esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cluster-Based Hierarchical-Approach for the Path Planning of Swarm
## 4 Model of the Proposed System
### Ant Colony Optimization  
The ant colony optimization (ACO) imitates the actual ants foraging for food using pheromones left over by previous ants [25]. The route traveled most by the ants has the most pheromones, and hence it helps the next ant in selecting the shortest path [26].  
The aircraft is flying from the takeoff to the destination using the edges [27]. The following supposition helps select the next edge of the path.  
Supposition 1: Assume that the \(k\)th ant is located at a node on time \(t\), the transit probability is given as;  
\[p_{a,b}^{k}(t)=\frac{\tau_{a,b}^{\alpha}(t)\mu_{a,b}^{\beta}(t)}{\sum_{b\in accept (a)}\tau_{a,b}^{\alpha}(t)\mu_{a,b}^{\beta}(t)} \tag{1}\]  
where \(p_{a,b}^{k}(t)\) is transit probability of the \(k\)th ant from node \(a\) to node \(b\), \(\tau_{a,b}(t)\) is the pheromone on the edge \((a,\,b)\), \(\mu_{a,b}(t)\) is the feasibility of transition from node \(a\) to node \(b\), \(accept(a)\) is the set of neighboring nodes of \(a\), \(\alpha\) is the parameter to control the influence of \(\tau_{a,b}(t)\), and \(\beta\) is the parameter to control the influence of \(\mu_{a,b}(t)\).  
When the algorithm starts, the initial pheromone rate changes with respect to the edges. Afterward, every ant that produced the outcome [28] starts the next cycle of the algorithm and resets the pheromone rate. The pheromone \(\tau_{a,b}(t)\) on the edge \((a,\,b)\) is;  
\[\tau_{a,b}(t+1)=(1-\rho)\cdot\tau_{a,b}(t)+\sum_{k=1}^{m}\Delta\tau_{a,b}^{k}(t) \tag{2}\]  
where \(\rho\) is the evaporation rate of pheromone (\(0\leq\rho\leq 1\)), \(m\) is the total number of ants, and \(\Delta\tau_{a,b}^{k}(t)\) is the rate of pheromone on the edge \((a,b)\). \(\Delta\tau_{a,b}^{k}(t)\) can be further defined as  
\[\Delta\tau_{a,b}^{k}(t)=\left\{\begin{array}{cc}(Q/L_{k})if\text{ }ant\text{  
**Definition 1**.: _MMACO restricts the pheromone trails to a predetermined maximum and minimum value represented as \(\tau_{max}\) and \(\tau_{min}\). Therefore, by refining the pheromone trails after every cycle, we use this formula to update the pheromone,_  
\[\tau_{a,b}(t)=\left\{\begin{array}{l}\tau_{max};\ \tau_{a,b}(t)\geq\tau_{max}\\ \tau_{a,b}(t);\ \tau_{min}<\tau_{a,b}(t)<\ \tau_{max}\\ \tau_{min};\ \tau_{a,b}(t)\leq\tau_{min}(t)\end{array}\right. \tag{6}\]
# Exploring Pre-Trained Models for Skin Cancer Classification
## 3 Materials and Methods  
The proposed method follows a structured pipeline for skin lesion classification, integrating deep learning models and eXplainable AI (XAI) techniques, as illustrated in Figure 1. Initially, raw skin lesion images undergo preprocessing, which includes resizing to ensure uniform dimensions, normalization to standardize pixel values for improved model convergence, and data augmentation to enhance model generalization. The study evaluates both convolutional neural network (CNN)-based architectures (ResNet50 and VGG19) and vision transformer (ViT) models (ViT-b16 and ViT-b32) to assess their performance in skin lesion classification.  
To enhance interpretability and provide transparency in decision making, the study employs XAI techniques, specifically LIME and integrated gradients, to highlight the critical regions influencing model predictions. The integration of XAI techniques aims to enhance both the classification accuracy and the reliability of automated skin lesion diagnosis. These techniques are applied to the top-performing model among those utilized in the study.
# Comprehensive Evaluation of Lateral Performance of Innovative Post in Sand
## 6 Parametric Study
### Performance of Single-Paddled Posts  
A single-paddled pile is anticipated to have a lower lateral resistance than double-paddled piles (i.e., the system that was tested in the field). The lower resistance is attributed to three factors: a lower moment of inertia, a smaller soil-pile interface area and the absence of soil confinement between the two plates. For the different innovative post configurations considered (i.e., S10, S12, S16, and S20), similar pile configurations with only one plate are considered in the analysis. Since wind loads can act on the paddled flange (Case 1) or on the opposite flange (Case 2), a study is conducted first to determine the worst-case scenario for the location of the paddle relative to the load direction. As demonstrated in Figure 12, Case 2 exhibited lower lateral resistance and, therefore, it is considered for the ensuing parametric study.  
The validated model was used in the analysis and the paddle was considered attached to the flange opposite to loading direction (i.e., Case 2). The pile lateral capacity was established based on pile-head lateral displacement failure criterion provided by [PERSON] and Cox [26], i.e., the lateral capacity is the load that caused a pile head lateral displacement of 13 mm. Table 6 compares the lateral load capacity of the single-paddle (SP) and double-paddle (DP) piles.  
As expected, DP posts have higher lateral load capacity owing to the additional stiffness and resistance related to the second plate. It is also noted from Table 6 that S16 exhibits the highest percentage increase in pile capacity (i.e., 40%) due to the second plate. This increase in capacity is a result of the plate size of S16 (L = 2.325 m and W\({}_{\rm p}\) = 0.5 m),  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Pile Type** & **Load at 13 mm (kN)** \\ \hline S10 & DP & 79 \\  & SP & 68 \\ S12 & DP & 88 \\  & SP & 72 \\ S16 & DP & 112 \\  & SP & 80 \\ S20 & DP & 165 \\  & SP & 145 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of lateral load capacity of single- and double-paddled posts.  
Figure 12: Comparison of load-displacement curves considering different load directions.  
which significantly increases the pile-soil interface area and passive resistance. Compared to the other post configurations, the lateral capacity of the double-paddled S20 improved only marginally, 13.7%, which can be attributed to the small ratio of plate-to-pile width: \(\mathrm{W_{P}}/\mathrm{W_{f}}=450/202=2.23\).
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 4 Design and Deployment
### Cloud Infrastructure  
The cloud side of our IoT architecture is relatively simple, using just one server as a database to store all data received from the deployment--data which arrives as periodic packages from the gateway node over its 3G link. Other cloud resources can then access this server to use the data for specific purposes, such as scientific modelling or visualisations. We chose to use MongoDB as our data storage technology due to its schema-free design that is highly flexible to different kinds of data--as is highly likely to be the case when using many different sensor modalities in the way that we have. Modelling and visualisation services are run on elastic cloud computing resources, scaling up the amount of processing power needed as their complexity increases.  
Besides receiving and storing data, the main purpose of our cloud infrastructure is to act as a management service for the deployment. This allows nodes to be registered as they get deployed (including their position, sensing attributes, and a logical group membership for each node) or for their details to be edited if they are later moved or changed and also for the sampling rate of deployed nodes to be changed if desired. The latter is done using logical node groups, such that the sampling rate affects a selected group of nodes (e.g., \"soil nodes\" or \"riverside soil nodes\"). The management service also records the last time it heard any data from each node and the last-reported battery level of each node to aid in the day-to-day running of the deployment. As part of this service, we have developed a mobile app through which environmental scientists can add and update details about deployed nodes as they make changes in the field of study (see Figure 9).  
While this architecture is sufficient for our current deployment, in the future, we would explore more efficient solutions for processing and data distribution, particularly for modelling and visualisation services--such as allowing some data processing to occur on the database server itself to avoid shipping large quantities of data to other locations.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 6 Graph-Based RA in CR Networks
### RA Methods in Underlay CR Networks  
A graph-coloring based dynamic channel allocation solution for an underlay CR mesh network, constrained by the SINR of both the PUs and SUs, is proposed in [75]. Each PU is served by a separate BS, while a single CAP serves all the SUs, with the CAP coordinating with the BSs. The CR network is modeled as an undirected graph which connects each pair of SUs if they have at least one common channel in their sets of perceived available channels. RA is performed via graph coloring on a conflict graph for each SU, which has the set of adjacent nodes as well as the set of edges (of the original graph), connecting that SU to its neighbors as vertices. They are connected via edges, which represent the SINR of each link. RA is performed through graph coloring that minimizes conflicting channel allocations between the links. The total throughput \(R_{T}\) (combining the throughputs of both PU and SU networks) increases linearly with the number of SUs. An alternative underlay CR scenario, with each SU being assigned only one channel, is considered in [76] for an edge-cutting RA method that aims to maximize the gains of both PUs and SUs. The channel allocation problem is modeled as a bipartite graph that connects the SU pairs and the channels, with the edges' weights being the utility function depending on the SUs' throughput and interference generated to the PUs. Then, each SU forms a preference list of desired channels, which is used to filter out channel allocations (edges) with low throughput potential. In this way, a smaller set of channels that have stable CSI is determined to maximize the SU's throughput. The proposed solution retains nearly constant error due to CSI variations, with the increase in the maximum number of edges. Clustering-based RA in an underlay CR network, subject to dynamic constraint for the interference perceived by each PU, is explored in [77]. An interference graph describes the CR network connecting the SUs, with the edges' weights being the channel gains. Then, the SUs are clustered such that their perceived channels have similar gains, and at the same time, the clusters' sizes are optimized to prevent inter-cluster interference. This is achieved by cluster centers chosen among the SUs, based on their channel quality. After the clusters are formed, channel and power allocations are performed separately. The total
## 3 Results and Discussion
### Cutting Force of Winter Wheat Residues  
Experimental studies have shown that the cutting force of winter wheat residues varies from 22 N to 34 N (Figure 7). Comparison of the control treatment with the treatments, where the soil and the plants were exposed to different biopreparations and their mixtures in spring, revealed a significant difference in the cutting force of winter wheat residues only between the control treatment and Treatment 7. In Treatment 7, the cutting force of winter wheat residues was 11 N higher than in the control. The results showed that the biopreparations and their mixtures did not have a significant effect on the force required to cut plant residues in almost all cases. [PERSON] and [PERSON] [40] state that biopreparations are more important for increasing plant productivity. In our research, biopreparations were applied to growing plants in spring, and several months passed before the harvest, so the effect on the cutting force was not significant. However, treatment of plant residues with biopreparation after the harvest and keeping them on the soil surface for two weeks resulted in a significant decrease in cutting force [38]. This was influenced by the intensified process of decomposition of plant residues, which depends on the type of plant residues, chemical composition, carbon-nitrogen ratio, lignin concentration, and other factors [64; 65]. Our research showed that winter wheat residues, regardless of treatment, were cut at a similar depth (Figure 7). The cutting depth varied from 16 mm to 20 mm, and no significant difference was observed. Summarizing the results of this type of experimental research, when the soil is tilled up to 100 mm with disc working parts, plant residues, regardless of the treatment, had the possibility to be cut in the top layer of soil. Plant residues are not only inserted, but also cut at the depth of cultivation, when the stresses of plant residues are lower than the penetration resistance of the disc part into the soil [38].
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 4 Model Details
### Semantic-Guided Color Histogram (SCH) Loss
#### 4.3.3 Semantic-Guided Color Histogram Loss  
To ensure that the color distribution of the enhanced image closely matches that of the ground truth, an \(L_{1}\) loss is employed to constrain the differentiable color histogram. The final semantic-guided color histogram loss is defined as follows:  
\[\mathcal{L}_{\text{SCH}}=\sum_{c}||H^{c}(I_{out})-H^{c}(I_{gt})||_{1} \tag{11}\]  
where \(H^{c}(I_{\text{out}})\) and \(H^{c}(I_{\text{gt}})\)represent the instance-level color histograms of the enhanced and ground truth images, respectively. The SCH loss effectively guides the optimization of the neural network, ensuring more natural and accurate color restoration.
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 3 Results
### VIs Development
Figure 13: Biplots of the numerical morphological features along the first two principal components. Both the VIs were plotted in the chart. Chart (**a**) includes the VIs’ average regression line’s slope during the six-year monitoring period (NDVIs_m and NDREs_m), while chart (**b**) includes the CV of the regression line’s slope (NDVIs_CV and NDREs_CV). Figure 16: The boxplots describe the VIs’ regression line’s slope seasonal mean and CV for all vineyards computed through the six vintages according to the training systems considered for this paper. The boxplots (**a,b**) display the mean regression line’s: (**a**) refers to NDVI, and (**b**) refers to NDRE. The boxplots (**c,d**) display the regression line’s slope: boxplot. (**c**) refers to NDVI and boxplot (**d**) refers to NDRE. Figure 14: The boxplots describe seasonal VIs’ regression line’s slope CV computed for all vineyards through the six vintages according to the cardinal direction classes used in this paper. Boxplots (**a**) refers to NDVI, and (**b**) refers to NDRE. Figure 15: The boxplots describe the seasonal average VIs’ regression line’s slope for all vineyards computed through the six vintages according to the location and topography classification used in this paper. In (**a,b**), data are grouped according to the location, while in (**c,d**), data are grouped according to the topography: (**a,c**) refer to NDVI, while (**b,d**) refer to NDRE.
###### Abstract  
Herbs and agro-food wastes are rich sources of bioactive compounds vital for organisms and valuable for many fields of industry. Therefore, in this study, green deep eutectic solvents (DESs) such as choline chloride/citric acid (ChClCitA), glucose/citric acid (GuCitA), glucose/urea (GuCt), betaite/citric acid (BCitA), and betaine/urea (BU) at a molar ratio of 1:1 for ultrasound-assisted extraction (UAE) of antioxidants from four herbs (chamomile--Cha, lemon balm--LB, mint--M, and nettle--N) and two agro-food wastes (buckwheat husk--BH and chokeberry pomace--ChoP) were proposed. The antioxidant capacity (AC) of the obtained extracts was evaluated utilizing three antioxidant assays: cupric reducing antioxidant capacity (CUPRAC = 0.0-429.9 \(\mu\)mol of Trolox (TE)/g); 2.2\({}^{\prime}\)-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS = 0.0-146.5 \(\mu\)mol TE/g); and 2,2-diphenyl-1-picrylhydrazyl (DPPH = 11.9-170.3 \(\mu\)mol TE/g). The LB extracts revealed the highest CUPRAC (59.3-429.9 \(\mu\)mol TE/g), ABTS (30.7-144.3 \(\mu\)mol TE/g), and DPPH (32.6-170.3 \(\mu\)mol TE/g) values. Due to the lowest antioxidant potential of LB extracts prepared using ChClCitA (AC = 30.7-59.3 \(\mu\)mol TE/g) and the highest AC demonstrated by extracts based on B.U (AC = 144.3-429.9 \(\mu\)mol TE/g), the UAE conditions using a new DES consisting of ChCl and U were optimized by the Box-Behnken design (BBD). Effects of three independent variables, molar ratios of the ChCl and U (mol/mol), water content (%), and sonication time (t) on the AC of LB extracts were studied by response surface methodology (RSM). The results of principal component analysis (PCA) and hierarchical cluster analysis (HCA) demonstrated that different DESs had great differences in the extraction of antioxidant compounds from herbs and agro-food residues.  
deep eutectic solvents; ultrasound-assisted extraction; antioxidant capacity; plant materials; Box-Behnken design Article
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 3 Generalized Random Petri Net Model for Plant Pest and Disease Spread Diagnosis
### Generalized Stochastic Petri Net
Thisallows the model to probabilistically express the occurrence of transitions, with the \(\lambda\) value for instantaneous transitions defaulting to zero, reflecting their characteristic of occurring immediately with no delay. The GSPN has an isomorphic relationship with continuous-time homogeneous Markov chains, which allows for the establishment of a corresponding Markov chain model by analyzing the reachability set of the GSPN. This isomorphism means that once the Markov chain reaches its equilibrium distribution, the long-term steady-state probabilities of the system can be calculated. By constructing and analyzing such a Markov chain, it becomes possible to effectively analyze the starting points of the plant pest and disease spread diagnostic system.
# I3 Mote: An Open Development Platform for the Intelligent Industrial Internet
## 4 Platform Hardware
### Wireless Connectivity  
The wireless connectivity unit (see Figure 6) of the I3 Mote is built around the CC2650, a multi-standard 2.4 GHz ultra-low power MCU. The CC2650 is designed to integrate the complete stack of standard protocols such as Bluetooth and 6 LoWPAN (IPv6 over Low Power Wireless Personal Area Networks), enabling seamless connectivity for industrial, consumer electronics and medical applications. The CC2650 embeds three different processors: application, radio and a dedicated sensor acquisition unit. The goal of this SoC architecture is to maximize performance while reducing power consumption.  
The radio controller is built around a 32-bit ARM Cortex-M0 processor. It is responsible for interfacing to the radio hardware and running the PHY layer of the protocol stack (both for IEEE 802.15.4 and BLE). The application processor is a 32-bit ARM Cortex-M3 running at up to 48 MHz. It is designed for higher-level protocol and application layers processing. The sensor controller is built around an ultra-low-power processor and is used to interface external sensors and collect analog/digital data autonomously while the rest of the system is in sleep mode. This module can be used as an alternative to the MSP432 when data processing is not necessary. In addition, the CC2650 includes various communication and cryptographic peripherals.  
The I3 Mote does not require external components for wireless radio communications. For this purpose, it includes a PCB Inverted F Antenna and all required matching components. The antenna is designed to be used with 2.4 GHz transmitters and receivers. The maximum gain is measured to be +3.3 dB and overall size is 25.7 \(\times\) 7.5 mm\({}^{2}\). Alternatively, the I3 Mote contains a 50 \(\Omega\) U-FL connector to use with any standard 2.4 GHz external antenna.  
Figure 5: Data fusion architecture.  
Figure 6: I3 Mote communications.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 2 Global Market/Bibliographic Data Analysis and Standards
### Standards  
The IoV is an evolving concept that involves the integration of vehicles with communication technologies and the Internet to enhance safety, efficiency, and transportation experience. To ensure interoperability, security, and efficiency within the IoV ecosystem, several standards have been developed. Here are some key standards relevant to the IoV with technologies used, description, merits, and demerits, as depicted in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Standards** & **Technology Used** & **Description** & **Merits** & **Demerits** \\ \hline \multirow{4}{*}{IEEE 802.11p} & \multirow{4}{*}{WAVE} & \(\bullet\) & This standard defines the use of the & \multirow{4}{*}{Dedicated to vehicular} \\  & & WAVE protocol suite. & & communication, \\ \cline{3-4}  & & & It operates in the 5.9 GHz DSRC band & enabling low-latency, \\ \cline{3-4}  & & and supports communication between & and high-speed data & \\ \cline{3-4}  & & vehicles and between vehicles and & exchange. & \\ \hline \multirow{4}{*}{ISO 21217} & \multirow{4}{*}{ITS—CALM} & \(\bullet\) & This ISO standard specifies the & \multirow{4}{*}{Provides a standardized framework for interoperable} \\  & & & It provides guidelines for & \\ \cline{3-4}  & & communication between vehicles and & communication between vehicles and & \\ \cline{3-4}  & & & applications such as traffic & \\ \cline{3-4}  & & management, emergency services, and & infotainment. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The key standards used in Internet of Vehicles.  
Figure 3: The assemblage of co-appearing keywords from the Scopus database.
## 2 Related Works
### Packet Feature-Based Methods  
The port-based classification method identifies traffic by protocol ports, like TCP port 80 for HTTP. However, in modern networks, the increasing number of non-standard and dynamic ports makes it hard to classify accurately by port numbers [9]. Deep Packet Inspection (DPI) technology deeply analyzes packet payloads to identify complex protocols and applications [10]. However, it performs poorly when handling encrypted traffic and private protocol traffic [11]. Packet feature-based methods either overlook the sequential characteristics within a traffic flow or extract them through intricate procedures [12]. Moreover, packet-based features are more unstable than flow-based features [13]