esa-sceva/satcom-chunk-collection huggingface dataset:
# Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System
## 2 System Model and Problem Statement  
In this section, we present the system model and the formulation of the optimization problem. For ease of reference, the important notations in the system model are listed in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(H\) & Flying altitude of UAV \\ \(T\) & Total system delay \\ \(c\) & Speed of light \\ \(a\) & Task allocation ratio \\ \(L\) & Total computing tasks \\ \(a,b\) & Constants determined by the environment \\ \(\theta\) & Elevation angle between UAV and the user \\ \(\omega\) & Beamwidth of UAV’s directional antenna \\ \(r\) & Distance between the user and the projection of the UAV on the ground \\ \(\varepsilon,\varphi\) & Path loss exponent \\ \(f_{c}\) & Carrier frequency \\ \(\eta_{LoS}\) & \(\eta_{NLoS}\) & Extra path loss of LoS link and NLoS link \\ \(\eta_{LoS}\) & LoS probability between the user and the UAV \\ \(dg_{2\Delta}\) & Distance between the UAV and the user \\ \(dg_{2\Delta G}\) & Distance between the user and the BS \\ \(\eta_{LLoS}\), PL\({}_{NLoS}\) & Path loss of LoS link and NLoS link \\ \(\overline{\text{PL}}\) & Average path loss of S2A channel \\ \(\beta_{0}\) & Channel gain at the reference distance of 1m \\ \(\zeta_{2\text{SG}}\) & Small-scale fading component \\ \(h_{2\Delta G}\) & Channel gain of Rayleigh fading \\ \(p\) & Transmit power of the user \\ \(G\) & Gain of the directional antenna \\ \(N_{0}\) & Spectral density of noise power \\ \(B_{\text{UAV}}\), \(B_{BS}\) & Bandwidth allocated by the user to offload tasks to the UAV and the BS \\ \(R_{\text{UAV}}\), \(R_{\text{BS}}\) & Achievable offloading rate from the user to the UAV and the BS \\ \(g_{2\Delta T_{2\Delta G}}\) & Transmission delay of the user on S2A and S2G links \\ \(f_{\text{UAV}}\), \(f_{\text{BS}}\) & CPU frequency allocated for computation on the UAV and the BS \\ \(t_{\text{UAV}}\), \(t_{\text{BS}}\) & Computation delay for the user processing tasks on the UAV and the BS \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of notations.
## 2 Materials and Methods
### Carbohydrate Analysis  
To determine the carbohydrate profile, high-performance liquid chromatography (HPLC) with a refractive index detector system (RID) [24] was used (Agilent, 1200 series).  
Sample preparation and analysis: Honey samples (5.0 g) were mixed with methanol:water (25:75, \(v/v\)) solution in a final volume of 50 mL, and the mixture was filtered through a disposable syringe filter 0.45 \(\upmu\)m before the injection. The sugars were separated into two columns: the Zorbax Carbohydrate Analysis column (4.6 mm ID \(\times\) 150 mm \(\times\) 5 \(\upmu\)m) coupled in series, protected by a guard column (NH2 Guard Cartridge, 4.6 mm \(\times\) 12.5 m). In the mobile phase, a mixture of acetonitrile:water (75:25, \(v/v\)) at a flow rate of 1.8 mL min\({}^{-1}\) was used. The column and the refractive index detector were maintained at 35 \({}^{\circ}\)C. The injection volume was 10 \(\upmu\)L. For the quantification, a five-point calibration curve was created and evaluated for each sugar.  
Calibrations curves: In the present research, we studied 16 different sugars: D(-)-Fructose, D-(+)- Glucose, D(+)- Saccharomyces, D-maltose monohydrate, D-(+)-Turanose, D-(+)-Trehalose dehydrate, Isomaltose, D(+)-Malottriose, D-(+)-Melezitose hydrate, Erlose, D-Raffinose pentahydrate, Melibiose, D-panose, Maltulose, Maltotetraose and Isomaltotriose in HPLC grade. To create the calibration curve for each sugar, stock solutions in a concentration of 100 mg mL\({}^{-1}\) were prepared, diluted in a methanol:water solution (25:75, \(v/v\)) and kept in the freezer. Five-point calibration curves in different concentrations were created for each sugar using the stock solutions. Specifically, for fructose and glucose, the concentrations ranged from 0.5 to 40 mg mL\({}^{-1}\) (0.5, 2, 10, 25, 40 mg mL\({}^{-1}\)), for sucrose 0.1 to 20 mg mL\({}^{-1}\) (0.1, 2, 5, 12.5, 20 mg mL\({}^{-1}\)) and for the rest of the sugars 0.1 to 10 mg mL\({}^{-1}\) (0.1, 2, 5, 7, 10 mg mL\({}^{-1}\)). Each mixture of standards (point of calibration curves) was analyzed five times, and the mean value was applied in the calibration curve.  
Method Validation: For the determination of the sugars, the HPLC method was validated for linearity, limit of detection (LOD) and limit of quantification (LOQ). Linearity was calculated by least squares linear regression analysis of the calibration curve. The LOD and LOQ values were calculated using the equations from JRC Technical Reports [25]. Ten blank samples containing the mobile phase were analyzed, and the standard deviation of the blank signals was calculated. The equations are given below:  
\[\text{LOD}=3.9\ \times\ \frac{S}{b}\]  
where: \(S\): Standard deviation of the blank signals; \(b\): Slope of the calibration curve.  
\[\text{LOQ}=3.3\ \times\ \text{LOD}\]
## 4 Theoretical Analysis: Analytical Approach
### Deformation in the Rigid Zone  
Displacements in this zone are caused by pull-out of the reinforcement. [PERSON] [18], [PERSON] [50], and [PERSON] [40] presented detailed formulas which can be used to calculate these kinds of deformations.  
For this purpose, the following governing equations can be used:  
1. The differential equation derived from the equilibrium of the reinforcing element (Figure 13) [18; 50]: \[\frac{dF}{dx}=-2B\tau,\] (8) where \(F\) denotes the tensile strength of the reinforcement, and \(B\) is the width of the reinforcement strip.  
Figure 12: Overview of forces acting on the reinforcing element.  
The equation describing strain in the reinforcement strip:  
\[\varepsilon=du/dx, \tag{9}\]  
where \(u\) is displacement.  
The correlation between sheer stress (\(\tau\)) and displacement (\(u\)):  
\[\tau=-Gu, \tag{10}\]  
where \(G\) denotes a coefficient of proportionality.  
The relationship between tensile strength and strain:  
\[F=E\varepsilon, \tag{11}\]  
where \(E\) is the stiffness of the reinforcement strip.  
Equations (8)-(11) give rise to the differential equation, which shows the distribution of forces along the reinforcement strip:  
\[\frac{d^{2}F}{dx^{2}}-\beta^{2}F=0 \tag{12}\]  
where \(\beta=\sqrt{\frac{2 BG}{E}}\). The solution for Equation (8) is the following function:  
\[F=C_{1}e^{-\beta x}+C_{2}e^{\beta x}, \tag{13}\]  
where \(C_{1}\) and \(C_{2}\) are coefficients determined from the corresponding boundary conditions:  
\[F(x=0)=F\text{ and }F(x=b)=0 \tag{14}\]  
It is possible to estimate the displacement (\(u_{pullout}\)) of the reinforcement strip using Equations (8), (10), (13), and (14).  
\[u_{pullout}=-\frac{F}{\beta E[1-\exp(-2\beta b)]}\cdot[\exp(-\beta x)+\exp(-2 \beta b)\exp(\beta x)] \tag{15}\]  
where \(b\) is the length of the reinforcement strip in the rigid zone (\(b=\text{L}-a\)), compared to Figures 11 and 12. The coefficient \(G\) (Equation (10)) can be calculated using \(\beta=\sqrt{\frac{2 BG}{E}}\). \(\beta\) can be determined using the following equation:  
\[C=-\frac{\beta E[1-\exp(-2\beta b)]}{1+\exp(-2\beta b)} \tag{16}\]  
Figure 13: Reinforcing strip.
## 7 Results and Analysis
### Discussion
#### 7.1.2 Energy-Latency Optimization
This would entail fine-grained 10 ms decision intervals for URLLC services, intermediate adjustments at 1 s intervals for eMBB traffic, and longer-term reconfigurations every 10 s for mMTC services, thereby aligning scheduling precision with the specific latency and reliability requirements of each service class. Moreover, although Figure 20 illustrates the consistent performance advantage of the proposed LSTM-based approach over conventional baselines, it also reveals a notable 12.7%  
Figure 20: Energy savings comparison across scheduling methods. efficiency gap between normal and high-load conditions. This observation motivates the exploration of load-aware LSTM models that can dynamically adjust their hidden layer sizes and internal representations in response to predicted traffic patterns. By incorporating such adaptive mechanisms, the scheduler could further improve its efficiency and resilience under varying network loads and dynamic operational conditions.
# Molecular and Hormonal Mechanisms Regulating Fleshy Fruit Ripening
## 3 Ripening Is Influenced by Multiple Hormones and Light
### Auxin Delays Ripening and Antagonises the Effects of Ethylene
#### 3.3.4 Salicylic Acid (SA)  
Plants synthesise salicylic acid (SA) from chorismite in the plastids, via either the isochorismate synthase (ICS) and/or the phenylalanine ammonia-lyase (PAL) pathways [171; 176]. The volatile derivative methyl salicate (MeSA) is generated by methylation of SA by a specific form of O-methyltransferaseasferase [177]. Application of SA delays ripening of banana and kiwifruit [178; 179] and there are several reports of the retardation of ripening by MeSA in mangos, papayas and sweet peppers [180; 181]. The effect of MeSA, however, may depend on dose and time of application. [PERSON] and [PERSON] [182] showed in tomato that low concentrations (0.1 mM) of MeSA applied at the mature green stage and 0.01 mM of MeSA at the breaker stage enhanced the production of colour, ethylene and respiration. At a higher concentration (0.5 mM), however, MeSA prevented ethylene production, respiration and colour development, and this was associated with suppression of accumulation of _ACS2_ and _ACS4_ transcripts and delayed accumulation of _ACO1_. MeSA also delays processes such as softening, in addition to ethylene synthesis and colour change, but it is not clear whether the delay in ripening is due exclusively to the inhibition of ethylene biosynthesis transcripts or whether other ripening genes are also directly affected.
# Monocular Camera-Based Robotic Pick-and-Place in Fusion Applications
## 2 Related Work
### Deep Learning-Based Methods
#### 2.1.1 2D Image Input with Traditional Computer Vision Algorithms  
Due to the availability of dense 3D data, depth cameras have become the first choice for most robotic manipulation research. Nevertheless, in certain exceptional scenarios, such as reflective surfaces with specular reflections and transparent objects, depth cameras fail to provide trustworthy observation data, as previously noted. Consequently, the monocular camera-based approach continues to occupy a significant position.  
Directly acquiring 3D information for robotic manipulation tasks through monocular camera images is not possible. The key challenge in such studies lies in relying on 2D information for estimating three-dimensional pose. A common approach to tackle this challenge is to leverage deep neural networks (DNNs) to extract 2D keypoints from the image. Through calibration, camera intrinsics and joint configuration can be obtained, then the perspective-n-point (PnP) algorithm is used to recover 3D information. Building on this approach, [PERSON] et al. [4] proposed a method to estimate robot pose using an eye-to-hand camera. This method computes camera extrinsics from a single image frame, thereby enabling online robot pose estimation. [PERSON] et al. [5] proposed a similar approach to address the challenge of the robotic manipulation of transparent glass objects.  
[PERSON] et al. [6] employed fast region with CNN to recognize the object and subsequently estimate depth by leveraging the camera's field of view (FOV), as well as the k-nearest neighbors (kNN) and fuzzy inference system (FIS) algorithms.  
Unlike all the above methods, [PERSON] et al. [7] extracted deep features from the input video frames with a deep CNN and then introduced the time sequence information by adding recurrent neural network (RNN) structures into the network so that the 3D knowledge could be learned.
# The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation
## 3 Results  
### Example No. 1 of Collision Situations Using the Proposed Model  
To achieve the objectives set in this study, the method of simulation research using an ECDIS simulator was applied, allowing for the implementation of pre-planned scenarios of ship encounters. In the simulation, each ship model was allowed the full use of the ship's equipment. Visualization of the sea area enabled the user to carry out visual observation. The navigator could also use any of the systems that a modern navigational bridge is equipped with, including ARPA and AIS, which were used in our scenarios. The ship models had full course and speed maneuvering capabilities.  
The following two ship encounter situations involving two vessel models (non-autonomous real-time) were selected for this study:  
* Crossing courses:
* Ship A sails on course 270\({}^{\circ}\), and ship B sails on course 000\({}^{\circ}\); the meeting vessels are of a medium size (length approx. 170 m), and both proceed full ahead and are visible to each other;
* Opposite or reciprocal courses:
* Ship A sails on course 180\({}^{\circ}\), and ship B sails on course 000\({}^{\circ}\); the meeting vessels are of a medium size (length approx. 170 m), and both proceed full ahead and are visible to each other.  
Figure 4: Model of transforming natural language into ontology.  
The chosen scenarios were defined as standard ship encounter situations, as presented in the Collision Regulations. At the same time, these situations raise many interpretation issues regarding the safety of the maneuver being carried out.  
Each situation was repeated several times on the simulator, depending on how the situation was developing. The recordings showed that every time the simulation was run, the communication processes and related ships' maneuvers ran differently. For the described simulations, appropriate scenarios were initialized and recorded, and this allowed for the starting situation to be re-played several times, and the navigators could subsequently take various actions.
## 1 Introduction
The samples were exposed to 100% CO\({}_{2}\) at a pressure of 5 bar for 2 h. Afterward, X-ray diffraction (XRD) and scanning electron microscopy (SEM-EDS) analyses were performed to determine the products of carbonation and to observe the morphology of the carbonation products. The results showed a 12% CO\({}_{2}\) uptake for EAFS, FA and cement, unlike lime and BFS that presented 40% and 7% CO\({}_{2}\) uptake, respectively. Similarly, cement and EAFS were comparable in terms of carbonation degree with a value of around 25%. The compressive strength (at 2 h) of EAFS reached 16.6 MPa, while that of FA was only 3.5 MPa. In this paper, the use of alkali-activated electric arc furnace slag (AAEAFS) as a sole precursor in the production of mortars has been investigated. Its main objective is to reach the optimization of the activator's composition based on maximized mechanical performance. Twenty-one different mixes were produced to study the influence of different alkali activator concentrations and ratios, to find the best alkaline solution for the EAFS mortars. Different silicate modulus ratios (0, 0.5, 1.0, 1.5, 2.0, 2.5) and Na\({}_{2}\)O/binder concentrations (4%, 6%, 8%, 10%, 12%) were selected for this study. Their effect on the performance was evaluated through comprehensive testing methodologies, including slump, shrinkage, flexural strength (FS), compressive strength (CS) and carbonation degree. A 28-day curing period, including a 7-day accelerated carbonation stage, followed. Finally, the correlations between the distinctive preparation conditions and the different tested parameters were graphically illustrated, serving the upcoming research studies toward the best future use of EAFS as a more sustainable cement-replacing material.
## 7 ECC-based Key Agreement Protocol
### Design  
ECC Background  
An _elliptic curve_ is of the form \(y^{2}=x^{3}+ax+b\). When defined over a finite field, all the points on the curve \((x,y)\) and the parameters \(a\) and \(b\) are limited to elements of the underlying field. A common  
\begin{table}
\begin{tabular}{c||c|c|c|  
class of finite fields used in ECC are _prime fields_\(GF(p)\), where \(p\) is a large prime number; the elements of this field are the integers in \([0,p-1]\). There are three basic operations that can be performed on elliptic curve points: _addition_ of two points \(P+Q\), _doubling_ of a point (addition to itself) \(2P\), and _scalar multiplication_\(nP\), where \(n\) is an integer. Algebraic formulae exist for both addition and doubling. Scalar multiplication is the repeated application of addition and doubling: \(nP=P+P+...+P\) for \(n\) times. To use ECC, two nodes \(A\) and \(B\) agree on what elliptic curve to use and a base point \(G\) on the curve; this information is not secret. Node \(A\) can generate a large random number \(q\) as its private key and derive its public key \(Q\) by using \(Q=qG\). If \(q\) is large, it is hard for an attacker to derive the private key \(q\) from the public key \(Q\) even if the attacker knows \(G\), due to the difficulty of the _elliptic curve discrete logarithm problem_ (ECDLP).  
We use the _Elliptic Curve Integrated Encryption Scheme_ (ECIES [19]), a public-key encryption scheme based on DHIES [28], to secure the _key agreement protocol messages_ between a more and a base station. DHIES (and ECIES) is secure against adaptive chosen-ciphertext attacks (see [28] for the security proof). The ECIES procedure is summarized in Figure 5 and described below. To protect the messages between node \(A\) (a more) and node \(B\) (base station) against eavesdropping and modification, node \(A\) generates a random number \(r\) and computes a secret \(S=rQ\), where \(Q\) is node \(B\)'s public key. Node \(A\) then uses a key derivation function (KDF) to generate two _transient keys_ from \(S\): a symmetric encryption key \(K_{s}^{{}^{\prime}}\) and a MAC (message authentication code) key \(K_{mac}^{{}^{\prime}}\). Node \(A\) also computes the point \(R=rG\), which is sent in the clear to node \(B\). Node \(B\) can derive the secret \(S\) using its private key \(q\) and the point \(R\): \(S=qR=q(rG)=r(qG)=rQ\). Node \(B\) then uses the same KDF to derive \(K_{s}^{{}^{\prime}}\) and \(K_{mac}^{{}^{\prime}}\) from \(S\). The transient keys are then used to encrypt and integrity-protect the messages between \(A\) and \(B\).
## 5 Conclusions
### Practical Implications  
The advantages of formative assessment include the system's capacity to provide real-time feedback to optimize teaching, personalize learning paths, and enhance student self-regulation. However, its challenges include limited data accuracy in noisy or multi-learner environments, potential privacy violations through continuous emotion monitoring, and the risk of weakening the emotional bond between teachers and students during direct interactions owing to overreliance on AI feedback. The application of an AI system evaluation in summative assessments can efficiently process large-scale data and enhance evaluation objectivity. However, challenges arise from the weak correlation between emotion and learning performance, difficulties in accurately assessing knowledge mastery, and complexities in capturing the trajectory of emotional changes in long texts for academic evaluations. Furthermore, future research could examine the correlation between sentiment data and learning performance. In conclusion, the AI-based text sentiment analysis system demonstrates innovative potential in formative assessment, with its dynamic feedback characteristics aligned closely with the teaching objectives. However, the application of summative assessments should be approached with caution, and the correlation between emotional data and academic performance requires further clarification.
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## 4 Experimental Simulation 7  
### Initial Value Sensitivity  
Extreme sensitivity to the system's initial conditions is the main dynamic characteristic of chaotic systems. Sensitivity refers to the fact that even a very small difference in initial values will result in two different sequences as the number of iterations increases. The chaotic system has good security performance because the small change in the initial value is difficult to estimate.  
The trajectories of the real-valued mapping points generated by the iteration of the Logistic chaotic map and the simulation results are shown in Figure 6a,b, respectively. Their system parameters are all four and the number of iterations is 70.  
As can be seen from the above Figure 6a, when the initial value is \(10^{3}\) orders of magnitude different, the two trajectories are irrelevant after only eight iterations. Figure 6b further shows that it only takes about 21 iterations to iterate two irrelevant trajectories, even when the initial values differ by an order of magnitude of \(10^{7}\). This shows that the information of the initial value state of the orbit is rapidly lost with the separation of the system orbit, which shows the long-term unpredictability of the system orbit. Therefore, the initial value sensitivity of the chaotic sequence makes the chaotic system have excellent security performance.  
Figure 5: Block diagram of receiver encryption.
# An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning
## 6 Conclusions  
In this paper, we examine the communication performance of a 5G NR-V2X within an asynchronous framework that combines offline training and online decision making with a Q-learning model. In order to mitigate the waste caused by redundant channel resources when the channel quality is good and the high delay and large BLERs caused by repeated retransmissions when the channel quality is poor, the \(T\)-delay scheme, \(K\)-repetition scheme and [T, K]-overlap scheme including 8 optimal transmission schemes are designed. In this study, the A-HARQ mechanism is realized through the decision-making ability of Q-learning's optimal learning model. The proposed A-HARQ for environmental awareness aims to optimize the utilization of limited channel resources and adaptively select the most appropriate actions based on the current system state. Finally, this approach enables the highest effective utilization of existing resources and channel quality and achieves the purpose of reducing the average BLER, average retransmission times, and RTT. In future studies, we will explore the utilization of neural networks to optimize the design of reward mechanisms, with the aim of achieving greater perfection.  
Conceptualization, M.-H.Y. and S.J.; methodology, M.-H.Y. and S.-Y.L.; software, M.-H.Y., S.J. and S.-Y.L.; validation, M.-H.Y. and S.-Y.L.; formal analysis, M.-H.Y.; investigation, M.-H.Y. and S.J.; resources, S.-Y.L.; data curation, M.-H.Y. and S.J.; writing--original draft preparation, M.-H.Y.; writing--review and editing, S.-Y.L.; visualization, M.-H.Y.; supervision, S.-Y.L.; project administration, S.-Y.L.; funding acquisition, S.-Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created during the study.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.