esa-sceva/satcom-chunk-collection huggingface dataset:
# GA-PSO Algorithm for Microseismic Source Location
###### Abstract  
Accurate source location is a critical component of microseismic monitoring and early warning systems. To improve the accuracy of microseismic source location, this manuscript proposes a GA-PSO algorithm that combines the Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). The GA-PSO algorithm enhances the PSO algorithm by dynamically adjusting the balance between global exploration and local exploitation through a sinusoidal function for the nonlinear adjustment of both learning factors, and an adaptive inertia weight that decreases quadratically with iterations. Additionally, the precision of the solutions is further improved through the crossover and mutation operations of the GA. In the simulated location model, the GA-PSO algorithm demonstrated the smallest error value, outperforming both the GA and PSO algorithm in terms of accuracy. Furthermore, the GA-PSO algorithm exhibited minimal sensitivity to wave speed fluctuations of \(\pm 1\%\), \(\pm 3\%\), and \(\pm 5\%\), maintaining the error within 0.5 m. The validation through the blasting experiment at the Shizhuyuan mine further confirmed the enhanced accuracy of the GA-PSO algorithm, with a location error of 20.08 m, representing an improvement of 59% over the GA and 43% over the PSO algorithm.  
microseismic source location; GA-PSO algorithm; dynamic learning factor; adaptive inertia weight +
Footnote †: journal: Applied sciences  
0000-0002-4000-1999-2233][PERSON]. [PERSON]  
0000-0001-5998-2286]F. [PERSON]  
0000-0002-3323-2333][PERSON]. [PERSON]  
0000-0002-4002-3333][PERSON]. [PERSON]
# Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
## 3 Dynamic Hard Shoulder Running Strategy Based on Hidden Markov Model
### Traffic Breakdown Predict Model under Hybrid Network
That is, with the sampling interval set to \(t_{c}\) minutes, the raw data can be used to compute the one-step transition probability matrix of the breakdown states. The one-step transition probability here refers to the probability of transitioning from the current state to the state after \(t_{c}\) minutes. Assuming that there are moments where the breakdown state transitions from state \(i\) to state \(j\) across all samples, we can estimate the hidden state transition probability matrix \(A\) based on these frequencies. \[a_{ij}=P\big{\{}S_{t}=s_{j}\lor S_{t-1}=s_{i}\big{\}}=\frac{f_{ij}}{\sum_{k=1}^{k =N}f_{ik}} \tag{9}\]  
(2) Learning of two-state corresponding probability matrix \(B\):  
Similar to the estimation of the hidden state transition probability matrix \(A\), the frequency is estimated by recording the number of occurrences of each display state (upstream density and penetration rate) under each hidden state (traffic breakdown type). The two-state corresponding probability matrix \(B\) is a large and sparse matrix with a size of \(N\times M^{N-1}\). (3) Learning of initial hidden state distribution:  
This model assumes prior knowledge of the hidden state at the initial moment and does not require parameter learning. If the breakdown state at the initial moment is denoted as \(k\), then only the \(k\)th bit of the initial hidden state distribution vector is set to 1, while all other bits are set to 0.
## 5 Simulation Setup
### Compressive Sensing Setup  
For CS application, we generate a realistic crowded star field with airy shaped PSF with PSF radius ranging from (1,5) pixel units. For \(n\times n\) image, we generate \(0.75\times n\times n\) star sources. The flux of the star sources ranges from 50 to 5000 units. In our simulations, we use \(m=0.25\times n\), where \(m\) is the number of CS measurements obtained. The number of measurements, \(m\), is a trade-off with the amount of error tolerance desired. We chose \(m\) value such that we would keep the average error tolerance to under 1%. A Bernoulli measurement matrix is used, which is varied during each Monte Carlo simulation. For a given \(R\) and \(\Omega\), we run 100 Monte Carlo simulations at each time sample, \(t\). The number of Monte Carlo simulations chosen are based on two factors: (1) available computational resources and (2) the minimum number needed, such that the average obtained over all Monte Carlo simulations is as close to the \"true\" average. In order to find the \"true\" average, we look at the standard deviation of the Monte Carlo simulations to verify that it is as close to 0 as possible. Using 100 Monte Carlo simulations, for \(R=7000\), we obtain the average standard deviation over all time samples and \(\Phi\) values to be \(1.2\times 10^{-11}\). Similarly, for \(R=42\),000 and \(R=1 AU\), we obtain the average standard deviation to be \(1.1\times 10^{-11}\) and 0.0, respectively. Hence, we use 100 Monte Carlo simulations because the standard deviation decreases to a minimal amount across that many number of simulations in our setup. For CS reconstruction, we use the greedy algorithm, orthogonal matching pursuit, as its computational time is relatively less than optimization algorithms.
## 3 UAV 6G Networks
### 6G Application Requirements
#### 3.2.3 Multimedia Transmission  
Based on mission profiles and to ensure the prompt provision of data to ground stations, unmanned aerial vehicles transport live data such as video, other sensors data for timely analysis and subsequent decision making. In future, modern multimedia services will be one of major demands through UAV platforms. These include multimedia applications related to virtual reality, 4K and beyond films, holograms, etc. These advance multimedia services require high data rates and bandwidth to provide true experience to users in connection with applications such as virtual reality and 3D holograms. The envisaged 6G network is capable of providing high bandwidth and throughput in UTM [74]. In order to ensure, seamless communication of UAVs with the ground control station and reliable traffic between the two ends, mandatory high bandwidth requirements are required to be promised, which will be offered by 6G in future. A data rate of 10 Gbps is anticipated in upcoming 6G technology [75]. The multimedia application of UAV is depicted in Figure 10.
# Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm
## 4 Proposed Algorithms
### The Proximal Operator of CNC Sparse Regularization  
As demonstrated by (8), the proximal operator of the nonsmooth regularization term plays a crucial role in solving the sparse regularization problem using the FBS algorithm. Therefore, we discuss the proximal operator of the CNC sparse regularization, which can be represented by  
\[\mathrm{prox}_{\lambda\phi_{b}}(y)=\arg\min_{x}\bigg{\{}\lambda \phi_{b}(x)+\frac{1}{2}\|x-y\|_{2}^{2}\bigg{\}}. \tag{15}\]  
According to the definition of \(\phi_{b}\) (10), Equation (15) means solving for \(x\) such that the following problem holds.  
\[\min_{x}\bigg{\{}\lambda\|x\|_{1}-\lambda s_{b}(x)+\frac{1}{2}\| x-y\|_{2}^{2}\bigg{\}}. \tag{16}\]Obviously, it is impossible to yield a closed-form solution of (16), but we can give an iterative solution. By Equation (6), we have  
\[\
abla(-\lambda s_{b}(x))=-\lambda b^{2}\bigg{(}x-\text{prox}_{\frac{1}{b^{2}}\| \cdot\|_{1}}(x)\bigg{)}. \tag{17}\]  
By using FBS iteration (8) to solve (16), we have  
\[x^{k+1} =\text{prox}_{\alpha\lambda\|\cdot\|_{1}}\bigg{[}x^{k}-\alpha \
abla(-\lambda s_{b}(x^{k})+\frac{1}{2}\|x^{k}-y\|_{2}^{2})\bigg{]}\] \[=\text{prox}_{\alpha\lambda\|\cdot\|_{1}}\bigg{[}x^{k}-\alpha \bigg{(}x^{k}-y-\lambda b^{2}\bigg{(}x^{k}-\text{prox}_{\frac{1}{b^{2}}\| \cdot\|_{1}}(x^{k})\bigg{)}\bigg{)}\bigg{]}. \tag{18}\]  
Finally, referring to [21; 43], we set the step size \(\alpha=1\). Then, we obtain the proximal operator of the CNC sparse regularization as  
\[\text{prox}_{\lambda\phi_{b}}(y)=\text{prox}_{\lambda\|\cdot\|_{1}}\bigg{[}y+ \lambda b^{2}\bigg{(}x^{k}-\text{prox}_{\frac{1}{b^{2}}\|\cdot\|_{1}}(x^{k}) \bigg{)}\bigg{]}. \tag{19}\]
## 4 Techniques Used in Academia to Improve Performance Metrics
### Temporal Diversity  
Time diversity can be achieved via (1) channel coding in conjunction with time interleaving or (2) simply by transmitting the packet several times. In the former scheme, the information is coded first and then dispersed in different coherent periods over time [14]. In the latter scheme, the messages are transmitted over different time slots with time separation.  
In channel coding with time interleaving, the coherent period should be larger than the coherence time of the channel in order to experience independent fading in transmission of different parts of the code words. The interleaving fails if the fading is very slow.  
In the retransmission scheme, if the time between consecutive transmissions is larger than the coherence time, then each transmission will see independent fading. Furthermore, if there is an end-to-end delay constraint or a large channel coherence time, the retransmission scheme fails to work.  
In [17], the author studies time diversity in conjunction with antenna diversity, based on CC2431 radio modules.
# Design of Machine Learning Solutions to Post-Harvest Classification of Vegetal Species
## 3 Experimental Evaluation
### Comparison
#### 3.2.3 Background-Segmented Images  
In many studies, the segmentation of the image background has often been correlated with a higher accuracy. This technique helps reduce the amount of background noise. Therefore, the less noisy the image, the better the classification. However, segmenting backgrounds using the different techniques in this study did not prove effective. It always performs lower than \(k\)-means clustering. Therefore, we drop the background segmentation at the early stages and only maintain \(k\)-means clustering.
## 3 Methodology
### Architectures of LSTM Units  
There are several architectures of LSTM units. We can observe the basic architecture of an LSTM memory block in Figure 1. A typical architecture of an LSTM unit consists of three gates: a forget gate, an input gate, and an output gate, in addition to the memory cell, which is part of the LSTM unit. Information is added or removed to the cell state through those gates. The forget gates determines what relevant information needs to be kept or removed during training. Values are determined according to the sigmoid function, typically 0 or 1. When the value is close to zero, then the information is ignored. Otherwise, when the value is close to one, then the information is kept. The input gate controls which information needs to be saved in the memory. The output gate determines which information should be exposed to the memory cell. Similarly, both gates are controlled by the sigmoid function, which determines whether the information needs to be processed or not [23].
## 3 RS Applications in Ocean
### Ocean Surface Current (OSC)
#### 3.2.8 Summary and Future Direction  
In summary, HF radar has widely been applied to estimate coastal and offshore OSC, while altimeters have mostly been used to measure global OSC. However, the range of the HF radars was limited to between 200 km to 600 km, which should be further improved to obtain more accurate information from coastal OSC. Moreover, HF radar and altimeters observations have data gaps (no data) due to the presence of obstacles, such as artificial features (especially for HF radar) and atmospheric conditions and clouds (especially for radiometers). Integrating these datasets with in situ data and OSC derived from TIR/Microwave radiometers and SAR systems could help reduce the gaps and improve the accuracy of the OSC map.  
Although the airborne RS systems provide high spatial resolution data for ocean applications compared to spaceborne systems, they have been rarely used for OSC estimation due to their high cost and lower coverage over ocean. The gap between spaceborne and airborne RS data has been recently filled by data derived from Unmanned Aerial Vehicles (UAVs). UAVs can provide affordable observations with very high spatial and temporal resolutions for OSC estimation and monitoring. Moreover, various sensors (e.g., optical, SAR, and LiDAR) can be mounted on a UAV to collect data over the ocean. Such multisource observations can drive new developments in the OSC estimation. RS CubeSats (i.e., mini satellite systems from 1 to 100 kg) have also created a paradigm shift in ocean applications by providing data with high temporal and spatial resolutions. These observations can efficiently be utilized to monitor a wide variety of ocean parameters describing the OSC.
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 4 Experimental Verification
### Discussion  
The 3D reconstruction for the weldments demonstrates that a point cloud that represents the complete geometric features of a complex spatial planar weldment can be generated by point cloud registration, with the incorporation of a well-calibrated turntable. Notably, this methodology remains effective regardless of the initial position and orientation of the weldments, thus addressing the issue outlined in the introductory section.  
Although welding is a relatively low-precision processing process, extracting weld seams from point clouds is still challenging. The reason not only lies in the difficulty of ensuring numerical accuracy when extracting geometric features but also in the fact that conventional feature extraction algorithms such as vanilla RANSAC tend to fail for complex workpieces. Experiments on plane segmentation reveal that conventional RANSAC algorithms exhibit commendable performance and acceptable accuracy solely for objects with rudimentary shapes. When confronted with intricate spatial structures, these algorithms either fail to detect certain planes or return inaccurate results. In contrast, the proposed improved RANSAC algorithm showcases the ability to accurately recognize planes from spatial workpieces, even those characterized by multiple angles and complex intersecting planes.  
The improvement of the proposed method over the conventional method varies from case to case. In the experiments conducted in this study, the average errors are reduced by 90.3% to 99.8%, and the standard deviations are reduced by 64.8% to 97.0%. Moreover, thanks to the optimizations introduced in Section 2.2, the improved RANSAC greatly eliminates the problems encountered by conventional RANSAC when dealing with complex workpieces. As shown in the experimental results, the proposed segmentation method ensures the extraction of necessary planes while avoiding the generation of erroneous planes. Since the conventional methods basically fail to generate the weld seams properly, we are unable to provide comparisons on WSE over the proposed method and conventional ones.
# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 2 Materials and Method
### Method
#### 2.3.3 The Quantitative Relationships between Climatic Factors and WUE, GPP, and ET  
To explore the response of WUE, GPP, and ET to climate change, this study utilized partial correlation analysis to examine the degree of association between them and climatic factors. Partial correlation analysis was employed to investigate the relationship between two variables, x and y, while simultaneously controlling for the influence of other factors on variable x [27]. This study employed partial correlation analysis to determine the correlation between WUE and four climatic factors: Tem, Prec, Solra, and VPD. The formula for calculating the partial correlation coefficient was as follows:  
\[r_{xy,z_{1}z_{2},z_{g}}=\frac{r_{xy,z_{1}z_{2},z_{g}}-r_{xz_{g}-z_{1}z_{2},z_{ g-1}}\cdot r_{yz_{2},z_{1}z_{2},z_{g-1}}}{\sqrt{\left(1-r_{xz_{g}z_{1}z_{2},z_{g-1}} \right)\cdot\sqrt{\left(1-r_{yz_{g}z_{1}z_{2},z_{g-1}}^{2}\right)}}} \tag{3}\]  
\(r_{xy,z_{1}z_{2},z_{g}}\) represents the partial correlation coefficient between variables x and y after eliminating the influence of variables \(z_{1}\), \(z_{2}\), \(\ldots\), \(zg\). A partial correlation coefficient greater than 0 suggests a positive correlation between WUE and the climate factor, while less than 0 suggests a negative correlation.  
In order to quantitatively describe the influence of climate factors on WUE, GPP, and ET, this study calculated the contribution rate of each climate factor to the change of WUE, GPP, and ET using the following method [41; 42]:  
\[\text{Contr.Y}_{\text{j}}=\frac{|R_{y;x_{i}}|}{\sum\limits_{i=1}^{i=n}|R_{y;x_ {i}}|}\times 100\% \tag{4}\]  
Y\({}_{\text{j}}\) is the contribution rate of climate factors to the dependent variables, including WUE, GPP, and ET. \(R_{y;x_{i}}\) is the partial correlation coefficient between climate factors and the dependent variables. Four climate factors, Prec, Tem, Solra and VPD, were considered in our study.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 3 Proposed ACS Architecture  
This section describes the proposed T2G network architecture. Its goal is to obtain a flexible, adaptable, and resilient communication between the train and the ground equipment in order to overcome the limitations of legacy routing. The key novelty of this architecture can be summarized in three aspects. First, to the best of our knowledge, there is no previous proposal that includes SDN and MPTCP working simultaneously. Second, MPTCP is configured to use a novel redundant scheduler, which provides spatial redundancy by duplicating every packet and sending it through different network interfaces at the same time. Third, as standalone MPTCP does not assure the use of disjoint paths, a locally customized SDN forwarding application is employed to improve path diversity. This application is able to compute and set redundant disjoint data paths--active and backup--between two given SDN switches. Moreover, the failover response time is minimized because the backup path is configured beforehand, so that the switching time is reduced to the time that takes to install a flow rule.  
Furthermore, the ACS can be logically divided into three architectural components depicted in Figure 2: core network, access network, and ACS functions.  
Figure 2: Proposed adaptable communication system (ACS) architecture; SDN—software-defined networking, EPC—evolved packet core, LTE—long-term evolution channel, eNB—eNodeB, AP—access point.
# Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions
## 1 Introduction  
The idea of a constellation of satellites appeared in the market about twenty years ago with Iridium and Globalstar as pioneering examples. They offered worldwide communication links competing with the terrestrial cellular network, having, however, limited success due to service costs mainly [1; 2]. We can now say that at that time the business model was not sustainable due to the small market and high initial and maintenance costs.  
Access to space is now broadening thanks to technology miniaturization and design experience. With a tremendous forecasted increase in the launch rate for small satellites (from pico-sized to mini-sized) [3], constellations are getting attention again from sustainable businesses [4].  
Constellations have their greatest potential in the communication field. The upcoming era of the Internet-Of-Things requires the communication infrastructure to handle huge amounts of data and to guarantee service in any geographical position. Constellations, however, also have great potential in weather science, safety/security and disaster monitoring [5].  
In the rapidly emerging business of satellite constellations, it is important to track and update the information about players and trends to guide future developments. While for other emerging satellite related markets such as the one of the nanosatellite revolution, several surveys have already been published, both technical [6] or market oriented [3], the authors are not aware of a similar effort towards satellite constellations. As remarkable exception, the very recent work in [7], is mainly devoted to Space Traffic Management. In [8] constellations are instead considered as part of the wider context of distributed satellite systems (DSS). They are characterized morphologically as those DSS having highdegrees of homogeneity and physical separation, but low functional and operational interdependence. The paper provides a historical perspective of DSS, their taxonomy, and an overlook of technological solutions at subsystem and system levels. However, it lacks a detailed review of constellations.  
In this review paper, we aim at the perspective of matching technical needs and technology availability for large constellations, while providing at the same time a detailed survey of the upcoming satellite constellations, and that is all articulated as follows. The main trends are examined in Section 2, considering the targeted application field, the constellation size and the expected time-to-completion. In addition to statistical aspects, technical challenges are also outlined and then expanded in Section 3. Issues, such as constellation management, communication efficiency, space traffic and deployment strategies, are analyzed after reviewing the work that has been done so far in the literature.  
In Section 4 conclusions are drawn, wherein we outline the main analogies between the upcoming constellations and the most promising trends for the solutions to technical challenges.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 1 Introduction
Specifically, we first aim to minimize the user-side overhead, as user devices are typically resource-constrained, such as mobile phones. Second, we focus on having the computational server provide the service with low communication cost. Compared to the computational resources that can be added locally, limited bandwidth resources will constrain the throughput and scalability of schemes with high communication overhead. Third, we aim to further reduce the computational cost on the server side, particularly when computational overhead becomes the performance bottleneck. Overall, our design seeks to minimize the end-to-end query time for users to enhance the reliability and effectiveness of secure indoor localization, a task that requires real-time performance.