esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed mmWave Assisted CR-IoV
### Dual Step Security Provisioning
#### 4.3.3 Mathematical Model of FA-TDoA
First, a hash value is generated for the SU ID, i.e., \(e=H[SU_{ID}]\) by using the Secure Hashing Algorithm-3 (SHA-3). 2. Select a secure random integer \(q\) from the range of \([1,\ h-1]\). Here, \(h\) represents the order of the base point. 3. Consider FourQ-curve which is represented as,  
\[d_{u,i}=\sqrt{(x_{i}-x_{u})^{2}+(y_{i}-y_{u})^{2}} \tag{9}\]0  
Here, \(a\) is the non-square function of the Mersenne prime. On this curve, the base point \(BP\), which is the prime number that satisfies the curve equation is derived. 4. From the \(BP,\ Pv_{sk}\) and \(Pu_{sk}\) are derived as follows,  
\[d_{u,i}=\sqrt{(x_{i}-x_{u})^{2}+(y_{i}-y_{u})^{2}} \tag{9}\]1  
The \(Pv_{sk}\) is chosen random manner and \(Pu_{sk}\) is derived from the private key. 5. Next, compute the curve point \((x_{1},y_{1})=q\times BP\). 6. Calculate signature pairs as, \(S_{1}=x_{1}mod\ h\) and \(S_{2}=q^{-1}(z+S_{1}Pv_{sk})mod\ h\). These signature pairs are generated as the function of the private key. Based on the above steps, a signature is generated by SU and it is embedded in the digital certificate. Then, this signature pair is validated by the CBS by using \(Pu_{sk}\) of that SU. In this manner, all SUs are validated by CBS before data transmission. the involvement of the dual-step security provisioning scheme improves the security level in the network by removing attackers and illegitimate SUs.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 2 State of the Art
### Related Works
#### 2.3.2 Simulations of BPLC Systems
The work examines BPLC topology in NS-3 where the channel capacity, depending on the branch length and the impedance, is simulated. The work is also focused on smart metering. In [57], NS-3 simulations in the frequency range 2-30 MHz are presented. The channel capacity was investigated depending on the cable length and the type of modulation technique. Simulated cooperative communication is intended to improve the efficiency and performance of the smart grid network. The simulation itself is created for a real in-home network based on MAC switching. The last related work [58] describes an adaptive OFDM modulation method for middle-voltage PLC systems in power distribution and utility networks. The paper focuses on fulfilling the delay requirements of the services within the distribution smart grid architecture, grouped according to priority. Distributed energy station control with a delay requirement of under one second is found in the group with high priority. This division into priority groups is defined by the range of the SNR values. The simulation experiments are based on fixed modulation types for each priority group of services and specific OFDM parameter settings where the QoS analysis was provided. The results lead to positive conclusions, where the presented method is recommended in order to improve communication in the band 2-30 MHz of the BPLC systems on the MV lines. Table 3 shows a paper overview of BPLC simulations for smart grid area and channel modelling for specified frequency bands.
## 2 Investigated Soils
### Details among the Soil Properties  
Reliable data are a major factor needed to perform a consistent factor analysis. Therefore, a very comprehensive literature survey was conducted to collect the required data with the desired soil properties. All the data have been gathered from the studies conducted on the soils of Cyprus included in governmental- and university-based research projects and peer- reviewed scientific articles.  
Soil properties, including liquid and plastic limits, clay fraction, montmorillonite, and calcium carbonate content were recorded from 207 different soil samples carefully selected from the literature, assuring that the same methodologies were used to determine them. The original research that the soil properties derived from are the studies of [PERSON]  
Figure 1: Main geological zones of Cyprus.  
et al. [40], [PERSON] et al. [41], [PERSON] et al. [42], [PERSON] and [PERSON] [43], [PERSON] and [PERSON] [44], [PERSON] and [PERSON] [45], [PERSON] and [PERSON] [46], [PERSON] [47; 48], and [PERSON] [49].  
All the soils are classified based on Unified Soil Classification System (USCS) for additional clarification. Based on this approach, three types of soils were noticed being used in this analysis. The dominant soil type is highly plastic clays (CH), representing 79% of the data, followed by low to medium plasticity clays (CL) and inorganic silts with high plasticity (MH) by a distribution of 12% and 9%, respectively.  
The sampling method and the derived depth of the soils vary based on the original research. This includes disturbed and undisturbed sampling from surface excavations and rotary core drillings within various depths. Although contaminated soils were not particularly identified within the data, it might be a promising future topic [50; 51].  
The liquid limit of the soils ranges from 34% to 210%. This wide range of liquid limits indicates the different origins of soils with higher limits, which possibly belong to igneous-origin clays. Additionally, high values of liquid limits indicate the presence of the dominant montmorillonite mineral. The plastic limit varies between a wide range of 14% to 55%. The variety of the soil properties due to different geological origins and the fact that the soils were sampled all over Cyprus better represent the soils of the island. The basic descriptive properties of the selected soils, including range, mean, and standard deviation, are tabulated in Table 1. Also, further visualization of the data can be observed using the box and whiskers diagram in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Soil Property** & **N** & **Minimum** & **Maximum** & **Mean** & **Std. Dev.** \\ \hline Liquid Limit (\%) & & 34.0 & 210.0 & 90.4 & 40.0 \\ Plastic Limit (\%) & & 14.0 & 55.0 & 30.9 & 11.2 \\ Clay Content (\%) & 207 & 10.0 & 84.0 & 40.1 & 15.7 \\ Montmorillonite & & 8.7 & 61.2 & 27.6 & 12.5 \\ Content (\%) & & 1.2 & 58.7 & 13.7 & 11.2 \\ CaCO\({}_{3}\) Content (\%) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the soils.  
Figure 2: Visual perspective of the soil properties.
## 3 Design of the Stress Wave-Based Communication System  
### System Architecture Design  
Based on the principles mentioned above, a new stress wave communication system is designed (Figure 2). The proposed system aims to directly generate modulated stress wave carrying information using the PPM. The entire system design process includes two parts, the hardware design and the software design.
# 2D vs. 3D Radiological Methods for Dental Age Determination around 18 Years: A Systematic Review
###### Abstract  
The age of a living human being can be determined by applying a number of different methods; the most diffused are skeletal and dental methods, both principally based on X-rays examinations. This systematic review assesses the current evidence regarding the accuracy and reliability of Cone Beam Computed Tomography (CBCT) vs. Orthopantomography (OPG) in age determination. A computerized systematic literature search of studies published up to January 2020 was conducted without language restrictions in order to identify articles comparing CBCT vs. OPG in dental anatomy evaluation, articles evaluating the accuracy of dental methods for age estimation, both with CT and OPG exams, and articles comparing CBCT vs. OPG in terms of radiation dose. CBCT was found to be more accurate compared to OPG in dental anatomy evaluation. When analyzing young adults, an estimation error of two years is considered forensically acceptable. The radiation dose of a CBCT exam is higher compared to an OPG exam. However, the difference is not as marked with small Fields Of View (FOV) and low-resolution protocols. Final conclusion was that a small FOV CBCT centered on the mandibular angle of a young patient is an accurate and safe method for dental age estimation around the age of 18.  
Keywords:cone beam computed tomography; panoramic radiography; orthopantomography; age determination by teeth; age estimation; forensic dentistry +
Footnote †: journal: Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy; [EMAIL_ADDRESS] (D.D.); [EMAIL_ADDRESS] (I.T.); [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (L.V.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (C.P.)
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 3 Distance Protection Methods
### Exclusion Zone  
To obtain the exclusion zone in the distance protection method, the procedure shown in Figure 7 is followed. In calculating the interference from all base station locations \((x,y)\), we need to consider: the transmission power of the base station, the antenna gain of the base station, the antenna gain of the FSS ES, and the path loss. One also needs to make sure the interference \((x,y)\) is less than the I/N protection threshold to be able to use this model. If the interference \((x,y)\) meets the protection threshold, the base station can be deployed at this location. The location \((x,y)\) is then marked in the red color. Finally, all red location points from the exclusion zone are outlined. Secondly, in determining the value of the path loss, six different methods are used, according to three different environments (RMa, UMa, UMi) and two different path loss types (LOS and NLOS). For calculating the BS antenna gain, three different antenna arrays are distinguished, including 4 \(\times\) 4, 8 \(\times\) 8, 16 \(\times\) 16.  
Figure 7: Flowchart of the distance protection method: Exclusion zone.
## 4 Node Activity Scheduling
### Performance Evaluation  
We now evaluate by simulation the performance of SERENA three-hop vertex coloring in various network configurations. Similar to EOLSR, we run 10 simulations per configuration and the result depicted in the curves is the average of these 10 simulations. Simulation parameters are those given in Table 2. Figure 9 depicts the number of colors needed when varying network density and number of nodes, like in [10]. We notice that the curves depicting the number of colors as a function of the node number are always under the first bisectrix. In other words, the number of colors is always smaller than the node number. Higher the difference, higher the benefits provided by coloring. The number of colors strongly increases with density and weakly with the node number. Compared with two-hop coloring as evaluated in [6], three-hop coloring requires about 50% additional colors. For instance, to color 200 nodes with a density 10, 28 colors are sufficient with two-hop coloring, whereas 43.4 colors are needed with three-hop coloring. However, if immediate acknowledgement is required, three-hop coloring is needed to avoid the color conflicts depicted in Figure 6.  
Figure 10 shows the number of rounds needed to color all network nodes. It represents the complexity of the coloring algorithm. This number of rounds is close to the number of nodes, whereas the worst theoretical case gives an upper bound of three times the number of nodes for three-hop coloring. In other words, after one node has colored itself, the only node that can color next is three-hop away. Such a configuration is unfrequent in usual wireless sensor networks in industrial environment.  
The average number of messages sent per node is given in Figure 11 for different densities and numbers of nodes. We observe that the number of messages like the number of colors strongly depends on network density and weakly with the number of nodes. Simulation results show that the number of messages generated by three-hop coloring is about twice the number generated by two-hop coloring. This is due to the fact that information about 1, 2 and three-hop neighbors is needed to color a node. These new results allow us to conclude that the immediate acknowledgement is exp  
Figure 9: Number of colors used by SERENA for different network configurations.  
colors and messages. However, it is needed to quickly confirm the good reception of a unicast message, which is not guaranteed in a wireless environment.
## 1 Introduction  
Unmanned aerial vehicle (UAV) systems are increasingly valued for their diverse applications in agriculture, aerospace, and military domains [1, 2]. Specifically, in agriculture, UAVs have revitalized modern farming practices [3]. Their extensive deployment in this sector not only enhances production efficiency and agricultural product quality but also introduces more intelligent and efficient solutions for various agricultural operations [4]. To overcome the limitations of single UAVs in handling complex tasks, research and practical applications are progressively adopting multi-UAV collaborative approaches to achieve greater overall benefits.  
As smart agriculture continues to advance, a single UAV often faces limitations in executing complex agricultural tasks. Consequently, the intricate and diverse nature of agricultural production chains underscores the significance of deploying multi-UAV systems in this field [5]. Addressing the task allocation challenge for multi-UAV systems involves assigning multiple UAVs to collaboratively perform various tasks while adhering to multiple constraints to minimize overall system costs [6]. Thus, the multi-constrained multi-UAV collaborative task allocation problem (MCMUAVCTAP) is classified as a nondeterministic polynomial (NP) problem, necessitating effective management of its inherent complexity. To accurately control multi-UAVs and efficiently formulate task allocation plans, solving the MCMUAVCTAP has become a pressing issue in the coordinated control of multi-UAVs [7].  
This study integrates the Tent chaotic mapping strategy and the Levy flight strategy into the Seagull Optimization Algorithm (SOA) to enhance search diversity and accelerate convergence. Furthermore, the spiral coefficient within the SOA is refined and applied to the MCMUAVCTAP. To evaluate the effectiveness of the proposed Tent-Levy Improved Seagull Optimization Algorithm (TLISOA), it was compared with various task allocation algorithms using real agricultural scenarios. The findings confirm the effectiveness of TLISOA in addressing the MCMUAVCTAP. This research aims to support the resolution of MCMUAVCTAP in agriculture, thereby fostering broader application and development of UAV technology in this sector. The main contributions of this study are summarized as follows: (1) establishing a single-objective optimization model for the MCMUAVCTAP; (2) proposing the TLISOA to address the MCMUAVCTAP; (3) conducting simulation experiments in agricultural contexts, demonstrating that the algorithm enhances the response speed and efficiency of multi-UAV systems.  
The structure of this paper is as follows: Section 2 reviews the current state of both domestic and international research on the topic. Section 3 introduces the single-objective optimization model developed for the MCMUAVCTAP. Section 4 describes the TLISOA. Section 5 presents simulation experiments comparing the proposed TLISOA with five other algorithms in multi-UAV collaborative task allocation for rice pest monitoring scenarios and provides a comparative analysis. Finally, Section 6 concludes this paper.
## 3 Results  
Out of 235 women who were willing to participate in the study, 63 were qualified. Of the 63 participants, 33 had an A/G ratio over 0.33 and qualified for the higher android fat group (HAF). After qualifying the participants to the appropriate group, the BMI and age did not differ significantly between the researched NAF and HAF groups. The only differences in HAF and NAF group parameters were fat percentage, waist-to-height ratio (WHtR), android/gynoid fat ratio (A/G) and visceral adipose tissue (VAT) (Table 1). HAF women presented a higher RSES score and were categorised as having higher self-esteem (Table 1). Different dietary behaviours than NAF characterised women in the HAT group. HAF women were more likely to consume milk and milk drinks at least once a day (OR: 3.3 95%CI: 1.1; 10.3) and fresh cheese curd products at least every other day (OR: 3.5, 95%CI: 1.1; 10.6) than the NAF group. There was no difference in fruit and vegetable intake (Table 2). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **HAF (n = 33)** & **NAF (n = 30)** & \(\boldsymbol{p}\) \\ \hline Age (years) & 31 \(\pm\) 6 & 28 \(\pm\) 7 & 0.06 \\ Body mass (kg) & 70 \(\pm\) 16 & 65 \(\pm\) 14 & 0.22 \\ Height (cm) & 166 \(\pm\) 6 & 166 \(\pm\) 8 & 0.91 \\ BMI (kg/m\({}^{2}\)) & 23.7 \(\pm\) 5.0 & 25.2 \(\pm\) 5.0 & 0.23 \\ Fat (\%) & 41 \(\pm\) 5 & 29 \(\pm\) 7 & 0.00 * \\ A/G (-) & 0.46 \(\pm\) 0.07 & 0.25 \(\pm\) 0.05 & 0.00 * \\ Visceral adipose tissue VAT (g) & 474 \(\pm\) 431 & 262 \(\pm\) 322 & 0.03 * \\ WHR (-) & 0.81 \(\pm\) 0.07 & 0.80 \(\pm\) 0.06 & 0.49 \\ WHtR (-) & 0.49 \(\pm\) 0.07 & 0.45 \(\pm\) 0.07 & 0.05 \\ Waist circumference (cm) & 82 \(\pm\) 13 & 75 \(\pm\) 12 & 0.04 * \\ Hip circumference (cm) & 100 \(\pm\) 11 & 94 \(\pm\) 9 & 0.02 * \\ Total lean mass (kg) & 41.15 \(\pm\) 5.70 & 39.90 \(\pm\) 5.37 & 0.37 \\ Nutrition knowledge score & & & \\ (points) & 15.8 \(\pm\) 5.7 & 14.6 \(\pm\) 6.0 & 0.42 \\ Rosenberg Self-esteem (RSES) & & & \\ scale 2 & \%(n) & \%(n) & \\ Low (\(<\)27 points) & 18(6) & 43(13) & 0.00 * \\ Medium (27–32 points) & 49(16) & 40(12) & \\ High (\(>\)32 points) & 33(11) & 17(5) & \\ Mean value of RSES & 30.3 \(\pm\) 4.0 & 27.2 \(\pm\) 3.8 & 0.05 \\ \hline \hline \end{tabular}  
* \(p<\) 0.05. \({}^{1}\) Group mean and standard deviation. \({}^{2}\) [PERSON] self-esteem scale is expressed as the percentage and number of women in each group, categorised into low, medium, and high total points as well as mean and standard deviation.  
\end{table} 
Table 1: Participants’ characteristics among higher-android-fat ratio (HAF) and normal-android-fat ratio (NAF) \({}^{1}\). HAF women were less likely to consume the red meat minimum every other day (OR: 0.2 95%CI: 0.0; 0.7) (Table 3). What was significant was that medium or high self-esteem was found to increase by three times the probability of a higher android/gynoid fat ratio.
## 5 Path Loss and Energy Consumption Models  
In BASNs, the signals transmitted by biosensors may diffract around the body or reflect a nearby distraction and then back at the body. Therefore, the link quality cannot be described by a simple power-distance law since each link between biosensors faces different fading, shadowing, polarization, interference, and correlation parameters in BASNs [40]. Considering these factors, the path loss (\(\mathcal{PL}\)) model [41] over the distance (\(d\)) between biosensor nodes \(\mathcal{S}_{i}\) and \(\mathcal{S}_{j}\) can be indicated as  
\[\mathcal{PL}(d)=\mathcal{PL}(d_{0})+10n\ell\varrho(\frac{d}{d_{0}}).\] (20a) The average path loss of the whole body can be computed as \[\mathcal{PL}(d_{0})=10*\ell\varrho(4\pi\cdot d_{0}(f))*\mathcal{S}_{\ell}\.\] (20b) The total path loss by considering the shadowing factor can be numerically computed as \[\mathcal{PL}=\mathcal{PL}(d)+X_{\sigma}. \tag{20c}\]  
In the energy consumption model [42], the energy required to transmit an \(\ell\) bit data message between two nodes separated by distance \(d\) can be calculated as  
\[{}_{tx}(d,\ell)=_{tx}(\ell)+_{amp}(d)*\ell. \tag{21a}\]  
The energy required by the transmit amplifier, with the distance \(d\leq d_{th}\) and \(d>d_{th}\), to maintain the lowest signal-to-noise ratio for effective wireless channel transmission is given by  
\[{}_{amp}(d)=\left\{\begin{array}{cc}{}_{fs}*d^{2},&\quad d\leq d_{th}\\ {}_{fs}*d^{4},&\quad d>d_{th}\end{array}\right.\, \tag{21b}\]  
where  
\[d_{th}=\sqrt{\frac{fs}{amp}}. \tag{21c}\]  
Consequently, the energy needed for receiving a bit (\(\ell\)) at the given reference distance (\(d\)) can be calculated as  
\[{}_{rx}(\ell)=_{elec}(\ell), \tag{21d}\]  
in which \(\mathcal{PL}\) (\(d_{0}\)), \(f\), \(\mathcal{S}_{\ell}\), E\({}_{e\ell ee}\), E\({}_{fs^{\prime}}\), E\({}_{amp^{\prime}}\) and \(d_{th}\) are, respectively, the path loss in dB at a reference distance, the frequency of operation, the speed of light, the transmitter energy or receiving circuit, the parameters in a free space model, the parameters in a multipath fading model, and the threshold in meters. During simulation studies, two other routing schemes, namely Energy Aware Link EfficientRouting Approach ELR-W [21] and Clustering Routing Protocol for Boday Area Sensor Network (CRPBA) [23] are used as comparisons to evaluate the performance of our proposed protocol in BASNs. The simulation parameters are given in Table 3.
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 2 Problem Description
The charging power is calculated based on the angle between the satellite's solar panels' normal vector and the sunlight vector, which changes with the satellite's attitude. \(P_{Camk}\) is the consumption power for satellite k observation; a satellite consumes electric energy while observing a target. \(P_{ SatelliteDataTransk}\) is the consumption power of satellite k during inter-satellite data transmission, where both data transmission input and output consume electric energy. \(P_{GroundDatatransk}\) is the consumption power of satellite \(k\) during satellite-ground data transmission. A satellite consumes electricity while downloading data to ground stations. \(P_{Attitudek}\) represents the consumption power of satellite \(k\) during attitude maneuvers. The formulas indicate that satellite observations, inter-satellite data transmission, satellite-ground data downloading, and attitude maneuvers all consume electrical power. This reflects the coupling of electricity and energy consumption between satellite observations and data transmission activities. Equation (12) represents the memory constraint; the occupied memory of storage is calculated as  
\[M_{k}=\int(R_{CamInk}t+R_{SatDataTransInk}t-M_{\text{era}}(t))dt\quad y_{il,k}=1, \tag{14}\]  
where \(R_{CamInk}\) represents the data rate generated by the camera for capturing images, and \(R_{SatDataTransInk}\) represents the code rate for inter-satellite data transmission between satellite \(l\) and satellite \(k\), which is the smaller of the two satellite transmission code rates. The observation and the data reception increase the occupied memory. The memory-erase event clears individual files; each observation of a target forms an image data file. During the processes of storing, transmitting, and erasing observation image data for a target, complete image data files need to be stored, transmitted, and erased. The satellite memory erase time is scheduled after the data are transmitted to other satellites or downloaded to the ground station. The occupied memory decreases correspondingly after data erasing. The equation reflects the memory occupancy coupling of satellite observations and data transmission. The calculations for attitude maneuver history, observation time window, data transmission time window, \(W_{k}\), and \(M_{k}\) can be found in reference [54]. Improved Tabu Search Method for Multi-Satellite Observation-Relay Transmission-Download Coupling Scheduling