esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Full-Order Envelope Model Derivation
\begin{array}{l}\frac{4v_{in}(t)}{\pi}\sin(\theta_{s}(t))=\\ \Big{(}R(t)+\frac{dL(t)}{dt}\Big{)}\tilde{r}^{s}(t)\sin(\theta_{s}(t))\,+L(t) \Big{(}\frac{d\tilde{r}^{s}(t)}{dt}-\omega_{s}(t)\tilde{r}^{c}(t)\Big{)}\sin (\theta_{s}(t))+v^{c}_{\mathbb{C}}(t)\sin(\theta_{s}(t)),\end{array} , \tag{19}\]  
\[ \begin{array}{l}0=\\ \Big{(}R(t)+\frac{dL(t)}{dt}\Big{)}\tilde{r}^{c}(t)\cos(\theta_{s}(t))+L(t) \Big{(}\frac{d\tilde{r}^{c}(t)}{dt}+\omega_{s}(t)\tilde{r}^{s}(t)\Big{)}\cos (\theta_{s}(t))+v^{c}_{\mathbb{C}}(t)\cos(\theta_{s}(t)),\end{array}  \tag{20}\]  
and  
\[ \begin{array}{l}\tilde{r}^{s}(t)\sin(\theta_{s}(t))=\mathbb{C}(t)\Big{(} \frac{dv^{s}_{\mathbb{C}}(t)}{dt}-\omega_{s}(t)v^{c}_{\mathbb{C}}(t)\Big{)} \sin(\theta_{s}(t))+\frac{dC(t)}{dt}v^{s}_{\mathbb{C}}(t)\sin(\theta_{s}(t)), \end{array}  \tag{21}\]  
\[ \begin{array}{l}\tilde{r}^{c}(t)\cos(\theta_{s}(t))=\mathbb{C}(t)\Big{(} \frac{dv^{c}_{\mathbb{C}}(t)}{dt}+\omega_{s}(t)v^{s}_{\mathbb{C}}(t)\Big{)} \cos(\theta_{s}(t))+\frac{dC(t)}{dt}v^{c}_{\mathbb{C}}(t)\cos(\theta_{s}(t)), \end{array}  \tag{22}\]  
respectively. Equating the coefficients at both sides of each equation, a set of four envelope dynamics equations with corresponding general initial conditions is established as [30,31]  
\[ \begin{array}{l}\frac{d\tilde{r}^{s}(t)}{dt}=\omega_{s}(t)\tilde{r}^{c}(t)+ \frac{1}{L(t)}\Big{[}\frac{4v_{in}(t)}{\pi}-\Big{(}R(t)+\frac{dL(t)}{dt} \Big{)}\tilde{r}^{s}(t)-v^{s}_{\mathbb{C}}(t)\Big{]},\end{array} \quad\tilde{r }^{s}(0)=I^{s}_{0}, \tag{23}\]  
\[\begin{array}{l}\frac{d\tilde{r}^{s}(t)  
and  
\[p_{in}(t)=v_{in}(t)i_{in}(t)=v_{in}(t)C_{in}\frac{dv_{in}(t)}{dt}, \tag{32}\]  
respectively. In case of lossless conversion,  
\[\overline{p}_{in}(t)=\overline{p}_{out}(t) \tag{33}\]  
with  
\[\overline{x}(t)=\frac{1}{T_{s}}\int\limits_{t}^{t+T_{s}}x(u)du \tag{34}\]  
denote the average value of \(x(t)\) over \(T_{s}\). The average output and input converter power are given by (cf. (10))  
\[\overline{p}_{out}(t)=R(t)\bigg{(}\frac{I_{M}(t)}{\sqrt{2}}\bigg{)}^{2} \tag{35}\]  
and  
\[\overline{p}_{in}(t)=\overline{v}_{in}(t)C_{in}\frac{d\overline{v}_{in}(t)}{ dt}, \tag{36}\]  
respectively. Equations (35) and (36) yield the input voltage dynamics given by  
\[\frac{d\overline{v}_{in}(t)}{dt}=-\frac{R(t)}{2\overline{v}_{in}(t)C_{in}}(I_ {M}(t))^{2},\ \ \overline{v}_{in}(0)=V_{0} \tag{37}\]  
with the corresponding general initial condition. The set of state-space Equations (23)-(26) and (37) and output Equations (27)-(30) form a full (fifth-order) envelope model of the system [32]. The corresponding block diagram is depicted in Figure 4.
## 1 Introduction  
In the last decade, a growing number of longitudinal echocardiographic studies focused on describing cardiovascular physiology in American-style football (ASF) athletes [1; 2; 3; 4; 5]. These studies made significant contributions to the understanding of the impact of ASF participation on the left ventricular (LV) remodeling phenotype, showing a higher prevalence of maladaptive LV remodeling among competitive athletes playing linemen field positions compared to their non-linemen counterparts [6]. Although a close interaction between exercise-induced LV and right ventricular (RV) adaptations has been confirmed in many sports disciplines [7; 8; 9], the RV response to ASF participation received less attention. To our knowledge, a single two-dimensional (2D) echocardiographic study analyzed in 24 athletes the RV response to ninety days of exercise throughout an ASF competitive season, showing no effect of team training on the RV morphology nor function [10]. The training regimen during ASF inter-season differs from that undertaken during the competitive period. Therefore, it remains unclear to what extend the inter-seasonal training, which implies more balanced dynamic and static exercises for all team players over a longer period compared to the competitive season could influence the RV structure. Moreover, given the complexity of the RV geometry, assessment of RV physiological adaptations in athletes is three-dimensional (3D) echocardiography preferable [11].  
The present study aimed to describe the RV response to the ASF inter-seasonal team mixed training. Since the assessment of RV regional changes is important for discerning pathological and physiological remodeling [12; 13], we used an in-house technique to obtain and assess the global and regional RV volume distribution using 3D echocardiography [14].
## 3 Mechanical Sealing and Thermal Expansion Sealing Mechanism
### Thermal Expansion Sealing Mechanisms  
The TMA Q400 thermomechanical analysis machine with a test accuracy of 10-4 \(\upmu\)m was employed to measure the linear thermal expansion coefficient (TEC, \(\upalpha\)) of the sealing material (Figure 4a). A copper sample with a dimensions of \(\updelta\)\(8\times 9\) mm was installed into a chamber to detect the continuous elongation of the sample under variable temperatures ranging from 25 \({}^{\circ}\)C to 600 \({}^{\circ}\)C. The thermal-induced elongation (\(\Delta l\)) evolution curve is demonstrated (Figure 4b).  
The copper sample's elongation generally increases with increasing temperature. The elongation decreased from 175 to 200 \({}^{\circ}\)C due to crystal structure transformation (Figure 4b). After 200 \({}^{\circ}\)C, the elongation showed an excellent thermal expansion effect; specifically, the sample was stretched by 5.9591, 15.5919, 29.5937, 44.5267, and 60.2160 \(\upmu\)m at 200, 300, 400, 500, and 600 \({}^{\circ}\)C, respectively. The average TEC of the copper sample tested is 11.6654 \(\upmu\)m/(m\({}^{\circ}\)C). For the adopted isotropic sealing material in our study (\(\upphi\)18 \(\times\) 21 mm), the lateral and axial elongations increased by 0.084 mm and 0.0980 mm at 400 \({}^{\circ}\)C, respectively; therefore, heating-induced expansion of sealing is conducive to re-sealing after the mechanical sealing scenario, which is a positive effect during hydraulic fracturing tests.
## 2 Problem Formulation  
Assume that we have \(N\) IID observed data vectors, \(\mathbf{X}=[\mathbf{x}_{0}^{T}\ \mathbf{x}_{1}^{T}\ \ldots\ \mathbf{x}_{N-1}^{T}]^{T}\), where \(T\) denotes transpose and each \(\mathbf{x}_{n}\) is an \(L\times 1\) complex-valued data vector conforming to a zero-mean multivariate complex Gaussian distribution \(\mathbf{x}_{n}\sim\mathcal{CN}(\mathbf{0},\mathbf{C})\) for \(n=0,1,\ldots,N-1\), and the \(\mathbf{x}_{n}\)s are mutually independent. In addition, we assume \(N\leq L\). The \(L\times L\) covariance matrix \(\mathbf{C}\) is a Hermitian matrix, so its inverse can be decomposed via the Cholesky decomposition as  
\[\mathbf{C}^{-1}=\mathbf{D}^{H}\mathbf{D}, \tag{2}\]  
where \(\mathbf{D}\) is a lower triangular \(L\times L\) matrix. And it has a testing model as follows.  
\[\mathbf{D}=\mathbf{D}_{B}+\sum_{k=1}^{M}b_{k}\mathbf{\Phi}_{k}, \tag{3}\]  
where \(\mathbf{D}_{B}\) is a known banded lower triangular matrix, with a bandwidth of \(m\), \(b_{k}\)'s are unknown complex-valued parameters, and \(\mathbf{\Phi}_{k}\)s are known basis matrices.  
Specifically,  
\[\begin{split} b_{1}&=[\mathbf{D}]_{m+2,1},\qquad \qquad\mathbf{\Phi}_{1}=\mathbf{e}_{m+2}\mathbf{e}_{1}^{T}\\ b_{2}&=[\mathbf{D}]_{m+3,2},\qquad\qquad\mathbf{ \Phi}_{2}=\mathbf{e}_{m+3}\mathbf{e}_{2}^{T}\\ \vdots&\qquad\qquad\qquad\vdots\\ b_{N-m-1}&=[\mathbf{D}]_{N,N-m-1},\quad\mathbf{ \[PERSON]}_{N-m-1}=\mathbf{e}_{N}\mathbf{e}_{N-m-1}^{T}\\ b_{N-m}&=[\mathbf{D}]_{m+3,1},\qquad\mathbf{ \[PERSON]}_{N-m}=\mathbf{e}_{m+3}\mathbf{e}_{1}^{T}\\ b_{N-m+1}&=[\mathbf{D}]_{m+4,2},\qquad\mathbf{ \[PERSON]}_{N-m+1}=\mathbf{e}_{m+4}\mathbf{e}_{2}^{T}\\ \vdots&\qquad\qquad\qquad\vdots\\ b_{M}&=[\mathbf{D}]_{N,1},\qquad\qquad\qquad \mathbf{\Phi}_{M}=\mathbf{e}_{N}\mathbf{e}_{1}^{T}\end{split} \tag{4}\]  
where \(M=\frac{(N-m-1)(N-m)}{2}\) and \(\mathbf{e}_{k}\) is an \(L\times 1\) vector with its \(k^{th}\) element being one and all other elements being zeros. The objective is to test whether the lower triangular Cholesky factor matrix \(\mathbf{D}\) is equal to the banded lower triangular matrix \(\mathbf{D}_{B}\). Let \(\mathbf{b}=[b_{1}\ b_{2}\ \ldots\ b_{M}]^{T}\), then the detection problem is equivalent to choosing between the following two hypotheses:  
\[\begin{split}\mathcal{H}_{0}&:\mathbf{b}=\mathbf{0}; \\ \mathcal{H}_{1}&:\mathbf{b}\
eq\mathbf{0};\end{split} \tag{5}\]
## 3 Experiments and Results  
The information saved in the DB used contained 121 variables, and these were obtained from the incoming alert calls converted into emergencies and attended by their corresponding PSAP, as shown in Figure 1. All the registered events were stored without any order or predefined pattern, but the geographical coordinates and several time references are available. To give an idea of the amount and quality of the available data in this DB, there were about 1,078,000 registered emergencies in the Quito PSAP only for 2014, and each event had valid information recorded in at least 50 of the 121 variables. These events were used as the basis for building and testing the system, providing us with the three described types of analytics and visualization, namely, temporal, geographical, and spatiotemporal.
## 4 Case Study and Analysis
### Aerodynamic Analysis
#### 4.3.1 Aerodynamic Analysis of Cross-Sectional Airfoils
As a result, Airfoil B has better aerodynamic performance than Airfoil A when the \(\alpha\) is smaller than 6\({}^{\circ}\) at \(Re\) = 4.0 \(\times\) 10\({}^{4}\). Besides, when \(\alpha\) is bigger than 7\({}^{\circ}\), the laminar separation bubble moves far  
Figure 28: Comparison of the test and CFD results under \(Re\)\(\approx\) 4 \(\times\) 10\({}^{4}\): (**a**) Thrust coefficient vs. advance ratio; (**b**) Power coefficient vs. advance ratio; (**c**) Propeller efficiency vs. advance ratio. away from the tail edge of the airfoil and has a reduced influence on the effective camber of the airfoil. Therefore, the lift coefficient of airfoil increases significantly and becomes big enough. In this case, Gurney flap is no longer needed to supplement the lift coefficient, while it can bring additional drag. As a result, when \(\alpha\) is bigger than 7\({}^{\circ}\), the improvement by the Gurney flap diminishes. Figure 30: Geometries of the cross-sectional airfoils on Prop A and Prop B at 0.75 R.  
Figure 29: Comparison of the test and CFD results under \(Re\approx 1.6\times 10^{5}\): (**a**) Thrust coefficient vs. advance ratio; (**b**) Power coefficient vs. advance ratio; (**c**) Propeller efficiency vs. advance ratio. When the Reynolds number is 2.0 \(\times\) 10\({}^{5}\), the influence of inertial force on the flow increases, and the laminar separation bubbles greatly decrease and even disappear, as shown in Figure 33. It can be seen that, compared with that at \(Re\) = 4.0 \(\times\) 10\({}^{4}\), the vortices induced by the Gurney flap grow bigger, which can bring additional drag to Airfoil B. Although the Gurney flap can also bring a bigger lift coefficient, the increase in the drag coefficient by the Gurney flap plays a more important role. Because the lift coefficient of the airfoil is large enough, and the airfoil's drag coefficient is relatively small at this Reynolds number. As a result, the aerodynamic performance of Airfoil B is inferior to Airfoil A at \(Re\) = 2.0 \(\times\) 10\({}^{5}\). Besides, it can be seen that the angle of attack corresponding to the maximum lift-to-drag ratio of Airfoil B is smaller than that of Airfoil A. That is the reason why the advance ratio corresponding to the design thrust of Prop A is smaller than that of Prop B.  
Figure 31: Aerodynamic performance of Airfoil A, and Airfoil B at different Reynolds numbers: (**a**) Lift coefficient vs. angle of attack; (**b**) Drag coefficient vs. angle of attack; (**c**) Lift-to-drag ratio vs. angle of attack. The aerodynamic analysis results of the cross-sectional airfoils preliminarily explain the reason why the aerodynamic efficiency improvement of Prop B relative to A decreased or even disappeared with the increase in Reynolds number.
## 2 Methods and Models
### The Models of the Main Subsystems  
The plant under analysis was modeled in MATLAB/Simulink [22] using MATLAB function blocks. The receiver tube of the LFR system, the TES unit, and the pipelines of the BOP were represented by dynamic models and the ORC unit by a quasi-steady state model since its thermal inertia is significantly lower compared to the other subsystems. Hence, the use of a quasi-steady state model for the ORC system can be considered acceptable since it does not affect the relaxation time of the overall plant, as further discussed in [9]. Therefore, in this way, it is possible to reproduce the transient operation of the real plant and to take into account the effect of the changing working conditions on its overall performance. As regard the solar field, the thermal power output was calculated according to Equation (1):  
\[\mathrm{P_{LFR,out}}=\mathrm{A_{sf}}\cdot\mathrm{DNI}\cdot\mathrm{cos}\;( \theta)\cdot\mathrm{\eta_{lopt}}\cdot\mathrm{\eta_{lrec}} \tag{1}\]  
where \(\mathrm{A_{sf}}\) is the net area of the primary collectors; DNI the direct normal irradiance; \(\theta\) the incident angle; \(\mathrm{\eta_{lopt}}\) the optical efficiency of the LFR, which depends on the incident angle modifier (IAM); and \(\mathrm{\eta_{lrec}}\) the receiver efficiency. As regard the optical efficiency, it was calculated as  
\[\mathrm{\eta_{lopt}}=\mathrm{\eta_{lopt,max}}(\theta=0)\cdot\mathrm{IAM}( \alpha,\,\sigma) \tag{2}\]  
where \(\alpha\) and \(\sigma\) are the solar elevation and azimuthal angles, respectively. The values of the maximum optical efficiency and the IAM were provided by the manufacturing company ELIANTO [23] based on a ray-tracing analysis. Eventually, with reference to the receiver efficiency, it is calculated by applying the well-known Forristal model [24] for the evacuated tubes. In particular, a one-dimensional longitudinal model was adopted, and the same numerical scheme used for the pipelines was applied. It is worth noting that in the case of the receiver tube, only the thermal inertia of the diathermic oil was considered while those of the metal parts and the glass tube were neglected. The TES unit was represented by means of a lumped model according to the guidelines of the IEA Task 32 report on advanced storage concepts [25]. For the scope of the analysis, the PCM was supposed to be isotropic and isothermal in each internal time step. The use of heat pipes was included into the model in terms of the maximum power exchanged with the oil (40 kW) and of a minimum temperature difference between the oil and the PCM (equal to 5 \({}^{\circ}\)C). Hence, the TES varied its temperature, as described in Equation (3):  
\[\Delta\mathrm{T_{TES(t+1)}}=\Delta\mathrm{T_{TES(t)}}\cdot\mathrm{e}^{[- \Delta\mathrm{t}\cdot\mathrm{int}\cdot\mathrm{timestep}\cdot\mathrm{f}]} \tag{3}\]  
where \(\Delta\mathrm{T_{TES(t)}}\) is the temperature difference between the diathermic oil and the PCM itself at time step t, while f depends on the convective Nusselt number of the PCM. To increase
## 4 Modified LLB Method with Deck Charge Technique
### Analysis Results for Modified LLB Method  
Figure 11 shows the computing simulation results for the modified LLB method. Explosives #1 detonated after 0 s, the rocks around the LLB hole were crushed, and the failure range increased over time. High-pressure gases can then push the crushed rocks to the large-diameter uncharged hole and excavation face (free faces). Meanwhile, new free faces corresponding to stage 2 were generated, as shown in Figure 9. Next, explosives #2 fragmented the remaining rocks after detonating at 20 ms, pushing the remaining crushed rocks to the large-diameter uncharged hole and newly created free faces.  
Figure 8: Schematic of charging pattern in the modified LLB method using the deck charge technique.  
Figure 9: Expected rock fragmentation process of the proposed modified LLB method: (**a**) stage 1, (**b**) stage 2, (**c**) stage 3, (**d**) stage 4.  
Figure 12 shows a comparison of the failure ranges of each model in the last step. The total number of eroded elements for the original LLB method was 31,267, which was about 1.12 times lower than that in the modified LLB method, which was 35,050. The overall fragmented ranges of the modified LLB method were less than those in the original method; however, the explosives concentrated at the end of the blast hole were used to crush surrounding rocks into smaller pieces in the original LLB method. The advance per round in the original and modified model was 0.23 m and 0.19 m, respectively, which is attributable to the deeply formed large-diameter uncharged hole (free face) before blasting. The failure range of the modified LLB method was 1.03-1.10 m, which is up to 1.14 times smaller than that of the original model.  
Figure 11: Numerical analysis results of the modified LLB method with deck charge technique: (**a**) 0 ms, (**b**) 0.5 ms, (**c**) 1.0 ms, (**d**) 1.5 ms, (**e**) 20.0 ms, (**f**) 20.5 ms, (**g**) 21.0 ms, (**h**) 21.5 ms.  
Figure 10: Geometry of the numerical simulation model for modified LLB method utilizing the deck charge technique: (**a**) front view and (**b**) side view.  
Specifically, the distance between the final failure point and the tunnel face when applying the modified model was about 0.62 m, which is about 1.66 times shorter than that of the original model. Therefore, the explosives are expected to achieve a much better effect of pushing away fragmented rocks to the tunnel face when the modified model is used. In addition, the modified LLB method is of great significance in that it forms new free faces due to pre-detonation during the first stage. Overall, although the focus of this analysis model was on the cut area, the modified LLB method is expected to yield better blasting efficiency in the entire blasting process. Although, this method may be less constructable due to an increase in workload compared to the existing method, the increase in advance per round by improving blast ability using the modified LLB method is a significant advantage for the overall construction schedule.
# Review of the Main Mechanical Testing Methods for Interlayer Characterization in Laminated Glass
## 3 Evaluation of Interlayer Properties According to EN 16613:2019
### Experimental Research  
Experimental tests were conducted according to EN16613--Annex A [24] under the four-point bending configuration (Figure 2). The laminated glass plates were manufactured by two float glass plies (thickness 8 mm) with PVB interlayer (thickness 0.76 mm), with the size of \(l_{s}=1100\) mm and \(b=360\) mm (Figure 2) and total thickness \(t=16.76\) mm. The distance between the central lines of supporting rollers was \(l=1000\) mm, and the distance between the central lines of upper bending rollers was 200 mm. The tests (3 specimens) were conducted at a room temperature of 20 \({}^{\circ}\)C. A dead load (F) of 1150 N was applied for 24 h, while the midspan displacement was measured with an LVDT, continuously acquiring with a sampling rate of 50 Hz for the first 20 min and then with a sampling of 5 Hz (Figure 2). Figure 3 shows the time-displacement curve, which was used to validate the numerical model in Section 3.2.3. According to standard prescription, the response of the specimen which exhibited the maximum displacement was considered.
# Implanting Intelligence in 5G Mobile Networks--A Practical Approach
## 5 Dynamic Network Slicing--A Dilemma for Highly Changing Environment
### Machine Learning Algorithms for Slice Prediction  
The training data (80%) is given to machine learning algorithms to train the model. We build the classification models, and the slice type attribute is used as the target variable. Three machine learning algorithms: Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and Neural Network (NN) are trained, and compared to classify the slice type of a request into eMBB (enhanced Mobile Broadband), URLLC (Ultra Reliable Low Latency Communications), mMTC (massive Machine Type Communications), and master slice based on the following parameters such as throughput, latency, equipment type, reliability, mobility, etc. RF is a supervised learning model mostly used to construct predictive models for both regression and classification problems. The reason behind choosing RF over the other algorithms such as Decision Tree or Naive Bayes is that we leveraged the well-structured data, therefore, the RF uses several sub-trees to lessen the chance of overfitting. The RF works well with large datasets and outputs accurate predictions. Moreover, it is capable of determining the missing values and maintaining the accuracy of the model even if some input data are missing. XGBoost is a branch of Gradient Boosting Machine (GBM) techniques used to build both classification and regression predictive models. XGBoost is an ensemble approach, where new models are built to eliminate residuals or other errors from earlier models before being integrated to provide the final prediction. Experimentally, XGBoost outperforms many ensemble classifiers in terms of  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Feature** & **Values** \\ \hline Use case & Smartphone, IoT Devices, Smart Transportation, Industry 4.0, AR/VR/Gaming, Healthcare, Smart City/Home \\ \hline User Equipment Category & LTE, 5G \\ \hline Technology Supported & LTE/5G, IoT (LTE-M, NB-IoT) \\ \hline Guaranteed Bit Rate (GBR) & GBR, Non-GBR \\ \hline Reliability & 0.01, 0.001, 0.000001 \\ \hline Latency (Ms) & 10, 50, 60, 75, 100, 150, 300 \\ \hline \hline \end{tabular}
\end{table}
Table 4: An Overview of DeepSlice Dataset.  
speed and performance. Lastly, a three-layer feed-forward neural network (FNN) with a ReLu activation function is implemented to predict the slice. The selected parameters used to train the classifiers are shown in Table 5. After training the classifiers the testing data (30%) are given to the model to predict the slice type.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 4 Graph-Based RA in Cellular Homogeneous and Het-Nets
### RA Methods Based on GSP
The number of APs, UEs and channels are fixed in the considered scenario. The usage of replay buffer has been shown to significantly decrease the effect of over-fitting for the GNN. Fast convergence is also indicated.