esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Today's perishable food supply chains must be resilient to handle volatile demands, environmental restrictions, and disruptions in order to meet customers' requirements. The enablers of the perishable food supply chain have not yet been explored. In this paper, a bibliometric systematic literature review has been conducted to identify the articles related to the perishable food supply chain. Next, with these identified articles, a map is created with bibliographic data using Vosviewer network visualization software, and then the enablers were identified by conducting keyword co-occurrence analysis. Later, a total interpretive structural modeling (TISM) is employed to analyze the interrelationships among enablers and then determine each enabler's hierarchies, further representing them in a diagram. Finally, the identified enablers are classified using cross-impact matrix multiplication applied to classification (MICMAC) analysis, and the graph is plotted. The results obtained from the deterministic assessment model provide the critical enablers for the perishable food supply chain. The obtained critical enablers and their hierarchies provide valuable insights for researchers in the context of perishable food supply chain for further study.  
total interpretive structural modeling (TISM); Vosviewer; perishable products; enablers; cross impact matrix multiplication applied to classification (MICMAC)  
1  
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controls the quantity and quality of the perishable products within their limited shelf life. An appropriate transportation process helps control variables such as vehicle routing, fuel consumption, and cost and makes the product reach the end customer before its deterioration. Hence, the identification of enablers is significant during PFSC. The critical enablers are identified using a deterministic assessment model. Additionally, it is necessary to find the trade-off between profits and sustainability in present times because of the rise in the effects of global warming [3].  
The current study aims to identify the significant enablers of PFSC by conducting a systematic literature review. The study's primary objectives are as follows:  
1. Identifying the important enablers in the perishable food supply chain;
2. Finding the interrelationships among enablers, hierarchies of each enabler, and most driving and dependency enablers in PFSC;
3. The classification of the enablers based on driving and dependency values using MICMAC analysis [4; 5].  
The remainder of this paper has been organized as mentioned below. Section 2 describes the significance of enablers' literature related to the current study and identification of enablers using Vosviewer network visualization software. Sections 3 and 4 elaborate on the levels of each enabler using the TISM-MICMAC methodology, followed by the findings and discussion in Section 5. Finally, implications and conclusions are presented in Sections 6 and 7, respectively.
# Speed Optimization in DEVS-Based Simulations: A Memoization Approach
## 2 DEVS Methodology
### Coupled Model  
The coupled model internally connects multiple atomic models or coupled models. Through this, a larger system can be expressed hierarchically in the form of relationships between nodes and child nodes as a tree structure. The mathematical expression of the coupled model DN is as follows:  
\[\begin{array}{c}\text{DN}=\textless X,\textless Y,\textless\text{D},\textless \text{EIC},\textless\text{EOC},\text{IC},\textless\text{SELECT}\textgreater{}\\ \text{X}\textless\text{is the input events set.}\\ \text{Y}\textless\text{is the output events set.}\\ \text{D}\textless\text{is the set of all component models in DEVS.}\\ \text{EIC}\subseteq\textless\text{X}\times\textless\text{i}\text{i}X_{i}\textless \text{is the external input coupling relation.}\\ \text{EOC}\subseteq\textless\text{i}\text{Y}_{i}\times\text{Y}\textless \text{is the external output coupling relation.}\\ \text{IC}\subseteq\textless\text{i}\text{X}_{i}\times\text{i}\text{i}Y_{i} \textless\text{is the internal coupling relation.}\\ \text{SELECT: $2^{M}$-$\varphi$}\to\text{D}\textless\text{is a function which chooses one model when}\\ \text{more than 2 models are scheduled simultaneously.}\end{array} \tag{2}\]  
EIC (external input coupling) describes all possible couplings between the input X coming from outside the coupled model and the input \(X_{i}\) of each internal model. Similarly, external output coupling (EOC) represents the coupling of the entire output event set Y of the coupled model with the event output \(Y_{i}\) of each internal model. IC (internal coupling) expresses the coupled relationship between two atomic models within a coupled model or other coupled models, and it represents the input event set \(X_{i}\) and output event set \(Y_{i}\) of all internal models. SELECT determines the priority of event processing for a set of models that are simultaneously active at the same time due to input/output events. If more than one model fires events at a time, the order in which the events are processed can cause confusion.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 2 Related Works  
Three key considerations in WSNs are coverage [11], connectivity [12], and battery lifetime [13]. Coverage refers to how well an area is monitored, while connectivity means that each node or device should be connected such that the data is sent back to the base station (BS). Any applied heuristic needs to fulfill the requirement of Q-coverage and P-connectivity, where Q is the minimum number of sensors monitoring any one point in the area, and P is the number of disjoint paths between any two sensors.  
In this section, we shall take a brief tour of the past approaches at solving these issues, starting with [14] in which the authors proposed a heuristic method called QC-MCSC to maximize the network lifetime while satisfying both the coverage and connectivity requirements. This method consists of three phases or layers where the output of one phase is the input of the next one. The first phase is the Coverage phase, which outputs the set of sensors that cover all the target areas. Next is the Connectivity and Redundancy Reduction phase, which takes the set of the first phase and applies the Breadth-First Search (BFS) algorithm to find the shortest path for each sensor. In this phase, unnecessary nodes are removed and any extra nodes are only added if the network is not fully connected, and the output is a minimal connected set cover of nodes. Finally, the Energy and Priority Updating phase determines the lifetime constant of each set cover from the second phase. The lifetime constant is the minimum lifetime available from sensors in a set cover. In this phase, if the battery life 'B' of one node is less than the energy needed for communicating and sensing 'E', the node is removed from the set cover. Through simulation, the authors compared the Q-Coverage Maximum Connected Set Cover (QC-MCSC) algorithm with the Three Phase Iteration Connected Set Cover (TPICSC) algorithm [15] and the results showed that the former achieves a higher lifetime. Furthermore, the results of the simulations reveal that the proposed heuristic (QC-MCSC) is very close to the actual optimal result.  
A difficulty that researchers face when they want to expand the coverage time span is the question of how to distribute the sensors into separate independent subdivisions, known as Disjoint Set Covers (DSC). This problem is identified as having an NP-hard complexity, and many researches have used the Boolean sensing model to solve it.  
To facilitate topic lookup for the reader, we shall broadly divide the remaining studies by the type of approach and/or the specific WSN issue being addressed.
## 3 Results and Discussion
### Conductivity of GO/CCs
#### 3.2.2 Effect of Different GO Concentrations on Electrical Conductivity  
Expanding on the results presented in the previous section, the electrical resistivity of GO/CCs is positively correlated with WL, but the electrical conductivity is still affected  
Figure 4: Effect of water loss on electrical conductivity: (a) specimen conductivity variation with water loss; (b) specimen electrical resistivity with water loss.  
by GO concentration when the specimen WL is the same. Figure 5 demonstrates the WL of specimens with different GO-doping concentrations after different drying times and the resulting resistivity values. In Figure 5a, the specimen is in a water-saturated state and thus the specimen's electrical conductivity is largely governed by ionic conductivity, while the electronic conductivity caused by the increased GO concentration of GO is hardly significant. Therefore, the resistivity of the specimen shown in the figure does not correlate with the GO concentration. As shown in Figure 5b-\(I\), with an increase in GO concentration, the resistivity of the specimen increases first and is followed by a decrease. The probable reason is that the higher GO concentration specimens had higher WL for the same drying time. The addition of 0.025%, 0.05%, and 0.075% GO concentrations reduced resistivity, but this effect failed to counteract the increase in resistivity due to water loss; therefore, the conductivity results showed a decrease with increasing GO concentration. However, the resistivity of the 0.1% GO concentration specimens showed completely different results at different stages. Although more water was lost at 0.1% GO than at 0%, 0.025%, 0.05%, and 0.075% concentrations (the WL of 0.1% was almost the same as the WL of 0.075%), the resistivity of the 0.1% GO concentration specimen was substantially lower. A possible reason is the percolation threshold effect of CCs [17]. It is considered that a 0.1% GO concentration in GO/CCs reaches the percolation threshold in the cement matrix, forming a complete conductive network with a substantial decrease in electrical resistivity. Therefore, the addition of GO at 0.1% concentration significantly reduced the electrical resistivity of GO/CCs, and GO may have reached the percolation threshold at 0.1% GO concentration.  
Figure 5: _Cont._
# DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System
## 2 Background
### IoT Security Overview
#### 2.3.1 Non Artificial Intelligence-Based Security Method  
Many of non-artificial intelligence-based security methods have been implemented to secure the IoT environment. A good example of these methods: identity-based encryption, watchdog, reputation and trust mechanisms, _Complex Event Processing_ (CEP) and lightweight cryptography. Table 1 summarize the presented techniques.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref** & **Year** & **Techniques** & **Metrics** & **IoT Layer** & **Simulator** & **Category** \\ \hline
[39] & 2019 & Generic homeproof & Detection rate, low cost and maintenance & Applications Layer & Simulation Data & Honeypot \\  & & utilizing VPN & & effort & & \\ \hline
[40] & 2016 & Identity-based encryption & Efficiency (overhead cost associated with computation) & Applications Layer & Simulation Data & Lightweight cryptography (LWC) \\ \hline
[41] & 2015 & Watchdog, reputation trust mechanisms & Detection rate, false positives and false negatives. & Network Layer & Simulator Cooja & Intrusion detection system \\ \hline
[42] & 2014 & External entity as the ISP or Security as a service (SECaS or SaaS) provider, to install access control rules in the network &......... & Network Layer & Captured the network activity using the Wireshark packet analyzer. & Emerging household appliances \\ \hline
[43] & 2014 & Complex event processing (CEP) & CPU utilization, memory consumption and processing time & Applications Layer & Expert an engine for CEP and event series analysis. & Real-time intrusion \\ \hline
[44] & 2013 & Dynamic variable cipher security certificate “one time one cipher” & Repetition rate & Sensor Layer & Simulation Data & Lightweight cryptography (LWC) \\ \hline
[45] & 2013 & Rule-based detection & Detection rate, false positives & Network Layer & Demo & Intrusion detection system \\ \hline
[46] & 2013 & End-to-End message security such as IPsec and DTLS & Detection rate, True positives rate and memory consumption & Network Layer & Contiki’s network simulator Cooja & Real-time intrusion \\ \hline \hline \end{tabular}
\end{table}
Table 1: Non-Artificial Intelligence-based Security Method.  
Figure 2: Attacks on IoT Layers.
## 4 Experiments and Analysis
### Dataset and Evaluation Metrics  
The IRSTD-1k dataset is designed specifically for identifying and dividing minor infrared objects. Offering 1000 authentic images, the IRSTD-1k dataset displays a range of target forms and sizes and dense, cluttered backdrops marked by \"weak\" and \"small\" features. Within this framework, the term \"weak\" refers to the low signal-to-noise ratio, faint contrast in the background, and weak infrared radiation intensity of the targets, while \"small\" indicates a lack of target pixels, making the collection of texture data during detection more complex.  
We constructed the IRMT-UAV dataset using infrared images captured by a mid-wave infrared camera within the 3-5 \(\upmu\)m wavelength range with a resolution of 640 \(\times\) 512 pixels and shooting distances ranging from 100 to 1200 m. This dataset primarily features small targets and consists of 3500 infrared images of drones collected over more than a year across different seasons, weather conditions, and complex backgrounds. The challenges posed by this dataset include varying target orientations, scales, and occlusions, with a high proportion of small targets, many of which are almost invisible to the naked eye. The dataset contains 4697 bounding box labels for drones. As shown in Figure 6, the scale variation of drone targets in our dataset is significant. With the increase in imaging distance, the normalized height and width of the targets can be less than 1%, meaning the targets occupy only a few pixels. Our dataset includes both surface targets occupying thousands of pixels and point targets occupying only a few pixels. Additionally, we analyzed the signal-to-noise ratio (SNR) distribution for 4697 drone instances. Due to changes in the background and imaging distance, the SNR at the drone targets varies significantly, with an average SNR of around 60.  
To evaluate the effectiveness of our proposed algorithm relative to other advanced (SOTA) algorithms, measures including precision, recall, and mAP50 are employed. Precision denotes the proportion of correctly identified positives (TP) out of the predicted positive cases. The computation proceeds in the following manner:  
\[Precision=TP/(TP+FP) \tag{16}\]Recall represents the proportion of actual positive samples that are correctly predicted as positive. The computation proceeds in this manner:  
\[Recall=TP/(TP+FN) \tag{17}\]  
mAP50 represents the mean average precision of the model at an IoU threshold of 0.5. The computation proceeds in this manner:  
\[mAP50=\frac{1}{C}\sum_{i=1}^{C}AP_{i\leq 0} \tag{18}\]
## 4 Principles of Fuel Cell
### Types of Fuel Cell
#### 4.1.3 Phosphoric Acid Fuel Cell (PAFC)  
PAFCs rely on an acidic electrolyte, as with PEMFCs, to conduct hydrogen ions. The reactions in the anode and the cathode are the same as the PEMFC reactions. Phosphoric acid (\(\mathrm{H_{3}PO_{4}}\)) is a viscous liquid that is contained by capillarity in the FC in a porous silicon carbide matrix. PAFCs are medium temperature fuel cells that conduct hydrogen ions, thus they are not as fuel-flexible as the high-temperature fuel cells that conduct oxidizing ions (e.g., \(\mathrm{O^{-}}\), \(\mathrm{CO_{3}^{-}}\)). While PAFCs are predominantly used for stationary power, they have also been implemented in some large-scale vehicles, such as public buses [44]. The overall reaction can be characterized as follows:  
\[\mathrm{H_{2}}+\frac{1}{2}\mathrm{O_{2}}\rightarrow\mathrm{H_{2}O} \tag{6}\]  
The half reaction associated with the phosphoric acid fuel cell is as follows:  
Oxidation reaction:  
\[\mathrm{H_{2}}\rightarrow\mathrm{2H^{+}}+2\mathrm{e^{-}} \tag{7}\]  
Reduction reaction:  
\[\frac{1}{2}\mathrm{O_{2}}+2\mathrm{H^{+}}+2\mathrm{e^{-}}\rightarrow\mathrm{H _{2}O} \tag{8}\]
## 3 Methods
### GBDT Model for Inverting AGB  
The gradient boosting decision tree algorithm is a highly effective machine learning model that constructs a series of weak learners and aggregates their predictions to produce the final output. By using the negative gradient of the loss function to fit residuals and iteratively correcting these residuals, the model progressively enhances its predictive performance. This approach offers high accuracy, robustness, and flexibility, making GBDT suitable for both regression and classification tasks. In this study, 80% of the samples were randomly selected as the training set, while the remaining 20% were used as the validation set. The eight bands and each pixel in the Sentinel-2 data corresponding to the vegetation index, tasseled cap transformation index, terrain factor and canopy cover, and tree height were input as explanatory variables in the model, and the AGB was used as the estimation variable. The model was implemented on the Google Earth Engine cloud platform usingthe gradient boosting regression tree algorithm. After verifying the model's accuracy using the validation set, the trained model was applied to each pixel in the Sentinel-2 imagery. This process would output the estimated aboveground biomass value of each pixel so as to obtain the GGP stand AGB of major tree species.
# An Improved Artificial Ecosystem Algorithm for Economic Dispatch with Combined Heat and Power Units
###### Abstract  
The most effective use of numerous Combined Heat and Power Units (CHPUUs) is a challenging issue that requires strong approaches to handle the Economic Dispatch (ED) with CHPUUs. It aims at minimizing the fuel costs by managing the Power-Only Units (POUs), CHPUUs, and Heat-Only Units (HOUs). The transmission losses are also integrated, which increases the non-convexity of the ED problem. This paper proposes a Modified Artificial Ecosystem Algorithm (MAEA) motivated by three energy transfer processes in an ecosystem: production, consumption, and decomposition. The MAAE incorporates a Fitness Distance Balance Model (FDBM) with the basic AEA to improve the quality of the solution in non-linear and multivariate optimization environments. The FDBM is a selection approach meant to find individuals which will provide the most to the searching pathways within a population as part of a reliable and productive approach. Consequently, the diversity and intensification processes are carried out in a balanced manner. The basic AEA and the proposed MAAE are performed, in a comparative manner considering the 7-unit and 48-unit test systems. According to numerical data, the proposed MAAE shows a robustness improvement of 97.31% and 96.63% for the 7-unit system and 46.03% and 60.57% for the 48-unit system, with and without the power losses, respectively. On the side of convergence, based on the average statistics, the proposed MAAE shows a considerable improvement of 47% and 43% of the total number of iterations for the 7-unit system and 13% and 20% of the total number of iterations for the 48-unit system, with and without the power losses, respectively. Thus, the suggested MAAE provides significant improvements in the robustness and convergence properties. The proposed MAAE also provides superior performance compared with different reported results, which indicates a promising solution methodology based on the proposed MAAE.  
artificial ecosystem optimizer; fitness-distance-based; economic dispatch; valve-point loading effect; combined heat and power units +
Footnote †: journal:  
sources in addition to renewable energy sources. The reduction of pollution emissions that contribute to global warming has also received international attention [3]. One of the national energy policy initiatives by China is to focus on the development of efficient, secure, and sustainable energy sources with an ideal management system [4]. Economic load dispatch is a critical optimization problem in power systems that necessitates good generator coordination, control, and management [5]. Because of the imposed identical and uneven constraints, it exhibits non-linear performance. In response, it has been recognized as a difficult multi-modal optimizing problem to address [6]. Therefore, it is critical that a search is undertaken for efficient, robust, and highly convergent optimization solutions to the non-linear and complex ED optimization problem, which considers CHPUs.
## 2 DC-DC Synchronous Buck Converter
### 2 \(\mu\)m 4H-SiC CMOS Technology Overview  
As previously discussed, SiC technology presents a significant window of opportunities alongside WBG materials. In response to these opportunities, advancements have been made in various SiC technology nodes, including the current work of the Fraunhofer Institute for Integrated Systems and Device Technology (IISB)'s 2 \(\mu\)m 4H-SiC CMOS. Prior advancements in larger nodes, such as IISB's 6 \(\mu\)m 4H-SiC CMOS [29], have facilitated the development and refinement of scaled technology nodes, which are targeted for applications in extremely harsh environments [21].  
The IISB has developed a 2 \(\mu\)m 4H-SiC CMOS technology, utilizing a fabrication process based on a double well design. Detailed information about the full process flow can be found in a recent publication [30]. This innovative technology enables CMOS circuits to operate in extreme conditions, up to 500 \({}^{\circ}\)C, thanks to the wide-bandgap material, SiC. This feature indicates a promising potential for further applications and benefits that have not been fully explored. Additionally, the availability of a Verilog-A BSIM4 SiC model, available for both p-channel and n-channel metal-oxide-semiconductor field-effect-transistors (MOSFETs), facilitates accurate circuit simulations, calibrated from 25 \({}^{\circ}\)C to 300 \({}^{\circ}\)C [31]. Critical parameters such as the threshold voltages and carrier mobility of these devices have already been characterized [32; 33; 34], enabling the design and simulation of circuits within the defined operational range. The literature also presents example circuits that compare SPICE simulations with measurement results up to 500 \({}^{\circ}\)C, showcasing the effectiveness of this technology [30].  
The validity range for the technology compact models has been carefully considered during the design process, as the aim is to ensure a viable design for manufacturing. Pushing the design too far beyond this validity range adds uncertainty, making it unclear whether the fabricated design would behave as intended. Table 1 summarizes the parameters defining the validity range, including the geometrical parameters such as length and width, as well as the bias voltages (\(V_{gs}\) and \(V_{ds}\)) and operating temperature.
# Energy-Efficient BWP Configuration for Multi-Slice Users
## 6 Performance Evaluation
### Results Highlight  
Based on these results, we can draw the following conclusions:  
* Most users are likely to select the single-numerology BWP configuration, since it provides a reduced power consumption. The latter increases with the frequency of BWP scanning (Figures 2 and 6).
* Both approaches (centralized and distributed) provide better energy efficiency and throughput while maintaining the UE's network sojourn time to a minimum thanks to the well-defined cost function (Figures 3-5, 7 and 8).
* As the number of users increase, the performances become closer to the legacy scheme since the network becomes saturated (Figures 6-8).
* The distributed and centralized approaches realize almost the same performances as can be explained by the PoA analysis (Figure 9).
* The distributed approach has the best performance in terms of convergence time while achieving almost the same QoS performance as the centralized approach (Figures 9 and 10). Hence, the distributed approach should be adopted for implementation.  
Figure 10: Convergence time as a function of the number of users.