esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Co-Barrier Efficiency of Air Curtains in Mine Refuge Chambers
###### Abstract
There are two kinds of air curtains commonly used in mine refuge chambers, one is an air knife air curtain, and the other is a pipeline air curtain. Due to the lack of a unified standard for door wall construction and air curtain systems, the air curtain system varies greatly from refuge to refuge, some of which are shown in Figure 1.  
At present, the research on pipeline air curtains in the mine refuge chamber is not mature and perfect. [PERSON] et al. [32] found that when the refuge chamber door frame is 1.55 m wide and 1.00 m high, the optimal hole diameter and hole spacing of the pipeline air curtain is 1.2 and 40 mm, respectively. [PERSON] et al. [33] tested the performance of a pipeline air curtain system in refuge chambers in terms of CO gas barrier performance, showing a  
Figure 1: Diagram of the structure and installation position of the mine refuge air curtain [31]. (**a**) Air knife air curtain installed on top side. (**b**) Pipeline air curtain installed on three sides. barrier efficiency of 65% within 2.25 min when the air curtain was installed on both sides of the door frame and at a wind speed of 10.2 m/s. [PERSON] et al. [34] studied the influence of structure size, installation position, and airflow angle on the barrier effect. The results show that when the CO\({}_{2}\) concentration in the outside environment is 2%, the air curtain with a hole diameter of 1 mm and a hole spacing of 15 mm can block CO\({}_{2}\) gases in the tunnel by 55-60% after 5 min. It can be seen from the above that their research object is mainly CO\({}_{2}\) gases. It can be seen that the parameters to achieve the best barrier effect of the air curtain are different for different application scenarios. At the same time, they did not deeply study the relationship between structural parameters, velocity, and barrier effect. The above study demonstrates that different application contexts and barrier item types require varying air curtain structural parameters, airflow velocity, installation positions, and airflow angles to obtain the greatest barrier effect. We are unable to immediately transfer air curtain systems from other application situations or conditions to the mine refuge, for this reason. To obtain the mathematical relationship between the parameter change and the efficiency of the air curtain, which further provides reference suggestions for pipeline structural design, this paper methodically investigates the influence of the aforementioned parameters on the efficiency of the air curtain.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 4 Results
### Empirical Cumulative Distributed Function (ECDF) Curves  
The ECDF curves shown in Figures 7-11 measure the probability of achieving a particular throughput. The y-axis represents the probability which ranges from 0-1, and the \(x\)-axis represents the throughput in Mbps.  
Figure 5: Wideband SINR distribution for 40 UEs.  
Figure 6: Wideband SINR distribution for 50 UEs.  
Figure 7: Edge UE throughput ECDF for 10 UEs per cell.  
Figure 8: Edge UE throughput ECDF for 20 UEs per cell.  
Figure 10: Edge UE throughput ECDF for 40 UEs per cell.  
Figure 9: Edge UE throughput ECDF for 30 UEs per cell.  
Figure 12 represents peak edge UE throughput; Figure 13 represents the average edge UE throughput, while Figure 14 represents the fairness metric.  
Figure 11: Edge UE throughput ECDF for 50 UEs per cell.  
Figure 12: Peak edge throughput for a number of user equipment per cell.
### SEVIRI Errors of Omission and Commission
#### 5.1.1 Sunglint  
Sunglint has only a minor impact on the performance of the SEVIRI detection algorithm. Regardless of the ROI, less than 4% of the MODIS active fire pixels that SEVIRI failed to detect were located adjacent to a SEVIRI thermal pixel affected by sunlight. In relative terms, sunlight accounted for no more than 5% of SEVIRI's omission rate in any ROI (Table 2).
# Information-Aware Secure Routing in Wireless Sensor Networks
###### Abstract  
Secure routing is crucial for wireless sensor networks (WSNs) because they are vulnerable to various attacks. In this paper, we propose a new secure routing protocol for WSNs in the presence of malicious nodes. For each relay node in the route, associated information such as its trust value and status is considered in the protocol. The trust value is defined as the attack probability of the node according to previous packet-forwarding behaviors, and the status is a hybrid metric that combines the residual energy and distance to the sink node. Therefore, the route generated by the protocol is secure against malicious attacks and globally optimal according to the associated information. We used an improved variant of the Dijkstra algorithm to generate the secure route for WSNs in the presence of malicious nodes. Compared with the Reputation-Based Mechanism to Stimulate Cooperation (RBMSC) model in the same simulation environment, the proposed model can maintain a higher delivery ratio, which verifies the effectiveness of the proposed model on the basis of global optimization. Furthermore, compared with the traditional Dijkstra algorithm, the packet loss ratio in the improved Dijkstra algorithm is lower because it can more effectively avoid malicious nodes, thus verifying the effectiveness of the improved algorithm.  
secure routing; trust calculation; attack probability; information-aware; global optimization Article
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## Proposed Framework  
This section highlights preliminaries, node distribution, energy model and connectivity model for LEACH-MEEC routing protocol. In LEACH-MEEC, we consider:  
* Location of base station is anchored and positioned outside the area of sensors distribution.
* The \(N\) sensor nodes are distributed randomly.
* All sensors are homogeneous in nature, having similar specification.
* Mobile sensor nodes can move randomly with a specified speed following a certain mobility model pattern.
* Mobile sensor nodes can communicate directly with base station.
# Analytical Approach in the Development of RF MEMS Switches
## 2 Analytical Approach to the Development of RF MEMS Switches of Electrostatic Type
### The Influence of Switching Characteristics of Switch  
RF MEMS switches differ in both the activation mechanism and the principle of switching--resistive and capacitive.  
The main criterion in the development of RF MEMS resistive type switches (see Figure 2) is the contact area. Large contact areas have less resistance and, therefore, lower contact temperature. The contact area is determined by the applied force and hardness of the material, as well as its ability to form a surface layer with high-resistance. It is also necessary to take into account the adhesive force in the contact--the cantilever or the switch membrane must be sufficiently strong to overcome the adhesive force after removal of the control voltage [1].  
Typically, switches with a resistive switching principle are used in a wide frequency range from 0 to 50 GHz, contact resistance of resistive switch based on gold, as a rule, lies in the range of 0.15-0.4 \(\Omega\), contact force on the site is from 80 to 500 \(\upmu\)N. However, a feature of resistive switches is the need to supply a sufficiently high control voltage--from 60 to 80 V [13; 16; 17; 34].  
Figure 1: Structure of the analytical approach.  
RF MEMS switches with capacitive switching principle (see Figure 3) operate by changing the capacitance between the waveguide and the grounded electrode. Typically, switches with capacitive switching principle are used as shunt switches [35] with operating frequencies from 10 to 100 GHz, losses 0.2 dB, isolation from 15 to 35 dB for operating frequencies [19; 36; 37].  
The main advantage of RF MEMS capacitive type switches is the ability to develop switches with low control voltage, since there is no need to make a significant effort to create a contact. However, RF MEMS devices of this type are sensitive to surface roughness and internal stresses in the membrane [38] due to the appearance of an additional air gap, which significantly affects the ratio of capacities in the ON and OFF states. The power handling of RF MEMS switches of the capacitive type does not exceed 7 W, since the current density can exceed the critical value due to the high-resistance of the membrane fastening compared to the switches of resistive type [1].  
For the manufacture of high-power handling RF MEMS switches (100 mW and above) more preferred type of contact is the resistive switching principle. This is due to the need to use membranes with a rigid mount for the capacitive switching principle due to the effects of arbitrary operation and high current density, which eliminates their advantages over switches with a resistive switching principle.  
For the manufacture of switches of medium and low-power handling (up to 100 mW), switch with capacitive switching principle has an important advantage; the ability to manufacture a switch with low-voltage operation, which allows the use of a single power supply circuit for semiconductor devices and to control the switches.
# A 0.18 um CMOS Millimeter Wave Antenna-on-Chip with Artificial Magnetic Conductor Design
## 2 Materials and Methods
### Parameter Analysis of Antenna Slot  
In this paper, we only simulated and analyzed the antenna slot structure, observed the effect of the slot on the antenna, and took the reflection coefficient of less than \(-10\) dB as the standard. The gap evolution was divided into three parts. First, the feed end was measured with the GSG structure, and the distance between PADs was W9 = 50 \(\upmu\)m. The evolution diagram and simulation results of the antenna slot structure are shown in Figures 8a-c and 9. Figure 8a has only one slot. The bandwidth of this type was 30 GHz-53 GHz, and there was a \(-17\) dB resonance at 43 GHz. In addition to the strip gap, Figure 8b dug another strip gap near the feeder line to obtain a bandwidth of 26.3 GHz-60.4 GHz. There was resonance at 34 GHz and 55 GHz, respectively: \(-25.3\) dB and \(-12.2\) dB. Compared with Figure 8a, Figure 8b had 11.1 GHz more bandwidth. For Figure 8c, we formed an L-shaped gap with  
Figure 6: Simulated gain of proposed antenna-on-chip.  
Figure 7: Simulated efficiency of proposed antenna-on-chip.  
the strip gap close to the feeder line by digging the strip gap. At this time, the bandwidth was 32.6 GHz-69.4 GHz. There was resonance at 44 GHz and 55 GHz, with individual values of \(-\)33.2 dB and \(-\)28.1 dB. Compared with Figure 8b, the bandwidth was 2.7 GHz more. It can be seen from the above results that the bandwidth of the antenna increased gradually after increasing the strip slot and L-slot. In Figure 8c, the overall bandwidth was 36.2 GHz, with the best operating frequency band.
## 4 Numerical Results
### Configuration Comparison  
In order to appreciate the effect of the fracture toughness on the mechanical behaviour of the fuselage undergoing the crash event, the displacement patterns were superimposed in Figure 10. Indeed, in Figures 10A and 10B, respectively, the vertical displacements of control point A and point B obtained for the three analysed configurations were superimposed. As shown in Figure 10A,B, for all the configurations, the slopes at the beginning of the crash event are identical straight lines. In fact, at the beginning of the crash event, although the structure deforms and the damage has started, the effects of the damage evolution were found almost negligible. Indeed, the effects of the in-plane fracture toughness were found more significant later during the crash event almost at the maximum deflection.  
Configurations I and III, characterised by a similar value of fracture energies for both materials, both show a maximum deflection of about 250 mm for control point A and about 230 mm for control point B. For both these configurations, a significant variation of stiffness has been found at 0.022 s for control point A (Figure 10A) and 0.027 s for control point B (Figure 10B). On the other hand, configuration II, characterised by the highest toughness values, shows a maximum deformation of about 220 and 180 mm, respectively, for control points A and B. These maximum values, as expected, are lower if compared to configurations I and III.  
Finally, as already remarked, the configuration with the highest toughness, differently from configurations I and III, amplifies the asymmetrical distribution of the damage between the left side (control point A) and the right side (control point B) of the fuselage induced by the unbalancing effect of the introduced mass.  
Moreover, in Figure 11, a comparison of damage energy dissipation is reported for the three fracture toughness energies. Configuration II dissipates a lower amount of energy if compared to the other configurations. Configurations I and III dissipate a relevant amount of the total energy as damage energy; consequently, a higher value of the maximum displacement was found for this configurations, compared to configuration II.  
Figure 10: Displacement vs. time for the three considered configurations: (**A**) Point A location. (**B**) Point B location.
###### Abstract  
In the development of decarbonization technologies and renewable energy, water electrolysis has emerged as a key technology. The efficiency of hydrogen production and its applications are significantly affected by power stability. Enhancing power stability not only improves hydrogen production efficiency and reduces maintenance costs but also ensures long-term reliable system operation. This study proposes a thermal control method that stabilizes hydrogen output by precisely adjusting the temperature of the electrolysis stack, thereby improving hydrogen production efficiency. Fluctuations in the electrolysis stack temperature can lead to instability in the hydrogen output and energy utilization, negatively affecting overall hydrogen production. To address this issue, this study introduces an innovative system architecture and a novel thermal control strategy combining fuzzy logic control with a long short-term memory neural network. This method predicts and adjusts the flow rate of chilled water to maintain the electrolysis stack temperature within a range of \(\pm 1\)\({}^{\circ}\)C while sustaining a constant power output of 10 kW. This approach is crucial for ensuring system stability and maximizing hydrogen production efficiency. Long-term experiments have validated the effectiveness and reliability of this method, demonstrating that this thermal control strategy not only stabilizes the hydrogen production process but also increases the volume of hydrogen generated.  
electrolysis stack; fuzzy logic control; hydrogen; long short-term memory; neural network; thermal control; water electrolysis +
Footnote †: journal: Applied sciences
## 3 Results and Discussion  
### XRD Investigations of NiO-ZnNb\({}_{2}\)O\({}_{6}\)  
To analyze the structure of NiO-ZnNb\({}_{2}\)O\({}_{6}\) and polyaniline, X-ray diffraction (XRD) spectroscopy was conducted. NiO and ZnNb\({}_{2}\)O\({}_{6}\) underwent sintering at 1100 \({}^{\circ}\)C for a duration of 4 h. As shown in Figure 2, the XRD analysis showed that both NiO and ZnNb\({}_{2}\)O\({}_{6}\) formed singular-phase structures.  
The major phases, which are NiO (PDF Card No: 00-047-1049), ZnNb\({}_{2}\)O\({}_{6}\) (PDF Card No: 01-076-1827), and polyaniline (PANI) (PDF Card No: 00-053-1717), are clearly seen in the X-ray diffraction (XRD) patterns for NiO, ZnNb\({}_{2}\)O\({}_{6}\) and polyaniline (Figure 2). The diffraction peaks for each component are consistent with their primary phases. By using the oxide mixture method, then appropriate calcination temperature and subsequent sintering, a single-phase structure was successfully obtained for NiO and ZnNb\({}_{2}\)O\({}_{6}\), eliminating possible intermediate phases formed by calcination during the process. The XRD analysis confirmed the absence of secondary phases in PANI, sintered NiO and ZnNb\({}_{2}\)O\({}_{6}\).  
FullProf, Suite, a software package ([[https://www.ill.eu/sites/fullprof/](https://www.ill.eu/sites/fullprof/)]([https://www.ill.eu/sites/fullprof/](https://www.ill.eu/sites/fullprof/))) for Rietveld refinement, was utilized to refine the crystal structures and quantify phase compositions. Specifically, it was employed to generate the experimental lattice parameters and unit cell volume presented in Table 1, while the XRD model was matched to the reference patterns of NiO, ZnNb\({}_{2}\)O\({}_{6}\), and PANI [26,27].  
Figure 1: Schematic diagram for the synthesis of PANI/(NiO-ZnNb\({}_{2}\)O\({}_{6}\)) composite materials.
# Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions
## 1 Introduction
The drawbacks include the huge search space, a lack of understanding of how (and why) exactly the developed controllers work, and consequently a lack of confidence in their robustness and generality. Moreover, conversely to the proposed approach of employing GP, end-to-end reinforcement learning requires a human-annotated training set of data, i.e., the mapping of the states of the car and the environment (e.g., consecutive frames of a video feed) into appropriate steering commands obtained from a human (expert) driver. However, in our work we assume that such perception information is not always fully accessible (or, even if accessible, not necessarily with the same quality as the information used for training), for example, when driving in heavy-traffic, poor-visibility conditions (fog, snow, rain, night, etc.) or immediately behind a heavy vehicle (bus, truck, etc.). Therefore, in our work we propose the use of very simple perception information: only the current lateral and angular deviation of the car from the intended trajectory (i.e., the center of the lane). With such simple perceptions, for a human driver it would have been virtually impossible to achieve driving (let alone on slippery roads) that is good enough to serve as a representative trainer for the learner. Rather than automatically (via GP), the steering controller of the car on slippery roads could be handcrafted by the developers applying various top-down approaches. Usually, handcrafted solutions are based on certain assumptions intended to simplify the complex dynamics of a skidding car. The challenge of these approaches is in deciding the adequate assumptions and abstractions for the domain-specific knowledge. In predictive control ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017), for example, the controller decides the value of the control signal based on the predicted, rather than the current, values of parameters pertinent to the state of the car and its environment. The prediction could be seen as approximating the values of these parameters from their current values, their rate of change, and the prediction time that corresponds to the latencies in the control loop. Such prediction would be very accurate if the latencies are well-known, the laws that govern the changes of these latencies are also well-known, and the rate of change is (nearly) constant during the duration of the latency. In contrast to these approaches, the proposed method of automated, heuristic development of SAF via GP--as elaborated in detail in Section 2.5--assumes very little of such knowledge. The remainder of the article is organized as follows. Section 2 elaborates on the adopted model of the car and its environment. It also explains the basics of the PD and PID servo-control models and the proposed approach of employing GP for evolution of the optimal SAF. Section 3 presents our experimental results, and Section 4 discusses the some of the limitations of the results obtained by GP. Finally, Section 5 draws a conclusion.
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
## 2 Model Test
### Test Materials and Preparation Process
#### 2.2.3 Micropiles and Top Slab  
The micropile protype was reinforced concrete piles with a diameter of less than 300 mm made of C30 concrete and HRB335 steel bars. The model micropile was a pre-fabricated pile made before the test, which was composed of aluminum rod and gypsum. The water cement ratio of gypsum is 1:0.7 (\(m_{ gypsum}:m_{water}\)), and its elastic modulus is about 4.5 \(\times\) 10\({}^{9}\) Pa. Moreover, the model micropiles had a length and diameter of 1.12 m and 3.5 cm, respectively. The spacing of the micropiles and the spacing of the rows were designed to be 24 cm and 14 cm, respectively. The composite elastic modulus of the micropiles was roughly 1.167 \(\times\) 10\({}^{10}\) Pa. The top slab of micropiles was simulated by the wood board with a thickness of 1.8 cm, and epoxy resin was used to connect it to the top of the micropiles. The manufactured micropiles and top slab are shown in Figure 4a,b.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Soil Specim**} & **Unit Weight** & **Water Content** & **Cohesion** & **Internal Friction Angle** & **Elasticity Modulus** \\  & \(\gamma\) **(kN\(\cdot\)m\({}^{-3}\))** & **W (\%)** & **c (kPa)** & **\(\Phi\) (\({}^{\circ}\))** & **E (kPa)** \\ \hline Model Soil & 22.3 & 11.8 & 18.5 & 25.0 & 2.46 \(\times\) 10\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main mechanical indexes of soil specimens.  
Figure 3: Model box (**a**) positive view; (**b**) lateral view.
## 2 Literature Review
### Trajectory Analysis Using Semantic Information  
Many studies have proposed trajectory classification methods that utilize data with both spatial-temporal and other semantic dimensions to increase accuracy. [PERSON] et al. proposed an estimation method for the demographics of user groups based on trajectories obtained by analyzing wireless access points and prior knowledge of mobile users [29]. [PERSON] et al. also proposed a method for predicting the demographics of geographic units by analyzing geotagged tweets [30]. Many other methods have also been proposed, corresponding to classification or prediction models for user groups based on additional geographic and social network information [6; 7; 8; 9; 10; 11]. However, these methods must process a considerable amount of prior knowledge data linked to mobile users and perform text analyses on posts in social network services.  
In this context, [PERSON] et al. proposed MasterMovelets, which designs features for trajectories with semantic dimensions, excluding spatial-temporal dimensions [12]. MasterMovelets can be used to examine the features of spatial-temporal trajectories with rich information regarding other semantic dimensions via a series procedure. This method increases classification accuracy by focusing on local subtrajectories in a target trajectory. Most of the research introduced so far assumes that the trajectories have semantic dimensions in addition to spatial-temporal dimensions and aims to increase the classification accuracy for such trajectories. However, user information linked to mobile device location data tends to have many missing values because of the nonuniformity of users' registration statuses. Therefore, given that homogeneous information is not always added to all user trajectories, these methods cannot be applied to trajectories, which tend to have missing information.