esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Assessment of the Occurrence of Infectious Diseases of the Skins of Potato Tubers and Yields  
Potato tubers were harvested in the second fortnight or week of September. The surface of the adjacent soil was cleaned from each tuber plot and washed under running water. The total yield is expressed in t\(\cdot\)ha\({}^{-1}\). Afterward, the yield increase in individual treatments was calculated and expressed as a percentage in relation to the control.  
The health assessment of the tubers was carried out immediately after harvesting on 100 tubers which were randomly taken from a batch of tubers obtained from individual plots (28 samples).  
The evaluation of the tuber infection by _R. solani_ was carried out according to the EPPO PP 1/32 (3) [72] standard, which adopted a four-stage scale of infection of the bulb surfaces.  
According to the scale:  
**0**--no disease symptoms; **1**--1% surface of tubers covered with sclerotia of _R. solani_; **2**--5%; **3**--10%; **4**--above 15%.  
Since there is no EPPO-developed scale for potato tubers infection by _Streptomyces scabies_ and _Helminthosporium solani_, the 5\({}^{\circ}\) scale developed by [PERSON] et al. [73] for common scab was used.  
According to the scale:  
**0**--no disease symptoms, **1**--1-10% of tuber surface with common scab/with symptoms of silver scurf (stains with changed tuber skin color); **2**--11-20%; **3**--21-50%; **4**--51-80%; **5**--81-100%.  
Figure 1: Hydrothermal conditions in the area of conducted research in 2015–2017. \({}^{*}\)\(K\leq\) 0.4—extremely dry month; 0.4 \(<\)\(K\leq\) 0.7—very dry; 0.7 \(<\)\(K\leq\) 1.0—dry; 1.0 \(<\)\(K\leq\) 1.3—quite dry; 1.3 \(<\)\(K\leq\) 1.6—optimal; 1.6 \(<\)\(K\leq\) 2.0—moderately humid; 2.0 \(<\)\(K\leq\) 2.5—humid; 2.5 \(<\)\(K\leq\) 3.0—very humid; \(K\)\(>\) 3.0—extremely humid [71].  
Intensification of the potato tuber diseases was expressed by the disease severity index (DSI), the average level of infection of the sample, and the percentage of tubers affected by the particular disease, respectively.  
DSI was computed according to the formula described by [PERSON] and [PERSON] [74]:  
\[DSI=\frac{\sum\limits_{i}n\cdot v}{i\cdot N}\cdot 100\%\]  
where \(DSI\)--disease severity index; \(n\)--number of plants with a given degree of infection; \(v\)--degree of infection; \(i\)--highest degree of infection; \(N\)--total number of plants analyzed.  
Average degree of infection of infected tubers (ADIIT) was determined in a batch of potatoes with clear symptoms of skin diseases.  
The effectiveness of the protection procedures studied was calculated according to the Abbott formula [75].  
\[E=\frac{A-B}{A}\cdot 100\%\]  
\(E\)--effectiveness; \(A\)--infection in the control combination; \(B\)--infection in the protected combination.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 1 Introduction
Furthermore, in diffusive molecular communication, most system models assume that the transmitting molecule is a point source. Assuming the transmitting molecule as a point source and simplifying the system model, it is possible to solve the diffusion equation in closed form. However, since the transmitting molecule has a volume in the real environment, the diffusion equation solved under the assumption of point source is no longer valid, so it is necessary to consider the volume of the transmitting molecule.
# Enhanced Path Planning by Repositioning the Starting Point +
## 5 Conclusions
### Research Limitations and Scope Boundaries  
While this research demonstrates significant improvements in path optimization, several limitations warrant acknowledgment. The optimization methodology applies specifically to scenarios where launch pad repositioning remains feasible, restricting applicability in fixed-depot operations such as commercial package delivery systems. The current analysis focuses primarily on distance-based energy consumption without accounting for variable factors including payload weight, atmospheric conditions, or altitude variations that influence real-world energy usage. Additionally, the methodology assumes static ground stations and predictable flight environments, which may not accurately reflect dynamic operational conditions encountered in practice.
## 3 Results and Discussion
### Creep Characteristics of Asphalt Mixtures
#### 3.2.2 Fitting Analyses of Creep Compliance  
To further quantitatively analyze the effect of diatomic and rubber particles on the creep characteristics of asphalt mixtures, Burgers model and modified Burgers model were adopted to fit  
Figure 8: Creep compliance test results: (**a**) suspended dense gradation and (**b**) framework dense gradation. the curve of the creep compliance (\(J(t)\)). The Burgers model and the modified Burgers are shown in Equations (12) and (13), respectively [27; 28; 29; 30]. \[J(t)=\frac{1}{E_{1}}+\frac{t}{\eta_{1}}+\frac{1}{E_{2}}[1-\exp(-\frac{E_{2}t}{ \eta_{2}})], \tag{11}\]  
\[J(t)=\frac{\varepsilon}{\sigma}=\frac{1}{E_{1}}+\frac{1}{AB}(1-e^{-Bt})+\frac {1}{E_{2}}(1-e^{-\frac{E_{2}}{\eta_{2}}t}), \tag{12}\]  
\[Min\,F=\frac{1}{N}\sqrt{\sum_{i=1}^{n}[(J(t_{i})-J^{\prime}(t_{i}))/J(t_{i})]^{ 2}}, \tag{13}\]  
The Burgers model is composed of a Maxwell model and a Kelvin model in series. \(E_{1}\) and \(\eta_{1}\) are the elastic modulus and the viscosity coefficient of the Maxwell model, respectively, and \(E_{2}\) and \(\eta_{2}\) are the elastic modulus and the viscosity coefficient of the Kelvin model, respectively. The modified Burgers model is to replace a sticky pot in Burgers with a sticky pot with consolidation effect, that is, \(\psi\eta_{1}\) is replaced by \((1-e^{-Bt})/(AB)\), and \(A\) and \(B\) are the parameters to be fitted. When the loading time tends to infinity, the creep deformation of the asphalt mixture can be calculated by Equation (15). \[\Delta L=\sigma_{0}J_{\infty}L=(1/E_{1}+1/(AB)+1/E_{2})\sigma_{0}L, \tag{14}\]  
where \(\sigma_{0}L/E_{1}\) is the instantaneous elastic deformation, \(E_{1}\) can reflect the ability of the asphalt mixture to resistance instantaneous elastic deformation, \(\sigma_{0}L/E_{2}\) is the viscoelastic deformation, \(E_{2}\) can reflect the ability of the asphalt mixture to resistance viscoelastic deformation; \(\sigma_{0}L/(AB)\) is the unrecoverable viscous deformation, and \(AB\) can reflect the ability of asphalt mixture to resist irreversible permanent deformation. According to the creep flexibility of eight groups of asphalt mixtures shown in Figure 8, the parameters of Burgers model and modified Burgers model were solved by EXCEL software, and the solution method is shown in Equation (14). The solution results of the parameters are shown in Table 7, and the comparison between the solution results of the two models and the experimental results is shown in Figure 8. From the R\({}^{2}\) of Table 7 and the comparison diagram of Figure 8, we can see that both the Burgers model and the modified Burgers model can better reflect the creep flexibility of asphalt mixtures. When describing the change of creep flexibility of eight groups of asphalt mixtures, the modified Burgers model is better than the Burgers model. Asphal mixture tends to converge in the later stage of creep deformation, so it is difficult to be described by Burgers model.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 5 FedOpt Evaluation
### Communication Efficiency  
In our experiments, we consider the communication efficiency among the cloud server and the users \(\Pi\) as they are the main entities of the whole system. In specific, during the aggregation phase, we assume there are \(n\) epochs in the whole training process and each user \(\Pi\) has a single thread with the security parameter \(\sigma\) is set to 512 and the size of each local gradient \(G_{\Pi}\) is 32 bits. In each epoch, the users \(\Pi\) aggregate the encrypted local gradients \(E_{\Pi}\) to the cloud server and receives the shared parameters \(E_{add}\) from the cloud server. Figures 5 and 6 show the comparison result of communication efficiency between FedOpt, FedAvg and PPDL on MNIST and CIFAR-10 datasets, respectively. In specific, we consider different numbers of gradients and different numbers of users for evaluation in Figures 5a,b and 6a,b respectively. Clearly, it can be demonstrated that the increasing numbers of gradients with the maximum numbers of users has the maximum communication efficiency. Compared to the FedAvg and PPDL, FedOPT has 56% and 38% more communication efficiency, respectively, on MNIST dataset. Similarly, FedOpt outperforms on CIFAR-10 dataset with 54% and 32% more communication efficiency as compare to the FedAvg and PPDL. The major reason behind this higher communication efficiency is that, FedOpt completely utilises pallier encryption [25] which helps in the rapid growth of cipher-text volume. In addition, SCA algorithm helps FedOpt in faster convergence in terms of training epochs with significant compression rate.
## 3 A Resilient Routing Protocol
### Routing Algorithm
#### 3.2.2 Route Maintenance and Adaptation  
Given the dynamic nature of MANETs, routes may become invalid over time due to node mobility, link failures, or variations in link quality. The protocol incorporates both reactive and proactive mechanisms to address these challenges.  
Nodes continuously monitor link statuses by analyzing acknowledgment messages, transmission delays, and signal strength variations. When a link failure is detected, the affected node notifies the SDN controller. The controller dynamically updates clustering results and recalculates routing paths using the DQN. Proactively, the protocol predicts potential route failures based on trends in network parameters, such as signal strength fluctuations and historical mobility patterns. By updating paths before failures occur, the protocol minimizes disruptions and enhances overall network reliability.
## 3 Problem Formulation and Modeling
### Modeling
#### 3.2.1 State  
Status refers to the current conditions of the self-driving bus, surrounding private vehicles, and signal lights. The state encompasses several aspects, including the following: the status of the autonomous bus, which includes parameters such as vehicle speed, acceleration, and direction; the status of private vehicles, encompassing vehicle type, position, and speed; and the status of signal lights, involving the remaining time of the current phase.  
At a given moment \(t\), the state of the intersection is represented by \(q_{j}\), which comprises information such as the number of vehicles in the current lane and the speed of incoming vehicles. This state can be expressed as a vector \(Q_{j}=(V_{1},V_{2},\ldots,V_{2N})\) with \(2N\) elements, where the first \(N\) element \(V_{i}\) denotes the number of vehicles in the current lane, and the latter \(N\) element denotes the speed of the incoming vehicle. The state space \(\mathcal{S}=\big{\{}s_{j}(t)\big{|}j=1,2,\ldots,M,t\in[0,T]\big{\}}\) is composed of the set of all possible states \(s_{j}\) at any given time \(t\).  
The state space, where real-time signal priority is adjusted based on the vehicle arrival rate, can be represented as a vector comprising \(2N+1\) elements:  
\[S(t)=(V_{1},V_{2},\ldots,V_{2N},q(t)) \tag{1}\]  
where \(V_{i}\) represents the number of vehicles in the current lane; \(q(t)\) is the vehicle arrival rate at time \(t\).  
The adjustment of traffic-light duration based on the vehicle arrival rate \(q(t)\) is mathematically expressed by Formulas (2) and (3):  
\[\Delta T_{i}(t)=\eta_{i}\cdot(q(t)-q_{\text{target}}) \tag{2}\]  
\[T_{i}(t)=T_{i}(t-1)+\Delta\dot{T_{i}}(t) \tag{3}\]  
where \(\eta_{i}\) is the traffic-flow response coefficient of the intersection \(i\); \(q_{\text{target}}\) represents the target traffic volume.; \(T_{i}(t)\) is the time \(t\) duration of traffic lights at the intersection \(i\).
# Study on Burr Formation and Tool Wear in Drilling CFRP and Its Hybrid Composites
## 3 Results and Discussion
### Tool Wear  
Drill wear is a serious problem in the drilling of holes, as it is necessary to prevent the damage of cutting tools and workpieces. The non-homogenous and anisotropic properties of fiber composite materials lead to a high wear effect on the cutting tool, resulting in the difficult drilling of these materials and delamination damage [42]. Therefore, it has great significance for the study of tool wear to evaluate tool life, durability, cutting performance, etc. In general, tool wear shows the tribological properties and physical characteristics at the tool-work interfaces in the real cutting process. For tool wear analysis, a total of 300 holes was, respectively, drilled on CFRP and C-AFRP using an 8 mm diameter carbide drill with a point angle of 90\({}^{\circ}\), a 2000 rpm spindle speed and a 200 mm/min feed rate. Figure 9 displays SEM images of the carbide drill after drilling 30 and 300 holes for CFRP and C-AFRP. As shown in Figure 9, the tool wear mainly occurs on the flank side, so this kind of wear is also called flank wear. This typically wear leads to a rapid dulling of the cutting edges. After drilling 300 holes, some edge chipping and severe flank wear (see illustrations) were found on the cutting lip and the flank face, respectively. Moreover, it can be clearly observed that the tool wear caused by drilling CFRP is significantly more serious than drilling C-AFRP in Figure 9. This may be related to the high wear-resistance properties of CFRP [49]. The existence of hard carbon fibers in CFRP leads to excessive tool wear. Generally, the mechanism of tool wear in drilling of composite laminates can be attributed to abrasive wear, adhesion and chipping. This abrasive wear is also called mechanical wear. As the main tool wear factor in drilling of composite laminates, it is mainly caused by the strong wear resistance of carbon fibers [50]. The specific tool wear in the drilling of CFRP and C-AFRP, according to hole number, is shown in Figure 10. The flank wear of the carbide drill began after 30 holes with the slightly faster progress up to 90 holes, and then the wear progress becomes slightly steadier from 120 holes until 300 holes. In the initial stage of drilling, because the surface roughness of the carbide drill is high in the early coupling stage, their actual contact area suffers high pressure and severe friction, resulting in faster tool wear in the initial stage. Then, after drilling nearly 90 holes, the carbide drill passes the rapid wear stage and reaches the normal wear stage, which indicates that the tool-working friction pair that controls the chip removal process is in a stable state. Similar conclusions were also reported by [44]. [PERSON] et al. [44] also proposed to evaluate the failure degree of tool wear--that is, the average flank wear land width greater than 0.25 mm is failure. However, the average flank wear land width in this experiment is much lower than 0.25 mm--that is, the tool has not failed.  
Figure 8: Burr formation in drilling of CFRP and C-AFRP.
### Sixth-Generation (6G) Technology  
The sixth generation (6G) of technology aims to achieve unheard-of performance levels in a network that goes beyond 5G. Instantaneous downloads and streaming of ultra-high-resolution information are made possible by 6G, which aspires to offer peak data speeds of up to terabits per second. Beyond faster data rates, 6G concentrates on seamless satellite and terrestrial network integration to provide worldwide coverage. It incorporates ideas like superior energy-efficient solutions, holographic communications, and networks with AI capabilities. By prioritizing sustainability in both construction and operation, 6G aims to address environmental concerns, marking a significant step towards eco-friendly telecommunications infrastructure. The comprehensive implementation of 6G is poised to revolutionize industries and reshape social connectedness in unprecedented ways. Despite its potential, 6G is currently in the early stages of research and exploration, laying the groundwork for the transformative advancements to come [8].  
The progression from 2G to 6G highlights a series of significant advancements and persistent challenges in mobile communication technologies. Second-generation networks, primarily designed for voice communication and limited data services like SMS, faced issues with low data rates and limited internet capabilities. Transitioning to 3G brought improved data speeds and mobile internet access, but was constrained by relatively high latency and inconsistent connectivity, which hindered real-time applications. The advent of 5G introduced enhanced data rates, reduced latency, and greater connectivity, supporting a broader range of IoT devices and high-bandwidth applications. However, 5G still encounters challenges such as limited coverage in rural areas, high energy consumption, and potential security vulnerabilities. In contrast, 6G aims to overcome these issues by delivering ultra-low latency, ubiquitous connectivity, and enhanced energy efficiency through AI integration, promising smarter, more adaptive networks that can meet the demands of future digital landscapes while addressing the shortcomings of its predecessors. Further,Table 1 expresses the features of each generation while Figure 1 shows the evolution of mobile radio networks.
# Design and Analysis of Systematic Batched Network Codes
## 5 Inner Code for Systematic Batches
### Recovery of Individual Message Packets
On the other hand, suppose that we apply the Gaussian elimination step at node \(1\) and the result should be \(\mathbf{H}^{(1)}\mathbf{D}=\mathbf{I}\). Then, node \(1\) transmits \(\mathbf{Y}^{(1)}\mathbf{D}\) instead of \(\mathbf{Y}^{(1)}\). In this case, if we still erase the second packet, the following node can recover \(2\) message packets. Moreover, since the Gaussian elimination step preserves the column space of the batch transfer matrix, (\(\mathrm{Col}(\mathbf{H}^{(u)})=\mathrm{Col}(\mathbf{H}^{(u)}\mathbf{D})\)), the rank and number of recoverable message packets at the destination node should be at least as good as in the recoding schemes without this step._  
Note that the recovery of the message packets at an intermediate node is a linear operation on a batch and hence can be regarded as a part of the inner code. The effect of the recovery of the message packets can be captured by the coefficient vectors: the same operation applied on the received packets of a batch is applied on the coefficient vectors as well. The destination node does not need to know the exact operations at each intermediate, but only the coefficient vectors of the received packets.
# Evaluation of Adhesion and Hysteresis Friction of Rubber-Pavement System
## 2 Objectives  
The main goal of this study was to investigate the effect of adhesion between rubber and pavement surface on friction at low speed. In addition, we evaluated the effect of rubber rheological properties on friction with pavements. These objectives were achieved by conducting the following tasks:  
1. Measure the surface energy components of different rubber and aggregate materials.
2. Calculate the adhesive bond energy between rubber materials and aggregates.
3. Measure the rheological properties of test rubber materials at different temperatures and loading frequencies.
4. Measure the coefficient of friction between rubber and hot mix asphalt (HMA) substrates.
5. Investigate the correlation between the calculated adhesive bond energy and the measured coefficient of friction between rubber and pavement surfaces.
6. Investigate the correlation between the rubber dynamic modulus and the coefficient of friction.  
We measured the surface free energy of test rubber materials using a Sessile drop device, while the surface energy components of aggregates were measured using a universal sorption device (USD). The surface free energy of the rubber materials and aggregates were used to calculate the adhesion between the rubber materials and aggregates used in preparing the HMA test substrates. The friction between rubber sliders prepared using test rubber materials and HMA substrates was measured using the dynamic friction tester (DFT) in dry conditions at low speed. In addition, we measured the dynamic modulus of test rubber materials at different temperatures and loading frequencies using a dynamic mechanical analyzer (DMA).
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 3 The Proposed Approach
### System Model and Case Study
#### 3.1.2 Application  
An application includes some modules. These modules are related by edges. Data as tuples are transferred between two modules by tuple mapping.  
* **Application module:** This module is a type of VM. The module's properties include MIPS, size, bandwidth, and the number of PEs. The number of modules in each FD is more than the number of PEs \(\sum_{i=1}^{C}Module_{i}>\sum_{j=1}^{K}FD_{j}\), where \(C\) is the total number of modules, and \(K\) is the total number of FDs. The application modules of the considered case study include an object detector, motion detector, object tracker, and user interface.
* **Application edge:** The application modules are connected by edges. Each application edge is between two modules. In fact, tuples are transferred between modules by edges. Each edge has two important features: CPU length and data size. In fact, \(\sum_{i=1}^{M}TCL_{i}\leq MIPS_{module}\), and \(\sum_{i=1}^{M}DS_{i}\leq RAM_{module}\). This means the total CPU length and the data size of all input tuples to a module must be less than or equal to the MIPS and RAM capacity of that module. \(TCL_{i}\) is the total CPU length, and \(DS_{i}\) is the data size of the \(ith\) tuple. \(M\) is the total number of tuples. \(MIPS_{module}\) is the module's MIPS.
* **Application tuple mapping:** The tuple is the input/output relationships of the application modules that send data from one module to another module (\(module_{i}\) to \(module_{j}\); \(i\
eq j\)).
* **Application loop:** Each workflow of modules is an application loop. Each application has some workflow that connects modules by edges.
## 7 The GNSS Self-Spoofing Attack on USVs  
A GNSS Self-Spoofing Attack on USVs occurs in situations where the owner of the vehicle wants to hide his/her real position in space for a false one. For example, some USVs are commercial fishing vessels outside of the allowable area and include their own onboard self-spoofer to indicate the position of the USV in the allowable fishing area (Figure 8).  
From a technical point of view, the GNSS self-spoofing of USVs is the same spoofing, but here we have an absolutely different way of using spoofing, and if conventional anti-spoofing is based on the choice of technical means to protect oneself from spoofing, then  
Figure 8: The basic GNSS self-spoofing scheme of USVs: at some point in time, the self-spoofer determines its position \((x_{s},~{}y_{s})\), introducing some delays into each of the satellite channels, introducing some other signal distortion, and transmitting the false position \((x_{s}+\Delta x_{s},~{}y_{s}+\Delta y_{s})\) on the same frequency, which aims to deceive GNSS receivers and create a false PVT solution. All USVs in the self-spoofer activation zone are positioned as \(\text{USV}_{i,~{}false}=(x_{s}+\Delta x_{s},~{}y_{s}~{}+\Delta y_{s})\).  
anti-self-spoofing is based on the choice of technical means for attacking this self-spoofing in order to neutralize it, or at least to register the fact of its use by some vehicle including USV.