esa-sceva/satcom-chunk-collection huggingface dataset:
## Literature Review
### Applications of Two-Sided Matching Theory  
In recent years, two-sided matching theory has been extended to a few applications. [PERSON] et al. (2016) proposed an optimal matching approach for one-shot multiattribute exchanges with simultaneous fuzzy information and indivisible demand considerations (18). [PERSON] et al. (2017) presented mathematical programming approaches to quickly find good stable or nearly stable matchings for single-rider, single-driver dynamic ridesharing (19). [PERSON] et al. (2017) formulated the cooperative spectrum sharing between multiple PUs and multiple SUs as a two-sided market and studied the market equilibrium under both complete and incomplete information (20). [PERSON] et al. (2020) applied the lexicographical method in order to solve the multi-objective linear programming model so as to obtain the optimal bilateral transaction matching pair [21]. [PERSON] et al. (2020) proposed an app-matching system and generalized deferred acceptance algorithms so as to match mobile applications with users [22]. [PERSON] et al. (2021) presented a two-sided matching framework in order to model the resource allocation among customers and manufacturers and leveraged the stable matching algorithm to optimize the matches between customers and AM providers [23]. [PERSON] et al. (2021) showed a realistic modelling of the V2V energy sharing problem and proposed a two-layer matching approach that can efficiently match the EVs [24].  
Our present study started from the above-mentioned existing literature, including matching agents and rules, matching approach and optimization, and matching mechanism and algorithm. To our knowledge, this is an innovative study in its aim to design a stable mechanism for the trading between coal mining and power companies. In the market in which coal mining companies and power generation companies participate, the trading matchings are different, as are the preferences. From the matching perspective, the essence of the trading that occurs between coal mining companies and power generation companies is to achieve an agreement between the coal mining companies' description of the coal that will be supplied and the power generation companies' description of the coal that has been demanded. In other words, the two sides behind the supply and demand have reached an agreement with the order. The coal mining and power generation companies may agree upon multiple orders and ultimately reach multiple matchings.
# Local Community Detection in Dynamic Graphs Using Personalized Centrality +
## 6 Guaranteed Ranking
### Results
We therefore present a possible approach for handling group rankings rather than individual rankings, where the difference in value between two groups is substantial. Such a group ranking would not require precise sorting of the centrality vector; instead, some variation of bucket sort could be used where vertices are grouped in buckets based on a particular range of scores. This would be applicable in applications where the user has some a priori notion of the centrality values themselves and can set the ranges of scores accordingly. In situations such as community detection, group ranking can be more useful since the values of vertices themselves are not of utmost importance. The group ranking can function similarly to the method presented earlier for individual ranking. Rather than looking for gaps between vertices \(i\) and \(j\), we can instead look for gaps between average values in the difference groups. In this section, we presented a new stopping criterion for iterative solvers that allows us to theoretically guarantee when we have the top \(R\) vertices in a personalized centrality vector. We can then use this set as the local community, as we have explored in previous sections. All the results in this section have dealt with static graphs. The extension to dynamic graphs is quite straightforward: whenever the personalized centrality vector is updated using the dynamic algorithm for personalized Katz, the same theory in Theorem 1 can be applied to the updated centrality vector. We can then guarantee sets of top \(R\) vertices in dynamic graphs as well, which translate to the local communities in changing graph data. Future work will develop additional theory for guarantees for PageRank as well and will explore adapting the theory to update on the fly in dynamic graphs instead of having to recompute the bound from scratch every time the graph changes. Figure 5: Speedup in iterations.
# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 3 Experiments  
### Dataset  
The dataset used in this paper is the bearing dataset from Case Western Reserve University(CWRU) and Polytechnic University of Turin(DIRG). These two datasets are widely used in the domain of machine learning research and testing. We conduct comprehensive experiments with the CWRU dataset, and also validate our model on the DIRG dataset.  
In the CWRU dataset, we choose the various motor loads as the domains. Under each motor load condition, faults ranging from 0.007 inches in diameter to 0.040 inches in diameter were seeded separately at the inner raceway, rolling element (i.e., ball) and outer raceway, and for the faults at the raceway, we only include those whose positions are at 6 o'clock. Besides, there is one more normal working condition for each motor load. In summary, there are four kinds of motor load and each of them has 10 fault modes. In terms of signal, use the signal collected from the driving end sensor whose sampling frequency is 12K. The bearing data of Case Western Reserve University used in this paper is shown in Table 1:  
For convenience, as shown in Table 1, this paper marks the normal signal as the No. 0 fault mode, and marks the 9 fault modes from B007 to OR021 as the No. 1 to No. 9 fault modes in sequence, and marks the 4 working conditions of 0-3 HP as the working conditions of No. 0-3.  
The signals are processed using wavelet transform and converted into an image, and the data is enhanced by the method of overlapping sampling. Sampling is performed at every 128 signal points, which means the adjacent samples have 128 signal points that are identical. Each sample has 1024 signal points, and 880 images are collected for each type of fault mode. Under motor load condition 0, the wavelet transform image of 10 fault modes is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fault Indices**} & \multirow{2}{*}{**Fault Position**} & \multirow{2}{*}{**Fault Diagram**} & \multicolumn{4}{c}{**Load (HP)**} \\ \cline{3-6}  & & & **0** & **1** & **2** & **3** \\ \hline
1 & & 0.007 & B007\_0 & B007\_1 & B007\_2 & B007\_3 \\
2 & Ball & 0.014 & B014\_0 & B014\_1 & B014\_2 & B014\_3 \\
3 & & 0.021 & B021\_0 & B021\_1 & B021\_2 & B021\_3 \\
4 & & 0.007 & IR007\_0 & IR007\_1 & IR007\_2 & IR007\_3 \\
5 & Inner raceway & 0.014 & IR014\_0 & IR014\_1 & IR014\_2 & IR014\_3 \\
6 & & 0.021 & IR021\_0 & IR021\_1 & IR021\_2 & IR021\_3 \\
7 & & 0.007 & OR007\_0 & OR007\_1 & OR007\_2 & OR007\_3 \\
8 & Outer raceway & 0.014 & OR014\_0 & OR014\_1 & OR014\_2 & OR014\_3 \\
9 & & 0.021 & OR021\_0 & OR021\_1 & OR021\_2 & OR021\_3 \\
0 & Normal & & normal\_0 & normal\_1 & normal\_2 & normal\_3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CWRU bearing dataset.  
Figure 7: Wavelet transform images of different fault modes under load condition 0.
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 5 Evaluation
### Results
#### 5.2.3 Delay  
Figure 10 illustrates the delay and benefits under different demand levels and CR using the proposed method. The proposed method demonstrates significant benefits across various demand levels, achieving reductions in delay ranging from 22.82% to 52.62%. Optimization benefits of the adaptive control method range from 22.82% to 34.88%, whereas with the inclusion of CBCVs, reductions in delay range from 30.57% to 52.62%. This proves that the proposed method can improve the ceiling of travel efficiency at intersections.  
At V/C ratios of 0.2 and 0.5, the increase in CBCVs yields positive but not significant benefits. At a V/C ratio of 0.8, a 30% penetration of CBCVs achieves maximum optimization benefits but thereafter shows a declining trend. This is because under high traffic demand, the optimization space for CBCVs is limited. In this method, signal control optimization significantly influences delay reduction, especially at high V/C ratios, whereas vehicle control focuses more on emission reduction, potentially causing conflicts and compromising delay reduction benefits.  
Table 6 presents the delay results for three control methods under the unbalanced traffic demand. The results indicate significant optimization of the proposed method under non-uniform traffic flows. Compared to fixed-timing control, the proposed method achieves emission reduction benefits ranging from 41.10% to 43.38%. This is due to the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Traffic Demand** & **Fixed Signal Control Method** & **Adaptive Signal Control Method** & **Compared to The Proposed Method** & **Compared to the Fixed Signal Control Method** \\ \hline I & 0.89 & 0.65 & 0.47 & 47.19\% & 27.69\% \\ II & 0.97 & 0.78 & 0.58 & 40.21\% & 25.64\% \\ III & 0.93 & 0.69 & 0.54 & 41.94\% & 21.74\% \\ IV & 1.01 & 0.82 & 0.59 & 41.58\% & 28.05\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Stop frequency under different transportation demand structures.  
Figure 10: Average delay (s) and benefits (%) compared with the baseline.  
rolling optimization framework adopted in signal optimization, which effectively captures and predicts vehicle arrivals within each optimization cycle to implement the most optimal control strategy. Compared to adaptive control, the inclusion of CBCVs provides an additional optimization space of 11.73% to 13.96%. This is because vehicle arrivals are optimized by the trajectory control in the lower-level model, further enhancing the ceiling of intersection control efficiency.
## 2 Data Quality Analysis  
This research firstly determines whether the received satellite navigation data are reliable. GPS and BDS use the Kepler orbit element for calculating satellite position, and thus there will be orbit and clock errors due to the fact the elements in the navigation data not presenting the true activity of satellites [1; 17]. By validating the ephemeris and almanac data for each satellite, the difference in satellite control ability and consistency between GPS and BDS was determined. GPS is regarded as a standard for comparing SIS data quality with BDS [18]. Five DQA methods were proposed in our previous research [19], as shown in Figure 3.  
They are:  
1. DQA1: Satellite position difference when ephemeris is updated.
2. DQA2: Satellite clock correction difference when ephemeris is updated.
3. DQA3: Ephemeris applicable period.
4. DQA4: Satellite position difference between almanac and ephemeris.
5. DQA5: Almanac applicable period.  
The navigation data used in DQA analysis were recorded from the 10 th of July to the 13 th of August 2014. This research uses the raw broadcast navigation data from the receiver without any filtering or data transformation. In the following analysis, the statistical results were estimated using all the data.  
Figure 3: Schematic diagram of five DQAs.
# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 3 Literature Review
### State-of-the-Art Object Recognition  
Object recognition has been and still is, one of the major topics of interest in the computer vision community. Object recognition can be performed through object classification or scene segmentation. While semantic segmentation is computationally more intensive for real-time applications, it would result in more accurate boundaries of the objects in the scene, which is more suitable for obstacle avoidance. For instance, object recognition would classify all objects in the scene using boxes whereas semantic segmentation will divide the scene by putting boundaries around the objects. The latter can potentially help the mapping part of the SLAM for a robot.  
Even though the technique involving convolutional neural networks (CNN) have been around for years [42], it has not been used for segmentation until recently. [PERSON] et al. [43] and [PERSON] [44] combined an image region proposal scheme with CNN as a classifier and performed near real-time inference. CNN-based frameworks for semantic segmentation have also achieved near real-time inference time although the common drawback still is the computational load. On the other hand, [PERSON] et al. [45] proposed a semantic segmentation method that took exiting deep CNNs [46; 47; 48] into a fully convolutional network (FCN) and performed segmentation from their learning representations. [PERSON] et al. [49] proposed a semantic segmentation method that was based on a very large encoder-decoder model, performing pixel-wise labeling and resulting in a very large number of computations.  
The ability to perform pixel-wise semantic segmentation in real-time is necessary for mobile applications. The above studies have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. To address this issue, computationally lighter convolutional networks have been presented in [16; 50]. In the authors' previous work [13], for the segmentation model to work on the Jetson board with the limited memory and processing power, ENet [16] which has a smaller architecture compared to the recent deep neural networks was implemented on the UGV. However, the model size and high memory usage of this model forced the authors to allocate a separate Jetson to this module. The resulting latency in data transformation between different modules, caused by integrating multiple Jetson through the network, made the system inefficient in fast movements. This latency forced the authors to restrict the speed of the UGV for real-time performance necessity. Therefore, one of the two main contributions of this paper focuses on proposing a new segmentation model by reducing the model size and memory usage while maintaining accuracy.
## 3 Results and Discussion  
### Particle Size Distribution in the W/O and W/O/W Emulsions  
The particle size of the first emulsion (W/O) was determined to verify its stability. As shown in Figure 2, the particle size obtained had a mean diameter of 720 \(\pm\) 32 nm, which was adequate because it has been reported that particle sizes lower than 1 \(\upmu\)m result in better stability, preventing coalescence and avoiding phase separation [17,20].  
With regards to the second emulsion (W/O/W), Figure 3 shows a bimodal distribution with two peaks located between 32-37 \(\upmu\)m and 66.8-77.5 \(\upmu\)m; the particle mean diameter was 35.52 \(\upmu\)m for the double emulsion.  
With respect to the encapsulation efficiency, as mentioned in Section 2.4, a calibration curve was constructed with a NaCl solution, and a linear relationship between the encapsulation efficiency and the system conductivity was observed. The encapsulation efficiency for the double emulsion (W/O/W) was \(89.5\pm 1.10\%\). The particle size bimodal distribution and \(89.5\pm 1.10\%\) encapsulation efficiency obtained were very similar to those previously reported [10].  
Figure 3: Particle size distribution of water in oil in water (W/O/W) double emulsion with a W/O emulsion to WPI ratio of 1:1.333.  
Figure 2: Particle size distribution of water in oil (W/O) emulsion with a 30% IP.
## 5 Vulnerabilities of SDN to LDDoS Attacks
### Deep Learning-Based LDDoS Detection
#### 6.2.1 Implementation and Traffic Analysis of Deep Learning Methods Based on LDDoS Attack Detection  
This section presents the implementation and deployment of LDDoS detection mechanisms based on deep learning as well as the analysis of traffic and datasets used. Table 3 shows a summary of the simulation environment analysis and traffic/datasets used by existing methods.  
In [82], the authors experimented with Minnet in a simulated environment where they created a small SDN topology and generated regular traffic and an LDDoS attack. They build a network topology scenario with one ONOS controller, two OpenFlow switches, a web server victim, six regular hosts and four malicious hosts. The authors used the Flow Collector module to generate regular traffic flows (which is responsible for requesting the flow statistics periodically through the REST API from the SDN controller). For generating LDDoS attacks traffic, they used an open-source tool Slowloris-HTTP [118]. In total, they generated 468,002,403 traffic flows, consisting of 355,758,086 regular flows and 112,244,317 LDDoS attack flows. The collected data are divided into 20% test data and 80% training data. The training datasets are then split into 20% validation data and 80% training data.  
In [84], the authors used Mininet to design and simulate the network. The network topology contains 1 Python-based Ryu controller, 6 OpenFlow switches, and 21 hosts. They performed experiments using two datasets. The first dataset was collected from the network topology traffic, and the second dataset is CAIDA. Each data collection contains both the training and test sets. The Hping3 tool creates the attack traffic in the first dataset. The idle timeout is set to 10 s, and the attack period is set to 9 s. The data from the network traffic dataset are used to construct the regular flow. The second dataset comprises normal traffic and LDoS attack traffic, with a total of 79 parameters and an 80% to 20% training set to test set ratio.
###### Abstract  
A sensitivity analysis was conducted to evaluate several factors, including dam height, bank slope gradient, water storage times, and phased panel filling, on concrete-faced rockfill dams (CFRDs). The analysis identified the three most significant factors to examine their impacts on the stress-deformation characteristics of CFRDs. The results show that the order of influence on the dam body's stress and deformation characteristics is as follows: dam height > bank slope gradient > water storage times > panel phased construction. From the perspective of stress-deformation of the face slab, water storage times predominantly affect tensile stress, while the bank slope gradient exerts the greatest influence on compressive stress. As the bank slope gradient decreases, the panel's lateral restraint diminishes, leading to a decrease in the panel's extrusion efficacy. Consequently, there are notable variations in the panel's compressive stresses. An increase in dam height correlates with escalating stress and deformation in both the dam and face slab. As the bank slope gradient decreases, the deformation of the dam and face slab, as well as the range of tensile stress of the face slab, also increase. In contrast to a single water storage scenario, the face slab has experienced greater stress and deformation during the initial impoundment under multiple impoundment conditions. Therefore, multiple water storage schemes result in reduced deflection, axial horizontal displacement, and tensile stresses both along the slope and axial in the face slab. Furthermore, the tensile area at the bottom of the face slab transitions into a compressive area.  
Keywords:concrete-faced rockfill dam; stress-deformation analysis; influence factor; sensitivity analysis; finite element +
Footnote †: journal: Applied Sciences
# CFD Design Performance Analysis for a High-Speed Propeller
## 3 Materials and Methods
The mesh characteristics are shown in Table 1. As the element size was progressively refined (from 0.5 m in the coarse mesh to 0.125 m in the fine mesh), the number of nodes and elements increased substantially. Despite this, the minimum edge length remained constant across all cases, preserving geometric fidelity in critical regions. The default mesh, characterized by an element size of approximately 0.256 m and containing over 130,000 elements, was selected as optimal. It provided a good balance between numerical accuracy and computational cost. Comparisons of key output parameters across the three mesh levels showed negligible variation between the default and fine meshes, indicating that mesh independence was achieved [25]. For the sensitivity mesh study, two cases were chosen: the v = 2 [m/s] with rpm = 3600 case and the v = 6 [m/s] with rpm = 3000 case as shown in Table 1. The analyzed sensitivity results are shown in the Section 4. * Setting boundary conditions is a crucial step in CFD simulations. Boundary conditions define how each surface interacts with the fluid. Additionally, users can choose from various turbulence models to accurately represent non-laminar flow behavior. This work considered two situations, as follows. * Boundary conditions for water speed 0 [m/s]. For this case, the following boundary conditions are valid: the fixed domain is declared as a free slip wall on the external cylindrical surface [26; 27]; both sides of the fixed domain are openings; the propeller limits are declared as smooth walls; the rotating domains rotate with the propeller's RPM (Figure 6). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multirow{2}{*}{**Fine Mesh**} & \multicolumn{2}{c}{**Default-Mesh**} \\  & & **Utilized in This Study** & **Coarse Mesh** \\ \hline Element size [m] & 0.125 & 0.25611 & 0.5 \\ Max size [m] & 0.25 & 0.51223 & 1 \\ Minimum edge length [m] & 3.37 \(\times\) 10\({}^{-4}\) & 3.37 \(\times\) 10\({}^{-4}\) & 3.37 \(\times\) 10\({}^{-4}\) \\ Number of nodes & 66,584 & 24,692 & 11,364 \\ Number of elements & 355,131 & 130,503 & 11,364 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The characteristics of mesh sensitivity. Figure 5: The external domain mesh. Boundary conditions for water speed bigger than 0 [m/s]. For this second case, the following boundary conditions are valid: the fixed domain is declared as a free slip wall on the external cylindrical surface; the fixed domain side situated in front of the propeller is declared as the inlet; the fixed domain side situated behind the propeller is declared as the opening; the propeller limits are declared as smooth walls; the rotating domain rotates with the propeller's RPM (Figure 7).
# Data-Driven Health Assessment in Flight Control System
## 1 Introduction  
For modern advanced aircraft, flight control system is indispensable to complete normal flight mission and ensure flight safety, which means to stabilize attitude and track scheduled path. So such system's health assessment is necessary to assist maintenance decision, operation schedule, and logistical support optimization, in which it is essential to avoid catastrophic accidents or even loss of human life. A great many scholars have paid attention to flight control or other airborne system's health monitoring and assessment, which may use system's theoretical model, system measured information, or system knowledge and so on.  
In this paper, a data-driven health assessment method based on fuzzy comprehensive evaluation and rough sets reduction for flight control system is proposed. Compared with other health assessment methods, the algorithm based on fuzzy comprehensive evaluation and rough set reduction proposed in this paper still has better performance when the data sample size is small. In addition, by introducing the rough set reduction algorithm, it is also possible to objectively calculate the weight of each subsystem, which makes the evaluation results more comprehensive and reliable.The fuzzy comprehensive evaluation is introduced to complete each factor's evaluation based on optimal evaluation valueand so as to avoid the less fault samples problem. Moreover, the rough set reduction is used to calculate the importance or weight of each evaluation factor, which eliminates the subjective and noise effects. Finally, the actual flight data is used to analyze the health state of the flight control system, which proves the effectiveness of the proposed method. The corresponding literature review about this problem is completed as follows.