esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 4 Discussion
### Limitations of Model Simulations and Validation Data  
This study was mainly aimed at enhancing the sensitivity of the MTCI to both leaf chlorophyll information and the LAI for CCC estimation. Therefore, wide ranges for the LCC, LAI, canopy structure, and soil background were used in the PROSAIL simulation to analyze the performance of the LAI-VIs and MTCI combination approach. In this study, the leaf parameters, such as the N, C\({}_{\text{w}}\), and C\({}_{\text{m}}\), were set to the defaults for wheat and soybean crops (Table 5). The variations of other leaf parameters were of little significance for evaluating the effects of the proposed methods for fusing LAI and chlorophyll information. However, these parameters may affect the CCC estimation models for different crops based on spectrum simulations. Previous studies have demonstrated that C\({}_{\text{w}}\) has little influence on the visible and red-edge bands commonly used for vegetation chlorophyll estimation [67], while N and C\({}_{\text{m}}\) can affect chlorophyll estimation [27; 68; 69]. The leaf parameters, such as N, should be adjusted according to the specific crop type if the proposed VI combination approach is applied to other crops.  
Although this study considered the variation in the solar zenith angle to represent daily and seasonal changes, the simulations and test data used for the modeling and validation were limited to nadir observations. The ground spectrum was obtained at the nadir, and the seven-day 300 m surface-reflectance MERIS product lacked information on the view zenith angle. Therefore, the simulation data in this study were also based on nadir observations. However, remote sensing data, especially satellite imagery, widely vary in the zenith angle. MERIS reflectance data may not be observed from a nadir or near-nadir view [70], which increases the uncertainty of the validation results for the MERIS satellite data. Although the view zenith angle was fixed as a nadir observation, the results from the simulated field and MERIS data all show the effectiveness of the proposed VI combination method for CCC estimation. The influence of the view zenith angle on different CCC estimation models should be evaluated in the future.  
Figure 13: Accuracy of CCC retrieval for soybean crops using MERIS data characterized by LAIs. (a) MTCI with the ULR approach. (b) MTCI and MTVI2 with the RFR approach.  
Although ENVISAT MERIS is no longer in work, it makes sense to use past data to produce products with long time series. In addition, Sentinel-3 OLCI is the successor to MERIS, and they have the same band settings and similar spectral response functions [58]. Therefore, the CCC retrieval methods developed for MERIS can generally be applied to Sentinel-3 datasets. In the future, we hope to take the synchronous measurement for Sentinel-3 and further test our algorithm.
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 4 CFS for Baseline Decorrelation Compensation  
The cross-track baseline length of LB-IRA ranges between 629-1000 m, which is far larger than that of SWOT. Though SWOT has chosen the Ka-band and near-nadir look angle for short baseline defection, its altimetric sensitivity is still much lower than that of LB-IRA. However, too-large baseline also decreases the signal coherence due to baseline decorrelation, preventing altimetric accuracy from further being improved. In this section, we introduce the carrier frequency shift (CFS) method for baseline decorrelation compensation, hence LB-IRA will work in cross-interferometry mode to achieve much higher altimetric accuracy than normal interferometry.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 4 Proposed Tree Routing Protocol
### Tree Construction  
As illustrated in Figure 3, the sink node periodically broadcasts a build message to form a tree structure. The build message includes the number of hops to the sink node (i.e., hop count). When a node receives a build message, it stores the hop count in the neighbor table and increments the hop count in the build message by one. Then, the node rebroadcasts the build message. Since a node chooses a neighboring node with the same or fewer hops as a parent node, the hierarchical level of nodes is autonomously determined based on the number of hops to the sink node. In addition, each node periodically broadcasts a hello message containing the proposed decision criteria (i.e., \(P\), \(B\), and \(E\)). Upon receiving the hello messages from the neighbors, each node stores the decision criteria in the neighbor table. These variables are used to choose a parent at each node. The details of the tree construction are given in Algorithm 1.  
```
Initialization:\(S_{id}\): The ID of sink node \(H_{i}\): The number of hops to the sink node at sensor node \(i\)\(NT\): Neighbor table Algorithm:
1:if\(node\_type=root\)then
2: periodically broadcast a build message \(B_{msg}\)
3:else
4: periodically broadcast a hello message \(H_{msg}\)
5:endif
6:upon receiving \(B_{msg}\) from node \(i\)
7: Update \(S_{id}\) and \(H_{i}\) of \(B_{msg}\) in \(NT\)
8:\(H_{i}\) in \(B_{msg}\gets H_{i}\) in \(B_{msg}+1\)
9: Broadcast \(B_{msg}\)
10: Call Parent_Selection 0
```  
**Algorithm 1** Tree initialization at node \(k\).
# Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
## 4 Experimental Study
### Simulations
#### 4.2.2 Quantitative Analysis  
To quantitatively study the reconstruction results, the following mean squared error (MSE) was introduced:  
\[MSE(X)=\frac{1}{N_{X}}\sum_{i=1}^{N_{X}}\Bigl{(}X^{3D}-X^{2D}\Bigr{)}^{2}, \tag{21}\]  
where \(=\{R_{z},\ \varepsilon\}\), \(N_{X}=50\) is the number of slices, and the superscripts 3D and 2D indicate the 3D model and the 2D slice image, respectively.  
Table 2 summarizes the reconstruction results of the algorithms using Equation (21), where the highest performance in each case is indicated with boldface.  
The results of Table 2 reveal that the GA algorithm outperformed the PSO, DE, FA, ABC, and GSA algorithms in almost all the cases by between 5%-84% (porosity accuracy) and 3%-15% (auto-correlation function accuracy). Moreover, it is worth mentioning that the performance of all algorithms was worst for the case of the SC material (higher average \(MSE(\varepsilon)\)) in terms of porosity accuracy, although the 2D slices of the 3D model were more correlated in this case (lower average \(MSE(\varepsilon)\)). This was justified by the fact that the morphology of the SC material is more complex than that of the SBA-15. This complex distribution of SC material phases makes the optimization problem more difficult to solve using evolutionary algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Sintered Copper (SC)**} & \multicolumn{2}{c}{**SBA-15**} \\  & _MSE(\(\varepsilon\))_ & _MSE(\(R_{z}\))_ & _MSE(\(\varepsilon\))_ & _MSE(\(R_{z}\))_ \\ \hline GA & **0.0095** & 0.0311 & **0.0005** & **0.0594** \\ PSO & 0.0107 & 0.0322 & **0.0005** & 0.0614 \\ DE & 0.0104 & 0.0312 & 0.0032 & 0.0676 \\ FA & 0.0105 & 0.0328 & **0.0005** & 0.0647 \\ ABC & 0.0104 & **0.0265** & 0.0015 & 0.0627 \\ GSA & 0.0100 & 0.0324 & 0.0032 & 0.0691 \\ \hline Average & 0.0103 & 0.0310 & 0.0015 & 0.0642 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reconstruction performance indexes of the algorithms.
## 1 Introduction
These methods include linear regression (LR), random forests (RF) [16], support vector regression (SVR) [17], artificial neural networks (ANNs) [18, 19, 20, 21], and convolutional neural networks (CNNs) [22]. In addition, we use three different bio-inspired search algorithms--particle swarm optimization (PSO) [23], the artificial bee colony (ABC) algorithm [24], and the firefly algorithm (FA) [25]--to search for optimal process parameters. We further developed a simplified 1D version of the original 2D Visual Geometry Group (VGG) 16 model [26, 27] to establish a surrogate model between the process parameters and the product quality attributes, as shown in Figure 1. This 1D VGG model is called the simplified VGG (SVGG) model, and it can be regarded as a process for controlling HSLA product quality attributes through its parameter inputs. To meet the product quality requirements of HSLA, we applied the PSO, ABC, and firefly algorithm to search for the optimal adjustable process parameters and then com  
Figure 1: Surrogate model. pare their performance. In our experiments, 9000 samples (meeting the quality requirements) were used to train the five different methods for establishing the surrogate model, and 299 samples (not meeting the quality requirements) were used to evaluate three bio-inspired process optimization search algorithms. The FA ultimately produced the most optimal selection of adjustable process parameters. Our experimental results further showed that all the adjustable process parameters used in our test samples were successful in determining whether the corresponding product attributes met the mechanical requirements of the product. The main contributions of this paper are as follows:  
1. We addressed the interesting problem of steelmaking process parameter optimization by proposing a simplified VGG model to build a surrogate model and then compared it with four other machine-learning methods. 2. We applied three different algorithms--PSO, the ABC, and the FA--to search for optimal process parameters and then evaluated their performance. Our experimental results demonstrated that the FA can achieve high performance and outperforms the other methods. The remainder of this paper is organized as follows. Section 2 reviews the machine-learning methods and bio-inspired algorithms. Section 3 describes our proposed method called the simplified VGG model + Firefly algorithm to search for optimal process parameters. Section 4 presents our experimental results and discussions. Finally, conclusions and remarks are given in Section 5.
## 3 Underwater Wireless Optical Communication System
### Performance Test of UWOC System  
Figure 14 shows the communication test process of the UWOC system for circuit design verification. A communication test was conducted under the conditions of daylight at a distance of 1 m in the air and ambient light from office LEDs, and the visible light system was found to consume 0.02 A of power when on standby for reception. For maximum power consumption, since the light was emitted every time bit 0 was transmitted, approximately 2.2 A of power was consumed when 0 \(\times\) 00 was continuously transmitted. When the data were typed for transmission using a terminal program, the text was received normally without any break.  
The PIN diode attached to the light receiver of the UWOC system is sensitive to light and vulnerable to ambient light and dimmed light, leading to increased noise and communication error rates. Also, as the communication distance increases, the LED output detected by the light receiver decreases, and the waveform also becomes ambiguous between the high and low signals. The two cases are shown as simple tests. Figure 15 shows the voltage signal detected by the light receiver in a waveform when the distance between the transmitter and the light receiver is at a communication distance of 1 m, and Figure 16 illustrates the waveform of the signal detected at a communication distance of 2.5 m such that differences between the high and low signals are reduced.  
Figure 14: Communication experiments on the ground.  
Figure 13: Comparison before and after RZ modulation.
# System for Evaluation and Compensation of Leg Length Discrepancy for Human Body Balancing
## 4 Human Body Balancing Algorithm
### Simulation Results
#### 4.1.2 Scenario 2: Human Body with LLD and Scoliosis
The height of the left force plate \(z_{L}\) is 424.4 mm, and the height of the right force plate \(z_{R}\) is 445.83 mm. The load difference between the left (\(m_{L}=37.46\) kg) and the right (\(m_{R}=37.54\) kg) legs on their respective force plates equals to 0.08 kg, which shows that the human body is balanced. The location of the LASIS marker along the \(z\)-axis is 894.8 mm, and the location of the RASIS marker along the \(z\)-axis is 891.2 mm. The difference in marker height is 3.6 mm, which denotes that the pelvis is not level. The height difference between the left and right force plate is \(\Delta z=-21.43\) mm which gives \(\Delta l=-21.43\) mm. The simulated difference in leg length was 25 mm, meaning that the LLD evaluation gained via the HBBA is not equal to the real-world LLD because the subject has scoliosis. The goal of the HBBA is to achieve a symmetrical load on the left and right leg, which was achieved, as shown in Figure 13c. Figure 13: Scenario 2: (**a**) \(CoM_{x}\), the position of the human body’s CoM; (**b**) reference height of the left (\(z_{Lerf}\)) and right (\(z_{Rref}\)) force plate, current height of the left (\(z_{L}\)) and right (\(z_{R}\)) force plate; (**c**) the load on the left (\(m_{L}\)) and right (\(m_{R}\)) leg; (**d**) the balancing trigger signal (\(Q\)) marking the start of a new HBBA iteration; (**e**) position of the LASIS and RASIS markers, which are located on the pelvis; and (**f**) left and right force plate height difference (\(\Delta z\)), which corresponds with the LLD evaluation (\(\Delta l\)) in the HBBA’s final step. A symmetrical load on the left and right legs will be achieved if an orthopedic shoe insole is made for the patient with an elevation of 21.43 mm under the right leg.
# Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach
## 3 Basic Knowledge of Consensus Algorithm
### Consensus Protocols  
CHP can produce electric and thermal power while PVT-HP prosumer can act as a producer or a consumer according to the load level, output power of PV and heat pump. Besides, the deviated electric power is balanced by the utility grid. Thus, the network is divided into electric network and thermal network, i.e., the electric network set \(V_{e}\) and the thermal network set \(V_{t}\) and note that \(V_{t}\subset V_{e}\). Let us define two stochastic matrices named row stochastic matrix \(R=[r_{ij}]\) and column stochastic matrix \(S=[s_{ij}]\) associated with the electric network of \(N\) nodes as follows:  
\[r_{i,j}=\left\{\begin{array}{ll}\frac{1}{|N_{i}^{-}|},&j\in{N_{i}}^{+}\\ 0,&j\
otin{N_{i}}^{+}\end{array}\right.\begin{array}{ll}s_{i,j}=\left\{ \begin{array}{ll}\frac{1}{|N_{i}^{-}|},&i\in{N_{j}}^{-}\\ 0,&i\
otin{N_{j}}^{-}\end{array}\right.\end{array} \tag{1}\]  
Similarly, \(X=[x_{ij}]\) and \(Y=[y_{ij}]\) are the stochastic matrices of thermal network of \(M\) nodes:  
\[x_{i,j}=\left\{\begin{array}{ll}\frac{1}{|M_{i}^{-}|},&j\in{M_{i}}^{+}\\ 0,&j\
otin{M_{i}}^{+}\end{array}\right.\begin{array}{ll}y_{i,j}=\left\{ \begin{array}{ll}\frac{1}{|M_{i}^{-}|},&i\in{M_{j}}^{-}\\ 0,&i\
otin{M_{j}}^{-}\end{array}\right.\end{array} \tag{2}\]  
For simplicity, the electric network is taken as an example for the following part. It is assumed that \(P(k)=[P_{1}(k),P_{2}(k),...,P_{N}(k)]\) denotes the state vector of all nodes in the electric network at iteration \(k\). For the initial value \(P(0)\), the following two lemmas will be helpful for the design of consensus algorithm [24].  
Lemma 1: _(Consensus): If the network is strongly connected, for the discrete consensus algorithm \(P(k+1)=RP(k)\), there holds that \(\lim\limits_{k\rightarrow\infty}P_{i}(k)=c,\forall i\in V_{e}\), where \(c\) is a constant._  
Lemma 2: _(Ratio Consensus): If the network is strongly connected, for the discrete consensus algorithm \(P(k+1)=SP(k)\), there holds that \(\lim\limits_{k\rightarrow\infty}P_{i}(k)=\kappa_{i}\sum\limits_{i=1}^{N}P_{i}( 0),\forall i\in V_{e}\), where \(\kappa_{i}\) is the \(i\)th element of the unit eigenvector corresponding to eigenvalue 1 of the matrix \(S\)._
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 5 Performance Evaluation
### Power Consumption
#### 5.2.3 RX Power Consumption  
While transmitting data (TX) involves short bursts, receiving data (RX) requires continuous listening, making it a major power drain compared to the energy saving sleep mode. This problem is amplified in nodes with high power consumption during listening, such as Tmote Sky. Their radio hardware consumes significantly more power while receiving, meaning that even brief listening periods significantly impact battery life. Because RX operations are often more frequent than TX ones, especially in protocols with frequent communication, high power consumption during listening dramatically reduces overall network lifetime. Nodes deplete their batteries faster, requiring more frequent maintenance or replacement. Therefore, reducing RX power consumption is crucial for extending network lifespan, particularly for nodes with high listening currents.  
Our mitigation strategy successfully reduced the RX duty cycle (the time the radio spends in listen mode). In the worst-case scenario, an unmitigated two-node attack resulted in an average duty cycle of 2.75%. By implementing our mitigation technique, the duty  
Figure 11: TX power consumption of target node in scenario with two attackers.  
Figure 10: TX power consumption of target node in scenario with one attacker.  
cycle dropped to 1.93%, translating to a significant energy savings of 49% on average in the scenario with one attacker, as shown in Figure 12.  
Figure 13 reveals a similar trend in the scenario with two attackers. Our mitigation strategy achieves an average of 46% energy savings in terms of RX energy consumption.
# Diet and Asthma: A Narrative Review
## 3 Airway Inflammation  
Asthma is associated with airway inflammation, hyperresponsiveness, and remodeling of the airways [9; 13; 41; 42; 43].  
Inflammation is a biological mechanism that eliminates detrimental stimuli as irritants and pathogens, and damages cells with the purpose of beginning the healing process [43]. Inflammation can be acute or chronic. Chronic inflammation is associated with adverse effects, such as damage and remodeling of healthy tissues, as it occurs in asthma [43]. A combination of genetic predisposition, environmental exposures, and potential changes in the microbiota and metabolites are the main factors that may lead to inflammation in the lower airways [15].  
Pathogens, toxins, pollutants, irritants, and allergens are recognized by toll-like receptors (TLRs) that activate inflammatory cells such as nuclear factor kappa-light-chain-enhancer of activated B cells (NF-\(\kappa\)B), which then release growth factors and pro-inflammatory cytokines to begin the resolution process [43]. Activated cells begin wound healing in damaged epithelial cells to repair them. This process is called tissue remodeling [43].  
A variety of inflammatory changes at the cellular level affect the airways that cause tissue remodeling in asthma [7; 15; 43]. Pathologic changes of this process include epithelial damage and dysfunction, goblet cell hyperplasia, increased thickness of reticular basement membrane, angiogenesis, increased subepithelial myofiblasts and fibrocytes, and hypertrophy of airway smooth muscle cells [7; 13]. These pathological changes lead to airway wall thickening, luminal narrowing, and mucus plugging, with small airway obliteration [13], resulting in airway hyperresponsiveness and a progressive obstruction of the airflow [7].  
Exhaled nitric oxide is a marker of airway inflammation, more specifically of eosinophilic inflammation [44; 45]. Nitric oxide (NO) is a reactive, free radical gas that forms in the airways when l-arginine is oxidized to l-citrulline [44]. It is produced by two enzymes: constitutive nitric oxide synthase (cNOS), which produces small amounts of NO, and epithelial inducible NOS (iNOS), which is activated by various inflammatory cytokines [44; 45]. In allergic asthma, airway inflammation is caused by the activation of a Th-2-mediated pro-inflammatory cytokine mechanism that promotes the expression of epithelial iNOS [44; 45]. However, high FeNO values are not always associated with eosinophilic asthma, as elevated levels can also be associated with eosinophilic bronchitis, allergic rhinitis, and viral or allergen exposure [44; 45].
## 2 Geodetic Studies of the Aegean Region  
Since it is clear to the scientific community that satellite geodesy can provide detailed information about the motions of plate tectonics and the deformation of the crust, it is widely applied. Particularly, the Aegean and the eastern Mediterranean constitute an ideal laboratory site for geodetic analyses, and especially for the determination of strain [41; 42; 43; 44; 45; 46; 47]. Therefore, various studies have been conducted in the Aegean region by implementing measurements recorded by either permanent or campaign GPS/GNSS stations [48; 49; 50; 51; 52; 53]. While preceding seismological studies only implied the gross geodynamic setting of the East Mediterranean (the relation between the Arabian, Nubian and Eurasian plates), the geodetic studies confirmed and measured plate boundaries and motions. Furthermore, they resulted in the separating of the region into several blocks, proposing potential geodynamic models (e.g., [54; 55; 56; 57]), which are complicated. Focusing on the Aegean, two distinct velocity clusters are observed throughout the area, distributed on both sides of the NAT (and its continuation) and considered as the boundary between the North and South Aegean [58; 59]. The most recent studies predominantly focus on developing crustal block modelling (e.g., [16; 57; 60; 61]), attempting to interpret their geodynamic activity, as  
Figure 2: **(a)** Earthquake activity in the northern Aegean for a 15 yr time period (2008–2022). **(b)** Focal mechanisms from the RCMT catalogue for the period between 1997 and 2022. Beachball and fault colouring is according to kinematics. Faults in both insets are taken from GreDaSS [39; 40].  
well as their relationship to seismic events (e.g., [62; 63; 64; 65]). Notably, the block modelling considers that each block partially receives the brittle tectonics effect [57; 66; 67], caused by tectonically active structures, while it is imprinted on the upper-crust strain. This North Aegean region strain is documented on the seismic slip and the gravitational collapse of the Aegean crustal wedge in response to slab roll-back at the Eurasian-Nubian convergence boundary [68; 69; 70], while in tandem, strike-slip faulting dominates in this region [17; 23; 26].
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 3 Proposed MAC Layer for Image Streaming over CRSNs
So, in this work, we proposed the idea of Joint Optimizing the Packet Size (\(\tau_{P}\)) and the Number of Spectral Sensing Iterations (\(NSSI\) or \(X\)), and we named it JOPSS. In fact, in our proposed JOPSS method, the goal is to minimize the average time spent on retransmission and the spectrum sending sensing time. For this purpose, the optimal packet size (\(\tau_{P}\)) and the optimal number of spectrum sending iterations (\(X^{*}\)) are estimated simultaneously based on the proposed model.