esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Trapped modes that exist in the waveguide have different engineering applications. Excited acoustic modes are due to the interaction between acoustic and flow fields. To further study the influence of nonpotential flow on trapped modes for a two-dimensional cavity-duct system, a numerical simulation method that combines the finite-element method (FEM) associated with a mixed formulation of the Galbrun equation and computational fluid dynamics (CFD) is proposed. The calculation model is composed of a two-dimensional waveguide with cavities and perfectly matched layers (PMLs) to limit the computational domain. The effects of the Mach number and different cavity lengths on the resonance modes are investigated.  
trapped mode; nonuniform fluid; Galbrun equation; finite-element method (FEM); computational fluid dynamics (CFD); perfectly matched layer (PML) +
Footnote †: journal: Applied
## 4 Data Silos and Fragmentation:
### Effect on Elapsed Time  
Data Security and Integrity: Blockchain's decentralized and immutable nature ensures that data stored on the blockchain is secure and tamper-resistant. This eliminates the need for centralized data repositories and redundant data validation, which can expedite the overall data security process.  
Transparency and Auditability: The blockchain maintains a transparent and tamper-proof record of all transactions and data updates. This provides a clear audit trail, which can streamline data validation and model aggregation processes, as well as enhance trust among participating healthcare institutions.  
Automated Smart Contracts: Smart contracts within the blockchain automate data access and sharing permissions, reducing the manual administrative overhead and streamlining the process of granting data access to authorized parties.  
Interoperability: Blockchain's use of standardized data formats and communication protocols promotes interoperability among different EMR systems. This can facilitate faster and more efficient data exchange during the federated learning process [34].
## 3 Datasets and Methods
### Aerial Photographs  
The aerial photograph dataset was obtained from French National Institute for Geographic and Forestry Information (IGN) database, and it comprised 41 orthorectified images, taken between 1934 and 2004, with a spatial resolution ranging from 0.10 to 0.85 m. From this dataset, 31 photographs were selected, based on visual evaluation, for their quality and potential for exploitation. They were then georeferenced. A subsection of the study zone was then extracted, and polygons of the identifiable _Spartina_ meadows were obtained, distinguishing between the two species based on the GNSS data and georeferenced ground photographs. The number of identified _Spartina_ patches (polygons) is variable, depending on the year and quality of the photograph. In order to consider the error associated with the visual determination of the _Spartina_ meadows' limits, three replicates of polygons were drawn for each patch. We then used the polygon surface of the three replicates to estimate the mean surface and standard deviation error of each patch.  
The most recent aerial photograph was taken during a drone flight that was performed on 21 June 2016 over the study zone. We used a DJI Phantom 2 drone, equipped with a GoPro Hero 4 Black Edition, mounted on a DJI Zenmuse H3-3D gimbal to counterbalance unwanted camera rotations and absorb vibrations. The flight was performed at a very low altitude, 30 m, in order to obtain high-resolution images. Ground control points were taken over the study area and georeferenced using a GNSS system. The image dataset was aligned by a structure-from-motion algorithm through Agisoft Photoscan software, from which we obtained a 3D model that was optimized and georeferenced using the ground control points. Finally, a high-resolution (5 cm) orthophotography was extracted from the 3D-model. This high-resolution orthophotography was used both as a reference image and ground truth for the validation of feature identification in the satellite images.
## 2 Literature Review
### Research on Factors That Influence People's Sentiments  
The study of the factors that influence people's sentiments mainly includes natural attributes and social attributes. The natural attributes are related to the spatial location of the people and the degree of their connection with the disaster. [PERSON] et al. used Twitter data posted during Hurricane Sandy to visualize and analyze user sentiment, and they concluded that user sentiment was highly correlated with user location [9]. [PERSON] et al. analyzed Hurricane Harvey microblogs in a spatio-temporal manner and found that people's sentiments were affected by different phases of the disaster, showing more negativity during the hurricane than before and after the hurricane [10]. [PERSON] et al. drew on Twitter data to reveal that people's emotional reactions are closely related to the events themselves, using the 2011 earthquake in Japan, the 2010 earthquake in Haiti, and influenza A (H1N1) as examples [11]. The social attributes mainly involve changes in disaster relief policies and the basic characteristics of individuals. Taking the 2013 Ya'an earthquake as an example, [PERSON] et al. analyzed the effect of the content of blog posts on residents' positive and negative sentiments, and they concluded that the factors that affect the fluctuations in residents' sentiments include relevant news media reports and the strength of government credibility [12]. [PERSON] analyzed the relationship between people's sentiments and cognitive differences in disaster scenarios by constructing an analytical framework of the disaster attribution theory [13].  
To sum up, the information posted by the public on microblogs and other new media platforms after a disaster reflect, to a certain extent, the degree of public concern and the changes in public sentiment regarding the disaster itself. The current research on changes in public opinion and sentiment mostly focuses on a single disaster or two similar disasters, and relatively little research is conducted on three or more disasters at the same time. Furthermore, the research on the influencing factors of the concerns and sentiments regarding earthquake disasters is still dominated by feature analyses, and it is still lacking in terms of specific influences. Therefore, based on Sina Weibo data, this paper takes the Sichuan Lushan M6.1 earthquake, the Maerkang M5.8 earthquake and the Luding M6.8 earthquake that occurred in 2022 as the research objects; mines the Weibo contents related to the three earthquakes; analyzes the spatial and temporal evolution characteristics of the number of relevant Weibo posts; presents the results in a visualized manner; and studies the influencing factors of people's attention. By using natural language processing to identify people's sentiments and to explore whether people's sentiments are influenced by the sentiments regarding disaster areas and official microblogs, this paper provides a reference for relevant government departments to monitor public opinion and guide people's sentiments after a disaster.
# Experimental Investigation on Flow-Induced Rotation of Two Mechanically Tandem-Coupled Cylinders
## 4 Results and Discussion
### L/D = 5.0, 5.5 and 6.0
#### 4.2.2 Oscillation Time History and Spectral Characteristics  
Similarly, in order to further study the vibrational characteristics of the FIR system with \(L/D\) = 5.0, 5.5 and 6.0, three typical flow velocities (\(U\) = 0.302 m/s, 0.556 m/s and 0.810 m/s) were selected for each working condition, to analyze the time history and spectral characteristics of the rotation angle \(\theta\), as shown in Figures 15-17, respectively.  
(1) \(U\) = 0.302 m/s: At this flow velocity, there was no significant difference in the time history of the three cases. The waveforms were stable and the \(\theta\) value was low, all maintained at about 5.8\({}^{\circ}\). The frequency bands were narrow and no frequency doubling features appeared. This indicated that the rotational oscillations of these three cases were relatively stable and the vibration energies were small. On the other hand, it could be seen that, when \(L/D\) = 5.0, 5.5 and 6.0, the FIR system started to vibrate with a smaller flow rate, and when the velocity reached 0.302 m/s, rotational vibration of a lower level would occur.  
(2) \(U\) = 0.556 m/s: The analysis of the time history showed that the waveforms of the three cases were stable. Compared with that of \(U\) = 0.302 m/s, the \(\theta\) all increased significantly and could reach about 18.5\({}^{\circ}\). Additionally, as can be seen from the spectral diagram, the \(f_{\rm osc}\) of each case was obvious and increased to 0.782 Hz, 0.781 Hz and 0.782 Hz. The rotational oscillations were stable and energies concentrated at this flow velocity.  
(3) \(U\) = 0.810 m/s: The change of time history of the three cases was similar, the \(\theta\) decreased significantly and the waveforms were disordered. Meanwhile, the \(f_{osc}\) of each case continued to increase, the frequency band became wider and there were 2-fold frequency doubling characteristics. The results indicated that, when \(U\) = 0.810 m/s, the rotational oscillation was suppressed significantly and it was unstable.  
Figure 16: Rotational oscillation time history and spectral characteristics at three typical velocities when \(L/D\) = 5.5: (**a**) \(U\) = 0.302 m/s (\(U_{r}\) = 2.975); (**b**) \(U\) = 0.556 m/s (\(U_{r}\) = 5.483); (**c**) \(U\) = 0.810 m/s (\(U_{r}\) = 7.991).  
Figure 15: Rotational oscillation time history and spectral characteristics at three typical velocities when \(L/D\) = 5.0: (**a**) \(U\) = 0.302 m/s (\(U_{r}\) = 2.877); (**b**) \(U\) = 0.556 m/s (\(U_{r}\) = 5.302); (**c**) \(U\) = 0.810 m/s (\(U_{r}\) = 7.726).  
Comprehensive analysis of the oscillation time history and spectrum characteristics of the FIR system with \(L/D=5.0\), 5.5 and 6.0 showed that, at these three typical velocities, the \(\theta\) first increased and then decreased, and the \(f_{osc}\) continued to increase, which was consistent with the typical response law of VIV. The rotational vibration of these three working conditions could reach a high level because of the larger rotation angle.
# Research on Kinematics Analysis and Trajectory Planning of Novel EOD Manipulator
## 1 Introduction  
In 2020, a sudden outbreak of the COVID-19 epidemic brought great changes to our lives. Presently, society is in the new normal of the epidemic. \"Before the outbreak of the epidemic, terrorist attacks by extremist organizations were at most once a week, and now increased by at least five times, averaging more than 20 times a month,\" a senior Iraqi intelligence official [1] has revealed. Most of the terrorist attacks are explosion attacks, and the traditional manual explosive disposal methods cannot meet the needs of anti-terrorist war. Automatic explosive ordnance disposal (EOD) robots can replace humans to work in an unknown environment, including reconnaissance, detection, explosive disposal, transportation of explosives, and so on.  
Now, some scientists have developed robots for dismantling explosive and burning devices. The British \"trolley\" [2,3] series, the German \"TEODOR\" [4] series and the American \"Andros\" [5,6] series have been developed and applied in battlefields or terroristactivities. In China, there are also the \"Lizards\" [7; 8] series of EOD robots, designed by the Institute of Automation of the Chinese Academy of Sciences, and the \"Super-D\" [9; 10] series of EOD robots developed by Shanghai Jiao Tong University, equipped in various subway stations, parks and other densely populated areas.  
Presently, the manipulators the EOD robots on the market are equipped with, such as ABB, YASKAWA and KUKA manipulators, are self-designed according to the robots' chassis and do not have universal application. Due to the design requirements of the project, for example, the weight of the mechanical arm is less than 50 kg, the arm span is required to be greater than or equal to 1000 mm and the maximum grasping capacity is greater than or equal to 10 kg. At present, most of the manipulators in the market do not match the mobile platform used in this paper, and their degrees of freedom and accuracy cannot meet the requirements of explosive removal. Aiming at the problems of mismatching poor flexibility and low precision of the ordinary manipulator in complex special explosive ordnance disposal processes, a new type of 5-DOF manipulator is proposed in this paper. The D-H model of the manipulator is established, and the forward and inverse kinematics are analyzed. The workspace of the manipulator is analyzed by Monte Carlo pseudo-random number method. MATLAB is used to simulate the trajectory planning, and the simulation data are used to verify the effectiveness of the design and analysis method.  
The rest of this paper is arranged as follows: Section 2 briefly introduces the composition of the EOD robot and the design and analysis of the manipulator mechanism. Section 3 carries out the kinematics analysis of the manipulator. Section 4 simulates its workspace. Section 5 introduces the trajectory planning, simulation and experimental verification of the manipulator.
# Lower- Limb Exosuits for Rehabilitation or Assistance of Human Movement: A Systematic Review
## 3 Review
### Clinical Studies
This is favorable because a high knee adduction is a risk factor for disease progression of medial knee osteoarthritis [39]. The peak of knee adduction moment was also diminished by incorporating an inflatable air bladder into the design of a knee brace [26]. Furthermore, soft knee braces had an immediate positive effect on pain, activity limitations, self-reported knee instability and knee confidence in osteoarthritis [33]. Stretching elastic elements over hip, knee and ankle improved gait velocity [35], cadence, step length and stride length [38] in post-stroke subjects. An intervention with the \"Regent Suit\" showed an improvement in gait speed and daily living activities in subjects with sub-acute stroke. In the former study, it was attributed to the elastic loading elements increasing the physical effort of the user and the proprioceptive feedback [23]. Including proprioception into the design of an exosuit is promising because it is an essential component to the motor control of the musculoskeletal system [61] and has been shown to be effective for motor function recovery in stroke [23] and other musculoskeletal diseases [62]. Hence, biofeedback or proprioception may also play a role in improving exosuit-user adaptation in the future. Active exosuits were able to increase the propulsion [8; 37], walk distance [28] and maximum walking speeds [29], improve ground clearance during swing [8; 41] and immediately reduce hip hiking and circumduction, used to advance the paretic limb [30] in stroke patients during gait. It has been shown that the use of active exosuits can induce a more symmetrical body movement and reduce metabolic energy consumption during hemiparetic walking [31]. The reduction in timed up and go (TUG) execution time indicated a potential improvement in functional mobility [42]. Additionally, it was indicated that tuning assistive profiles for exosuit assistance is important, so post-stroke patients can fully exploit the exosuit assistance [40].
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 2 SAR Interferometric Processing
### SAR Interferometry Fundamentals
Figure 2: Conceptual InSAR computational scheme for deformation measurements: block diagram of the main processing stages.
# Stochastic Game Analysis of Cooperation and Selfishness in a Random Access Mechanism
## 4 Problem Formulation
### Model Description  
We consider a wireless network consisting of two groups of users sharing a common transmission channel, as illustrated in Figure 1. Let us consider a case where the first group consists of cooperative users and let the second group be to the group of selfish users. Let \(M\) be the number of users in the first group and \(N\) the number of users in the second group, and let \(m\) and \(n\) be the number of backlogged users in each group. The arrival flow of packets to each source in each group follows a Bernoulli process with parameters \(p_{c}\) and \(p_{nc}\), respectively. Similarly, let \(q_{c}\) and \(q_{nc}\) be the retransmission probability of backlogged users in each group.  
We define \(Q_{c}(i,m)\) and \(Q_{nc}(i,n)\) as the transmission probability of \(i\) unbacklogged nodes in the first and the second groups, respectively.  
\[Q_{c}(i,m)=\binom{M-m}{i}p_{c}^{i}(1-p_{c})^{M-n-i}, \tag{1}\]  
\[Q_{nc}(i,n)=\binom{N-n}{i}p_{nc}^{i}(1-p_{nc})^{N-n-i}. \tag{2}\]Similarly, we define \(Q_{c}^{r}(i,m)\) and \(Q_{nc}^{r}(i,n)\) as the retransmission probability of \(i\) back-logged nodes in the first and the second groups.  
\[Q_{c}^{r}(i,m) =\binom{m}{i}q_{c}^{i}(1-q_{c})^{m-i}, \tag{3}\] \[Q_{nc}^{r}(i,n) =\binom{n}{i}q_{nc}^{i}(1-q_{nc})^{n-i}. \tag{4}\]
# Impulsive Noise Removal with an Adaptive Weighted Arithmetic Mean Operator for Any Noise Density
## 3 Weighted Arithmetic Mean Based Fuzzy Filter Algorithm
### Noise Detection Phase Based on Fuzzy Open-Close Operators
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**FilterMeasure** & **M-SSIM** & **MSE** & **PSNR\({}_{\text{HVS}}\)** & **VIF** \\ \hline \hline \multirow{2}{*}{DBA} & & & & & & & 5 & 10 \\ \cline{2-10}  & & & & & & & 15 & & 60 \\ \cline{2-10}  & & & & & & & 70 & 75 \\ \cline{2-10}  & & & & & & & 80 & 85 \\ \hline \multirow{4}{*}{Hosseini} & 35 & 40 & 35 & 40 & 40 & 50 & 70 & 75 \\ \cline{2-10}  & 45 & 75 & 70 & 75 & 65 & 70 & 80 & 85 \\ \cline{2-10}  & 80 & 85 & 80 & 85 & 75 & 85 & 90 & 95 \\ \cline{2-10}  & 90 & 95 & 90 & 95 & 90 & 95 & 98 & \\ \cline{2-10}  & 98 & & 98 & & 98 & & & \\ \hline \multicolumn{1}{c}{[PERSON]} & 5 & 5 & 5 & 5 & 15 & & & \\ \cline{2-10}  & 5 & & & & 5 & 35 & 35 & 40 \\ \cline{2-10}  & & & & & 40 & 45 & 45 & 50 \\ \cline{2-10}  & & & & & 50 & 55 & 55 & 60 \\ \cline{2-10}  & & & & & 60 & 65 & 65 & 70 \\ \cline{2-10}  & & & & & 70 & 75 & 75 & 80 \\ \hline \multirow{4}{*}{AWAM} & 10 & 15 & 10 & 15 & 10 & 15 & 15 & 20 \\  & 20 & 25 & 20 & 25 & 20 & 25 & 25 & 30 \\ \cline{1-1} \cline{2-10}  & 30 & 35 & 30 & 35 & 30 & 35 & 35 & 40 \\ \cline{1-1} \cline{2-10}  & 45 & 50 & 40 & 45 & 40 & 45 & 45 & 65 \\ \cline{1-1} \cline{2-10}  & 55 & 60 & 50 & 55 & 50 & 55 & 70 & 75 \\ \cline{1-1} \cline{2-10}  & 65 & 70 & 60 & 65 & 60 & 65 & 80 & 85 \\ \cline{1-1} \cline{2-10}  & 75 & 80 & 70 & 75 & 70 & 75 & 90 & 95 \\ \cline{1-1} \cline{2-10}  & 85 & 90 & 80 & 85 & 80 & 85 & 98 & \\ \cline{1-1} \cline{2-10}  & 95 & 98 & 90 & 95 & 90 & 95 & & \\ \cline{1-1} \cline{2-10}  & & 98 & & 98 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Densities of salt-and-pepper noise for which the row filter is not surpassed by any of the rest in accordance with the Wilcoxon test. A green cell implies that the filter obtains the greatest mean value for that noise density, significantly outperforming the competitors in accordance with the Wilcoxon test. On the other hand, a lime cell implies that although the filter achieves the greatest mean value for that noise density, the difference is not significant according to the Wilcoxon test with respect to those filters coloured in yellow. Tables 3 and 4 show the means and standard deviations using the performance measure \(\mathrm{PSNR}_{\mathrm{HVS}}\) of the filters considered for all noise densities. The meaning of the color of the cells is similar to the meaning of the color of the cells of Table 2. The AWAM filter corresponds to the last row and 9 of the 20 possibles boxes, are coloured green and 10 in lime. It means that the AWAM filter almost always outperforms all others filters and in all noise levels. More specifically, in 9 of the 20 noise levels, it significantly outperforms all other filters and in 10, it outperforms them but not significantly.
## 2 Results  
### Optimization of VOCs Extraction  
The extraction temperature, extraction time and addition of water during blending were optimized for extraction of vegetable VOCs through HS-SPME by using broccoli as a model vegetable. Extraction efficiencies of VOCs at different temperatures (20 \({}^{\circ}\)C and 37 \({}^{\circ}\)C) were evaluated (Figure S1a). Data suggested that more peaks and higher peak abundances were observed for extraction of the broccoli sample at 37 \({}^{\circ}\)C as compared to extraction at 20 \({}^{\circ}\)C. Moreover, VOCs profiles at 37 \({}^{\circ}\)C showed higher reproducibility in total ion chromatograms (TICs). Therefore, in order to obtain more comprehensive volatile information all subsequent incubations and extractions were conducted at 37 \({}^{\circ}\)C.  
Different extraction times (30, 60, 90 and 180 min) were also examined as the releasing rate of diverse VOCs may vary significantly. In this study, we were more interested in the rapid-emitting VOCs, thus we implemented the SPME extraction immediately after the vegetable tissues were disrupted in an enclosed sampling system. Although there were more peaks detected with increasing extraction time (Figure S1b), most of these VOCs were eluted after 11 min. These late-eluted peaks were VOCs with relatively high molecular weight and high boiling point. Nevertheless, it was noted that the profiles before 11 min were constituents with lower molecular weight and boiling point not affected significantly by increasing sampling time. Moreover, a long sampling time may sacrifice the reproducibility and performance of the SPME fibre. In view of these reasons, 30 min was thus selected as the extraction time for subsequent studies.  
Adding a small amount of water into the vegetable samples during blending might help homogenize the tissue to enhance extraction, thus both conditions, i.e., with or without water added, were evaluated during preliminary sample treatment investigations. It was noted that addition of water would help homogenize the vegetable to some extent. However, the addition of water led to less peaks extracted from the vegetable at 37 \({}^{\circ}\)C for 30 min (Figure S1c), which was probably due to the dissolution of VOCs in water.  
Thus, throughout this VOCs profiling investigation, all the vegetables were extracted at 37 \({}^{\circ}\)C for 30 min without addition of water during blending. A typical TIC of VOCs extracted from crushed broccoli tissues under optimal extraction conditions is displayed in Figure S1d, which demonstrates a comprehensive capture of various VOCs emitted from the plant tissues.