esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Validation  
Using the formulated model, the prediction of tidal volume was also obtained using data from the reference instrumentation (\(\widehat{\text{TV}_{\text{REF}}}\)). Moreover, the formulated model was validated by performing a leave-one-subject-out cross-validation procedure using either wearable-device data (HRWD and BRWD) or breath-by-breath data (HRREF and BRREF), in addition to age and BMI. Specifically, the model was trained on 8 subjects (one less than the total number of subjects) and tested on the remaining one; thus, the procedure was repeated nine times.
## 2 Material and Methods
### Sample Entropy  
The SE index assigned a non-negative value to the corresponding data series, reflecting the complexity of each sequence, with larger values corresponding to more irregularity in the data [28]. Mathematically, SE is defined as the negative natural logarithm of the probability that two sets similar for \(m\) data points are also similar for \(m+1\) data points, given a tolerance distance \(r\), with the exception of self-matches. Thus, given a time-series dataset of length \(N=\{x(n),\,n=1,\ldots,N\}\), the \(k=1,\ldots,N-m+1\) vectors of length \(m\) are formed as \(X_{m}(k)=\{x(k+i),i=0,\ldots,m-1\}\). As a distance function \(d[X_{m}(i),X_{m}(j)]\), \((i\
eq j)\) we used the Euclidean distance, computed as the maximum absolute distance between the scalar elements of the vectors. If the distance between the elements is below a tolerance \(r\), then is counted as \(B_{i}\). The counting element of different vectors is calculated and normalized as  
\[B^{m}(r)=\frac{1}{N-m}\sum_{i=1}^{N-m}\frac{B_{i}}{N-m-1}. \tag{1}\]  
Figure 2: Examples of time-domain visual inspection to evaluate CFAE signal quality and the discarding process applied to 1, 2, and 4 s length segments. **(a)** First 8 seconds of a CFAE with an abnormal amplitude (drift) in the sixth second. The 1 s length element \(q_{1}^{(6)}\) has been discarded. The goodness of the 2 s and 4 s length were derived and, respectively, elements \(q_{2}^{(3)}\) and \(q_{4}^{(2)}\) were also discarded. **(b)** First 8 seconds of a CFAE with signal lost, indicated by amplitude ranges near 0 mV. Elements \(q_{1}^{(6)},q_{1}^{(7)}\), and \(q_{1}^{(8)}\) were discarded. The goodness of the 2 s length was affected and elements \(q_{2}^{(3)}\) and \(q_{2}^{(4)}\) were discarded. Finally, the 4 s length element \(q_{4}^{(2)}\) was discarded as well.  
The repetition of the described process for vectors of length \(m+1\) makes it possible to calculate \(B_{m+1}(r)\); hence, SE can be defined as  
\[SE(m,r,N)=-\ln\Bigg{[}\frac{B^{m+1(r)}}{B^{m}(r)}\Bigg{]}. \tag{2}\]  
The maximum template length \(m\) was set to 2 samples and the tolerance \(r\) to 0.35 times the standard deviation of the segment. This setup was selected taking into consideration previous studies with an in-depth analysis of SE parameters, testing other combinations of \(m\) and \(r\) to achieve optimized classification of AF events, which are directly dependent on AF organization [29]. SE was applied to 1, 2, and 4 s length sequences datasets. The results are reported with range, mean, and standard deviation.
## 2 Materials and Methods
### Implementing Digital Signal Processing Techniques for CSI Data Analysis
#### 2.5.1. Hampel Filtering  
Raw CSI data from the ESP32 are inherently noisy, containing numerous outliers--data points markedly distant from the rest. Ideally, CSI data should form a continuous sequence, making outliers effectively noise. Hampel Filtering is employed to eliminate these pronounced outliers. An illustrative result of this filtering is displayed in Figure 6, contrasting the original noisy input against the refined output with fewer outliers. We adjust the filtering parameters, window size, and \(\sigma\), here referred to as \(\alpha\) and \(\beta\), respectively.  
For a raw signal \(\{x_{i}\}_{i=1}^{n}\), the Hampel filter is defined as follows:  
1. Window and Median: For each \(i\) in the range \(\alpha\leq i\leq n-\alpha\), define the window \(\{x_{i-\alpha},\ldots,x_{i+\alpha}\}\) and compute its median \(\text{Median}_{i}\). \[\text{Median}_{i}=\text{median}(x_{i-\alpha},\ldots,x_{i+\alpha})\] (2)
2. Median Absolute Deviation: Calculate the median absolute deviation for the window:  
Figure 5. Steps of the mapping rule.  
\[\text{MAD}_{i}=\text{median}(|x_{j}-\text{Median}_{i}|)\quad\text{for}\quad j\in \{i-\alpha,\ldots,i+\alpha\} \tag{3}\]
3. Threshold: Define the threshold \(T\): \[T=\beta\cdot\text{MAD}_{i}\] (4)
4. Filtering: Replace \(x_{i}\) with \(\text{Median}_{i}\) if \(x_{i}>\text{Median}_{i}+T\): \[x_{i}=\begin{cases}\text{Median}_{i}&\text{if }x_{i}>\text{Median}_{i}+\sigma\cdot\text{MAD}_{i}\\ x_{i}&\text{otherwise}\end{cases}\] (5)
## 2 Materials and Methods
### Inhibition of Bacterial Growth  
_Escherichia coli_ (_E. coli_) and _Staphylococcus Epidemidis_ (_S. epidermidis_) were selected to evaluate the antibacterial activity of drugs loaded in PLA. The bacteria were previously grown aerobically to exponential phase in lysogeny broth (LB) (Lennox) (tryptone 10 g yeast extract 5 g, NaCl 5 g, pH 7.2). Growth experiments were performed placing three specimens (1.5 cm \(\times\) 0.5 cm \(\times\) 0.5 cm) of each sample in tubes of 15 mL; neat PLA, PLA-CHX 3 wt-% and PLA-TCS 3 wt-%. After this, 2 mL of broth culture containing \(1\times 10^{3}\) colony forming units (CFU) were seeded in each sample-containing tube. The cultures were incubated at 37 \({}^{\circ}\)C and agitated at 80 rpm. Aliquots of 100 \(\mu\)L were taken at predefined time intervals for absorbance measurement at 650 nm in a reader plate. Thus, turbidity was directly related to bacterial growth. The maximum bacterial growth (control) was that determined in broth culture with the unloaded PLA specimen and it was used to calculate the relative growth of the bacteria in the presence of the drug loaded samples. Values were averaged considering three replicas. Agar diffusion tests were performed in Petri dishes of 90 mm and seeded separately with \(1.5\times 10^{8}\) CFU/mL of each bacterium. Four samples were placed on each Petri plate; neat PLA (as negative control) and samples loaded with 1.5 wt-%, 3 wt-% and 6 wt-% of each drug. Inhibition halo images were taken after incubation of samples with bacteria for 24 h at 37 \({}^{\circ}\)C.
## 2 A Systematic Literature Review  
The SLR performed follows the PRISMA (Preferred Reporting of Items for Systematic Reviews and Meta-Analyses) protocol. This includes a step-by-step description of the search strategy protocol, eligibility criteria, and evidence extraction. The search is performed following the proposed RQs to design search strings using Boolean operators. The search is performed through index bases as follows: WoS, Dimensions, Scopus, PubMed, OpenAlex, and Google Scholar. Figure 1, inspired by the illustration from Ref. [33], displays the complete list of search strings. It should be noted that the syntax was formed after a large number of iterations of strings and keywords, so the syntax with the highest number of hits was adopted. Below, Table 1 shows the results of the initial research. The search strategy consists of screening, an eligibility assessment, and an in-depth exhaustive evaluation of studies for reaching a panel consensus. In the screening phase, 338 studies were identified. By applying the Exclusion Criteria-Not English (EC-NENG), studies that were not written in English were excluded (n = 10). Records that were not articles (n = 140) were excluded by Exclusion Criteria-Not Article (EC-NART). Furthermore, the duplicates (n = 69) were manually removed. The remaining 119 studies were deemed relevant and were subjected to an in-depth reading in accordance with the RQs. For this purpose, six additional exclusion criteria (EC) were introduced: EC1--review papers (n = 9), EC2--appearance and surface characteristics or mechanical characteristics of the material tests (n = 58), EC3--monograph (n = 1), EC4--studies on patients (n = 20), EC5--3D modelling and software (n = 6), and EC6--year out of scope [1 January 2020--1 May 2025] (n = 1), while the remaining five studies were not available for download (n = 5). The Inclusion/Exclusion criteria are detailed in Table S1. Finally, nineteen articles (n = 19) are identified as relevant for further analysis. Figure 2 shows the PRISMA flow diagram. Figure 1: Search strings and index bases used for the synthesis of studies. The extraction of content data included the following: the title of the paper, the journal that the paper was published in, the searched database, the year of publication of the paper, the type of publication (academic article, feasibility study, or in vitro study), the type of dental application, the type of 3D printing technology, the type of material used for fabrication, the advantages and disadvantages of 3D printing technologies, and printed part characteristics (parameters).
## 2 Materials and Methods
### Calibration Process  
The intrinsic and extrinsic parameters of each camera were calculated using the Matlab (2016a, The MathWorks Inc., Natick, MA, USA) camera calibration toolbox [14, 15]. A 1500 \(\times\) 1000 mm chessboard (15 \(\times\) 10 100 mm squares) was recorded underwater in different positions using both cameras at 120 fps with a 1920 \(\times\) 1080 resolution, wide window. The focal length, the principal point coordinates, the pixel skew, three radial distortion coefficients (2 nd, 4 th, and 6 th orders), and two tangential distortion coefficients were estimated based on 81 frames extracted from the footages recorded with each camera [16]. Afterward, the extrinsic parameters were obtained simultaneously for each camera using a rectangular rigid calibration body with 6 marks (1500 \(\times\) 1000 mm). Markers were placed horizontally at 0, 750, and 1500 mm and vertically at 0 and 1000 mm. The calibration body  
Figure 1: Top view schematic representation of the experimental setup.  
was positioned above the second lane, with the upper edge on the water surface, and its center was aligned with a 12 m marker in the pool. In this procedure, the trolley was aligned with the static camera.  
These procedures conclude the calibration of the static camera, but represents only the first two steps in moving camera calibration. For the first method applied for moving camera calibration, the previous extrinsic parameter calculated simultaneously with the static camera was not used. Instead, the extrinsic parameters (camera position and orientation, 6 DOF) were calculated frame by frame based on the pool reference frame determined by the pool markers (Figure 1) in each one of the experimental protocols (M1).  
The second method applied for the moving camera calibration is based on the camera principal point (M2). The principal point is the point on the image plane onto which the perspective center is projected, and in this way, it is the point that provides the best symmetry of distortions [17]. If the camera rotations and translations in the direction perpendicular to the image plane were constrained (using a trolley rail for instance), the translation of the real coordinates of the principal point maps camera translation. With this information, it is possible to translate the camera coordinates obtained by first using extrinsic parameter estimation (using the 6 marks rectangular rigid calibration body) to obtain the pool coordinates. In this method, the more reference points there are available, the better the results, since the camera position is only known when the principal point is aligned with the real marker., while in the other frames, the camera position is interpolated. In the pool where the test was performed, the separation between the tiles was perfectly visible to the camera, which provided us with reference points every 190 mm.
# Convolutional Adaptive Network for Link Prediction in Knowledge Bases
## 4 Experimental Setup
### Experimental Implementation  
The hyper-parameters for the CANet in the experiments were chosen by using a grid search throughout training. The following are the hyper-parameter ranges: input matrices and hidden layer dropout rate [0.1, 0.2, 0.3, 0.4, 0.5], learning rate [0.0001, 0.001, 0.005], relation and entity embedding dimension [50, 100, 200], batch size [64, 128, 256], and label smoothing [0.0, 0.1, 0.2, 0.3]. Different kernel sizes [\(2\times 3\), \(2\times 5\), \(2\times 7\), \(2\times 9\)] were tested in the experiments.  
Both datasets performed well with the following hyper-parameters: relation and entity dimension sizes of 200, batch size of 128, and label smoothing of 0.1. For FB15k-237 and WN18 RR, we set the learning rate to 0.0001, the input matrix dropout rate to 0.4, the hidden layer dropout rate to 0.4, and the kernel size to 2 \(\times\) 7; for FB15k, WN18, and YAGO3-10, we set the learning rate to 0.001, the input matrix dropout rate to 0.2, the hidden layer dropout rate to 0.3, and the kernel size to 2 \(\times\) 5. We used the program library PyTorch [31] to apply  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**\# Entities**} & \multirow{2}{*}{**\# Relations**} & \multicolumn{3}{c}{**\# Triplets**} \\ \cline{5-6}  & & & _Train_ & _Valid_ & _Test_ \\ \hline FB15k & 14,951 & 1345 & 483,142 & 50,000 & 59,071 \\ FB15k-237 & 14,541 & 237 & 272,115 & 17,535 & 20,466 \\ WN18 & 40,943 & 18 & 141,442 & 5000 & 5000 \\ WN18 RR & 40,943 & 11 & 86,835 & 3034 & 3134 \\ YAGO3-10 & 123,182 & 37 & 1,079,040 & 5000 & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Real-world KB datasets.  
our methods and reproduce DistMult, ComplEx, and ConvE on the PC with an NVIDIA GTX 2070S.
###### Abstract  
Low Earth Orbit (LEO) satellites can provide high-speed and low-delay services for terrestrial users; however, the rapid movement of LEO satellites and the insufficient size of the LEO constellations incurs the instability of the transmission links. The rapid movement of LEO satellites also leads to frequent handovers. Fortunately, GEO/LEO heterogeneous satellite systems can remedy this shortcoming. As the handover decision strategy which makes the selection among the available satellites will directly impact the performance of GEO/LEO heterogeneous satellite systems, we propose a two-step access and handover decision strategy for heterogeneous satellite networks in this paper. Firstly, a GEO/LEO network selection is carried out based on utility functions that reflect user's QoS requirements. Then, the multi-attribute decision making (MADM) method is used to select the specific LEO satellite if users select LEO satellite network. We also propose an Importance-TOPSIS scheme to improve the weight setting for handover attributes. Simulation results show that the proposed method can reduce the number of handovers and the forced termination probability of the system, and the overall throughput of the system is also improved.  
heterogeneous satellite networks; handover; network selection; multi-attribute decision making; utility function Article  
A Two-Step Handover Strategy for GEO/LEO Heterogeneous Satellite Networks  
2021 11 795 11050795
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 1 Introduction
However, despite their ability to process graph data with rich relational information among objects, to the best of the authors' knowledge, GNNs have not yet been employed in structural optimization problems. The advantages of GNNs include their ability to compute and learn based on graphs, considering the dependencies among objects, and their capability to predict entities at the node, edge, and graph levels. These advantages make the use of GNNs promising for the structural optimization of trusses. In this study, a novel GNN-based technique is developed to efficiently solve the size optimization problems of trusses with a fixed topology and shape. The primary objective is to reduce the computational cost while maintaining an acceptable level of solution accuracy. The technique combines a PSO algorithm for iterative search of the optimal cross-sectional area of the truss members with a GNN-based surrogate model trained to approximate nodal displacements of trusses with different sets of cross-sections during most iterations of the optimization process. The training dataset is generated using a simple finite element model (FEM) to determine the nodal displacements in various truss structures, which are then transformed into a graph representation where the truss joints and members are presented, respectively, as vertices and edges. The nodes and edges within the graphs are associated with input/output features. Specifically, the edge input feature corresponds to the cross-sectional area of the truss member, while the node input features include truss joint coordinates, joint external forces, and support conditions. The nodal displacements serve as the nodal output features. By leveraging the trained GNN-based surrogate model, the number of FEMs needed to analyze truss structures is minimized, leading to a reduction in computational time. The remainder of the paper is organized as follows. Section 2 presents the objective and significance of the research. Section 3 describes the development of the proposed GNN-based optimization technique and provides an overview of the PSO implementation for truss size optimization problems. Section 4 demonstrates the accuracy and effectiveness of the proposed technique by using the solution of three distinct truss optimization problems: a 10-bar plane truss, a 72-bar space truss, and a 200-bar plane truss. Finally, Section 5 provides a summary and concluding remarks.
## 3 Void Avoidance Algorithm (VAA)
### Assumption  
We assume that a number of sensor nodes are randomly deployed on an unobstructed roughly plane sensing field with a BS. Sensor nodes participating in the network are aware of the coordinates of their geolocation either from a GPS device or from other means [3-5]. As shown in Figure 2, all sensor nodes within communication range r of a node n are considered as neighbors of n and have bidirectional links with node n. The IEEE 802.11 MAC [23] sends link-level acknowledgements for all unicast packets. Neighbors can communicate directly with each other. The entire sensor nodes know their respective neighbors' geolocation via neighbor discovery protocol. All sensor nodes are static in location. Data packets are sent from sensor to BS. In VAA, each sensor maintains the cost and forwards the packet according to the high-cost-to-low-cost rule. Cost depicts distance from the BS to the sensor, so we call that cost is distance cost (DC).
## 2 Validation and Ancillary Dataset  
### OMI L1B and OMI Surface LER Product (OMLER)  
Because GEMS is not scheduled to be in orbit until 2019, the hyper-spectral UV-visible measurements of OMI, which has specifications similar to those of the GEMS, are used here as a proxy to test the retrieval over Asia. The OMI instrument [31] is a nadir-viewing imaging spectrometer that measures the solar radiation backscattered by the Earth's atmosphere and surface over the entire wavelength range from 270 to 500 nm with a spectral resolution of about 0.5 nm. The 114\({}^{\circ}\) viewing angle of the telescope corresponds to a 2600-km-wide swath on the surface, which enables measurements with daily global coverage. The 740 wavelength bands along the satellite track provide global coverage in one day. The spatial resolution is 13 km \(\times\) 24 km (along track \(\times\) across track) at nadir in the nominal operation mode and the pixel size can be resolved to 13 km \(\times\) 13 km for detecting urban-scale pollution events. The instrument was launched in July 2004 onboard NASA's Earth Observing System (EOS) Aura platform as a follow-up to the TOMS.  
The hyper-spectral capabilities of the OMI instruments enable the measurement of key air-quality components such as tropospheric ozone (O\({}_{3}\)), nitrogen dioxide (NO\({}_{2}\)), sulphur dioxide (SO\({}_{2}\)) and aerosols. In the present study, the hyper-spectral radiances ranging from 350 nm to 500 nm were used to retrieve AOD, SSA and ALH over Asia. Because of a row anomaly, the OMI Lv1B datasets from 2005 to 2007 were used for the algorithm test.  
Figure 1 shows the frequency distribution of the scattering angle in the viewing geometry of GEMS and OMI. Compared with OMI, the scattering angle of GEMS has a narrower distribution that shifts towards the backscattered direction over time. However, the cross-track measurements of OMI cover a wider scattering-angle range than do GEMS measurements. This comparison indicates that, in regard to angular coverage, LEO measurements can be used as a proxy for GEO measurements.  
The OMI surface reflectance climatology data product OMLER (v003; [PERSON] et al. (2008)) was adopted to correct for the surface contribution to the atmospheric signal. The level-3 global gridded product provides the monthly climatology of OMI Lambert Equivalent spectral surface Reflectance (LER) for 23 wavelengths between 307 and 500 nm with a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). The LER products are obtained by finding the minimum value of Rayleigh-corrected reflectance within a temporal window under the assumption that aerosol scattering increases upwelling radiance. The 5-year temporal window from 2005 to 2009 was applied to the LER-v003 product.
# Design and Development of an Embedded Controller for a Hydraulic Walking Robot WLBOT
## 4 Embedded Controller Software Design
### Joint Control Algorithm
#### 4.4.3 Impedance Control  
Since hydraulic walking robots are typically modeled as rigid body systems, they have forces or torques as input to their dynamics, and therefore being able to apply precise joint torques to a robot has many advantages. Contrary to the position control, impedance control, as one of the force controls, can significantly increase the versatility and compliance of the robot, which will have outstanding advantages in the interaction with the environment, especially in the walking experiment. The general concept of impedance control was introduced by [PERSON] in the seminal work [41; 42] and can nowadays be considered as a classical control approach in robotics. Thus, we design a joint impedance controller in the joint space as Figure 10 shows. The force inner loop PID controller adopts the same form of the first row of Equation (9). The impedance control can be described as a virtual spring-damper system or a PD controller, which acts as the position outer loop. The control law and the feedback are shown in Equation (12).  
\[\left\{\begin{array}{c}\tau_{d}=K_{i}e+C_{i}e\\ \tau_{f}=(A_{1}P_{1}-A_{2}P_{2})l_{i}\end{array}\right. \tag{12}\]  
where \(K_{i}\) and \(C_{i}\) are the stiffness and damping of the virtual joint spring; \(e\) and \(e\) are the error of angle and angular velocity; \(A_{1}\) and \(A_{2}\) are the acting area of piston and rod chamber; \(P_{1}\) and \(P_{2}\) are the pressure of piston and rod chamber; \(l_{i}\) is the force arm of output force of the joint hydraulic cylinder.  
Figure 10: The block diagram of impedance control.