esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Heat-to-Power Applications
### Supercritical Organic Rankine Cycles  
As mentioned in the previous section, the supercritical organic Rankine cycle (ORC) presents the exact same configuration as the supercritical steam Rankine cycle (see Figure 3), however the working fluid is an alternative fluid rather than water. The organic fluids that can be used in temperatures below 400 \({}^{\circ}\)C do not need to be highly superheated in most of the cases, a fact that leads to higher cycle efficiency [25]. This allows to exploit low-grade heat sources that otherwise would be wasted. However, unlike large-scale utility steam power plants where the use of supercritical cycles has been accomplished, in small/medium-scale systems using a supercritical ORC, the selection of the appropriate components becomes very challenging. Moreover, low-temperature operation (below 100 \({}^{\circ}\)C) brings some additional restrictions, since a limited number of fluids can be used for such purposes and the cycle configuration does not allow any flexibility.  
Supercritical ORC systems are studied in several publications [26; 27; 28; 29; 30; 31] especially for geothermal and industrial heat recovery applications, where the temperature of heat source is adequate for a supercritical transition. The number of experimental references can be a good track of R&D progress on the supercritical ORC technology. Even though the theoretical investigation is becoming important  
Figure 2: The supercritical Rankine cycle with multi stage reheat.  
in this field, only few ORC units running at supercritical conditions are reported so far [32], due to the excessive pressure levels and safety concerns. Most experimental systems refer to a low temperature operation and an accordingly low production, however the results concerning the cycle's efficiency are very promising. Some experimental investigation has also been carried out for larger systems as presented below.
# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
## 3 Methodology
### Phase 1: Exploring Gender Bias in Different CNN- Based Face Presentation Attacks Detection Methods
#### 3.3.2 Exploring Gender Bias in Detecting Face Presentation Attacks Using VGG16 Architecture  
VGG16 is another popular CNN model that is used in detecting spoof. In this step VGG16 was trained on SiW without using any previous weights, so VGG16 learned from scratch in this stage.  
**Model Construction:** VGG16 model was built from scratch. It consists of a specific sequence of convolutional and MaxPool layers. The total numbers of the convolutional and the MaxPool layers used in the model are 16 and 5 respectively. After the model was constructed, it was only trained on the training dataset of SiW without using any previous weights.  
**Hyper-parameters Setting:** Table 3 shows the values of hyper-parameters used in experiments with VGG16 for binary classification.  
Phase 2: Exploring Mitigation of Gender Bias in Detecting Face Presentation Attacks Using VGG16 Architecture and the Debiasing Variational Autoencoder (DB-VAE)  
In this phase, the VGG16 was used to evaluate bias in presentation attack detection. Then it was used as an encoder in the debiasing variational autoencoder (DB-VAE) proposed in [9] instead of the standard CNN model. The purpose of using the (DB-VAE) here is to investigate its ability to reduce bias in face presentation attack detection.
## 2 Numerical Methods and Theoretical Model
### Problem Description and Computational Method  
The computational domain and projectile geometry are shown in Figure 1a, \(D\) is the diameter of the projectile, \(L\) refers to the length of the projectile. The size of computational domain is \(200D\ \times 190D\). The value of aspect ratio \(L/D\) is chosen to be 5 and 8 in the simulation, respectively. Three kinds of water entry angle are simulated in this paper, \(10^{\circ}\), \(45^{\circ}\)and \(80^{\circ}\), two kinds of initial water entry velocities, 100 m/s and 500 m/s, are investigated. The densities of water and air are 998.2 Kg/m\({}^{3}\), 1.225 Kg/m\({}^{3}\), respectively. The saturation pressure is assumed to be 3540 Pa. For simplicity, we use the combination of Angle_L_Velocity to specify the simulated cases, such as 10_5D_V100 means water entry with the initial angle of \(10^{\circ}\), velocity of 100 m/s, and the aspect ratio \(L/D\) of 5.  
Figure 1: _Cont_.  
An unstructured grid consisting of 383,800 cells is used to calculate the simulation and the local mesh near the projectile is shown in Figure 1b. Three other grids consisting of 232,500, 306,300 and 437,300 cells are also used to check the grid sensitively of the solutions. According to the number of cells, the cell density from sparse to dense is numbered as I, II, III, IV, respectively. The comparison of the cavity shape at \(T=30\) under different cell density conditions is shown in Figure 1c. By comparing the cavity shapes for these grids, it can be found that the cavity shapes for cell density III and IV are almost the same. Considering the calculation accuracy and cost, the cell density III will be used to carry out the following simulation.  
For numerical simulation, we use the commercial CFD software Fluent [22] to solve the governing equations. The top of the computational domain is specified as pressure inlet boundary; while the sides and bottom of the domain are set to be pressure outlet boundary with the pressure equals to the local static pressure. The time-dependent governing equations are discretized by using the finite volume method, and the PISO scheme is used to solving the pressure-velocity coupling algorithms. The second order upwind scheme is used for the convection terms and the central-differenced for the diffusion term in the momentum equations. The PRESTO scheme is used for the pressure interpolation. The QUICK scheme is used for the transport equation for the volume fractions.  
In this research, the minimum cell size of the grid is set about \(5~{}\times 10^{-6}\) m, the time interval (\(\Delta t\)) of the simulation is calculated based on the CFL condition (Courant number is less or equal to one.).
# Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques
## 2 Proposed Method  
In our previous work [4; 11], we successfully developed an eye-tracking method designed for faces in diverse environments and user conditions. This method involved 11-point eye-nose shape tracking, which was based on the supervised descent method (SDM) [14] for non-occluded faces. Building on the success of our previous work that extensively employed machine-learning-based computer vision algorithms for pupil tracking, we now extend our approach to utilize the unique capabilities of event camera imaging. In this section, we describe how we adapt and apply our previously developed machine-learning-based algorithm to the context of event camera data. Specifically, we present the design of our novel pupil-tracking algorithm, which incorporates feature extraction, eye-nose detection, and alignment methods tailored specifically for event camera imaging. By building upon the principles established in frame-based eye-tracking research and combining them with the insights from our previous work on bare-face eye tracking, we aim to unlock the potential of event camera imaging for more sophisticated and effective pupil tracking. This novel approach holds promise for advancing eye-tracking technologies and expanding their applications in various real-world scenarios.
# AHC: Advanced Network-Hiding Access Control Framework
## 5 Experiments and Analysis  
### Experimental Setting  
Based on the AHAC framework proposed above, we carried out corresponding experimental settings aligned with the research motivation of this article. The goals primarily focused on verifying the performance, high availability, extensibility, and compatibility of AHAC. Additionally, the time cost and performance of the AHAC encryption algorithm were compared and analyzed in detail against SPA. In this setup, both the server and client environments used the Linux operating system. The hardware configuration included an Intel Core(tm) i5-3470 CPU @ 3.22 GHz x 4 processor, Mesa Intel HD Graphics 2500 (IVB GTI) graphics card, with 8.0 GiB memory and 1.0 TB disk capacity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Port Knocking** & **SPA and Enhancement** & **AHAC** \\ \hline Dev Language & Python, Bash & C, C++ & C/C++, Python, Java, Go \\ \hline Communication & Port sequences & Single package authorization & Noise Protocol, ECDH \\ \hline Architecture & Simple, client-server & Complex, encrypted packets, firewall & Advanced, scalable, Noise Protocol \\ \hline Authentication & Basic Port Knocking & UDP knocking IP amplification issue & Device fingerprint, UDP and TCP knocking \\ \hline Encryption & None & RSA, AES & Noise Protocol, and ECDH \\ \hline Performance & Low overhead & Moderate overhead, efficient & Optimized, minimal overhead \\ \hline Ability for & & & \\ Network-Hiding & & & \\ \hline Availability & Low to medium load & Higher load & High availability, scalable clusters \\ \hline Extensibility & Limited & Complex implementation & FIDO + AHAC, highly scalable, easy integration \\ \hline Compatibility & Most systems & Various systems, may need integration & High, multi-platform, future-proof \\ \hline Security & Basic, sequence secrecy & Strong encryption, key management risk & Address hidden, mutual authentication \\ \hline Use Case & Basic access control & High-security scenarios & Scalable, high-security environments \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Port Knocking, SPA, and AHAC.
# Research on the Mechanism and Dynamic Characteristics of Intermitt Failure of Electrical Connectors
## 2 Analysis of the Electrical Connector Intermittent Failure Mechanism
### Analysis of the Degradation of the Contact Resistance  
The common failure mode of the electrical connector includes contact failure, insulation failure, and mechanical failure. The contact failure accounts for the largest number of all electrical connectors' failures. The reasons for contact failure are as follows:  
* Creep and stress relaxation. The creep and stress relaxation both lead to a decrease in the elastic deformation of connectors. According to the Hertz contact theory [32], the conductive area of the contact spot will decrease with the decrease in elastic deformation, resulting in a significant increase in contact resistance.
* Metal migration. The metal migration mainly causes variation in contact area, then contact resistance fluctuates.
* Oxidation and corrosion. The contact area is easily corroded by oxygen. Thus, the oxidized film, which is composed of an insulating material, will cover the conductive spot gradually. Then, the size of the conductive spot will decrease, and the resistance will increase.  
The growth of oxidized film on the surface easily causes contact failure. The growth process is shown in Figure 2. Initially, the film is formed around the conductive spot. As the contact surface is continuously oxidized, the oxidized film accumulates gradually. Finally, the oxidized film, which is made of a nonconductive material, covers the original conductive contact area. When the whole contact surface is covered by oxidized film, the electrical connector fails.  
Figure 1: Current passing through the contact surface.
## 3 System Model and Architecture Design
### Energy Efficient Algorithm for SBD2D Based on Data Scheduling  
Beekeeping is an activity mostly operated in remote areas, where the use of grid power is inaccessible and the use of batteries to power sensors is not a feasible option, because their life cycle is very short and requires frequent replacement; hence, the system becomes expensive. Therefore, the new algorithm is designed to ensure the system's power efficiency, thereby allowing farmers to monitor their beekeeping activities every day. Figure 4 is a flowchart of the proposed model.  
The methodology used to achieve energy efficiency in this case is based on the use of a 1:10 ratio for the active to standby ratio. The algorithm works by considering the number of components connected to the smart beehive sensor network. The components are assigned priority based on the importance of the parameter they monitor. Based on the priority assigned, some components will always be in active mode, whilst others are scheduled. For example, temperature and humidity are important parameters that need to be closely monitored, and as such, DHT11 is always active. Based on the measured values, DHT11,in turn, instructs the digital fan and thermoelectric heater to be ON or OFF, according to the pre-set condition. However, other sensors are scheduled in the order of a 1:10 active to standby or sleep ratio and the servo motor is always in standby mode until it receives instruction from the appropriate transducer.  
Algorithm 1 further schedules data transmission. Instead of all the sensors and actuators transmitting data at once, the data transmission is performed in different iterations, i.e., five iterations. This means that some sensors and actuators are kept in active mode, while others are in standby mode for transmission. Different sensors and actuators respect the process of transmission within five iterations and a time slot of 2 s. For all iterations, there is an initialization period of 60 s, so that all the sensors and actuators would be in active mode to transmit data. However, due to the data scheduling process, each sensor or actuator would take a short time of two seconds to transmit data, while the other sensors wait for the transmission to be completed. Due to the scheduling, the data reach their destination quicker, since there is limited interference and consequently the energy efficiency is maintained. The results show that, if there are multiple transmission attempts of data from the source to the destination, the system's energy consumption tends to be high. Furthermore, when all the sensors transmit data from the source to a gateway or cloud at the same time, there is high interference, which implies data loss, forcing the sensor to make multiple transmissions. However, in our proposed algorithm, due to scheduling the transmission, the loss of data is reduced, and consequently, QoS is guaranteed; thus, the energy efficiency will also be maintained.  
Figure 4: Proposed block diagram integrating data scheduling.
# Closing Force Evaluation of a Sample Return Capsule for a Phobos Sample Return Mission
###### Abstract  
The mission objective of the Phobos Sample Return is to collect and return 100 g of Phobos' surface material to Earth inside a tight enclosure composed of a Vault, a Sample Container and sealing elements. One important aspect of the project was the development of a closing mechanism capable of ensuring a pushing force high enough compared to the available force of the robotic arm (40 N). The need for a higher pushing force derived from the design tests which were carried out to experimentally determine the necessary force to overcome the resistance of the sealing element when the vault is closed. Two types of sealing elements, custom made for this project, along with two SC with different geometrical shapes in the sealing area were tested. For better accuracy, the tests considered the imposed operational temperature domain for the vault, ECSS standards and the test rig set-up being performed at environmental temperature (+20 \({}^{\circ}\)C), \(-\)20 \({}^{\circ}\)C, \(-\)60 \({}^{\circ}\)C and +70 \({}^{\circ}\)C. The results of the tests highlighted that the negative temperature has a significant influence over the closing force, as this force is increasing once the operational temperature is decreasing. Based on the work performed, the most suitable type of sealing element was identified, in particular the piston geometry which allows a smaller force to close the vault.  
Published: 9 July 2021  
25 August 2021  
31 August 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Published: 2021 by the authors. License MDPT, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/40/](https://creativecommons.org/licenses/by/40/)]([https://creativecommons.org/licenses/by/40/](https://creativecommons.org/licenses/by/40/))).**  
**Keyword:** Phobos; Sample Return Mission; sealing element; closing force; design test
## 3 Parameter Analysis
### Influence of the Slope Angle of Reef Bottom Layer on Site Responses
#### 3.1.1 Influence on the Responses of the Reef Platform  
Figure 5 shows the distribution of peak acceleration at the reef platform. For different slope \(\theta\) values, the horizontal peak acceleration increases rapidly from the midpoint of the reef platform to both sides but decreases rapidly in the area close to the corner points at both sides. This phenomenon is most obvious when \(\theta=65^{\circ}\). When the slope is \(\theta=65^{\circ}\), the horizontal peak acceleration of each point on the reef flat is greater than the corresponding results for \(\theta=30^{\circ}\) and \(45^{\circ}\). This may be explained as follows. The bottom opening width of the reef island terrain corresponding to \(\theta=65^{\circ}\) is the smallest, resulting in the smallest internal space of the entire terrain. When seismic waves enter this small space, they will scatter repeatedly within it and become difficult to come out, thus causing the above phenomenon. In addition, for a fixed slope, there are significant differences in the results under different input ground motions, which is especially reflected in the results of \(\theta=30^{\circ}\) under Iwate motion. This reflects that the spectral characteristics of the input ground motion have a significant impact on the distribution of horizontal peak acceleration on the reef platform.  
The distribution of vertical peak acceleration on the reef platform is more complex. This is mainly reflected in the range of about \(-120\)-\(120\) m, that is, within the range of about \(1/4\) of the platform width on both sides of the original point. Within this range,  
Figure 5: Spatial distribution of peak acceleration of reef flat (\(L=500\) m).  
the vertical peak acceleration corresponding to the slope \(\theta=65^{\circ}\) is greater than for the slopes \(\theta=30^{\circ}\) and \(45^{\circ}\). The explanation for this is the same as that for the horizontal peak ground motion above. The spectral characteristics of the input ground motion also have a significant impact on the vertical peak acceleration distribution on the reef platform, and high-frequency ground motions will cause larger vertical peak acceleration on the platform.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 4 Reverse Packet Elimination
### Packet Size  
In our investigations, we utilize a max packet size of 127 bytes for data packets and the RPE-cancelling packets are set to 23 bytes. 23 bytes is the minimum packet size of an 802.15.4 frame carrying the necessary headers and two bytes containing the RPE-cancelling payload.  
Figure 4: Illustration of Reverse Packet Elimination (RPE) step 1 (**a**) and step 2 (**b**).  
Figure 5: Illustration of RPE step 3 (**a**) and step 4 (**b**).  
Link quality is defined by its BER and the BER differs for each link according to its propagation characteristics. Assuming the BER of each bit is independent, if there is a bit error in a packet, the packet is dropped. PER in Equation (1) denotes the packet error rate for a packet with n bits.  
\[PER=1-(1-P_{b})^{n_{bits}} \tag{1}\]  
Writing packet reception rate (PRR) as PRR = \(1-\text{PER}\), the following relation is found between the PRR of two packets with different sizes (short and long packets, namely \(PRR_{s}\) and \(PRR_{l}\)), with both packets experience the same BER.  
\[PRR_{s}=1-PRR_{l}^{\frac{L_{s}}{\tau_{l}}} \tag{2}\]  
where \(L_{s}\) and \(L_{l}\) are the numbers of bits in the short packet and long packet respectively. Equation (2) shows that the packet size matters in terms of PRR. Since the cancelling packet is significantly smaller than the larger upstream packet it has some advantages; it has higher PRR. Furthermore, the smaller RPE-cancelling packets require less transmission time as they contain fewer bits than a large data packet. This causes reduced energy consumption. Finally, due to higher PRR, the time the radio is active is lower, energy consumption decreases due to fewer retransmission attempts. From Equation (2) we find that if a 127 bytes data packet experiences a PRR = 70%, then a 23 bytes RPE-cancelling packet experiences a PRR = 93.7% under the same BER condition.  
To put this into perspective, assume the topology in Figure 4 where the source sends replicated packets along Path A and Path B. After a while, the link quality between 1 and 3 deteriorates from 70% to 20% for the 127 bytes packet. Assume that a 127 bytes packet then has a \(PRR=20\%\), a 23 bytes RPE-cancelling packet have \(PRR=74.7\%\) at the same link. This means that theoretically, a RPE-cancelling packet can traverse from 1 to 3 and stop the large packet from utilizing all its retransmission attempts. In essence, this decreases resource utilization at nodes when a change in link quality occurs. As mentioned earlier, smaller packets consume less energy than larger packets. The main contributor to energy consumption in WSN is transmitting and receiving packets [22]. Based on this understanding the energy consumption of transmitting and receiving a packet is proportional to the packet size. Thus, transmitting short RPE-cancelling packets requires less energy than longer data packets.
## 1 Introduction
In addition to the contributions stated above, the proposed systems also collects thermopile and ambient temperature data which are transferred to a computing device wirelessly at an interval of 2 s.  
* An artificial neural network (ANN) is trained on the thermopile array, and ambient temperature sensor data can provide a significantly higher R\({}^{2}\) score for wind speed and direction estimation than the SVM and LR methods. * For the evaluation of system, 96 h of data are used for training the ANN model and 72 h of data are used for the testing the model performance. This paper is structured as follows: in Section 2, a brief discussion of related work and research is presented. Section 3 introduces the mechanical design and working principle of the proposed system. In Section 4 the collected dataset, regression results and comparison between different regression methods are discussed. Section 5 draws the conclusion for the presented study and discusses limitations and future directions.
## 5 Performance Analysis  
### Deployment of Nodes  
Consider a 100 m \(\times\) 100 m square area with N sensor nodes distributed at random as shown in Figure 4, using the following settings: a total of 50 nJ/bit is the initial energy, with a maximum transmission distance of 200 m and a data aggregation energy of 5 nJ/bit/message. By our proposed EEWBP method, nodes' weights are calculated using a variety of parameters, as shown in the following Table 4. Our optimized weighted clustering method uses less energy than LEACH, CACP, CPCP, and CUCA when it comes to average node energy consumption or overall power consumption. We have the ability to lower the number of transmissions by a large margin since our technique selects CH by using multi-weight parameters like residual energy, the distance between and the degree of each node, an average flight speed, and a trust value [41].  
Figure 3: Proposed protocol structure.  
Cluster headcount, cluster construction, and consistency are the most critical factors that we employ in our evaluation process. Time, energy expenditure, network lifetime [42; 43; 44], and the probability of success are all considered. In our proposed protocol, MATLAB is used as a simulation platform in this experiment.