esa-sceva/satcom-chunk-collection huggingface dataset:
# M-Emu: A Platform for Multicast Emulation
## 3 Implementation
### Statistics Tools  
To help users analyze the performance of multicast algorithms, we developed some statistical tools that can conveniently calculate various common multicast evaluation metrics. When each node receives or forwards packets, it records the packet information in memory according to the format shown in Figure 3.  
Self ID is the ID of the current node. The link ID is the ID of the link through which the packet enters the current node. The Src Node ID indicates the ID of the node that sends the packet. The Dst node ID indicates the ID of the destination node of the data packet. The timestamp refers to the time when the packet is forwarded or received by the current node.  
The data statistics module of the control plane periodically records the packet information and MFT of each node into the text file as the raw data. M-Emu provides analysis scripts for these raw data to generate some common multicast evaluation metrics, such as:  
1. The MFT load. The MFT load of each node is obtained by calculating the MFT size of each node.
2. The Link load. With the link ID as the index, the amount of data passing through the link throughout the experiment can be obtained. In addition, the load changes of links in different time periods can be analyzed according to time stamps.
3. The Multicast tree cost. With the multicast ID as the index, all the links that the multicast tree passes through can be obtained, and then the cost of the entire multicast tree can be obtained.
4. The End-to-end delay. According to the multicast ID and packet type, the time when the DR sends data and the time when the DR receives data can be counted, and then the end-to-end transmission delay can be calculated.  
In this form, most of the user's statistical needs can be satisfied. Users can add some specific information to the existing package information to meet their personalized needs.
## 3 Results
### Influence of the Glottal Gap on the Phonatory Process  
Question 1: How does glottal closure insufficiency, expressed by GGI, influence the fluid-structure-acoustic interaction within disordered dynamics?  
The results of the statistical tests are presented in Table 4. The group differences of all phonation parameters used were tested, which occurred firstly between the three GGI groups and secondly between the two vibration characteristics (Groups and Group\({}_{\text{A}}\)). There were 8 out of 13 tests (61.5%) between all three GGI groups and 10 out of 24 post hoc tests (41.7%) that showed statistically significant differences. Statistically significant differences were found in 6 out of the 8 GAW parameters. Most differences in the direct comparison of two GGI groups were found between GGI\({}_{1}\) and GGI\({}_{2}\) with 4 statistically significant differences. Between GGI\({}_{1}\) and GGI\({}_{3}\), there were 2 statistically significant group differences (Table 4) and 2 of the p-values were slightly above the corrected p-value of 0.017; both with p-values of 0.018. Regarding aerodynamic parameters, the SPL values between the three GGI groups were statistically significantly. For the harmonic measures, CPP\({}_{\text{A}}\) was statistically significantly different between GGI\({}_{1}\) and GGI\({}_{3}\).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Kruskal-Wallis-test**} & \multicolumn{3}{c}{**Post hoc tests (corrected**} & \multirow{2}{*}{**Mann-Whitney-U-/k-test**} \\  & & & **significance level p = 0.017)** & \\ \hline  & **GGI\({}_{1,23}\)** & **GGI\({}_{1,2}\)** & **GGI\({}_{1,3}\)** & **GGI\({}_{2,3}\)** & **Groups\({}_{\text{A}}\)** \\ \hline  & \multicolumn{4}{c}{**(A) GAW Measures**} & \multicolumn{2}{c}{-} \\ \hline
**ALR (a.u.)** & **0.040** & 0.776 & **0.001** & **0.003** & **0.000** \\ STIFFNESS (frames\({}^{-1}\)) & **0.014** & **0.014** & 0.018 & 0.300 & 0.085 \\ ASQ (a.u.)** & 0.054 & - & - & - & **0.040** \\ CQ (a.u.) & 0.005 & 0.002 & 0.018 & 0.511 & **0.002** \\ QQ (a.u.) & 0.000 & 0.000 & **0.000** & 0.124 & 0.870 \\ SQ (a.u.) & 0.113 & - & - & - & 0.187 \\ ASI (a.u.) & **0.017** & **0.016** & 0.825 & 0.036 & 0.429 \\ PAI (a.u.) & **0.017** & 0.027 & 0.606 & 0.020 & **0.009** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical results for the GAW, aerodynamic, and harmonic parameters. 1  
Figure 7: Fundamental frequencies \(f_{0}\) subdivided into GAW, subglottal, audio measurements for (**a**) Subharmonic oscillations (**b**) Aperiodic oscillations.
## 5 Experimental Results
### Zero Crossing Comparison  
A zoom for AC current results is shown in Figure 22 for the following two cases: (a) the PI-SMC with fixed hysteresis band; and (b) the SMC with the proposed AHB. In Figure 22a, ripple increments are observed just before and after the zero crossing obtaining a \(THD_{i}=4.83\%\). In Figure 22b, the zero crossing is softer than the one with fixed hysteresis band. This approach represents a better performance during zero crossing and a reduction of \(THD_{i}\) of 2.67%.
## 3 Understanding Biofilms
### Biofilm Formation
#### 3.3.4 Dispersion  
The dispersion phase is a vital part of the biofilm's lifecycle, wherein cells that were formerly part of the community break apart and disperse to new locations [82; 83].  
Various environmental cues typically trigger this phase, including nutrient depletion, shifts in oxygen levels, waste accumulation, chemical signals and changes in flow dynamics. These changes are crucial in signaling bacteria within the biofilm to initiate the dispersion process [84; 85]. As a result, the bacteria undergo significant genetic changes. Genes that maintain the biofilm structure decrease activity, while those that promote mobility and dissociation increase activity. This alteration in gene expression is vital in preparing the cells for detachment [86; 87].  
A crucial element of dispersion is the reduction in the production of EPS. As the synthesis of these structural and protective chemicals decreases, the biofilm matrix weakens, and bacteria generate specialized enzymes that aid in its degradation. This promotes the separation of cells from the biofilm [88]. For instance, dispersion B (DspB), an enzyme, has been demonstrated to facilitate the breakdown of poly-N-acetylglucosamine (PNAG), a significant polysaccharide found in biofilms produced by different bacteria that create biofilms [89]. In numerous Gram-positive bacteria, such as _S. aureus_, biofilm dispersal is governed by the Agr QS system, which monitors bacterial cell density. Upon reaching a specific threshold of bacterial population, the Agr system initiates the synthesis of Phenol-Soluble Modulins (PSMs). These diminutive, amphipatible peptides exhibit cytotoxic properties and facilitate the disintegration of the biofilm matrix, hence enhancing the liberation of bacteria from the biofilm into the surrounding milieu [90; 91].
# Effects of Voice and Lighting Color on the Social Perception of Home Healthcare Robots
## 4 Discussion
### Implications and Limitations  
The findings of this study can be used as a reference for designing a robot with acceptable social perceptions. Robots with high social interactivity are generally assigned high warmth scores. For example, if a companion robot has a child voice or an adult female voice, the most suitable lighting colors are warm and white light. The use of an adult male voice combined with cool or white light is recommended if the robot must be skillful or highly competent. Moreover, the robot's voice should match its lighting, appearance, and other design features. Mismatches in these factors can cause discomfort during interactions, which reduces the acceptance of robots.  
This research has certain limitations. First, in this study, a fundamental frequency (F0) was used to control voice type; thus, other voice characteristics could not be analyzed. Many studies have indicated that controlling F0 is the most effective method for producing different types of voices [10]. Other acoustic parameters, such as format, may not influence the social perception of a robot. Future research could explore more acoustic parameters to reach more comprehensive results. Second, this study mainly analyzed a humanoid robot; mechanical robots were not included in this research. Although humanoid robots represent a mainstream intelligent robot appearance, future research should focus on a wider range of robot appearances. Third, cultural backgrounds can influence perceptions of colors. Cross-cultural comparisons can be conducted in future studies to extend the results of the present study.
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 1 Introduction
Many works on the IAE approach have focused on how to adapt the filter statistical information, which is the matrices \(Q\) and \(R\), for improving the filter performance. In contrast, a relatively small number of researchers have paid attention to the adaptation of error covariance matrix \(P\), of which approach is known as the adaptive fading Kalman filter (AFKF). Note that AFKF can be significantly effective for the estimation of the fault parameters. Incomplete information about the dynamic equation caused by faults in the system can be compensated by increasing the magnitude of the error covariance matrix using a forgetting factor. Meanwhile, the convergence property during the estimation procedure becomes a critical issue when the estimation of fault parameter is combined with an FTC based on adaptive control. In AFKF, the convergence property is determined by the forgetting factor. A large value of forgetting factor leads to a sluggish estimation transition in the transient phase, while a small value of forgetting factor results in the poor estimation of the parameter change caused  
Figure 1: Fault classification according to time characteristics. by the fault. Consequently, it is necessary to regulate the forgetting factor so as to improve both the estimation transition and its convergence time. In order to improve the convergence property during the estimation of fault parameters, this paper presents a novel adaptive fuzzy fading Kalman filter in which the forgetting factor is determined by a fuzzy system. The use of the fuzzy system is an attractive approach for Kalman filtering, not only because of its simplicity but also its effectiveness. The benefit of a fuzzy system in Kalman filtering has been verified through various studies [9; 10; 11]. The remaining part of the paper is organized as follows. In Section 2, the existing works on the Kalman filter and joint Kalman filter are discussed. In Section 3, the proposed adaptive fuzzy fading Kalman filter is explained. A set of numerical simulations are presented in Section 4, to demonstrate the effectiveness of the proposed method, followed by concluding discussions in Section 5.
## 2 Materials and Methods
### Shear-Wave Speed Estimates  
The S2000 generated a shear-wave speed grayscale elastogram adjacent to the B-mode image. See Figure 2a. A radiologist (RS) used the B-mode image to identify skin, adipose and parenchyma. Six ROIs were placed on each tissue type on the B-mode image. The S2000 provided on-screen annotations showing the position of each region of interest (ROI) in both the B-mode and the elastograms, and the mean \(c_{s}\) within the ROI.  
Figure 1: Schematic of transducer positions used to obtain shear-wave speed (\(c_{s}\)) estimates from the breast. Eight B-mode image and elastogram pairs were acquired from each breast.  
Figure 2: Showing: (**a**) An example B-mode image/elastogram pair acquired using the S2000 with ROIs marked on skin and parenchyma tissues (Volunteer 2); (**b**) an Aixplorer elastogram containing artefacts, overlaid with transparency on a B-mode image (Volunteer 2). In this example, \(c_{s}\) values were estimated by placing ROIs (Q-boxes) in the regions free from artefacts; (**c**) the same image where the ROIs were positioned without knowledge of the position of the artefacts, achieved by reducing the opacity of the displayed elastogram to zero. Note that the Aixplorer only displays the last 4 ROIs selected.  
The Aixplorer provided an elastogram superimposed on the B-mode image where red and blue indicated greater and lesser \(c_{s}\), respectively, as shown in Figure 2b. The smallest available ROI, 2 mm in diameter, was placed at six different locations on the skin, adipose and parenchyma, respectively. The anatomical regions of interest and the \(c_{s}\) measurement points selected by the radiologist and annotated on S2000 images were used to guide the placement of the ROIs on Aixplorer elastograms, which was performed by EH.  
Image artefacts of high \(c_{s}\) at regularly spaced intervals were observed in Aixplorer elastograms. These are visible as a regular pattern of greater shear wave speed in Figure 2b and as vertical lines of greater shear wave speed illustrated using an elastogram of a homogenous region of the phantom in Figure 3a. To study the effect of this artefact, the Aixplorer analysis was performed twice, once by choosing measurement locations that avoided the artefacts using both the B-mode image and the elastogram to position the ROIs, as in Figure 2b, and once with measurement locations selected using only the B-mode image by increasing the \(c_{s}\) scale and reducing the opacity of the on-screen display of the elastogram to zero (see Figure 2c). In some elastograms, it was not possible to avoid some overlap of ROIs with regions that appeared to contain artefacts. In some cases, minimising the inclusion of artefacts reduced the number of measurement positions available. The number of \(c_{s}\) estimates obtained using the Aixplorer when avoiding artefacts varied between two and six.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
###### Abstract  
The concept of land surface temperature (LST) encompasses both surface energy balance and land surface activities. The study of climate change greatly benefits from an understanding of the geographical and temporal fluctuations of LST. In this study, we utilized an improved version of the TFSW algorithm to retrieve the LST from the Medium resolution spectral imager II (MERSI-II) data for the first time in Pakistan. MERSSI-II is a payload for the Chinese meteorological satellite Fengyun 3D (FY-3D), and it has the capability for use in various remote sensing applications such as climate change and drought monitoring, with higher spatial and temporal resolutions. Once the LSTs were retrieved, accuracy of the LSTs were investigated. Later, LST datasets were used to detect the spatiotemporal variations of LST in Pakistan. Monthly, seasonal, and annual datasets were utilized to detect increasing and decreasing LST trends in the regions, with [PERSON]'s slope estimator tool. In addition, we further revealed the long-term spatiotemporal variations of LST by utilizing Moderate Resolution Imaging Spectrometer (MODIS) LST observations. The cross-validation analysis shows that the retrieved LST of MERSSI-II was more consistent with the MODIS MYD11A1 LST product compared to the MYD21A1. The spatial distribution of LSTs demonstrates that the mean LST exhibits a pattern of spatial variability, with high values in the southern areas and low values in the northern areas; there are areas that do not follow this trend, possibly due to reasons of elevation and types of land cover also influencing the LST's spatial distribution. The annual mean LST trend increases in the northern regions and decreases in the southern regions, ranging between \(-\)0.013 and 0.019 \({}^{\circ}\)C/year. The trend of long-term analysis were also consistent with MERSSI-II, excepting region II, with increasing effects. This study will be helpful for various environmental and climate change studies.  
remote sensing: spatiotemporal; land surface temperature; MODIS; MERSI-II
# Experiment on Flexural Fatigue Performance of Precast Bridge Deck Joints with Loop Connections
###### Abstract  
With the background of bridge industrialized construction, as a convenient construction form of a joint, a joint with loop connections has been applied in the connection of the precast bridge deck. As a relatively new type of connection structure, the fatigue performance and degradation law of a loop connection joint are still not clear. In this paper, different flexural fatigue specimens are designed based on the application situation of the loop connection joint. After 0 to 2 million fatigue-loading cycles, the fatigue-loading process is suspended and the static flexural performance of the specimen is tested. The load-deflection curves of specimens under static loading remain roughly linear, and the slope changes little. After \(200\times 10^{4}\) fatigue-loading cycles, the failure mode of each specimen is still presented as typical pure flexural failure. In addition, the strain of steel and concrete changes little, as well, and the stiffness degradation of each specimen is not obvious. It is indicated from this experiment that the fatigue-loading cycles has little effect on the mechanical properties of the concrete and loop bars, and the joint with loop connections has good fatigue performance.  
fabricated bridge; concrete joint; loop connection; flexural fatigue experiment; performance analysis; failure mode; stiffness degradation Article
# A Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
## 2 Related Works
### Trust in Automation  
Quantitative models of trust can generally be categorized by different dimensions: probabilistic models, deterministic models, cognitive models, and neural models. Additionally, they can be classified into offline models and online models based on the type of input data used to generate predictions.  
(1) Offline Trust Models  
Traditional trust models are mostly offline and use a set of predefined parameters as the input to generate predictions. These models often rely on feedback loops to determine the next state of a human-automation system at a given time, including trust levels, reliance on automation, and task performance. [PERSON] et al. used time series analysis to model the dynamic impact of system failures on operator trust, predicting trust levels through a trust transfer function based on failure occurrences, automation, and human performance [14]. [PERSON] et al. extended decision field theory to model the relationship between trust in automation and reliance behavior [15]. [PERSON] et al., building on the psychological literature, established a third-order linear automation trust model linking trust directly to experience [16]. [PERSON] et al. refined [PERSON]'s model by introducing additional parameters to construct a second-order linear automation trust model, achieving more accurate predictions of trust dynamics [17].  
Offline trust models are suitable for evaluating the performance trends and overall efficacy of human-automation systems under different initial conditions. They provide researchers with insights into how various factors interact and influence human behavior. Additionally, since these models rely solely on initial parameters, they are well-suited for predicting system behavior during the design phase of automation systems.
## 2 Infrastructure, Instrumentation and Calibration Methods
### Calibration and Validation Methods
#### 2.2.3 Backscatter Coefficient Cal/Val  
In addition to the signal propagation time, a radar altimeter measures the power level of these radar signals [15]. The relation between the amplitude of the transmitted and received echo signals provides information about the characteristics of the imaged surface on the ground. This is expressed as the back-scatter coefficient (sigma-naught, or sigma-0). The sigma0 coefficient is used to retrieve wind speed, and empirical models for the sea state bias required to determine sea surface height.  
Calibration of the sigma-naught coefficient can be obtained using either active or passive targets with known and stable radar cross-section, as they are easily detected within the altimeter backscatter records. Previously, a sigma-naught transponder was developed by the European Space Agency and used to calibrate the ENVISAT RA-2 altimeter [16]. This old transponder has been recently (2019) refurbished and is expected to support Sentinel-3 sigma-naught calibrations in 2020 [17].  
In support of Copernicus Sentinel-6, a new range and sigma-naught transponder is currently under development and is expected to be installed at the PFAC. The new processing technique of fully focused SAR altimetry [18] may also provide sigma-naught calibration with passive corner reflectors [17] and this will also be investigated at the PFAC. Details on these future plans are given in Section 6.
## 5 Discussion and Conclusions
### Influence of Design Parameters  
#### 5.2.1 Influence of the Index Angle  
As observed in Figure 14, the index angle (\(\gamma\)) had little influence on the mean outlet flowrate; however, it had a great influence on the outlet flow ripples. Referring to Figure 13, it can be seen that the index angle enabled a phase difference between the flow from the two gear pairs. Noting that the flow of the tandem pump was obtained by summing the flow from the two gear pairs, it was workable to attenuate the flow ripple of the tandem pump by properly selecting the index angle, therefore allowing the peak value of the flowrate from one gear pair to coincide with the valley value of the flowrate from the other gear pair. This is believed to be the reason why the index angle of 13.85\({}^{\circ}\) (\(\lambda/2\)) led to the greatest decrease in the flow ripple. As shown in Figure 16, the flow ripple was reduced by 66% from the maximum value (\(\gamma=0\)) by properly selecting the index angle (\(\gamma=13.85^{\circ}\)).  
Figure 18: Flow ripples of the tandem pump and the single pump under different operating conditions: (a) 500–1500 rpm; and (b) 2000–3000 rpm.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 3 Models and Methods  
Figure 5 shows the overall framework of the proposed method, where a large number of ideal simulation signal samples are generated as source domain data \(D_{S}\) to train an ideal intelligent DOA estimation model. The source task \(T_{S}\) is DOA estimation under ideal conditions. We collect a small number of actual signal samples as target domain data \(D_{T}\). Under the effect of the knowledge transfer of \(D_{S}\) and \(T_{S}\), the model is fine-tuned. The target task \(T_{T}\) is DOA estimation in the actual environment.  
The covariance matrix of the phase difference between elements can be regarded as a direction image. It not only contains all information about the direction of an incident signal, but also eliminates some noise and interference [28]. Therefore, we use CNN to construct the DOA estimation model. The CNN can continuously extract the features of data in layers. The multi-layer linear and nonlinear processing units of CNN are superimposed in a hierarchical manner, which provides the ability to learn complex representations at different levels of abstraction. Thus, it is good at learning the complex characteristics of array signals.  
Figure 4: Phase difference of ideal simulation signal and actual signal between the first and second elements. (**a**) Phase difference of ideal simulation signal. (**b**) Phase difference of actual signal.