esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data
###### Abstract  
Freshwater lakes supply a large amount of inland water resources to sustain local and regional developments. However, some lake systems depend upon great fluctuation in water surface area. Poyang lake, the largest freshwater lake in China, undergoes dramatic seasonal and interannual variations. Timely monitoring of Poyang lake surface provides essential information on variation of water occurrence for its ecosystem conservation. Application of histogram-based image segmentation in radar imagery has been widely used to detect water surface of lakes. Still, it is challenging to select the optimal threshold. Here, we analyze the advantages and disadvantages of a segmentation algorithm, the Otsu Method, from both mathematical and application perspectives. We implement the Otsu Method and provide reusable scripts to automatically select a threshold for surface water extraction using Sentinel-1 synthetic aperture radar (SAR) imagery on Google Earth Engine, a cloud-based platform that accelerates processing of Sentinel-1 data and auto-threshold computation. The optimal thresholds for each January from 2017 to 2020 are \(-14.88\), \(-16.93\), \(-16.96\) and \(-16.87\) respectively, and the overall accuracy achieves 92% after rectification. Furthermore, our study contributes to the update of temporal and spatial variation of Poyang lake, confirming that its surface water area fluctuated annually and tended to shrink both in the center and boundary of the lake on each January from 2017 to 2020.  
Poyang lake; Otsu method; Google Earth Engine; water occurrence; hydrological dynamics; water area changes +
Footnote †: journal: Geo-Information
# Proof of Optimum (PoO): Consensus Model Based on Fairness and Efficiency in Blockchain
## 5 Application and Findings
### Complexity Analysis of PoO  
In terms of time complexity, PoO involves solving a Traveling Salesman Problem (TSP) for each block, which is an NP-hard problem. The TSP has a worst-case time complexity of O(n!), which makes it computationally expensive for large instances. However, PoO uses a genetic algorithm to solve the TSP, which has a lower time complexity than brute-force algorithms. The genetic algorithm has a worst-case time complexity of O(n\({}^{2\text{m}}\)), where n is the number of nodes and m is the number of generations.
## 2 Methodology  
The flow of the study is described in Figure 1. The LiDAR system was used in the study because it can cover a large area in a short time to produce consistent results. With a drone, it is able to capture data in remote, unsafe, or difficult-to-access areas with lower safety risks, though the accuracy is highly dependent on the quality and calibration of the system's scanner, INS (Inertial Navigation System), and GNSS (Global Navigation Satellite System) components, and it also requires skill to process the data and recognize inaccuracies.  
The periodically acquired drone LiDAR data are preprocessed with direct georeferencing and outlier filtering for the surface point cloud. The processed point cloud is used for the elevation change measurement, and the accuracy was assessed with the surface settlement data. Then, the two data went through a regression for long-term settlement and consolidation prediction. Finally, the drone data were used for the settlement and consolidation prediction for points without surface settlement.
# A Miniaturized Design for a Terahertz Tri-Mirror CATR with High QZ Characteristics
## 2 Design of a Miniaturized Tri-Mirror CATR
### Synthesis of Shaped Mirrors and System Design
For such a field distribution, when the system's primary mirror radius, \(r_{\rm max}\), is fixed, the QZ width of the output field can be increased by appropriately enlarging the expected QZ radius, \(r_{\rm q}\). Consequently, the parameter \(r_{\rm q}\) is also treated as an optimization variable for enhancing QZ characteristics. For the primary mirror, as its aperture size decreases, the edge diffraction effects of the mirror increase sharply. Simultaneously, to improve the system's QZ usage ratio, the method of increasing the output field QZ width adopted during the shaped mirror design also results in a relatively divergent energy distribution at the primary mirror's edges, further exacerbating edge diffraction. Therefore, to reduce the impact of edge diffraction on the system's QZ, the rolled-edge treatment in [32] is applied to the edge of the primary mirror in the tri-mirror CATR system. Following the above design process, a miniaturized tri-mirror CATR system working at 100-500 GHz with an aperture of 0.2 m is developed. Its configuration structure is illustrated in Figure 2, and the main structural dimensions of the entire system are 0.61 m \(\times\) 0.2 m \(\times\) 0.66 m.
## 5 Discussion
### Cross-Examination of Sea-Surface Calibration Results  
The Corsica, France calibration facility calculated the sea-surface height bias of Sentinel-3A and Sentinel-3B as +16 mm (S3A) and +19 mm (S3B), respectively [29]. The mean values of all passes calibrated at the PFAC have been (Table 7) -7.93 mm (S3A) and +4.70 mm (S3B), respectively. The deviation (S3 MPC minus PFAC) between the calibration results is of the order of +2.5 cm for Sentinel-3A and +1.5 cm for Sentinel-3B. This discrepancy is within the reported FRM uncertainty of the PFAC sea-surface calibration results (\(\pm\)34.5 mm, Table 5).  
There are several other reasons that may explain the discrepancy between PFAC and S3 MPC such as: (1) the S3 MPC Cal/Val results are based on processing baseline No. 2.33 to 2.45 while the PFAC applied the latest PB.2.61, (2) Sentinel-3A Cal/Val results from the S3 MPC refer to cycles 1 to 47, but this work is applied till cycle 56, (3) the S3 MPC Cal/Val results for Sentinel-3B cover four cycles during its tandem phase with Sentinel-3A and not under its nominal orbit as determined for these PFAC results, and (4) in contrast to transponder Cal/Val where S3 MPC used the same data (CDN1 transponder) as the PFAC, sea-surface calibration is carried out at different geographic locations, with different instrumentation and processing methodologies.
# A Literature Survey on Offline Automatic Algorithm Configuration
## 6 Multi-Objective META-EAs
### Model-Based Methods
#### 2.4.3 Meta-Tuner  
[PERSON] et al. [102] proposed a model-based tuning method, Metatuner, which is based on the sequential optimization of perturbed regression models. Their experimental results proved that this method finds competitive algorithm configurations when compared with the results of Iterated Racing, SMAC, and ParamILS methods in different problem scenarios and gives information about the interaction between parameters and the relevance of each parameter. However, in the final, they discussed this method's drawbacks, such as the number of parameters that can be tuned, and other issues for advancing and improving their method.  
We summarized the main advantages and disadvantages of widely used model-based algorithm configuration methods in Table 3.  
* SMAC can search the full configuration space which means it does not require discretized parameter space like PARAMILS [6].
* SMAC overcomes two limitations of other SMBO methods, one of them is handling numerical and categorical parameters and can be used for multiple problem instances that show different features [6].
* SMAC considers the instance features and parameter interactions.
* Performance prediction model used in SMAC also considers the model's variance/uncertainty in its predictions and this leads to an improvement of the model itself [6; 13].
* As a disadvantage, SMAC can only use to optimize the runtime of the algorithm and it cannot deal with all instance types, such as NP-hard problems [10].  
\begin{table}
\begin{tabular}{p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt}} \hline \hline
**Methods** & **Advantages** & \multicolumn{2}{c}{**Disadvantages**} \\ \hline \multirow{4}{*}{SPO+} & \multirow{4}{*}{\(\bullet\)} & Their intensification mechanism reduces the total required time of the parameter tuning process; and their new prediction models show much better performance than the previous model [14; 86]. & \multirow{4}{*}{\(\bullet\)} & Although there are considerable improvements in SMBO-based methods, all of them still have three key restrictions, first one is, that it can handle only numerical parameters; it can only configure the algorithm for a single test problem. Moreover, it has not been an early terminating procedure for poor-performing parameter configurations [6]. \\ \cline{1-1} \cline{3-3}  & & & & \\ \cline{1-1
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 3 Materials and Methods
### Network Structure  
We propose a multi-scale strip pooling network (MSPNet) for road extraction from remote sensing images as illustrated in Figure 3. Encoder-decoder networks are applied to many computer vision tasks, and their superiority has also been validated [19; 29]. Therefore, we apply one as the overall architecture of our proposed MSPNet. The ResNet [30] is widely used in image recognition because of its outstanding performance for feature learning. The ResNet contains a series of residual neural network models, which have different numbers of layers. The CNN-based model with more layers generally improves the performance, while it requires a higher computational cost and training time. We will provide analysis of the performance and parameter cost for a ResNet series network in the next subsection. Here, we employ ResNet-34 pre-trained on ImageNet [31] as the encoder.  
The roads in remote sensing images are narrow, long span, and straight distribution that often produce fragmented road segments. To improve the connectivity of roads, our proposed multi-scale strip pooling (MSP) module is sequentially added to the first three skip-connection paths to extract linear features of roads at multiple scales. Different from the roads, most background objects have bulk shapes. Considering the advantages of traditional pooling, we adopt the Pyramid Pooling Module (PPM) [32] to effectively learn complex features that are added to the last skip-connection path. Due to the complex distribution of roads and other objects, the use of PPM will enhance the learning ability  
Figure 3: Overall architecture of the proposed multi-scale strip pooling network (MSPNet). The encoder module contains four residual blocks, the MSP module is applied to extracting linear features of the roads, and the PPM module is used to learn complex features from different scales. The decoder module is applied for gradually upsampling the resolution of the output feature map to get the final per-pixel prediction.  
of complex features and thus improve the performance. In the decoder module, we apply transpose convolution for upsampling the feature maps to an appropriate size and \(1\times 1\) convolution for adjusting the channels of feature maps; then, we concatenated with the corresponding output feature map of the MSP module in the skip-connection path.
## 1 Introduction
### Research on Situation Awareness Indicators
Existing studies have not analyzed the factors affecting remote tower controllers' situational awareness comprehensively and deeply enough and lack a unified and effective assessment model. As a core factor affecting the safety and efficiency of air traffic control, the accuracy and validity of the assessment of situational awareness are crucial. Accurately assessing the situational awareness level of remote tower controllers is of non-negligible significance for optimizing the controller training system, improving the design of remote tower systems, and ensuring the safe and efficient operation of air traffic. However, there are differences in the existing research methods and indicator selection, which makes it difficult to compare and integrate the results of the studies and fails to provide strong support for practical applications. Therefore, this study is conducted to fill these research gaps, comprehensively and deeply explore the level of situational awareness of remote tower controllers, and construct a set of scientific and reasonable assessment index system and model, so as to provide theoretical basis and practical guidance for improving the safety and efficiency of air traffic management.
## 4 Experiments
### Load Balancing
Figures 6 and 7 depict load balancing effect in grid and random topologies respectively by using symbols defined in Table 4. The size of a blue circle is proportionally enlarged as N increases. N, representing traffic load, is defined in Table 4 as well. The larger the circles, the more traffic-imbalanced the network is. Figure 6 shows the comparison of flow distribution at each node in grid topology. Figure 6a shows AOMDV scenario, where large blue circles surround the gateway and it means imbalanced \begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Description** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} \\ \end{tabular} } & data gateway \\  & CBR source \\  & Node with traffic load above 10\% of total source data traffic, \\  & where, N = (Node’s load/Total source data traffic) \(\times\) 10. N is 1, \\  & when the load is from 10\% to 19\%; N is 2, when the load is \\  & from 20\% to 29\%, and so on. \\  & Node with traffic load under 10\% of total source data traffic \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Symbol description. Figure 6: Load distribution in gird topology (total node is 85). (**a**) AOMDV; (**b**) LBMR. Figure 7: Load distribution in random topology (total node is 100). (**a**) AOMDV; (**b**) LBMR. network traffic is aggregated at some nodes, especially at Layer 1. Therefore, bottlenecks are prone to occur at Layer 1. On the contrary, LBMR in Figure 6b is with small circles, which indicates the network traffic is much more balanced. Therefore, LBMR successfully alleviates the network bottleneck issue in AOMDV. For the grid topology shown in Figure 6, Figure 6b shows all layer 1 nodes have the same loading in LBMR but Figure 6a shows half of layer 1 nodes have large loading burdens but the other half have much less burden. In Figure 7, three big dashed circles represent three ranges which are 50, 100, and 150 m away from the gateway respectively. Although nodes are randomly deployed in uniform distribution, it is obvious that the number of heavy-loaded nodes in LBMR scenario is relatively less than that in AOMDV, especially nodes nearby the gateway are perfectly load-balanced.
## 4 System Architecture
### Pilot  
Before transmitting, the pilots must add the signal to estimate the channel. A system's minimum pilot density and pilot position are critical. The pilot spacing used for the OFDM symbol was studied and implemented in [9].  
Let \(N\) be the OFDM symbol's length, \(\Delta f\) be the spacing between subcarriers, and \(N*\Delta f\) be the OFDM system's bandwidth.  
Let \(T_{s}\) be the spatial sampling period and \(\tau\) be the time delay between spatial sampling. As indicated below, \(\Delta p\) is the maximum pilot spacing:  
\[\Delta p\leq\frac{N\Delta f}{2\tau/T_{s}} \tag{7}\]  
The spacing between two adjacent pilots would be close for suitable interpolation performance. It should be emphasized, however, that the accuracy of the estimation is not proportional to the number of pilots. In circumstances where the channel is over-estimated, pilots placed too closely may compromise performance.
## 3 Circuit Simulation and Experiment
The simplified model solely focuses on the primary factors, which influence the measurement speed of the PDSH circuit and necessitate the integration of circuit dynamic performance and bandwidth for accurate pulse peak value measurements. The holistic approach enables the construction of a rational PDSH circuit that incorporates critical considerations. To further reduce voltage step errors, it is advisable to select diodes with smaller equivalent capacitance, or to utilize a specific circuit configuration to ensure that the output voltage of the OTA exceeds its negative saturation voltage.
## 4 Discussion
### Sense of Presence, Environmental Stimuli and Scenarios  
Several primary studies revealed that different environmental variables and spatial attributes have significant effects on the intensity and qualities of presence experienced. Specifically, presence was modulated when manipulating sounds [23; 59], smells [22; 23; 26; 32; 70], temperature [21; 23], texture [24], spatiality [19], socio-spatial qualities [20; 70], etc. Taking a more global approach, a solid line of research related to the interactions between the different landscape typologies of scenarios and sense of presence was also found [1; 5; 12; 20].  
These results emphasize the need to study the experience of presence in interrelation with the characteristics of the built context, since they directly affect its spatial presence dimension, the subjective feeling of \"being there\" in a virtual environment, as well as other dimensions. Therefore, researchers should go beyond investigating presence as an isolated phenomenon, and to move toward conceiving it within a comprehensive framework of interaction between the properties of the simulated environment and the internal processes of users and their individual characteristics. Only through an ecological approach will it be possible to identify optimal design principles for virtual environments that will make them capable of evoking high levels of spatial presence. This will also make it possible tounderstand how different built space configurations impact on the cognition, affectivity and behaviors of users.
## 5 Three-Dimensional Path Planning Algorithms  
The top challenge in autonomous navigation of UAVs is planning an obstacle-free route from the start to the destination. Encountering obstacles is possible, especially for missions like law enforcement, package delivery, and first aid in urban areas. Most of the path planning algorithms for UAVs are derived from pre-existing algorithms designed for ground robots. These algorithms are often 2D and need to be modified into 3D for aerial vehicles. The complexity to design and the demand for high performance computerson-board the UAVs are challenges that incurred by the 3D path planners. The obstacle-free 3D path planning process demands an intensive computational burden that often limits the maximum cruising capability of the UAV. The effect of this computational burden is true for both free and cluttered environments as long as image processing has to occur.  
Commonly known 3D path planning algorithms are \(A^{\star}\) with its variants, Rapidly-Exploring Random Tree (RRT) with its variants, Probabilistic RoadMaps (PRM), Artificial Potential Field (APF), and Genetic or Evolutionary algorithms. These algorithms can be categorized into two: sampling-based and node/grid-base algorithms. Sampling-based algorithms connect randomly sampled points (subset of all points) all the way from start to the goal points thereby creating random graphs from which a graph with shortest path-length is selected. The algorithms include RRT, PRM, and APF.  
Node/grid-based algorithms, unlike sampling-based algorithms, exhaustively explore throughout consecutive nodes. These algorithms include \(A^{\star}\) and its variants. In search for an obstacle-free path, the algorithm takes in an image of the environment and discretize it into grid cells that includes the current (start) location of the UAV and the goal location. The \(A^{\star}\) algorithm has two functions to prioritize the cells to be visited. These two functions are the cost function, which calculates the distance from the current cell to the next cell, and the heuristic function, which calculates the distance from the next cell to the cell that contains the goal. With the objective of minimizing the sum of these two functions, the cells to be visited are heuristically prioritized. In the case of 3D search, the cost function calculates distances from the current cell to all 26 neighboring cells, and the heuristic function calculates distance from the 26 cells to the cell that contains the goal. In a cluttered environment with complex occlusion, highly dense grid cells are required, which in turn increase the computational burden, and thus the selected path may not be optimal.
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
## 2 Materials and Methods
### Plasma Sources and Plasma Treatment  
For treatment of the selected spices, a unique microwave (2.45 GHz) plasma source (MPS) operating at atmospheric pressure argon [30; 31] was used in these investigations. In contrast to previous MPSs operating at atmospheric pressure, which delivered plasmas in the form of cylindrical plasma columns or flames, the MPS used in the presented study generated a very convenient plasma sheet. Such a shape of plasma can be easily implemented into a technology line (i.e., over the conveyor belt). This type of MPS may be based on a rectangular waveguide or on a strip-line structure [32]. In this experiment, we used a setup based on a WR 340 standard rectangular waveguide (Figure 1). Argon plasma was generated inside a flat quartz box and protruded out of the box. The flat quartz box was inserted into the waveguide through rectangular slits in the section of reduced height of the MPS. As a protection from microwave leakage, the discharge quartz box was surrounded by metal shielding. The working gas (argon) was introduced into the quartz box by two opposite ducts placed in its upper part. The MPS had an integrated three-stub tuner to match the impedance. Using the tuner, the microwave reflected power was less than 5% of the microwave forward power and there was no need to use the protection isolator. Microwaves were generated by a standard magnetron (CEFEMO 2M240H), commonly used in kitchenette microwave ovens. The magnetron was powered from a Dipolar MagDrive (Sweden) power supply. The microwave power absorbed by the plasma was 600 W. At the microwave power (incident power--reflected power) absorbed by the plasma at 600 W, the device consumed approximately 920 W of electrical power from the power grid. The argon flow rate was set at 20 L/min. Under these conditions, the plasma sheet outside the box was about 50 mm width, 10 mm length and 1 mm thick, and the temperature of the plasma was about 900-950 K [25]. The treated spices were placed on a Petri dish on the motorized (with stepper motor) linear stage. The linear stage was placed 20 mm below the open side of the quartz box (Figure 2). The diameter of the Petri dish was 60 mm. The linear stage moved the Petri dish so that the plasma scanned its area from one side to the other. One full scan of the Petri dish's area lasted 15 s. The samples were treated by plasma from 15 (single scan) up to 60 (four scans) seconds.