esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 3 SDN-Driven Traffic Migration Engineering (TME) Model  
### Network Model and Problem Formulation  
In this paper, the issues considered in the elaboration of the network mainly include the following aspects: (1) An initial value of energy resources for each traffic control devices (i.e., OVSs), which needs to consume a certain amount of energy resources to monitor and forward data traffic; (2) Throughput of the network; (3) The average delay of the network, namely E2E latency; (4) The OPEX of the network, that is, the optimal number of the used OVSs within 1 km\({}^{2}\) of the physical layer in order to achieve the best effects of (1)-(3) at the same time. That is to say, we achieve the optimal resource allocation under the optimal KPIs and the minimum OPEX in the eMBB application scenarios. The network model and problem formulation are described as follows.  
In the actual physical layer network, the actual position of SDN devices with communication function is random and dynamic, and the mapping symmetric OVSs in corresponding virtual networks also have the characteristics of dynamism and randomness. In this paper, we set up a random virtual network for the study of our problem, and the topology of the virtual network is shown in Figure 1. The blue nodes in Figure 1 represent OVSs, and the central location (red \(\star\)) is the virtual macro base station (MBS) of the virtual network. Additionally, all OVSs in the network transmit the data traffic to the base station through multihop communications.  
Figure 1: The virtual slice network model.  
Let \(G(V,E,B,R)\) denote a weighted virtual network protocol, where \(V\) represents the set of OVSs; \(E\) represents the set of links (edges) and each link is associated with weights \(B\) and \(R\). Table 1 summarizes the notations used in this paper.
# Removal of Heavy Metal Cd Element from Paddy Soil by Geo-Electrochemical Technology
## 2 Overview of the Study Area  
Quanzhou County (25\({}^{\circ}\)37\({}^{\prime}\) to 26\({}^{\circ}\)18\({}^{\prime}\) N, 110\({}^{\circ}\)41\({}^{\prime}\) to 111\({}^{\circ}\)14\({}^{\prime}\) E), located in the northeast of Guangxi, dominates the upper reaches of the Xiang River and serves as a key point on the Hunan-Guangxi corridor. The county is in a sub-tropical humid monsoon climate zone, with a frost-free period of 298 d per year and an average annual temperature of 17.7 \({}^{\circ}\)C. Its primary characteristics include strong solar radiation, abundant sunlight for most of the year, an average multi-year sunshine duration of 1404.0 h, and plentiful rainfall with an average multi-year precipitation of 1565.9 mm, although it is unevenly distributed throughout the seasons. The area of Quanzhou County is approximately 4000 km\({}^{2}\), with karst areas accounting for more than one-third of the total land area. It is a typical karst hilly basin region, with carbonate rocks dominating the lithology of the strata [21]. Quanzhou County is a national basic farmland demonstration county and one of the national commodity grain production base counties, making it an important agricultural county in Guangxi province. However, previous investigations found that farmland soil in Quanzhou County is contaminated with heavy metals to varying degrees, with Cd showing the highest level of contamination. The farmland is considered moderately polluted [21]. The average Cd concentration of 288 air-dried farmland soil samples reaches 0.43 mg/kg, exceeding the background value of agricultural soil in the north-eastern region of Guangxi(0.18 mg/kg) [22]. Therefore, it is urgent to implement effective technical measures to remediate and control Cd contamination of the paddy soil of Quanzhou County. The geo-electrochemical technology is expected to play a key role in the in situ treatment of Cd-contaminated paddy fields.
applied sciences Article  
Experimental Determination of the Coefficient of Friction on a Screw Joint  
[PERSON] *(c) and [PERSON]  
Department of Design and Monitoring of Technical Systems, Faculty of Manufacturing Technologies with a Seat in Presov, Technical University of Kosice, Bayerova 1, 080 01 Presov, Slovakia  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +421-55-602-6337  
**Featured Application: The results of this paper can help in lubricant selection when frequently disassembling and assembling a screw joint or when using a screw joint with fine mechanics. By applying the results in practice, it is possible, in certain cases, to minimize the tightening torque as the main parameter for screw joints.**  
This paper deals with the coefficient of determination of screw connection friction between the thread and the matrix. The coefficient of friction was measured using a laboratory device with an M20 screw connection without any grease and, subsequently, plastic grease was added (CX80 silicone, lithium, and copper grease). When grease is added, the friction in the threads and screw heads is limited and consistently retained. When tightening by torque, which represents the prevailing assembly method in standard screwing practice, only part of the torque is effectively used to create axial force and pre-stress. The rest of the torque is employed in friction suppression between threads and converted into heat. In general, the coefficient of friction depends on diverse factors such as the roughness of the thread surface, the gradient angle of the helix, and the grease properties. The tightening torque represents a primary parameter in the experimental measurements, monitored using a digital torque spanner, and generates an axial force in the screw. Based on the aforementioned parameters, the objective of this paper was to monitor changes in the coefficient of friction between the thread of the screw and the matrix in the case of different grease types. The actual coefficient of friction was calculated through the exponential equation of the torque balance. First measured was the load of the bolted joint without the use of grease, where the average value of the coefficient of friction was 0.44732; this value served as a benchmark for comparison to the measurements with the use of grease. The measurements showed that the value of the friction coefficient was reduced by 30.57% when using lithium grease, by 40.56% when using silicone grease, and by 47.64% when using copper-based grease, making the latter the most suitable for the application. Without appropriate greasing, friction suppression was accompanied by extremely high torques, which resulted in insufficient screw prolongation.  
bolded joint; load; axial force; coefficient of friction; grease
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Regulatory Challenges in AI Healthcare: Adapting to AI's Evolving Nature
#### 5.2.3 Regulatory Adaptation for Advanced AI Models in Healthcare  
The emergence of advanced AI models like LLMs, including GPT-4, poses new challenges for regulatory bodies like the FDA, which have previously overseen AI-based medical technologies. These models' scale, capabilities, and potential impact necessitate reevaluating existing regulatory frameworks. The real-time adaptation of LLMs and the adaptive and autodidactic functions of deep learning algorithms call for regulatory frameworks capable of addressing these systems' adaptability and self-teaching nature. To address these challenges, there is a need to create new regulatory categories specifically for LLMs, distinct from existing AI-based medical technologies. Continuous monitoring and validation of these systems are crucial to ensuring their accuracy and validation across different populations. Ensuring transparency and interpretability in AI decision-making processes is also essential, albeit challenging, due to the complex nature of AI algorithms. Lastly, the regulation of AI in healthcare must consider ethical aspects, including fairness, non-discrimination, and respect for patient autonomy, to prevent potential biases in AI models that could lead to healthcare disparities. Adapting regulatory frameworks for AI in healthcare is imperative to manage the unique challenges posed by evolving AI technologies effectively. This adaptation requires a multifaceted approach, encompassing continuous monitoring, creating new regulatory categories for advanced models like LLMs, focusing on transparency, ethics, and data privacy, and integrating ethical considerations into the regulatory process [27].
## 3 Analytical Method for Spatial-Temporal Prediction of the Extreme Pressure
### Spatial-Temporal Distribution of Pressure State Model
#### 3.2.4 Definition of the Extreme Pressure and Pressure Regions  
According to the time sequence, Equations (9) and (12), Equations (13) and (14) do not coincide in time. Thus, the maximum and minimum values of \(p_{s}\) are \(\pm 4\). The condition where \(p_{s}\) could reach \(\pm\) 4 is called the worst condition.  
In the post-train stage, \(p_{s}=4\) represents that during a short time, the pressure in the tunnel rises continuously due to the influence of the successive compression waves from four PWSs, as shown in labels 17-20 in Figure 1. \(p_{s}=-4\) indicates that in a short time, the pressure continuously decreases under the influence of the successive expansion waves from the four PWSs, as shown in labels 13-16 in Figure 1. In the worst condition, the region where \(p_{s}\) could reach 4 is called the positive pressure region, and those with \(p_{s}=-4\) are called the negative pressure regions, where the positive and negative pressure amplitudes are greater than the other region of the tunnel.  
The intersection of the positive and negative pressure regions is the peak pressure region, where the pressure fluctuates greatly. MPP is the point where the pressure rises for the longest time in the positive pressure region. MNP is the point with the longest pressure drop time in the negative pressure region. MPP and MNP are also called extreme pressure. The MPPR is the region with MPP and MNP as the endpoints, indicating where the maximum peak-to-peak pressure (the largest difference between the positive and negative pressure peaks) would appear after train passage. It is usually the place with serious fatigue damage due to the largest alternating load.  
Owing to the attenuation during wave propagation, only the superposition of the influential pressure waves (with less attenuation) is considered in this study. European standards EN 14067-5 [49] indicate that in the post-train stage, the pressure fluctuation is still significant for a period (\(\leq\)10 \(L_{tu}/c\)). Thus, in this study, for the entry-induced PWS, \(n_{w}\leq 10\), for the exit-induced PWS, \(n_{w}\leq 4\).
## 3 Results and Discussion
### Analysis of Various Heat Transfer Contributions  
Further insight into the heat transport mechanism of the suspension fluid flowing in the tube can be gained by analyzing the following quantities: the dimensionless outer wall heat transfer (\(q^{*}_{w,o}\)), the dimensionless inner wall heat transfer over the cooled section (\(\left(q^{*}_{w,i}\right)_{L_{b}}\)), the dimensionless sensible heat transfer (\(\left(q^{*}_{w,i}\right)_{L_{b,\;sen}}\)), the dimensionless latent heat transfer (\(\left(q^{*}_{w,i}\right)_{L_{b,\;lat}}\)), and the dimensionless wall conduction heat transfer (\(\left(q^{*}_{w,i}\right)_{cond}\)). The respective plots for these heat transfer contributions are analyzed to identify the general features of the competing mechanisms of sensible and latent heat transfer.  
First, for the considered parameters, the axial fluid conduction over the cooled section accounts for approximately 0.2% of the total heat transport in tube flow (data not shown). In Figure 6a, the individual heat transfer contributions do not change significantly with \(t_{w}\) but increase with increasing \(k^{*}_{wf}\), albeit at different increments. At low \(k^{*}_{wf}\) values of 0.1 and 1, the latent heat transfer is the dominant mechanism for convective transport in the suspension flow, and this contribution decreases considerably as \(k^{*}_{wf}\) increases. For \(k^{*}_{wf}=0.1\), each of the contributions to the heat transfer rate is quite low. When \(k^{*}_{wf}=1\), the latent heat transfer (\(\left(q^{*}_{w,i}\right)_{L_{b,\;lat}}\)) accounts for 78.67% of the inner wall heat transfer, whereas the sensible heat transfer (\(\left(q^{*}_{w,i}\right)_{L_{b,\;sen}}\)) accounts for only 21.1% of the inner wall heat transfer; that is, most of the energy transfer is in the form of released latent heat. For \(k^{*}_{wf}=10\), the latent heat transfer (\(\left(q^{*}_{w,i}\right)_{L_{b,\;lat}}\)) accounts for 55% of the inner wall heat transfer, and the sensible heat transfer (\(\left(q^{*}_{w,i}\right)_{L_{b,\;sen}}\)) increases to 44.82%. This result can be explained by inspecting Figure 3b.
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Mathematical Description of Network Reconfiguration for Ship Power Systems
#### 2.2.4 Objective of Balancing Generator Efficiency  
It is necessary to rationally utilize each generator in the reconfiguration process and consider the efficiency of each generator to avoid its inefficiency and adverse effects on the generator's output power, thermal load, noise and vibration, and energy consumption. The generator's working efficiency is balanced as an objective setting function as Equation (10):  
\[\min\!f_{4}=\min\!P_{Ni}/P_{Gi}\quad i\in N_{G} \tag{10}\]  
where \(P_{Ni}\) is the active power output of generator \(i\); \(P_{Gi}\) is the active power rating of generator \(i\); and \(N_{G}\) is the set of four generators. Equation (10) itself is the interval \([0,1]\) normalized value, \(\min\!f_{3}=0\) means when all generators are running at full load, and \(\max\!f_{3}=0\) means all generators are out of operation. Shown as Equation (11)  
\[U(f_{4})=f_{4} \tag{11}\]  
A mathematical model of the objective composite function for reconfiguration of a ship's distribution network can be obtained. Shown as Equation (12):  
\[\min\!f=\delta_{1}U(f_{1})+\delta_{2}U(f_{2})+\delta_{3}U(f_{3})+\delta_{4}U(f _{4}) \tag{12}\]\(\delta_{1}\), \(\delta_{2}\), \(\delta_{3}\), and \(\delta_{4}\) are the weighting coefficients of \(f_{1}\), \(f_{2}\), \(f_{3}\), and \(f_{4}\). In this paper, we adopt the hierarchical analysis method to determine the weights, and establish the scale matrix with a level from \(1\) to \(9\). Shown as matrix A:  
\[A=\begin{bmatrix}1&3&5&9\\ 1/3&1&3&5\\ 1/5&1/3&1&3\\ 1/9&1/5&1/3&1\end{bmatrix} \tag{13}\]  
We calculate the maximum eigenvalue of \(A\)\(\lambda_{\rm max}=4.073\); the corresponding eigenvector is \([\ 0.8982\ 0.3951\ 0.1765\ 0.0773]\), where the number of targets \(n=4\). The consistency index formula is \(\delta_{\rm CI}=(\lambda_{\rm max}-n)/(n-1)=0.024\), \(\delta_{\rm RI}=0.9\), and the consistency ratio is \(\delta_{\rm CR}=\delta_{CI}/\delta_{RI}=0.027<0.1\). Therefore, it meets the consistency test. The eigenvectors of matrix \(A\) can be normalized to obtain the weight vector \([0.9055\ 0.3975\ 0.3200\ 0.0779]\).  
The mathematical model of the objective composite function for network reconfiguration of a ship's power system is given by.  
\[\text{min}f=0.9055U(f_{1})+0.3975U(f_{2})+0.3200U(f_{3})+0.0779U(f_{4}) \tag{14}\]
## 2 OBS Networks
### Functional Mechanisms of OBS Nodes
#### 2.2.1 Burst Assembly  
This is the process of assembling incoming data packets into bursts. In general, each edge node maintains multiple queues to aggregate the data packets from client networks according to their destinations and QoS requirements (see Figure 3(a)). A burst is then assembled from one of these queues according to a given assembly algorithm whose purpose is to decide when to stop the data aggregation process. Several assembly algorithms have been proposed and investigated up to date [6,7,21,22], but they usually have to take the following parameters into account: a preset timer, minimum burst size and maximum burst size. The timer is used by the edge node to decide when to assemble a new burst. The minimum and maximum burst size parameters determine the length of the assembled burst. These parameters require careful setting because long bursts can hold network resources for long time periods, prone to higher burst losses, while short bursts result in increased number of CPs.
## 5 Data Processing and Artificial Intelligence Methods
### Holistic Data Processing  
It is quite clear to most experts that a holistic approach is needed in precision livestock farming [1; 4; 13; 62; 67; 71; 76; 77]. This can be achieved by integrating and analyzing data from different data sources together. The data-driven perspective has been the focus of several publications. A review has also been conducted in this area, summarizing the data integration methods and algorithms used, and detailing the goals and potential of this approach [63]. It is now clear that, as well as at the individual-animal-monitoring level or at the farm level, data integration includes data from monitoring IoT devices and the use of other, more traditional data sources. Rumen boluses have not yet been investigated in studies in terms of their role and significance in data integration. It has become clear that only AI algorithms can achieve good results in this area. On the second and third level of data processing the suggested methods, there are the computationally more intensive deep neural network algorithms. Now, these methods are more and more widespread in any application (including from self-driving cars to smart buildings) [78]. In this area, the most frequent applications are the video-based data analysis methods. However, the video cameras are not able to be applied comfortably in stables, and their usage is expected to be limited in the future, but their data-processing algorithms are pioneering in precision agriculture. It is expected that these type of algorithms (e.g., CNN) with many modern techniques, such as network pruning, weight quantization, and transfer learning, will become important in data processing [79; 80]. Other proposed algorithms are fuzzy, neuro-fuzzy systems, and dimension reduction methods used in big data systems, or deep neural network-based solutions [1; 4; 49; 57; 67]. It is important to mention the ongoing need and the solutions that are coming to the fore today for the interpretability of the AI models. This has been a classically large problem for AI systems; however, with the proliferation of explainable AI, the models can be interpreted, helping the proposed interventions obtain more user acceptance [81; 82].
## 1 Introduction
Agricultural spraying robots can orient themselves in the working environment, as exemplified by the real-time kinematic (RTK) system used in many robots. RTK helps to estimate precise positioning, velocity, and timing indicators, which can be used to generate robot motion trajectories and sequences [50]. Plant recognition systems such as various lasers, LiDAR, and other sensors are used for precision spraying to recognize plants. Plant recognition is based on color transformation and the Ostu automatic threshold algorithm, where the sprayed zone is seen as a two-dimensional rectangle. Studies have shown that pesticide use can be reduced by up to 46% using recognition systems [49]. The research so far mainly focused on the quality of robotic spraying, and the accuracy of different systems such as steering, spraying, or plant recognition [51; 52]. The use of spraying robots in agriculture is a relatively new but rapidly evolving field, and therefore, spray drift studies are relatively scarce. When analyzing spray drift studies in gardens, most of them are carried out with tractor sprayers or drones [53]. It is therefore necessary to carry out spray drift studies using a spraying robot and to assess the effectiveness of this technology when the sprayed liquid is subjected to lateral wind at certain velocities. The hypothesis of this study is that use of DRAs will help significantly reduce spray drift when we use spraying robots. Also, as the lateral wind velocity increases, the effect of DRAs on spray drift mitigation will decrease. The aim of this study is to determine the influence of lateral wind velocity and three different DRAs on the spray of artificial plants and spray drift using an agricultural spraying robot.
## 3 Results and Discussion
### High-Risk Zones Created by Cruise Ships' Routes
#### 3.1.5 SuSac Island  
Longitudinal northwesterly and southwesterly routes as well as transversal southerly and northerly routes often pass along the coast of Susac island. Cruise ships on northwesterly and souteasterly longitudinal routes connecting south Adriatic east-coast destinations and north Adriatic destinations (and vice versa) do not use the Central Adriatic Separation Scheme. They proceed along outer island routes passing the south shores of Susac island. Transversal routes pass the eastern and western shores of Susac island. Cruise ships on southern transversal routes depart from northern Adriatic ports or the port of Split and proceed to the western Adriatic coast or join the longitudinal corridor on the way to Dubrovink, Kotor or the Oranto strait. Northerly transversal routes connect western Adriatic ports, the Oranto strait, Dubrovnik or Kotor to central Adriatic ports or northern Adriatic ports.  
During the research period, 101 cruise ships passed less than 3 M from Susac island with average retention periods of 13.84 min, 265 cruise ships passed with 3 M to 6 M distances from the south shores of Lastovo, while 30 cruise ships kept distances of 6 M or more from the south island shores (Table 15). Traffic monitoring showed that two cruise ships met less than 3 M from the island shores seventeen times in a 90 min period.