliers, identified as values exceeding three standard deviations from the mean for each feature or methanol output, were removed, affecting less than 1% of the data, to mitigate noise impact [17]. Missing values were absent due to the controlled synthetic generation process.  
The prepared dataset, comprising the five input features and the target variable (minimum methanol quantity), was then formatted for compatibility with Python-based ML libraries (e.g., scikit-learn and XGBoost), facilitating the subsequent model implementation. This preparation ensured a reliable foundation for training, testing, and evaluating the predictive performance of the proposed models under consistent conditions.
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 3 Method
### Framework Overview  
As shown in Figure 4, the D-SWIM framework is composed of two parts: the _software compilation tool_, (SW tool), and _hardware generation tool_ (HW tool). The HW tool generates hardware components following the D-SWIM method. Note that D-SWIM mainly optimizes the buffer hardware which is a general component of the streaming architecture, where specific arithmetic units (operators) are  
Figure 3: (**a**) Shows that the image lines are sequenced into a stream and then clipped to multi-pixel blocks; FPGA accepts one block in each cycle. (**b**) Shows the general pixel buffer in which the BRAM-misalignment issue occurs. (**c**) Shows the SWIM buffer avoids the BRAM-misalignment using specific BRAM partition.  
generated by FPGA synthesis tools without further optimization. According to the principle of stream processing in Figure 1, the Buffer of D-SWIM is composed of multiple line buffers (LBs), and the number of LB (\(N_{LB}\)) is equal to the height of a 2D stencil (denoted as \(H\)). Details on the construction of LBs will be elaborated in Section 3.2. Inside the buffer hardware, the Controller module provides the control signals to the underlying BRAMs to realize certain buffer behavior. Note that we employed an Instruction Memory to provide the control words which can be pre-generated and loaded into the memory during run-time. By doing so, the D-SWIM hardware can accommodate the arbitrary image size by quickly switching the control words in the Instruction Memory in a few cycles instead of performing FPGA reconfiguration. The SW tool generates the specific instruction words for the Controller based on the pixel block size and image size, that will be described in Section 3.3.  
The design parameters used in D-SWIM are listed in Table 1. \(N_{blk}\) is the number of pixels in one block; \(N_{line}\) is the number of pixels in one image line (image width), and \(N_{height}\) is the image height; \(N_{line-max}\) is the largest possible value of \(N_{line}\) that decides the volume of the Buffer; and \(H\) is the height of the 2D stencil pattern that determines the number of image lines stored in the buffer (number of LB). Table 1 also highlighted the use scope of each parameter. Note that the HW tool only invokes \(N_{blk}\), \(N_{line-max}\), and \(H\), which are independent to the image size.
## 3 Methods
### Environment Attention Module
#### 3.4.1 Trajectory Density Map  
A large amount of pedestrian trajectory data explains the degree of \"preference\" that pedestrians have for different areas, indicating that the model needs to pay more attention to these areas. This way, we can address the issue that previous work, which only extracts environmental information, cannot establish a connection with pedestrians. In order to obtain the trajectory density values for each spatial location, this paper divides the trajectory data using a Gaussian kernel function based on historical trajectory data, resulting in a trajectory density map for spatial locations in each scene. This trajectory density map illustrates the areas of the congregation for pedestrians within the scene and their certain walking preferences. The Formula (11) for the Gaussian kernel density. Figure 5 shows the spatial probability density feature map of the dataset in this paper.  
\[K(x_{i}^{t},y_{i}^{t})=e^{-\gamma\left\|x_{i}^{t}-y_{i}^{t}\right\|^{2}} \tag{11}\]  
where \(\gamma\) is the hyperparameter of the Gaussian kernel function. Coordinates data \(\left(x_{i}^{t},y_{i}^{t}\right)\) is the coordinates of the target pedestrian \(i\).  
Figure 5: Spatial probability density map, where (**a**) is the scenario from the ETH dataset, (**b**) the scenario from the hotel dataset, (**c**) the scenario from the zara1 dataset, and (**d**) the scenario from the zara2 dataset. The thermal bar scale on the right side of the figure represents the density of trajectories in different regions of the scenario. The coordinate origin of these four images is at the bottom left corner. The different density values reflect the density of pedestrian historical trajectories in various areas of the scene and indicate the level of “preference” pedestrians have for different regions. When the density value of a particular area is high, the model needs to focus on the pedestrian density and trajectory distribution within that area to avoid predicting trajectories that result in “collisions”.
## 2 Materials and Methods  
### Experimental Photovoltaic Greenhouse  
The experimental photovoltaic greenhouse was built in the agricultural science practice base of Haidian campus of Hainan University (110\({}^{\circ}\)33\({}^{\prime}\) east longitude, 20\({}^{\circ}\)05\({}^{\prime}\) north latitude). The greenhouse span was 5.4 m with totally three spans and 16.2 m in total length; the standard width was 4 m with totally four widths and 16 m in total width; the floor area of the green house was 259.2 m\({}^{2}\). The shoulder height was 2.5 m, and the ridge height was 3.7 m. The direction of the greenhouse was from north to south, with a lean-to roof towards the south. The main structure of the greenhouse was a hot-dip-galvanized steel pipe. The roof other than the photovoltaic modules was covered with a 0.15 mm thick film. The zigzag vent was covered with a 40 mesh insect-proof screen. All the facades of the greenhouse were covered with a 90% shading net (the experimental greenhouse was small in size, and hence, the shading net was used to protect it from ambient scattered light, but for mass production, an insect-proof screen will be used), and the sunshade net was a plastic net formed by bending the warp and wfet wires with knitting needles and by intertwining them. It was inlaid and fixed on the greenhouse facade. The 90% shading rate can theoretically block 90% of the light flux from entering the room, so that the roof became the only light-transmitting channel. The photovoltaic modules adopted polycrystalline silicon battery pack with a size of 1650 mm \(\times\) 990 mm and a capacity of 250 W. Four modules were contained in one group, and six groups were deployed in one span. Each greenhouse was deployed with three spans (72 modules), with an 18 kW capacity in total. The installed capacity per unit area of the photovoltaic greenhouse was about 69 W/m\({}^{2}\), the annual average daily power generation hours in Haikou area is about 4.56 h, and the theoretical average daily power generation of the experimental greenhouse is about 82.08 kWh. A 0.6 m wide photic belt was arranged between each two modules for film covering, and the film covering rate was 76.9% (notes: covering rate = photovoltaic module coverage/(photovoltaic module coverage + photic belt coverage); the film coverage between front and rear spans is a photovoltaic array spacing designed to avoid the photovoltaic modules which are not sheltered from sunshine during 9:00-15:00 in winter [14]. This is not considered in the coverage rate calculation). All the spaces between roof covering modules and between film coverings were sealed by sealants for roof rain-proof. The rainwater on the roof coverings was discharged to the rain pipes at two sides of the greenhouse through the gutter on the shoulder. The simulation model and the real structure of the greenhouse are shown in Figure 1.  
Figure 1: Simulation model and real structure of the experimental photovoltaic greenhouse: (**a**) simulation model; (**b**) real model.
## 11 Conclusions  
This research involved an in-depth analysis and optimization of the HCS of the MAR system. By establishing a mathematical model, it is clear that the system is a nonlinear and strongly coupled system with two inputs and two outputs.  
The HCS designed based on ADRC utilizes its characteristics of a low requirement for modeling accuracy and effective compensation for comprehensive disturbances to achieve decoupling control of the system. In response to the problem that the traditional TD cannot stably track the high-frequency position feedback signal of the MRC, a new differential tracker, SYSTD, is constructed. The simulation shows that SYSTD balances rapidity and stability well and significantly improves the tracking accuracy of the position feedback signal.  
At the same time, the proposed sensor-less control method simplifies the system structure and improves the accuracy of position estimation by winding survey coils to estimate the position of the MRC.  
Verified by simulation and experiments, the improved HCS of the MAR system has good dynamic and static characteristics and robustness. The positioning accuracy can reach \(\pm\)5 \(\upmu\)m in simulation, and the control accuracy can reach \(\pm\)2 \(\upmu\)m in experiments. It can effectively achieve decoupling control. The research results of this paper lay a solid foundation for the practical application of the MAR system and are expected to promote the wide application of this technology in fields such as coordinate survey.  
Future work will integrate temperature compensation and extend the framework to include 3D multi-axis coordination.  
Conceptualization, G.T., C.T., J.S. and S.D.; methodology, G.T., C.T., J.S. and S.D.; software, G.T. and S.D.; validation, G.T. and S.D.; formal analysis, G.T. and S.D.; investigation, G.T. and S.D.; resources, G.T. and S.D.; data curation, G.T. and S.D.; writing--original draft preparation, G.T. and J.S.; writing--review and editing, G.T. and S.D.; supervision, C.T. and J.S.; funding acquisition, C.T. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the General Program of Chongqing Natural Science Foundation, grant number CSTR2022 NSCQ-MSX1350, and the National Key Research and Development Project, grant number 2019 YFB1707505.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors would like to thank the students from the Power Electronics and Electric Drive Innovation Base of the School of Electrical and Information Engineering at Beihua University.  
The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.