esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Modeling of Da'anshan Landslide  
### Numerical Tool for Landslide Modeling  
Numerical modeling is an applicable method for analyzing complex geophysical mass flows and offers an effective tool for exploring disaster mechanisms via inverse analysis [44; 57] or parameter sensitivity analysis [3; 58; 59]. Some numerical tools have been widely applied in geological hazard analysis, such as r.avaflow [55; 57; 59; 60], D-Claw [2; 12; 60; 61], TITAN2D [62], DAN (DAN3D) [17; 52; 63; 64], and Massflow [31; 65].  
DAN3D [64] is a Lagrangian implementation of the depth-averaged model with equivalent fluid assumption. DAN3D was derived in a Lagrangian coordinate system, with the x- and y-coordinate located in the plane tangent to the terrain and the z-coordinate oriented in the bed-normal direction [27]. Therefore, before numerical simulation, it is necessary to convert the digital elevation model in the global coordinate system into the digital terrain in the Lagrangian coordinate system mentioned above. DAN3D has achieved the integration of two different dynamic models and has been applied to the analysis of disasters [27; 32]. The other numerical tools mentioned above are mainly depth-averaged dynamic models and have not yet been integrated with other models such as the sliding block model [18] and the mass point method (lumped mass model) [14; 15].  
The numerical tool r.avaflow \"[[http://avaflow.org](http://avaflow.org)]([http://avaflow.org](http://avaflow.org)) (accessed on 8 October 2022)\" was designed as a plug-in based on open-source GIS software (GRASS GIS V7.8). It utilizes C language to execute core compute modules and the R and Python languages for auxiliary tasks such as data transfer and post-processing of results. The V2.4 pre-release version of r.avaflow includes a mixture flow (single-phase) model and a multiphase flow model [25]. Several enhancements have been implemented within this version, including an enhanced drag force module [66] and virtual mass force formulation [67]. As open-source software, the functions of r.avaflow can be easily customized or extended. The versatility of r.avaflow has been verified in various disaster events [43; 44; 56; 57; 59; 68; 69].  
In this study, the numerical tool r.avaflow was utilized, which provides a computational framework based on the total-variation-diminishing (TVD) non-oscillatory central differencing (NOC) scheme [70]. For modeling the Da'anshan rockslide-debris avalanche event, a customized model that integrates the multi-block model (Section 2.1) and the depth-averaged model (Section 2.2) was implemented in r.avaflow. The influence of bed entrainment effects was also considered (Section 2.3). The advantages of this integrated model will be discussed. In this study, all the numerical simulations and the output results were carried out with the help of GRASS GIS.
## 3 MIMO System Model and the Implementation Steps of Channel Estimation
### Basic Autoencoder  
The autoencoder and LSTM networks are used to evaluate the system coefficients in the MIMO system. In deep learning, an autoencoder [34] is used to obtain feature vectors by utilizing an \"encoder-decoder module\" in which the encoded signal is reconstructed by means of a decoder, and the encoder in the network compresses the input signal. Decoding is carried out to reconstruct the compressed data to their original input signal. The autoencoder consist of an input layer, output layer, and hidden layer. The encoder and decoder are the two stages of operation performed in the autoencoder structure. Initially, the input is fed to the encoder with low dimension and then to the decoder structure. The decoder reproduces the initial data. The measurements of the data are decreased by training the output layer and then fed to the next hidden layer. Here, the input data are represented by \(W\), and the encoded result is determined using the formula in Equation (4).  
\[Rf=y(c)=\beta(EC+n_{c}) \tag{4}\]  
In Equation (4), the term \(\beta\) represents an activation function. The input signal is again reconstructed using the decoder as given in the formula provided in Equation (5).  
\[C_{1}=h(w)=\alpha(EU+n_{u}) \tag{5}\]  
In Equation (5), the term \(\alpha\) denotes the decoder's sigmoid activation function, and the terms \(n_{c}\) and \(n_{u}\) indicate the biasing vectors of the decoder variables. The decoder reconstructs the signal back to its original signal during reconstruction, hence information loss will occur.  
The autoencoder must be trained to reduce the information loss using the variables \(\phi=(E,n_{u},n_{c})\), and this function is given in Equation (6).  
\[\eta=\min_{\phi}A\left(C,C^{\prime}\right)=\min_{\phi}A(C,h(g(c))) \tag{6}\]  
Random and continuous information loss can be measured using the cross-entropy function and by square error value, respectively. These functions are given by Equation (7).  
\[A(\phi)=\sum_{o=1}^{m}\left[c_{i}\log(u_{i})+(1-c_{i})\log(1-u_{i})\right] \tag{7}\]The autoencoder reduces the dimensionality of the signal. Anomaly detection of images is possible using an autoencoder in the network. BER and signal noises are reduced by using an autoencoder system.
## 6 Applications of UAV Computing
### Healthcare  
Drones can help manage various public health issues, including epidemics and dangerous illnesses such as COVID-19. During the COVID-19 pandemic, for example, certain governments and nations used drones to scan the virus's fast spread, identify patients, and then estimate mortality risk factors using AI algorithms to the acquired data. Drones have been used to monitor crowds, transmit public announcements and information, spray disinfectants, and take thorough temperature readings in various residential environments [171]. The lifeguard community often uses UAVs for search and rescue operations that need quick action. Instead of utilizing human-crewed airplanes, which may take longer to deploy,  
Figure 6: UAV Computing Application Domains.  
UAVs' adaptability quickly assesses the rescue scenario [172]. Using UAV computing, AI, and computer vision techniques to imitate visual reality might lead to new applications that allow for speedier and real-time judgments in emergencies. UAVs, for example, identify impediments by flying over them, collecting data, extracting the essential aspects, and making choices using machine vision and pattern recognition technology. People with vision impairments can navigate more quickly in this manner. This technology is anticipated to be a critical component of the healthcare revolution [172].
## 2 Principle of the Miniature Michelson Interferometer (MMI)
### Angular Error Measurement of the Nanopositioning Stage  
The principle of angular error measurement is based on the autocollimation principle. A collimated laser beam emitted from a laser diode is divided into a transmitted measurement beam (P-polarized beam) and a reflected reference beam (S-polarized beam) by a polarizing beam splitter (PBS1), as shown in Figure 1. The measurement beam reflected by a moving mirror (mirror 3) is split into two beams by a beam splitter (BS2). The reflected beam is used to measure pitch and yaw errors of the nanopositioning stage by an autocollimator (AC1), comprising a focus lens (FL1) and a four-quadrant photodetector (QPD1).  
Figure 2 shows the optical layout of the miniature autocollimator kit. BS2 is mounted on an angle mirror mount for easy angle adjustment. QPD1 is positioned on the focal point of FL1. When mirror 3 is rotated with a pitch angle or a yaw angle, the position of the focused spot on QPD1 is moved along the \(x\)- or \(y\)-directions, accordingly. The relationship between the angle and laser spot movement can be expressed by the following equations:  
\[\theta_{pitch}=\frac{\Delta x}{2f}\text{ and }\theta_{yaw}=\frac{\Delta y}{2f}, \tag{1}\]  
where \(\theta_{pitch}\) and \(\theta_{yaw}\) are the pitch and yaw errors, \(\Delta x\) and \(\Delta y\) are the corresponding spot shifts on QPD1, and \(f\) is the focal length of FL1.
## 2 Materials and Methods
### Application to Real Samples  
The method was used for the determination of ALP and FLT in five surface water samples that were collected from the rivers of Sperchios and Gorgopotamos of the province of Fthiotida, Greece on 27 March 2021 (River Water, RW 1 and RW 2, respectively), from the dock of Stylida and the sea of Karavomylos of the same province on 29 March 2021 (Sea Water, SW 1 and SW 2, respectively) and the sea of Aretsou of the Municipality of Kalamaria, Thessaloniki, Greece on 25 May 2021 (SW 3).  
Each sample was vacuum filtered with a membrane filter of 0.22 \(\upmu\)m for the removal of microorganisms and precipitates. Nine analyses were conducted for each sample, three without the addition of a stock solution, three with the spiking of 25 ng/mL and three times with the spiking of 50 ng/mL.
## 5 Experimental Validation
### Experimental Result for Shading Pattern 1  
Figure 18 shows the experimental results for shading pattern 1 (1, 0.8, and 0.6 kW/m2) in which the GMPP is located at the right side of the PV curve. The powers of GMPP and two local MPPs are 143.6, 118.7, and 64.52 W, respectively.  
Figure 18a depicts the power, voltage, and duty cycle graphs obtained using the incremental conductance MPPT algorithm. Here, the conventional INC can track GMPP under SSC after some perturbations of the duty cycle. The power fluctuation (137.676-140.98 W) around GMPP under SSC is also not high. Consequently, 99.59% of power efficiency is obtained from shading pattern 1. The INC scheme requires at least 0.18 s to converge the operating point toward the GMPP location with duty cycle changes from 0.181 to 0.22. Conversely, the conventional P&O algorithm can track the GMPP location within 0.2 s with duty cycle changes between 0.2 and 0.218 (Figure 18b). The P&O MPPT  
Figure 17: Experimental results under STC: (**a**) INC-MPPT; (**b**) P&O-MPPT; and (**c**) proposed MPPT.  
experiences a lesser amount of power oscillation around GMPP (139.8-143.06 W) than the INC. Hence, the P&O algorithm achieves a similar amount of tracking efficiency (99.62%) as the INC algorithm. Figure 18c shows the PV characteristic waveforms and tracking results of the proposed MPPT technique. The operating point of the PV array is rapidly driven and adjusted toward the GMPP location, requiring only 0.12 s tracking speed, where the proposed algorithm is satisfied and verified with the simulation test. Having a certain number of iterations in the duty cycle perturbation, the proposed method stops the operating point at the GMPP and maintains a 0.223 duty cycle under SSC. As comparison under this shading pattern 1, both conventional INC and P&O failed to remove power oscillations under the steady-state condition, whereas the proposed method experienced no power fluctuations. Nevertheless, the proposed scheme showed 99.92% power efficiency during the GMPP tracking period under SSC.  
Figure 18: Experimental comparison of three MPPT techniques at the right GMPP of the PSC: (**a**) conventional INC; (**b**) conventional P&O; and (**c**) coordinated PI-MPPT algorithms.
## 3 Gear Train Efficiency and Meshing Force Model Analysis  
### Power Flow and Efficiency Calculation of Star Gear Train  
The mechanism motion diagram of the herringbone tooth star gear train in a GTF is shown in Figure 2. Here, the planet carrier is fixed, and the gear train is essentially a fixed-shaft gear train with power splitting. The total power input by the sun gear is split through the five-way star gear and then output through the internal gear. The design parameters of the star wheel in the herringbone gear train are the same, and the installation position is evenly distributed around the central wheel. However, in actual use, due to the problems of manufacturing accuracy, installation accuracy, load deformation, thermal deformation, etc., the load of each branch is uneven, the power diversion is uneven, and the meshing efficiency of the gear train also changes. For the herringbone tooth star gear train in a GTF with high efficiency requirements, establishing an accurate efficiency calculation model can facilitate further research on the optimization and improvement of efficiency.  
Here, the load factor on each branch is set to \(\xi_{i}\). (\(i\) = 1, 2,..., 5); then:  
\[\xi_{i}=\frac{P_{\rm{rei}}}{P_{\rm{av}}} \tag{1}\]  
Figure 1: Power loss ratio at different efficiencies.  
Figure 2: The mechanism motion diagram of herringbone tooth star gear train.  
where \(P_{\rm{rei}}\) is the power actually allocated on each branch, and \(P_{\rm{av}}\) is the average power of each branch.  
According to the power flow of the star gear train, this star gear train with power splitting is equivalent to a hybrid unit. As shown in Figure 3, the meshing efficiency of each branch is set as \(\eta_{\rm{mi}}\); then, the expression of gear train meshing efficiency \(\eta_{\rm{m}}\) is:  
\[\eta_{\rm{m}}=\frac{1}{n}{\sum\limits_{i=1}^{n}{\xi_{i}\eta_{\rm{mi}}}} \tag{2}\]
# Slip Estimation for Mars Rover Zhurong Based on Data Drive
## 4 Results and Discussion  
### Ground Tests  
We repeated the hold-out validation on the models of each type ten times to ensure a better stability and reliability of results. Twenty models were obtained in total with BP and GA-BP; the prediction results are summarized in detail in Table 3. A boxplot was used to show the distribution of evaluation results for each model in the following, as shown in Figure 8: the maximum value, upper quartile, median, lower quartile, and minimum value are represented from top to bottom.  
Figures 9 and 10 present the prediction results of five test groups for slip_x and slip_y, respectively. As can be seen in Figures 8a,c and 9a,c for most test groups, after GA optimization, the MAE and RMSE of slip_x and slip_y were reduced to varying degrees at the same time. Combining the data in Table 2 to obtain the MAE results for slip_x as an example, the MAE results of BP were between around 3.020 and 3.314%, with an average of 3.164%, and the MAE results of GA-BP were between around 2.987 and 3.216%, with an average of 3.108%. The MAE predicted by GA-  
Figures 11 and 12 show part of the prediction results of slip_\(x\) and slip_\(y\) in group (1), respectively. Since the testing set data were randomly selected, the testing data points were not continuous, and so it was not suitable to show all testing data since they were too dense and no trend could be seen. In order to show the prediction results more clearly, Figures 11 and 12 only show the prediction of 100 data points. The predicted results were consistent with the trends of actual values, but the actual slip_\(x\) and slip_\(y\) had a larger variation range, while the prediction data of BP and GA-BP were relatively concentrated. According to statistics, out of 1911 test points in group (1), there were 987 slip_\(x\) points predicted by GA-BP, which was closer to the actual slip_\(x\) than the points predicted by BP, and the amount of 1078 slip_\(y\) GA-BP prediction points was closer to the actual slip_\(y\) than the BP prediction points. It can be seen from the above analysis that GA-BP had a higher prediction accuracy than BP.
## 3 Results and Discussion  
### The Monitoring of Geodynamic Processes  
The current tectonic regime of subneriodional transpression in the Tien Shan region leads to changes in the integral macroscopic parameters of geological media, resulting in variations and anomalies being observed in continuous measurements of electrophysicalparameters such as apparent electrical resistivity. Successful monitoring examples, such as the Kambarata experiment, demonstrate that certain azimuthal rotations make the apparent electrical resistivity most sensitive to simultaneous seismic events. The developed effective media approximation theory allows for mathematical definition of the effective electrical conductivity tensor (\(\sigma^{eff}\)) for gradient anisotropic media by averaging the spatial dependence of parameters over a small, physically finite volume.  
Experimental data for the Tien Shan region demonstrate clear decreases and increases in electrical resistivity values along orthogonal azimuths (Figures 2 and 3). Cleavage microcracks, which can be parallel or perpendicular, are common in rock masses and contribute to the observed anisotropy in changing physical parameters. The present study identifies key points with complex relationships between variations in apparent electrical resistivity and the stress-strain state of crustal parts in order to investigate geodynamic processes in seismically active zones. Slow fluid redistribution between fracture and pore systems during seismic event preparation is also reflected in variations in electric conductivity, as shown in [29].  
Effective parameters such as electric conductivity are useful for studying the impact on geological media, and EMA theory allows for their manipulation. Apparent resistivity and impedance phase values are employed to assess effective parameters, which are highly sensitive to geological media. The choice of coordinate system affects the effective electric conductivity, and switching to another system can result in gradient anisotropic media.  
Appendix A provides general expressions for effective electric conductivity, while the following subsection explores several models of rock deformation and their reflection in macro anisotropic electrical parameters that are relevant for active electromagnetic monitoring considering different kinds of monitoring system configurations.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 5 Discussion
For instance, since TCP for multihop wireless networks needs to deal with packet losses differently according to the cause of losses such as congestion, channel noise, and mobility [63], the cross-layer design issue involving TCP and RPL needs to be explored.
# Experimental Validation and Evaluation of a Coupled Twist-Camber Morphing Wing Concept
## 4 Flight Tests  
Even considering the estimated low roll authority, flight testing was attempted with the current wing prototype. For that purpose, a simple tail and boom fuselage was built to carry the necessary avionics and battery for gliding flight. Bungee launching was used to get the aircraft with sufficient speed to climb and glide.  
During this short initial flight, the wing was able to turn left, right and maintain flight with wings leveled as can be seen in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Power (W)** & **\#1** & **\#1.* & **\#2** & **\#2.* & **\#3** & **\#3.* & **\#4** & **\#4.* & **\#5** & **\#5.* & **\#6** & **\#6.* \\ \hline Unloaded & 0.0 & 16.2 & 0.7 & 15.9 & 4.4 & 16.7 & 12.4 & 14.4 & 22.6 & 21.3 & 26.3 \\ Loaded & 0.0 & 20.1 & 0.8 & 22.0 & 7.0 & 21.6 & 28.4 & 22.9 & 26.4 & 32.6 & 27.0 & 37.9 \\ \(\Delta\) (\%) & 0.0 & 24.1 & 14.3 & 38.4 & 59.1 & 29.3 & 129.0 & 59.0 & 17.9 & 44.2 & 26.8 & 44.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Actuation power for different configurations.  
Figure 13: Morphing wing load test.  
The pilot in command described the roll control authority as not abundant but sufficient to ensure roll control at least under the calm wind flight conditions at which the test was made.
## 2 Materials and Methods
### Experimental Setup  
To investigate the biodegradability and toxicity of the PAM flocculant under anaerobic conditions, samples of thickened digested SS (3 g) were dispensed into 120 mL vials and supplemented with a liquid medium (mineral solution without sulfide and bicarbonate [19]) to 50 mL. In the vials designated as \"T\" (for toxicity), a mixture of acetate, propionate, and butyrate (60, 10, and 30 mM, respectively) was added as a substrate. The VFA ratio was selected based on previous experiments [12; 16]. The samples with the medium without the additional substrates were designated \"B\" (for biodegradability). All samples were then supplemented with the PAM to the final concentrations of 0 (control samples), 5, 40, 80, 200, and 400 mg/g TS. To establish anaerobic conditions, the vials were purged with nitrogen and sealed with rubber stoppers and aluminum caps. The experiment was carried out for 40 days in accordance with [PERSON] et al. [13].  
The possible flocculant application to restore methanogenesis in the bioreactor destabilized by the accumulation of high VFA concentrations was studied in the next experiment. It was carried out in 500 mL vials with 100 mL of a digesting mixture in a batch mode. For the rapid accumulation of VFAs and inhibition of methanogenesis, the following were used:  
* a low inoculum to substrate ratio (ISR = 30%/70% on VS basis);
* a high proportion of readily degradable co-substrate, s-OFMSW, which was 45% of the total VS of the digesting mixture (a mixture of inoculum, co-substrates and tap water). Accordingly, the second co-substrate, namely SS, represented 25% of the total VS of the mixture;
* a high concentration of VS in the mixture (40 g/kg).  
All that resulted in the accumulation of excessive VFA, a pH decrease, and termination of methanogenesis. After 7 days, the vials were supplemented with a 1% water solution of the flocculant to the final concentrations of 20, 40, and 60 mg PAM/g TS. After addition of the PAM solution all vials were purged with nitrogen.  
To achieve the same mixture volume and moisture content, relevant amounts of water were added to the control vials and those with 20 and 40 mg PAM/g TS. Some vials were incubated under non-mixing conditions, while others were mixed (100 rpm).  
All experiments were conducted under thermophilic conditions (50 \(\pm\) 0.5 \({}^{\circ}\)C).
## 7 Discussion
### Geographical Extent  
In the context of STLF, geographic scope plays a critical role in shaping the performance efficiency of forecast models. The case sensitivity of a forecasting model becomes evident as the same methodology may yield different performances in distinct locations or countries due to factors such as temperature, humidity, windspeed, rainfall, and cloud cover. In particular, variations in climatic conditions lead to dynamic patterns in energy consumption, highlighting the need for adaptive and location-specific forecasting models. This nuanced understanding enhances our appreciation of the complex interplay between geographical characteristics and forecast accuracy and further emphasizes the case sensitivity inherent in STLF. Out of 196 countries [282], 44 nations, including the USA and China, have contributed to STLF models and datasets. Collaborative efforts from organizations such as EUNITE and APEC have further enriched the dataset (Figure 9), underscoring the global commitment to STLF research. There are six countries in North Africa, of which only Egypt and Morocco have presented studies based on their datasets.  
Figure 9: Country-wise models developed and datasets utilized in STLF.  
Tanzania is the only East African country out of 19 working on STLF, while 4 out of 6 East Asian countries are involved. India and Iran are notable contributors in South Asia. In West Asia, Turkey, Tanzania, and Jordan are actively developing models. In Western Europe, Spain, the Netherlands, France, Italy, Ireland, Denmark, Switzerland, and Portugal conduct STLF research. In Eastern Europe, the Czech Republic, Greece, Poland, Hungary, and Slovenia are notable. Colombia and Chile are the leaders in South America. Developed regions such as the United States, Canada, Australia, and Japan are actively involved in developing electric load forecasting datasets and implementing various STLF models, in addition to the collaborative efforts of EUNITE and APEC.