esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Identification Methods
### Particle Swarm Optimization
#### 3.3.2 Constriction Factor  
The constriction factor method, also known as the constriction coefficient method, was introduced by [PERSON] et al. [53] to improve the convergence and stability of the PSO algorithm. It involves the use of factor \(\aleph\), which can be calculated using Equation (12) as follows:  
\[\aleph=\frac{2}{\left|2-\varphi-\sqrt{\varphi^{2}-4\varphi}\right|} \tag{12}\]  
where \(\varphi=c_{1}+c_{2}\), \(\varphi>4\). The constriction factor is inversely proportional to \(\varphi\), so when \(\varphi\) is set to 4.1, for example, the constriction factor becomes 0.73. This value is then multiplied by the three terms in Equation (9): the velocity, \(pBest\), and \(gBest\). Empirical studies have shown that the constriction factor method can improve the performance of PSO compared to the original algorithm. However, it does not offer a mechanism for preventing premature convergence, which can occur when the optimization process reaches a satisfactory solution before fully exploring the search space.  
The updated velocity equation incorporating the inertia weight and the constriction factor is as follows:  
\[v_{k+1}(i)=\aleph(w_{k}v_{k}(i)+c_{1}r_{1}(pBest_{k}(i)-x_{k}(i))+c_{2}r_{2}(gBest _{k}-x_{k}(i))) \tag{13}\]  
In this research, the selected variant of PSO is referred to as the standard PSO (SPSO) for comparison with the hybrid methods. The SPSO will be integrated with the QN method to create a hybrid approach. The pseudo-code of the SPSO algorithm is presented in Algorithm 1. The algorithm will terminate when the maximum number of iterations (\(k_{max}\)) is reached.  
```
0:\(N\), \(k_{max}\), \(x_{min}\), \(x_{max}\), \(w_{min}\), \(w_{max}\), \(\varphi\), \(c_{1}\), and \(c_{2}\)
1: Set \(k=0\), randomly initialize particles' positions \(x_{i}(k)\) and velocities \(v_{i}(k)\)
2:for each particle \(P_{i}(k),\ i=1,2,\ldots,N\)do
3: Set \(pBest_{i}(0)=x_{i}(0)\), \(gBest(0)=pBest_{1}(0)\)
4:endfor
5:repeat
6: set \(k=k+1\)
7:for each particle \(P_{i}(k),\ i=1,2,\ldots,N\)do
8: Evaluate \(Fcost(x_{i}(k))\) using Equation (8)
9:if\(Fcost(x_{i}(k))<Fcost(pBest_{i}(k))\)then
10:\(pBest_{i}(k)=x_{i}(k)\)
11:endif
12:if\(Fcost(pBest_{i}(k))<Fcost(gBest(k))\)then
13:\(gBest(k)=pBest_{i}(k)\)
14:endif
15: Update velocity \(v_{i}(k+1)\) and position \(x_{i}(k+1)\) using Equations (13) and (10)
16:endfor
17:untilSPSO termination criterion is satisfied
```  
**Algorithm 1** SPSO.
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 6 Conclusions  
Field data collection has been a crucial agronomic task for a long time, but at present, sustainability issues, green deal rules, climate change challenges, and safe food expectations demand crop monitoring with enhanced requirements. On the other side, human efforts should be minimized and research has been looking for solutions to automate monitoring, maximizing outcome details. Proximal and remote sensing offer images with enhanced spatial resolution or almost continuous environmental data. This availability of large amounts of data is essential, but algorithms are fundamental methods to automatically extrapolate relevant results. On the contrary, when limited acquisitions are available, algorithms offer segmentation approaches or methods to rationally merge data from different sources. The synergy between sensing and algorithms is a relevant characteristic of IoT, repeatedly analyzed in this paper, and it represents the key factor to stimulate the evolution towards the next generation of intelligent farming.  
This study shows the high interest of the research community in applying algorithms to a variety of different agronomic aims. The reliability of the analyzed technologies is proved by many papers, and the adoption of new architectures such as transformer is encouraged. Efforts should be dedicated to improving their generalization and usability in real scenarios, allowing stakeholders to concretely take advantage of the technological achievements.  
Conceptualization, G.P.C., D.T. and P.B.; methodology, G.P.C., D.T. and P.B.; software, G.P.C., D.T. and P.B.; validation, G.P.C., D.T. and P.B.; formal analysis, G.P.C., D.T., P.B. and M.C.L.; investigation, G.P.C., D.T. and P.B.; resources, G.P.C., D.T. and P.B.; data curation, G.P.C., D.T. and P.B.; writing--original draft preparation, G.P.C., D.T., P.B. and M.C.L.; writing--review and editing, G.P.C., D.T. and P.B.; visualization, G.P.C., D.T. and P.B.; supervision, G.P.C., D.T. and P.B.; project administration, D.T. and P.B.; funding acquisition, D.T. and P.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of Next-GenerationEU, partnership on the Agritech National Research Center (NRRP--Mission 4 Component 2, Investment 1.4--D.D. 1032 17 June 2022, CN0000002). This manuscript reflects only the authors' views and opinions; neither the European Union nor the European Commission can be considered responsible for them.  
Data Availability Statement:Data sharing is not applicable.  
The authors declare no conflicts of interest.
## 2 Overview of Artificial Intelligence and Machine Learning in Road Transport Systems
### Advance Approaches for Incident Detectors of Road Traffic Accidents Using AI and ML
#### 2.5.5 Incident Detector through Vehicle Ad Hoc Networks  
The majority of systems designed for VANET incident detection rely on individual cars to infer the existence of an incident from reports from other vehicles [72]. To construct a network with a large coverage area, a technique called a vehicular ad hoc network (VANET) uses automobiles as wireless routers or mobile nodes. Therefore, understanding the mobility model representing the actual behavior of vehicle traffic is necessary before beginning the simulation. To properly model vehicular mobility, we must differentiate between macro- and micro-mobility descriptions [69]. VANET transforms each participating vehicle into a router or wireless node, enabling vehicles up to 300 m distant to join a vast network. Automobiles can link to other cars and connect vehicles to form a mobile internetwork when they are outside of the signal range and out of the network [73]. IoT is a key network in VANET that links all physical objects and entities to the internet and transmits data without involving humans. IoT enables connectivity in the form of objects-to-objects (vehicle-2-infrastructure) and things-to-humans (vehicle-2-human). IoT uses cutting-edge communication technologies to make devices smarter, reducing the consequences of population expansion, climate change, human mobility, and environmental changes. A potential initiative in intelligent transportation is VANET. It illustrates how MANET networks can support internal management and inter-node communication without relying on external infrastructure [74].  
First, topology-based routing techniques apply network link information to determine the best route for packet forwarding. These routing protocols can also be split into hybrid, reactive, and proactive routing. In order to choose the subsequent forwarding hops, location-based routing systems communicate geographic position data with nearby nodes obtained using GPS sensors. Third, for cluster-based routing protocols, a node is chosen as the cluster head in each cluster, which manages communications inside and between clusters. Direct links connect nodes inside a cluster, and cluster heads handle communications between clusters. Fourth, VANETs typically share information via broadcast routing techniques. The fifth is the geocast routing protocol, which sends packets from a source to every node in a specific area. This routing strategy is advantageous for numerous VANET applications [75].  
In order to exchange information for purposes related to safety, comfort, or entertainment, vehicles are equipped with on-board Units (OBUs), which enable them to communicate with their surroundings (including other vehicles, roadside units (RSUs), people, and sensors).VANET is a terrestrial networking paradigm [76].
# Assessing Interactive Web-Based Systems Using Behavioral Measurement Techniques
## 4 Data Analysis
### Eye-Tracking Analysis
#### 4.3.2 Jarir Website
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Participants** \\ \end{tabular} & **Total Time** & **TTFF** & \
\begin{tabular}{c} **Average Fixation** \\ **Duration** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Fixations** \\ \end{tabular} & **Spent Time** & **Revisits** \\ \hline Participant 1 & 600 s & 1.3 s & 0.41 s & 1011 & 412 s & 89 \\ \hline Participant 2 & 324 s & 1.91 s & 0.3 s & 484 & 146 s & 21 \\ \hline Participant 3 & 287 s & 0.78 s & 0.32 s & 470 & 149 s & 65 \\ \hline Participant 4 & 401 s & 2.8 s & 0.27 s & 511 & 139 s & 60 \\ \hline Participant 5 & 428 s & 3.1 s & 0.29 s & 443 & 128 s & 49 \\ \hline Average & 408 s & 2 s & 0.32 s & 584 & 195 s & 57 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Eye-tracking identified usability issues for Extra website. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **TTFF** & **Average Fixation Duration** & **Number of Fixations** & **Spent Time** & **Revisits** \\ \hline Search bar & 0.01 s & 0.34 s & 14 & 4.76 s & 6 \\ \hline Main menu & 0.3 s & 0.11 s & 3 & 0.33 s & 1 \\ \hline Compare button & 0.17 s & 0.24 s & 6 & 1.44 s & 3 \\ \hline Item card & 0.01 s & 0.32 s & 16 & 5.12 s & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Fixation data for Task 2. Figure 17: Finalizing comparison—jarir. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **TTFF** & **Average Fixation Duration** & **Number of Fixations** & **Spent Time** & **Revisits** \\ \hline Search bar & 0.02 s & 0.33 s & 13 & 4.29 s & 2 \\ \hline Main menu & 3.05 s & 0.16 s & 6 & 0.96 & 4 \\ \hline Add to Cart button & 0.2 s & 0.27 s & 3 & 0.81 s & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Fixation data for Task 3. Figure 19: Adding an item to cart. Figure 18: Using the search bar to locate items. * Eye Tracking Discovered Usability Issues  
Table 19 shows the identified usability issues using eye tracking and the severity rating for the website. The usability issues pointed out problems related to user behavior. * Non-Task-Related Observations  
Further analysis of user behavior on the website revealed the following:  
1. The users did not register any notable interest in the homepage despite its colorful design and attractive banners. However, the users' notice of the main banner was more than that for the Extra website. 2. User scan paths were somewhat regressive, tracing back and forth between certain areas on the page. On the Jarir website, this was also due to the crowded banners with small fonts and icons.
# An Intelligent Collaborative Charging System for Open-Pit Mines
## 6 Simulation Experiments and Result Analysis
### Construction of Charging Scenario  
The blasting area boundary and borehole coordinates were extracted from the CAD design drawings of a large open-pit iron mine. The coordinates of the boundary points and boreholes were stored in a TXT file for subsequent area modeling and path planning. Within the blasting zone, both traditional charging vehicle operations and the intelligent cooperative charging method were simulated, followed by a comparative analysis of the experimental results. The schematic diagram is detailed in Figure 7.
###### Abstract  
Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, and nonexistent tools. Therefore, the present research is conducted to minimize the dangers in actual tests, increase the documentation through a platform repository, and solve the autonomous trajectory of a maze with obstacles. The methodology consisted of: replicating a scenario with the parrot robot in the gazebo simulator; then the computational resources, the mechanism, and the available commands of the robot were studied; subsequently, it was determined that the genetic micro-algorithm met the minimum requirements of the robot; in the last part, it was programmed in simulation and the solution was validated in the natural environment. The results were satisfactory and it was possible to create a parrot robot in a simulation environment analogous to the typical specifications. The genetic micro-algorithm required only 100 generations to converge; therefore, the demand for computational resources did not affect the execution of the essential tasks of the robot. Finally, the maze problem could be solved autonomously in a real environment from the simulations with an error of less than 10% and without damaging the robot.  
trajectory planning; mobile robot; micro-algorithm; genetic algorithm; simulation +
Footnote †: journal:  
Article  
Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 3 YOLOv8-TLC Algorithm
### C2f-DS Module  
As the depth of the network increases and the number of parameters expands, the detection speed of steel surface defect models gradually slows down. To address this issue, the C2f-DS module is proposed, which achieves high computational speed with low storage requirements, thereby improving the overall efficiency of the defect detection model. In addition by incorporating a distributed shift mechanism, Distributed Shift Convolution (DSConv) [40] can model complex local feature distributions more accurately, thus significantly improving the feature representation capability of the model. DSConv is an efficient quantized convolution operator that decomposes the traditional convolution kernel into a Variable Quantized Kernel (VQK) and distributed shift components, aiming to reduce computational and storage overhead. The distributed shift components consist of two tensors: Kernel Distribution Shifter (KDS) and Channel Distribution Shifter (CDS). The framework is shown in Figure 5.  
During network pretraining, the weight tensor filters are divided by depth into blocks of length \(B\), with each block sharing a single floating-point value. All these blocks are then quantized. The memory saved for each tensor using this method can be expressed by the following formula:  
\[p=\frac{b}{32}+[\frac{C_{i}}{B}]/C_{i} \tag{13}\]  
In the equation, \(C_{i}\) represents the number of channels, while \(b\) refers to the chosen hyperparameter setting.  
Leveraging the advantages of DSConv in low storage and high computational speed, we designed the C2f-DS module, as shown in Figure 6. This module adjusts the spatial positions of the convolution kernels without increasing the number of parameters, which reduces the neglect of important information during the convolution process and enables more effective extraction of local features. This enhancement not only improves the utilization of computational resources in steel surface defect detection but also elevates the overall performance of the model.  
Figure 4: TSP structure.  
Figure 5: DSConv frame diagram.
## 6 Results and Discussion
### Reliability Assessment
When evaluating the outcomes from these two different scenarios, from a practical viewpoint, it is worth mentioning that the weight of each indicator should be chosen based on the requirements of the system designer.
# Approximating a Minimum Dominating Set by Purification
## 2 Preliminaries  
A formal description of our domination problem is as follows. Given a simple connected graph \(G=(V,E)\) with \(|V|=n\) vertices and \(|E|=m\) edges, a subset of vertices \(S\subseteq V\) is a _dominating set_ in graph \(G\) if any vertex \(v\in V\) is adjacent to some vertex \(x\) from this subset (i.e., there is an edge \((v,x)\in E\)), unless vertex \(v\) itself belongs to set \(S\). The objective is to find a feasible solution with the minimum possible size \(\gamma(G)\).  
Given a vertex \(v\in V\), \(N(v)\) is the set of neighbors or the open neighborhood of \(v\) in \(G\), that is, \(N(v)=\{u\in V:(u,v)\in E\}\), we denote by \(\delta_{G}(v)=|N(v)|\) the _degree_ of vertex \(v\) in \(G\). We let \(\delta_{G}=\min_{v\in V}\{\delta(v)\}\) and \(\Delta_{G}=\max_{v\in V}\{\delta(v)\}\). The _private neighborhood_ of vertex \(v\in S\subseteq V\) is defined by \(\{u\in V:N(u)\cap S=\{v\}\}\); a vertex in the private neighborhood of vertex \(v\) is said to be its _private neighbor_ with respect to set \(S\). For further details on the basic graph terminology, see [5].  
In [22], a dominating set is formed in two stages. At the first stage, an initial dominating set \(S=\{v_{1},\ldots,v_{k}\}\) is created by a fast greedy algorithm, where \(v_{i}\) stands for the vertex included at iteration \(i\) of this greedy algorithm. At the second purification stage, a special iterative procedure is applied to reduce the initial dominating set. This iterative procedure will be referred to as the purification procedure 0, abbreviated P0. It is based on the analysis of the flowchart of the greedy algorithm, represented as a special kind of spanning forest \(T\) of the original graph \(G\) and consisting of the vertices of set \(S\). This forest is the union of the so-called _clusters_ that organize vertices from set \(S\) in special graphical structures. These structures are formed at the first stage of our algorithm, while the greedy algorithm generates set \(S\). Clusters represent important dependencies in the dominant set that allows its purification in different ways. In particular, the order in which the vertices of each cluster are purified is important and affects the outcome of the purification process. A cluster can be seen as a specific type of spanning forest of the initial graph \(G\). Different clustering methods yield a collection of different types of rooted trees. By a traversal of these trees, we will purify the initial dominating set at stage 2. Different combinations of a particular clustering and traversal/purification method result in different overall purification algorithms of different efficiency.  
In the following sections, we describe different tree traversal methods starting with a formal definition of a cluster in the next section. Then, we describe how we carry out the purification process during a traversal of each cluster. We combine different clustering and traversal methods and obtain different purification procedures.
# Modified Joint Source-Channel Trellises for Transmitting JPEG Images over Wireless Networks
## 3 Construction of the Combined Source-Channel Trellis  
In this section, we describe the design of our modified trellis and the method of decoding messages encoded in the trellis using our modified optimal algorithm. For this purpose, a low-complexity JSCC scheme with expurgating trellis is developed.
## 1 Introduction  
### Background  
Boiling heat transfer is a highly efficient thermal management mechanism widely utilized in various industrial applications, including power generation [1], data centers [2], and the thermal management of electronics [3]. Its superior heat transfer performance and ability to dissipate large heat fluxes with minimal temperature differences make it indispensable. The continuous pursuit of higher performance and miniaturization [4] necessitates ongoing efforts to further enhance boiling heat transfer, particularly by increasing the critical heat flux (CHF) and the heat transfer coefficient (HTC) [5,6], while simultaneously reducing wall superheat. This demand has sparked significant interest in modifying boiling surfaces across macro-, micro-, and nano-scales [7,8]. The primary objectives of these surface modifications are to increase the effective heat transfer area significantly, facilitate optimized vapor-liquid pathways, promote nucleation site density, and enhance overall bubble dynamics [9].  
While extensive research on surface modification techniques, such as surface coating [10, 11, 12], bi-conductive surfaces [13], laser-textured surfaces [14] surface roughness effects [15,16], has demonstrated effectiveness in improving pool boiling HTC and CHF, their widespread industrial adoption is often hindered by high costs and complexities associated with multiple manufacturing steps [7,17]. This presents a critical research gap. Binder-jetting 3D printing (BJ3 DP) offers a compelling solution characterized by its simplicity, controllability, and cost-effectiveness. This additive manufacturing approach has the potential to yield comparable or even superior heat transfer performance compared to surfaces produced by traditional methods, such as machining, coating, and chemical processing. Our unique contribution lies in the application of BJ3 DP for fabricating specific porous copper structures designed for enhanced boiling heat transfer, providing a novel and scalable pathway for advanced thermal management solutions.
## Results
### LLLT Promotes the Transfer of NF-kB from the Cytoplasm to the Nucleus  
To elucidate the molecular mechanisms underlying osteoclast differentiation, the activation of RANKL-induced nuclear translocation of p65, a subunit of NF-kB, was evaluated via confocal microscopy. RAW 264.7 cells were stained with DAPI (blue, nuclei) and Alexa 488 (green, p65). In the cells of the control group, p65 was located in the cytoplasm, and, in the cells treated with RANKL, p65 was confirmed to transfer from the cytoplasm to the nucleus. In addition, it was found that in the group treated with RANKL and LLLT, lasting for 15 s, the expression of p65 increased more than in the group treated with RANKL alone, and it was clearly transferred from the cytoplasm to the nucleus (Figure 4). Therefore, this result suggests that LLLT irradiation increases the expression of NF-kB in RAW 264.7 cells, promoting the transfer from the cytoplasm to the nucleus, thereby inducing the transcription of various genes.  
Figure 3: LLLT causes a difference in the expression of osteoclast-forming genes. RAW 264.7 cells were irradiated with LLLT (5–30 s) and incubated for 24 h in the presence of RANKL (10 ng/mL), then total RNA was extracted, and gene expression was analyzed by real-time RT-PCR. Each value represents the mean \(\pm\) S.D. * \(p<\) compared to control group; compared to RANKL alone, \({}^{\dagger}\)\(p<\)0.05.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 3 Proposed Solution  
### OFQS Objective Function  
To overcome the lacks of the metrics traditionally used by RPL and allows the multi-instances, we introduce the tunable multi-objective metric \(mOFQS\) to be used by \(OFQS\). The \(mOFQS\) metric adapts automatically to the number of instances in the network depending on their criticality level by tuning its parameters jointly. \(OFQS\) is derived from \(MRHOF\) as it relies on the same rank calculation mechanism, it adopts hysteresis to prevent routing instabilities by reducing parent switches under a certain threshold.