esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Based on the hydrogen compressor, this paper analyzed the characteristic performance, the flow field, and the aerodynamic load on the impeller of three models (UGV, TEB, and SIS). The SIS methods could provide a new idea for designers to deal with a comparable problem in the compressor. Based on the simulation results and analysis, some conclusions are summarized.  
1. The wake of the guide vane excited the aerodynamic load at \(f_{i}\), threatening the impeller's safety. The self-induced slot method was a way to improve the dire situation. The SIS method did not influence the characteristic line of the compressor and was the same as the UGV model.
2. The SIS method changed the wake style behind the guide vane in the radial inlet. Due to the differential pressure, the self-induced slot made the flow pass through the slot from the hub region to the shroud region and resulted in the wake style changing from the pure wake to the weak wake near the shroud region. However, it enhanced the wake intensity near the hub region. Due to the style changing of the wake, it decreased the amplitude at \(f_{i}\) near the shroud region and increases the amplitude at \(f_{i}\) near the hub region of the impeller in the SIS model.
3. As a passive control method, the SIS was not needed for extra power outside the compressor. Although the self-induced slot could not achieve the effect similar to the trailing edge blowing method, it decreasing the amplitude near the shroud region was more meaningful, and appropriately increasing the amplitude near the hub region was acceptable.  
Conceptualization, J.X. and X.L.; methodology, J.X.; software, L.Z.; validation, X.K. and H.L.; formal analysis, X.L.; investigation, J.X.; resources, J.X.; data curation, L.Z.; writing--original draft preparation, J.X.; writing--review and editing, A.M.; visualization, J.X.; supervision, H.L.; project administration, J.X.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare
# Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge
## 6 Experiments and Analysis
### Memory and False Rate  
In order to analyze the memory and false rate of READ, this section compares READ with DCDS, VBFA, CSE and SRLA algorithm. Table 3 shows the average memory occupancy and error rate of READ and comparison algorithms in different experimental data sets. False positive rate (FPR), false negative rate (FNR) and false total rate (FTR) are three kinds of false rates. Let N represent the number of super points, \(N^{-}\) represent the number of super points that are not detected out by an algorithm and \(N^{+}\) represent the number of hosts whose cardinalities are less than the threshold, but detected as super points by an algorithm. Then, \(FPR=100*N^{+}/N\%\), \(FNR=100*N^{-}/N\%\), \(FTR=FPN+FNR\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Experiment Traffic** & **Algorithm Name** & **Memory (MB)** & **FPR (\%)** & **FNR (\%)** & **FTR (\%)** \\ \hline \multirow{6}{*}{Caida 2015\_02\_19} & DCDS & 384.00 & 0.72 & 0.32 & 1.04 \\  & VBFA & 320.00 & 0.92 & 0.15 & 1.07 \\  & CSE & 512.00 & 2.02 & 1.26 & 3.28 \\  & SRLA & 320.63 & 0.76 & 0.83 & 1.59 \\  & READ & 323.00 & 0.87 & 0.71 & 1.58 \\ \hline \multirow{6}{*}{Caida 2016\_01\_21} & DCDS & 384.00 & 0.77 & 0.84 & 1.61 \\  & VBFA & 320.00 & 1.78 & 0.40 & 2.18 \\ \cline{1-1}  & CSE & 512.00 & 3.86 & 3.21 & 7.07 \\ \cline{1-1}  & SRLA & 320.63 & 0.82 & 1.01 & 1.84 \\ \cline{1-1}  & READ & 323.00 & 1.03 & 0.40 & 1.42 \\ \hline \multirow{6}{*}{IPtas 2017\_10\_23} & DCDS & 384.00 & 5.00 & 0.00 & 5.00 \\  & VBFA & 320.00 & 5.43 & 0.00 & 5.43 \\ \cline{1-1}  & CSE & 512.00 & 1.39 & 1.27 & 2.66 \\ \cline{1-1}  & SRLA & 320.63 & 2.42 & 0.55 & 2.97 \\ \cline{1-1}  & READ & 323.00 & 2.45 & 0.44 & 2.89 \\ \hline \multirow{6}{*}{IPtas 2018\_03\_08} & DCDS & 384.00 & 5.59 & 0.02 & 5.61 \\ \cline{1-1}  & VBFA & 320.00 & 6.56 & 0.00 & 6.56 \\ \cline{1-1}  & CSE & 512.00 & 1.44 & 1.40 & 2.84 \\ \cline{1-1}  & SRLA & 320.63 & 3.36 & 0.56 & 3.91 \\ \cline{1-1}  & READ & 323.00 & 2.96 & 0.32 & 3.28 \\ \hline \end{tabular}
\end{table}
Table 3: Memory and false rate.  
Table 3 shows that READ occupies less memory than DCDS and CSE, and only 3 MB more memory than VBFA. In terms of error rate, the error rate of READ is close to that of SRLA algorithm.
## 2 Significance of Research  
The widespread integration of PRC systems, especially in scenarios where conventional concrete and steel fall short of contemporary strength standards, underscores an urgent requirement for connections that enhance both seismic resilience and separability. The deficiencies in material characteristics result in heightened brittleness in standard precast beam-column joints, leading to a decrease in ductility and considerable damage during moderate to severe seismic incidents. In response to this challenge, the present research conducts an in-depth evaluation of existing precast connection methodologies, complemented by an innovative finite-element modeling initiative designed to assess the efficacy of a novel device called the ACPH.  
A core focus of this investigation is to ascertain whether the ACPH can meet crucial seismic performance criteria, particularly adequate ductility, effective hysteretic behavior, and dependable protection for beams and columns, when employed within structures built from materials that do not meet established strength benchmarks. To this end, four distinct precast frame configurations are scrutinized, varying in the number of bays and stories. This systematic approach allows for a detailed analysis of the ACPH's influence on base shear demands, fundamental periods, localized damage patterns, and potential failure mechanisms. The research emphasizes the performance of PRC components that are substandard relative to prevailing seismic codes.  
Furthermore, a significant objective is to juxtapose the ACPH against conventional precast joints designed to emulate the behavior of monolithic cast-in-place structures, often labeled as wet connections. The intention is to evaluate whether this innovative hinge can achieve safety levels that equal or exceed those of established techniques, while also presenting notable advantages in terms of post-earthquake separability. As performance-oriented seismic standards increasingly necessitate concise documentation of equivalent or superior performance for new connection systems, this study enhances its numerical assessments with calibrations derived from prior subassembly testing. By utilizing real seismic data, exemplified by the Mw 7.7 Kahramanmaras earthquake, the research evaluates the dynamic response of the ACPH during intense seismic occurrences. Collectively, these endeavors aim to elucidate the potential of a replaceable plastic hinge to remedy prevalent deficiencies in precast frameworks characterized by inadequate material properties, ultimately proposing a practical avenue for the development of more resilient and easily recoverable structural systems.
# Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster
## 5 Discussion
### The State of Distributed DL Inference Using Spark TensorFlow Distributor  
To recap, this paper mainly researched the effectiveness of distributed DL inference by using a 2-node Pi cluster. Contrary to the expectations of the authors, adding another Pi node for DL inference offered no significant speedup as there was no significant performance difference between the 2-node Pi cluster and the single Pi. Occasionally, the cluster performed worse than its standalone counterpart in some parts of the experiments.  
Using just two Pi 4B nodes for processing-heavy Spark programs in standalone mode is also not recommended, as there would be memory allocation issues, particularly with the executor and driver. As all the nodes only had 4 GB of RAM, this might have played a major role in this outcome. From this, the RAM capacity for a 2-node cluster configuration should have the appropriate amount of RAM depending on the use case of the application, as it is hard to find out the minimum amount of RAM capacity that will eliminate memory exceptions. For this reason, referring to the Apache Spark documentation is highly  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Experiment 1 Test**} & \multirow{2}{*}{**Execution Time (s)**} & **Speedup Over** & **Accuracy** \\ \hline
**Dataset** & **Model** & & **InceptionV3** & **Difference** \\ \hline \multirow{2}{*}{CIFAR10} & InceptionV3 & 3810.57 & \multirow{2}{*}{184.35} & \multirow{2}{*}{25\%} \\  & MobileNetV3 & 20.67 & & \\ \hline \multirow{2}{*}{ImageNetV2} & InceptionV3 & 6529.43 & \multirow{2}{*}{7.88} & \multirow{2}{*}{12\%} \\  & MobileNetV3 & 828.33 & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Accuracy and speedup differences over InceptionV3 using MobileNetV3 for Configuration 1.  
Figure 10: Mean speedup with the enhancements versus default Pi 4B.  
recommended, as every Spark application will have different resource requirements. For the experiments seen in this report, the minimum is ideally 6 GB, as DL models such as the InceptionV3 alone could easily occupy 3 GB of RAM during model inference. The size of the dataset is also a factor, as ImageNetV2 was found to have occupied 2 GB of RAM during testing. Another way to work around the memory limitations is to run Spark on YARN, though this is not ideal as it introduces processing overhead as per the discovery in [29]. In summary, it is better to use a single Pi which would be better off being enhanced as per the findings of this report.  
At the time of writing, the technology behind the distributed version of TensorFlow, the Spark TensorFlow Distributor, has not matured enough to allow for distributed inference that scales well with the number of nodes in a cluster, as currently, each node is still executing inference independently without synchronisation across the cluster. The authors deliberate that the Spark MLlib library will remain the status quo of the go-to ML framework for use with Spark clusters.
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 3 Proposed Model
### Random Destination with Pheromone Zone Mobility Model  
We enhance the Random Waypoint model by selecting the next destination in the border of the scanning area in order to solve the insufficient exploration at the boundary area in the Random Waypoint model. When UAVs fly from one border to the other border straight, the center area is still frequently reconnoitered. Thus, the proposed model guides the UAV to move in a zigzag pattern toward the destination in the border area. This zigzag pattern makes the target difficult to predict the route and results in the visiting rate in each area to be possibly even, as well. We manage the visited count in a zone with a suitable size and induce UAVs to follow the zones with lower visited count to the destination in the border.  
The brief algorithm of the proposed mobility model is shown in Algorithm 1. A UAV selects a border zone and flies to the zone through several waypoints. When the UAV arrives at the intermediate waypoints, it selects a next waypoint in the next zone. In addition, the UAV updates its pheromone  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Type of aircraft & Fixed-wing \\ Rotation radius & 500 m \\ Communication range & 8000 m \\ Scan area of camera & 2000 \(\times\) 1000 m \\ Flight speed & 150 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the simple UAV.  
Figure 3: The visited counts: (**a**) Random Waypoint model; and (**b**) Pheromone Repel model.  
information about intermediate waypoints and shares the information with other UAVs. Thus, the model consists of three procedures: (1) selecting border zone; (2) selecting next zone and waypoint; and (3) exchanging information. In the procedure of selecting border zone, we describe the types of zone such as the border zone and the immediate zone, and the method of selecting border zone. The procedure of selecting next zone includes the method of establishing the intermediate zone in order to have the zigzag pattern until reaching the border zone. In the procedure of exchanging information, we explain how to share the pheromone information between UAVs. Based on the shared information, the UAV stochastically selects the next zone in which reconnaissance is insufficient.  
```
/* - \(P_{curr}\): the current position of the UAV - \(Z_{curr}\): the current zone of the UAV - \(W_{z}\): the set of waypoints visited in the zone \(z\) - \(Ph_{info}\): the pheromone information or the list of \(W_{z}\) */  Initialize \(P_{curr}\) and \(Z_{curr}\) as a starting position. whiletruedo \(Z_{dest}\gets selectNextBorderZone(Z_{curr})\)do \(Z_{next}\gets selectNextZone(Z_{curr},Z_{dest},Ph_{info})\) \(W_{next}\gets selectWaypoint(Z_{next})\) while\(P_{curr}\
eq W_{next}\)do  Control the UAV to move to \(W_{next}\)  end while \(W_{z}\gets W_{z}\cup\{W_{next}\}\) \(Z_{curr}\gets Z_{next}\) while\(Z_{curr}\
eq Z_{dest}\);  end while
```  
**Algorithm 1**UAV Mobility Model
# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 4 Results and Analysis  
### Experimental Dataset  
The data used in our experiments come from the large ADNI public database of the United States. The sample image is a weighted MRI, with a magnetic flux of 1.5. The number of samples is 602, including 50 AD cases, 332 NC cases, and 220 MCI cases. The description of the data information is shown in Table 1, which lists the numbers, ages, simple mental state table (MMSE), and clinical dementia rating (CDR) of AD, NC, and MCI, respectively. The total score of MMSE is 30 points. It evaluates scores according to the educational level. If the score is below the cut-off value, it is a functional defect, and a score above the cut-off value is normal: illiterate (unedicated) is 17 points, primary school (education years \(\leq\) 6 years) is 20 points, and secondary school or above (education years \(\geq\) 6 years) is 24 points. CDR is used to assess the severity of dementia: healthy (CDR = 0), suspected dementia (CDR = 0.5), mild dementia (CDR = 1), moderate dementia (CDR = 2), and severe dementia (CDR = 3).  
The information on features is shown in Table 2. For the NC-AD group data, the total number of features is 312, including 303 texture features and 9 morphological features. For the NC-MCI group data, the total number of features is 314, including 303 texture features and 11 morphological features. For the MCI-AD group data, the total number of features is 316, including 303 texture features and 13 morphological features. This paper mainly studies the binary classification problem. In the actual diagnosis process, it is not only necessary to identify the patients from normal people however additionally to consider the degree of disease. After all, MCI in the later stage is easily confused with AD. Therefore, we additionally need to consider the distinction between MCI and AD.
# Bearing Capacities and Failure Behaviors of F-Type Socket Joint in Rectangular Pipe Jacking Tunnel
## 4 Parametric Analysis  
### Analysis of Cross-Sectional Dimensions  
According to the Technical Regulations for Rectangular Pipe Jacking Engineering (T/CECS716-2020), the cross-sectional dimensions of 1625 mm \(\times\) 1075 mm, 6000 mm \(\times\) 4300 mm, 7000 mm \(\times\) 5000 mm, 7700 mm \(\times\) 4500 mm, 9100 mm \(\times\) 5500 mm and 10,400 mm \(\times\) 7500 mm were selected for analysis and study to compare their joint bending moment-relative corner relationships. This is shown in Figure 20.  
As can be seen from Figure 20, under the action of the joint bending moment, a section of the joint is compressed and the other end is opened, and the compression amount is much smaller than the opening amount. The maximum amount of joint opening with the joint bending moment is more significant, there is an approximately linear relationship for the different cross-sectional size of the pipe joint, the relative angle of the joint under the action of the joint bending moment and the law of change are basically the same, but the larger the cross-sectional size of the pipe joint, and the larger the joint bending moment and relative angle, the greater the rotational stiffness. It is calculated that for each increase of 1.3 m to 2 m in section size, the rotational stiffness increases from 160 kN-m/rad to 170 kN-m/rad.
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 2 Materials and Methods
### Generator
Through element-wise multiplication of the amplitude mask output \(Mask\in\mathbb{R}^{T\times F\times 1}\) and the magnitude spectrum of the original speech input \(Y_{m}\), background noises can be suppressed, and enhanced frequency magnitude is achieved: \(\hat{X}^{Mask}\in\mathbb{R}^{T\times F\times 1}\). The mask is multiplied by trigonometric functions that can take on positive or negative values, thereby reconstructing the complex spectrum with the correct phase. The real and imaginary parts of the complex spectrum are processed by the complex decoder to output both the real part \(\hat{X}^{Comp}_{r}\in\mathbb{R}^{T\times F\times 1}\) and the imaginary part \(\hat{X}^{Comp}_{i}\in\mathbb{R}^{T\times F\times 1}\). Finally, the masked magnitude is combined with the phase to obtain a magnitude-enhanced complex spectrum. Through element-wise addition with \(\left(\hat{X}^{Comp}_{r},\hat{X}^{Comp}_{i}\right)\), we eventually obtain the final complex spectrum \(\left[\hat{X}_{r},\hat{X}_{i}\right]\), as shown in Equation (6):  
\[\left\{ \begin{array}{l}\hat{X}^{Mask}_{r}=\hat{X}^{Mask}\cos(\theta_{X})\\ \hat{X}^{Mask}_{i}=\hat{X}^{Mask}\sin(\theta_{X})\\ \hat{X}=\hat{X}_{r}+j\hat{X}_{i}=\left(\hat{X}^{Mask}_{r}+\hat{X}^{Comp}_{r} \right)+j\Big{(}\hat{X}^{Mask}_{i}+X^{Comp}_{i}\Big{)}.\end{array} \right. \tag{6}\]  
The reconstructed complex spectrum \(\hat{X}\) undergoes inverse power-law compression, after which the frequency-domain signals are converted back to time-domain signals via the ISTFT to reach enhanced clear speech \(\hat{x}\).
# Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging
## 3 Methods
### New Initialization  
A wise initialization of sensing matrix could be of great worth, which directly determines the iteration efficiency of our algorithm and affects the line backtracking search direction of \(\Phi^{(k)}\). Thus, we proposed a new method of initializing \(\Phi\) based on the characteristics of the loss Equation (15). Firstly, \(\Theta\) is initialized according to the pseudo inverse \(\left(\Theta^{H}\right)^{\perp}\) of \(\Theta^{H}\) at the assumption that Gram matrix is equal to an identity matrix, \(\Theta^{H}\Theta=I\left(\Theta^{H}\left(\Theta^{H}\right)^{\perp}=I\), assuming \(\left(\Theta\right)^{\perp}=\left(\Theta\right)^{-1}\right)\), the equation is followed as:  
\[\Theta\Theta^{H}\left(\Theta^{H}\right)^{\perp}=\Theta, \tag{27}\]  
and:  
\[\left(\Theta\Theta^{H}\right)^{-1}\Theta\Theta^{H}\left(\Theta^{H}\right)^{ \perp}=\left(\Theta\Theta^{H}\right)^{-1}\Theta. \tag{28}\]  
So the initial \(\Theta^{(0)}\) is expressed as:  
\[\Theta^{(0)}=\left(\Theta^{H}\right)^{\perp}=\left(\Theta\Theta^{H}\right)^{- 1}\Theta=\left(\Phi\Psi(\Phi\Psi)^{H}\right)^{-1}\Phi\Psi. \tag{29}\]  
Thus, the initial sensing matrix \(\Phi^{(0)}\) is gotten from \(\Theta^{(0)}=\Phi^{(0)}\Psi\), and \(\Phi^{(0)}\) is denoted as:  
\[\Phi^{(0)}=\left(\Phi\Psi(\Phi\Psi)^{H}\right)^{-1}\Phi. \tag{30}\]  
The \(\Phi\) of Equation (30) is the random binary sensing matrix that needs to be initialized randomly at first. For example, in our single-pixel imaging system, \(\Phi\) can be initialized as 0-1 binary random matrix and part Hadamard random matrix.  
The above method of initializing \(\Phi\) takes full advantages of the two approximate terms in the loss Equation (15) assuming the two ideal situations of \(\Theta=\Phi\Psi\) and \(\Theta^{H}\Theta=I\) and simultaneously combines the characteristics of the two terms, which conforms to the properties of solving the objective Equation (15). Consequently, \(\Theta^{(0)}\) obtained by our initialization and the fixed \(\Psi\) are input to the step 1 of our algorithm as known variables for training more optimized sensing matrix \(\Phi\). After applying our initialization, the risk of local minima and instability the solution of \(\Phi\) may fall into is ameliorated by the fact that the initialization of \(\Phi^{(0)}\) by Equation (30) has already been in the neighborhood of a desirable attraction basin of the global optimum or a local optimum. Thus, compared with other initialization methods such as the direct random binary initialization on \(\Phi\), our initialization method can train more optimized sensing matrix, theoretically approximating the global optimum that is almost equivalent to the ideal solution of \(\Theta=\Phi\Psi\) and \(\left(\Phi\Psi\right)^{T}\Phi\Psi=I\) simultaneously. In other words,this new initialization method facilitates the loss function \(F(\Phi)\) to approximate the global optimal solution with higher efficiency and greater probability.
## Data and Methods
### Factors Affecting the SMAP FT Classification Accuracy  
The mean annual FT classification agreement between WMO SAT observations and global SMAP FT classification values was evaluated according to potential landscape noise factors, including topographic heterogeneity, surface water fraction, and the percent tree cover within a grid cell. These factors represent physical landscape attributes known to affect FT accuracy, including surface water contamination of Tb and FT retrievals over adjacent land areas within a grid cell; terrain-driven microclimate and FT heterogeneity occurring beneath the effective resolution of the satellite footprint; and a degraded surface and soil microwave signal in dense vegetation areas [8; 40; 41]. The spatial proportion of open water (fw) within each 9 km and 36 km SMAP grid cell was derived from a 300 m global water body map for the 2010 epoch (from 2008 to 2012) produced by the European Space Agency (ESA) [42]. The fw (%) map defines static open water body cover and does not account for temporally dynamic variations in surface inundation. The MODIS MOD44B 250 m vegetation continuous field (VCF) version 6 product [43] was used to derive the amount (%) of forest cover within each 9 km and 36 km SMAP grid cell. Global 30 m digital elevations (GDEM V2) [44] were used to characterize the terrain heterogeneity within each 9 km and 36 km EASE-Grid cell. Terrain heterogeneity was defined as the spatial standard deviation (SD) of the elevation distribution within each SMAP grid cell. A drop-in-bucket averaging of finer-resolution pixels was used to resample to the 9 km or 36 km resolution EASE-Grid format.
## 2 Materials and Methods
### A-D4s
#### 2.4.2 A-D4S Operation Concept  
Drone vehicles are typically kept at base or mobile petrol reserves, equipped with a secure communication channel using a mobile or satellite or detected communication infrastructure. Once approval for an operation is given, a drone can take off within LOS or/and beyond LOS (BLOS) conditions and start surveillance, while the command-and-control center can maintain high levels of vigilance concerning the drone's location and altitude, issuing appropriate commands related to the operation. While other members of the command-and-control center observe the received video stream, data and suggestions from AI can establish better situational or environmental awareness during monitoring to establish and facilitate decisions. Once the event is finished, the drone can be called back to be rechecked for the next operation.