esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 4 Proposed System
### Communication Subsystem  
The communication network of the proposed system is composed of a small number of aircrafts. In order to simplify the network architecture, the scanning strategy adopted without area subdivision is relatively simple and somehow sacrifices the efficiency of the scanning process, which could result in longer scanning time for a fixed area due to the overlapping approach used in image capture. However, this practice leads to improved security while sweeping the area. The UAVs have a coordinated flight course, running similar routes at a constant speed, as it is possible to determine future points and predict the network course. Thus, the proposed scanning strategy allows setting a flight plan that minimizes the distance between network nodes to avoid signal loss problems and the use of optimized end-to-end delivery techniques with a unicast packet.  
Bearing this in mind, two types of communication protocols are used, that is, U2U and U2S. The U2U protocol is capable of performing the communication between the UAVs that compose the aircraft squadron in flight, thus allowing for the collaborative work. The U2S protocol is responsible for ensuring data exchange between the ground BS and the UAVs network.
# Effects of Microbiota on the Soiling Process of PV Modules in Arid Zones of the Atacama Desert
## 2 Materials and Methods
### Microbiological Characterization of soiling
Sequencing using Nanopore technology was performed on the GridION Mk platform by utilizing the MinKNOW software (v22.12.15) and a FLO-MIN106 flow cell (R9.4) for a period of 6 h. For the base calling and demultiplexing of the reads in Fast5 format, the Guppy program (v6.4.6) was used. After the base calling from the fastq files, sequencing adapters and indexes were identified and removed using Porechop ([[https://github.com/rrwick/Porechop](https://github.com/rrwick/Porechop)]([https://github.com/rrwick/Porechop](https://github.com/rrwick/Porechop)), accessed on 27 July 2023). Data post-processing was performed using EMU software (version 5.6) [18], which was specifically designed and optimized for analyzing sequencing data with higher error rates, such as those from ONT sequencing. This program utilizes an expectation-maximization (EM) algorithm to accurately profile the microbiota, which reduces false negatives. Subsequently, the filtered reads were annotated against a 16S rRNA database using a cutoff abundance of 0.01. The taxes below this threshold were excluded from subsequent analyses. Finally, an operational taxonomic unit (OTU) table was generated to show the relative abundance of each species in the samples. Data post-processing was conducted using the Microbiome Analyst platform [19]. The phylogenetic tree was built using the full 16S sequences in the Geneious tree builder algorithm from the Geneious prime software (v. 2022.1.1). For this, a global alignment with free end gaps was selected as the alignment method, with a 65% similarity (5.0\(/-\)4.0) cost matrix. To build the tree, a Jukes-Cantor model was used to estimate the genetic distances, and neighbor-joining was used as a tree-building method with no outgroup. A bootstrap resampling method was used with 100 replicates. The branch labels on the tree represent the consensus support (%).
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 5 Conclusions  
The existing object detection methods have a high number of parameters and computations, which is not favorable for practical application in the agricultural environment. Therefore, this study proposes a lightweight crop pest detection method based on convolutional neural networks, named YOLOLite-CSG.  
The baseline model of the method proposed in this study is YOLOLite, built by optimizing the YOLOv3 structure. To optimize the performance of the method in termsof precision and the number of parameters and computations, this study introduces k-means++ for YOLOLite to improve the quality of the prior box, and embeds lightweight sandglass blocks to further reduce the parameters and computations and to extract more spatial information. In addition, coordinate attention was introduced to guide the feature extraction process.  
The experimental results on the CP15 dataset show that the crop pest detection precision of the method proposed in this study is higher than that of existing object detection methods, and the number of parameters and computations are lower, which is better for practical applications. Future research should focus on exploring more methods to optimize the actual performance of crop pest detection methods, so as to migrate the method proposed in this study to UAVs or other embedded devices in the agricultural environment for efficient crop pest detection.  
Conceptualization, Z.C. and R.Q.; methodology, Z.C. and R.H.; software, R.H.; validation, R.H., W.D. and J.Z.; formal analysis, R.H.; investigation, R.Q.; resources, R.Q. and R.H.; data curation, W.D.; writing--original draft preparation, R.H.; writing--review and editing, Z.C. and R.Q.; visualization, M.L.; supervision, Z.C.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the authors.  
We would like to especially appreciate the support and help of the assistant editor, [PERSON], during the process of submitting the paper.  
The authors declare no conflict of interest.
## 3 Model Inversion and Discussion
### The Influence of \(P\) in Model Objective Function on Inversion Results  
Figure 7 is the cross-section through the x-axis composed of two 2-D magnetic dyke models.  
Set the magnetic susceptibility \(\chi=0.01\) SI and \(0.02\) SI. When \(p=1.0\), \(1.1\), \(1.3\), \(1.5\), \(1.7\), \(2\), we used Algorithm 2 to perform smooth inversion for Model 3 (Figure 8). The inversion results are shown in Figure 9. From the inversion results, we can observe that when \(p\) was large, the restored magnetic susceptibility was lower than the real value. The smaller \(p\) was, the closer the inversion result was to the real value. However, on the other hand, the larger \(p\) was, the larger the range of abnormal bodies distribution will be compared with the real model. Moreover, there will be interference between different anomalies to produce false anomalies. Generally speaking, \(p\in[1,\ 1.5]\) was a suitable choice without the constraint of the upper bound of magnetic susceptibility. In Figure 10, due to the setting of the termination conditions, the field values obtained by inversion with each \(p\)-norm can fit the observed data. In summary, to balance the smoothness of the model and maintain the edge of the restored model, we set \(p=1.2\) in the subsequent work.  
Figure 8: Susceptibility model 3 and its \(\Delta\)T field. (**a**) Spatial distribution of magnetic susceptibility of model 3, (**b**) the \(\Delta\)T of model 3.  
Figure 7: The cross-section through the x-axis of the model.  
Figure 9: The inversion results of model 3 by using different \(p\) value. (**a**) \(p=1.0\), (**b**) \(p=1.1\), (**c**) \(p=1.3\), (**d**) \(p=1.5\), (**e**) \(p=1.7\), (**f**) \(p=2\).
# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 2 Proposed Methodology
### Explainable AI  
In high-stakes areas like breast cancer detection, where open ness and trust are crucial, XAI has become an essential feature of medical picture analysis. Despite their impressive accuracy, traditional DL models frequently operate as \"black boxes,\" providing no insight into the decision-making process. In therapeutic settings, where comprehension of the reasoning behind a prediction is just as crucial as the forecast itself, this opacity presents a substantial obstacle to adoption. Grad-CAM is one gradient-based visualization approach that has been created to improve model interpretability in order to meet this difficulty. By emphasizing the prominent areas of an image that have the most impact on the model's prediction, Grad-CAM offers visual explanations that promote diagnostic validation and foster clinician trust [65]. Calculating the gradient of the class score \(y^{c}\) (i.e., the output of a target class \(c\)) in relation to the feature mappings Ak of a convolutional layer is how Grad CAM operates mathematically. For every feature map \(k\), the important weight \(\alpha_{k}^{c}\) is determined as follows:  
\[\alpha_{k}^{c}=\frac{1}{Z}\sum_{i}\sum_{j}\frac{\partial y^{c}}{\partial A_{ij} ^{k}} \tag{12}\]  
The gradient of the class score with regard to the activation at spatial position \((i,j)\) is \(\partial y^{c}\), \(\partial A_{ij}^{k}\), where \(Z\) is the total number of pixels in the feature map \(A^{k}\). After a weighted combination of the feature maps, the final Grad-CAM heatmap \(L_{Grad-CAM}^{c}\) is produced.  
Only the features that have a positive impact on the class of interest are kept after a ReLU activation.  
\[L_{Grad-CAM}^{c}=\text{ReLU}\left(\sum_{k}a_{k}^{c}A^{k}\right) \tag{13}\]  
The regions that most influence the model's choice are graphically shown by superimposing this heatmap, which has been up sampled to the original picture quality. For this investigation, the outputs of the improved MobileNet models (both FLA-enhanced and DOA-enhanced) were subjected to Grad-CAM on three datasets: the fusion dataset, Breast Ultrasound, and MIAS. This allowed us to visualize how the model interprets various pathological features such as masses and calcifications, thereby validating the clinical relevance of predictions. The integration of Grad-CAM not only augments diagnostic performance but also aligns with the growing demand for ethically responsible and explainable AI in healthcare applications [66; 67; 68].
## 2 Materials and Methods
### The Microbiological Diagnostic Procedure
#### 2.5.3 Next-Generation Sequencing (NGS)  
The amplification of hypervariable regions 2-9 of the 16s rRNA genes was performed with an Ion 16S Metagenomics kit (ThermoFisher, Waltham, MA, US), according to the manufacturer's instructions. A total of 3.5 ng of each isolated DNA sample was used. After amplification, purification, and joining of amplicons, 150 ng of each of the pooled amplicons were used to construct barcoded libraries using an Ion Plus Fragment Library kit (ThermoFisher, Waltham, MA, US), according to the manufacturer's instructions. The prepared libraries were then cleaned of unbound adapters and barcodes. Barcoded library concentrations were quantified using an Ion Universal Library Quantitation kit (ThermoFisher, Waltham, MA, US). Further steps involved PCR emulsion dilution with 10 pM of all samples pooled in 12 \(\upmu\)L, followed by preparation of the basic template for sequencing with the Ion OneTouch(tm) 2 System device and an Ion PGM(tm)  Hi-Q View Kit (ThermoFisher, Waltham, MA, US). Concentration of Ion Sphere beads-ISP (empty and polyclonal ISP washing) was performed with an Ion OneTouch ES kit and the Ion OneTouch 2 instrument (ThermoFisher, Waltham, MA, US), according to the manufacturer's instructions. Sequencing was carried out with an Ion PGM(tm)  Hi-Q View Sequencing Kit on the Ion PGM(tm)  System using the Ion 316(tm)  chip. The analysis of the obtained resultswas conducted using the IonReporter software 5.20 [44; 45; 46; 47]. In the present study, our focus was on nine main periodontal pathobionts: _Aggregatibacter actinomycetemommitans_, _Porplyomonas gingialis_, _Fusobacterium nucleatum_, _Treponema denticola_, _Tannerella forythia_, _Prerotella intermedia_, _Parximonas micra_, _Eubacterium nodatum_, and _Campylobacter rectus_.
## 3 Effects of Storage and Maintenance Modes on Battery Performance
### Effects of Storage SOC on Battery Performance
#### 3.5.1 Effects of Storage SOC on Battery Capacity  
Table 8 demonstrates the charge retention rates and the capacity recovery rates of Ni-MH battery modules, maintained with different storage SOC. We can see that, with the 50% storage SOC, the charge retention rate was 16.4% less with batteries from manufacturer A and 2.2% higher with batteries from manufacturer B, compared to the 30% storage SOC scenario. The average values show that 30% SOC was more conducive to maintaining the charge retention of Ni-MH battery modules. Moreover, these two storage modes had similar effects on the battery capacity recovery rates.
## 4 Experimental Results and Discussion
### Comparison with Existing Methods
Among the 10-fold cross-validation, PL-MSMM achieves the highest accuracy. Its overall diagnostic performance is significantly superior to that of the other models under consideration. In summary, these experimental results show that the proposed PL-MSMM method performed excellently in turnout data fault diagnosis. This provides a feasible fault diagnosis method for practical applications and can achieve efficient fault diagnosis. To enhance the illustration of the fault diagnosis results, Figure 7a-d display the confusion matrix generated by four different models. The confusion matrix provides a visual representation of the relationship between the model's predicted results and the actual labels across various categories, presented in a matrix format. In the confusion matrix, each column represents the predicted categories, while each row corresponds to the true categories of the data. The elements on the main diagonal of the matrix indicate the number of correct classifications for each respective category. \begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Hyperparameters** & **Training Range** \\ \hline CAE & \(\{r,b,e\}\) & \(\{[0.0001,1],[32,256],[10,1000]\}\) \\ CNN & \(\{r,b,e\}\) & \(\{[0.0001,1],[32,256],[10,500]\}\) \\ MSMM & \(\{C,\tau\}\) & \(\{[0.001,1000],[0,10]\}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter setting and tune range of the models used in the comparison. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Testing**} & \multicolumn{4}{c}{**Training**} \\ \hline
**Method** & **Accuracy (\%)** & **Precision (\%)** & **F1-Score (\%)** & **Accuracy (\%)** & **Precision (\%)** & **F1-Score (\%)** \\ \hline CAE & 97.02 & 97.11 & 97.07 & 97.87 & 97.96 & 97.92 \\ CNN & 97.56 & 97.65 & 97.60 & 97.97 & 98.06 & 98.01 \\ MSMM & 98.11 & 98.17 & 98.14 & 98.07 & 98.12 & 98.09 \\ PL-MSMM & 98.67 & 98.70 & 98.68 & 98.74 & 98.77 & 98.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average testing and training accuracy, precision, and F1-score for different methods. Figure 6: \(Cont\). Figure 6: The accuracy under each fold: (**a**) CAE, (**b**) CNN, (**c**) MSMM, and (**d**) PL-MSMM. Figure 7: Confusion matrix from 10-fold cross-validation: (**a**) CAE, (**b**) CNN, (**c**) MSMM, and (**d**) PL-MSMM.
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 4 Adaptive Kalman Filter
### ML-KF Loop  
The KF needs to predict the information at the next moment using the estimated phase and frequency. However, data bit reversals will lead to 180-degree phase ambiguity. KF will output an incorrect result in this case. Therefore, data bits must be stripped off or estimated if KF is used. As illustrated above, the cost function value will change its symbol if the data reversal happens. This can be used to judge the data reversals. In the update epoch k, the cost function value at the initial point (\(L(\theta_{0})\)) is calculated first. If \(L(\theta_{0})>0\), the data bit is the same as the last bit and the phase does not need to be corrected. On the contrary, if \(L(\theta_{0})<0\), the data reversal happens and the phase needs to be corrected by adding \(\pi\). In this way, the data bits can be estimated.  
The block diagram of the ML-KF loop is shown in Figure 6. The integration results are sent to the MLE estimator. The frequency and phase is estimated first by LM iteration processing. Then, \(\hat{A}\) and \(\sigma^{2}\) are estimated by (8) and (28), respectively. They are used to update the observation noise covariance matrix \(R_{k}\) and estimate \(C/N_{0}\) by (27) and (29), respectively. \(\hat{f}\) is corrected by adding \(\pi\) if \(L(\theta_{0})<0\). All these information is set to KF to get fair estimates of phase and frequency. Finally, the output of KF is used to adjust the frequency and phase of the carrier NCO.  
In terms of the structure of the receiver, the ML-KF loop can be used to replace the traditional PLL or FLL absolutely. This is because they use the same input (the correlation outputs of the prompt branch) and output the same results (Doppler frequency and phase residuals) to adjust the carrier NCO. Therefore, a standard DLL architecture is compatible with the proposed method. Actually, they can be seen as two independent loops. Therefore, the perfect wipe off of the PRN can be assumed to avoid the influence of irrelevant factors.
## 4 Results and Discussion
### Performance on House A  
In House A, two residents live together, and the activities of one resident are classified based on the active presence of the other. Table 4 displays the performance of ten classifiers on the ARAS dataset using five-fold cross-validation. The results indicate that the Gated Recurrent Unit (GRU) model performed the best, followed by the random forest model, while the Gaussian naive Bayes model delivered the lowest performance when evaluated using the metrics. The performance of other classifiers listed in Table 4 is also not poor, comparatively.  
We evaluated the performance of various models using the House A data in different splitting ratios (80:20, 70:30, and 60:40) for training and testing. The results of the applied models are shown in Figure 4. The figure indicates that, for the most part, the performance of the same classifiers did not vary significantly across different splitting ratios. Figure 4c-e show that the Gaussian naive Bayes (GNB) and KNN classifiers performed differently in terms of the recall, macro F-1, and weighted F-1 scores for different splitting ratios. The performance of the other classifiers remained consistent across different splitting ratios in regard to most of the metrics, suggesting that splitting ratios do not significantly impact performance. However, there was a significant difference in performance among the classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F-1 Score** & **Macro Average F-1** & **Weighted Average F-1** \\ \hline Decision Tree & 1.0 & 0.99 & 0.97 & 0.98 & 0.99 & 1.0 \\ Gaussian Naive Bayes + & 0.96 & 0.33 & 0.96 & 0.49 & 0.73 & 0.97 \\ Random Forest **1** & 1.0 & 0.99 & 0.98 & 0.99 & 0.99 & 1.0 \\ LGBM & 1.0 & 0.99 & 0.96 & 0.98 & 0.99 & 1.0 \\ Support Vector Machine & 1.0 & 0.99 & 0.96 & 0.98 & 0.97 & 0.99 \\ Logistic Regression & 1.0 & 0.94 & 0.90 & 0.92 & 0.96 & 1.0 \\ k-Nearest Neighbors & 0.99 & 0.98 & 0.84 & 0.90 & 0.89 & 0.98 \\ Gradient Boosting Machine & 1.0 & 0.98 & 0.87 & 0.92 & 0.96 & 1.0 \\ LSTM Technique & 1.0 & 0.99 & 0.97 & 0.98 & 0.98 & 1.0 \\ GRU Technique * & 1.0 & 0.99 & 0.99 & 0.99 & 0.99 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative performance of the ten applied machine learning and deep learning classifiers on the House A data from the ARAS multi-resident dataset with 5-fold cross-validation. In the table, an asterisk (*) indicates the overall best performance, (**) indicates the second-best performance, and (+) indicates the worst performance.  
Figure 4: _Cont_.
## 3 Modified CNN Based on Dropout and the SGD Optimizer
### Activation Function Based on Leaky ReLU  
A traditional CNN often uses nonlinear functions, such as Tanh [21], ReLU [22], and Sigmoid [4], shown in Figure 5, to become an activation function.  
The sigmoid function, which maps a real number input to the [0, 1] scope, encounters two problems as an activation function. (1) Gradient saturation: When the function activation value approaches the extremum 0 or 1, the gradient of the function tends to become 0. For the cost of the reverse propagation of the \(l\) layer neurons \(\delta^{(l)}\), the formula is \(\delta^{(l)}=(w^{(l)})^{T}\delta^{(l+1)}*f^{\prime}(x_{j}^{l})\), where \((w^{(l)})^{T}\) represents the weight of the \(l\) neurons in the \(l\) layer. When the neuron reverse propagation cost in the \(l+1\) layer \(\delta^{(l+1)}\) approaches 0, the computed gradient also approaches 0 to achieve the goal of not adjusting the update parameters. (2) Constantly positive weight: The average value of the function output is not 0, which causes the neuron layer to yield the signal input of the nonzero mean. This activity makes the data of the input neuron positive. Consequently, the weight becomes positive. These problems result in a slow parameter convergence and affect training efficiency and the model recognition effect. The tanh function can map a real input into the [\(-\)1, 1] range but it is actually a variant of the sigmoid function, i.e., \(\tanh(x)=2 sigmoid(2x)-1\). Moreover, the tanh function also exhibits the gradient saturation problem.  
Figure 5: Nonlinear function. (**a**) Sigmoid function; (**b**) Tanh function; (**c**) ReLU function; (**d**) Leaky ReLU function.  
The ReLU function \(f(x)=\max(0,\ x)\) (\(x\in(0,\ +\infty)\)) (Figure 5c) has the following features. (1) Unsaturated gradient: The formula for the gradient is \(l\{x>0\}\). Therefore, the problem of gradient dispersion in the reverse propagation process is alleviated, and the parameters in the first layer of the neural network can be updated rapidly. (2) Low computational complexity: The ReLU function sets the thresholds, i.e., if, \(x<0\) then \(f(x)=0\); if \(x>0\), then \(f(x)=x\). Unfortunately, ReLU units can be fragile during training and can \"die\" [23]. A \"dead\" ReLU always outputs the same value.  
The Leaky ReLUs activation function [24] with a small positive gradient for negative inputs was designed to solve the \"dead\" problem of ReLU. The formula of the Leaky ReLU is \(f=max(0.01x,x)\), which has all the advantages of the ReLU activation function but without the \"dead\" problem.  
This study uses the Leaky ReLU function as the activation function because of its advantages to solve the gradient saturation problem and improve convergence speed.