esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Temperature Collection
Figure 6 shows examples of tank temperature changing behavior on sunny days during summer and winter. As seen, the peak temperature for the east and west sides shifted due to the sunlight direction changes in the morning and the evening. Moreover, in most cases, there was a positive initial thermal gradient (\(\Delta\)T \(>\) 0) on the tank wall thickness between 9:00 a.m. to 6:00 p.m., which is suitable for performing IRT. Therefore, this implies that the best time for performing IRT can be on sunny days and sometime between 9 a.m. to 6 p.m., with a preferred time in the afternoon (2:00 p.m. to 6:00 p.m.) when both east and west sides have comparable temperatures. In addition, since the maximum \(\Delta\)T for the east and west sides happen at different times during the day, it is hypothesized that it may be better to evaluate each tank twice a day: once in the morning and once in the afternoon. For this reason, in this study, the IRT has been completed once in the morning and once in the afternoon for each heating-cooling combination to study the accuracy of the last hypothesis. The feasibility of implementing this hypothesis in the field for evaluating a real tank has been studied by conducting field testing as discussed in Section 3.8. Figure 3: The non-uniform temperature on the tank (picture was taken in the afternoon when the sun was on the west side). Figure 4: Temperature change on the tank wall at three different elevations of the top, middle, and bottom from two different sides of east and west measured via attached thermocouples: (**a**) at 10:30 a.m. and (**b**) at 4:30 p.m.  
Figure 5: Temperatures of the HDPE tank, water, and ambient on two cloudy days during winter, measured via attached thermocouples. Figure 6: Temperatures of the HDPE tank, water, and ambient on two sunny days during **(a)** summer and **(b)** winter measured via attached thermocouples.
# Lightning Interferometry with the Long Wavelength Array
## 3 Results
### Positive Leader Tips
Although the deconvolution algorithm correctly identified much of the emission seen in the dirty image, there is still additional unlocated emission along other channels. The faintness of the positive leader tips' emission, coupled with the rather crude deconvolution routine herein, causes the positive leader tips to come and go from the maps of the lightning flash. By examining the dirty images directly, it becomes clear that the positive leader tip is a continuously radiating feature. Figure 6e shows a streak image of the dirty image brightness along the positive leader channel line depicted in Figure 6a,b. The color shows the indicated received power of the dirty image using the scale in Figure 6c. Portions of the dirty image that have negative intensity are not physical and are masked in white. Both the positive leader tip and the needle activity are identifiable, with the positive leader tip being a continuous feature in front of the needle activity along the channel. Because of the finite angular resolution of the LWA, both the positive leader tip and the needle emission cause brightness along an extended length of the positive leader path in the streak image, obscuring the separation between them. Careful examination indicates that the positive leader tip is 1-2 km in front of the needle emission. The results also indicate that the positive leader propagates roughly 4.5 km in 48 ms at a nearly constant velocity of 9.4 \(\times\) 10\({}^{4}\) m/s. This is significantly faster than the 2 \(\times\) 10\({}^{4}\) m/s reported in previous radio studies of in-cloud positive leaders [4; 8; 39; 40; 41], but similar to studies of positive cloud-to-ground leaders [42; 43]. The conversion to linear distance was achieved by assuming that the positive leader was propagating purely horizontally at a constant altitude, similar to the methods used in [PERSON] et al. [33]. The altitude used was 6200 m above the ground (or 7600 m above sea level), based on the ENTLN pulse locations for this flash. The nearest sounding (85 km to the north) indicates a temperature of \(-\)15 \({}^{\circ}\)C at this altitude, a reasonable height for the main negative charge layer for the storm. We estimate an error of around 1 km in the altitude of the positive leader, and the velocity estimate does not change greatly within this range. The unusually high velocity of this positive leader may be related to the detectability of the emission from the positive leader tip.
## 3 Methodology
### Place of Anemometer Installation  
With a wind speed meter located far enough to detect a gust, the controller gives the actuator the signal of the setpoint to adjust the pitch angle in advance, and by the time the gust is in the turbine, the rotor does not generate an overshoot of force that rotates the turbine above the cut-off speed, as this can cause damage to the mechanical system.  
The data to determine the appropriate distance for the placement of an anemometer are the maximum rotation speed of the pitch angle equivalent to 3\({}^{\circ}\)/s; therefore, the time for the pitch angle to rotate from 0\({}^{\circ}\) to 90\({}^{\circ}\) is 30 s. In addition, the minimal speed considered for a gust is 10 m/s and the maximum wind speed historically presented at the installation site is 15 m/s, so the distance calculated to detect a wind speed of 15 m/s, 30 s in advance, is 450 m.  
The mean of communication between the anemometer and controller located in the tower is carried out using the MODBUS industrial communication protocol with a transmission speed of 19,200 bps and a maximum distance of 1200 m [40], so the delay in communication is not considerable.  
The locations of the wind turbine and the proposed place for the installation of the anemometer are shown in Figure 8.  
Figure 8: Installation area of the wind turbine and anemometer. Letter A indicates the installation site of the wind turbine, and letter B is the installation site for the anemometer.  
Figure 7: Wind direction distribution.  
A dimensional analysis of the nearby buildings located in a straight line between the wind turbine and the anemometer is carried out to verify that the wind path is not affected and the wind speed measurements are not altered. Scale diagrams of the wind turbine and the buildings, with the height differences between buildings and the wind turbine, are shown in Figure 9.
## 5 Results and Analysis  
In this section, we evaluate if the proposed optimization framework, described in Section 4, could be successfully used to derive truth tables for scheduling application requests in order to minimize both the driver workload and the queuing of actions. The analysis concerns with the development of the truth table are represented in Table 2. This truth table is suitable for scheduling of \(m=5\) application requests, each of them belonging to one of the priority classes listed in Section 3: \(\text{FCW}\in\text{W}\); system interaction \(\in\text{D}\); turn indicators \(\in\text{OP1}\); volume increase of infotainment system \(\in\text{OP2}\); phone call \(\in\text{OP3}\).  
Specifically, the truth table is carried out by defining five truth functions \(f_{i}\), with \(i\in\{1,\,\ldots,\,5\}\), which suggest if the application requests could be executed or not. In this decision process are involved 11 parameters: \(n=5\) DVE parameters, i.e., DVE 1, DVE 2, DVE 3, DVE 4, and DVE 5, measured by the status recognition and monitoring system, that, as described in Section 2.1, allow us to represent 32 possible DVE states; queue length parameter LQue; and \(m=5\) application requests (App. 1 = FCW, App. 2 = system interaction, App. 3 = turn indicators, App. 4 = volume increase, App. 5 = phone call) to take into account multiple and simultaneous actions.
# Late Fusion-Based Video Transformer for Facial Micro-Expression Recognition
## 2 Related Works
### Vision Transformer  
The transformer is a highly successful model in natural language processing and has recently been applied to computer vision. The best-known vision transformer is ViT [17], which replaces word tokens in a sentence with patch tokens in an image. The biggest difference between the transformer and CNN, which was the mainstream in computer vision, is that it uses self-attention operations, not convolution operations. Self-attention is an operation that allows each token to represent contextual information within the group to which it belongs, rather than representing an individual meaning. For that, self-attention converts the input token into individual query, key, and value, and calculates a scaled dot product [18] between them. Because self-attention models the relationship of patches within the image they belong to, unlike CNN, the vision transformer has a global receptive field. In addition, each query, key, and value depends on the input data, so unlike CNN, the transformer has a property of adaptive weight aggregation, making it more expressive. However, as it has a large capacity, it is necessary to learn with large amounts of data to achieve good performance. Many studies have been proposed to achieve similar performance to CNN with the same amount of data. Among them, the Swin transformer [13] used in this paper is a study to solve the above problem by borrowing some of the pyramid structure and locality of CNN.
## 5 Simulation Results
### Overall Performance of the Recovery Planning Framework
The configuration of the UAV swarm and the trajectory of the mother aircraft is the same as the previous simulation. In each case, the Monte Carlo simulation performs 100 runs. The results of the mean value, the standard deviation, and the best value of the objective function is shown in Table 6. Clearly, the mean values with a non-maneuvering mother aircraft are lower than those with a maneuvering mother aircraft, respectively. This is caused by the inappropriate distances and the heading angles of the UAVs, so that they do not have recovery time windows. Besides, the mean values are close to the best values, which indicates that the UAVs can be recovered efficiently by using the recovery planning framework. In addition, we can see that there is a slight increase in the standard deviation as the size of the UAV swarm increases, but they are in an ideal range. Overall, the results of the Monte Carlo study further validate that the recovery planning framework has a good performance in the aerial recovery problem for the UAV swarm. This framework ensures that the mother aircraft recovers the most UAVs in the most efficient manner despite the quantity change for the UAVs, and the flyable paths of the UAV swarm can be designed by using the homotopic path planning approach. Figure 11: _Cont._  
Figure 11: Representative recovery results. (**a**) 10 UAVs with a non-maneuvering mother aircraft; (**b**) 10 UAVs with a maneuvering mother aircraft; (**c**) 20 UAVs with a non-maneuvering mother aircraft; (**d**) 20 UAVs with a maneuvering mother aircraft; (**e**) 30 UAVs with a non-maneuvering mother aircraft; (**f**) 30 UAVs with a maneuvering mother aircraft.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
###### Abstract
To solve this problem, this paper proposes the use of a multisink WSN structure and presents a new routing protocol for multisink WSNs that is based on transmission power control and energy balance (PCEB-MS). The basic premise is as follows: a number of sink nodes are placed in the middle of the coalmine tunnel at random intervals (i.e., not more than the maximum communication radius of the sink), and the transmission power between the sinks is controlled to ensure network coverage. In the coverage of each sink, an individual region, which includes the cluster head and universal nodes, optimizes the selection mechanism of the candidate cluster head using a non-uniform clustering idea to ensure  
Figure 1: The haulage roadway. that the sensor node that has the largest residual energy becomes the priority candidate cluster head. To enable this step, it is necessary to calculate the non-uniform competition radius and the competition method required by the candidate cluster heads, by considering the distance from the residual energy and the cluster head to the sink. Selection of the cluster head is necessary to enable the energy consumption balance. The main innovative points of this paper include the following:  
1. The use of multisink WSNs in a coalmine tunnel to enable transmission of data to the sink in a rapid and timely manner. 2. The provision of a new routing protocol in a coalmine tunnel based on a multisink WSN structure. 3. The new algorithm has good network performance, including good connectivity, power efficiency, and delay. 4. By targeting underground coalmine tunnels with multisink WSNs, each sensor node can transmit data to the sink in either one hop or two hops. Figure 2 shows the architecture of the multisink WSN, which includes more than one sink, many sensor nodes, and one mine communication switch in the underground coalmine. The sink and sensor nodes obtain location information with UWB technology [3; 4], the sink nodes connect each other to the mine communication cable, and the sensor node transfers data in one hop or two hops to the sink. All of the data are sent to the mine communication switch and are then transferred to the switch, certain types of servers, and routers on the ground. Finally, the relevant department obtains production information in real time. This paper is organized as follows: Section 2 describes related studies. Section 3 explains the network and transmission power control model used in this paper. Section 4 presents the detailed design and introduces the PCEB-MS algorithm. Section 5 analyzes the parameters in the PCEB-MS algorithm. Moreover, Section 6 presents the simulation experiment for this study. The final section provides the conclusion and future research directions.
# Vibration Analysis of a Piezoelectric Ultrasonic Atomizer to Control Atomization Rate
###### Abstract  
In this work, a mechanical vibrational analysis of an ultrasonic atomizer is carried out to control its atomization mass transfer rate. An ultrasonic atomizer is a device constructed with a piezoelectric ring coupled to a metallic circular thin plate with micro-apertures. The mechanism of mass transfer by atomization is a complex phenomenon to model because of the coupling effect between the fluid transfer and dynamic mechanics controlled by a piezoelectric vibrating ring element. Here, the effect of the micro-apertures shape of the meshed thin plate coupled to a piezoelectric ring during vibration, as well as the resonance frequency modes, are numerically studied using a finite element analysis and compared with theoretical and experimental results. Good correlations between the predicted and experimental results of the resonant frequencies and atomization rates were found.  
atomization rate; finite element analysis; micro-apertures; piezoelectric; resonant frequency; ultrasonic atomizer +
Footnote †: journal: Applied Sciences
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 3 Related Work  
### SUCI Protection in 5G  
The introduction of encrypted subscriber identity in 5G comes from the fact that, in the technology of previous generations (2G, 3G, 4G), the plaintext transmission of subscriber identity (e.g., IMSI) during user registration suffers from user privacy leakages. An attacker, with open-source hardware and software tools, can act as a man-in-the-middle between the UE and the base station to eavesdrop on the plaintext IMSI without the knowledge of the network operator or the user. The captured IMSI is then utilized to track the user location and eavesdrop on user private conversations [1]. For example, [PERSON] et al. [13] realized this attack (also known as an _IMSI catcher_) in a 4G/LTE network using software-defined radios (Ettus USRP B210) and open-source software tools (OpenBTS and Open Air Interface). While there are research works conducted to reveal the presence of IMSI catchers [14; 15; 16], the 3 GPP standard addresses the issue by introducing a Subscription Concealed Identifier (SUCI) in the 5G authentication and key agreement protocol [6].  
In 5G, the user encrypts its identifier (IMSI/SUPI) using public-key cryptography, turning it into a concealed identifier (SUCI) that serves as a component of 5G-AKA during the authentication and key agreement protocol. The 3 GPP proposes two discrete-logarithm-based elliptic curve cryptography (ECC) algorithms to calculate the SUCI. The protection scheme identifier field in the SUCI format specifies the algorithms as X25519 and secp256r1 as discussed in Section 2.2.1.
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 1 Introduction
From the end of the nineties, incoherent digital holography techniques began to emerge again with new possibilities [13; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]. One notable invention in the field of incoherent digital holography is Fresnel incoherent correlation holography (FINCH) which was also developed based on the self-interference principle [23]. FINCH was able to twist one of the fundamental laws of optics, the Lagrange invariant, in order to enhance its transverse image resolution. Alternative versions of incoherent holography systems called self-interference digital holography (SIDH) techniques, were developed and implemented for applications of adaptive optics [28; 32]. More recently, a generalized version of the self-interference incoherent digital holography technique called coded aperture correlation holography (COACH) has been developed [46]. In this survey, we review various designs and applications of the COACH systems and other close techniques. This review consists of four main sections. The development of COACH architectures, with different modalities and characteristics, is reviewed extensively in the following section. In the third section, various applications based on COACH techniques are discussed. The final section summarizes the review.
# An Algorithm to Generate a Weighted Network Voronoi Diagram Based on Improved PCNN
## 4 Network Voronoi Diagram Construction Based on the Improved PCNN
### Initialization
#### 4.1.1 Points' Projection  
The first step of the algorithm is projecting all the points in the point cluster onto the road segments, aiming at locating the positions of initial neurons (points in the point cluster) on the road network in the improved PCNN model. As shown in Figure 5, perpendiculars from points \(P_{1}\)-\(P_{5}\) to the nearest road segments are able to be found. The intersection (marked as \(P_{1}\)'-\(P_{5}\)') between the perpendiculars and the road segments can be regarded as the projection of the points.
## 3 Results and Discussion
### Agoronomic Potential
#### 3.1.1 Macronutrients of the Biosolids and Animal Waste
[41] in 7 WWTP in Swaziland (South Africa) found average concentrations of 29.95 \(\pm\) 7.54 kgN ton\({}^{-1}\) and 18.33 \(\pm\) 5.89 kgP ton\({}^{-1}\). Regarding animal waste, the average concentration of N in the WWTP of the present research (IBRS WWTP) was 975% higher than the average concentration in cattle manure (lowest concentration of N between animal residues), with a mean concentration of 3.45 \(\pm\) 1 kgN ton\({}^{-1}\) and 224% higher than the average concentration of N in chicken manure, 15 kgN ton\({}^{-1}\), which contains the highest concentration of N between organic residues from animals. When comparing the average concentration of P in IBRS WWTP between animal residues, we observed that it was higher than in the manure of chickens, with the highest concentration of P (15 kg P ton\({}^{-1}\)) between animal residues. Yet it was 1787% higher than the quantity of P (1.15 \(\pm\) 0.21 kg P ton\({}^{-1}\)) in pig manure, which contains the lowest quantity between organic residues from animals. Regarding potassium, the average concentration of K in the WWTP of the present research was similar to the concentration of other tropical climate WWTPs. A highlight was for chicken manure with a concentration of 8 kgK ton\({}^{-1}\) and sheep manure with 11.9 kgK ton\({}^{-1}\). IBRS WWTP's Ca and Mg had low quantities, differing from the other WWTPs presented in the present study, which can be explained by the fact that the WWTP of the present study receives industrial and treated effluent, which reduces the concentration of those nutrients. Another possible explanation is that the sewage treatment systems differ in their techniques and procedures before they generate biosolids [41]. It is known that the biosolids composition varies significantly between the systems, as well as over time [42]. Among organic residues from animals, the quantity of Ca for manure of chickens, 24 kgCa ton\({}^{-1}\), significantly stands out. At last, another nutrient with a high concentration in the biosolids was S in the WWTP of the present research (11.13 \(\pm\) 1.76 kgS ton\({}^{-1}\)). Despite the clear potential of this byproduct (biosolids), given the values here presented and compared with different types of effluent and processes generating biosolids, the population still gives a certain attention to organic waste [43]. Especially in regulatory and supervisory bodies, when the byproduct is of human origin, there is a rejection, particularly toward biosolids [7]. Ref. [44] observed that efforts can be made in three aspects to encourage the adoption of food fertilized with biosolids, namely, provision of nutritional information and microbiological quality of the product to resolve the population's doubts; effective price system that can make composting products more competitive; and more flexible policies to facilitate the applications.