esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The aim of this study was to investigate balance responses following perturbations in the mediolateral direction at different walking speeds. Previous studies investigating muscle synergies underlying dynamic balancing responses after inducing balance perturbations during gait have suggested that the same set of muscle synergies used during unperturbed walking is also used in perturbed walking [3,14]. However, these studies were performed at intermediate to high walking speeds (around 1.2 m/s). In line with these previous studies, our results for a high walking speed of 0.8 m/s demonstrated that four similar muscle synergies did indeed underly both unperturbed and perturbed gait. In our study, we also examined muscle synergies at substantially lower walking speeds (0.4 and 0.6 m/s). For these lower walking speeds, the muscle synergies for perturbed and unperturbed walking differed considerably. The number of muscle synergies for perturbed gait that were similar to those for unperturbed walking was significantly less than four for low walking speeds, with mainly the push-off synergy being substantially different.
# Development of New Soft Wearable Balance Exercise Device Using Pneumatic Gel Muscles
## 1 Introduction
This type of device has a number of beneficial characteristics, such as being lightweight, flexible, and inexpensive, compared with large exoskeleton devices. Although pneumatic artificial muscles are not able to generate large forces, previous studies reported that they can aid human movement and activities of daily living. Wearable walking suits using a pneumatic artificial muscle were reported to decrease muscle activity during gait [18]. In addition, a wearable force-feedback glove using pneumatic artificial muscles decreased upper limb muscle activity during upper limb movement [19]. Thus, soft wearable assistive devices may also contribute to the improvement of balance function. In the current study, we designed a new wearable assistive device for improving balance function by generating small perturbations using pneumatic artificial muscles. In designing a device that could be applied in balance exercises, several features were incorporated, including lightweight design, the ability to generate small perturbations with a small power source, and the ability to assess reactive postural control. The main contribution of the current paper is the development of a new wearable balance exercise device that is lightweight, portable, and able to generate small perturbations during exercise. Considering these feature requirements, we used pneumatic artificial muscles and a small CO2 tank for a wearable balance exercise device. Moreover, we used a three-dimensional accelerometer to determine whether this device was able to generate small perturbations and to examine whether users exhibited reactive postural control after the perturbations. This paper is organized as follows. The background and purpose of this study are introduced in Section 1. In Section 2, the materials involved in the wearable balance exercise device are described, including the pneumatic artificial muscles and measurement protocol. In Section 3, the results of a series of tests of the device and the effects of the generated perturbations are presented. Section 4 discusses the effects of this device. Finally, Section 5 draws conclusions based on the current findings and provides several suggestions for future research.
# A Study of Wind Shear Influences on the Aerodynamic Performances of a UAV Airfoil
## 3 Results and Discussion
### Effect of Shear Wind on Lift-Drag Ratio  
Based on previous velocity analysis, it can be concluded that a positive gradient of shear wind decreases lift and drag, a negative gradient increases lift and drag, and logarithmic distribution decreases lift and drag, which shows that variations of lift and drag under shear wind are consistent with each other. In order to comprehensively evaluate the effect of shear wind on airfoil lift and drag, this section seeks the optimal performance of airfoil in a shear wind field through the study of the lift-drag ratio of the airfoil. The effectiveness of an airfoil is highly dependent on the lift-drag ratio which is an important parameter of airfoil aerodynamic performance.  
Figure 13a shows that the maximum lift-drag ratio of an airfoil, as well as the corresponding angle of attack, increases significantly when the velocity gradient is increased. When the gradient is reduced to \(dV_{\text{W}}/dh=1.52\), the maximum lift-drag ratio is decreased by 28.8% for the \(dV_{\text{W}}/dh=3.05\) condition. Considering decreases in lift and increases in lift-drag ratio at a positive gradient, it can be deduced that airfoil drag has been significantly reduced. Therefore, a positive gradient is more conducive to the cruising flight of a vehicle, which is the reason why birds can cruise long distances in shear wind fields with very little energy consumption. If aircraft could adopt air energy in shear winds, they will achieve long-distance flight with very low energy consumption.  
Figure 12: Lift curves of the airfoil under a linear and logarithmic distribution of shear winds.  
A negative gradient will significantly reduce the maximum lift-drag ratio of the airfoil and its corresponding angle of attack. As a result, the maximum lift-drag ratio is lower than the one of the gradient-free case, and the corresponding angle of attack is shifted forward, as shown in Figure 13b. Considering the increased lift, increased drag and decreased lift-drag ratio, it can be deduced that the drag has been significantly increased due to the negative gradient effect. Thus, a negative gradient effect is not conducive to the cruise flight of aircraft. On the contrary, it is beneficial for landing and reducing glide distance as larger lift and drag are both required in the flight situation. The logarithmic distribution of shear wind will also reduce the lift, drag and lift-drag ratio as shown in Figure 14. The maximum lift-drag ratio and the corresponding angle of attack are slightly decreased compared with those of the gradient-free case.
## 2 Materials and Methods  
The architecture of the proposed method for the detection and estimation of articulated objects is presented in Figure 1.  
The purpose of the system is to build a point-cloud-based environment model, which takes into account information about the articulated object's position, the handle, and the axis of rotation or translation. The entire system for building an environment model using information about articulated objects was created in ROS Noetic (Robot Operating System), which is a meta-system used to develop robotic software. The system for building an environment model using information about articulated objects consists of five main components (Figure 1): a Kinect camera, a handle detector, a front detector, a module that determines axes of rotation, and a module that builds the environment model. The diagram depicts the tasks of each node and their relationships. The Kinect camera publishes RGB and depth images, which are converted into a point cloud. The handle and front detector modules operate solely on RGB images. The prediction results from both detectors are forwarded to the rotation axis detection module and the scene model building node. The rotation axis detection module determines the rotation axis of rotating fronts in the RGB image and publishes the prediction results to the module that builds the scene model.  
The detected objects are utilized to construct an environment model, incorporating information about articulated objects. Based on the handles and fronts found in the image, their positions in 3D space are estimated. For translational fronts, the positions of the translation axes are estimated using the normal vectors of the front surfaces.
###### Abstract  
In this study, we present the first results of detecting ionospheric irregularities using non-typical GPS observations recorded onboard the Geostationary Operational Environmental Satellites (GOES) mission operating at -35,800 km altitude. Sitting above the GPS constellation, GOES can track GPS signals only from GPS transmitters on the opposite side of the Earth in a rather unique geometry. Although GPS receivers onboard GOES are primarily designed for navigation and were not configured for ionospheric soundings, these GPS measurements along links that traverse the Earth's ionosphere can be used to retrieve information about ionospheric electron density. Using the radio occultation (RO) technique applied to GPS measurements from the GOES-16, we analyzed variations in the ionospheric total electron content (TEC) on the links between the GPS transmitter and geostationary GOES GPS receiver. For case-studies of major geomagnetic storms that occurred in September 2017 and August 2018, we detected and analyzed the signatures of storm-induced ionospheric irregularities in novel and promising geostationary GOES GPS observations. We demonstrated that the presence of ionospheric irregularities near the GOES GPS RO sounding field of view during geomagnetic disturbances was confirmed by ground-based GNSS observations. The use of RO observations from geostationary orbit provides new opportunities for monitoring ionospheric irregularities and ionospheric density.  
ionospheric irregularities; auroral irregularities; radio occultation; geomagnetic storms; GPS; geostationary satellite; GOES; ROTI
## 6 Simulation Results
### Transmission Using Proposed Algorithm  
Performance of the proposed algorithm is evaluated in a two-fold approach--the energy harvested at the switch, and the QoS achieved at the destination. This evaluation allows us to determine the superiority of the switch with respect to the existing schemes.
## 4 Experimental Results
### Dataset and Exploratory Analysis
#### 4.1.2 Word Cloud Analysis  
In this section, we employed word cloud analysis to derive into the linguistic and statistical attributes of suicide-related communications, with a particular focus on differentiating these from regular social media posts. Figure 5 revealed that words such as \"like\", \"want\", \"feel\", and \"know\" frequently appear in the content of suicidal notes. However, it is noteworthy that these words are predominantly used in their negated forms, including phrases like \"don't like\", \"don't want\", \"don't feel\", and \"don't know\". The recurrent usage of these negations points toward a pattern of reluctance of denial in expressing positive sentiments or desires. The predominance of such negations may be indicative of underlying depressive states or suicidal ideation. This linguistic tendency reflects a sense of dissatisfaction, helplessness, or lack of control over personal emotions and circumstances.
## Numerical Examples
### The Software and Computational Meshes and Grids for the Test Cases
The DH object embedded in the simulation domain needs to be specified before the mesh generation. The surface of the DH object is the inner boundary for our mesh generation. Note that the fractures and the associated 2D fracture meshes are also the internal boundaries used to constrain the 3D mesh generation. In this study, the 2D fracture geometry is obtained based on the FAB file generated from the FracMan software. Figure 6b,c further zoom in the local region in the simulation domain and present the generated meshes for the 2D fractures and 3D matrix. In this case, the average node distance for 2D triangular mesh and 3D tetrahedron mesh in the HD model is approximately 0.125 m, and the number of nodes for the fractures and matrix is 412,174. Therefore, the numbers of 2D triangular and 3D tetrahedron elements are 16,734 and 2,511,187, respectively. This study used the same node distance of 0.125 m for the cell size specified in the ECPM model (i.e., DarcyTools). The number of total cells in the ECPM model was 524,288 based on  
64 \(\times\) 64 \(\times\) 128 in x-, y-, and z-directions. Note that the mechanism for grid generation in DarcyTools follows a 221 rule that two adjacent cells always have a cross and longitudinal size ratio of 2:1 at most [27; 28; 29]. Figure 5: The computational mesh of the test Case I for the HD model: (**a**) The generated mesh for the fractures (in blue color) and the associated matrix (in black color); (**b**) The inner view of the mesh for the fractures (in blue color) and matrix (in black color); (**c**) The mesh for fractures; (**d**) The close view of the generated nodes and elements for the connections of fractures (in blue color) and matrix (in black color) in the HD model.
## 2 Background Study
### Building Blocks of CNN  
Figure 1 demonstrates the building block of CNN.  
Figure 1: Building blocks of CNN.  
Convolution: The main step here constitutes feature extraction from the input image. It is the first step in CNN. There is an input image, a feature detector, and a feature map. The filter can be applied block by block to the input image.  
ReLU: ReLU stands for a rectified linear unit and is the next step to the convolution layer. It is the activation function frequently used in CNN. Convolution is a linear operation, whereas the data of the real world is non-linear; however, other activation functions can also be applied like Sigmoid, Tanh and Leaky ReLU.  
Pooling: The size of the representation of the input is reduced as the pooling progresses. With this, the detection of the objects becomes possible irrespective of their location. With the help of pooling, the number of required parameters is reduced, thereby reducing the computational power. Hence, it can control overfitting.  
Flattening: one of the simple steps wherein the flattening is carried out of the pooled feature maps into a sequence of long vectors.  
Fully connected layer: this helps to combine the attributes and features that can predict the classes.  
The Figure 2 depicts the classification pipeline using CNN. The image is passed as an input to a stack of Conv and Pool layers which act as feature extractors. The feature maps are obtained as the output after the Conv and Pool layers are stretched out to a 1-dimensional vector. The 1-D vector is then passed on to the classification layer. The softmax translates the probability from the output score of the fully connected layer. Depending on the class having the highest probability, the name of the image is obtained.  
The classification pipeline is modified to perform the localization using a CNN. For localization, the bounding box needs to be drawn around the object. In Figure 3, the classifier obtains the class scores and classifies the image using the softmax function. The BBox Regressor obtains the bounding box by using the L2 loss. The classifier and BBox Regressor are combined by considering the maximum confidence score and its corresponding coordinate to complete the localization.  
There are various methods of object detection as described below:
## 3 Numerical Results
### Uncertainty Analysis of NLS Equation with Initial Value
#### 3.1.1 Estimate Analysis
The multi-fidelity method based on data-driven low-fidelity model has the same convergence speed as the MC method. Under the same budget \(10^{5}\), MSE estimated by the MFMC method is about 30 times lower than the MC method. For the same convergence rate, this means that the speed of the MC estimation is increased by 30 times. In other words, the MSE estimation of MFMC is 0.1 when the calculation budget is 300, but the budget of MC is 9000 with the same value of MSE. To further study the stability of the MFMC method, Figure 3a shows that the MSE of the standard MC and MFMC estimator changes with runtime when the numbers of samples are 100, 500 and 1000. We can see, with the increase of running time, that MSE decreases under different sample numbers, indicating that MFMC is stable in the time evolution direction. On the other hand, the MSE of the MFMC is estimated by the number of model evaluations \(m\) and the coefficients \(\alpha_{2},\ldots,\alpha_{k}\), while the number of model evaluations \(m\) and coefficients \(\alpha\) are determined by the sample correlation coefficients and sample variances. Table 3 give the sample correlation coefficients computed from 100, 500 and 1000 samples. When the number of samples increases, the sample correlation coefficients change only slightly. Table 4 list the sample variance calculated by the same samples as before. Since calculating coefficients \(\alpha\), only the sample variance ratios \(\sigma_{1}/\sigma_{i},i=1,2\) affect the value of coefficients. When increasing the samples 100 to 500, the ratios \(\sigma_{1}/\sigma_{i}\) also change very little. Hence, the disturbance of sample variances and sample correlation coefficients has minimal effect on MSE estimated by MFMC method. Figure 3b reports the proportion plot of the number of model calculations \(m\) for MFMC estimator when the sample numbers are  
Figure 2: The budgets of estimated MSE for the standard MC and MFMC estimators range from \(p=10^{3}\) to \(10^{5}\). 100, 500 and 1000. As the number of samples increases, the change of percentage is small, and it is confirmed that small perturbations in the sample estimates lead to small changes in the number of model evaluations \(m\).
## 4 Review of the Routing Protocols
The heterogeneous nature of sensors requires prioritised scheduling and forwarding mechanisms, which are provided by QoS-based routing protocols through QoS priority routing QoS modules for delay and reliability. Although these protocols provide QoS, their designs are complex, which makes them less scalable. Cluster-based protocols are designed to reduce energy consumption for sensor nodes where, in a limited region, a cluster is created, and a cluster head is selected for communication outside the cluster. This increases the overall network lifetime by reducing the multiple communication attempts of various nodes through cluster heads. However, the overheads involved in the cluster-head selection among nodes make it less energy efficient and, ultimately, less scalable; if a large number of nodes make a network, then the head selection process will get complex and consume more energy. In cross-layer approaches, the network layer involves different layers to perform energy-efficient and delay-bounded routing; however, due to dynamic network conditions and mobility, these protocols do not deliver the expected results.
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## 2 Methods
### Models and Set-Up
#### 2.1.1 The Circulation Model NEMO  
NEMO (Nucleus for European Modelling of the Ocean, [34]) is a framework of ocean-related computing engines, from which we use the OPA (Ocean Parallelise) package (for the ocean dynamics and thermodynamics) and the LIM3 (Louvain-la-Neuve Sea Ice Model) sea-ice dynamics and thermodynamics package [34]. In OPA, six primitive equations (momentum balance, the hydrostatic equilibrium, the incompressibility equation, the heat and salt conservation equations and an equation of state) are solved, where the  
Figure 1: **(a)** The North Sea (depth in m) topography as used for the model simulations and buoy locations (red circle: Fino3, yellow circle: Elbe). **(b)** Magnification of the German Bight showing the HZG drifter trajectories released at the location of their respective numbers. Deployment periods of the HZG drifters are shown on the top left corner.  
Arakawa C grid is used in the horizontal. In the vertical, terrain-following coordinates, z coordinates or hybrid \(z\)-\(s\) coordinates can be chosen. Previously, NEMO was applied to the Baltic Sea and the North Sea area in uncoupled mode [35], coupled to atmospheric models [36] and forced with a wave model [18; 19; 25]. For the northwestern European Shelf, NEMO is used as a forecasting model in the COPERNICUS Marine Services [23; 37; 38]. The horizontal model resolution is about 2 nm with 51 \(\sigma\)-levels in the vertical providing instantaneous hourly surface (0.6 m uppermost level thickness) velocity fields. The study domain is 48.0-62.5\({}^{\circ}\) N and \(-\)4.7-13.2\({}^{\circ}\) E, which includes the North Sea, Skagerrak and Kattegat (Figure 1). The hourly atmospheric forcing is taken from subsequent short-range forecasts from the regional atmospheric model COSMO-EU, operated by the German Weather Service (DWD). Atmospheric pressure and tidal potential are included in the model forcing. River run-off is provided in the form of a daily climatology based on river discharge datasets. Lateral open boundary and initial condition fields (temperature, salinity, velocities and sea level) are derived from the MetOffice Forecasting Ocean Assimilation Model (FOAM) AMM7 (7 km horizontal resolution [23]), currently used by the Copernicus Marine Environment and Monitoring Service (CMEMS) as an operational service.
## 2 The Analytic Network Process
### Analysis of ANP Results  
ANP is an improved model proposed to adapt to the non-independent hierarchical structure. Therefore, the calculation ideas of the two models are roughly the same. In addition, the reliability of AHP has been fully verified in previous studies, and AHP is considered reliable in the study. In order to eliminate the impact of data differences on the weight results, ANP can be fully proven to be highly reliable while considering the relevance of different risk factors. In the study, the same set of statistics was selected to be calculated with ANP and AHP, respectively, and the calculation results were compared and analyzed to comprehensively evaluate the differences between the two methods.  
Figure 4 above shows the ANP model built with Super Decision software, and Figure 7 shows the AHP model with only linear hierarchies. The weights of the AHP model were calculated (Figure 8) in the study, and the data of ANP and AHP were processed with SPSS software. The results are shown in Figure 9.  
The analysis of the weights of ANP and AHP by SPSS shows that the range of the weights of ANP is 0.2099, and the range of AHP is 0.2146, R(AHP) > R(ANP). Figure 6 also clearly shows that the ANP exists in a relatively more balanced distribution of weights. Because of the network hierarchy of circular domination within the same level and the correlation between the elements, the ANP model incorporated a feedback mechanism. Therefore, to a certain extent, the priority of risk factors that can affect other elements is improved, making the weight of indicators closer to the actual situation.  
Figure 8: Secondary index weights in AHP.  
Figure 7: Structure of AHP models.  
In Figure 9, the weights of some risk elements in ANP differ greatly from those in AHP. Due to the space limitation, \"Pipelines\" is used as an example in the paper. In actual production, many valves, flanges, and other safety accessories are installed on pipelines. Such type of security accessories is numerous and easily overlooked. This also explains why \"safety accessories\" is the highest weighted risk element. Furthermore, the failure of such fittings can easily lead to a pipe rupture, which in turn can cause a series of interlocking reactions; Similarly, pipe rupture may cause the failure of some pipe fittings, thus increasing the probability of accidents. Considering the interaction between \"Pipelines\" and \"safety accessories\", the results of ANP reduce the difference between the two weights. The above also proves that the weight of the ANP model is closer to reality.