esa-sceva/satcom-chunk-collection huggingface dataset:
# Lagrangian Modeling of Marine Microplastics Fate and Transport: The State of the Science
## 4 Numerical Lagrangian Models
### D-Waq Part  
Delft3D, as open-source software, can be applied for simulating flows, the transport of sediments, tidal currents, water quality, waves, etc. in various water settings. Delft3D has several modules as shown in Figure 2. The modules are linked to each other in order to simulate the multi-dimensional transport and hydrodynamics (in 2D or 3D) [117]. Particle transport and dynamical spatial distribution are simulated by the PART module of Delft3D using particle-tracking modeling using the inputs provided by the FLOW module (i.e., two- or three-dimensional flow data). In other words, velocity and other hydrodynamic parameters are computed by the FLOW module using an offline coupling and provided as inputs for the D-WAQ PART module.  
D-WAQ PART is not capable of considering the impact of the biofouling process on the trajectories of micro-size particles via increasing the density and size of the particles since it cannot consider the thickness of the biofilm layer, and so, the density of a fouled particle. D-WAQ PART also lacks a specific tool to provide insight and appropriately predict complex processes including degradation as a long-term and aggregation as a short-term process.  
Particle trajectories are computed over time in three dimensions by modeling the advection and dispersion processes [118]. As stated above, each individual particle can be advected by water currents and wind or wave drag as additional sources and also displaced randomly due to diffusion in order to explain unresolved processes.  
The velocity of particles due to advection includes the local current velocity prepared by the hydrodynamic model in the Eulerian velocity field and the wind velocity at the location of a particle as shown in Equation (13) [58]. The wind-drag coefficient is related to object characteristics as an empirical parameter. In addition, D-WAQ PART employs a random-walk particle-tracking model in order to describe the movement of particles due to diffusion processes. The diffusion is described using kinetic gas theory and the theory of Brownian motion [107].  
Figure 2: Schematic diagram of the Delft3D structure adapted from [75], with permission from Elsevier, 2021.  
D-WAQ PART addresses beaching and washing-off using the probability of being washed off, as shown in Equation (16).  
Many numerical models, including D-WAQ PART, are not capable of reading the unstructured mesh information provided by an Eulerian model. However, some of the other Delft-3D modules (e.g., D-Water Quality and GPP) can solve the advection-diffusion equation numerically on arbitrary, unstructured grids and structured, shaped grids such as triangles, rectangles, and curved elements.  
On the other hand, D-WAQ PART utilizes the linear and block interpolation implemented for all time functions between time intervals [107]. In the linear interpolation, D-WAQ PART interpolates the hydrodynamic inputs linearly between the time records while it interpolates the two different subsequent inputs with similar time as the block interpolation.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 4 System Architecture
### Bandwidth Limitations
The essence of the unlicensed spectrum is that any certified device can operate within it, with only minimal restrictions on the uses that can be made. The unlicensed spectrum encourages manufacturers to collaborate in the development of open standards and compete to deliver low-cost components and user equipment. However, there are limitations in the use of this spectrum, namely due to the difficulty in finding a common universal standard [47]. Having an updatable database online on the routers is the main obstacle since each country has its free frequencies. Within each country, the frequency ranges available may vary from region to region, which significantly complicates implementing a standard system using TVWS. However, it is still a good alternative for wireless long-distance communications as its broadcast nature could allow for transmitting software updates and ML models to the entire network. More recently, a new model for wireless communications has emerged that appears to complement existing technologies to solve the limitation of coverage of the terrestrial network. This satellite network has been considered in the construction of IoT systems to provide global and uninterrupted IoT services. Considering the limited energy consumption of the IoT device, the LEO satellite is a good candidate for providing IoT services due to its low propagation loss. Meanwhile, the LEO satellite also has advantages in the low propagation delay and in provisioning continuous global coverage, which satisfies the requirements of a global IoT network. The LEO satellite generally acts as an access point in a satellite communication network, making a network like LoRaWAN in the topology domain. Therefore, when combining LoRaWAN with the LEO satellite system, LEO satellites mainly play the role of LoRaWAN gateway, which is used to acquire data from devices, operate and control access [41]. In order to meet the needs of IoT by satellite systems, in [48] the authors introduced LEO satellite constellation technology to support IoT telecommunications systems. These have unique advantages compared to geostationary satellite communication systems since they orbit closer to the earth. The constellation of LEO satellites (usually less than 2000 km) is more efficient in terms of time. In terms of propagation delay, quantified by a round trip time (RTT), the LEO satellite constellation has an RTT of less than 100 ms. In comparison, the RTT of geostationary systems is greater than 600 ms. Thus, the constellations of LEO satellites are presented as a good bet for the future to make Internet access available in remote regions, rural areas, among others, where terrestrial technologies, by cable or fiber, are not possible to be installed.
# Design of a Hand-Launched Solar-Powered Unmanned Aerial Vehicle (UAV) System for Plateau
## 5 Field Experiment
### Flight Parameters of HighAltitude Experiment
Figure 20 shows different payloads applied to endangered animal monitoring. Figure 19: Rudder—roll angle response. Figure 18: The flight path of the solar-powered UAV: (**a**) Circular flight path without wind, (**b**) quadrilateral flight path with 7 m/s wind. The Figure 21 shows the cruising power of the UAV with two different payloads. The dotted line in the figure is the average cruising power, which can be used to estimate the aircraft's battery endurance to determine whether the UAV is capable of flying across the night, which is a prerequisite of perpetual flight. Different seasons have different hours of sunshine, which is closely related to the flight endurance. As can be seen from the Figure 21a, the cruising power of the UAV with payload 1 was about 27 W, which means the battery endurance of the UAV was 13.3 h, which was enough to allow the UAV to cross the night (perpetual flight) in summer. In summer (take July 1 as an example), the flight time that the solar cell power can meet the cruise consumption is about 11.7 h, and the night time is 12.3 h to simplify the neglect of transition time. In winter (take December 10 as an example), the maximum solar irradiance and sunshine duration are shorter, and considering the influence of low temperature in winter on battery discharge efficiency, the flight endurance that the solar cell power can meet the cruise consumption is only 6.1 h, which means the UAV cannot cross the night. If the UAV takes off at midnight, it reaches its energy balance point when batteries run out and the UAV will gain its maximum endurance of 29.7 h.  
As can be seen from the Figure 21b, the cruising power of the UAV with payload 1 was about 39 W, which means the battery endurance of the UAV was 9.2 h and the UAV cannot cross the night with payload 2. The maximum endurance in summer and winter are 29.3 h and 20.8 h, respectively. On the whole, at an altitude of 4600 m, the MP Solar UAV can achieve autonomous climb, cruise, hover, and descent control, completing the feasibility verification of the whole process of control system  
Figure 21: Cruise power data of the UAV with different payload configurations at altitude of 4700 m: (**a**) Small fixed-focus camera with low-power transmission, (**b**) optical-infrared combined optoelectronic pod with remote digital transmission. Figure 20: Different payloads for endangered animals: (**a**) Optical image, (**b**) infrared image. design. The cruise power data of the UAV with different loads indicate that it is feasible for the UAV to achieve perpetual flight at an altitude of 4600 m, which verifies the correctness of the design method. The permanent flight of solar UAV requires both seasons and mission loads. Less ideal seasons and more stringent mission requirements will prevent the UAV from achieving perpetual flight, but it still has better endurance, which is the unique advantage of solar-powered UAV.
# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 2 Materials and Methods
### Classification of Urban Buildings  
Urban building classifications are key input variables for demographic and social-economic models [32; 33]. Thus, urban building classification plays an important role in map updating, urban modeling, urban growth analysis, and change monitoring [34]. The urban population is mainly distributed in residential buildings. The correlation between the population distribution and non-residential buildings is relatively small, and the number of residential buildings reflects the size of the permanent population. With the advent of the big data era and the rise of dynamic data acquisition technologies, the availability of building data has been increased, which can be used for population estimation at the fine-scale. How to classify urban buildings in a scientific and efficient manner is crucial for obtaining fine-scale population data and improving the level of urban population management.  
The _POIs_ in cities mainly include physical buildings in cities that are closely related to people's lives, such as schools, banks, and supermarkets. _POI_ data describes the spatial and attribute information of buildings, so it can be reclassified to identify the types of buildings in a city. Considering the uneven distribution of _POI_ data, this paper obtains functional classifications of buildings based on _TF-IDF_. This paper predicts the probability of building types based on the frequency statistics for _POIs_. The TF value indicates the frequency of a _POI_ as a building in a certain area, and the _IDF_ value indicates the inverse frequency of the _POI_ in a subdistrict. The result obtained by multiplying both is the probability that a building belongs to a certain type (Figure 2).  
Figure 2: Building classification process.
# Reduced-Order Model Based on Neural Network of Roll Bending
## 4 Neural Network
### Network 'Force'  
The next stage of using the neural network was the use of the same training and testing sets to train the network to predict the rolling force. The NN 'force' is shown in Figure 8b. Here, the rolling force, \(F\), is treated as the output variable. The sheet convexity, \(c\), which was the result of the FE calculations, becomes an input. Table 2 presents the results of training the NN 'force' for various variants of network configuration.  
The 'accuracy' column shows the percentage of variants in which the error is less than 1000 kN (2% of the range). NN1f-NN3f were trained on 3575 points of datasets and showed little effectiveness. Analysis of the FEM results shows that the reason for such low effectiveness may be the insufficient distinction of results due to a large range of changes in the sheet convexity (from \(-0.9\) to \(0.6\) mm). Because negative convexity values (concavity) lead to instability of the rolling process, rolling of concave sheets is unacceptable. Therefore, only 2067 variants, in which the sheet convexity is positive, were selected for the new training set. As a result, the most effective network, NN4f, was obtained with one hidden layer with 10 neurons. The error of this network is almost three times lower, and its accuracy is the highest.  
The effectiveness of NN4f can be assessed by comparing the results of the calculation of this network with the results of NN7c. Figure 11 shows, in the same coordinate system and under the same conditions, the dependence of the rolling force on the sheet convexity calculated by the NNs 'force' and 'convexity'. These calculations were performed for several examples of the roll convexity, anti-bending forces, and the sheet width. As can be seen, the plots do not differ much. This can be an additional confirmation of the proper operation of both networks. A comparison of NN7c and NN4f, presented in Figure 11, can be treated as a cross-validation of the NN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network**} & **Network Configuration** & **Mean Square Error** & **Correlation** & **Accuracy, \%** \\ \hline NN1f & 5-10-1 & 0.0359 & 0.9894 & 47.9 \\ \hline NN2f & 5-10-5-1 & 0.0379 & 0.9882 & 44.7 \\ \hline NN3f & 5-20-5-1 & 0.0378 & 0.9882 & 43.7 \\ \hline NN4f & 5-10-1 & 0.0135 & 0.9973 & 79.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the training of the NN ’force’ for various configurations.  
Figure 11: The rolling force needed to achieve a specific sheet convexity.
# Development and Preliminary Evaluation of a Lower Body Exosuit to Support Ankle Dorsiflexion
## 2 Exosuit Functionality and Design  
### Functionality  
The purpose of the developed exosuit is to support and enhance the ankle movements of the sagittal plane during gait. The two main functions include (1) the actuation of the plantarflexion and (2) the actuation of the dorsiflexion. During rehabilitation, it is intended to train a gait-dependent activation of the impaired muscles. Current non-articulated solutions often lower the activity of dorsiflexion muscles due to a \"bracing effect\", which is often provided from passive orthosis devices made of material with a high degree of stiffness [36]. Additionally, a resistance to plantarflexion makes it also difficult to achieve a sufficient forward propulsion during gait [37]. On the one hand, this exosuit intends to provide an active impulse to the ankle during swing phase, so that it is possible to gain toe clearance without an inhibition of the simultaneous muscle activation. On the other hand, the push-off is supported to achieve a powerful plantarflexion without restricting the movement.  
The gait cycle (GC) is illustrated in Figure 1. The gait cycle can be divided into the stance phase from 0 to 60% GC, during which the leading limb (right side in illustration) has contact with the ground. At the following swing phase, from 60 to 100% GC, the foot is lifted off the ground and swings forward to the next heel strike. The exosuit supports the ankle at two sub-phases during gait. At the end of the stance phase, the heel lifts up and the forefoot pushes off the ground due to a plantarflexion moment of the ankle. The system actuates and supports this degree of freedom by pulling up the heel until the entire foot takes off and leads to the swing phase of gait cycle. During the first half of swing phase (proxy. 25% of the gait cycle) the foot is lifted up by a dorsiflexion movement of the ankle joint. Whereas the entire swing phase lasts \(\sim\)400 ms [38], the foot is actuated and lifted during the first 50% in \(\sim\)200 ms and is held until heel strike.  
Figure 1: Illustration of supporting function during gait cycle.
# An Improved Skewness Balancing Filtering Algorithm Based on Thin Plate Spline Interpolation
## 2 Materials and Methods
### Removing the Outliers Based on Morphological Operations  
Due to the influence of external environments or the instrument itself, the obtained LiDAR point clouds always contain outliers. These outliers can be further classified as low and high outliers, as shown in Figure 1. High outliers are commonly generated by laser pulses reflected from aircraft or birds flying in the sky, while low outliers are always caused by multipath effects. Both of these kinds of outliers have a direct influence on the filtering results. Thus, both kinds of outliers should be removed before filtering.  
In this paper, the outliers are removed based on morphological operations. Since raw point clouds have no regular distribution, the LiDAR data should be organized first [37]. This paper organizes the 3-D LiDAR data as 2.5-D data using pseudo-grids, as shown in Figure 2. First, the cellsize of each grid should be defined according to the density of point clouds. In general, the cellsize is equal to \(\sqrt{1/\lambda}\), where \(\lambda\) is the density of point clouds. In so doing, we can try to make the grids with no points as few as possible. If there are more than one point falling into one grid, only the lowest elevation is kept as the characteristic value of the grid, as shown in Figure 2.  
Figure 1: A horizontal profile of the digital surface model (DSM) generated using raw light detection and ranging (LiDAR) point clouds without denoising.  
Figure 2: Pseudo grids construction for point clouds.  
Generally, the outliers are isolated points and their elevations are far from the main body of the LiDAR points. Thus, this paper applies morphological dilation operation to the grids (\(IM\)) generated in last step. If the characteristic value of a grid changes greatly before and after the morphological dilation, the grid is labeled as a noisy grid. This step can be denoted as Equation (1).  
\[\Delta H_{i,j}=abs\big{(}IM_{i,j}-Dilation\big{(}IM_{i,j}\big{)}\big{)}, \tag{1}\]  
where \(IM_{i,j}\) is the characteristic value of the grid \((i,j)\), \(Dilation\big{(}IM_{i,j}\big{)}\) is the characteristic value after morphological dilation, and \(abs\) is used to calculate the absolute value.  
Morphological dilation is used to select the largest characteristic value in the structure element [23]. This dilation can be denoted as Equation (2). The structure element is a 3 \(\times\) 3 window as shown in Figure 3. If \(\Delta H_{i,j}\) is greater than a predefined threshold that can be set as a constant, such as 5 m, the corresponding grid is labeled as a noisy grid. Considering that the outliers are generally isolated points, the outlier can be detected if the number of points with similar elevations is less than 3 in the noisy grid.  
\[Dilation\big{(}IM_{i,j}\big{)}=\max_{i\in[-1,1],j\in[-1,1]} \tag{2}\]
# An Improved Jacobi-Based Detector for Massive MIMO Systems
###### Abstract  
Massive multiple-input-multiple-output (MIMO) is one of the key technologies in the fifth generation (5G) cellular communication systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) presents a near-optimal performance. Due to the required direct matrix inverse, however, the MMSE detection algorithm becomes computationally very expensive, especially when the number of users is large. For achieving the high detection accuracy as well as reducing the computational complexity in massive MIMO systems, we propose an improved Jacobi iterative algorithm by accelerating the convergence rate in the signal detection process.Specifically, the steepest descent (SD) method is utilized to achieve an efficient searching direction. Then, the whole-correction method is applied to update the iterative process. As the result, the fast convergence and the low computationally complexity of the proposed Jacobi-based algorithm are obtained and proved. Simulation results also demonstrate that the proposed algorithm performs better than the conventional algorithms in terms of the bit error rate (BER) and achieves a near-optimal detection accuracy as the typical MMSE detector, but utilizing a small number of iterations.  
Keywords:the massive MIMO system; Jacobi algorithm; the steepest descent method; the whole-correction method; signal detection +
Footnote †: journal: Information
## 5 Local Scale Analysis
### Results
This increased vulnerability is attributed to their location above deep-seated gravitational slope deformations or near previously occurred landslides, making them more susceptible to reactivation. Figure 12: The four sites analyzed: Alpenzu Grande (**a**), Noversch (**b**) Gressoney-La-Trinité (**c**), and Orsia (**d**). Photographs by the authors. Figure 14: The local scale analysis results (improved hazard calculation, assessed vulnerability and worth exposed values, and final risk score) for Noversch (**a**), Gressoney-La-Trinité (**b**), and Orsia (**c**). Analysis made with data collected in the field with the vulnerability scores listed in Table 3.  
Figure 13: Local-scale analysis workflow for the Alpenzu Grande (Area 01) site: improved hazard calculation with a more detailed DTM (\(2\times 2\) m) and assessed vulnerability and worth exposed values for the area to compute potential damage and assess the final risk score. In particular, Alpenzu (Area 01) and Orsia (Area 04) are on the edge of areas affected by gravitational slope deformations, a common issue in the region characterized by scattered houses or small historical centres. Area 03 in Gressoney-La-Trinite experienced an evacuation in 2018 due to an unstable boulder, affecting residents, tourists, and services, and disrupting transportation routes. Area 02, Noversch, was impacted by the 2000 floods, where intense rainfall exceeded regional averages, causing soil saturation and slope sliding. The analysis underscores the importance of detailed vulnerability estimation in assessing potential damage and total risk. At-risk municipalities should enhance their documentation with information on building materials, age, floors, and inhabitants to improve risk assessments. The presence of Walser houses and historical settlements increases vulnerability and risk, highlighting the need to prioritize safety and heritage protection in building renovations. Comprehensive risk analysis should guide new construction locations, especially considering tourism development in the Lys Valley.
## 3 Simulation and Experimental Results
### Effect of the Filter Surface Roughness on the Combined Beam Quality  
Beam quality is another important evaluation index for the design of the FSBC system, and the edge filter as the combining element also has a significant effect on this parameter. In our study, we also developed a preliminary analysis on the relationship between the filter's surface roughness and the combined beam quality.