esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Perceived Lengths (cm)  
Statistical analysis results showed that single-session real rPMS compared with single-session sham rPMS had an enlargement effect on perceived lengths (i.e., perceived wrist length and perceived hand lengths of where the thumb, index, middle, ring and little finger locate) in general. MANOVA showed that the within-subject effect of time (Pillai's Trace) was statistically significant, F (18, 21) = 1.706, \(p\) = 0.037, \(\mathrm{np^{2}}\) = 0.087. There was an interaction effect between time and group (Pillai's Trace), F (18, 21) = 1.940, \(p\) = 0.013, \(\mathrm{np^{2}}\) = 0.097. We looked further into the interaction effect between time and group in the univariate test, as shown in Table 1. Enlargement of perceived wrist length and hand lengths of where the thumb, index, and middle finger locate was not statistically significant; perceived wrist length, real rPMS (4.35 \(\pm\) 0.16) compared with sham rPMS (4.54 \(\pm\) 0.16), F (3, 36) = 1.803, \(p\) = 0.151, \(\mathrm{np^{2}}\) = 0.046; perceived hand length of where the thumb locates, real rPMS (7.48 \(\pm\) 0.46) compared with sham rPMS (7.3 \(\pm\) 0.45), F (3, 36) = 1.212, \(p\) = 0.309, \(\mathrm{np^{2}}\) = 0.032; perceived hand length of where the index finger locates, real rPMS (11.05 \(\pm\) 0.62) compared with sham rPMS (11.71 \(\pm\) 0.60), F (3, 36) = 1.779, \(p\) = 0.155, \(\mathrm{np^{2}}\) = 0.046; perceived hand length of where the middle finger locates, real rPMS (12.14 \(\pm\) 0.63) compared with sham rPMS (12.72 \(\pm\) 0.61), F (3, 36) = 2.675, \(p\) = 0.051, \(\mathrm{np^{2}}\) = 0.067. However, perceived hand lengths of where the ring and little finger locate were enlarged by real rPMS with statistical significance; perceived hand length of where the ring finger locates, real rPMS (10.95 \(\pm\) 0.60) compared with sham rPMS (11.48 \(\pm\) 0.59), F (3, 36) = 4.611, \(p\) = 0.004, \(\mathrm{np^{2}}\) = 0.111; perceived hand length of where the little finger locates, real rPMS (8.88 \(\pm\) 0.59) compared with sham rPMS (9.73 \(\pm\) 0.57), F (3, 36) = 3.189, \(p\) = 0.027, \(\mathrm{np^{2}}\) = 0.079.
## 2 Background and Methods
### Location-Based Services  
Location-based services (LBS) are mobile applications that give information depending on the location and context of the user [18; 19]. The largest and probably the most popular LBS applications (including driver assistance, passenger information and vehicle management) are the mobile (car or pedestrian) navigation systems [20]. These systems are created and designed to help people during wayfinding activities in different environments [21].  
Location-based information can be conveyed to users through an overview map or as turn-by-turn instructions. According to [PERSON] [22], limited-sized screens provide a limited overview. This can be compensated with good wayfinding instructions [23]. [PERSON] and [PERSON] [24] highlighted that bottom-up and top-down mechanisms drive human visual attention. Unexperienced users process animated displays based on perceptual salience and not thematic relevance [25]. According to [PERSON] [26], users can detect a maximum of four moving objects simultaneously.  
The 'Geographic Information Displays' (GID) offered by smartphones can be examined from (a) GIScience, (b) cartographic and (c) a cognitive science perspective [27].  
1. The main challenge--from the GIScience perspective--is the 'context' (information for a person, place or object characterization)--that is, adaptation, inference, management and modeling [28]. According to [PERSON] et al. [29], people's behavior will change if they receive more information about the environment (e.g., spatial and task contexts often alter during navigation). Technical systems should solve this problem.
2. In order to help the user's understanding, graphic elements on the display should change according to many visual variables [30; 31]. The more sophisticated the visualizations are, the better the performance of a navigation-related task. To this end, new functions have been developed, e.g., multi-scale traversal routes in a simultaneous representation, reducing the need to zoom in and out for orientation [32]. The extent of a visualization task's performance depends on expertise [33; 34; 35] and emotional context [36].
3. GIDs should support the user's mental representation of the variety of spatial conditions that can be used during navigation [27]. For navigation, spatial information must be translated from one reference frame to another [37]. GIDs can facilitate this transition by providing a track-up map that improves navigation efficiency [38]. With the help of the GID's instructions, the decision-making process of the user de creases [39; 40], or rather distracts the visual attention from the environment--the space is less experienced directly by the users [41; 42].
## 3 Modified F/DR Algorithms
### DR-D-k Algorithm  
In this strategy, cells with the highest demand are less likely to be exchanged in the sorting list. This approach probabilistically avoids the creation of a local minimum that results from the DR priority scheme. In particular, the DR-D-k algorithm proposes to swap the cells in the list following the algorithm:  
1. Generate an ordering list following the DR criteria.
2. Exchange the cell \(L\{i\}\) at the end of the list with a probability of \(\frac{k}{100}\Big{\{}1-\frac{D_{l}(i)}{\max\mathbf{D}}\Big{\}}\), where \(k\) is a number between 0 and 100, \(D_{L\{i\}}\) is the demand of the cell \(L\{i\}\), and \(\max\mathbf{D}\) is the maximum value of the demand vector \(D\).
## 1 Introduction
Compared with the neural network and fuzzy control, its control strategy is simpler, and the influence of the initial value selection on the controller is reduced since the tuning parameters are less. In [18], it uses the first-order Taylor series expansion to propose an adaptive estimator of uncertainty for the robust control of electrically driven manipulators, to improve the computational efficiency and ease of implementation, the higher-order terms of Taylor series expansion are ignored. Instead, the higher-order terms may cause approximation errors and the estimator performance may be reduced. However, for the Fourier series, due to the characteristic of the basis function of the Fourier approximation, its approximation effect on the periodic function is the most obvious. Furthermore, in [17], it uses the Legendre polynomial to approximate the uncertainty factors online, and the Legendre coefficients can be tuned using the adaptive laws derived from the stability analysis. However, it is difficult to select a reasonable value for the error boundary in [17], too conservative values would cause high-frequency chattering of the system, and too low would increase tracking error. Furthermore, for the underwater manipulator of this article (as shown in [9]), under the drive of hydraulic system, there is joint lag in the joint control of the manipulator. However, the above control methods, such as [17], do not consider the influence of joint lag on the trajectory tracking control. For the above considerations, this paper proposes an adaptive sliding mode proportion integral differential (PID) control method for underwater manipulator based on Legendre polynomial FAT. In our work, a sliding model PID controller is designed to accelerate the system response and reduce joint lag. The adaptive law is used to estimate the error boundary, and reduce the chattering of the system. Furthermore, according to the analysis of Lyapunov stability, it is demonstrated that the tracking error can be uniformly asymptotically converged. Based on the pool experiments of the joint step response and trajectory tracking, the effectiveness of the proposed method is verified by comparing the joint error and control quantity of the proposed method and the comparison method in [17]. This paper is organized, as follows. Section 2 describes the joint control problem of the underwater manipulator, and gives the dynamics model and its uncertainties. In Section 3, the adaptive sliding mode PID control method is studied based on Legendre polynomial FAT, and contains the stability analysis. Then in Section 4, the effectiveness of the proposed method is verified by pool experiments. Finally, we make a brief conclusion in Section 5.
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 3 Fouling in MD, OD and OMD  
The performance of the membrane can be affected by the deposition of a fouling layer on the membrane surface or in the membrane pores [31]. The decline of water permeate flux is attributed to both temperature and concentration polarization effects as well as fouling phenomena [32].
## 2 Materials and Methods
### Flux Data Processing  
Raw 10 Hz data from the infrared gas analyzer and sonic anemometer were processed for 30-minute runs using the EddyPro 6.1 software (Li-Cor Inc., Lincoln, NE, USA). The data was checked for data spikes and they were removed, following the methods of [PERSON] [(27)]. A coordinate rotation using the planar fit method [(28)] was applied to the sonic anemometer data to remove tilt errors as well as linear trends. The Webb-Pearman-Leuning (WPL) correction for density effects due to heat and water vapor transfer [(29)] was applied to correct the calculated CO\({}_{2}\) fluxes.  
Quality control was applied to each 30-min eddy-covariance data block and data from north and south systems were combined. The processed NEE data for each sensor was filtered and combined into one data set based on the prevailing wind direction and the sensor's quality control parameters. Data from the north facing tower was used for wind directions between 315 and 45 degrees, the south facing tower datasets were used for wind directions between 135 and 225 degrees. For the remaining wind directions, the average of both sensors was used.  
The footprint model was then applied to the map with estimated fetch values and angle to screen out measurements that did not originate from the study area. Finally, only measurements which had footprints where at least 70% of the CO\({}_{2}\) flux originated from within the marsh area surrounding the flux tower were used in this study, which mostly occurred during daytime conditions. This combined NEE was further filtered based on marsh inundation values. The filtered data was then used to calculate the GPP.  
The friction velocity threshold (u\({}^{\star}\)) for nighttime fluxes at our study site was 0.10 ms\({}^{-1}\) and determined per [PERSON] et al. [(30)]. We used the median of the absolute deviation method by [PERSON] et al. [(30)] to detect outliers within each 30 min block of flux data. A total of 37%of the observations were removed, with a greater percentage of removed observations from nighttime conditions.
## 5 Results and Discussion
### Effect of the Diffusion of Submicron Particles
In the first stage, when small particle trajectories change from diffusion to focus, \(R\) decreases rapidly as \(d_{p}\) increases. The reason for this is that the increase in particle inertia and the flow drag force weakens the particles escaping from the  
Figure 11: The streamline in a fully expanded micronozzle. Figure 12: Al particle with variable shape factors in different processes. axial direction, promoting the aerodynamic focusing of the particles. As \(d_{p}\) continues to increase, the intersection process(II) for the particles appears, which is the second stage. \(R\) gradually increases at first and then decreases. This is because the position of the particle trajectory intersection moves towards the throat at first. After a particle size increases beyond a special particle size, the intersection moves to the nozzle outlet. This can be also observed in Figure 10d-f. At the third stage, the particle inertial force dominates the behavior of the submicron particles. The fluid drag force seems less obvious, so the diffusion radius of the particles increases slightly when \(d_{p}\) increases. As the particle density increases, the critical diameter of the particle diffusion radius decreases in these three stages; that is, the diffusion radius of the particles changes at a smaller size. The change in \(R\) of the Al particles in the first stage is particularly more obvious than that of the Cu particles. On the contrary, the change in \(R\) of the Al particles is smaller in the third stage. Figures 12 and 13 suggest that the different properties of particle trajectories with the same shape factor are determined by the particle size and particle density. In conclusion, a converging-diverging micronozzle with a fully expanded supersonic flow is significant for the aerodynamic scheme of particle focusing in micro cold spay. The dynamic focusing of particles is the result of the competition between inertial force and drag force. Submicron particles with larger inertias will be useful in aerodynamic focusing in a higher drag force flow field.
## 5 Implementation of Proposed Scheme in FANET
### Signing and Verifying Phase  
In this part, the _RMEC-UAV_ first selects \(\omega\) from {1, 2,..., \(n-1\)} and then computes \(\Delta_{1}=\omega/\mathcal{E}_{\text{\tiny{TV}}}\), \(\Delta_{2}=\beta_{\text{\tiny{TV}}}/\omega\), and then sends it (\(\Delta_{1}\), \(\Delta_{2}\)) to the M-UAV. Further, the M-UAV proceeds as follows:  
* It selects (\(\tau\), \(\varphi\)) from {1, 2,..., \(n-1\)};
* It computes \(\mathcal{R}=\mathcal{A}\)(\(m\), \(\Delta_{1}\), \(\Delta_{2}\), \(\sigma_{\text{\tiny{mav}}}\)) and \(\mathcal{Z}=\mathcal{R}+\varphi\);
* It sends \(\mathcal{Z}\) to the _RMEC-UAV_. The _RMEC-UAV_ generates the partial blind signature \(\mathcal{S}^{*}=\mathcal{O}_{\text{\tiny{TV}}}-\mathcal{Z}.\beta_{\text{\tiny{TV }}}\) and sends it to the M-UAV;
* \(\tau\), and transfers it (\(\delta^{**}\), \(r\)) to the BS/IoT.  
Then BS/IoT can verify the blind signature if either of the following equalities are satisfied: \(r^{*}=\) (\(m\), Ns) = \(r=\) (\(m\), Ns) or \(r^{*}=r\).  
Figure 3: Initialization and registration phase.
## 3 Uncertainty-Controlled Remaining Useful Life Prediction with a Data Augmentation Strategy
### Wiener-LSTM Bearing RUL Prediction Model
#### 3.2.3 Optimization of the Hyperparameters by PSO Algorithm  
It is well known that the performance of the network is influenced by the hyperparameters, such as the learning rate, number of neurons, maximum epochs, and so on. However, these hyperparameters are always tested by so many trials to seek the optimal structure of the network. As one of the optimization algorithms, the PSO algorithm has been widely used in several optimization tasks of the network [41] because of the fast convergence and robust applicability. The structure of the Wiener-LSTM model with PSO optimization is shown in Figure 4. The Wiener-LSTM model consists of one LSTM layer and one dense layer. Thus, the hyperparameters in this model include the number of hidden layer neurons (\(ln\)), initial learning rate (\(lr\)), and drop rate factor (\(dr\)). The process of the optimization can be divided into four steps:  
* Step 1: Parameter initialization. The particle dimension, population size, iterations, learning factors, inertia weight, velocity, and position are determined.
* Step 2: Initialize the particle positions and velocities, then generate population particle (\(ln,lr,dr\)) at random.
* Step 3: The loss function in Equation (23) is chosen to be the fitness function in the PSO algorithm here. The particle position and velocity are updated by epoch. The extreme individual value and extreme global value are then updated by computing the fitness value in accordance with the new situation.
* Step 4: Judge whether the termination conditions are met. If satisfied, the algorithm ends and outputs the optimization result (\(ln,lr,dr\)); otherwise, return to Step 1.
###### Abstract  
It is fundamental to acquire accurate point cloud information on rock discontinuities efficiently and comprehensively when evaluating the stability of rock masses. Taking a high and steep cliff as an example, we combined 3D laser scanning and UAV photogrammetry technology to collect rock data, and proposed an intelligent identification method for rock discontinuities based on the multi-source fusion of point clouds. First, the 3D-laser-collected point cloud data were used as the basis to fuse with the UAV-photogrammetry-collected data, and the unified coordinate system and improved ICP algorithm were used to obtain the complete 3D point cloud in the study area. Secondly, we used neighborhood information entropy to achieve adaptive neighborhood-scale selection and to obtain the optimal neighborhood radius for the KNN search, to effectively calculate the point cloud normal vector and rock mass orientation information. Finally, the KDE algorithm and DBSCAN algorithm were combined for rock discontinuity clustering to achieve intelligent identification and information extraction of the rock structural plane. The clustering results were imported into the DSE program developed based on Matlab to calculate the discontinuity spacing and continuity of the rock mass structure, and to efficiently obtain the parameters of rock mass occurrence. The research results showed that this method can effectively solve the problem of incomplete-data-acquisition ground 3D laser scanning in complex geological conditions, and UAV photogrammetry prone to blurred images in depressed areas. When the extraction results were compared with the field-measured rock occurrence, the average dip angle error was about 2\({}^{\circ}\), the average dip direction error was 1\({}^{\circ}\), and the recognition results met the accuracy requirements. The research results provide a feasible scheme for the identification and extraction of discontinuities of high and steep rock masses.  
rock discontinuities; multi-source point cloud; point cloud fusion; automated extraction 2022
## 2 Ice Accretion Tests
### Testing Cases  
The testing cases are listed in Table 1. Circular cylinders were used to simulate the pipeline and the wind hanger. Ten circular cylinders with various \(D\) values were selected to study the potential size effect of the ice accretion. The small-diameter cylinders (\(D<500\) mm) were made of plexiglass, while the large diameter ones were made of thin stainless-steel plates. A layer of thin ice quickly formed on the cylinder made of plexiglass and the stainless-steel plate around the 10 th sprinkle cycle (Spraying times \(N=10\)). At this point, the interfaces of the models of both materials were composed of the same ice, and the subsequent icing processes were almost the same. Thus, the influence of the two materials could be ignored. Generally, the angle between the wind hanger and the horizontal plane (\(\gamma\)) was 0\({}^{\circ}\)-60\({}^{\circ}\). The cylinders with \(D=25\)-150 mm were also taken as examples to study the effect of \(\gamma\) on the ice accretion on wind hangers. The \(\gamma\) of the wind hanger was changed by adjusting the relative heights of the two ends.  
The section steels (angle steel, U-steel, and I-beam) were made of stainless steel. Three different sizes of section steels were used to investigate the potential size effect of the ice accretion for three \(\gamma\) values. \(\gamma=0^{\circ}\) and 180\({}^{\circ}\) for the angle steel meant that the model was supported by its horizontal and vertical plates, respectively. \(\gamma=0^{\circ}\) and 90\({}^{\circ}\) for the U-steel meant that the model was supported by its short and long plates. The ends of the circular cylinders and section steels were sealed to avoid the entry of water. The ice sizes were measured after every 20 spraying cycles and the end of sprinkling.  
Considering the interference of the truss stiffening girder, pipeline, grate plate, and guardrail, three typical pipeline suspension girders, that is, A, B, and C (Figure 3), from real bridges were chosen to investigate their ice accretion characteristics. The scale ratios of girders A, B, and C were 1/6, 1/5, and 1/6.4, respectively. The lengths (\(L\)), widths (\(B\)), and heights (\(H\)) of the sectional models are listed in Table 1. The truss was simulated by angle or channel aluminum. The pipeline was simulated by PVC tubes or steel pipes. The grate plate and guardrail were simulated by plexiglass. For each testing case, the experiments were conducted twice to verify their repeatability.
# Development of a Perfusing Small Intestine-Liver Microphysiological System Device
## 1 Introduction  
The 3 Rs--replacement, reduction, and refinement--of scientific experimentation are increasingly being regulated globally, and in Europe, animal testing has been banned for certain safety evaluation studies [1]. Therefore, methods for evaluating safety and toxicity without using animals are essential to the development of novel treatments and drug interventions. The alleviation of animal models can be challenging for certain biomedical research techniques; for example, the study of organ-organ interactions in static culture systems, such as cell cultures in dishes and plates, for predicting pharmacokinetics and safety in humans is complicated.  
The microphysiological systems (MPS) model has garnered increasing attention as a new tool for cell assays in drug discovery research by combining microfluidic devices and cell culture technologies [2]. The MPS model functions as a technological system that mimics physiological blood flow, which enhances expression and functions in cells that cannot be suitably tested using technologies such as conventional static culture [3]. Overthe past few years, numerous research grants, including one from the Japan Agency for Medical Research and Development (AMED), have been invested in MPS globally [4]. Such an investment reveals the magnitude of expectations for research related to MPS. The functions of various organs and tissues, such as the lung, liver, kidney, and gut, have been studied using MPS models [5]. Liver MPS models are being developed to accurately predict metabolic abilities and toxicity during the drug discovery process [6]. However, the applicability of liver MPS models is unknown. Researchers have developed a polydimethylsiloxane (PDMS) liver-on-a-chip device to evaluate drug absorption by investigating the drug responsiveness to culture cryopreserved human primary hepatocytes and investigating its responsiveness to determine its drug absorption [7]. Furthermore, MPS devices have been proposed for investigating multi-organ functions for predicting organ interactions [5]. The MPS reported by [PERSON] et al., which comprised a slide glass and PDMS sheets with microchannels fabricated via photolithography, cultured Caco-2 cells in the intestine component and HepG2 cells in the liver component [8]. Based on this report, we employed MPS technology to imitate the small intestine and liver in vivo in our laboratory with the aim of developing a suitable model for evaluating pharmacokinetics and safety in a multi-organ system. Therefore, we developed a perfusing small intestine-liver connected device (hereafter referred to as a small intestine-liver device).  
To evaluate our device, we selected human iPS cell-derived small intestinal epithelial cells (HiEs) produced in our laboratory [9] and hepatocytes, which are widely used for pharmacokinetic and safety research by pharmaceutical companies. We then co-cultured hepatocytes and HiEs using the proposed small intestine-liver device and determined its utility for pharmacokinetic analyses.