esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Tunnel field-effect transistor (Tunnel FET) with asymmetric spacer is proposed to obtain high on-current and reduced inverter delay simultaneously. In order to analyze the proposed Tunnel FET, electrical characteristics are evaluated by technology computer-aided design (TCAD) simulations with calibrated tunneling model parameters. The impact of the spacer \(\kappa\) values on tunneling rate is investigated with the symmetric spacer. As the \(\kappa\) values of the spacer increase, the on-current becomes enhanced since tunneling probabilities are increased by the fringing field through the spacer. However, on the drain-side, that fringing field through the drain-side spacer increases ambipolar current and gate-to-drain capacitance, which degrades leakage property and switching response. Therefore, the drain-side low-\(\kappa\) spacer, which makes the low fringing field, is adapted asymmetrically with the source-side high-\(\kappa\) spacer. This asymmetric spacer results in the reduction of gate-to-drain capacitance and switching delay with the improved on-current induced by the source-side high-\(\kappa\) spacer.  
t 20202021 April 20202027 April 202026 March 202  
power consumption [22]. Accordingly, many groups who are studying tunnel FETs have applied the underlap junction between gate and drain and low drain doping to suppress ambipolar current.  
Here, a silicon tunnel FET with high- and low-\(\kappa\) spacers on source- and drain-side, respectively, is proposed with line-tunneling to improve the on/off current and switching speed. In general, using a high-\(\kappa\) spacer in the conventional tunnel FET (i.e., there is no line-tunneling) is not an effective solution to improve on-current, because the fringing field makes the source energy band depleted, leading to high tunneling resistance [23; 24]. That's why additional source-to-channel junction optimization or low-\(\kappa\) spacer are required to improve the tunneling current. On the contrary, in tunnel FET with line-tunneling, fringing field effects improve the tunneling current without any junction optimization. This is because the area underneath the spacer is not source, but an epitaxially grown silicon channel region. Therefore, the higher fringing field is applied, the larger BTBT is generated between the channel and source region. However, in spite of the line-tunneling scheme, the high-\(\kappa\) spacer approach, unfortunately, degrades gate-to-drain capacitance by a higher fringing field, which affects poor switching characteristics. That is why the low-\(\kappa\) spacer is intentionally formed on the drain-side as asymmetric spacer. Consequently, the proposed architecture gives better alternating current (AC) switching characteristics by increasing on-current and decreasing gate-to-drain capacitance, simultaneously. In addition, the process integration for the asymmetric spacer is introduced in terms of process feasibility.
# Observer-Based Consensus Control for Heterogeneous Multi-Agent Systems with Output Saturations
## 3 Main Results  
In this section, we construct the consensus algorithms for heterogeneous agents with the output saturations and consider the cases of leaderless and leader-following. The proposed algorithms are composed of three parts, as shown in Figure 1. The first part is the nonlinear observer to measure the state using the measured output, the second part is the distributed reference generator to generate the common trajectory, and the third part is the controller to track the common trajectory based on the output regulation theory.  
Figure 1: Block diagram of agent \(i\) with the observer-based consensus algorithm.
## 2 Establishment of Interface Model and Simulation Experiment  
### Establishment of Interface Model  
Firstly, Ni crystal cell was imported and cleaved along the (1 0 0) surface, and then the constructed crystal cell was optimized. The thickness of the Ni layer is 3.4 nm. The super cell was established by extending the optimized structure along the direction of U and V. Then, a three-dimensional periodic cell with a vacuum layer thickness of 0 A was established, and its lattice parameters were a = 5.0 nm, b = 7.5 nm, c = 3.4 nm, and \(\alpha=\beta=\gamma=90^{\circ}\). After full energy optimization, a circular arc groove was cut with a depth of 1.2 nm.  
Secondly, two PC all-atomistic molecular chain models with different chain lengths (N = 5, 25) were established. The chemical formula for PC is (C\({}_{16}\)H\({}_{14}\)O\({}_{3}\))\({}_{\mathrm{n}}\). After structure optimization, two amorphous crystal cell models with the same total number of atoms and chain numbers of 50 and 10, respectively, were constructed with a density of 1.2 g/cm\({}^{3}\). The crystal cell parameters were adjusted to be equal to the Ni crystal cell a and b values, so that the interface model could be established later. Thirdly, the smart method structure optimization [15] of PC crystal cell models was carried out at a temperature of 0 K to find the lowest energy structure adjacent to it. The smart method structure optimization combines three energy minimization methods: the steepest descent method, the conjugate gradient method, and the Newton method. Then, the temperature factor was added to search for the lowest energy conformation of the system through energy disturbance; that is, 5 cycles of annealing were conducted to cause the system to be in an equilibrium state. Furthermore, 20 PS preheating was conducted at 500 K to bring the polymer to a molten state. Finally, the interface models of the two PC systems and Ni layers were established, respectively, as shown in Figure 1. In order to avoid the influence of 3d periodic boundary conditions on the height, a vacuum layer of 2.0 nm was set above the model.
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
## 1 Introduction
The IPDA technique foreseen for MERLIN relies on DIAL (Differential Absorption Lidar) measurements using a pulsed laser emitting at two frequencies around a methane line multiplet at 1.64 \(\upmu\)m. One wavelength is accurately locked to a spectral feature of the CH\({}_{4}\) absorption manifold, minimizing any small frequency shifts, to diminish potential systematic errors. The other frequency is selected to have negligible CH\({}_{4}\) absorption and is used as the reference. The MERLIN instrument concept, the measurement approach, and frequency selection are similar to the CHARM-F instrument, mentioned above. An Optical Parametric Oscillator (OPO) pumped by a Nd:YAG laser will serve as the MERLIN transmitter to make use of DLR's long-term experience with such devices for atmospheric water vapor profiling [67]. With respect to space application, this technical concept will benefit from ESA's laser development program in connection to the Earth Explorer Aeolus (ALADIN) and EarthCARE (ATLID) [68]. Further heritage on the instrument readiness level is provided by the NASA/CNES CALIPSO mission that uses high average power Nd:YAG lasers as Lidar transmitters in space over a period of more than 10 years [69; 70]. To mitigate the risk of the spaceborne pump laser development for MERLIN, the conceptual design will make use of the so-called \"Future Laser System\" FULAS development, which is a cooperation between ESA and DLR to advance spaceborne laser technology [71]. This active method does not require reflected sunlight, and enables measurements to be made in all seasons at all latitudes, during daylight and night. The differential approach, the size of the laser spot (\(\sim\)120 m at the surface along the track), and the selective sampling guarantees low systematic errors (target \(<\) 3.7 ppb) and measurements in broken cloud condition, with almost no contamination by aerosol or water vapor. MERLIN observations promise to enhance the effective constraints brought by satellite data on methane surface emissions once assimilated into atmospheric modelling systems. Given the experience in the development and operation of previous Lidar missions in space, a mission lifetime of three years for MERLIN appears feasible. In this manuscript, the MERLIN mission objectives (Section 2), the methodology employed (Section 3), the different mission elements (Section 4), a first analysis of the expected performances (Section 5), and conclusions are presented.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## 4 Establishment of LTE-R Time Synchronization SPN and MC Models  
### Basic Concepts of Stochastic Petri Nets  
An SPN is generally defined as a six-tuple array \(\mathit{SPN}=(P,\,T,\,F,\,K,\,M,\lambda)\), where:  
* \(P=\{P_{1},\,P_{2},\,\ldots,P_{n}\}\) is a finite set of repositories, and \(n\) is the number of repositories;
* \(T=\{t_{1},\,t_{2},\,\ldots,\,t_{n}\}\) is a finite set of changes, and \(m\) is the number of changes, satisfying \(P\cap T=\Phi\) and \(P\cup T=\Phi\);
* \(F\subseteq I\cup O\) is a set of directed arcs, where \(I\) represents the set of transition input arcs, \(I\subseteq P\times T\); \(O\) represents the set of transition output arcs, \(O\subseteq T\times P\); the forbidden arc is allowed in \(F\); and the forbidden arc only exists in the arc from the depot to the transition;
* \(W\):\(F\to N^{+}\) is an arc function, where \(N^{+}(1,\,2,\,3,\,\ldots\,)\);
* \(M\):\(P\to N\) is the marking of the Petri net. \(M_{0}\) is the initial marking, which indicates the initial state of the system;
* \(\lambda=\{\lambda_{1},\lambda_{2},\,\ldots\,\lambda_{m}\}\) represents the average firing rate set associated with the transition.
## 5 Prototype System
### Extraction and Indexing
#### 5.1.6 Indexing  
For the representation engine to offer users the extracted features, the features along with their timestamp information need to store in an efficiently retrievable manner. To do this all the data related to video segments and the multimodal features along with the timestamp are stored as documents in a tables or cores in Solr search platform [67]. Solr is written in JAVA, uses Lucene indexing and searching engine [68]. A simple web request is used to access the information which is served in standard machine-readable format such as JSON (JavaScript object notation). Following is an example of a video segment indexed by Solr.  
'id': \"[PERSON]_2014S-480p_c99_2\",  
\"video\":['[PERSON]_2014S-480p\"],  
\"num\":[2],  
\"segmenter\":['C99\"],  
\"Start_time\":['00:02:42\"],  
\"End_time\":['00:07:58\"],  
\"seg_text\":['n\
\tSothereismoregoingonhere,  
but I'mnotgoingto  
talktoomuchaboutthistoday,  
becauseIwantfocousonwealth  
inequality.  
Soletmejustshowyouaversimple  
indicatoraboutthe  
incomeinequalitypart.  
Sothisistheshareoftotalincome  
goingtothetop10 percent... \"],  
Similarly, information about multimodal feature is indexed for the representation engine to quickly search it. Following is an example of a multimodal feature indexed in Solr.  
```
!id^:^ChrystiaFreeland_2...^, \"video^:[^ChrystiaFreeland_2013G-480p^], \"segmenter^:[^C99^], \"Expange^:[^Efficient^], \"Modalize\":[^Visual\"], \"Name^:[^Keyframes^], \"seg_num^:[^11], \"FeatureValue\":[^CF/C_1_vis.html^], \"_version_\":1579238985017851904]
```
###### Abstract  
The high-biofidelity dummy used to evaluate occupant protection under blast conditions is often costly and vulnerable. To address the limitations of low-cost, simplified dummy head-neck structures, which exhibit significant differences in mechanical properties compared to high-biofidelity dummies, a virtual-real fusion-based test method for assessing occupant head-neck injury in under-body blast impacts is proposed. A simplified dummy head-neck physical model, designed based on human biomechanical characteristics, is constructed for physical testing. A mapping test model based on 1D convolutional neural network (1D-CNN) is developed as the virtual counterpart to process physical model test data, specifically head and chest centroid accelerations, into head centroid acceleration and upper neck axial compression force matching the Hybrid III 50 th numerical model. Pendulum collision tests are conducted to simulate under-body blast impacts, generating multiple sets of head and chest acceleration data. Under identical loading conditions, the head centroid acceleration and upper neck axial compression force of the Hybrid III 50 th numerical model are computed. The parameters of the mapping test model are then optimized using these simulated experimental datasets based on non-dominated sorting genetic algorithm II (NSGA-II). Validation through blast experiments demonstrates that the proposed method achieves high accuracy, with errors of 10.9% for the Head Injury Criterion (HIC\({}_{15}\)) and 2.4% for upper neck maximum axial compression force compared to the Hybrid III 50 th numerical model calculations. This approach effectively bridges the gap between biofidelity and cost-efficiency in dummy testing for blast impact scenarios.  
virtual-real fusion; test method; head-neck; blast impact injury; physical model; mapping test model; optimize Article
# Ex-Situ Evaluation of Commercial Polymer Membranes for Vanadium Redox Flow Batteries (VRFBs)
## 2 Materials and Methods
### Ex-Situ Characterizations
#### 2.2.4 Membrane Ionic Conductivity Measurements and Area Resistance  
A two-probe conductivity cell with two Pt strips (distance: 1.6 cm) was mounted on a Teflon block as both AC current injectors and voltage measurement probes were employed for membrane conductivity measurement [38]. A piece of 2.4 \(\times\) 1.0 cm membrane was immersed in deionized water for 24 h to be fully hydrated before measuring and then sandwiched between the two Teflon blocks. The impedance measurement was conducted immediately following filling water into the open windows of the Teflon block to avoid membrane dehydration. A Solatron 1260 (Solartron Analytical, Farnborough, UK) impedance/gain-phase analyzer was used to measure the resistance. The applied perturbation amplitude of the AC signal was 10 mV RMS (root mean square) over a frequency range 10 MHz to 100 Hz. All measurements were performed at an ambient temperature of 21 \({}^{\circ}\)C. Membrane in-plane ionic conductivity in water (\(\sigma\)) was calculated by Equation (4):  
\[\sigma=\ \frac{L}{A\cdot R} \tag{4}\]  
where \(\sigma\) is the ionic conductivity of the membrane (S\(\cdot\)cm\({}^{-1}\)), \(L\) is the length of the membrane between the potential probes (cm), \(R\) is the resistance (\(\Omega\)) obtained from the high-frequency intercept of the semicircle with the real impedance axis, and \(A\) is the membrane cross-sectional area (cm\({}^{2}\)). For each sample, three pieces of membrane were tested to calculate the average ionic conductivity and standard deviation.  
The membrane area resistance was also measured in 2 M H\({}_{2}\)SO\({}_{4}\) in an H-cell, which was separated by a membrane with an effective area of 1 cm\({}^{2}\). The cell was filled with 2 M H\({}_{2}\)SO\({}_{4}\) in each half cell. Two carbon rod electrodes were connected with Solatron 1260 (Solartron Analytical) impedance/gain-phase analyzer to measure the area resistance over a frequency range from 100 to 1 MHz. The membrane was pretreated in a 2 M H\({}_{2}\)SO\({}_{4}\) solution for 24 h. The membrane area resistance (R) is calculated by Equation (5):  
\[\text{R}=\text{A}\times(\text{R}_{1}-\text{R}_{2}) \tag{5}\]  
where A is the membrane active area (cm\({}^{2}\)), R\({}_{1}\) is the resistance of the cell with the membrane (\(\Omega\)) and R\({}_{2}\) is the resistance of the cell without the membrane (\(\Omega\)).  
The ion selectivity represents the ratio of ionic conductivity over vanadium ion permeability and was calculated by Equation (6).  
\[S=\frac{\sigma}{P} \tag{6}\]  
where \(S\) is the ion selectivity of the membrane (S\(\cdot\)s\(\cdot\)cm\({}^{-3}\)); \(\sigma\) is the ionic conductivity of the membrane (S\(\cdot\)cm\({}^{-1}\)); \(P\) is the diffusion coefficients of VO\({}^{2+}\) (m\({}^{2}\cdot\)s\({}^{-1}\)).
## 2 Experimental Design and Method
### Measurement and Analysis  
Before weighing, the filters were conditioned in a desiccator at least for 24 h in a temperature- and humidity-controlled chamber. After gravimetric determination of particle mass on an electronic microbalance (ME 5-F, [PERSON], Gottingen, Germany), inorganic elements including heavy metals were quantitatively analyzed by Energy Dispersive X-ray Fluorescence spectrometry (ED-XRF, ARL QUANTUMYX High Performance ED-XRF, ThermoFisher Scientific, Seoul, Korea) [15].  
Ions were extracted from the particulate matters collected on a quartz filter in an ultrasonic bath. Five cations (Na\({}^{+}\), NH\({}_{4}\)\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\)) and three anions (Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{2-}\)) were analyzed using an ion chromatography (Metrohm 930 & 883, Herisau, Switzerland). Bulk carbon contents such as organic carbon (OC) and elemental carbon (EC) were quantified with a TOT analyzer (Thermal/Optical Transmittance, Sunset Lab., Tigard, OR, USA). The concentration of OC was measured in a helium atmosphere at 31 \({}^{\circ}\)C to 840 \({}^{\circ}\)C, and the EC was measured in an oxygen atmosphere at 550 \({}^{\circ}\)C to 870 \({}^{\circ}\)C [16]. One punch of 1.5 cm\({}^{2}\) was directly analyzed following the procedure reported in the protocol of the National Institute for Occupational Safety & Health (NIOSH 5040). Automatic split time was always used for the distinction between EC and pyrolyzed OC.  
The obtained data were statistically correlated by the software package SPSS v15.0 for Windows. Possible relationships between elements and carbons were analyzed through a multivariate exploration of the data.
## 4 Performance Analysis and Discussion
### Future Roadmap in Shared Situational Awareness Training and Research  
Given the low latency and high integrity achieved in our proposed platform, the digital twin approach to provide real-time collaborative decision-making training is highly viable through the above-proposed setup. The practical implication of this paper is to contribute a feasible and economical (partially open-source-based) solution in enhancing human factors training in air traffic operations. Multiple stakeholders in air traffic operations have been considered instead of only some. The communication and mental model disconnect issues are expected to be addressed through a thorough and extensive use of the proposed platform. On the academic research side, future studies can attempt to use the platform to conduct digital twin-based experiments. The subjects' performance can be evaluated online such that data-driven, adaptive, and intelligent automation aids can be further designed to address human factors involved in aviation operations. Therefore, the current solution serves as a cornerstone, as well as a starting point, for further investigation regarding the shared situational awareness issue in an objective approach.
## 3 System Model
### The Establishment of Attribute Indexes
#### 4.1.3 Buffer Idleness  
Because the data transmission in WSNs has the characteristics of \"many to one\", it may lead to numerous traffic data converging to some key nodes at the same time. It may run out of energy prematurely because of the overload and reduce the network lifetime. Thus these nodes discard data  
Figure 2: Distinguish routing process based on DS fusion rules.  
because of limited buffer, which will affect the reliability of data transmission. Therefore, the buffer idleness is established to measure the ability of nodes to receive data, which is expressed as follows:  
\[BI(j)=1-\frac{Q_{c}^{j}+Q_{in}^{j}-Q_{out}^{j}}{Q_{\max}},j\in FN(i) \tag{7}\]  
where \(Q_{c}^{j}\) is the current traffic data of node \(j\), \(Q_{in}^{j}\) is the traffic flowing into the node \(j\), \(Q_{out}^{j}\) is the traffic flowing out of the node \(j\), and \(Q_{\max}\) is the maximum buffer length of each node.
# Designing Procedure Execution Tools with Emerging Technologies for Future Astronauts
## 2 Habitat Integration
### Prototype  
For this project, both a physical and a software prototype were constructed. The physical prototype consisted of an ISS locker constructed using foam core and integrated with IoT sensors (see Figure 2a,b). These sensors, connected to an ESP8266 chip, detected changes in the state of the habitat, such as the locker door being opened or closed. Based on these changes, feedback was provided to the users which allowed them to react accordingly in the procedure. Simultaneously, an iOS application was designed and built to enable a user to walk through a given procedure with AR assistance. This application was built using ARKit, Apple's AR platform for iOS devices, which enables developers to produce apps that interact with the world using the device's cameras and sensors. ARKit is able to recognize 3D objects and anchor augmented objects using world orientation tracking. By graphically rendering 3D models, the application provides visual instruction on the steps the user should follow. The iOS application published information to the server about the user's progress and the real-time information from each of the IoT sensors' state. This prototype demo allowed users to complete several steps of the procedure successfully through the illustration of individual steps.  
In this demo, we were able to successfully demonstrate that users could complete procedure steps with guidance provided by the digital tool and its overlaid visualization to the real-life locker prototype. For this initial research, the ability of the user to complete the demo using the SHARP tool was the main feedback demonstrating the usability of AR technology displayed through an interface. This was the first step in demonstrating thatan untrained user could follow a complex set of instructions as those seen in spaceflight operations. The digital tool allowed for automated step-by-step guidance of the procedure. The IoT sensors placed within the prototype enabled the prototype tool to acknowledge the step the crew member had just completed and to automatically reflect the changes in the procedure viewer. Augmented reality provided the opportunity to look beyond what the crew member could physically see, including systems placed behind covered panels in the prototype, which would have otherwise called for a dismantling of the panel just for diagnosis purposes.
## 4 MiniGrid Hybrid Power System Description
### Diesel Generator  
Due to the fact that the PV and wind modules have intermittent natures, the diesel generator will ensure the off-grid hybrid power system's stability and reliability. In addi  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Electrical Parameters**} \\ \hline Rated power at STCs & 295 Wp \\ Open circuit voltage, \(V_{oc}\) & 39.5 V \\ Maximum power point voltage, \(V_{mp}\) & 32.3 V \\ Short circuit current, \(I_{sc}\) & 9.75 A \\ Maximum power point current, \(I_{mp}\) & 9.14 \\ Temperature coefficient of maximum power & \(-0.39\%\)/\({}^{\circ}\)C \\ Temperature coefficient of open circuit voltage & \(-0.29\%\)/\({}^{\circ}\)C \\ Temperature coefficient of short circuit current & 0.05\%/\({}^{\circ}\)C \\ Module efficiency & 18.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical parameters of the PV module [38].  
Figure 8: Power curve of the wind turbine.  
tion, in this study the diesel generator is considered as the base case reference for relative comparison using LCOE and NPC. In HOMER Pro, we input the load profile and select \"Autosize Genest\" for the DG specification, and set the maximum annual capacity shortage fraction of the system operational constraint to 0%. The generic 10 kW diesel generator with efficiency of 30%, and fuel curve slope of 0.286 L/h/kW were considered for the initial case from HOMER Pro catalogue. Then, the software automatically adjusted to the proper capacity and fulfilled the system's operational constraints.