esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Properties of Sand-Sawdust Mixture for Modeling Deposit Soil
## 4 Normalized Shear Modulus and Damping
### Normalized Damping Ratio  
Figure 11 represents the effect of sawdust content and confining pressure on the \(\lambda\)-log\(\gamma_{\text{d}}\) of the mixture. In the figure, the makers are the experimental data and lines are associated with the fitting curves in the same color. Figure 11a indicates that the sawdust content had a remarkable effect on the \(\lambda\)-log\(\gamma_{\text{d}}\) curves in the shear strain range of \(\gamma_{\text{d}}>5\times 10^{-3}\). At the same shear strain amplitude, the damping ratio decreased as the sawdust inclusion increased. This phenomenon can be explained in a way that, when a higher percentage of sawdust is used, the mixture has more elastic deformation at the given confining pressures. Therefore, the \(\lambda\) values decayed as the sawdust percentage increased. Figure 11b shows that in the shear strain range of \(10^{-4}\) to \(10^{-2}\), with an increase in confining pressure at the same shear strain amplitude, the \(\lambda\) values decreased for the confining pressures of 20, 50, 100, 150, and 200 kPa. It can also be observed that for \(\sigma_{3}>20\) kPa, with the increase of \(\sigma_{3}\), the \(\lambda\) values grew faster as the shear strain amplitude increased. For \(\sigma_{3}>20\) kPa, the increase in the confining pressure caused the mixture to be compressed, and due to the elasticity of sawdust, the plastic strain decreased and caused the damping ratio to decrease.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sawdust Content (\%)** & \(\lambda_{\text{max}}\) & \(\alpha\) & \(\mathbf{R^{2}}\) \\ \hline
40 & 0.17701 & 0.5076 & 0.8894 \\
33 & 0.19788 & 1.2034 & 0.8762 \\
25 & 0.25159 & 0.8527 & 0.8901 \\
20 & 0.32114 & 0.8592 & 0.8899 \\
0 & 0.36422 & 0.9874 & 0.8102 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fitted maximum damping ratio and parameter \(\alpha\) for various sawdust contents.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\sigma_{3}\) **(kPa)** & \(\lambda_{\text{max}}\) & \(\alpha\) & \(\mathbf{R^{2}}\) \\ \hline
10 & 0.1839 & 1.0797 & 0.8959 \\
20 & 0.1814 & 0.6060 & 0.7919 \\
50 & 0.1755 & 0.6250 & 0.8197 \\
100 & 0.1949 & 0.8599 & 0.7499 \\
150 & 0.1874 & 0.9951 & 0.7199 \\
200 & 0.2206 & 1.7013 & 0.9792 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fitted maximum damping ratio and parameter \(\alpha\) for various confining pressures.
# Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks +
## 2 Preliminaries
### Hybrid Message Passing Inference
#### 2.1.4 Message Passing Inference for Hybrid BNs  
For a conditional hybrid BN (i.e., Types 1, 2, 3, 11, 14 and 17 in Figure 1), the Pi/Lambda functions and the Pi/Lambda messages can be extended as follows [29].  
The Pi function for a discrete node B (i.e., Types 1, 2 and 3) is given by Equation (3). The Pi function for the continuous node \(X\) of Type 17 is given as follows:  
\[\pi(X)=\sum\limits_{\mathbf{A}}\int\limits_{\mathbf{U}}P(X|\mathbf{A}, \mathbf{U})\prod\limits_{i}\pi_{X}(A_{i})\prod\limits_{j}\pi_{X}(U_{j})\,d \mathbf{U}, \tag{11}\]  
where \(d\mathbf{U}\) is the \(m\)-dimensional differential with \(\mathbf{U}=\{U_{1},U_{2},\,\ldots,U_{m}\}\), \(\pi_{X}(A_{i})\) denotes the Pi message from the discrete parent \(A_{i}\) to \(X\) and \(\pi_{X}(U_{j})\) denotes the Pi message from the continuous parent \(U_{j}\) to \(X\). Derivation of Pi functions for Types 11 and 14 is straightforward by use of Equation (11).  
The Lambda function for Types 11, 14 and 17 is given by Equation (8). The Lambda function for the discrete node \(B\) of Type 3 is given as follows:  
\[\lambda(B)=\prod\limits_{i}\lambda_{C_{i}}(B)\prod\limits_{j}\lambda_{\gamma_ {j}}(B), \tag{12}\]  
where \(\lambda_{C_{i}}(B)\) denotes the Lambda message from the discrete child node \(C_{i}\) to \(B\) and \(\lambda_{\gamma_{j}}(B)\) denotes the Lambda message from the continuous child node \(Y_{j}\) to \(B\). Derivation of Lambda functions for Types 1 and 2 is straightforward by use of Equation (12).  
The Pi message for Types 11, 14 and 17 is given by Equation (9). The Pi message for Type 3 is given as follows:  
\[\pi_{Y_{j}}(B)=\alpha\left[\prod\limits_{i}\lambda_{C_{i}}(B)\prod\limits_{k \
eq j}\lambda_{Y_{k}}(B)\right]\pi(B) \tag{13}\]  
The Lambda message for Types 1, 2 and 3 is given by Equation (6). The Lambda message for the continuous node \(X\) of Type 17 can be written as follows:  
\[\lambda_{X}(U_{j})=\int\limits_{X}\lambda(X)\sum\limits_{\mathbf{A}}\int \limits_{\overline{\mathbf{U}}}P(X|\mathbf{A},\mathbf{U})\prod\limits_{i}\pi_ {X}(A_{i})\prod\limits_{k\
eq j}\pi_{X}(U_{k})\,d\overline{\mathbf{U}}\,dX \tag{14}\]  
and  
\[\lambda_{X}(A_{i}=a)=\int\limits_{X}\lambda(X)\sum\limits_{\overline{\mathbf{ A}}}\int\limits_{\mathbf{U}}P(X|A_{i}=a,\overline{\mathbf{A}},\mathbf{U})\prod \limits_{k\
eq i}\pi_{X}(A_{k})\prod\limits_{j}\pi_{X}(U_{j})\,d\mathbf{U}\,dX, \tag{15}\]  
where \(d\overline{\mathbf{U}}\) is the \((m-1)\)-dimensional differential with \(\overline{\mathbf{U}}=\{s|s\in\mathbf{U}\text{ and }s\
eq U_{j}\}\), \(\overline{\mathbf{A}}=\{s|s\in\mathbf{A}\text{ and }s\
eq A_{i}\}\) and \(a\) denotes a state of \(A_{i}\). The first equation is for the message to the continuous parent \(U_{j}\) and the second equation is for the message to the discrete parent \(A_{i}\). These apply respectively to Types 11 and 14.
###### Abstract  
Purpose: This work aimed to develop a novel indicator of upper limb manipulative movements. A principal component analysis (PCA) algorithm was applied to kinematic measurements of movements of the upper limbs performed during an everyday activity. Methods: Kinematics of the upper limb while drinking from a mug were investigated using the commercially available Xsens MVN BIOMECH inertial sensor-based motion capture system. The study group consisted of 20 male patients who had previously suffered an ischaemic stroke, whilst the reference group consisted of 16 males with no disorders of their motor organs. Based on kinematic data obtained, a set of 30 temporal and kinematic parameters were defined. From this, 16 parameters were selected for the determination of a novel indicator, the Upper Body Index (UBI), which served the purpose of assessing manipulative movements of upper limbs. Selection of the 16 parameters considered the percentage distribution of the parameters beyond the standard, the differences in mean values between the reference group and the study group, and parameter variability. Results: Analysis of kinematics allowed for the identification and selection of the parameters used in the development of the new index. This included 2 temporal parameters and 14 kinematic parameters, with the minimum and maximum angles of the upper limb joints, motion ranges in the joints, and parameters connected with movement of the spine recorded. These parameters were used to assess motion in the shoulder and elbow joints, in all possible planes, as well as spine movement. The values of the UBI indicator were as follows: in the case of the reference group: \(13.67\pm 2.40\) for the dominant limb, \(13.71\pm 3.36\) for the non-dominant limb; in the case of the stroke patient group: \(130.86\pm 75.07\) for the dominant limb, \(155.58\pm 170.76\) for the non-dominant limb. Conclusions: The developed UBI made it possible to discover deviations from the standard performance of upper limb movements. Therefore, the index may be applicable to the analysis of any sequence of movements carried out by the upper limb.  
upper limb; indicator methods; kinematics; PCA principal components algorithm
## 4 Conclusions  
A successful combination of aperture coupling and probe feeding methods is demonstrated in this paper. The main advantages of this compact package are its broadband character and low cross-polarization levels. A mirrored geometry between radiating elements and the feeding network is used to create the \(2\times 2\) array. This design occupies less space around the radiation elements, which reduces the size of the antenna and improves its efficiency. In this study, the suppressed cross polarization is verified for a 3-D half power beamwidth area in the whole operating bandwidth (10.55-14.25 GHz). For all of the main directions, the cross-polarization level is less than \(-30\) dB. With a \(0.5\,\lambda_{0}\) element spacing, the proposed \(2\times 2\) array can be easily used as a sub-array to design a large array. Hence, the proposed array could be useful in applications requiring small element spacing, wide operating bandwidth, and lower cross-polarization levels. It has good potential to use the proposed structure as a subarray in a larger array for a wide spectrum of practical applications such as satellite remote-sensing antennas.  
Conceptualization, F.K., A.A.-e.-E. and H.B.; validation, F.K. and A.G.; writing--original draft preparation, F.K.; writing--review and editing, H.B. and A.G.; funding acquisition, H.B. and L.T.; Supervision, H.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: The data presented in this study are openly available.  
This research was supported by FRQNT and NSERC.
# COVID-19 Outbreak Prediction with Machine Learning
## 1 Introduction
These ML methods are limited to the basic methods of random forest, neural networks, Bayesian networks, naive Bayes, genetic programming, and classification and regression tree (CART). Although ML has long been established as a standard tool for modeling natural disasters and weather forecasting [44; 45], its application in modeling outbreak is still in the early stages. More sophisticated ML methods (e.g., hybrids, ensembles) are yet to be explored. Consequently, the contribution of this paper is to explore the application of ML for modeling the COVID-19 pandemic. This paper aims to investigate the generalization ability of the proposed ML models and the accuracy of the proposed models for different lead times. The state-of-the-art machine learning methods for outbreak prediction modeling demonstrate two major research gaps for machine learning to address. Firstly, advancement in time-series prediction of outbreak and, secondly, improvement of SIR and SEIR models. Considering the drawbacks to the existing SIR and SEIR, machine learning can certainly contribute. This paper contributes to the advancement of time-series prediction of COVID-19. Consequently, an initial benchmarking is given to demonstrate the potential of machine learning for future research. The paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models. The remainder of this paper is organized as follows. Section 2 describes the methods and materials. The results are given in Section 3. Sections 4 and 5 present the discussion and the conclusions, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Journal** & **Outbreak Infection** & **Machine Learning** \\ \hline
[39] & Transboundary and & & \\  & Emerging Diseases & Swine fever & Random Forest \\ \hline
[35] & Geospatial Health & Dengue fever & Neural Network \\ \hline
[42] & BMC Research Notes & Influenza & Random Forest \\ \hline
[41] & Journal of Public & & \\  & Health Medicine & Dengue/Aedes & Bayesian Network \\ \hline
[38] & Informatica & Dengue & LogitBoost \\ \hline
[8] & Global Ecology & & \\  & and Biogeography & H1N1 flu & Neural Network \\ \hline
[34] & Current Science & Dengue &
\begin{tabular}{c} Adopted multi-regression \\ and Naive Bayes \\ \end{tabular} \\ \hline
[36] & Environment International & Oyster norovirus & Neural Network \\ \hline
[37] & Water Research & Oyster norovirus & Genetic programming \\ \hline
[43] & Infectious Disease Modelling & Dengue &
\begin{tabular}{c} Classification and regression \\ tree (CART) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notable machine learning (ML) methods for outbreak prediction.
## 2 Modeling of TSDs  
The intention is to realistically model and use TSDs to efficiently mitigate wind-induced vibrations. These devices are basically liquid containers, where the sloshing motion under oscillations counteracts the base excitations of the structure that they are mounted in. TSDs contribute with added mass, damping and stiffness, similarly to TMDs. Such elements are added to highrise structures near the top. Design considerations rely on knowing the exact structural parameters that define their inherent deformations patterns. Specifically, we need to compute the particular eigenmodes and imply proper normalization. Figure 1 shows these main concepts. Here, the basic definition of a TSD is also included, specifically marking the initial water height \(h_{0}\) and the definition of the wave in time \(t\) by the varying height \(h(x,t)\) for the sloshing in direction \(x\).  
All formulas needed for the design depend on the basic definition used in defining a TSD. The fundamental sloshing frequency of the water \(f_{w}\) inside a TSD can be expressed using the linear wave theory [16],  
\[f_{w}=\frac{1}{2\pi}\sqrt{\frac{\pi g}{L}\tanh\left(\frac{\pi h_{0}}{L}\right)}. \tag{1}\]  
In Equation (1), \(g\) is the gravitational acceleration and \(h_{0}\) and \(L\) mark the basic geometric parameters of the initial water height and tank length. In the following, \(a\) denotes the amplitude of the horizontal displacement, whereas \(f_{ext}\) and \(\omega_{ext}\) mark the (circular) frequency of the external excitation. Three main characteristics influencing the behavior of a TSD are summarized in Equation (2):  
\[\begin{split}\text{depth ratio}&\frac{h_{0}}{L},\\ \text{frequency ratio}&\beta=\frac{f_{ext}}{f_{w}} =\frac{\omega_{ext}}{\omega_{w}},\\ \text{amplitude ratio}&\frac{a}{L}.\end{split} \tag{2}\]  
The water content can be calculated using the proper density \(\rho_{w}\), according to Equation (3):  
\[m_{w}=L\;W\;h_{0}\;\rho_{w}. \tag{3}\]  
For a rectangular device, only a part of the total liquid mass fully contributes to the sloshing motion. Other parts do not provide any momentum in the desired motion because of the existence of recirculation zones. Therefore, the water mass can be divided into the effective mass in motion \(m_{eff}\) and the rest \(m_{0}\). Equation (4) provides the relations based on the linear potential flow theory [17]:  
\[\begin{split} m_{eff}&=\frac{8\tanh\frac{\pi h_{0}}{L }}{\frac{\pi^{3}h_{0}}{L}}m_{w},\\ m_{0}&=m_{w}-m_{eff}.\end{split} \tag{4}\]
## 6 System Evaluation and Model Prediction Performance  
To assess the efficacy of the proposed functions within the FELL platform, this study employed five prominent deep neural network models to predict the SoH of electric vehicle batteries. Additionally, we conducted an analysis comparing the prediction accuracies of these models. The models utilized encompassed multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and multi-head self-attention transformer architectures. Evaluation metrics employed to gauge prediction performance consisted of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE). The electric vehicle battery usage dataset [23] comprises 38,222 samples. The training dataset constitutes 70% of the total dataset, approximately 26,755 samples. The validation dataset and test dataset each represent 15% of the total dataset, consisting of approximately 5733 samples each.
## 5 Discussion
### Topography Effects on Tree Density and Forest Productivity  
We observed a negative relationship between TD and surface elevation. This trend is coherent across the used TD estimates (i.e., ALS- or in situ-based) and scales (i.e., plot, landscape) and is in line with results documented in the literature [108]. The decreasing TD with increasing surface elevation can be associated with the availability of resources (i.e., temperature, water, nutrients) and the competition for them among trees. Additionally, mechanical pressure (e.g., storm damages), in combination with slow growing rates, could explain the topography impact on TD. Our results show stronger negative trends for north-facing slopes (i.e., R\({}^{2}\) \(\sim\)0.6 with a negative slope) than south-facing slopes (i.e., R\({}^{2}\) \(\sim\)0.2-0.3 with a negative slope) at plot scale. This finding suggests that, and provides further evidence that, in subalpine forest ecosystems resource availability strongly determines TD, since north-facing slopes are generally colder and thus show even larger temperature gradients that are less favorable for tree growth.  
TD and FP are positively related across scales. This finding is expected, since a co-variance of both variables with height-dependent gradients of growth-limiting factors and eventually canopy density exists. Our findings show stronger positive dependency for north-facing plots (i.e., R\({}^{2}\) \(\sim\)0.6) than south-facing plots, confirmed at landscape scale as well (showing a moderate dependency with an R\({}^{2}\) \(\sim\)0.3). Therefore, we may conclude that the more trees, the higher the total productivity of a forested area, and vice versa. Associated with this is the negative relationship between FP and surface elevation. This finding is in line with an early and often-cited study by [PERSON] et al. [109] and other studies [108; 109; 110; 111]. As for TD, the negative relationship is plausible for the investigated alpine environment, since surface elevation dependent gradients of growth-limiting factors (e.g., nutrients, temperature, water) are likely to affect tree growth. [PERSON] et al. [108], for example, conclude that a reduction of tree productivity towards the upper tree line is directly associated to decreasing temperature. We also observed a strong decline of FP in lower elevations in the vicinity of a river in the valley bottom. Causes for the reduced productivity are related to steep terrain and unfortunate soil conditions (rock, nutrient limitation) due to flooding events of the river.  
However, it must be noted that the use of simple vegetation indices as a proxy for GPP, in combination with difficulties in describing the radiative transfer of complex canopies (i.e., the fractions of direct and diffuse irradiance components) [84], likely contributes to uncertainty in the FP estimates. Further analyses considering larger elevation gradients and more appropriate remote sensing approaches (e.g., SIF) are suggested in order to yield a more general view on forest growth patterns in alpine environments and underlying causes such as resource limitations.
## 3 Computational Methodology
### NBO Analysis  
In order to gain more insights on the elementary steps of interest, NBO calculations [28] were performed for a sequence of different geometry configurations related to different intrinsic reaction coordinates (IRC points = A, B, C,...). NBO charges (\(Q_{IRC\_point}^{atom}\)) of selected atoms in the different IRC points were obtained, and comparing them against the NBO atomic charges corresponding to their immediately preceding IRC point, allowed the determination of consecutive changes of charge (\(\delta Q_{AIRC}^{atom}\), where \(\Delta\)IRC = B - A, C - B, etc.) for each atom of interest [29]. Then, analysis of those changes of charge where done.  
Additionally, from the NBO outputs corresponding to the intermediates (INT) and transition states (TS) of interest, Wiberg bond indexes (\(B^{bond}\)) [30], were determined and used to calculate the bond evolution (\(\delta B^{bond}\)) according to Equation (5), which is zero when the bond has not changed, and the unity when the bond has totally evolved. The synchronicity (Sy) [31] of the step of interest was also computed by using Equation (6).  
\[\delta B^{bond}=\frac{B_{TS_{i}}^{bond}-B_{INT_{i-1}}^{bond}}{B_{INT_{i}}^{ bond}-B_{INT_{i-1}}^{bond}}; \tag{5}\]  
\[Sy=1-\frac{\sum_{k=1}^{n}\left|\delta B_{k}^{bond}-\delta B_{average}^{bond} \right|}{\left(2n-2\right)\delta B_{average}^{bond}}. \tag{6}\]  
In these equations, \(i\) is the _i_-th step of the mechanism that represents the step of interest, \(n\) is the total number of bonds considered to the analysis (bonds that have not significantly changed are neglected), \(\delta B_{k}^{bond}\) is the bond evolution of the _k_-th bond, and \(\delta B_{average}^{bond}\) is the average of whole \(\delta B_{k}^{bond}\).
## 3 Multilayer Measurement Methodology
### Measurement Tools, Performance Indicators, and Measurement Methodology
#### 3.2.1 Performance Indicators of Open-Source Measurement Tools  
Our methodology employs the open-source tools _iperf3_, _ping_, and _iw_ for obtaining performance indicators of multilayers. Here, we explain the characteristics of each measurement tool. The _iperf3_ is one of the representative measurement tools, and its main feature is its active measurement of the throughput by TCP and UDP traffic at the transport layer. The throughput indicator is often used as a primary communication performance indicator because it directly affects applications. In addition, the measurement log provides various metrics for TCP and UDP communications. In a TCP measurement log, while a sender (_iperf3_ client) provides throughput and retries results, a receiver (_iperf3_ server) does throughput. On the other hand, in UDP measurement, a sender provides throughput, and a receiver does throughput, jitter, and lost datagrams. Table 2 summarizes the performance indicators employed in this paper.  
The _ping_ is a well-known network command to verify connectivity at the IP layer. It provides RTT and packet loss information as a network performance indicator.  
The _iw_ command is a configuration utility for wireless devices. It runs on Linux and provides various functions, such as device configuration, scanning, and station statistics for RSSI, Tx/Rx bytes, Tx/Rx packets, Tx/Rx bitrates, and Tx failed. We employ RSSI and Tx bitrate to help to evaluate wireless link quality. RSSI is commonly used to show wireless conditions and is familiar to non-specialists. On the other hand, the Tx bitrate indicates the logical transmission speed for the modulation and coding scheme currently used at the Phy/MAC layers. Although it is not a familiar indicator, it is also easier to understand wireless conditions due to using concrete transmission speed values. Note that we employonly the Tx bitrate because our preliminary experiments showed that the Rx bitrate was the same value as the Tx bitrate.