esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
While this approach eliminates the friction associated with Bowden cable mechanisms using hydraulic transmission, three short cables connecting the actuators to the effector through PTFE tubes still suffer from friction and hysteresis. Additionally, a pulley for displacement amplification was required due to the limited stroke of the pouch actuator. Pouch actuators, as seen in Figure 1a and initially introduced in [14], are characterized by their low profile, low cost, and having nearly zero volume when deflated, making them highly suitable for applications in confined spaces. However, their theoretical contraction ratio is limited to 36.3% of their unactuated length. Various approaches have been explored to enhance the contraction ratio of pouch actuators [15; 16; 17; 18; 19]. These approaches include internal and external constraints [15], rigid amplification mechanisms [16], series-parallel hybrid arrangements [17], the addition of gussets [18], and stacked pouches pushing outer structures [19]. These methods can improve contraction ratios, but their structures and manufacturing processes are relatively complex. Figure 1: A pouch actuator. (**a**) A contraction-based pouch actuator [14]. (**b**) A folded pouch actuator [20]. In contrast, folded actuators (shown in Figure 1b, as an example), constructed from an airbag, can achieve large angular displacements and generate considerable force outputs [20; 21; 22; 23]. They exhibit flexibility, adaptability, simplicity, low profiles, and low costs. When deflated, their volume is minimal, yet they can achieve significant expansion upon inflation. So far, they have been used predominantly with soft wearable devices to assist joint movement. More recently, artificial muscles based on folded pouch actuators achieved impressive contraction ratios (up to 92.9%) [24; 25; 26]. Moreover, robotic elbows, jumping robots, and soft grippers have been developed based on folded pouch actuators [25; 26]. This paper introduces a novel soft CDPR based on folded hydraulic pouch actuators designed for MISs, specifically colorectal surgeries, as an improvement over a range of existing CDPR mechanisms. The proposed design allows the end-effector to be directly connected to the actuators, resulting in there being no friction or hysteresis, which are issues typically associated with the use of Bowden cables in previous CDPR-based robots [12; 13]. The robot is a monolithic device crafted entirely from low-cost, flexible plastic film laminates, enabling the robot to be single use, which has certain advantages in MISs when compared with reusable devices that require sterilization before each use.
## 4 Correlation Analysis  
### Failure Duration Analysis  
Figure 7a-c plots the change characteristics of FD-traffic flow rate under different rates of CAVs (Ra). To reduce the random error, the FD values of 10 simulation results of each scenario were taken as an average. As is shown in Figure 7a-c, the overall trend of the 10 scenarios in each graph remains consistent, in that the flow rate positively correlates with FD.  
Comparing the curves of scenarios 1, 2, 3, and 4, it can be found that the relationship between FD and the number of bus arrivals per hour is obvious. Comparing the curves of scenarios 2, 5, 6, and 7, the dwell time in these four scenarios is 30, 40, 50, and 60 s, respectively, and it can be concluded that the FD value increases with the dwell time. The reason might be that the longer the bus stops, the greater the probability of stop failure. Comparing the FD-flow rate change relationship of scenarios 2, 8, 9, and 10, it can be found that the failure occurs less in the double-birth bus stop, while many more failures  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Bus Flow Rate** & **Dwelling** & **Station Type** \\  & **(veh/h)** & **Time (s)** & **Time (s)** \\ \hline
1 & 10 & 50 & Single-borth bus bay stop \\
2 & 20 & 50 & Single-borth bus bay stop \\
3 & 30 & 50 & Single-borth bus bay stop \\
4 & 40 & 50 & Single-borth bus bay stop \\
5 & 20 & 30 & Single-borth bus bay stop \\
6 & 20 & 40 & Single-borth bus bay stop \\
7 & 20 & 60 & Single-borth bus bay stop \\
8 & 20 & 50 & Single-borth curbside bus stop \\
9 & 20 & 50 & Double-borth no-overtaking bus bay stop \\
10 & 20 & 50 & Double-borth overtaking bus bay stop \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation schemes.  
Figure 6: Flowchart of simulation model operation.  
occur in the single-beth bus stop. This reveals that the number of bus berths has the most significant impact on the failure duration, and the reason is that the greater the number of berths, the lower the probability and dwelling time of bus parking and waiting.  
Moreover, it can be observed that the value of FD decreases as the penetration of CAVs increases, indicating that FD is negatively correlated with Ra level. One of the reasons might be that CABs take less time than human-driven buses in the stopping process, especially in the behavior of changing lanes, stopping, starting, etc.
## 2 Background and Related Work  
### Earthquake Source Parameter Estimation  
In remote sensing, the Monte Carlo method has widely been used for earthquake or volcanic source parameter estimation. To reduce the computation time, many researchers used techniques that randomly sample only a portion of the entire geodetic measurement, such as quadtree sampling [14], or defined bound constraints based on previous seismic studies conducted in the same study area. [PERSON] et al. [15] estimated the source pa rameters and its uncertainties for the 2015 Wolf volcano using the Okada and Yang models and the Levenberg-Marquardt method. Studies by [PERSON] et al. (2016) and [PERSON] et al. (2017) used the downhill simplex optimization and the Okada model to estimate the source parameters of the 2003 Bam earthquake and the 2009 Yao'an earthquake, respectively. [PERSON] et al. (2018) estimated the source parameters of the 2008 Quetame earthquake using the nonlinear interior-point optimization and the Okada model.  
Recently, studies estimating the source parameters using a statistical approach or machine learning have also been proposed. [PERSON] and [PERSON] (2019) and [PERSON] et al. (2020) used the Bayesian approach based on the Markov Chain Monte Carlo. [PERSON] (2018) and [PERSON] et al. (2022) applied a genetic algorithm and artificial neural network for source parameter estimation. [PERSON] and [PERSON] (2019) proposed a parameter search space reduction method based on the principal component analysis to accelerate nonlinear optimization.
## 3 Results
### Sparse Graphs of Pores, Connectivity  
Four variables related to pore connectivity, which refers to the number of other pores a pore shares connection voxels with, as derived from the sparse graphs of pores, were found to be significant in relation to firing temperature (\(p<0.05\)) (Table 5).  
Low-fired sherds tend to have higher connectivity values for the maximum (correlation with firing temperature 0.8) (Figure 14, left), mean (correlation 0.9), and standard deviation (correlation 0.9). One explanation is that dissolution of feldspars, cracking of quartz grains, and other firing effects on the non-clay components can cause a large number of pores within that disintegrating grain to be in contact with other pores [70]. These non-plastics tend to be more numerous in lower-fired materials. The inverse is true for the percentage of pores that are unconnected, with the higher-fired sherds having higher values (correlation 0.8) (Figure 14, right). At higher temperatures, vitrification proceeds and forms a dense matrix with isolated pores formed during the release of gases.  
Figure 14: Maximum pore connectivity mean values (**left**) go down with increased firing temperature, indicating more isolated unconnected pores form in the higher-fired ceramic products. This situation is also indicated by the fact that the percentage of pores unconnected (**right**) is shown to rise with higher firing temperatures. Points are mean of 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ceramic**} & **Pore Connectivity** & **Pore Connectivity** & **Pore Connectivity** & **\% of the Pores** \\  & **Max** & **Mean** & **SD** & **Unconnected** \\ \hline
1 & 53.5 & 2.92 & 2.03 & 1.5 \\
2 & 28.0 & 2.13 & 1.59 & 3.0 \\
3 & 17.0 & 1.86 & 1.34 & 5.0 \\
4 & 21.0 & 2.39 & 1.56 & 2.0 \\
5 & 18.5 & 2.19 & 1.50 & 3.0 \\
6 & 20.0 & 2.24 & 1.55 & 2.3 \\
7 & 14.0 & 1.60 & 1.3 Figure 15 illustrates the difference in connectivity between a sample from the lowest-fired sherd in the experiments (ceramic 1, 900 \({}^{\circ}\)C) [31] and the highest (ceramic 12, 1320-1380 \({}^{\circ}\)C) [65] using the sparse graphs of pores. Ceramic 1 (Figure 15, upper), with a higher volume percentage of pores (27.6%), high maximum pore connectivity (60), and mean connectivity value of 2.82, has only 1.5% of its pores unconnected so they are hard to pick out within this fully packed pore system. In contrast, the sample from ceramic 12 (Figure 15, lower) with only 2.27 vol.% pores, has a maximum connectivity of 5, mean of 0.99, and 15% of its pores unconnected to any other pores. The unconnected ones stand out here, and it is apparent that many pores are connected to only one other pore.  
Figure 15: Sparse graphs of pores: (**upper**), low-fired ceramic 1; (**lower**), high-fired ceramic 12. Pores are colored by connectivity, with dark red for high connectivity down through degrees of red to white for low connectivity. Connections color-coded by length.
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 4 Computational Results
### Test Problem P20 Results and Comparisons  
As stated previously, the 20 facility test problem, P20, was taken from [PERSON] and [PERSON] (2018). See Table 5 for a summary of the results. The proposed GA results are compared with the results from the tabu search (TS) heuristic which consisted of the boundary search heuristic (TS/BSH) presented in [PERSON] and [PERSON] (2018) and the multi-start simulated annealing algorithm (MS-SA) presented in [PERSON] and [PERSON] (2018). It is important to note that the test problem was solved once using TS/BSH, but the test problem was solved 10 times (i.e., 10 runs) using MS-SA heuristic, and the computation time was not given. Recall, the test problem was solved five times using the proposed GA. However, the proposed GA obtained the minimum TWD of 1147 and improved the best-found TWD in the literature (1151.4), which is bolded in Table 5, by 0.38% in much less CP time compared to TS/BSH. Also, notice how much lower the mean and standard deviation of the proposed GA are compared to the MS-SA. As a result, the proposed heuristic outperformed the other heuristics with respect to solution quality and/or CP time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Min TWD** & **Mean TWD** & **Std. Dev.** & **\% Improvement** & **CP Time** \\ \hline Proposed GA & 5667.17 & 5667.17 & 0 & \(-\)0.53 & 1 m \\ MPL/CPLEX & **5637.16** & – & – & 0 & 21.3 h \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of results for test problem P8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem Instance** & **Gen_Size** & **Max_Num_Cross** & **Max_Duration (Minutes)** \\ \hline P8 & 10 & 20 & 1 \\ P20 & 30 & 60 & 15 \\ P50 & 40 & 80 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: GA heuristic parameter setting.
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### Temporal Interpolation  
#### 3.3.1 Interpolating _in-Situ_ Measurements to ASTER Dates  
Previous studies tended to apply linear interpolation to either satellite data or field data to achieve the temporal consistency between them, as conducted by [PERSON] _et al._[16] and [PERSON] _et al._[35]. This is feasible when the apart dates between the observation of _in-situ_ data or satellite data are short or the parameters (such as fractional vegetation cover) are assumed to be steady in a short time. [PERSON] _et al._[29] interpolated the _in-situ_ measurements to the dates of satellite observations by a cubic model, which derived a smooth and consistent temporal shape. However, the cubic interpolation is sensitive to the amount of samples and the outliers. Random errors in limited _in-situ_ data might result unreasonable temporal curves. As mentioned above, a data gap from 18 June to 3 July existed in the temporal profiles of the _in-situ_ measurements, overlapping with the fast growing period of vegetation. Either linear or cubic interpolation might not be capable of capturing the temporal curves of crop growth precisely. More precise representation of FAPAR time series is thus required. [PERSON] _et al._[46] proposed a method to represent vegetation phenology as series of piecewise logistic functions of time. [PERSON] _et al._[47] applied the logistic model to fit the time series of vegetation index. The logistic representation is advantageous, because the growth speed at different stages can be described accurately in a simple form. Therefore, it is employed in the study for temporal interpolation, expressed as:  
\[\mathrm{FAPAR}_{t}=\ \frac{b_{3}}{1+e^{b_{1}+b_{2}\times t}}+b_{4} \tag{7}\]  
where \(b_{1}\) and \(b_{2}\) are fitting parameters; \(b_{3}+b_{4}\) is the maximum FAPAR value and \(b_{4}\) is the initial background FAPAR value; and FAPAR\({}_{t}\) is the FAPAR value at the observation date \(t\). The parameter \(b_{4}\) is set to 0 for bare soil background; the value of \(b_{3}+b_{4}\) is set to 1.0 for complete absorption of PAR by vegetation. In implementation, only sites with more than three dates of observations were considered to ensure the reliability of the fitted equations. The time series of thirteen field sites were fitted using the logistic model respectively and then temporal interpolation was performed.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 3 Data and Methodology
### Deep Learning Models
#### 3.2.7 SE-RegNet Model  
The SE-RegNet model advances wetland image segmentation through its sophisticated management of intricate, high-resolution spectral-spatial features. Although traditional models such as RegNet efficiently execute hierarchical feature extraction, SE-RegNet amplifies this function by integrating a Squeeze-and-Excitation (SE) block, facilitating channel-wise feature recalibration [41]. This enhanced characteristic renders SE-RegNet especially adept at interpreting the complex aspects of wetland biomes [42].  
In this research, the SE-RegNet model begins with an initial convolution layer for feature extraction, followed by a series of SE-RegNet blocks. These blocks incorporate a special 'Squeeze-and-Excitation' (SE) operation, dynamically recalibrating channel-wise feature maps to emphasize relevant features. Each block combines a convolutional layer with a MaxPooling2D layer for spatial dimension reduction. The architecture culminates in a fully connected output layer designed for multi-class categorization, using'sparse_categorical_crossentropy' for loss and 'adam' for optimization. This structure allows SE-RegNet to balance feature relevance and architectural complexity effectively. The model employs 200 epochs, determined through cross-validation results, assessing aspects such as model complexity, learning rate, and potential for overfitting or underfitting. Dropout layers and batch normalization are incorporated to mitigate overfitting. The detailed architecture of the model is presented in Figure 9.  
Figure 8: The architecture of the Inception-RegNet model.
## 5 Conclusions  
Many IEQ evaluation models have been developed over the past two decades. They have varied greatly, including IEQ aspects and indicators, data collection and analysis methods, etc. Most consider the four main elements of IEQ: IAQ, thermal comfort, visual quality, and acoustic quality. However, more IEQ aspects (such as layout and furnishing) were also relevant to the overall IEQ performance, suggesting that they should be considered in future studies. The IEQ model is an effective and valuable tool with which to merge all the IEQ aspects into one summative index, which could assist building designers and managers in designing and maintaining a comfortable indoor environment. Several studies mainly used data collection (such as on-site measurement, questionnaire surveys, and simulation) and data analysis methods (such as regression analysis, AHP, and SEM) were summarized in the current study, which showed a need for a more uniform methodology. Moreover, two IEQ model-updating methods--the Bayesian approach and the open IEQ acceptance model--were presented, which could incorporate quantitative data (such as cleanliness and layout) and continuously revise the existing model with new data. Hence, they are potential methods for future IEQ model development.  
This study summarized all the procedures involved in developing IEQ models in ten questions. The answers to these questions will help to promote IEQ model development and eventually integrate the IEQ models into a comprehensive profile that provides a reference for building operation, maintenance, and design.  
Conceptualization, D.Z. and L.-T.W.; methodology, D.Z. and L.-T.W.; writing--original draft preparation, D.Z.; writing--review and editing, K.-W.M. and L.-T.W.; supervision, K.-W.M. and L.-T.W.; project administration and funding, K.-W.M. and L.-T.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the General Research Fund (Project no. 15217221, PolyU P0037773/Q86B), the Collaborative Research Fund (CRF) COVID-19 (Project no. PolyU P0033675/C5108-20G), Research Grants Council of the Hong Kong Special Administrative Region, China, and partially supported by PolyU internal fund (P0043713 and P0043831).  
Not applicable.  
No data were collected in this study.  
The authors declare no conflict of interest.
# Video-Based Parking Occupancy Detection for Smart Control System
## 2 Materials and Methods
### Jetson TX2  
Jetson TX2 is NVIDIA's Compute Unified Device Architecture (CUDA)-capable embedded artificial intelligence (AI) computing device, which has high power efficiency. This supercomputing module brings genuine AI processing to end devices, and has low power consumption. It is equipped with a graphics processing unit (GPU)-based board with NVIDIA 256 core pascal architecture and a 64-bit hex core ARM (version 8) central processing unit (CPU), which contains a specialized architecture for AI applications. It enables real-time deep learning applications in small form-factor products such as drones. Jetson TX2 has 8 GB of low-power double data rate synchronous dynamic random access memory and supports PCIe (version 2.0) and various peripherals such as general-purpose inputs/outputs (GPIOOs), display, camera, universal serial buses (version 3.0 and 2.0), Ethernet, and 802.11 ac wireless local area networks. Therefore, creating intelligent applications quickly and anywhere for prototyping is simple. NVIDIA provides the required drivers and CUDA toolkits for Jetson TX2 through the JetPack software development kit (version 4.2), which is used to automate basic installations for Jetson TX2, and includes Board Support Packages and libraries specialized in  
Figure 4: Architecture design of the automatic streetlight and smart street parking control system.  
Figure 5: System methodology.  
deep learning and computer vision. These features of the GPU-based board helped us implement the parking detection system in street scenes and in the implementation on the streetlight.  
The high-performance, low-energy computing capabilities of deep learning and computer vision provided an ideal platform for embedded projects with high computing demands. To use the deep learning method to support the entire architecture, we moved object detection, image judgment, and analysis onto the Jetson TX2 to achieve parking space detection and streetlight control. The image-based system was a valid choice.  
To directly receive real-time images interfacing the street camera, these data aid in tracking the statuses of the streetlight and parking grids from the roadside. The Jetson TX2, which receives live feed images, compares images at the time and recalls the deep learning model to detect moving objects. To simulate the environment of the detection model and street lamp, we used a webcam to receive real-time images and use the GPIO pin of the Jetson TX2 to control six light-emitting diode (LED) lights (see Figure 6).
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 2 Related Work
### The Opportunistic Networking Environment (ONE) Simulator  
The Opportunistic Networking Environment (The ONE) simulator is a Java program which makes complex DTN simulations more realistic [7]. The ONE has been widely used for evaluating DTN routing and application protocols. This simulator is able to use various movement models from synthetic models to real movement traces to create a motion model of nodes, forwarding messages between nodes through different DTN routing protocols and visualizing the mobility motion of nodes and message transfer in its graphical user interface.  
The movement model of nodes, inter-node contacts, routing and message handling are the main functions of the ONE simulator. Visualization, reports and post-processing tools are used for analyzing and collecting the results [27]. Figure 1 shows an overview of the ONE simulator, which has four modules, namely, movement models, routing, event generator and visualization, and results.  
The output is produced by the visualization and results module. ONE is capable of visualizing results of simulation in two ways: via an interactive Graphical User Interface (GUI) and by generating images or graphs from the reports, which are created in the simulation [27]. The ONE simulator can be run through two methods: a GUI method and a batch method. The GUI method is used for visualizing, testing and debugging whereas the batch mode is used for running multiple simulation scenarios with different parameters.  
As each node has an energy budget, which is consumed for node activities such as forwarding, receiving, radio scanning and generating message, the energy mechanism [28] is implemented in the ONE simulator to analyze the impact of energy consumption on the DTN routing protocols. This energy module is added within the routing module in the ONE framework as shown in Figure 2.  
This work extends the open-source ONE simulator by allowing existing DTN protocols to make forwarding decisions based on the available energy resources. It also extends the energy modelling to include energy-harvesting features, where the energy is replenished from solar power.  
Figure 1: Overview of the ONE simulator [6].