esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
It is well known that glaucoma comes first in cases of irreversible vision loss and is the second leading cause of blindness worldwide [1]. Nowadays in developed countries, less than 50% of people are unaware of their diagnosis, mainly because of the asymptomatic nature of chronic glaucoma [2].  
Trabeculectomy remains the gold standard for glaucoma surgery despite high rates of complications [3; 4; 5]. Choroidal effusions, hypotony, shallow anterior chambers, and hyphema are known as early postoperative complications [6; 7; 8]. Late complications are often bleb-related, including leakage, blebitis, and endophthalmitis. These complications are more common if antimetabolites like 5-Fluorouracil and Mitomycin C are used [9].  
Outflow resistance is caused primarily by the juxtacanilicular trabecular meshwork and, especially, by the inner wall of the Schlemm's canal. This fact is used for the concept of the trabecular meshwork bypass [10]. It is presumed that 35% of the outflow resistance arises in the inner wall of the Schlemm's canal [11].  
In addition to the procedures already mentioned, we focus in this work on an enhanced Schlemms canal (SC), which can be addressed both internally and externally. According to the current classification, the ab interno methods include trabecome, iStent, Hydrus, the results presented in this work, and high-frequency deep sclerotomy (HFDS) [12]. The trabecome was introduced in 2004 and has an electroablation of an arc of trabecular meshwork where direct access to the collector channels is given. The intraocular pressure (IOP) reduction of up to 40% is stated in the literature for this ab interno method [13; 14]. Another approach for a micro-invasive glaucoma surgery (MIGS) from interno is the iStent. With this device, the aqueous humor is drained directly into the [PERSON]'s canal, thus avoiding the resistance of the trabecular meshwork [15]. Another representative of the ab interno procedure of the [PERSON]'s canal microsten is the so-called hydrus. It is an 8 mm long crescent-shaped open structure that the bend and sludge canal adapts. It is introduced into the [PERSON]'s canal by a clear corneal incision [16]. The HFDS is an internally operative glaucoma method that was introduced in 1999. Initially, this surgical procedure was known as sclerothalamotomy ab interno (STT ab interno), and its name was only changed to HFDS in 2012. The aim of the new nomenclature was to describe the procedure more precisely. Regarding the procedure, there were no differences between STT ab interno and HFDS [17; 18; 19; 20].  
The aim of all MIGS is that, in addition to being effective, they can be carried out simply, quickly and inexpensively with the longest possible effect. The aim of this study is to show the clinical 4-year investigations of this trans-trabecular surgery using the Schlemm's canal as an outflow pathway combined with cataract surgery.
## 6 Challenges and Future Research Scopes
### Intelligent Management and Orchestration  
There are several challenges yet to be addressed regarding the management and orchestration (MANO) of virtualization such as placement, chaining, scaling, and fault diagnosis. In the study presented by [PERSON] et al. [97], the authors proposed a federated DRL-based algorithm for SFC orchestration. A machine learning-based autonomous framework for predicting and placing VNFs is proposed by [PERSON] et al. [36]. Reference [98] introduces an intelligent orchestration for edge cloud networks. This orchestration is built on the OpenNebula SDN controller and machine learning models. Intelligent management and orchestration (iMANO) is another compelling research area in edge cloud networks.
## 4 Electric Field Decomposition Using the Indicatrix  
The decomposition of the electric field in uniaxial and biaxial crystals has been previously described by [PERSON] et al. [14] and others [13].  
In a uniaxial crystal, due to symmetry, the refractive index in two of the crystal axes is equal (\(n_{1}=n_{2}\
eq n_{3}\)). This results in the field decomposing into an ordinary (\(e_{o}\)) and extraordinary (\(e_{e}\)) optic axis, where the refractive index of the ordinary is \(n_{e_{o}}=n_{1}=n_{2}\), whereas that of the extraordinary depends on the angle of the incident ray vector (\(\hat{k}\)) and the high-symmetry axis \(\theta_{k}\), such that \(n_{e_{e}}=n_{3}\cos\theta_{k}\). As previously stated, this is true except in the special case of \(\theta_{k}\to 0\), where \(\hat{k}\) is aligned down the optic (high-symmetry) axis and the crystal appears isotropic.  
In a biaxial crystal (\(n_{1}\
eq n_{2}\
eq n_{3}\)), the decomposed electric field vectors are determined by the extrema of the ellipse that bisects the indicatrix ellipsoid in the plane normal to \(\hat{k}\) (Figure 1 right). In our model, we determine these vectors numerically for each \(\hat{k}\) or crystal orientation. A random vector is generated perpendicular to \(\hat{k}\), (\(\hat{r}\)) and normalized, and the cross product of the two is used to find the third corresponding perpendicular vector (\(\hat{s}\)). Then, \(\hat{r}\) and \(\hat{s}\) are iteratively rotated about \(\hat{k}\) (i.e., in the plane of the ellipse as shown in Figure 1) until the maxima solutions of Equation (1) are found (those being the semi-major axes of the ellipse). It is important to note that this method requires the condition that \(n_{3}>n_{1}\) & \(n_{2}\). Figure 3 shows the calculation of the intrinsic birefringence by computing the difference between the refractive index of the two decomposed vectors as a function of orientation such than minima represent points where the axis are equal and hence the location of the optic axes. This was performed for two test case crystals using the literature values of refractive index for topaz and muscovite, in can be seen how the relative distribution of the refractive indices determines the position of the optic axes.  
Figure 3: Calculation of intrinsic birefringence by subtraction of indicatrix projection ellipse semi-major axes from each other as a function of \(\theta_{k}\) & \(\phi_{k}\). Distributions are shown in 2D (left) and 3D spherical (right) projections for a topaz crystal (**a,b**) with refractive indices of \(n_{1}=1.618\), \(n_{2}=1.620\), and \(n_{3}=1.627\) and muscovite (**c,d**) crystal with indices \(n_{1}=1.563,\ n_{2}=1.596\), and \(n_{3}=1.601\). Maxima denote areas of high birefringence while the minima correspond to points where the refractive indices are equal and hence an optic axis. The small differences in refractive index cause a significant difference in separation of the optic axes as seen in the 3D projections (black lines). An example of a uniaxial crystal is shown in the Supplementary Materials.
## 3 Results and Discussion
### Simulation Results
From the obtained E-field and power density results, the maximum exposure values corresponded to the HD 802.11 ay system, which was the predominant system in this case (realistic case), yet remained with very low levels: E-field levels lower than 2 V/m and power density levels lower than 0.1 W/m\({}^{2}\) for all cases. In order to gain insight into the maximum exposure levels that potential users in this scenario could be exposed to, Figure 12b presents the total E-field and power density levels received for both systems operating at the same time for the HD and MD cases. In conclusion, for the worst case of HD when all working places of the library had active users using one of the two considered systems, the maximum E-field and power density exposure levels remained below 2 V/m and 0.1 W/m\({}^{2}\), respectively, for all user locations. Figure 11: CDF of the received E-field and Power density for Case II at the XY bi-dimensional plane at head height when the people were working on the first floor of the library: (**a**) UL and DL comparison for HD and MD and (**b**) the total E-field exposure and power density for HD and MD. In addition, Figure 13 shows the environmental E-field and power density exposure levels considering the XY bi-dimensional plane at the height of a seated person in a working space on the first floor of the library for Case III HD (realistic case conditions). From the results obtained, we could conclude that there were no significant variations in the E-field or power density level distribution, and the highest levels were encountered within the user working spaces inside the bookshelves, as these spaces were subject to higher reflection phenomena produced by the bookshelf's walls. Nevertheless, as it has been clearly remarked before, the higher E-field values encountered remained lower than 2 V/m, and the higher power density levels remained lower than 0.1 W/m\({}^{2}\). The current regulation limits based on the ICNIRP guidelines [54] state that when determining the compliance for frequencies between 2 and 300 GHz, the incident power density must be considered. Thus, it can be stated that the obtained power density for all the cases was far below the current regulation limit for the general population for these frequency bands, which was 10 W/m\({}^{2}\)[54]. Figure 12: CDF of the received E-field and power density for Case III at the XY bi-dimensional plane at head height when the people were working on the first floor of the library: (**a**) 5G and 802.11 ay comparison for HD and MD and (**b**) the total E-field exposure and power density for HD and MD.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 3 Probabilistic Localization Model and System Design  
In the previous section, a feature selection approach based on averaged MI was introduced. The average MI approach provides a selection of the most dependent spectra characteristics, while the actual feature value and most related directions are not considered. In this chapter, those two feature properties are investigated and integrated with the localization mapping progress. With the proposed mapping operation, not only are the features exploited in advance, but the localization robustness will also be further improved.  
Aiming at localizing the sound source in a real-world environment with presence of noise and interference, the localization problem is considered probabilistic mapping between the feature vector and source location \(\mathbf{\Theta}_{\text{IP}}=(\theta,\varphi)\), which can be expressed as:  
\[\hat{\mathbf{\Theta}}_{\text{IP}}=\underset{\mathbf{\Theta}_{\text{IP}}}{ \operatorname*{arg\,max}}\,P(\mathbf{\Theta}_{\text{IP}}|\hat{\mathbf{v}}) \tag{10}\]  
where \(P(\mathbf{\Theta}_{\text{IP}}|\hat{\mathbf{v}}_{p})\) represents the probability of the sound source located at \(\mathbf{\Theta}_{\text{IP}}\) with a given feature vector \(\hat{\mathbf{v}}_{p}\). In the interaural-polar coordinate system, the generic ITDs are the same when the sources are placed on the same sagittal plane; thus, it is more applicable to estimate the azimuth \(\theta\) first, and then estimate the elevation \(\varphi\) with a given \(\vartheta\). Therefore, the probability \(P(\vartheta|\psi_{p})\) can be represented based on the conditional chain rule by a multiplication of two posteriors as:  
\[P(\mathbf{\Theta}_{\text{IP}}|\hat{\psi}_{p})=P(\vartheta|\hat{\mathbf{\varphi}})P( \varphi|\vartheta,\hat{\mathbf{\varphi}}) \tag{11}\]  
where \(P(\varphi|\hat{\psi}_{p})\) represents the probability of the source located at the sagittal plane, labelled by \(\vartheta\), and \(P(\varphi|\vartheta,\hat{\mathbf{\psi}}_{p})\) represents the probability of \(\beta\) with given \(\alpha\) and \(\hat{\psi}_{p}\). Then, by substituting (11) into (10), the estimated source location \(\hat{\mathbf{\Theta}}_{\text{IP}}=\hat{f}_{\Theta_{\text{IP}}}(\hat{\psi}_{p})\) can be obtained. There are two obvious advantages of adopting such localization architecture. First, in the training process, the feature characteristics caused by \(\vartheta\) and \(\varphi\) are separated, so the localization model can be more explainable and a more accurate localization result can be achieved [21]. Second, in the testing process, the \(\vartheta\) and \(\varphi\) can be estimated in parallel, and then high computation efficiency in contemporary parallel hardware can be achieved.  
To estimate the \(P(\alpha|\hat{\psi}_{p})\) and \(P(\beta|\alpha,\hat{\psi}_{p})\) for the real-world source localization, the estimation approaches should be able to tackle the amount of noise in the input feature vector. In the next section, the RF method is introduced to select the most important features and provide a robust estimation.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Females**} & \multicolumn{3}{c}{**Males**} \\  & & **SJ** & **CMJ** & **CMJA** & **SJ** & **CMJ** & **CMJA** \\ \hline Jump height & \(r\)= & 0.88 & 0.88 & 0.83 & 0.80 & 0.85 & 0.75 \\  & \(p\)= & ** & ** & ** & ** & ** & ** & ** \\ Impulse & \(r\)= & 0.40 & 0.32 & 0.56 & 0.50 & 0.26 & \(-\)0.65 \\  & \(p\)= & 0.07 & 0.13 & * & * & * & 0.17 & ** \\ Minimal ND knee angle & \(r\)= & 0.26 & \(-\)0.52 & \(-\)0.28 & \(-\)0.15 & \(-\)0.08 & 0.04 \\  & \(p\)= & 0.17 & * & 0.17 & 0.30 & 0.39 & 0.45 \\ Minimal D knee angle & \(r\)= & 0.28 & \(-\)0.47 & \(-\)0.26 & \(-\)0.10 & \(-\)0.05 & 0.04 \\  & \(p\)= & 0.16 & * & 0.18 & 0.37 & 0.43 & 0.45 \\  & \(r\)= & \(-\)0.08 & 0.41 & 0.27 & 0.10 & 0.03 & 0.06 \\  & \(p\)= & 0.40 & 0.07 & 0.18 & 0.37 & 0.46 & 0.43 \\ Maximal RFD FP1+2 & \(r\)= & 0.75 & 0.05 & 0.12 & 0.65 & \(-\)0.13 & \(-\)0.39 \\  & \(p\)= & ** & 0.44 & 0.34 & ** & 0.32 & 0.09 \\  & \(r\)= & 0.78 & 0.15 & 0.13 & \(-\)0.05 & \(-\)0.13 & \(-\)0.50 \\  & \(p\)= & ** & 0.30 & 0.33 & 0.43 & 0.32 & * \\  & \(r\)= & 0.70 & \(-\)0.01 & 0.06 & 0.47 & \(-\)0.08 & \(-\)0.27 \\  & \(p\)= & ** & 0.48 & 0.42 & * & 0.39 & 0.18 \\ Torso incline & \(r\)= & \(-\)0.21 & \(-\)0.18 & \(-\)0.29 & 0.46 & \(-\)0.23 & 0.19 \\  & \(p\)= & 0.23 & 0.26 & 0.16 & * & 0.21 & 0.26 \\ Maximal angular velocity ND knee & \(r\)= & 0.70 & 0.69 & 0.60 & 0.53 & 0.54 & 0.21 \\  & \(p\)= & ** & ** & * & * & 0.24 \\ Maximal angular velocity D knee & \(r\)= & 0.75 & 0.80 & 0.72 & 0.73 & 0.55 & 0.37 \\  & \(p\)= & ** & ** & ** & ** & 0.10 \\ Maximal angular velocity ND shoulder & \(r\)= & & & 0.14 & & & 0.17 \\  & \(p\)= & & & 0.31 & & & 0.29 \\ Maximal angular velocity D shoulder & \(r\)= & & & 0.06 & & & 0.05 \\  & \(p\)= & & & 0.41 & & & 0.43 \\ \hline \hline \end{tabular} Note: SJ = squat jump; CMJ = countermovement jump; CMJA = countermovement jump with arm swing; D = dominant; ND = non-dominant; RFD = rate of force development; FP = force plate; * < 0.05; ** < 0.01; ** < 0.001.  
\end{table} 
Table 1: Correlation results between volleyball spike jump height and variables during general jump types in females and males. Figure 3: Each participant’s mean jump heights of all jump types and correlation between VSJ and general jump types in females and males. Male VSJ height = 0.23 + 0.65-SJ height + 2.93-10-5-SJ maximal RFD FP2,  
\[\text{Male VSJ height = 0.37 + 0.96-CMJA height - 1.23-10-4.CMJA impulse.} \tag{3}\]  
No improvements were found for the simple models of CMJ (\(R^{2}\) = 0.76, \(p\) < 0.001) and CMJA (\(R^{2}\) = 0.65, \(p\) < 0.001) in females and CMJ in males (\(R^{2}\) = 0.69, \(p\) < 0.001). Statistical power to obtain improved models was at \(1-\beta\) = 0.53 for CMJ in females, \(1-\beta\) = 0.22 for CMJA in females, and \(1-\beta\) = 0.33 for CMJ in males. Statistical power to detect correlation effects. Statistical power to detect correlation effects \(r\geq 0.50\) was at \(1-\beta\) = 0.89.
## 3 Methodology
### Activity 3: Defining the Objectives, Definition, and Relevance
#### 3.4.2 Modularization Strategies  
We suggest breaking down the implementation into distinct, manageable components--such as ontology modeling, source schema analysis, mapping definition, and query optimization (see Section 4). Each can be handled using specialized tools or by domain experts, reducing the burden on a single development team.
## 3 Mathematical Formulation of the Problem
### Total Quality Cost
#### 3.5.1 False Alarm Cost  
The notion of false alarm is introduced when the limits of the card are exceeded, even if the process is under control. In this case, another cost called the false alarm cost is built into the model. The probability \(\beta_{2}\) presents the false alarm where the control chart detects a point that exceeds the control limits while the process is under control. So, the central value of the card remains constant, \(\Delta=\delta_{p}\times\sigma_{0}=0\):  
\[\mu_{1}=\mu_{0}+\delta_{p}\times\sigma_{0}=\mu_{0}\]  
and the probability of achieving a false alarm is provided by:  
\[\beta_{2}=\left\{Prob\big{(}\;\overline{X}_{s}\;\leq\;LCL\;\big{|}\;\mu_{1}=\mu \big{)}+Prob\big{(}\;\overline{X}_{s}\;\geq\;UCL\;\big{|}\;\mu_{1}=\mu_{0})\;\right\} \tag{34}\]  
\[\beta_{2}=F\Bigg{(}\frac{LCL-\mu_{1}}{\frac{\sigma_{0}}{\sqrt{n}}}\Bigg{)}+1-F \Bigg{(}\frac{UCL-\mu_{1}}{\frac{\sigma_{0}}{\sqrt{n}}}\Bigg{)} \tag{35}\]\[=1+F\Bigg{(}\frac{\mu_{1}-k_{p}\ \times\ \frac{\sigma}{\sqrt{n}}-\mu_{1}}{\frac{ \sigma_{0}}{\sqrt{n}}}\Bigg{)}-F\Bigg{(}\frac{\mu_{1}+k_{p}\ \times\ \frac{\sigma}{\sqrt{n}}-\mu_{1}}{\frac{\sigma_{0}}{\sqrt{n}}}\Bigg{)}=1+F \big{(}-k_{p}\big{)}+1-F\big{(}k_{p}\big{)}=2\times F\big{(}-k_{p}\big{)} \tag{36}\]  
So, the average number of checks (of successive samples) that you must have before obtaining a false alarm (\(AOP_{2}\)) is given by:  
\[AOP_{2}=\frac{1}{\beta_{2}} \tag{37}\]  
Let \(S\) be a discrete random variable indicating the samples' number as long as the production process is under control. \(S\) can take values between 0 and \(m\). So, the probability distribution related with the random variable \(S\) is presented by:  
\[P(S=m)={p_{1}}^{m}\times(1-p_{1}) \tag{38}\]  
Thus, the average number of samples as long as the production process is under control can be expressed by:  
\[E(S)=\sum_{i=0}^{m}\Big{\{}i\times{p_{1}}^{i}\times(1-p_{1})\Big{\}} \tag{39}\]  
The false alarm cost is expressed by:  
\[C_{FA}=C_{F}\times\frac{E(S)}{AOP_{2}} \tag{40}\]  
\[C_{FA}=2\times C_{F}\times E(S)\times F\big{(}-k_{p}\big{)}\]
## 3 Proposed Watermarking Framework
### Structure of Watermarking Network to Be Trained
#### 3.2.5 WM Extractor Network  
The extraction network is a reversely symmetrical structure to the WM pre-processing network except for the number of filters used. It reduces the resolution of the watermarked and attacked image and extracts the WM information. It consists of three CL-BN-AF (ReLU) blocks and one CL-AF (tanh) block, that is the last block. We set the stride of all CLs to 2 for down-sampling. The number of filters used in the CLs is 128, 256, 512, and 1, respectively. This network uses mean absolute error (MAE) between the extracted WM (\(WM_{ext}\)) and the original WM (\(WM_{o}\)) as a loss function (\(L_{2}\)). The reason why MAE is used for the extraction network is that the WM information consists of binary values (compare to Equation (1) that uses MSE for the host image information). It is determined empirically based on the data from lots of experiments. This is shown in Equation (2).  
\[L_{2}=\frac{1}{XY}\sum_{i,j}^{XY}|WM_{o}(i,j)-WM_{ext}(i,j)| \tag{2}\]  
Here, \(X\times Y\) is the resolution of the WM information.
## 4 Trajectory Prediction
### LSTM Predictor  
The recurrent neural network (RNN) can process time series data. However, when the time span is large between the cell that holds the relevant information and the cell that uses it, RNN has difficulty connecting the relevant information due to gradient vanishing and explosion problems. Therefore, LSTM can improve the performance of the original RNN by introducing the three-gate architecture (input gate, forget gate and output gate). Since the arm motion data is a set of time series data, it is suitable to use LSTM to predict the motion trajectory.  
At the beginning, we tried to use the network structure of multiple middle layers, but the training time is too long and the prediction effect is poor. Through the comparison of experimental results, the three-layer network structure is finally determined to be used. The input layer and the output layer are full connection layers, each containing three nodes, representing the coordinate values of \(x\), \(y\) and \(z\) respectively. The middle layer is composed of the basic LSTM cell, and tanh hyperbolic tangent is used as the activation function.
# Finite Element Analysis of Thermal Stress and Thermal Deformation in Typical Part during SLM
## 4 Emulation Results and Discussion
### X/Y/Z Uniaxial Plane Stress  
Furthermore, the plane state of the uniaxial stresses of X, Y, and Z axes were analyzed at the panel height of 0.5 mm. As presented in Figure 7a, the uniaxial stress of X alternated between the two sides, which was in accordance with the relationship between the acting and reacting forces. Therefore, the plane stress tension was offset and the plane stress balance was reached. Figure 7b presents a similarity to the thermal stress distribution. The reason for this stress alternation was that following the melting of a layer of powder, the edge position was cooled first, thus becoming the stress distribution boundary. The subsequent solidification position caused the tension to increase as the central formation was symmetrical to the tensile stress on both sides of the boundary from the edge to the internal cooling center. Moreover, the internal solidification contraction produced contraction pressure, while as the cooling was extended to the center, the equilibrium compressive stress was produced. As presented in Figure 7a,b, from the center of geometry, the tension was drawn to the pressure zone and subsequently to the edge. Specifically, as presented in Figure 7c, the Z direction thermal stress cloud edge stress was high and the center tension was low, which led to the parts' susceptibility to warping deformation.  
Figure 6: Development trend of thermal–residual stress before and after the removal of the substrate: (**a**) residual Stress; (**b**) residual strain.  
The distributions of axial stress are presented in Figure 7d-f. \(\sigma_{\rm x}\) expresses the stress variation law of the extended molten pool along the scanning direction, \(\sigma_{\rm y}\) expresses the change law of thermal stress in the trajectory direction, and \(\sigma_{\rm z}\) expresses the stress change principle along the layer thickness accumulation. Each group could be divided into three areas, as presented in Figure 7d-f. The stress in Area I was positive and the edge was cooled first, therefore producing high residual tensile stress. The stress in Area II was the maximum compressive stress of the opposite force. The stress in Area III was the pull force from the edge tensile stress to the center. From the graph of the \(\sigma_{\rm x}\)/\(\sigma_{\rm y}\)/\(\sigma_{\rm z}\) comparison, the distribution curve regularity was the same, but the amplitude varied highly. Therefore, the maximum residual stress of 316L stainless steel under this process parameter was \(\sigma_{\rm xMax}=295\) MPa, \(\sigma_{\rm yMax}=283\) MPa, and \(\sigma_{\rm ZMax}=305\) MPa, while the residual stress was lower than the 316L yield strength of \(310\) Mp \(<\sigma_{\rm s}<500\) MPa. Additionally, these values were lower than the tensile strength \(600\) MPa \(<\sigma_{\rm p}<750\) MPa [42].
## 7 Current Challenges and Future Research Directions
### Exploiting SDN Capabilities for LDDoS Detection  
SDN adds new qualities that did not exist in traditional IP networks, resulting in a distinct network design from traditional IP networks. SDN-based networks employ OpenFlow as southbound interfaces, and messages such as packet-in, packet-out, flow-mod, echo-reply, echo-request, error messages, and hello messages have the capacity to reverse network state. In other words, these messages and the features of the packets may be utilized by machine learning in the SDN to identify LDDoS attacks. However, based on a review of the relevant literature, implementing these messages features is a shortcoming in leveraging the SDN's potential for LDDoS detection.