esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations
### Results  
#### 4.3.1 Influences of Ferry Nodes  
This simulation evaluates the influence of the ferry node on the message forwarding by comparing ECRF with ECR and SFWP. From Figures 4 and 5, it was clear that ECRF with ferry nodes had better performance than ECR, and ECRF was also superior to SFWP with the same number of ferry nodes. In the simulation of Infocom 2006, with the growth of the number of messages in the network, the delivery ratio and the average delay in ECRF were stable and maintained at about 65% and 1600 s, respectively, while the delivery ratio in SFWP was reduced by about 52% and average delay decreased about 25%. The delivery ratio in ECR was reduced by about 50% and average delay decreased about 25%. The situation in the synthetic trace was roughly similar. These simulation data are explained by: The ferry nodes aid the improvement of the message forwarding performance, and the degree of influence is related to the location of the ferry node.  
Figure 4: Influences of ferry nodes on delivery ratio: (**A**) In Infocom 2006; and (**B**) in synthetic trace.
# A Novel Energy Optimization Control Strategy for Electric Drive System Based on Current Angle
## 2 Electric Drive System Loss Model  
The general electric drive system (EDS) is shown in Figure 1, which is composed of permanent magnet synchronous motor (PMSM) and inverter (six insulated gate bipolar transistor (IGBT) modules). In order to optimize the energy consumption characteristics of EDS, the loss model of EDS needs to be established. The loss of EDS is divided into motor loss and inverter loss. Among the motor losses, copper and iron losses are controllable, whereas stray loss and mechanical loss were very small and almost unchanged, which have little effect on EDS efficiency, so they can be ignored. Inverter loss (loss of six IGBT modules) is divided into conduction loss and switching loss.
## 2 Materials and Methods
### Analysis of Sugars, Organic Acids  
Sugars and organic acids were quantified according to [PERSON] et al. [21] with some modifications using 0.150 g of sample. The determination of the content of sugars and organic acids was conducted using high-performance liquid chromatography (HPLC-DAD-RID) (Hewlett Packard 1100 series; Willmington, DE, USA). A Supelcogel TM C-610H column (30 cm \(\times\) 7.8 mm) and a Supelguard precolumn (5 cm \(\times\) 4.6 mm) (Supelco,Bellefonte, PA, USA) were used for separation. The absorbance was measured using a diode-array detector (DAD) at 210 nm for the organic acids detection and a refractive index detector (RID) was used for the detection of sugars. Standards of organic acids (citric, fumaric, malic, oxalic, phytic, and tartaric) and sugars (arabinose, fructose, galactose, glucose, maltose, and sucrose) were obtained from Sigma (St. Louis, MO, USA). Calibration curves, with a concentration range between 1 and 10 g L\({}^{-1}\), were used for the quantification of organic acids and sugars and showed good linearity (r\({}^{2}\)\(\geq\) 0.999). This analysis was run in triplicate, and the results were expressed as g kg\({}^{-1}\).
## 2 System Model
### Relay Selection Methods  
#### 2.4.1 Hybrid Partial Relay Selection (H-PRS)  
In the conventional PRS protocol [35], the relay providing the highest channel gain at the first hop is selected to forward the data to the destination. Mathematically speaking, we write  
\[\mathrm{R}_{a_{1}}:\gamma_{\mathrm{SR}_{a_{1}}}=\max_{m=1,2,...,M}\left( \gamma_{\mathrm{SR}_{m}}\right), \tag{20}\]  
where \(\mathrm{R}_{a_{1}}\) is the chosen relay with \(a_{1}\in\left\{1,2,...,M\right\}.\)  
For the PRS protocol proposed in [37], the best relay is selected by the following strategy:  
\[\mathrm{R}_{a_{2}}:\gamma_{\mathrm{R}_{a_{2}}\mathrm{D}}=\max_{m=1,2,...,M} \left(\gamma_{\mathrm{R}_{m}\mathrm{D}}\right), \tag{21}\]  
where \(a_{2}\in\left\{1,2,...,M\right\}.\)  
Combining Equations (17) and (18) and Equations (20) and (21), the e2e OP of the PRS methods in [35; 37] can be expressed, respectively, as  
\[\mathrm{OP}_{\mathrm{PRS1}}=\mathrm{Pr}\left(C_{a_{1}}<C_{\mathrm{th}}\right) =\mathrm{Pr}\left(\tfrac{(1-\alpha)\mathrm{T}}{2}\mathrm{log}_{2}\left(1+\min \left(\Psi_{1a_{1}},\Psi_{2a_{1}}\right)\right)<C_{\mathrm{th}}\right), \tag{22}\]  
\[\mathrm{OP}_{\mathrm{PRS2}}=\mathrm{Pr}\left(C_{a_{2}}<C_{\mathrm{th}}\right) =\mathrm{Pr}\left(\tfrac{(1-\alpha)\mathrm{T}}{2}\mathrm{log}_{2}\left(1+\min \left(\Psi_{1a_{2}},\Psi_{2a_{2}}\right)\right)<C_{\mathrm{th}}\right). \tag{23}\]  
In our proposed PRS protocol, if \(\mathrm{OP}_{\mathrm{PRS1}}\leq\mathrm{OP}_{\mathrm{PRS2}}\), the best relay is selected by (20), and if \(\mathrm{OP}_{\mathrm{PRS1}}\mathrm{>}\mathrm{OP}_{\mathrm{PRS2}}\), the selection method in (21) is used to choose the relay for the cooperation (the operation of the H-PRS protocol will be described in the next sections). As a result, the outage performance of the H-PRS protocol is expressed as  
\[\mathrm{OP}_{\mathrm{H-PRS}}=\min\left(\mathrm{OP}_{\mathrm{PRS1}},\mathrm{OP }_{\mathrm{PRS2}}\right). \tag{24}\]  
Next, the obtained throughput of this protocol is calculated by  
\[\mathrm{TP}_{\mathrm{H-PRS}} =\left(1-\alpha\right)\mathrm{TC}_{\mathrm{th}}\left(1-\mathrm{OP }_{\mathrm{H-PRS}}\right). \tag{25}\]
# A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things +
## 6 Conclusions  
In this paper, a novel quantitative trust model for a clustering environment in IoT-MANET has been proposed. The prediction-based trust model can collaboratively compute the resultant trust of a node using direct trust and recommendation trust opinions from other nodes. The proposed evidence-based trust model utilizes the probabilistic model of \(\beta\) distribution to calculate the direct trust of a node under review. From the resultant or final trust of the node under review, the theory of ARMA/GARCH has been used to predict the future behavior of the node, which is derived from its past behavior. Using a weighted combination model, the direct trust evidence and collected recommendation evidences are combined so that the effect of malicious reporting can be minimized. Extensive simulation study shows that the model outperforms the similar trust-based protocols in terms of false positive detection, even for a highly congested network. The routes have been established between source and destination only including the trusted nodes in the path. Simulation has been carried out to compare the efficacy of the scheme and the results are very satisfying in terms of packet delivery fraction, packet drop rate, packet delivery rate, and throughput compared to clustering and trust-based protocols.  
In the future, we will set up a testbed of 10 laptops and implement our proposed trust-based model to validate the simulation results. The proposed trust model can be applied in IoT-WSN and subsequently in smart cities to provide security. Further, it can provide security in software-defined mobile ad hoc networks (SD-MANET), where SDN controllers can collect the recommendation trust and compute the resultant trust value of their nodes.  
**Author Contributions:** All the authors have contributed equally at each phase of this work.  
**Funding:** The authors extend their appreciation to the deanship of scientific research at King Saud University for funding this work through research grant number NFG-15-01-22.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Asymptotic Analysis  
In the performance evaluation of wireless communication systems, the asymptotic analysis at the high average SNR level has been a popular evaluation methodology to observe how the channel parameters affect the performance and obtain more significant insights for system deployments. Generally, the diversity order and the coding gain are two performance metrics of interest to compare different modulation systems/fading models in the previous works. In high average SNR regions, the asymptotic OP or ABEP (ASEP) can be defined as \(P_{x}=(G_{c},\mathcal{T})^{-G_{d}}\), where \(G_{c}\) denotes the coding gain, \(G_{d}\) is the diversity order,and \(P_{x}\) represents the OP or ABEP/ASEP. In what follows, we will derive the asymptotic analytical expressions of the aforementioned communication systems over \(\alpha\)-\(\mu\)/IGA fading channels by taking the OP and the ABEP of DPSK as examples.
## 2 Materials and Methods
### Methods
```
Input: populationSize, orderedPopulation  Output: orderedPopulation, bestFitness, P 1 function FitnessEval() 2 for\(i\gets 0\) to populationSize do
3 \(\textit{fitness}\leftarrow\textit{FitnessFunction\_call\_to\_gazebo\_simulator}\)
4 \(\textit{orderedPopulation}[i][0]\leftarrow\textit{fitness}\)
5 \(\textit{orderedPopulation}[i][1]\gets i\)
6 endfor
7  for\(i\gets 1\) to populationSize-\(1\)do
8 for\(j\gets 0\) to populationSize-\(i\)do
9  if\(\textit{orderedPopulation}[j][0]\) >\(\textit{orderedPopulation}[j][0]\)do
10 \(\textit{aux}\leftarrow\textit{orderedPopulation}[j][0]\)
11 \(\textit{orderedPopulation}[j][0]\leftarrow\textit{orderedPopulation}[j+1][0]\)
12 \(\textit{orderedPopulation}[j+1][0]\leftarrow\textit{aux}\)
13 \(\textit{aux}\leftarrow\textit{orderedPopulation}[j][1]\)
14 \(\textit{orderedPopulation}[j][1]\leftarrow\textit{orderedPopulation}[j+1][1]\)
15 \(\textit{orderedPopulation}[j+1][1]\leftarrow\textit{aux}\)
16 endif
17 endfor
18 endfor
19 for\(i\gets 0\) to populationSize do
20 \(k\leftarrow\textit{orderedPopulation}[i][1]\)
21 for\(j\gets 0\) to chromosomeSize do
22 \(\textit{aux}[i][j]\leftarrow\textit{population}[k][j]\)
23 endfor
24 endfor
25 for\(i\gets 0\) to populationSize do
26 for\(j\gets 0\) to chromosomeSize do
27 \(\textit{P}[\textit{populationSize-i-1}]\leftarrow\textit{aux}[i][j]\)
28 endfor
29 \(\textit{bestFitness}\leftarrow\textit{orderedPopulation}[i][0]\)
30 endfor
```  
**Algorithm 4** FitnessEval  
The fitness function is described in Equation (1). \[\mathrm{F(x_{i})=G-x_{i}} \tag{1}\]  
where G is the goal and x\({}_{i}\) is the final position obtained for each individual simulation where i = 1, 2, 3,..., n.
# Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
## 2 Related Work  
This section presents the recent and relevant image processing-based models that have used different color spaces and techniques to identify leaf chlorosis and assess the quality of crops. [PERSON] et al. proposed a model based on a support vector machine (SVM) and advanced imaging processing techniques, such as image binarization, mask, and filling approaches for the extraction of selective color features, such as a* (CIELAB color space), G (green from RGB color space), and H (hue from HSV color space) to detect the yellow and rotten lettuce leaves in a hydroponics system [18]. The model has achieved an accuracy of 98.33%. [PERSON] et al. proposed a model based on [PERSON]'s method and k-means clustering technique to detect faulty regions in leaves [19]. [PERSON] et al. developed the HSV and decision tree-based method for the greenness identification of maize seedling images captured in the outdoor field [20]. [PERSON] et al. proposed a methodology based on the color analysis technique to determine the quality of tomato leaves using [PERSON]'s method, SVM, k-NN (k-nearest neighbor), and multi-layer perceptron (MLP) [21]. Their model obtained an accuracy of 86.45% when classifying the healthy tomato leaves from the diseased tomato leaves and an accuracy of 97.39% when classifying the type of disease suffered by a diseased leaf. [PERSON] et al. developed a system based on a LAB (L*: lightness, a*: red/green value, b*: blue/yellow value) space-based color histogram, k-nearest neighbors, and random forests to detect the quality of apple leaves. This approach has achieved an accuracy of 98.63%.  
These models have made great contributions to literature, but some limitations are observed. For instance, most models have used images belonging to one scenario. Either they are taken in a lab environment (indoor) or outdoors in open-air fields. Secondly, some models have used non-destructive chemical approaches to collect the preliminary data, particularly while assessing the quality of plants based on chlorophyll content, nitrogen level, or nutrient deficiency. Considering the aforementioned, in this study, a fully automated, low-cost, and non-destructive model is proposed that is built while considering a variety of lettuce images from different sources.
## 3 Our System Model and Primer
### Transmission Power (\(P\), \(\eta\))  
The transmitted power from the stationary terrestrial base station is \(P_{\text{S}}\) while that from the flying base station is \(P_{\text{F}}\), where the subscript indicates the transmission source. While the stationary base station generally has a greater overall power transmission power budget  
Figure 1: Telecommunications architecture. The wireless links drawn in dashed arrows are between the user (\(U\)), the flying base station (\(F\)), and the stationary terrestrial base station (\(S\)). The flying base station relies on the stationary base station to provide connectivity to the core network and the rest of the network in wired communications, drawn in solid arrows.  
than the flying base station, it also generally serves many more users for the connectivity provision. For example, in the US, as of 2021, there were an estimated 420,000 base stations across the major cellular service providers, serving 208 million data-only users (cellular only, not supporting WiFi) [24], many of which were sensors and smartwatches constantly in use and connectivity. \(P_{\text{S}}\) is the transmitted power for a user using the communication link, which is often significantly smaller than the entire transmission power budget of the stationary base station serving multiple users. The stationary base station can also fix its \(P_{\text{S}}\) by standard practice because of equity between the users and its fixed cell size.  
Because both the stationary and flying base stations serve the same purpose--to serve the user, albeit being physically distinct and located in different hardware and locations--we introduce a system control parameter, \(\eta\), to define the ratio between the transmission powers, i.e., \(\eta=\frac{P_{\text{F}}}{P_{\text{S}}}\), to characterize the power allocations between the base stations. A greater \(\eta\) indicates that the flying base station can allocate more transmission power to the user relative to that of the stationary base station.
## 1 Introduction
However, even though perfluorinated PEMs such as Nafion have been widely used as a separator in VRFBs, owing to their high proton permeability and chemical stability, they suffer from the significant crossover of cationic redox species during operation, which can result in a decrease in the battery efficiency [32]. In addition, the employment of expensive perfluorinated PEMs such as Nafion could significantly elevate the system cost [33]. Among various approaches to solving this problem, the use of AEMs has received much attention due to their low permeability of cationic species by the Donnan exclusion effect, and potentially low membrane cost [33; 34]. To be successfully used in VRFB, however, their poor chemical stability in harsh acidic environments, and relatively low ion conductivity should be further enhanced [33]. For example, the ion conductivity of Tokuyama A201 is shown to be about 42 mS cm\({}^{-1}\)[29] which is much smaller than that of Nafion (86 mS cm\({}^{-1}\) for Nafion 1035 [35]). Among the various kinds of IEMs, pore-filled IEMs (PFIEMs), which are composed of a thin and mechanically strong porous substrate film, and polyelectrolyte that fills the pores, are known to provide both the high ion conductivity and mechanical strength [36; 37; 38; 39]. They could also be fabricated by a simple and cheap manufacturing process [39]. Recently, we have developed novel pore-filled AEMs (PFAEMs) for application to electrochemical energy conversion systems [11]. The results demonstrated that the PFAEMs, consisting of a thin porous polytetrafluoroethylene (PTFE) substrate and highly cross-linked ionomer, could provide both excellent electrochemical properties and alkaline stability [11]. Unfortunately, however, the power generation was much poorer than those of the state-of-the-art such as the Tokuyama A901 [28]. In this work, we have dramatically improved the performances of the PFAEMs by choosing a proper porous substrate and adjusting the cross-linking degree for successful applications to ADLFC and VRFB. Namely, two different kinds of porous PTFE substrate, i.e., hydrophobic and hydrophilic grades, were chosen and utilized for the comparative study, and membrane cross-linking was also finely controlled. Moreover, we have systematically characterized the prepared membranes via various experimental analyses, including membrane-electrode assembly (MEA) tests, VRFB charge-discharge tests, Fenton oxidation, vanadium oxidative stability, and crossover rate evaluations, etc.
## 2 Materials and Methods
### iTransformer Model
\tag{6}\]  
\[Attention_{i}=\text{softmax}(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{7}\]  
\[MultiHead(Q,K,V)=Concat(Attention_{1},Attention_{2},\cdots,Attention_{i})W^{O} \tag{8}\]  
where \(X\) represents the input multivariate time series matrix; \(Q\), \(K\), and \(V\) are the query, key, and value matrices, respectively. \(W^{Q}\), \(W^{K}\), and \(W^{V}\) are the learned weight matrices; \(d_{k}\) is the scaling factor; Softmax is the activation function; \(Concat\) is the concatenation function; \(W^{O}\) is a learnable weight matrix responsible for the linear transformation of the concatenated outputs from multiple attention heads; \(Attention_{i}\) represents the attention distribution of the \(i\)th head, and \(MultiHead(Q,K,V)\) denotes the multi-head attention distribution. When iTransformer is directly used for prediction, its projection layer maps the multivariate feature information extracted by the TmBlock module directly to the final predicted values of the target variables. However, in this study, the iTransformer is utilized for feature extraction, where its output forms a high-dimensional feature matrix for each variable, denoted as \(X\in R^{N\times d}\) (where \(N\) is the number of variables and \(d\) is the feature dimension). In this context, the projection layer is used to structurally reorganize the principal multivariate features extracted by the TmBlock module, transforming them into a time-dominant input format compatible with subsequent models (such as BiLSTM). The specific process is as follows:  
\[x_{t}=MLP([X;p_{t}]),t=1,2,\cdots,T \tag{9}\]  
where \(p_{t}\) denotes the learnable positional encoding at time step \(t\), and MLP represents the multi-layer perceptron. By inputting the feature variables and positional encodings output by the iTransformer, a feature representation \(x_{t}\in R^{d}\) is generated for each time step. These representations are ultimately combined to form a sequential feature matrix \(X^{\prime}\in R^{T\times d}\), which serves as the input to the BiLSTM. This process achieves the mapping from variable space to temporal sequence space, enabling the model to effectively capture the dynamic dependencies of cooling load variations over time.
## 9 Mobility Environment
### Discussion  
The routing problem of CRAHN is a challenge that requires a flexible, robust, and dynamic solution. The routing challenge degrades the quality of routing, leading to a high packet-drop rate. In an endeavor to address the routing issue, several studies have either modified or designed new algorithms. This study investigated PDR and packet-loss and proposed an OCS-AODV scheme employing the levy-flight distribution. The scheme is optimized for the discovery of stable routes with sufficient residual energy. The simulation results show that the proposed scheme addressed the PDR and packet loss and outperformed the CS-DSDV and ACO-AODV schemes.  
The DSDV protocol has some disadvantages because it is a proactive protocol and has the challenge of making consistent updates even when the state of the network is stable. DSDV keeps outdated paths for a prolonged duration, which makes it not reliable for ad hoc networks. Given the results, we observed that the proposed scheme can transmit data for longer periods without experiencing problems with the battery power of nodes because the scheme can allow the mobile nodes to adjust energy consumption when idle and when  
Figure 7: The performance of the three schemes in terms of packet-delivery ratio and packet-loss metrics. (**a**) Depicts the PDR of the three routing protocols (**b**) Depicts the dropped packets results of the three protocols.  
transmitting to conserve energy. The ACO algorithm takes a long time to converge, and coding does not achieve optimal results.
## 3 Reducing Transmission Overhead without Compromising Throughput
### LARep Design
#### 3.2.2 Phase 2: Determining Message Progress towards the Destination  
In this section, we propose the Message Progress Tracking (MPTrack) mechanism, which determines the progress messages have made towards their respective destination nodes. At the source node, MPTrack associates to every generated message, \(m\), two properties, namely \(srcDstDist_{m}\) and \(w_{m}\). As shown in Equation (4), the \(srcDstDist_{m}\) property of a message holds the Euclidean distance  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Slot** & **Location** & **GPS Coordinates** & **Location Reference** \\ \hline \(\Delta t_{1}\) & \(a\) & \((x_{a},y_{a})\) & \(\langle x_{a},y_{a}\rangle\) \\ \(\Delta t_{2}\) & \(b\) & \((x_{b},y_{b})\) & \(\langle x_{a},y_{a}\rangle\) \\ \(\Delta t_{3}\) & \(c\) & \((x_{c},y_{c})\) & \(\langle x_{a},y_{a}\rangle\) \\ \(\Delta t_{4}\) & \(d\) & \((x_{d},y_{d})\) & \(\langle x_{d},y_{d}\rangle\) \\ \(\Delta t_{5}\) & \(e\) & \((x_{e},y_{e})\) & \(\langle x_{e},y_{e}\rangle\) \\ \(\Delta t_{6}\) & \(f\) & \(\begin{pmatrix}x_{f},y_{f}\end{pmatrix}\) & \(\langle x_{e},y_{e}\rangle\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recording location information in the location table with PoiTrace.  
Figure 13: Map of a fictional city.  
between \((x_{src},y_{src})\), the location of the source node, and \((x_{dst},y_{dst})\), the location of the destination node. This information remains the same throughout the lifetime of the message. The other message property, \(w_{m}\), is initialized to the source node's forwarding utility for the destination node, \(d_{m}\), and is updated whenever the message is transmitted (i.e., received or sent). The updates are done according to Equation (5). Suppose node \(i\) sends to node \(j\) a message, \(m\), that is destined for node \(d_{m}\). Node \(i\), the sender, updates the \(w_{m}\) property of its own copy of \(m\) with node \(j\)'s (i.e., the receiver's) forwarding utility for \(d_{m}\), i.e., \(U_{j}(d_{m})\). Node \(j\), the receiver, also updates the \(w_{m}\) property of its own copy of \(m\) with, \(U_{j}(d_{m})\), its own forwarding utility.  
\[srcDstDist_{m}=\sqrt{\left(x_{src}-x_{dst}\right)^{2}+\left(y_{src}-y_{dst} \right)^{2}} \tag{4}\]  
\[w_{m}(new)=w_{m}(old)+U_{j}(d_{m}) \tag{5}\]  
The \(srcDstDist_{m}\) property signifies the relative number of copies required to guarantee the delivery of \(m\) to the destination node. Hence, the higher the value of \(srcDstDist_{m}\), the more copies of \(m\) is required in the network. \(w_{m}\) on the other hand, indicates the relative number of copies of \(m\) existing in the network. As \(m\) takes more steps towards the destination, the value of \(w_{m}\) increases respectively, based on the forwarding utilities of the relays traversed.