esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytic Derivation of Ascent Trajectories and Performance of Launch Vehicles
## 2 First Sizing of a Launch Vehicle
The red arrow defines the velocity and the blue arrow the thrust direction  
It is important to know that if the total mass \(M_{1}\), the payload mass \(m_{pay}\) and the values \(\epsilon_{k}\)ith the five unknow, \(Isp_{k}\) are given, there is an optimal selection of \(U_{k}\), maximizing the Tziolkowski velocity (4). The procedure to identify the optimal subrocket mass ratios \(U_{k}^{*}\) is in Appendix A. The parameters \(\epsilon_{k}\), \(Isp_{k}\) can be easily guessed at the first step of the rocket design, and the maximum payload mass \(m_{pay}\) to be inserted into a specific reference orbit is the performance characterizing a space launcher. With \(\epsilon_{k}\), \(Isp_{k}\), and \(m_{pay}\) fixed, the total mass \(M_{1}\) determines the optimal mass distribution \(U_{k}^{*}\) (staging) and the maximum Tziolkowski velocity, according to Appendix A. If this velocity is equal to the velocity required to inject the payload mass \(m_{pay}\) into the reference orbit, \(M_{1}\) is the launcher lift-off mass achieving this performance. One point to underline is that the velocity to be provided by the thrust is not equal to the orbital velocity of a spacecraft on a circular orbit of altitude \(h\): \(V_{a}=\sqrt{\frac{\mu}{h+R_{e}}}\). In fact, we must consider the losses: these depend on each phase of the ascent trajectory. In the present Section it is assumed that the launcher velocity due to thrust must be overcome by 30% the required value for orbit injection. The orbit velocity relative to the Earth \(\tilde{V}_{R}\) depends on the latitude \(L\) of the launch site and on the azimuth \(\psi\)  
\[e_{i}=\frac{m_{si}}{m_{i}} \tag{1}\]5  
\[e_{i}=\frac{m_{si}}{m_{i}} \tag{1}\]6 The structural mass ratio of different stages taken from the database ©[PERSON] [18]. \[e_{i}=\frac{m_{si}}{m_{i}} \tag{1}\]7 The specific impulse of different stages taken from the database ©[PERSON] [18]. where \(V_{E}=\omega_{E}\,R_{E}\,\cos(L)\) is the velocity of the launch site and the azimuth angle \(\psi\) is related to the launch site latitude and orbit inclination by the equation: \(\sin\psi=\frac{\cos i}{\cos L}\). Then, the required velocity is here defined as  
\[e_{i}=\frac{m_{si}}{m_{i}} \tag{1}\]8  
Figure 4 shows the relative (in blue) and required (in red) velocities to achieve a reference circular orbit of altitude h = 650 km with different inclinations from a launch site at L = 30 deg N. Table 2 shows the minimum mass at lift off for a micro-launcher having parameters (5) and (6) for different payload masses (from 100 to 1000 kg) to be injected into the reference orbit (\(V_{req}=9805\,\)m/s). The ratio \(\frac{M_{1}}{m_{pay}}\) is constant.
# Effective Scheme for Inductive Wireless Power Coil Design Using Scan-and-Zoom Optimization
## 3 Proposed Coil Design Method
### Scan-and-Zoom Algorithm  
An appropriate optimization algorithm should be chosen to reduce the number of FEA executions in the FEA-based method. However, most gradient-based optimization algorithms require closed-form equations in FEA design problems. For this reason, the scan-and-zoom algorithm, one of the search-based optimizations, is adopted in this paper. The scan-and-zoom algorithm finds the optimal value by narrowing the search range while increasing the resolution and does not require the computation of closed-form equations for the gradient of the cost function. The algorithm is terminated by the following termination conditions: (1) The difference between the best value of the previous step and the best value of the current step is within the tolerance, or (2) the distance between the neighboring search points reaches within acceptable resolution. The scan-and-zoom algorithm will be described below. First, the optimization problem is defined as follows.  
\[\begin{array}{l}\max/\min f(X)\\ s.t\,X_{lower}\leq X\leq X_{upper}\end{array} \tag{6}\]  
The center point \(X_{ci}\) is chosen and \(\Delta X_{i}\) specifies the search span for determining the search region. The maximum number of search levels \((nL)\) and the number of search points per level \((nP)\) is defined. In each search level, \(i\) is stored and the optimal solution \(f_{i,opt}(X_{i,opt})\) and the location \(X_{i,opt}\) of the optimal solution in that level are stored after scanning the number of search points. Finally, the termination conditions are checked if \(\Delta f_{i}\leq\epsilon_{1}\) or \(\Delta R_{1}\leq\epsilon_{2}\), where  
\[\Delta f_{i}=\left|f_{i,opt}\left(X_{i,opt}\right)-f_{i-1,opt}\left(X_{i-1,opt }\right)\right| \tag{7}\]  
\[\Delta R_{i}=\frac{\left|X_{i,upper}-X_{i,lower}\right|}{nP-1}. \tag{8}\]  
\[X_{i,upper}=X_{ci}+\Delta X_{i} \tag{9}\]  
\[X_{i,lower}=X_{ci}-\Delta X_{i} \tag{10}\]\[\Delta X_{i}=\frac{X_{upper}-X_{lower}}{2} \tag{11}\]  
If the condition is satisfied, then the algorithm is terminated. Otherwise, the center point will be chosen to be equal to the optimal solution point and the search region is zoomed by reducing \(\Delta X\) by \(\Delta X_{i}\) half. The algorithm flow chart is summarized as shown in Figure 3.
# PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning
## 6 Discussion
### Directions for Future Work
#### 6.2.2 Underrepresented Taxa  
Through the course of collecting features and analyzing the resulting table of biomolecular features, we recognized that several properties of features in the taxonomy are underrepresented. These areas warrant further attention, and several examples are given below.  
1. **Residue-Specific Atomic Subsets**. Previous work by [PERSON] and [PERSON] included three atomic structural partitions [54]. In one representation, they included the entire structure for atom partitioning, in a second representation they only included primary and secondary shell residues in the binding site, and in a third representation they selected subsets representing the interactions between two atomic elements. However, this approach excludes allosteric networks and amino acid level properties. To alleviate this unintended bias, scientists should partition structures by residue properties and by computationally or experimentally elucidated allosteric residue interaction networks.
2. **Amino acid specific features**. Currently, few amino acid specific representations exist, thus this is a major growth area for protein-specific structural dynamic features. As mentioned, amino acid motion (and their restriction, such as in active site preorganization) is essential for protein function, although without the tools to experimentally measure amino acid motion, insights cannot be drawn from amino acid dynamics. New features should address amino acid specific dynamics, and extend these dynamic models beyond the common but rudimentary Principal Component Analysis (PCA) to describe more complex motions with simple equations.
3. **Structural trajectories: \"dynamicalization\" of structural features.** It would be computationally trivial to expand all structural features to dynamic structural features through molecular dynamics simulation. Recent work has improved the efficiency and analysis of these simulations by integration with DL modeling [55; 56] and given guidance to practitioners for which models they should use with their systems of study [57].
4. **Evolutionary trajectories**. In order to make evolutionary methods more accessible and useful and to take advantage of existing evolutionary knowledge, many desired features can be computed along an evolutionary trajectory. This is accessible for most computational methods and may be powerfully applied to evolutionary design tasks.
## 2 Methods
### Slope Model and Layout of the Sensors  
Table 1 lists the dimensional information of the slope model. We used uniform rounded sands to construct homogeneous non-cohesive slope models. The source of the sands is Vietnam. After the sieve analyses, the grain size parameters of the sands were obtained as D\({}_{50}\) = 1.74 mm, D\({}_{10}\) = 0.94 mm, and D\({}_{90}\) = 3 mm. The model's average moist unit weight is approximately 1.447 t/m\({}^{3}\), while the dry unit weight is about 1.429 t/m\({}^{3}\). The water content by weight is measured at around 1.5%. To ensure that the slope materials are loose  
Figure 1: Setup of the flume test for wave erosion on slope. (The blue arrow indicates the direction of water flow.)  
and easily damaged, we did not compact them during the model-building process. After the slope model was built, we let the model sit still for 5 h to allow moisture in the model to distribute naturally before conducting the tests.  
The layout of sensors is as shown in Figure 2. This study used 3 accelerometers (Acc 1-3) and 4 pore pressure gauges (PP1-4) to record the seismic signals and pore pressure of the slope models. The wave level is measured by two wave gauges (WL1, WL2), positioned 7.4 and 1.4 m away from the toe of the slope model, as shown in Figure 1. We also placed a microphone (Mic) above the surface of the slope model to record the acoustic signals of slope erosion and slide/collapse caused by waves. The specifications of the accelerometers and the microphone are listed in Supplementary Tables S1 and S2. In addition, 3 cameras were set up during the tests to record the front, top, and side views of the slope model.
## 4 Conclusions  
In this study, we prepared the hyperbranched polyester grafted GO (GO-H202) and the iPP/GO composites. Results of XPS, FT-IR, and TEM revealed the successful grafting of H202 onto GO, and TGA indicated that the weight ratio of grafting was about 35 wt %. DSC analysis and POM observation were carried out to investigate the role of GO and GO-H202 on the crystallization kinetics of the composites. Results of conventional cooling and heating scan suggested that the addition of GO enhances the nucleation rate and crystallizability of the composites, while GO-H202 exhibits a higher crystallization acceleration effect compared with GO. Results of isothermal crystallization kinetics and self-nucleation isothermal crystallization kinetics showed that the overall crystallization rate (involving both nucleation and crystal growth) and crystal growth rate increase after the addition of GO and GO-H202. The crystallization acceleration of GO-H202 became stronger compared with GO. Moreover, the variation trends of Avrami exponent \(n\) with the isothermal crystallization temperature \(T_{cISO}\) changed significantly from PP, GPP to GHPP, which might imply that the addition of GO and GO-H202 led to different crystallization dimensionalities during the isothermal crystallization of the composites.  
Conceptualization, Z.H.; methodology, L.L.; validation, L.L. and B.Y.; formal analysis, X.S. and X.L.; resources, L.H.; writing--original draft preparation, Z.H.; writing--review and editing, L.L. and B.Y.; visualization, P.L.; project administration, L.L. and B.Y.  
This research was funded by National Natural Science Foundation of China, grant number 51702282, the Key industry technology innovation projects of Chongqing, grant number CSTC2017 zdcy-zdyf0297 and the leading Talents of Scientific and Technological Innovation of Chongqing, grant number cstccxljrc201720.  
We gratefully acknowledge the financial support from the National Natural Science Foundation of China (51702282), the Key industry technology innovation projects of Chongqing (CSTC2017 zdcy-zdyf0297), and the leading Talents of Scientific and Technological Innovation of Chongqing (cstccdjrc201720).  
The authors declare no conflict of interest.  
Figure 10: Polarized optical microscopy (POM) images during the isothermal crystallization at 130 \({}^{\circ}\)C of (**a**) neat PP, (**b**) GPP, and (**c**) GHPP.
## 3 Concept
### Continuous ML in a Real Mining Environment  
The purpose of the conceptual view (Figure 3) is to illustrate how continuous ML could be applied into real underground mining environments. Emulations (Figure 2) would be performed prior to the deployment of ML-based models into a real mining environment (step 4 in Figure 3).  
A router node could be attached to a moving vehicle. The router would have other on-board devices (e.g., video camera, Lidar, and sensors), which collect data from the environment. The role of the edge node(s) is to act as a fixed access point(s) for communication, offline training of models, and optionally provisioning of inference services to router(s). The control center acts as a coordination point for managing the vehicles and services deployed in them. Additionally, the moving vehicles may communicate via other vehicles with mesh networking.  
After emulation(s) are executed (Figure 2), the best performing ML-based model may be deployed directly to the router for facilitating decision making. Additionally, a new ML-based model may be trained from scratch, or an existing model may be retrained (transfer learning). In such cases, the best-performing ML-based training algorithm(s) may be reused for training in the real mining environment. When models are trained/retrained in the production environment (step 6), new monitoring data are collected from the real mining environment (step 5). ML-based models may be continuously evaluated (step 7) and deployed (step 8) into real mobile routers or into edge device(s) for facilitating decision-making purposes.
# Monitoring Perishable Commodities Using Cellular IoT:
## 2 Materials and Methods
### Hardware Architecture
#### 2.2.1 Components and Sensors
To measure the CO\({}_{2}\) concentration in the environment, the SCD41 from a Sensirion (Laubisruetstrasse, 50, Stafa, Switzerland) sensor [61] was used, which allows a measurement range from 0 to 40,000 ppm with a high accuracy of \(\pm\)40 ppm and 16-bit resolution. Moreover, it offers I\({}^{2}\)C communication and low-power operation (0.4 mA). The option of measuring the concentration of volatile organic compounds (VOC), such as ethylene, was discarded due to the time required to heat up the sensor before the measurement could be taken, together with high power consumption. The technical description of the sensors is summarized in Table 1.
# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 3 Results
Further visualization of the results is shown in Figure 11, where cumulative electrical energy production for each PVA (Figure 11a) is shown, and a comparison between estimated cumulative production and measurements for each PV system is provided (see Figure 11b). Finally, a comparison between the estimated production using detected tilt angles and the optimal tilt angles for each PV system is shown in Figure 11c. These results highlight the importance of considering site-specific factors for PV systems' tilt angle optimization. Although some systems (A and D) were already close to optimal configuration, others (B and C) faced practical limitations that restricted their optimization potential. The results have demonstrated that even small improvements in arrays' tilt angle can lead to measurable increases in energy production, especially during several years of operation. Furthermore, in many cases, adjustment of the tilt angles per array does not require significant on-site workload. \begin{table}
\begin{tabular}{c c c c c c} \hline PV System & Meas. [MWh] & Sim. [MWh] & Opt. [MWh] & MAPE (Meas., Sim.) & Improvement (Sim., Opt.) \\ \hline A & 208.316 & 196.492 & 199.471 & 5.68 \% & 1.52 \% \\ B & 34.261 & 36.552 & 37.601 & 6.69 \% & 2.87 \% \\ C & 23.251 & 22.130 & 22.5909 & 4.82 \% & 2.08 \% \\ D & 46.814 & 46.695 & 48.985 & 0.26 \% & 4.90 \% \\ \hline \end{tabular}
\end{table} 
Table 3: Average electrical energy produced of the given PV systems in the timeline 2018-2022, with comparison between simulated expected energy production and the calculated error using MAPE metric. Further comparison is provided between simulated energy production using detected tilt angles and optimal tilt angles. Figure 9: Optimal tilt estimated with the proposed method for each array for all four PV systems. The black lines represent the real slopes used by the arrays within the given PV system. Colors of the arrays correspond to colors in Figure 8b.
## 3 Discussion of Results from Sensitivity Analyses
### Tanks-Fuselage Integration Studies Based on Full-Section Layouts  
#### 3.2.1 Sensitivity to \(t_{0}\) and \(t_{ins}\) under Different Layout Constraints  
The results presented in this section were obtained using the data in Table 8, assigning to \(t_{ins}\) a variable value and setting as a constant the tube length \(L\) = 5.9 m (which provides \(L/D_{str}=1.5\) for the main tank, Tank#0). The number of tanks in the section was not fixed a priori and 3 possible conditions were considered: no constraints on the \(R_{min}\) value (Figure 19a), \(R_{min}\) = 0.5 m (Figure 19b), and \(R_{min}\) = 0.5 m with a 0.8-m-wide catwalk (Figure 19c). Some examples of cross-sections are illustrated in Appendix D, for \(t_{ins}\)= 50 mm.  
Figure 19: Sensitivity of number of tanks (left) and burnt LH\({}_{2}\) per pax-km (right) to \(t_{0}\) and \(t_{ins}\), with range indication: (**a**) without \(R_{min}\) constraint; (**b**) with \(R_{min}\) = 0.5 m; (**c**) with \(R_{min}\) = 0.5 m and catwalk width = 0.8 m.  
The grey areas in Figure 19 charts indicate that the \((L/D_{str},t_{ins})\) pair is not able to fulfil the minimum internal pressure requirement for the whole mission duration; therefore, the solution is indicated as not feasible for safety reasons. A possible countermeasure to this problem would be the implementation of a specific pressure-dependent strategy to control the LH\({}_{2}\) outflow from each tank, whereas in this preliminary study a constant LH\({}_{2}\) flow was considered for all the non-empty tanks.  
The main observations concerning the results shown in Figure 19 are:  
* The need to define a proper \(R_{min}\) value is not only related to manufacturability but derives from thermodynamic reasons too. In fact, too small LH\({}_{2}\) volumes are affected by minimum internal pressure problems which lead to a wide unfeasible region (Figure 19a);
* When the \(R_{min}\) constraint is introduced, the solutions which maximise the ranges and minimise LH\({}_{2}\) per pax-km consumption, are in the neighbourhood of \(t_{0}=90^{\circ}\) and \(t_{ins}=50\) mm and are characterised by only 1 tank in the section (Figure 19b). In this case, the seat capacity is 204;
* The introduction of the catwalk breaks the symmetry of the solution, moving the optimum far away from the configuration with \(t_{0}=90^{\circ}\). In the case at hand, the optimal condition is reached for \(t_{0}\) close to \(80^{\circ}\), without any effects on seat capacity.  
The LH\({}_{2}\) per pax-km consumption is slightly affected by different number of passengers, which vary because \(t_{ins}\) has a weak but not null effect on tanks' total lengths, according to Equation (2). The seat capacity of the previous cases can be increased by 1 row for several \((L/D_{str},t_{ins})\) combinations, whereas few cases allow to add 2 rows.
## 2 Geometrical Details and Mathematical Models
### Governing Equations  
Continuity equation:  
\[\frac{\partial\overline{U}_{i}}{\partial x_{i}}=0 \tag{1}\]  
Momentum equation:  
\[\overline{U}_{j}\frac{\partial\overline{U}_{i}}{\partial x_{j}}=-\frac{ \partial P}{\partial x_{i}}+\frac{\partial}{\partial x_{j}}\left[\
u\left( \frac{\partial\overline{U}_{i}}{\partial x_{j}}+\frac{\partial\overline{U}_{j }}{\partial x_{i}}\right)-\overline{u_{i}u_{j}}\right] \tag{2}\]  
with:  
\[-\overline{u_{i}u_{j}}=\
u_{t}\left(\frac{\partial\overline{U}_{i}}{\partial x _{j}}+\frac{\partial\overline{U}_{j}}{\partial x_{i}}\right)-\frac{2}{3}\rho k \delta_{ij} \tag{3}\]  
Turbulent eddy viscosity (\(\
u_{t}\)):  
\[\
u_{t}=C_{\mu}f_{\mu}\frac{k^{2}}{\overline{\varepsilon}} \tag{4}\]  
Modified dissipation rate (\(\overline{\varepsilon}\)):  
\[\overline{\varepsilon}=\varepsilon+D \tag{5}\]  
Turbulent kinetic energy (\(k\)) equation:  
\[\overline{U}_{j}\frac{\partial k}{\partial x_{j}}=\frac{\partial}{\partial x _{j}}\left[\left(\
u+\frac{\
u_{t}}{\sigma_{k}}\right)\frac{\partial k}{ \partial x_{j}}\right]+P_{k}-\widetilde{\varepsilon}-D \tag{6}\]  
Turbulent energy dissipation rate (\(\overline{\varepsilon}\)) equation:  
\[\overline{U}_{j}\frac{\partial\overline{\varepsilon}}{\partial x_{j}}=\frac {\partial}{\partial x_{j}}\left[\left(\
u+\frac{\
u_{t}}{\sigma_{\varepsilon }}\right)\frac{\partial\overline{\varepsilon}}{\partial x_{j}}\right]+C_{1}f _{1}\frac{\overline{\varepsilon}}{\overline{k}}P_{k}-C_{2}f_{2}\frac{ \overline{\varepsilon}^{2}}{k}+E \tag{7}\]\[Re_{y}=\sqrt{k}y/\
u\text{; }Re_{t}=k^{2}/(\varepsilon\
u)\text{; }y^{*}=u_{\varepsilon}y/\
u\text{, }y\text{ is the distance from the wall, }u_{\varepsilon}\text{ is Kolmogorov velocity scale, and }u_{\varepsilon}=(\
u e)^{1/4}\text{.}\]
###### Abstract  
A variety of volume electron microscopy techniques have been developed to visualize thick biological samples. However, the resolution is limited by the sliced section thickness (\(>\)30-60 nm). To preserve biological samples in a hydrated state, cryo-focused ion beam scanning electron microscopy has been developed, providing nm resolutions. However, this method is time-consuming, requiring 15-20 h to image a 10 um thick sample with an 8 nm slice thickness. There is a pressing need for a method that allows the rapid and efficient study of thick biological samples while maintaining nanoscale resolution. The remarkable ability of mega-electron-volt (MeV) electrons to penetrate thick biological samples, even exceeding 10 um in thickness, while maintaining nanoscale resolution, positions MeV-STEM as a suitable microscopy tool for such applications. Our research delves into understanding the interactions between MeV electrons and frozen biological specimens through Monte Carlo simulations. Single elastic scattering, plural elastic scattering, single inelastic scattering, and plural inelastic scattering events have been simulated. The electron trajectories, the beam profile, and the intensity change of electrons in each category have been investigated. Additionally, the effects of the detector collection angle and the focal position of the electron beam were investigated. As electrons penetrated deeper into the specimen, single and plural elastic scattered electrons diminished, and plural inelastic scattered electrons became dominant, and the beam profile became wider. Even after 10 um of the specimen, 42% of the MeV electrons were collected within 10 mrad. This confirms that MeV-STEM can be employed to study thick biological samples.  
Monte Carlo simulation; MeV-STEM; thick biological samples; nanometer resolution; electron specimen interaction +
Footnote †: journal: Physics Letters B