esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 1 Introduction
### Contributions  
In this paper, we present an overview of UAV-enabled IoT platform for disaster management. The following are the main contributions of this paper:  
* We present a framework for research in UAV-enabled IoT platform for disaster management such as wildfire management.
* We highlight critical hurdles in the success of UAV-enabled IoT platform for disaster management.
* We discuss the use of NDN architecture to enhance the performance of the ground IoT network.
* We provide a case study to show the significance of NDN architecture in the design of efficient ground IoT network that can detect and monitor wildfire and send notifications to people in the affected areas.
* We highlight future research directions for UAV-enabled IoT platform for disaster management.  
The the remainder of the paper is organized as follows: Section 2 briefly reviews the literature on UAV-enabled IoT platform for disaster management and applications of UAV-enabled IoT systems in disaster management. The architecture and key challenges are presented in Section 3. In Section 4, we present a case study to use NDN architecture for IoT Platform to increase its performance. Finally, the conclusion is drawn and future research directions are highlighted in Section 5.
# HSAW: A Half-Face Self-Attention Weighted Approach for Facial Expression Recognition
## 4 Proposed Method
### Self-Attention Weighted  
\(V_{all}\), \(V_{left}\), and \(V_{right}\) are three feature vectors extracted by the feature extraction network from \(I_{all}\), \(I_{left}\), and \(I_{right}\), respectively. Owing to facial asymmetry, the intensity of emotional expressions varies between the left and right sides of the face which, in turn, influences the final classification outcomes differently. To encapsulate this variance within the model, this paper employs a self-attention mechanism that effectively acknowledges and integrates the distinct contributions of each half-face to emotional expression recognition. The self-attention weight module comprises a fully connected layer (FC) and a sigmoid activation function. This configuration derives expression information from the half-face region's local feature vectors \(V_{left}\) and \(V_{right}\) and quantifies it as weight values. These weights digitally represent the contribution of the half-face's local features towards the final prediction outcome in the classification process, thereby encapsulating the nuanced impact of each half-face on emotion recognition.  
The contribution of each local feature vector \(V_{i}\), denoted as the weight \(w_{i}\) within the self-attention mechanism module, is calculated using the following approach:  
\[w_{i}=\sigma(W^{T}V_{i}),i\in\{left,right\} \tag{2}\]where \(V_{i}\in R^{(121\times 1)}\) represents the local feature vector of the half-face and \(W\in R^{(121\times 1)}\) represents the parameters of the fully connected layer. \(\sigma(\cdot)\) represents the sigmoid activation function. In contrast, the entire face \(I_{all}\) possesses a wealth of global information; thus, its contribution score is assumed to be fixed at 1. To formalize the relationship between the half-face and the entire face, a loss function has been designed, expressed as follows:  
\[\mathcal{L}_{w}=L_{1}(w_{left}+w_{right},1) \tag{3}\]  
where \(L_{1}(\cdot)\) represents a smooth \(\ell_{1}\) loss function [21]. The value of 1 constrains the weights of the left and right faces, such that they approximate the weight of the complete face in terms of their impact on the final prediction outcome. This loss function is instrumental in guiding the module to allocate weights within defined limits, ensuring that the contributions of the half-faces are balanced and aligned with the comprehensive facial information. This balancing act facilitates a more accurate and holistic interpretation of facial expressions by acknowledging the integral role of both global and local facial features in emotion recognition.
## 4 Results
### Kinetics and Adsorption Isotherms  
A kinetic study was carried out to analyze the adsorption performance over time. The fitting parameters of the selected models were estimated by non-linear fitting, and are listed in Table 6; it was found that the pseudo second order model best described the adsorption kinetics of Pb(II) ions onto the CP, YP, BP, and OBP bioadsorbents, based on the correlation coefficient values. The fitting of experimental data to this model indicated that the process was governed by chemical adsorption,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Heavy Metal** & **Adsorbent** & **Removal Yield (\%)** & **Metal Uptake (mg/g)** \\ \hline \multirow{3}{*}{Pb(II)} & Yam peel & 81.24 & 82.24 \\  & Banana peel & 90.12 & 91.12 \\ \cline{1-1}  & Cassava peel & 98.19 & 99.19 \\ \cline{1-1}  & Oil palm bagasse & 39.41 & 40.41 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Adsorption results of lead ions onto biomass-derived adsorbents.  
Figure 6: Energy Dispersive X-Ray spectrogram of: (**a**) YP, (**b**) CP, (**c**) BP, and (**d**) OPB biomasses after the adsorption process.  
due to the formation of chemical bonds between the adsorbent and the adsorbate at the active sites. According to the value estimated for constant K\({}_{2}\), it was shown that the initial sorption rate for Pb (I) ions over the CP biomass was also high, achieving high adsorption efficiency [37,46].  
The fitting parameters for adsorption isotherms are reported in Table 7. It was found that the Freundlich model reached the highest R\({}^{2}\), indicating that bioadsorbent surfaces are heterogeneous, which is consistent with the results obtained during biomass characterizations. Also, the adsorption mechanism seems to be multilayered, with a non-uniform distribution of heat on the surface of the adsorbents, and the removal of Pb(II) ions occurred by chemisorption [34,47]. The Freundlich parameter (n) reached a value of 1.87 for the OBP biomass, suggesting that the chemical bonds formed between Pb(II) ions and OBP were strong, and that the adsorption process was favorable. On the other hand, the values of n for CP, YP, and BP were relatively low, which indicated that the binding of Pb(II) ions onto adsorbent surface was weak [14,43].  
It is important to point out that this research was limited to the adsorption experiments of lead ions onto four different residual materials; desorption cycles were not performed. However, hydrochloric and nitric acids can be used for desorbing under concentrations ranging from 0.1-1 M [48]. Despite efforts to recover the adsorbent, final disposal of contaminated biomass at the end of its useful life presents a challenging issue. The application of cement-based immobilization technologies may represent an attractive solution to such a problem. In addition, the presence of soluble organic compounds in the treated water presents another field for prospective research.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 3 Fire Detection Approach
### IoT Devices and Sensor Models  
Raspberry Pi 4 has its own OS that is pre-installed with anIntegrated Development Environment (IDE). This allows the user to conveniently use the python problem. Furthermore, the Raspberry Pi 4 is equipped with a set of pins, which you can easily access to test out sensors, etc. We tested the MQ-2 flammable gas and smoke sensor because it was the only thing that was available to us. However, there are a lot more similar sensors that can be used depending on the situation.  
Sensors similar to MQ-2 are as follows:  
* MQ-2: measures methane, butane, liquefied petroleum gas (LPG), smoke;
* MQ-3: alcohol, ethanol, smoke;
* MQ-4: methane, compressed natural gas (CNG);
* MQ-5: natural gas, LPG;
* MQ-6: LPG, butane;
* MQ-7: carbon monoxide;
* MQ-8: hydrogen gas;
* MQ-9: carbon monoxide, flammable gasses;
* MQ131: ozone;
* MQ135: air quality;
* MQ136: hydrogen sulphide gas;
* MQ137: ammonia;
* MQ138: benzene, toluene, alcohol, propane, formaldehyde gas, hydrogen;
* MQ214: methane, natural gas;
* MQ216: natural gas, coal gas;
* MQ303A: alcohol, ethanol, smoke;
* MQ306A: LPG, butane;
* MQ307A: carbon monoxide;
* MQ309A: carbon monoxide, flammable gas.  
We chose to go with the MQ-2 not only because it was the only thing that was available to us, but also because it measures methane, butane, LPG, and smoke.
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 2 Related Work
By searching the network, the node satisfying the condition is found as the optimal next-hop data forwarding node for data delivery. Reference [22] proposed a new method to find the nearest neighbor from the current sending node in a given angle range as the next-hop node. In the routing algorithm based on location information, the greedy data forwarding strategy is the most efficient and simple. Based on the definition of the particle fitness function with neighbor node information, a single-hop routing clustering protocol (DPSOCA) based on discrete particle swarm optimization is proposed in [23]. Reference [24] proposed a new routing protocol called greedy peripheral coordinator routing (GPCR) algorithm, which does not require external information of the node, nor the entire network information. It only realizes the data forwarding between nodes by coordinating the relationship between nodes. However, it is easy for this selection mechanism to fail in the case of low node density. The improved greedy traffic  
\[ \begin{split}\sum\limits_{j=1}^{k}\int_{\lambda_{j}}\log\frac{p(v_{j} \lambda_{j}|\lambda_{j}\mu,\Lambda,\psi)}{\lambda_{j}}\mathrm{d}\lambda_{j}\\ =\sum\limits_{j=1}^{k}E[\log p(v_{j}|\lambda_{j};\mu,\Lambda,\psi) +\log p(\lambda_{j})-\log p(\lambda_{j})]\end{split}  \tag{13}\]  
Therefore, it can be obtained:  
\[ \begin{split}\mu=\frac{1}{k}\sum\limits_{j=1}^{k}v_{j},\quad \Lambda=(\sum\limits_{j=1}^{k}(v_{j}-\mu)\mu_{v_{j}|\lambda_{j}}^{T})(\sum \limits_{j=1}^{k}\mu_{v_{j}|\lambda_{j}}\mu_{v_{j}|\lambda_{j}}^{T}+\Sigma_{v_{ j}|\lambda_{j}})^{-1}\\ \psi=\frac{1}{k}\sum\limits_{j=1}^{k}v_{j}v_{j}^{T}-v_{j}\mu_{v_{ j}|\lambda_{j}}^{T}\Lambda^{T}-\Lambda\mu_{v_{j}|\lambda_{j}}^{T}\chi_{i}^{T}+ \Lambda(\mu_{v_{j}|\lambda_{j}}\mu_{v_{j}|\lambda_{j}}^{T}+\Sigma_{v_{j}| \lambda_{j}})\Lambda^{T}\end{split}  \tag{14}\]  
The \(\mu_{v_{j}|\lambda_{j}}\) and \(\Sigma_{v_{j}|\lambda_{j}}\) can be obtained \(\mu_{v_{j}|\lambda_{j}}=\mu+(\Lambda\Lambda^{T}+\psi)\Lambda^{T^{-1}}\lambda_{ j}\), \(\Sigma_{v_{j}|\lambda_{j}}=\Lambda-(\Lambda\Lambda^{T}+\psi)\Lambda^{T^{-1}}1\). _E-step_ estimates the weights of the Gauss model by conjecture the parameters of the Gauss model. _M-step_ is the weight of the Gauss model based on the _E-step_ estimate. Repeat the above two steps until the fluctuation is small and approximately reaches the extreme value.
## 5 Experimental Results and Analysis
### Performance Evaluation  
To evaluate the performance of the proposed intrusion detection model, we used several widely applied metrics: accuracy, detection rate (DR), precision, recall, false positive rate (FPR), and F1-score. These metrics can be calculated based on four basic metrics of the confusion matrix, as shown in Table 1: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). TP and TN indicate that the attack and normal records are correctly classified, respectively; FP represents a normal record that is incorrectly predicted as an attack; and FN represents an attack record that is incorrectly classified as a normal record.  
Accuracy is the proportion of all predictions that are correctly identified as \"Attack record\" and \"Normal record\". Accuracy is the most intuitive performance measure of an intrusion detection system. It directly reflects the superiority of the system. Accuracy is defined as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}. \tag{29}\]  
The detection rate (DR) is the proportion of the actual attack records that are correctly classified. DR is an important value for measuring the performance of the intrusion detection system. DR is defined as follows:  
\[DR=\frac{TP}{TP+FN}. \tag{30}\]  
Precision is the proportion of all predicted attack records that are actually attack records. Precision is defined as follows:  
\[Precision=\frac{TP}{TP+FP}. \tag{31}\]  
Recall is a measure of coverage and can indicate the ratio of the actual attack records that are correctly identified. Recall is also considered as the detection rate. With a certain accuracy, the recall rate of the classifier is required to be as high as possible. The recall is defined as follows:  
\[Recall=\frac{TP}{TP+FN}. \tag{32}\]  
The false positive rate (FPR) generally indicates the probability that normal records are incorrectly predicted as attack records. The FPR affects the performance of the intrusion detection system. The FPR is defined as follows:  
\[FPR=\frac{FP}{FP+TN}. \tag{33}\]  
The F1-score is the harmonic mean of precision and recall. In other words, it can be interpreted as a weighted average of the precision and recall. The F1-score takes false positives and false negatives  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Test result positive** & **Test result negative** \\  & **(Predicted as an Attack)** & **(Predicted as a Normal Record)** \\ \hline Actual positive class (Attack record) & True positive (TP) & False negative (FN) \\ Actual negative class (Normal record) & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The four basic metrics of the confusion matrix.  
into account. The F1-score is usually more useful than accuracy, especially if you have an imbalanced class distribution. The F1-score is defined as follows:  
\[F1-score=\frac{2(Precision*Recall)}{Precision+Recall}=\frac{2 TP}{2 TP+FP+FN}. \tag{34}\]
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 4 Markov Decision Process
### Reward Function  
We define the reward function \(R\) to maximize the sum data rate while satisfying the defined constraint. To calculate the data rate according to the SINR, we use a modified Shannon capacity formula [31]. The data rate of the mobile device can be denoted as  
\[R=\frac{1}{N}\sum_{\gamma=1}^{N}Blog_{2}(1+\frac{D^{-\alpha}\cdot T\cdot|g|^{2}} {\sigma^{2}+I}) \tag{5}\]  
where \(\gamma\) and \(B\) represent the number of iterations and the bandwidth of the mobile node, respectively.
# Systematic Review and Meta-Analysis of Melatonin Quantification in Wine
## 2 Materials and Methods
### Inclusion/Exclusion Criteria and Data Extraction  
Our primary purpose was to review every single paper for new evidence related to melatonin concentration in wine. The results of the searches were filtered by the English language criterium and document type. Because information published in one original article can also be found in a different document, e.g., review, conference paper, etc., it is crucial to meticulously de-duplicate the data. To avoid introducing duplicate information into the meta-analyses, the exclusion criteria were review, book chapter, conference paper, short survey, note, letter, and article commentary. The title and abstract of papers were evaluated and categorized with the terms \"retain\" or \"exclude\", and relevant documents were downloaded by one investigator (G.L.R.). Articles eligible for assessment were those involving measurements of melatonin concentrations in wine samples. The references lists of the collected articles were also screened manually to find additional articles. Then all retained full texts were read by two investigators ([PERSON] and C.A.) to assess each article's suitability for inclusion based on the pre-specified inclusion and exclusion criteria. We excluded all articles that did not provide the average melatonin concentration of the studied wine in a numerical manner. Afterward, the data from each study, including country, method of detection, sample pretreatment, variety of wine (red, white/sparkling, rose or dessert), type of grapes (e.g., Malbec, Feteasca Neagra (FN), CS), the concentration of melatonin in wines and the standard deviation error, were extracted independently using a data collection form by all investigators. Any disagreements were resolved through discussion between investigators.
# Design and Test of Vertical Axis Rotating Cutters for Cutting Corn Roots and Crown
###### Abstract  
In this study, the bionic cutter and the multi-curve cutter were designed for cutting crowns and roots, respectively. Two types of cutters were integrated into the device. This integration aims to address the issues of the poor effect of cutting the root-crown, the high disturbance rate of the soil, and the high power consumption of the device. The cutters for cutting crowns imitating the outline and action of a cat's claw were designed based on reverse engineering technology. The multi-curve cutters for cutting roots were designed based on the distribution characteristics of roots in different soil layers. The discrete element method (DEM) was employed to simulate the process of cutting the root-crown. The accuracy of the DEM simulation result was verified by comparing it with the field test result. The result showed the device could cut the root-crown efficiently, which facilitated the decomposition of the root-crown into organic matter. While minimizing soil disturbance and power consumption, this design effectively maintained soil moisture retention, reduced erosion, and created favorable conditions for subsequent crop growth. The qualified rate of root-crown length, the rate of soil disturbance, and the power consumption of the device were significantly affected by the forward speed of the device and the rotational speed of the cutter shaft. The qualified rate of root-crown length, the rate of soil disturbance, and the power consumption of the device would be increased with the increase in the rotational speed of the cutter shaft. With the increase in the forward speed of the device, the rate of soil disturbance and the power consumption of the device were also increased, but the qualified rate of root-crown length was decreased. To minimize the rate of soil disturbance and the power consumption of the device while meeting the national standard for the qualified rate of root-crown length, the optimal operating conditions were that the forward speed of the device was 0.71 m\(\cdot\)s\({}^{-1}\) and the rotational speed of the cutter shaft was 380 r\(\cdot\)min\({}^{-1}\). At this time, the qualified rate of root-crown length was 90.54%, the rate of soil disturbance was 18.56%, and the power consumption of the device was 3.835 kW. This study provides technical support for designing the device for cutting the root-crown, and, more importantly, offers a sustainable root-crown management solution that addresses the key challenge in the modern conservation tillage system, effectively balancing root-crown cutting efficiency with soil health preservation.  
corn root-crown; bionic cutter; multi-curve cutter; DEM method; full-factorial test +
Footnote †: journal: Applic
# Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets
## 5 Image Dehazing Method Based on Full Polarization Information Inversion and Image Fusion
### Basic Principle of Image Dehazing Based on Full Polarization Information Inversion
The image processing flowchart of the dehazing method based on full polarization information inversion and image fusion proposed in our paper is shown in \[I^{target}(x,y)=S^{target}_{0\_{lp}}(x,y)+S^{target}_{0\_{inp}}(x,y) \tag{10}\]1 The preprocessed polarization image and light intensity image are used as inputs for the entire image processing; the dehazing polarization fusion enhancement image is obtained in the final. The calculation methods for each parameter and image acquisition methods are detailed in the following paper.
## 2 Related Study
### Equipment Process Degradation Level  
The algorithm employed was an AE, which is a technique based on unsupervised learning. This approach involves computing the root mean square error (RMSE) by comparing the reconstructed output values of the model with the numerical input values. The calculated RMSE serves as an indicator of the equipment process degradation level, where smaller and larger values indicate healthier and poorer states, respectively [7; 8].
# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 5 Experiment Analysis
### Experimental Setting  
The model proposed in this study is based on deep neural networks, and there are many well-known hyperparameters. However, in training the model, is mainly performed on parameters including batch size (Batch_size), training epochs (Training_Epoch), learning rate (Learning_rate), graph embedding size (Ebedding), number of attention heads, main task weights (\(\lambda_{min}\)), Interactive traffic threshold for origin and destination station (set \(\theta_{out}=\theta_{in}\) in the experimental process of this section to simplify the parameters).  
During the training process, an early stopping mechanism (with a patience parameter of 50) was also used, and a stochastic gradient descent (SGD) optimizer was selected. The iteration decay rate was set to 0.95, and the loss function was squared loss. Firstly, the size of Batch_2 will affect the degree of gradient descent and convergence speed of the model. Increasing it will reduce the number of epochs and improve the training speed of the model, but it will consume more computing resources, resulting in a longer training time for the model. Too small a model and difficult to converge, set Batch_Size to 64. Secondly, in terms of training_epochs, each epoch will update the model parameters based on the loss function of gradient descent. As the epoch increases, the model fitting effect gradually changes from underfitting to overfitting. The appropriate epoch value makes the model converge and the performance evaluation indicators tend to be stable. Therefore, the experiment sets epoch = 80. In terms of learning rate (Learning_rate), the highest usage frequency in deep learning is set to 0.001. In terms of Ebedding size, this chapter considers four specifications of networks [32; 64; 128; 256]. After multiple experiments, it was found that when Ebedding = 128, the RMSE value of the model is the smallest, as shown in Figure 7. The number of GAT heads was compared experimentally according to [1; 2; 4; 8]. After multiple experiments, it was found that when the number of GAT heads was set to 4, the performance indicator RMSE was the smallest. During the experiment of learning weight parameters for the main task, multiple experiments were conducted with [1/3, 1/2, 2/3, 3/4, 1]. It was found that when the main task learning weight was set to 0.5, the RMSE value was the lowest. In terms of traffic threshold parameters in subtask learning, the range was set to [65%, 70%, 75%, 80%, 85%, 90%] for multiple experiments. It was found that as the traffic threshold increased, the performance indicator RMSE value of the model gradually decreased first and then gradually increased, reaching the lowest RMSE value at 80%, and the model had better representation ability. Hyperparameter impact analysis as shown in Figure 11.