esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network Approach to MPPT Control and Irradiance Estimation
## 2 Theoretical Background
### Neural Network Model of PV Module  
In recent years, neural networks (NN) have been increasingly applied to various engineering problems, due to their capability to solve complex and nonlinear problems. In general, authors have proposed the usage of different NN architectures for photovoltaic modeling [11; 12; 13; 14; 15]. Here we detail the procedure of photovoltaic modeling with one-hidden-layer feedforward NN, because this network later will be used for the design of the MPPT algorithm and irradiance estimator.  
The feedforward NN model with one hidden layer and three neurons is shown in Figure 3. The solar irradiance \(G\), temperature \(T\) and voltage \(V\) represent inputs to the neural network, while the module current \(I\) is chosen as output. The output current of the NN model is given by:  
\[\hat{I}=net(\mathbf{u})=\mathbf{W_{2}}\tanh\left(\mathbf{W_{1}}\mathbf{u}+ \mathbf{b_{1}}\right)+b_{2}, \tag{6}\]  
where \(\mathbf{u}=\left[\begin{array}{cc}T&G&V\end{array}\right]^{T}\), whereas \(\mathbf{W_{1}},\mathbf{W_{2}},\mathbf{b_{1}}\) and \(b_{2}\) are the coefficients that need to be learned from the available input/output training sets.  
In order to facilitate the learning process, each input can be normalized by using the formula:  
\[u_{i,new}=\frac{u_{i}-u_{i,\min}}{u_{i,\max}-u_{i,\min}},i\in\left\{S,T,V \right\}, \tag{7}\]  
where index \(max/min\) denotes the maximal/minimal value of \(S,T,V\) in the training set. A similar operation can be performed with the output training data.  
The neural network coefficients are optimized by using the Levenberg-Marquardt algorithm or some other second-order algorithm. The cost function is defined as the mean square error (MSE) between the predicted and actual current:  
\[J=\frac{1}{\sum_{i=1}^{M}N_{i}}\sum_{i=1}^{M}\sum_{k=1}^{N_{i}}\left(\hat{I}- I\right)^{2} \tag{8}\]  
where \(N_{i}\) is the number of available points of \(I\)-\(V\) curve for specific irradiance \(G_{i}\) and temperature \(T_{i}\), while \(M\) denotes the number of different \(I\)-\(V\) curves.  
Operating points making up the training set may be collected by experiments under controlled environmental conditions [17]. The environmental data need to be uniformly distributed in order to avoid overfitting/underfitting in the ranges that have more/less training data. Moreover, it is important to cover all \(G\) and \(T\) values that can occur in practical scenarios, because the NNs do no have good extrapolation properties. In case that only a small amount of experimental data or manufacturer datasheet information is available, the parameters of the equivalent circuit should be determined first. After that, the training data can be synthesized by using Equations (1)-(5). However, it has been shown that the PV models built directly from the measured data usually have greater accuracy than the equivalent circuit models [16].  
Figure 3: The architecture of neural network model of the PV.
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 3 The Proposed Critical Action Information Extraction Network
### Feedback Attention-Guided Module (FAGM)  
To further enhance the capability of CNNs to capture sub-action information, we propose a Feedback Attention-Guided Module (FAGM). The FAGM consists of attention modules applied to the temporal, channel, and spatial dimensions. Firstly, the low-level and high-level features are separately fed into the Temporal Attention Module (TAM), to take more attention to frames that are informative with salient motion regions. Next, the features processed by TAM are then combined and passed into the Channel Attention-Guided  
Figure 4: Adaptive Fusion Module (AFM). For the displacement tensor \(\mathbf{D}_{i}\) with different frame intervals, we first concatenate them along the channel dimension, then apply average pooling, MLP, and the sigmoid function to generate adaptive weights. These adaptive weights are then added to the concatenated displacement tensor. Finally, we use three \(1\times 3\times 3\) separable convolutions to convert it into features with the same dimensions as the original RGB features.  
Module (CAGM) and the Spatial Attention-Guided Module (SAGM). CAGM and SAGM modules leverage contextual information to guide the low-level feature learning, to focus on the channels and spatial regions most relevant for promoting video action classification.
## 2 Materials and Methods
### Egg Quality  
External and internal egg quality traits and egg proportions were assessed at 29 and 39 WOA. Approximately 15 eggs per treatment (165 eggs in total) were collected in the morning. Measurements were performed by one experimenter within 4 h of egg collection, except for eggshell weight and thickness (i.e., waited until dried). All significantly deformedeggs (4 and 9 deformed eggs at week 29 and 39, respectively) were discarded and excluded from the analysis. Eggs were analysed for both external (eggshell thickness, breaking strength and reflectivity, and egg length, breadth, and shape index) and internal quality (albumen height, yolk height, yolk diameter, yolk index, yolk colour, and Haugh unit). Egg length (dimension between the poles) and breadth (dimension at the equator) were measured using a Kincrome 0-150 mm Digital Vernier calliper (Kincrome, Scoresby, Victoria, Australia). Then, egg shape index was determined as the egg length divided by egg breadth. Eggshell reflectivity was determined with a shell reflectivity meter (Technical Services and Supplies, Dunnington, York, UK), while eggshell breaking strength and egg internal quality traits were measured using a digital egg tester (DET6500, Nabel Co., Ltd., Kyoto, Japan). Egg yolks were transferred onto preweighed filter papers (diameter 90 mm, CAT No.1541-090, Whatman, Buckinghamshire HP7 9 NA, UK) and weighed. For eggshell weight and thickness, the eggshell residue was washed in slow-running tap water to remove the albumin from the eggshell surface, and then the eggshell was air-dried thoroughly for at least 48 h. Eggshell weight was taken using an Adventurer TM Precision analytical balance (Model AX423, Ohaus(r), Newark, NJ, USA). Eggshell thickness, including the shell membrane, was measured on two pieces of shell collected from different places on the egg using a custom-built gauge (Mitutoyo Dial Comparator Gauge, Model 2109-10, Kawasaki, Japan), and the average value was used for the analysis. To measure egg proportions, egg albumen weight was calculated by subtracting yolk weight and shell weight from the intact egg weight. Percentages of albumen, yolk, and eggshell were obtained by dividing weights of these egg components by the total egg weight.
## 3 Results
### Diffusion Behavior of EO-PG in Relation to Biological Activities  
Table 9 presents the values obtained for the parameters of diffusion and biological activities of desorption of the EOPH in the PG in all eight of the tests carried out.  
In order to provide a comprehensive explanation of the findings, it was necessary to select the most pertinent axes for the analysis. The Kaiser criterion was used to select axes F1 and F2, which had eigenvalues greater than 1 (\(\lambda_{1}\) = 2.79 and \(\lambda_{2}\) = 1.64), corresponding to percentages of 46.56% and 27.27%, respectively. Together, these axes accounted for 73.83% of the information, and their eigenvalues represented a significant portion of the total inertia, indicating that the sum of the inertia explained by each axis was substantial.  
The mapping displayed in Figure 9 provided several interpretations. First, the diffusion coefficient \(D\) and the flux \(F\) were perpendicular in the correlation circle, indicating that these two variables were not correlated and were independent. Second, the diffusion coefficient \(D\) was inversely correlated with the mortality \(M\)% and the length of the aerial part \(L_{A}\), while the flux \(F\) was inversely correlated with the weight \(W\) and the length of the root part \(L_{R}\).  
Figure 8: Simulation of concentration profiles for 15 days (T = 25 \({}^{\circ}\)C; H = 70%; C = 0.01 \(\mu\)L-g\({}^{-1}\)-cm\({}^{-3}\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**Desorption Parameters**} & \multicolumn{2}{c}{**Insecticidal**} & \multicolumn{2}{c}{**Germination Parameters**} \\ \cline{2-7}  & \multicolumn{2}{c}{**F (\(\times 10^{4}\)) Dz (\(\times 10^{6}\)) (g\(\cdot\)h\({}^{-1}\).cm\({}^{-1}\)) (cm\({}^{2}\).h\({}^{-1}\))**} & \multicolumn{2}{c}{**M (\%)**} & \multicolumn{2}{c}{**W (mg)**} & \multicolumn{2}{c}{**L\({}_{\mathbf{A}}\) (cm)**} & \multicolumn{2}{c}{**L\({}_{\mathbf{R}}\) (cm)**} \\ \hline
1 & 8.4152 & 12.40 & 33.33 \(\pm\) 5.40 & 638.5 \(\pm\) 0.2 & 3.4 \(\pm\) 0.1 & 3.1 \(\pm\) 0.1 \\ \hline
2 & 5.4625 & 5.68 & 38.55 \(\pm\) 5.64 & 999.0 \(\pm\) 0.4 & 7.0 \(\pm\) 0.2 & 8.2 \(\pm\) 0.2 \\ \hline
3 & 7.2241 & 7.92 & 20.20 \(\pm\) 4.93 & 637.5 \(\pm\) 0.2 & 4.3 \(\pm\) 0.1 & 4.9 \(\pm\) 0.1 \\ \hline
4 & 9.2375 & 6.80 & 66.67 \(\pm\) 7.21 & 515.5 \(\pm\) 0.2 & 7.8 \(\pm\) 0.2 & 4.0 \(\pm\) 0.1 \\ \hline
5 & 5.8732 & 14.64 & 21.84 \(\pm\) 3.11 & 726.0 \(\pm\) 0.3 & 7.8 \(\pm\) 0.2 & 3.9 \(\pm\) 0.1 \\ \hline
6 & 5.7223 & 7.36 & 54.23 \(\pm\) 6.29 & 909.5 \(\pm\) 0.4 & 10.0 \(\pm\) 0.2 & 5.5 \(\pm\) 0.1 \\ \hline
7 & 6.4254 & 8.77 & 50.41 \(\pm\) 6.04 & 768.3 \(\pm\) 0.3 & 5.2 \(\pm\) 0.1 & 5.7 \(\pm\) 0.1 \\ \hline
8 & 8.764 & 6.24 & 30.21 \(\pm\) 5.07 & 817.22 \(\pm\)0.4 & 8.9 \(\pm\) 0.1 & 5.1 \(\pm\) 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Diffusion parameters and biological activities of EOPH desorption in the PG.
## 3 Results and Discussion
### Assessment of Cd Distribution in Different Soil Fractions  
The Cd content in each fraction obtained by sequential chemical extraction is shown in Table 3. The results indicate that Cd recovery after sequential extraction exceeded 75% for all treatments with or without HANPs, indicating the effectiveness of the method in assessing the distribution of Cd among geochemical fractions. The distribution of Cd among the geochemical fractions depends on soil characteristics (Table 1). The sandy-loam texture, characterized by a low clay content, reduces the soil capacity to retain Cd. In addition, the low organic matter content limits the formation of organic-metallic complexes, while the acidic pH increases Cd solubility by enhancing competition between H\({}^{+}\) and heavy metals for sorption sites on soil particles. These characteristics explain the relatively high proportion of Cd associated with bioavailable fractions (F1-F3), particularly in untreated soils. The high ECEC, predominantly influenced by exchangeable Al\({}^{3+}\), contributes to the mobility of Cd. The Al\({}^{3+}\) occupies exchange sites that might otherwise bind Cd, facilitating the presence of the metal in soil solution. This is relevant to the inhibition of quinoa germination and growth observed during phytotoxicity tests and pot experiments. The efficacy of HANPs in mitigating Cd toxicity seems to depend on their ability to immobilize the metal in less bioavailable forms, as evidenced by the increased F6 content and reduced bioavailable fractions observed in the sequential extraction results. The non-presence of Cd in the fractions from the control soils, both untreated and treated with HANPs, confirms the lack of natural Cd in the soil samples, as indicated in the pseudototal metal analysis (Table 1). This confirms that Cd comes from the experimental treatments. In the Cd-treated soils, the proportion associated with the most bioavailable fractions (F1, F2, and F3) ranged from 36.6% (Soil + Cd 5 mg kg\({}^{-1}\)) to 60.4% (Soil + Cd 25 mg kg\({}^{-1}\)). These fractions, representing exchangeable and weakly bound Cd, are responsible for the risks of quinoa exposure to Cd and its potential mobility in soil. There was no significant difference in the distribution of Cd for the sum of F1-F3 between soils treated with HANPs and untreated soils. This suggests that HANPs did not significantly reduce the proportion of Cd in bioavailable forms under the conditions tested. The persistence of bioavailable Cd in HANP-treated soils is consistent with the
## 4 Proposal
### System Overview  
This section presents an overview of this approach that utilizes existing signs in the building. Figure 2 provides an overview of the integration functions for running this method in _INSUS_ with the help of the _SEMAR_ server. Since the main process of the proposed system is based on Python language and located in the _SEMAR_ server, we utilize an _application programming interface (API)_ to integrate these two components. First, the camera captures an image containing signs as the input, which is called a _sign image_. The _image transmission function_ sends it to the _SEMAR_ server via HTTP communication using the REST API service in base64 format. Upon receiving the data, the _object detection function_ identifies the sign in the image and saves the result as an _isolated sign image_. Then, the _text extraction function_ recognizes and extracts text from the _isolated sign image_. The _database matching function_ selects the room number in the Room Database that is most closely similar to the extracted text to determine the room coordinates. We employ the _Levenshtein distance_ to calculate the similarity between the extracted text and the room number. The _SEMAR_ server sends the room coordinates back to _Unity_ via HTTP through the _Unity Web Socket_ module. Finally, _Unity_ uses the new coordinates to update the user's location.  
The connection between Unity and the _SEMAR_ server through the REST API service is visualized in Figure 3. Since the REST API is based on the HTTP communication protocol, we employ the _Unity Web Request_ module on the Unity side to send an encoded base64 sign image to the _SEMAR_ server using JSON data. On the _SEMAR_ server side, due to the utilization of Python as the main process, we employ the FastAPI library to receive the image and process the image via _YOLOv8_, _PaddleOCR_, _Levenshtein distance_, and the _regular expression_ library to select corresponding room coordinates in the database. Finally, the _SEMAR server_ sends the room coordinates back to _Unity_ to update the user's current coordinates through the _Unity Web Request_ module in JSON data.
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 3 Design of Bottom Guidance Law Based on Sliding Mode Control Theory
### Design of the Impact Time Control Guidance Law
#### 3.2.3 The Extension of the Guidance Law under the Maneuvering Target  
For maneuvering target, in order to achieve the effective attack on the target under the designated time, the strategy of predicting the collision point is adopted. The target prediction point \((x_{TP},y_{TP})\) is shown in Figure 2.  
The coordinates of the target prediction collision point can be expressed as:  
\[\begin{split} x_{TP}&=x_{T}+(V_{T}\cos\gamma_{T})t_{go} \\ y_{TP}&=y_{T}+(V_{T}\sin\gamma_{T})t_{go}\end{split} \tag{36}\]  
where, \((x_{T},y_{T})\) is the target coordinates at the current time, \(V_{T}\) is the target velocity, \(\gamma_{T}\) is the target flight path angle. \(r_{P}\) is the relative distance between the UAV and predicted collision point, \(\lambda_{P}\) is the corresponding leading angle. By replacing \(r\) and \(\lambda\) with \(r_{P}\) and \(\lambda_{P}\) respectively, and bringing them into the guidance law Equation (24), it can attack the maneuvering target in the designated time.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 2 Datasets  
Two datasets are used to evaluate the methods described in this work, one dataset collected in the Keweenaw Waterway in August, 2014, (abbreviated as KWAUG14) [14] and the other dataset from the Surface Processes and Acoustic Communication Experiment performed off the coast of Martha's Vineyard in 2008 (abbreviated as SPACE08) [15].  
* The KWAUG14 experiment lasted about 4.5 days. The water depth was about 10 m. A waveform of 8.8 s in the frequency band [14; 20] kHz was transmitted with a fixed power every 15 min over a link of 312 m. The waveform is modulated by the orthogonal frequency-division multiplexing (OFDM) technique with a quadrature shift keying (QPSK) constellation. Each transmission consists of 20 OFDM blocks.
* The SPACE08 experiment was conducted off the coast of Martha's Vineyard at the Air-Sea Interaction Tower by Woods Hole Oceanographic Institution from 14 October to 1 November 2008 [15]. The water depth was about 15 m. An OFDM communication waveform within the frequency band [8; 18] kHz was transmitted every two hours. The experiment consisted of several different receiver arrays, but to commonize the datasets, in this work only the data collected from a single receiver array (S3, located 200 m from the source) and modulated by the QPSK constellation are used.  
The KWAUG14 experiment took place in fresh water with mostly good weather conditions, while The SPACE08 experiment took place in the sea environment over a wider variety of weather conditions. Additional measurements of turbulence, turbidity, and temperature are not included in this study. It is expected that additional variation of these parameters would create a more diverse set of multipath effects and this warrants further study.  
While only a single transmission distance from each dataset is included in this work, our previous work [8] considered different receiver arrays from the SPACE08 experiment and showed that the conventional methods worked well on the shorter and longer distance channels, at 60 m and 1 km respectively. The influence of temperature and ice cover isn't considered in this work, but a study of channel condition variation [16] shows that an observable difference in CIRs and SNR is present with ice cover. It is expected that these changes (different transmission range, temperature, and ice cover) could be learned by the methods proposed in Section 5.
# Sustainable Pavement Management: Harnessing Advanced Machine Learning for Enhanced Road Maintenance
## 4 Results and Analysis
### Statistical Insights  
In this section, statistical methods are employed to assess the performance disparities observed among machine learning models in this study. The statistical tests aim to address the question: To what extent do the differences in performance across models vary in terms of F1 scores and accuracies?  
To achieve this, a series of \(t\)-tests were conducted to compare the mean F1-scores of EfficientNetB3 + SwinGAN with other models such as ResNet50. The \(t\)-test was selected  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Run** & **Tr. Acc.** & **Ts. Acc.** & **Tr. Loss** & **Ts. Loss** & **F1 Score** & **Efficiency** & **Computational Demand** \\ \hline ResNet50 & 1 & 72.9\% & 69.0\% & 2.97 & 3.02 & 0.80 & Moderate & High \\  & 2 & 73.5\% & 70.1\% & 2.90 & 2.95 & 0.78 & Moderate & High \\  & 3 & 74.2\% & 71.0\% & 2.85 & 2.90 & 0.81 & High & High \\ ResNet18 & 1 & 75.0\% & 72.6\% & 2.65 & 2.82 & 0.83 & High & Moderate \\  & 2 & 76.0\% & 73.5\% & 2.60 & 2.75 & 0.85 & High & Moderate \\  & 3 & 77.1\% & 74.3\% & 2.50 & 2.65 & 0.82 & High & Moderate \\ EffNetB3 + SwinGAN & 1 & 78.3\% & 76.7\% & 1.47 & 1.60 & 0.87 & Very High & Low \\  & 2 & 79.0\% & 77.4\% & 1.40 & 1.50 & 0.91 & Very High & Low \\  & 3 & 79.8\% & 78.2\% & 1.33 & 1.45 & 0.90 & Very High & Low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results for pavement distress classification models.  
under the assumption of normality in the distribution of mean F1-scores derived from multiple independent model runs. The hypotheses for these tests are stated as follows:  
* H0: There is no statistically significant difference between F1-scores from EfficientNetB3 + SwinGAN and ResNet50.
* H1: There is a significant difference in the F1-scores between EfficientNetB3 + SwinGAN and ResNet50.  
These \(t\)-tests result, then, in a \(p\)-value equal to 0.0029 when comparing EfficientNetB3 + SwinGAN with ResNet50. Clearly, such a \(p\)-value is way lower than the standard threshold of 0.05; therefore, the F1-score differences are statistically significant. This strong statistical evidence supports our claim that EfficientNetB3 + SwinGAN has architectural advantages for the efficient handling of image classification tasks, especially pavement distress imagery.  
For the two models, the difference in their mean F1-scores is within 5% to 15% with confidence, which statistically upholds the significance of the performance increase when using EfficientNetB3 + SwinGAN. Figure 7 illustrates the fluctuation of precision scores over epochs across different convolutional neural network models, offering insights into their precision performance and convergence patterns.
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 3 FLGR Protocol
### Selection Criteria and Parameters for Assessment
The distribution degree of the nodes in CNR represents the distribution of nodes in the CNR of CN, and it is denoted by \(\Omega\) in this paper. In the FLGR protocol, if the nodes in CNR are evenly distributed along the communication boundary, the distribution of the nodes in CNR is optimal. The probability that the CN is selected as the next forwarding node is the highest. However, if the nodes in CNR are centrally distributed near the CN, the distribution of the nodes in CNR is the worst. Taking the CN \(n_{2}\) of EN \(n_{1}\), for example, node \(n_{4}\) and \(n_{5}\) are in the CNR of CN \(n_{2}\), and the optimal distribution and worst distribution are shown as follows. As shown in Figure 2a, there are two nodes (\(n_{4}\) and \(n_{5}\)) in the CNR B of CN \(n_{2}\), and they are uniformly distributed along the communication boundary. In this situation, the parameter \(\Omega\) of CN \(n_{2}\) is the largest. By contrast, the distribution of nodes \(n_{4}\) and \(n_{5}\) in Figure 2b is the worst, and the parameter \(\Omega\) of CN \(n_{2}\) is smallest. Thus, taking the CN \(n_{k}\) for example, if \(Z_{k}\
eq\varnothing\), and there are \(m\) nodes in its CNR, the optimal locations of the \(m\) nodes, denoted by \(\mathbf{L}^{opti\_kj}\), can be obtained as follows:  
\[l^{opti\_kj}_{u}=\left(x_{k}+\cos(\frac{\theta^{zero}_{kj}*u}{m+1}+(\theta_{kj }-\frac{\theta^{zero}_{kj}}{2})),y_{k}+\sin(\frac{\theta^{zero}_{kj}*u}{m+1}+( \theta_{kj}-\frac{\theta^{zero}_{kj}}{2}))\right),m\geq 1 \tag{5}\]  
As shown in Equation (5), taking the node \(n_{k}\) as the origin of coordinates, the angle \((\frac{\theta^{zero}_{kj}*u}{m+1}+(\theta_{kj}-\frac{\theta^{zero}_{kj}}{2}))\) represents the angle of the \(u\)th optimal distribution node relative to the CN \(n_{k}\). In this paper, the distribution function is defined as \(f(\mathbf{L},\mathbf{L}^{opti},m)\), and the parameter \(\Omega_{kj}\) of CN \(n_{k}\) can be obtained as follows:  
\[\Omega_{kj}=f(\mathbf{L},\mathbf{L}^{opti},m)=\frac{\sum\limits_{i=1}^{m}\min \limits_{j=1}^{m}\left(\left\|l_{i}-l^{opti}_{j}\right\|_{2}\right)^{2}}{m*R^{ 2}},m=1,2,\ldots \tag{6}\]  
In Equation (7), \(\mathbf{L}=(l_{1},\ldots,l_{m})\) are the positions of \(m\) modes in CNR. According to the distribution function, the value range of parameter \(\Omega\) is \((0,1]\).