esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization and Geometrical Configuration of MICEPH  
In this work, the proposed form of submarine pressure hull is a multiple intersecting cross-elliptical hull, constructed from Carbon/Epoxy composite (USN-150) with stacking sequence \([(\alpha/-\alpha)_{4}]_{s}\). The optimization is performed using the ANSYS V14.5 (ANSYS, Canonsburg, PA, USA) parametric design language (APDL) to maximize buckling load and minimize the buoyancy factor under the constraints of failure strength and deflection (\(\delta_{max}\)) according to the design requirements. The model was built using SHELL99 for the shell and BEAM189 for the ring and long beams [53,54]. The material properties and the strength parameters are presented in Table 1[55]. Figure 3 shows the proposed MICEPH utilized in this study. Figure 3 shows the multi-objective optimization procedure flow chart. The random design generation method (RDGM), which is a sub type of the sub problem approximation used in (ANSYS) will be considered in this study. On the other hand, Table 2 illustrates the results of the multi-objective optimization for MICEPH which indicates the optimal design point and objective function. The table contains the failure index (\(FI\)) for the maximum stress (\(F_{\text{MAXF}}\)) and Tsai-Wu (\(F_{\text{TWSR}}\)) failure criteria. The optimal objective function (\(MOF\)) is 0.0487, the buoyancy factor (\(B.F\)) is 0.197, and the buckling strength factor is 4.056, with a total hull weight of 394.57 kg. The optimal angle-ply orientation (\(\alpha\)) is 49\({}^{\circ}\), with a layer thickness (\(t\)) equal to 1.294 mm. The major diameter (\(D_{major}\)) and minor diameter (\(D_{minor}\)) are 2.0 m and 1.6836 m, respectively. The intersecting angle (\(\theta\)) is 40\({}^{\circ}\), and the radius (\(R\)) is equal to 0.50 m.
## 3 Methods
### Deep Learning Techniques
#### 3.1.2 Convolutional Neural Network  
The CNN is another type of DL architecture that has gained more attention in the last few years. The CNN is an unsupervised multilayer feed-forward neural network. It consists of one input layer, one output layer, and the hidden layer that can include any combination of the convolutional layer, nonlinearity, pooling layer, fully connected layer, and regularization. Figure 2 illustrates a typical CNN architecture for binary rumor detection.  
The CNN has been proven to perform effectively in image classification. Researchers found it a powerful method also in the natural language processing field, such as text classification [31; 32; 33]. In [3], authors investigated the influence of CNN on rumor detection task. They found that the CNN can capture rumor features well when the hidden layer is tuned gradually.  
Figure 1: The original LSTM [20].  
Figure 2: Illustration of a typical CNN architecture for rumor detection.  
Convolutional layer: For textual data, a convolutional layer is connected to the input layer for extracting features around a particular window, \(h\), of words, \(w\), referred to as a filter. To capture the useful features, the filter slides across the data. The length of the filter is called the kernel size or window size. Once the features are extracted, the output is passed forward to the next layer. * Nonlinearity: Here, the goal is to include nonlinear properties in the network. The most used nonlinearity functions in CNN are tanh, sigmoid, and relu. [PERSON] and [PERSON] [3] found that the tanh activation function yielded better results compared to sigmoid and relu. Thus, in this paper, we followed their recommendation and empirically assessed the results. * Pooling layer: Often, the convolutional layer generates feature maps with high dimensionality. Thus, the role of the pooling layer is to reduce the dimensionality by applying a function such as max pooling, average pooling, and stochastic pooling. * Regularization layers: Similar to the traditional ML methods, the DL also suffers from an overfitting problem. Regularization methods such as early stopping, dropout, and weight penalties are type of techniques that are used for reducing the testing error [34].
# Shock Attenuation and Electromyographic Activity of Advanced and Novice Equestrian Riders' Trunk
## 1 Introduction  
Low back pain (LBP) in the equestrian athlete negatively impacts performance by acting as a distractor that alters the coordination dynamics of the horse and rider [1]. Most authors agree that LBP is the most common chronic injury experienced by equestrian athletes [1, 2, 3, 4, 5], and it has been linked to poor postural control [6] and lack of balance, stability and alignment of the skeleton at the pelvic level [7]. [PERSON] et al. (2015) reported a significantly higher incidence of back pain in amateur riders when compared to advanced riders [8]. On the other hand, high volumes and repetitive motions have previously been correlated to the high prevalence rates of LBP in the athletic population [9]. This contradiction may be explained by the more harmonic riding technique of the advanced riders, and one could conclude that a very important factor influencing the prevalence of LBP in epistemic sports is the level of expertise, i.e., it is skill-dependent. Understanding the biomechanical differences between advanced and novice equestrianism is therefore of major interest.  
In the last decade, several publications have compared the interactions in the horse-rider system of advanced and novice riders at a mechanical level by looking at joint angles [10] and inertial data [11, 12]. It is known that the pelvis of a horse rider acts as a communication channel, physically interacting with the horse's behaviour [11], and great differences can be observed between advanced and novice riders [11, 12]. [PERSON] et al. (2005) showed that advanced riders achieved a higher level of synchronicity and coordination with the horse's movement when compared to novice riders [13]. Coordinationdynamics, also known as harmony [14], are strongly related to the postural control of the rider [15], which greatly depends on the coordination and neuromuscular awareness of the core muscles [16]. Publications analysing biological differences between advanced and novice riders are sparse [16; 17] and rarely consider mechanics and biology simultaneously [18].  
This study aims to describe how the rider's neuromuscular system (NMS) copes with external mechanical strain when exposed to the postural perturbations elicited by the horse in motion, based on inertial and surface electromyographical (sEMG) measures. Both parameters were described and correlated, then compared between advanced and novice riders to portray the skill-dependent differences of the interactions between the horse and rider.  
We hypothesized that advanced riders would show a greater ability to attenuate the shockwave generated by the horse's movement by having a higher overall sEMG level and earlier muscle activation.
## 4 Model Validation via Static-Load Deflection Test
Figure 5a shows the predicted displacement versus applied static load when the bearing radial clearance is 230, 250, and 270 \(\upmu\)m and the measured data from the static-load deflection test. The prediction results tend to agree with the test results overall, and show high correlation especially in the range \(\pm\)100 N-\(\pm\)200 N, although the actually designed nominal radial clearance is 200 \(\upmu\)m. This means that actual assembled radial clearance is slightly larger than the designed value, which might be caused by a manufacturing error in height of the bump foils. It is interesting that the curve measured from the test exhibits a nonlinear behavior, particularly in the low-load regions (\(\lesssim\)\(\pm\)70 N). This phenomenon is believed to be mainly attributed to the manufacturing and assembling uncertainties of the bearing and shaft. Note that the test result indicates that there are four loading processes: pull-loading and -unloading as well as push-loading and -unloading. During these processes, the measured displacements and static loads form a hysteresis loop, whose area is related to the energy dissipation and slope correlates with the structural stiffness [28]. Figure 5: Comparison of the measured and predicted (**a**) displacements versus the applied static loads and (**b**) structural stiffness versus the displacement: prediction cases are clearances of 230, 250, and 270 \(\upmu\)m.  
Figure 5b illustrates the estimated structural stiffness as a function of the displacements from the prediction and test. All the stiffness curves are estimated by differentiating the static load versus displacement fitting functions with respect to the displacement, and the functions are listed in Table 2. The results indicate that stiffness curves of the analysis tend to agree with the curve of the test especially in the low-displacement regions, whereas the major discrepancy occurs in the high-displacement regions. As a result, the presented structural model of GFPBs is effective enough to predict structural behavior of GFPBs in the lightly loaded condition. In general, dynamics of rotor-bearing systems with lightly loaded condition are dominated by bearing stiffness than bearing damping effects since the stiffness determines system critical speeds and whirl frequency of instabilities. However, in some cases of high-load conditions, the model may not guarantee the accuracy of the prediction result due to the fact that the model cannot capture viscoelastic behavior of polymer layers.
# Scaling of Rain Attenuation Models: A Survey
## 5 Research Scopes and Challenges
### Enhancement of Composite Scaling Technique  
According to the study in [65], the liquid water content (LWC) can affect the attenuation over a wide frequency range up to 1000 GHz. It was shown that the attenuation due to the LWC is almost similar to the standard atmosphere condition, but within 1 GHz and 100 GHz, the attenuation due to the LWC is lower than the standard atmospheric condition. Consequently, in a particular region at a specific time, the LWC showed a more significant variation. To model the attenuation in such an area, it needs a separate procedure to calculate the attenuation due to the unusual LWC. It was mentioned in [9] that environmental visibility can affect the attenuation in terrestrial and slant links. Moreover, in [66; 67], the perspective relationship was studied among the factors such as visibility, precipitation, and the presence of fog. In many regions, fog is a regular seasonal phenomenon, so the effect of the GHz-range frequency bands' link can also be modeled according to the resulting attenuation due to fog or visibility at different frequency bands.
## 2 Materials and Methods  
### Cell Lines and Viruses  
VeroE6-eGFP cells were generated and maintained as described previously [35]. A549-hACE2 cells were purchased from InvivoGen (San Diego, CA, USA) and were cultivated in RPMI-1640 medium (Sigma, St Louis, MO, USA) supplemented with 10% (\(\
u/v\)) heat-inactivated fetal calf serum (HIFCS; Biowest, Nuaille, France) and 0.5 ug/mL puromycin (Gibco, Waltham, MA, USA). HeLa-hACE2 cells were purchased from Creative Biogene (New York, NY, USA). The cells were maintained in Dulbecco's Modified Eagle Medium (DMEM; Lonza, Basel, Switzerland) supplemented with 10% (\(v/v\)) HIFCS and 0.5 ug/mL puromycin. Both A549-hACE2 and HeLa-hACE2 antiviral assays were performed using medium with the same composition as the respective culture media but without puromycin. Caco-2 cells were maintained by Frankfurt University in Minimal Essential Medium (MEM; Sigma, St Louis, MO, USA) supplemented with 10% (\(v/v\)) FCS, 100 IU/mL of penicillin, and 100 ug/mL of streptomycin (all from Sigma, St Louis, MO, USA) following established procedures [36]. VeroE6 cells (ATCC CRL-1586) were maintained by Utrecht University in DMEM (Gibco, Waltham, MA, USA) supplemented with 10% (\(v/v\)) FCS, 20 mM of HEPES (Lonza, Basel, Switzerland), 0.075% sodium bicarbonate (Gibco, Waltham, MA, USA), penicillin (100 IU/mL), and streptomycin (100 IU/mL) following established protocols [37; 38]. VeroE6 TMPRSS2 cells were obtained from the laboratory of [PERSON] (Rotterdam, the Netherlands) and were cultured by Utrecht University using the same medium composition as VeroE6, but supplemented with hygromycin.  
Virus stocks were generated and titrated in the VeroE6 cell line (SARS-CoV-2 Belgium (strain BetaCov/Belgium/GHB-03021/2020 B.1 lineage A, SARS-CoV-2 Omicron variant (strain B.1.1.529-BA.1), SARS-CoV-1 (strain human coronavirus 19/Germany/FrankfurtFFM1/2020)) or in the Huh7 cell line (human coronavirus 229E) (HCoV-229E (strain AlphaCoV/ATCC/VR-740))). Titrations were performed using an endpoint dilution assay by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR), microscopic cytopathic effect (CPE) scoring, or immunofluorescent staining. Titers were calculated using the Reed-Muench method and reported in tissue culture infectious doses per mL of sample (TCID\({}_{50}\)/mL).
## 3 Problem Statement and Formulation  
### System Model  
We consider a downlink OFDMA system, as depicted in Figure 1, where multiple transmitters (BSs) communicate with multiple users within each cell and share one frequency band per cell. The multiple BSs share the downlink resource with each user and spread a resource block (RB) in OFDMA. We assume that the transmit and receive terminals are equipped with a single antenna. Note that the OFDMA RBs allocated to different users within the same cell are orthogonal to each other since the intra-cell interference does not exist. Hence, the inter-cell interference across different cells is the leading performance limiting factor for throughput evaluation.  
For convenience, we denote the transmitters as BS and the set of active users as \(\mathcal{U}_{i}\), \(i=1,2,\ldots,K\) that employed with sub-carriers in multi-cell networks. The direct channel gain between the \(i^{th}\) BS-user pair over channel \(n\) is denoted by \(h_{i,i}^{n}\) and the interference channel between the \(i^{th}\) BS and the \(i^{th}\) user over channel \(n\) is denoted by \(h_{i,j}^{n}\). Therefore, the received signal at \(i^{th}\) user over channel \(n\) is given by  
\[\mathcal{Y}_{i}(n)=h_{ii}^{n}x_{i}+\sum_{\
eq k}^{K}h_{ij}^{n}x_{j}+n_{i} \tag{1}\]  
Here, \(n_{i}\) is the AWGN power spectral density (PSD) at destination user \(i\) with distribution \(n_{i}\sim\mathcal{N}\big{(}0,\sigma_{i}^{2}\big{)}\) such that \(\sigma_{i}>0\). Note that the confidential messages transmitted by different sources are independent of each other. In such cases, each source message can be kept confidential from all other unintended users. The transmitting power of a BS \(i\) in channel \(n\) is represented as \(P_{i}^{n}\). We also represent the downlink power allocation vector of the system as \(\mathbf{p}\triangleq\textit{vec}\Big{\{}\mathbf{p}^{(1)},\ldots,\mathbf{p}^{ (N)}\Big{\}}\), where \(\mathbf{p}^{(n)}\triangleq\big{(}P_{1}^{n},\ldots,P_{i}^{n}\big{)}^{T}\). Therefore, the SINR for the \(i^{th}\) user over channel \(n\) can be written as  
\[SINR_{i}=\frac{P_{i}^{n}\big{|}h_{ii}^{n}\big{|}^{2}}{\sum_{j\
eq k}^{K}P_{j}^ {n}\big{|}h_{ij}^{n}\big{|}^{2}+\sigma_{i}^{2}} \tag{2}\]  
Accordingly, the corresponding achievable rate at the \(i^{th}\) user served by BS \(i\) under channel \(n\) can be expressed as  
\[R_{i}=\log_{2}(1+SINR_{i}) \tag{3}\]  
The achievable data rate \(R_{i}\) can be maximized using the optimization of transmit power constraints. It is to be noted that the power of AWGN at each destination user has  
Figure 1: An illustration of downlink resource allocation for multi-cell and multiple user systems.  
been normalized, while the effects of multipath fading and path loss have been modeled as a location-dependent channel variance.
## 4 Results
### Profile \"Old Ice\"  
Figure 6 presents various data sets along the flow line of profile \"Old Ice\", located in a slow moving area outside the main trunk of 79 NG (see also Figure 1). As the flowline is heading towards the area studied by [PERSON] et al. (2018) we mark in Figure 1) their study site, allowing for a direct comparison. Visual inspection of neither optical nor radar satellite imagery indicate any heavily crevassed region in this profile, nor lakes, but numerous rivers. Our firn modelling results indicate that the ablation zone is consisting predominantly of an ice surface with a density above pore-close off (see also Figures S2 and S3), blocking surface melt water to penetrate into the glacier, fostering surface run-off. The steep slope in this area leads is further intensifying run-off through rivers. In-situ deposition of impurities from nearby soil, as observed by ([PERSON] et al., 2018) is likely to either remain on the surface or to be washed off with the run-off. Sentinel-2 data indicates a change from clean late Holocene ice to early Holocene ice and also the transition to Pleistocene ice, as also reported by [PERSON] et al. (2018). With the onset of dirty Holocene ice, both, winter and summer periods show a smooth decline in HH and HV backscatter. Also the transition in reflectance of various bands in S2 is smooth, with a drop of about 0.1 in the visible range over the last 60 km towards the ice margin. In details, referring to areas in Figure 6, the average reflectance value for the areas with elevation above 1000 m a.s.l. (distance from the ice margin > 20 km) showed on April about 0.98, 1.00 and 0.075 for bands 3, 8 and 11, respectively. The values in areas closer to the ice margin (<1000 m a.s.l.) were 0.94, 0.95 and 0.08 for  
Figure 5: Profile “Northern Branch” with the same panels as Figure 4.  
the above mentioned bands and same date. The indexes were both close to each other highlighting the occurrence of similar surface types (Index1: \(-\)0.012 and \(-\)0.007: Index2: 0.86 and 0.85). The same analysis evidences on July reflectance values significantly different: 0.89, 0.77 and 0.026 far from the ice margin; 0.79, 0.55 and 0.009 close to the ice margin. While the Vis-NIR index (Index 1) highlighted an important difference (0.18 against 0.07), the NIR-SWIR index (Index 2) showed limited increase (0.93 against 0.97).
## 2 Background Theory
### Spatial Attention Mechanism  
Using the attention mechanism, [PERSON] et al. [42] transform the spatial information in the original image into a different space while maintaining the key information. A spatial converter module is used to perform the necessary spatial transformation of the spatial domain information in order to extract the key information. The input feature map data are passed through the maximum pooling layer and the average pooling layer to generate feature maps. The two feature maps are then merged by concatenating the operation to construct a feature map with one channel. After this, the spatial attention of the corresponding feature map is obtained after the sigmoid activation function. Similar to the channel attention module, the spatial attention mechanism does not alter the dimensional information of the data.  
According to the flow chart in Figure 4, the channel attention is calculated using Equations (9) and (10), where \(conv\) is the two-dimensional convolution operation, \(\sigma\) denotes the sigmoid activation function, \([A;B]\) stands for the concatenate operation of matrix \(A\) and matrix \(B\), \(SA_{F_{in}}\) indicates the attention weight matrix of the input features, \(F_{out}\) represents the output feature map after attention enhancement, and \(*\) denotes the matrix multiplication.  
\[SA_{F_{in}}=\sigma(conv([Avgpool(F_{in});Maxpool(F_{in})])) \tag{9}\]  
\[F_{out}=SA_{F_{in}}*F_{in} \tag{10}\]
## 3 Optimization Strategy
### Optimization Model of Cross-Sectional Airfoil with Gurney Flap
The Reynolds number of each cross-sectional airfoil on the propeller can be expressed as follows:  
\[Re_{\text{target}}=\frac{\rho c\sqrt{{V_{\infty}}^{2}+\left(2\pi n_{s}r\right) ^{2}}}{\mu} \tag{6}\]  
where \(\mu\) is the dynamic viscosity coefficient at the cruise state of the propeller. Objective function of the cross-sectional airfoil optimization  
The aim of airfoil optimization is to maximize the efficiency of each blade element. The efficiency of blade element can be expressed as follows:  
\[\eta_{\text{element}}=\frac{\text{d}T\cdot V_{\infty}}{\text{d}F\cdot 2\pi n _{s}r} \tag{7}\]  
As shown in Figure 10, the thrust (d\(T\)) and rotational drag (d\(F\)) of each blade element can be expressed by the lift (d\(L\)) and drag (d\(D\)) as follows:  
\[\left\{ \begin{array}{l}\text{d}T=\text{d}L\cos\varphi-\text{d}D\sin\varphi\\ \text{d}F=\text{d}L\sin\varphi+\text{d}D\cos\varphi\end{array} \right. \tag{8}\]  
Figure 10: Force diagram for a blade element. Then, the efficiency of each blade element can be written as  
\[\eta_{\text{element}}=\frac{K\cos\varphi-\sin\varphi}{K\sin\varphi+\cos\varphi} \cdot\tan\varphi_{0} \tag{9}\]  
It can be proved by calculating the derivative function of Equation (9) with respect to \(K\) that when \(\varphi\) and \(\varphi_{0}\) are known, \(\eta_{\text{element}}\) is a monotonically increasing function of \(K\). It indicates when \(K\) gets the maximum value, \(\eta_{\text{element}}\) will also get the maximum value. So, the lift-to-drag ratio \(K\) was used as the objective function in the optimization of the corresponding cross-sectional airfoil. Thus, the optimization model of the cross-sectional airfoil can be constructed as follows:  
\[ \begin{array}{ll}\text{find}&\mathbf{x}=[\ a_{0},\cdots,a_{4},b_{0},\cdots,b_{ 4}]\\ \text{max}&K\\ \text{s.t.}&Re=Re_{\text{target}}\\ &C_{l}=C_{l-\text{target}}\\ &t=t_{\text{target}}\end{array}  \tag{10}\]  
To ensure the accuracy of the design results, the CFD method based on the \(\gamma\)-\(Re_{\theta}\) model was used to calculate the aerodynamic data of the cross-sectional airfoil with a Gurney flap in the optimization. In addition, the computation grid of the airfoil can be automatically updated with the change of the shape (see Figure 11). The results of the grid dependency study of the airfoil with a Gurney flap show that when the number of cells increases to 42,000, the aerodynamic characteristics of the airfoil become stable and hardly change with the increase in the number of cells. Taking the calculation accuracy and calculation efficiency into consideration, the size of the computational grid used for calculation is set at 54,000.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 4 Methodology
### LogEDL
#### 4.5.1 Transformer Encoder  
We treat log sequences as sentences in natural language and use a Transformer Encoder to extract the semantic vectors of log keys and capture the input log sequence contextual semantic information through a multi-head attention mechanism. The Transformer Encoder consists of multiple transformer layers. Each transformer layer consists of a multi-head self-attention and a position-wise feed-forward sub-layer using residual connection around the two sub-layers followed by layer normalization [8]. The sequence of logs \(S=\{k_{1},\dots,k_{t},\dots,k_{T}\}\), where \(T\) is the sequence length, then represents each log key \(k_{t}\) as an input representation \(x_{t}\), where the representation \(x_{t}\) is obtained by using the tokenizer, resulting in \(X=\{x_{1},\dots,x_{t},\dots,x_{T}\}\). Secondly it is used as input to the Transformer Encoder to obtain a \(d\)-dimensional semantic vector \(h_{t}\), where \(d\) is set to 256. Finally, we obtain the sequence of semantic vectors, written as \(H=\{h_{1},\cdots,h_{t},\cdots,h_{T}\}\). To summarize, the Transformer Encoder represents the log keys with semantic vectors, which preserves the semantic information of the events; similar log keys also have similar semantic vectors, mitigating the effect of unstable logs and noise.