esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study we characterized the process-structure interactions in melt extrusion-based 3D bioplotting of polycaprolactone (PCL) and developed predictive models to enable the efficient design and processing of scaffolds for tissue engineering applications. First, the effects of pneumatic extrusion pressure (0.3, 0.4, 0.5, 0.6 N/mm\({}^{2}\)), nozzle speed (0.1, 0.4, 1.0, 1.4 mm/s), strand lay orientation (0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\)), and strand length (10, 20, 30 mm) on the strand width were investigated and a regression model was developed to map strand width to the two significant parameters (extrusion pressure and nozzle speed; \(p<0.05\)). Then, proliferation of NIH/3T3 fibroblast cells in scaffolds with two different stand widths fabricated with different combinations of the two significant parameters was assessed over 7 days, which showed that the strand width had a significant effect on proliferation (\(p<0.05\)). The effect of strand lay orientation (0\({}^{\circ}\) and 90\({}^{\circ}\)) on tensile properties of non-porous PCL specimens was determined and was found to be significantly higher for specimens with 0\({}^{\circ}\) lay orientation (\(p<0.05\)). Finally, these data were used to develop and experimentally validate a finite element model for a porous PCL specimen with 1:1 ratio of inter-strand spacing to strand width.  
3D Bioplotting; scaffolds; finite element analysis; tensile strength; PCL +
Footnote †: journal: Applied Physics Letters
### Article Structure  
The paper is organized into sections to provide a comprehensive overview of FSO communication systems in the context of 5G and beyond. Having introduced the background and motivation along with the objectives and scope of the paper, Section 2 delves into the fundamentals of FSO communication systems, detailing their components and performance metrics. Section 3 addresses the various challenges that FSO systems face, including atmospheric turbulence, weather-induced signal degradation, and alignment issues. Section 4 explores the enabling technologies crucial for advancing FSO communication, such as AO, modulation schemes, and error correction codes, as well as the integration of hybrid systems. Section 5 highlights emerging trends, including the use of artificial intelligence and machine learning to optimize system performance. Section 6 discusses the open challenges and future prospects, providing insights into potential research directions and long-term applications. Finally, Section 7 concludes the paper by summarizing the key findings and emphasizing the importance of continued innovation in the field. The structure and organization of the manuscript adhere to the framework illustrated in Figure 3. Each section is meticulously arranged to follow the sequence and logic depicted in the figure, ensuring a coherent and comprehensive presentation of the content.
# Analyzing the Characteristics of Soil Moisture Using GLDAS Data: A Case Study in Eastern China
## 3 Results and Discussion
### Discussion  
As we know, GLDAS output can be highly uncertain [12; 13], but if we combine data validation with in-situ observations, it may help us to overcome the limitation of short time scales and restricted space. With the advanced development of models and assimilation methods, we believe GLDAS outputs will be accurate and thus useful to help understand the correlation of soil moisture with climate elements and the causation of meteorological elements. In the future, we will focus on the further development of GLDAS output and its support to more studies of soil moisture's responses and effects on climate change globally or regionally, such as in eastern China.
# Analysis, Design, and Experimental Research of a Novel Wheelchair-Stretcher Assistive Robot
## 2 Mechanical Structure Design of the Wheelchair-Stretcher Assistive Robot
### Control of the Walking Mechanism  
The walking mechanism mainly realizes six kinds of motions, which are forward, backward, left, right, turning both sides. The main control board transmits four PWM pulses respectively to control the walking mechanism. In order to ensure the accuracy of the moving process, the compound position control method is adopted to control six kinds of motion. The so-called composite motion adopts velocity control between two discrete position nodes and detects the position at the same time. The position error of this node is taken as part of the theoretical velocity control of the next node and the theoretical velocity is corrected. The control block diagram is shown in Figure 20.  
Figure 19: Control block diagram of the wheelchair-stretcher assistive robot. STM32: STMicroelectronics 32-bits Microcontroller; GPIO: general purpose input/output; RV: rotate vector.  
Figure 20: Control block diagram of compound position control method.  
According to the figure, the position error of this node due to speed control is,  
\[\Delta p_{\rm e}=p_{\rm d}-p_{r} \tag{28}\]  
where \(p_{\rm d}\) is the reference value of the position, \(p_{r}\) is the real value of position detected by the controller.  
The velocity of the next node is:  
\[v^{\prime}{}_{d}=P\Delta p_{e}+v_{d}. \tag{29}\]
###### Abstract  
Sulfur recovery unit (SRU) thermal reactors are negatively affected by high temperature operation. In this paper, the effect of the fuel mass fraction on the combustion and fluid flow in a SRU thermal reactor is investigated numerically. Practical operating conditions for a petrochemical corporation in Taiwan are used as the design conditions for the discussion. The simulation results show that the present design condition is a fuel-rich (or air-lean) condition and gives acceptable sulfur recovery, hydrogen sulfide (H\({}_{2}\)S) destruction, sulfur dioxide (SO\({}_{2}\)) emissions and thermal reactor temperature for an oxygen-normal operation. However, for an oxygen-rich operation, the local maximum temperature exceeds the suggested maximum service temperature, although the average temperature is acceptable. The high temperature region must be inspected very carefully during the annual maintenance period if there are oxygen-rich operations. If the fuel mass fraction to the zone ahead of the choke ring (zone 1) is 0.0625 or 0.125, the average temperature in the zone behind the choke ring (zone 2) is higher than the zone 1 average temperature, which can damage the downstream heat exchanger tubes. If the zone 1 fuel mass fraction is reduced to ensure a lower zone 1 temperature, the temperature in zone 2 and the heat exchanger section must be monitored closely and the zone 2 wall and heat exchanger tubes must be inspected very carefully during the annual maintenance period. To determine a suitable fuel mass fraction for operation, a detailed numerical simulation should be performed first to find the stoichiometric fuel mass fraction which produces the most complete combustion and the highest temperature. This stoichiometric fuel mass fraction should be avoided because the high temperature could damage the zone 1 corner or the choke ring. A higher fuel mass fraction (i.e., fuel-rich or air-lean condition) is more suitable because it can avoid deteriorations of both zone 1 and heat exchanger tubes. Although a lower fuel mass fraction (i.e., fuel-lean or air-rich condition) can avoid deterioration of zone 1, the heat exchanger tubes may be damaged. This paper provides a guideline for adjusting the fuel mass fraction to reduce the high temperature inside the thermal reactor and to ensure an acceptable sulfur recovery.  
SRU thermal reactor; fuel mass fraction; sulfur recovery; H\({}_{2}\)S destruction; SO\({}_{2}\) emission; thermal reactor temperature
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 3 Model Introduction and Problem Analysis  
### Satellite Edge Computing Architecture  
In this paper, we introduce a classic satellite edge computing architecture, as shown in Figure 1. Task requests uploaded by users on the ground can be continually received by the LEO satellite during its motion. These tasks include the analysis of monitoring data from some sensors, assistance with communication from ocean-going ships, requests for emergency communication from the ground, and analysis of remote sensing images from the satellite itself. Traditionally, the satellite acts as a relay node to transmit the tasks back to the ground central station, i.e., the ground cloud center, for batch processing. However, as the satellite gains access to more powerful computing resources, it may think about processing tasks on the satellites while in orbit instead of sending them back to the ground cloud center. The satellites will carry the edge computing servers. The functional components are shown in Figure 1. The satellite edge computing server can autonomously perform work, such as resource allocation and task scheduling, in orbit. This edge computing satellite model, which is close to the users on the ground, may efficiently decrease the backhaul network traffic while also reducing task-processing latency.  
When a satellite receives many tasks, it considers the resource usage of each satellite in the constellation. In addition, a suitable strategy for offloading in orbit is developed. The inter-satellite offloading issue for a single dependent task in this edge computing satellite scenario is the focus of this article. Each satellite can establish contact with the four satellites around it using the inter-satellite link (ISL), as depicted in Figure 2. Due to the various operation conditions, each satellite has a unique load state, which can be broadcast to other satellites via the ISL. For example, when SAT1 receives a complete task request, it will select the appropriate low-load satellite in the constellation to offload parts of subtasks, while the high-load satellite cannot be selected for offloading.  
Figure 1: Satellite edge computing architecture.
# SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning
## 4 Experimental Results
### Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Method** & \begin{tabular}{c} **Accuracy** \(\uparrow\) \\ **(AP\({}_{8.5}\), \%)** \\ \end{tabular} & \begin{tabular}{c} **Speed** \(\uparrow\) \\ **(FPS)** \\ \end{tabular} & \begin{tabular}{c} **No. of Model** \\ **Parameters** \(\downarrow\) **(M)** \\ \end{tabular} \\ \hline \multirow{7}{*}{\begin{tabular}{c} Tiny YOLOv4 \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} Proposed SAFP \\ with CBAM [45] \\ \end{tabular} } & 50\% Pruning & 76.8 & 28.4 & 2.4 \\ \cline{2-5}  & & 75\% Pruning & 74.1 & 40.2 & 1.0 \\ \cline{2-5}  & & 87.5\% Pruning & 71.4 & 52.9 & 0.4 \\ \cline{2-5}  & & 50\% Pruning & 77.4 & 28.4 & 2.4 \\ \cline{2-5}  & & 75\% Pruning & 74.9 & 40.2 & 1.0 \\ \cline{2-5}  & & 87.5\% Pruning & 72.3 & 52.9 & 0.4 \\ \hline \multirow{7}{*}{
\begin{tabular}{c} Tiny YOLOv7 \\ YOLOv7 \\ \end{tabular} } & \multicolumn{3}{c}{Baseline TinyYOLOv7} & 81.9 & 18.3 & 6.0 \\ \cline{2-5}  & & 50\% Pruning & 79.3 & 27.2 & 2.3 \\ \cline{2-5}  & & 75\% Pruning & 77.6 & 38.4 & 1.0 \\ \cline{2-5}  & & 87.5\% Pruning & 74.7 & 50.7 & 0.4 \\ \cline{2-5}  & & 50\% Pruning & 81.0 & 27.2 & 2.3 \\ \cline{2-5}  & & 75\% Pruning & 78.5 & 38.4 & 1.0 \\ \cline{2-5}  & & 87.5\% Pruning & 75.8 & 50.7 & 0.4 \\ \cline{2-5}  & & 87.5\% Pruning & 74.7 & 50.7 & 0.4 \\ \cline{2-5}  & & 50\% Pruning & 81.0 & 27.2 & 2.3 \\ \cline{2-5}  & & 75\% Pruning & 78.5 & 38.4 & 1.0 \\ \cline{2-5}  & & 87.5\% Pruning & 75.8 & 50.7 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed method with TinyYOLOv4 and TinyYOLOv7 for pig dataset on a TX-2. Figure 12: Attention maps from the proposed method and the conventional filter-pruning techniques [51; 52]. The attention maps from the proposed method predominantly focus on the target objects. In contrast, using the conventional filter-pruning techniques was observed as lacking this focused attention on the objects.
## 5 Discussion and Practical Tests
### Issue of COVID-19 Pandemic  
The issue of the COVID period is critical. As shown in Figure 2, a peak is identified in May 2020, corresponding to the economic and financial distresses due to the COVID-19 pandemic. To test if the issue of the COVID-19 pandemic affects our conclusion, we exclude the data after 2020 and rerun the models. The results are consistent with the following notions: First, \(\wp_{2}{}^{EUA}\) is 7.0437, and its standard deviation is 0.4887. Moreover, \(\wp_{2}{}^{WTI}\) is 4.4603, and its standard deviation is 0.3060. These results imply Regime II (I) as a high (low)-volatility regime, consistent with Table 5. Second, \(\wp_{2,2}\) (0.2363) is higher than \(\wp_{1,1}\) (0.1083), implying the intensity of the volatility spillover effect in carbon-energy markets. Third, \(\wp_{1,2}\) (0.3418) is higher than \(\wp_{2,1}\) (0.0979), which means that the volatility spillover direction is from the energy market to the carbon market. Overall, our conclusion on the intensity and direction of volatility spillover effect in carbon-energy markets is robust to the subperiod. However, as shown in Table 5, the estimate of \(\wp_{2,2}\) is 0.3609, which is higher  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **MAE** \\ \hline _Panel A: MAE (Mean Absolute Error)_ & \\ \hline _Bivariate GARCH-CCC model_ & 2.3185 \\ _Bivariate GARCH-DCC model_ & 2.3143 \\ _(\(-\)1.0906)_ & 2.1470 \\ _Bivariate SWARCH model with state-varying correlations_ & (\(-\)8.9225) *** \\ \hline _Panel B: MAE (Mean Square Error)_ & \\ \hline _Bivariate GARCH-CCC model_ & 9.9811 \\ _Bivariate GARCH-DCC model_ & 9.9981 \\ _(\(0.3579\))_ & 8.5664 \\ _Bivariate SWARCH model with state-varying correlations_ & (\(-\)5.5544) *** \\ \hline \hline \end{tabular}  
* Notes: This study adopts MSE and MAE, the two commonly used forecasting performance criteria, to evaluate the performance of various models in EUA–WTI portfolio variance forecasting. We adopt the bivariate GARCH-CCC model as a benchmark and calculate the statistic for the difference in MSE and MAE (see the figure in parenthesis). *** represents significance at the 1% level.  
\end{table}
Table 7: Performance of EUA–WTI portfolio variance forecasting.  
than all the correlation estimates obtained with the subperiod data. The result implies the COVID-19 pandemic enlarges carbon-energy market volatilities and correlations.
# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 2 Contextualisation
### Evolution of the Object Detection Architectures  
Object detection algorithms' main purpose lies in accurately identifying and locating objects in images or videos, making it a valuable practical technology in the real world. OD algorithms allow the recognition of objects in an image and have increasingly received attention from researchers in the last ten years. In 2012, AlexNet [20] was a game changer for the field of computational inference (see Figure 3). This architecture was capable of generating models for detecting objects in an image and, for each object, output its location, a predefined label, and a prediction confidence value. Mainly, they are grouped into two categories [21; 22]--single-stage detectors (e.g., YOLO versions [23; 24; 25; 26], SSD [27], RetinaNet [28], RefineDet [29], EfficientDet [30]) or two-stage detectors (e.g., R-CNN [31], Fast R-CNN [32], Faster R-CNN [33]).  
As OD algorithms belong to the DL family, a large amount of data is required for the learning process, and special attention needs to be given to the quality of data. For example, it is well known that the performance of OD algorithms is improved when the training uses large amounts of labelled data.  
The conventional procedure when using supervised DL can be subdivided in the following operational steps: (i) collect a dataset as large as possible; (ii) assign labels to images, pixels, or regions; (iii) train models on that dataset; and (iv) place the model to production. So far, this strategy has worked with different degrees of success, albeit with high human and computational resource costs. Another type of approach, known as active learning, consists in using the knowledge learned by the model (from a small dataset) to filter and rank the images provided to the specialists during the annotation task. It is a powerful technique for improving data efficiency (i.e., avoiding significant overlap between data instances [34]), as it allows supervised learning algorithms to achieve the desired performance using a small training set at time, diluting the annotation efforts by more but lighter sessions.
# Characteristics of Carbon Fluxes and Their Environmental Control in Chenhu Wetland, China
###### Abstract  
Carbon dioxide (CO\({}_{2}\)) flux measurements were conducted throughout the year 2022 utilizing the eddy covariance technique in this study to investigate the characteristics of carbon fluxes and their influencing factors in the Chenhu wetland, a representative subtropical lake-marsh wetland located in the middle reaches of the Yangtze River in China. The results revealed that the mean daily variation of CO\({}_{2}\) flux during the growing season exhibited a U-shaped pattern, with measurements ranging from \(-12.42\) to \(4.28\)\(\upmu\)molCO\({}_{2}\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\). The Chenhu wetland ecosystem functions as a carbon sink throughout the growing season, subsequently transitioning to a carbon source during the non-growing season, as evidenced by observations made in 2022. The annual CO\({}_{2}\) absorption was quantified at 21.20 molCO\({}_{2}\cdot\)m\({}^{-2}\), a figure that is lower than those documented for specific subtropical lake wetlands, such as Doping Lake and Poyang Lake. However, this measurement aligns closely with the average net ecosystem exchange (NEE) reported for wetlands across Asia. The correlation between daytime CO\({}_{2}\) flux and photosynthetically active radiation (PAR) can be accurately represented through rectangular hyperbola equations throughout the growing season. Vapor pressure deficit (VPD) acts as a constraining factor for daytime NEE, with an optimal range established between 0.5 and 1.5 kPa. Furthermore, air temperature (T\({}_{\text{a}}\)), relative humidity (RH), and vapor pressure difference (VPD) are recognized as the principal determinants affecting NEE during the nocturnal period. The association between T\({}_{\text{a}}\) and NEE during the non-growing season conforms to the van't Hoff model, suggesting that NEE increases in response to elevated T\({}_{\text{a}}\) during this timeframe.  
the middle Yangtze reaches; Chenhu wetland; eddy covariance; carbon dioxide flux Article
## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.1 Comparisons Between Groups A and B
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Feature** \\ **Selection Mode** \\ \end{tabular} & \
\begin{tabular}{c} **Feature Subsets** \\ \end{tabular} & **AUC** & **Accuracy** & **Precision** & **Recall** & **Specificity** \\ \hline \hline \multirow{8}{*}{**T**} & Full dataset & All 25 cytokines & 0.588 & 0.700 & 0.708 & 0.700 & 0.700 \\ \cline{2-8}  & & IL-1 RN, CXCL,10, CCL3, CXCL9, IL-7, HGE, IL-15, IL-13, IL-1\(\alpha\), IFN-\(\gamma\) & 0.777 & 0.725 & 0.730 & 0.725 & 0.725 \\ \cline{2-8}  & & IL-1 RN, CXCL,10, CCL3, CXCL9, IL-7, HGE, IL-15, IL-13, IL-1\(\alpha\) & 0.790 & 0.700 & 0.708 & 0.700 & 0.700 \\ \cline{2-8}  & & IL-1 RN, CXCL,10, CCL3, CXCL9, IL-7, HGE, IL-15 & 0.795 & 0.725 & 0.756 & 0.725 & 0.725 \\ \cline{2-8}  & & Information gain & IL-1 RN, CXCL,10, CCL3, CXCL9, IL-7, HGF & 0.804 & 0.700 & 0.738 & 0.700 & 0.700 \\ \cline{2-8}  & & IL-1 RN, CXCL,10, CCL3, CXCL9, IL-7, HGF & **0.810** & **0.750** & **0.798** & **0.750** & **0.750** \\ \cline{2-8}  & & IL-1 RN, CXCL,10, CCL3, CXCL9, IL-7 & 0.746 & 0.700 & 0.738 & 0.700 & 0.700 \\ \cline{2-8}  & & IL-1 RN, CXCL,10, CCL3, CXCL9 & 0.730 & 0.625 & 0.633 & 0.625 & 0.625 \\ \cline{2-8}  & & IL-1 RN, CXCL,10, CCL3 & 0.744 & 0.650 & 0.652 & 0.650 & 0.650 \\ \cline{2-8}  & & IL-1 RN, CXCL10 & 0.665 & 0.575 & 0.577 & 0.575 & 0.575 \\ \cline{2-8}  & & IL-1 RN & 0.605 & 0.550 & 0.551 & 0.550 & 0.550 \\ \hline \multicolumn{8}{c}{
\begin{tabular}{c} Univariate \\ Analysis \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Naive Bayes models for disease severity, i.e., discriminating between discharged patients without IMV (Group A) and discharged patients requiring IMV (Group B). Models were based on full datasets or feature subsets of cytokines, routine blood analyses, or hybrid data selected by an information gain algorithm or univariate data analysis. Figure 4: t-SNE representations for Group A (blue) versus Group B (red), considering 25 serum cytokines (**a**), 9 routine blood analyses (**b**), and 34 cytokines and routine blood analyses (**c**); and feature subsets of serum cytokines (**d**), routine blood analyses (**e**), and cytokines plus routine blood analyses (**f**) that led to the best predictive models. Overall, our results were comparable to other predictive models regarding the need for IMV. For example, [PERSON] et al. achieved an AUC of 0.820 for predicting the need for IMV, based on CRP, procalcitonin, D-dimers, respiratory rate and blood oxygen saturation [54]. [PERSON] et al. achieved an AUC of 0.860 in a model that included the following features:
## 1 Introduction
### Contributions  
This paper addresses the complexity challenge in massive MIMO systems by proposing two linear iterative detection methods. The proposed methods avoid the complicated high-dimensional matrix inverse involved in MMSE detectors, thereby significantly reducing the computational complexity. The first method we propose is the symmetric accelerated over-relaxation (SAOR)-based detection method. This approach accelerates the convergence speed in challenging scenarios under higher-order modulation schemes by carefully choosing the relaxation and acceleration parameters. The SAOR method leverages symmetric iteration to not only accelerate convergence but also maintain computational efficiency. In iterative detection methods, the choice of the initial solution is crucial, as an efficient initial estimate can substantially decrease the total number of iterations needed to realize the desired detection accuracy. Given the diagonally dominant nature of the MMSE filtering matrix in massive MIMO, we propose utilizing the diagonal component. We show that the SAOR detector achieves the desired error performance with fewer iterations compared to conventional AOR-based detection algorithms. Moreover, thanks to its symmetric iteration, the SAOR method has a notable advantage over the AOR method. This symmetry enables the effective use of CG acceleration. Consequently, we introduce a new and efficient SAOR signal detection method named CGA-SAOR. The proposed CGA-SAOR method incorporates CG acceleration to further enhance the convergence rate. This integrated approach greatly improves performance compared to both the AOR and SAOR methods. Additionally, we provide a detailed computational complexity analysis and numerical results to demonstrate the efficacy of the proposed detection algorithms. The numerical results show that the proposed iterative detection algorithms achieve near-MMSE detection performance with a significantly lower computational burden. In addition, the proposed detectors considerably outperform recently developed iterative detection algorithms while maintaining virtually the same detection complexity, making them highly attractive solutions for massive MIMO systems.