esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Results Analysis
#### 4.5.1 Fed-DTB Performance Under Low-Complexity Conditions  
The results for the configuration with 10 clients provide an in-depth analysis of the system's performance under low-complexity conditions. This setup was essential to evaluate the framework's ability to detect and exclude untrustworthy clients while maintaining robust performance. Figures 9 and 10 illustrate the evolution of accuracy and loss over 10 rounds. Initially, the accuracy was moderate at 62.91%, affected by the presence of malicious clients, while the loss was high at 1.8026. Over successive rounds, as malicious and low performance clients were identified and excluded, accuracy steadily improved, stabilising at 88.09% by round 7, while loss decreased sharply to 0.7225. These results highlight the system's rapid convergence and effectiveness in enhancing its performance through iterative rounds.  
The detailed behaviour of key performance metrics, including precision, recall, and F1 Score, is depicted in Figure 11. These metrics demonstrated significant improvement after  
Figure 10: Loss evaluation.  
Figure 9: Accuracy evaluation.  
the exclusion of low-trust clients in the early rounds. By round 10, the precision reached 89.65%, recall stabilised at 88.09%, and the F1 Score achieved 88.24%, indicating the system's ability to strike a balance between false positives and false negatives. Furthermore, the ROC-AUC metric, shown in Figure 12, exhibited consistently high values, culminating at 0.9886 in the final round, reflecting the model's exceptional discriminatory ability between benign and malicious data sources.  
The effectiveness of the trust mechanism in identifying and excluding untrustworthy clients is demonstrated in Figure 13. Four malicious clients were excluded by round 2 due to their misleading information, while two low-performance clients were excluded in rounds 3 and 5, leading to a marked improvement in the trustworthiness of the remaining clients. This exclusion process directly contributed to the observed enhancements in model performance. The corresponding computational efficiency is demonstrated in Figure 14, where the training time decreased from 32.1 s in the first round to 3.2 s in the final round, reflecting the reduced computational overhead as untrustworthy clients were excluded and the data quality improved.  
Figure 11: Precision, recall, and F1 Score performance.  
Figure 12: ROC-AUC evaluation.  
Finally, the performance metrics for each round are summarised in Table 1. This table provides a detailed breakdown of metrics such as accuracy, loss, F1 Score, the number of excluded clients, and total round time, showing the stabilisation of the system's performance after round 7. These results confirm the system's capability to deliver robust and efficient performance in a low-complexity setup with 10 clients.
## 4 Analysis of Influencing Factors
### Influence of the Horizontal Spacing Between the Foundation Pit and Tunnel
#### 4.2.2 Tunnel Vertical Displacement  
Figure 11(b) shows the tunnel's maximum vertical displacement resulting from the completion of the foundation pit excavation at various horizontal spacings. The diagram shows that as the horizontal spacing increases from 0.5 H to 3 H, the tunnel uplift decreases by 15.38%, 33.33%, 47.43%, 60.26% and 78.21%, respectively, and the settlement decreases by 42.08% and 72.92%, respectively. The maximum values of tunnel uplift and settlement gradually decrease with increasing horizontal spacing. Moreover, the rate of change in the settlement value is notably greater than that of the uplift value, indicating that the change in horizontal spacing has a more pronounced impact on tunnel settlement.
## 3 Protocol Analysis on Modbus and 5G
### Modbus Protocol
#### 3.1.1 Modbus RTU Protocol  
The serial bus is used to transmit Modbus RTU data, where the application data unit (ADU) of Modbus RTU is divided into four fields, including 1 byte address code, 2 bytes cyclic redundancy check (CRC) check code, 1 byte function code, and N bytes data. We can determine the only destination device based on the address code and ensure the reliability of data transmission according to the CRC check code. The frame structure of the Modbus RTU in the data link layer is shown in Figure 1.
# Secure Beamforming in 5G-Based Cognitive Radio Network
## 3 System Model
### Proposed Technique in Cascaded Transmission of MIMO-NOMA Network
The above proposed solution is situated at the BS and the derivation for the secrecy rate is calculated as follows [12]:  
\[R_{pu}^{l_{A}}=G_{pu}^{l_{A}}\sum_{m=1}^{L}v^{m,A}+l_{pu}^{l_{A}}\sum_{m=1}^{L}v ^{m,\ B}+w_{pu}^{l_{A}} \tag{3}\]6  
\[R_{pu}^{l_{A}}=G_{pu}^{l_{A}}\sum_{m=1}^{L}v^{m,A}+l_{pu}^{l_{A}}\sum_{m=1}^{L}v ^{m,\ B}+w_{pu}^{l_{A}} \tag{3}\]7The total secrecy rate in a cluster is given by  
\[R_{pu}^{l_{A}}=G_{pu}^{l_{A}}\sum_{m=1}^{L}v^{m,A}+l_{pu}^{l_{A}}\sum_{m=1}^{L}v ^{m,\ B}+w_{pu}^{l_{A}} \tag{3}\]8  
The proposed method in an Equation (16) will convert a total secrecy rate of a cluster to the total throughput of a cluster. The BER from the above formulation is calculated as  
\[R_{pu}^{l_{A}}=G_{pu}^{l_{A}}\sum_{m=1}^{L}v^{m,A}+l_{pu}^{l_{A}}\sum_{m=1}^{L}v ^{m,\ B}+w_{pu}^{l_{A}} \tag{3}\]9  
where Q (\(.\)) is the Gaussian Q-function and is defined as  
\[S_{su}^{l_{A}}\overset{\text{\tiny{def}}}{=}\left[\log_{2}\!\left(1+\psi\! \left|S_{su}^{l_{A}}v^{l_{A}}\right|^{2}\right)-\max_{m,m\
eq l}\log_{2}\! \left(1+\psi\!\left|C_{su}^{m,A}v^{m,A}\right|^{2}\right)\right]^{+} \tag{4}\]0  
The channel capacity is also greatly increased by the proposed method and is formulated as  
\[S_{su}^{l_{A}}\overset{\text{\tiny{def}}}{=}\left[\log_{2}\!\left(1+\psi\! \left|S_{su}^{l_{A}}v^{l_{A}}\right|^{2}\right)-\max_{m,m\
eq l}\log_{2}\! \left(1+\psi\!\left|C_{su}^{m,A}v^{m,A}\right|^{2}\right)\right]^{+} \tag{4}\]1  
where B is the channel bandwidth, which is taken at 1 MHz. The throughput of the channel can be expressed as  
\[S_{su}^{l_{A}}\overset{\text{\tiny{def}}}{=}\left[\log_{2}\!\left(1+\psi\! \left|S_{su}^{l_{A}}v^{l_{A}}\right|^{2}\right)-\max_{m,m\
eq l}\log_{2}\! \left(1+\psi\!\left|C_{su}^{m,A}v^{m,A}\right|^{2}\right)\right]^{+} \tag{4}\]2  
The proposed technique is skillful in allocating the multi-cells and multi-clusters in which the number of antennas is not limited at the BS.
# A Secure IoT-Based Irrigation System for Precision Agriculture Using the Expeditious Cipher
## 2 Materials and Methods
### Proposed IoT System Architecture  
The main objective of our proposed system is the addition of a lightweight cryptography layer to the agriculture IoT ecosystem, as shown in Figure 2. The function of this layer is to create a secure communication channel between the sensing/actuator layer (IoT nodes) and the subscriber layer, thus protecting sensors' published data from eavesdropping and theft, as well as from unauthorized changes to sensitive data that can negatively impact crop development. In addition, the secure channel protects the irrigation decision made by the data analytics (DA) entity regarding the irrigation time and the quantity of water that is returned to actuators from any alteration while meeting the requirements of IoT-constrained devices in terms of memory usage and power consumption by ensuring the application of lightweight cryptography techniques.  
Figure 3 depicts the architecture of our proposed secure IoT-based smart irrigation system. Figure 3a illustrates the general system architecture and Figure 3b shows the hardware components of the proposed system. The proposed architecture consists of five layers: the sensing/actuator layer, the lightweight cryptography layer, the communication layer, the network layer, and the application layer.
## Materials and Methods
### Sample Preparation  
The specimens for each designed architected metamaterial are produced using fused deposition modeling (FDM) process of additive manufacturing (3D printing) technology, due to its merits of economy and efficiency. See Ref. [35; 36] for more information about FDM process. The 3D printer used to manufacture these samples is the I3 Mega printer (Anycubic Co, Ltd.) with the build material poly lactic acid (PLA+) polymer. The printing resolution is set to be 0.2 mm and the thickness of each strut for all samples is 0.5 mm. The nozzle temperature is set to be 200 \({}^{\circ}\)C and the bottom plate 60 \({}^{\circ}\)C. The CAD models of the samples are first created by Inventor 2019 and then input into the 3D printer software Simplify3D for printing path planning. Single filament is used for each strut to avoid strength or stiffness reduction and poor connection of adjacent filaments, which is commonly observed in traditional multi-filaments FDM process [37]. Single filament process can be guaranteed by the reasonable settings of nozzle diameter and extrusion rate. The nozzle diameter is controlled to be 0.08 mm with the extrusion rate of 2 in experiments. The actual strut thickness of the 3D-printed samples is then checked using microscopic image processing software. Due to the limitation of the 3D printer, a slight deviation of strut thickness and relative density is observed and shown in Table 1, which is mainly caused by the printing process [38].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Strut Thickness (mm)**} & \multicolumn{2}{c}{**Average**} & \multicolumn{2}{c}{**Theoretical**} & \multirow{2}{*}{**Error**} \\  & **1** & **2** & **3** & **Thickness (mm)** & & **Thickness (mm)** & \\ \hline P-0 & 0.482 & 0.467 & 0.511 & 0.487 & & 2.6\% \\ P-1 & 0.458 & 0.493 & 0.487 & 0.479 & & 4.2\% \\ P-2 & 0.497 & 0.476 & 0.465 & 0.479 & & 4.2\% \\ P-4 & 0.483 & 0.492 & 0.480 & 0.485 & & 3.0\% \\ \hline \hline \end{tabular} Note:
# Design and Construction of a Low-Cost Test Bench for Testing Agricultural Spray Nozzles
## Abbreviation  
* \(a\) acceleration/deceleration value during transients, m/s2
* _L_a_ trails section covered by the nozzle at constant acceleration/deceleration, m
* _L_r_ trails section covered by the nozzle at constant speed, m
* _t_a_ acceleration/deceleration time (transient time), s
* _t_r_ time while the nozzle moves at constant speed (steady state time), s
* _v_r_ desired constant nozzle speed, m/s
* _A_i_ area of droplet \(i\) detected by ImageJ, pixel
* _D_i_ diameter of droplet \(i\), pixel
* \(D\)10} arithmetic mean diameter, m
* \(D\)20} surface mean diameter, m m
* \(D\)30} volume mean diameter, m m
* \(D\)32} Sauter mean diameter (SMD) or the diameter of a drop having the same volume to surface area ratio as the total volume of all the drops to the total surface area of all the drops, m m
* _D_v0.1, _D_v0.9} volumetric diameters below which smaller droplets constitute, respectively, 10% and 90% of the total volume, m
* _D_v0.5} volumetric median diameter (VMD), below which smaller droplets constitute 50% of the total volume, m m
* _NMD_ number mean diameter, the droplet diameter below which the droplet diameter for 50% of the number of drops are smaller, m m
* _RSF_ relative span factor, a dimensionless parameter indicative of the uniformity of the drop size distribution
* _SMD_ Sauter mean diameter, m m
* _VMD_ volume median diameter, m m
* \(V\)100, \(V\)200} proportion of total volume of droplets smaller than, respectively, 100 and 200 m in diameter, %
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 2 JPS Algorithm  
The JPS algorithm is a pathfinding algorithm that uses pruned neighbor rules as the search direction of nodes and the position of forced neighbors as the judgment of jump points. The JPS algorithm extends the jump points by iterative computation and by selecting the least costly node among the candidate jump points as the subsequent path. The JPS algorithm's method of determining the current jump point x consists of the following three parts:  
1. If node \(x\) is a start point or a target point, then node \(x\) is a jump point. 2. If node \(x\) has at least one forced neighbor, then node \(x\) is a jump point. 3. If the search direction from the parent node of node \(x\) to \(x\) is diagonal and there is a point in the horizontal or vertical direction of \(x\) that satisfies condition 1 and 2, then \(x\) is a jump point. _JPS Algorithm for Pruned Neighbor Rule and Forced Neighbors_  
In the JPS algorithm, the neighboring nodes in each direction need to be considered when searching from the starting node. When the JPS algorithm determines a specific search direction, it expands in that direction and does not need to calculate the generation value of nodes in that direction until it meets an obstacle or jump point. If a path is blocked, all nodes along that direction will be pruned without further consideration. While JPS is extending a path in a particular direction, it identifies a set of natural neighbors for a node under evaluation. When the extension direction is a straight line, the natural neighbor of the current point \(x\) is defined as the next node in the same direction. That is, JPS will continue to search along the direction of the current natural neighbor. When the extension direction is diagonal, the natural neighbors of the current point \(x\) include three nodes, which are the next node along the extension diagonal, and the next
## 3 Proposed Improved ADRC Strategy
### Characteristic Analysis of Improved ADRC
#### 3.3.1 Disturbance Estimation  
By applying the Laplace transform, the disturbance estimation transfer function of traditional ESO and cascaded ESO can be derived:  
\[\frac{z_{2}}{x_{2}(s)}=\frac{{{\omega_{o}}^{2}}}{{{(s+{\omega_{o}})}^{2}}} \tag{20}\]  
\[\frac{z_{2}(s)+v_{2}(s)}{x_{2}(s)}=\frac{{{\omega_{o}}^{2}}\big{(}2{s^{2}}+4{ \omega_{o}}s+{{\omega_{o}}^{2}}\big{)}}{{{(s+{\omega_{o}}^{2})}^{4}}} \tag{21}\]  
In which \(\omega\) Fixed at 300 rad/s. As can be seen from the figure, the amplitude gain of cascaded ESO is relatively higher, which means that it has a better estimation ability for disturbances. In addition, the phase delay in the disturbance estimation process is better to some extent. Authors can derive the transfer functions of traditional ESO and cascaded ESO regarding disturbance estimation errors:  
\[\frac{z_{2}(s)-x_{2}(s)}{x_{2}(s)}=-\frac{s(s+2{\omega_{o}})}{s+{{\omega_{o}}^ {2}}} \tag{22}\]  
\[\frac{z_{2}(s)+v_{2}(s)-x_{2}(s)}{x_{2}(s)}=-\frac{s^{2}(s+2{\omega_{o}})^{2} }{s+{{\omega_{o}}^{4}}} \tag{23}\]  
According to the Final value theorem, both the traditional ESO and the cascade ESO can realize the no difference estimation when estimating the step disturbance. Figure 8 shows the time-domain response curves of the two observers mentioned above when evaluating slope disturbances. The cascaded ESO can still achieve relatively accurate interference when the interference is a slope signal with slope \(k\), but applying traditional ESO will result in the state-error of \(-2\,k/\omega_{o}\).
## 3 Modelling Framework
### Traction-Separation Law
#### 3.3.1 Damage Initiation (Separation Initiation)  
Damage initiation is defined as part of the interaction properties, using damage for the traction-separation law. There are a lot of damage initiation criteria available in the literature that are implemented in Abaqus as well, such as the maximum nominal stress criterion, quadratic stress criterion, maximum separation criterion, and quadratic separation criterion. In this thesis, the maximum nominal stress damage criterion is used. With this option, the damage will initiate when the maximal nominal stress exceeds the critical value, which is the maximum strength of the material. Figure 4 shows the typical traction-separation response for all three directions to the contact faces. A crack can appear when the maximum principal stress calculated in its integration points meets the criterion of Equation (3), in the case of the XFEM, or Equation (4), in the case of the cohesive surface technique. A more detailed explanation of these techniques can be found in [17]. The initiation and propagation criteria for the cohesive surface technique are combined in a single formulation and defined in the interaction module of Abaqus.  
\[max\Big{\{}0,\frac{\sigma_{maxys}}{\sigma^{*}}\Big{\}}\geq 1 \tag{3}\]  
\[max\Big{\{}\frac{\langle t_{n}\rangle}{t_{n}^{*}},\frac{t_{s}}{t_{n}^{*}}, \frac{t_{t}}{t_{t}^{*}}\Big{\}}=1 \tag{4}\]  
\(\sigma_{maxys}\) is the calculated maximum principal stress and \(\sigma^{\star}\) is the maximum strength of the material. \(n,s,t\): normal, shear, and tangential components of the interfacial tractions, respectively. (*) represents the maximum interfacial tractions. A purely compressive displacement (contact penetration) or a purely compressive stress state does not initiate damage, as indicated by the Macaulay bracket \(\langle\rangle\).
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 2 Efficient Fuzzy MPC Algorithms with the Advanced Construction of the Dynamic Matrix
### Optimization Problem in the Numerical and Analytical Versions of the Algorithms
#### 2.3.3 Utilization of Analytical Versions of the Algorithms  
Due to the fact that the dynamic matrix \(\mathbf{A}_{k}\) changes, in general, in every time step, then in each time step, the formula (46) or (48) allowing calculating the control action should be used by the algorithm. As a consequence, appropriate rows of the matrix \(\left(\mathbf{A}_{k}^{T}\cdot\mathbf{\kappa}\cdot\mathbf{A}_{k}+\mathbf{\Lambda}\right)^{-1}\) (corresponding to the control increments for the current sampling instant \(\Delta u_{k|k}^{m}\)) should be calculated by the algorithm in each time step.
## 1 Introduction
Accurate estimation of LBV data is crucial for understanding a wide range of flames and flame stabilization. Furthermore, it describes laminar and turbulent premixed characteristics within the laminar flamelet regime. LBV also plays a fundamental role in validating the chemical kinetics of the fuel and its combustion characteristics, such as flame stability, heat release rate, and diffusion probability. Therefore, it is a significant factor in calculating fuel performance and emissions [15; 16; 17]. Furthermore, according to the Gibbs free energy for the combustion product, the overall reaction for ammonia is 4 NH\({}_{3}+3\)O\({}_{2}\to 2\)N\({}_{2}+6\)H\({}_{2}\)O, which indicates that there \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Property/Fuel** & **NH\({}_{3}\)** & **H\({}_{2}\)** & **CH\({}_{4}\)** \\ \hline Boiling temperature at 0.1 MPa (\({}^{\circ}\)C) & \(-\)33.4 & \(-\)252.9 & \(-\)161 \\ Lower heating value (MJ/kg) & 18.6 & 120 & 50.0 \\ Flammability limit (\(\phi\)) & 0.63–1.40 & 0.10–7.1 & 0.50–1.7 \\ Adiabatic flame temperature (K) & 1800 & 2110 & 1950 \\ Laminar burning velocity (m/s) & 0.07 & 2.91 & 0.37 \\ Auto ignition temperature (\({}^{\circ}\)C) & 650 & 520 & 630 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fundamental combustion characteristics and thermal properties of ammonia, hydrogen, and methane fuels [1]. are no final products of NO\({}_{x}\). When comparing the formation of NO in methane and ammonia, it is observed that NO produced from NH\({}_{3}\)/air flames is lower than that from CH\({}_{4}\)/air flames under stoichiometric conditions. This trend continues for lean conditions with increased NO production. However, for the rich side of stoichiometry, increasing the equivalence ratio leads to a more rapid decrease in NO generated from NH\({}_{3}\)/air flames compared to CH\({}_{4}\)/air flames [18]. In a few words, the mole fraction of NO produced by ammonia flames increases with an increase in the equivalence ratio (\(\phi\)) and peaks at 0.9. However, beyond this point, there is a rapid decrease in the NO mole fraction on the rich side. When the equivalence ratio exceeds 1.3, NO production becomes almost negligible, typically around 10-100 parts per million (ppm). Rich side combustion is considered one of the effective methods for reducing NO\({}_{x}\) emissions. However, it is important to note that this approach can lead to a sharp decrease in combustion efficiency due to the rapid increase in unburned NH\({}_{3}\). Another unwelcome product in the rich side combustion of ammonia is N\({}_{2}\)O, which has a global warming potential 300 times higher than CO\({}_{2}\)[1]. Given these observations, conducting slightly rich ammonia combustion would be preferable to achieve higher flame speed while minimizing the formation of NO\({}_{x}\).
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
## 4 Numerical Results
Figure 5: Average end-to-end throughput performance in AS case versus the average SNR with different PS coefficients \(\rho\). Figure 6: Average end-to-end throughput performance versus different PS coefficients \(\rho\) in PS aided two-way satellite-terrestrial relay network with ILS, AS, and FHS. Figure 7 shows the average energy efficiency performance in AS case versus different PS coefficients \(\rho\). The average energy efficiency performance in the ILS case is significantly higher than the one in AS and FHS cases. The average energy efficiency performance in three cases significantly increases as the PS coefficients \(\rho\) increase from 0.1 to 0.4 or 0.5 and gradually decreases as the PS coefficients \(\rho\) increase from 0.5 to 1. The increase of average energy efficiency performance from 0.1 to 0.4 or 0.5 is because increasing average end-to-end throughput exceeds the benefit of reducing energy consumption by energy harvesting at the user source node. The decrease of average energy efficiency performance from 0.5 to 1 is due to the benefit from reducing the total energy consumption by energy harvesting at the user source node, which exceeds the benefit from increasing average end-to-end throughput.
## 2 Methods
### Present-Day Fertilisation Experiments  
To evaluate the ability of JULES to reproduce belowground fertilisation observations, we replicated the three-year belowground fertilisation experiments conducted by [40]. JULES was initially spun up for 10,000 years by repeating the forcing data from 1901-1921. The model was then run for the period 1901-2016 for a control (_Control_) simulation.  
During the field experiment, [40] added slow-release nitrogen fertiliser equivalent to 8 g[N] m\({}^{-2}\) at a depth of 45 cm in September 2007, to mimic increased nitrogen-availability at the thaw-front as a result of permafrost thawing. This deep fertiliser then froze and became available to the plant roots about half way through the subsequent growing season in mid 2008. A similar amount was added at a depth of 10 cm in June 2008 to represent increased nitrogen mineralisation in response to climate warming. The impact of the slow-release fertiliser on plant biomass was measured after three years. The slow release fertilisation was prescribed as a constant input to JULES of 0.0073 g[N] m\({}^{-2}\)day\({}^{-1}\) for the three growing seasons. This is equivalent to approximately 2.67 g[N] m\({}^{-2}\)year\({}^{-1}\). The \(N^{+}_{NearSurface}\) simulation had fertiliser added near the surface at 10 cm, whilst \(N^{+}_{ThawDepth}\) had fertiliser added near to the thaw-front at 47.3 cm, and \(N^{+}_{Both}\) (not shown) had fertiliser added at both 10 cm and 47.3 cm. All fertilisers were added at the same time as during the [40] experiment. In each simulation, including \(Control\), nitrogen deposition is also added to the top layer of the soil based on external input taken from an ACCMIP multi-model dataset interpolated to annual fields [53; 59]. These present day simulations were used to evaluate the ability of the vegetation in JULES to respond to the nitrogen addition for the \(CN_{high}Root_{25}\) and \(CN_{low}Root_{60}\) vegetation types and when both types of vegetation are present (\(PFT_{compete}\)).