esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 5 Application Layer
### Data Aggregation Techniques  
Data aggregation is a process of aggregating the sensor data using aggregation approaches. The algorithm uses the sensor data from the sensor node. It then aggregates the data by using some aggregation algorithms such as the centralized approach, LEACH (low-energy adaptive clustering hierarchy) [51], TAG (Tiny Aggregation) [52], etc. These aggregated data are transferred to the sink node by selecting the efficient path.  
Centralized Approach: This is an address-centric approach where each node sends data to a central node via the shortest possible route using a multi-hop wireless protocol [53]. The sensor nodes simply send the data packets to a leader, which is the powerful node. The leader aggregates the data, which can be queried. Each intermediate node has to send the data packets addressed to the leader from the child nodes. In such a scenario, many messages have to be transmitted where the total number of messages equals the sum of external path lengths for each node.  
In-Network Aggregation: In-network aggregation is the global process of gathering and routing information through a multi-hop network. It processes data at intermediate nodes to reduce energy consumption, hence, increasing network lifetime [54]. In-Network aggregation, there are two approaches. First, with size reduction, data sets are combined and compressed to reduce the data packet's length. In the second, with mergingdata packets, received data from diverse neighbors are merged into one data packet to be transmitted to the sink.  
Tree-Based approach: This approach is the construction of an aggregation tree with a minimum spanning tree. In this tree, data are forwarded to the sink where data sources are the WSN nodes, considered as leaves [55]. In this tree, each node relays data to its parent. The data flow in the tree starts from leaves (nodes) up to the sink where parent nodes do the data aggregation.  
Cluster-Based Approach: In this approach, the WSN network is divided into clusters [56]. In each cluster, a cluster head is elected to aggregate data from WSN nodes of the cluster, and cluster heads establish a network to route the aggregated data to the sink.  
COUGAR Approach: This approach suggests a query layer that supports data aggregation based on queries [57]. In this approach, users can issue queries to the WSN where the query layer processes user queries and generates a cost-effective query plan. The main parameter that is considered by this approach is the communication cost, which includes the cost of routing and data aggregation over the WSN.
## 2 Theory  
### The Double Regularization Conductivity Imaging Method  
The central equation used in the gradient-based electrical conductivity imaging method is as follows [36]:  
\[\
abla\varphi^{tr}\cdot\
abla\rho+\
abla^{2}\varphi^{tr}\cdot\rho-2\omega\mu_{ 0}=0 \tag{1}\]  
where \(\rho=1/\sigma\), \(\mu_{0}\) is the free space permeability, \(\varphi^{tr}\) is the transceive phase, and \(\omega\) is the Larmor frequency. Several assumptions are applied to derive Equation (1), which assumes \(\
abla\big{|}B_{1}^{+}\big{|}=0,\
abla\big{|}B_{1}^{-}\big{|}=0\), \(\sigma^{2}\gg(\omega\varepsilon)^{2}\) and neglects spatial derivatives of \(B_{z}\). With the first-order central differential scheme, the discretized Equation (1) in an image matrix \(M\times N\times L\) can be written as [37]:  
\[\begin{array}{c}\Big{(}\frac{\rho_{i,j+1}-\rho_{i,j-1}k}{2\Delta x}\Big{)} \cdot\frac{\partial\varphi^{tr}}{\partial x}+\Big{(}\frac{\rho_{i+1,j}-\rho_{ i,j-1}k}{2\Delta y}\Big{)}\cdot\frac{\partial\varphi^{tr}}{\partial y}+\Big{(} \frac{\rho_{i,j+1}-\rho_{i,j,j-1}}{2\Delta z}\Big{)}\cdot\frac{\partial\varphi ^{tr}}{\partial z}\\ \hskip 142.26378 pt+\rho_{i,j}k\cdot\Big{(}\frac{\partial^{2}\varphi^{tr}}{ \partial x^{2}}+\frac{\partial^{2}\varphi^{tr}}{\partial y^{2}}+\frac{ \partial^{2}\varphi^{tr}}{\partial z^{2}}\Big{)}=2\omega\mu_{0},\end{array} \tag{2}\]  
where \(i=1,2\cdots N\), \(j=1,2\cdots M\), \(k=1,2\cdots L\), \(\Delta x\), \(\Delta y\), \(\Delta z\) are the spatial resolution along the \(x\), \(y\), \(z\) directions, respectively. Equation (2) is applied to each point of the imaging region, and then an inverse problem is obtained:  
\[A\rho=b, \tag{3}\]  
where \(A\) is the sparse matrix with size \(M*N*L\times M*N*L\), \(b=2\omega\mu_{0}\). According to [PERSON] [37], Equation (3) is constructed into an optimization problem with additional double regularization terms of total variation (TV) and wavelet transform. This method is abbreviated as \"DRM\" in this study. Thus, the corresponding optimization problem is written as:  
\[\rho=\text{argmin}\frac{1}{2}\|A\rho-b\|_{2}^{2}+\lambda_{1}\|\rho\|_{tr}+ \lambda_{2}\|W\rho\|_{1}, \tag{4}\]  
where \(W\) is a wavelet transform, \(\|\rho\|_{tr}=\sum\limits_{i,j}\sqrt{\big{(}\
abla_{x}\rho_{i,j}\big{)}^{2}+ \big{(}\
abla_{y}\rho_{i,j}\big{)}^{2}}\). \(\lambda_{1}\) and \(\lambda_{2}\) are the parameters of TV and wavelet transform, respectively. This problem is solved using the Split Bregman method [40]. However, ADMM was used in this study and is introduced in detail in a later section.
# Feature Mining: A Novel Training Strategy for Convolutional Neural Network
## 4 Results and Discussion
### Image Classification
#### 4.1.1 CIFAR Classification
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **ResNet-56** & **ResNet-110** & **ResNet-164** & **DenseNet-100** & **WRN-28-10** & **PyramidNet-110** \\ \hline Baseline & 73.71 \(\pm\) 0.28 & 74.71 \(\pm\) 0.23 & 74.98 \(\pm\) 0.23 & 77.25 \(\pm\) 0.20 & 79.35 \(\pm\) 0.20 & 81.54 \(\pm\) 0.31 \\ Dropout & 73.81 \(\pm\) 0.27 & 74.69 \(\pm\) 0.33 & 75.15 \(\pm\) 0.11 & 77.45 \(\pm\) 0.12 & 79.21 \(\pm\) 0.15 & 81.31 \(\pm\) 0.24 \\ SpatialDropout & 74.38 \(\pm\) 0.17 & 74.76 \(\pm\) 0.12 & 75.34 \(\pm\) 0.09 & 77.97 \(\pm\) 0.16 & 79.63 \(\pm\) 0.09 & 81.60 \(\pm\) 0.17 \\ DropBlock & 73.92 \(\pm\) 0.10 & 74.92 \(\pm\) 0.07 & 75.51 \(\pm\) 0.14 & 77.33 \(\pm\) 0.09 & 79.44 \(\pm\) 0.24 & 81.57 \(\pm\) 0.21 \\ \hline FM (1 layer) & **74.55 \(\pm\) 0.11** & **75.79 \(\pm\) 0.08** & **76.51 \(\pm\) 0.13** & **78.24 \(\pm\) 0.12** & **79.75 \(\pm\) 0.16** & **82.13 \(\pm\) 0.25** \\ FM (2 layers) & **75.35 \(\pm\) 0.07** & **76.90 \(\pm\) 0.13** & **78.31 \(\pm\) 0.21** & **78.78 \(\pm\) 0.09** & **80.90 \(\pm\) 0.22** & **82.87 \(\pm\) 0.15** \\ \hline \(p\)-Value & 0.018003 & 0.000634 & 0.000193 & 0.039 & 0.00495 & 0.01008 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Validation accuracy (%) on the CIFAR100 dataset using the CNN architectures of ResNet-56, ResNet-110, ResNet-164, DenseNet-100, WRN-28-10, and PyramidNet-110-270 (top accuracy in bold). FM denotes Feature Mining. The \(p\)-value is the statistical comparison between FM (2 layers) and the best-performing Dropout method within the independent-samples \(t\) test. (independent-samples \(t\) test, \(p\)-value = 0.000322 < 0.001) of CS-KD. This shows that Feature Mining does not conflict with these methods; even in the case of improving the performance of the front layer (BYOT) or reducing the gap within the class (CS-KD), the performance can still be improved by strengthening the learning of the local features. **Comparison with the data augmentation method:** We investigated orthogonal usage with other types of regularization methods such as Mixup [12]. Mixup utilizes convex combinations of input pairs and corresponding label pairs for training. We combined our method with Mixup regularization by combining the input and label processed by Mixup with ResNet-110, which uses Feature Mining on the last two layers. Table 3 shows the effectiveness of our method combined with Mixup regularization. Interestingly, this simple idea significantly improved the performance of the classification task. In particular, our method improved the top-1 accuracy of Mixup regularization from 79.22% to 81.04% (independent-samples \(t\) test, \(p\)-value = 0.001 < 0.01) on ResNet-164, which proves our method is also compatible with the data augmentation method.
# Applying Deep Learning to Medical Imaging: A Review
## 6 Ethical Considerations for Using DL Methods
### Explainability and Interpretability  
The black-box nature of DL models is another ethical concern in medical imaging, as it can make it difficult to understand how they arrive at their predictions, and to identify potential errors or biases [170]. This lack of transparency and interpretability can limit the usefulness of DL in clinical settings, where explainability and interpretability are critical for building trust and confidence among healthcare providers and patients. To address these issues, researchers have proposed various methods for enhancing the explainability and interpretability of DL models, such as attention mechanisms, saliency maps, and counterfactual explanations [44]. These methods can help to improve the transparency and interpretability of DL models and facilitate their integration into clinical workflows.
## 3 Results
### Intermediate Phases
#### 3.2.2 Zn-Blende-CoO Polymorph  
Following the previous results, a fresh commercial Co(CH3 COO)2.4H2O sample was heated in the X-ray chamber up to 332 \({}^{\circ}\)C under a nitrogen atmosphere. At this temperature, CoO with Zn-blende structure seemed to be stabilized as single phase (the 2D plot depicted in Figure 7a shows the thermal evolution of the sample). From 332 \({}^{\circ}\)C, the sample was quenched to RT. The XRD pattern of the obtained green product, shown in Figure 7b, matches that of PDF-00-042-1300, which corresponds to the cubic CoO.  
Figure 7: (a) X-ray powder diffraction thermal evolution of commercial cobalt (II) acetate heated under N2 atmosphere from 300 to 332 \({}^{\circ}\)C in 2 \({}^{\circ}\)C steps. The experiment was stopped as soon as only Zn-blende-type structure for CoO was observed; (b) XRD pattern of the final product obtained at 332 \({}^{\circ}\)C, showing the two characteristics peaks of Zn-blende-type CoO (PDF-00-042-1300).  
Under the experimental conditions described above, the wurtzite polymorph was not stabilized. As we mentioned in the introduction, a wurtzite polymorph nucleates from Zn-blende and the size of the formed primary nuclei strongly influences the growth of the wurtzite polymorph [13; 23]. If these nuclei are larger, the obtained final phase is the Zn-blende; if they are small, however, the wurtzite polymorph can be stabilized [14; 15]. Pure wurtzite CoO has been obtained from the thermal decomposition of cobalt acetyl-acetonate in refluxing benzyl ether [21]. Moreover, [PERSON] et al., [22] studied the main parameters determining the growth of either hexagonal or cubic polymorphs (solvents or surfactants, heating rate, reflux temperature or time...). According to the thermal decomposition of the acetate, the nucleus of Zn-blende CoO must have been large enough to prevent its transformation into the wurtzite polymorph that was never obtained as a single phase. The Zn-blende/wurtzite ratio was strongly dependent on the conditions in which the experience was carried out (see Section III.3. in Supporting Information).
# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 2 Preliminaries and Problem Definition
### Dnn  
The multilayer perceptron (MLP) is a class of artificial neural network (ANN) that consists of at least three layers (input, hidden, and output layers) of nodes [49]. Except for input nodes, each node is a neuron that uses an activation function (i.e., step, sigmoid, _tanh_, ReLU: Rectified Linear Unit), which determines whether to output the received data to the next layer. The MLP is a machine learning solution and has been applied to various applications such as speech recognition, image recognition, and machine translation software. However, the MLP faces the problems of vanishing gradient, the reasoning of new facts, and the inability to process new data. These problems were solved by the development of deep learning [50].  
DNNs have recently become a hot topic for image processing technology, reduced computational cost due to graphics processing unit development, and new machine learning techniques. DNNs have been actively studied to help solve the problem of complex and nonlinear functions. In addition, DNNs exhibit excellent learning performance on unclassified data; thus, they are used in various fields, such as artificial intelligence, graphic modeling, optimization, pattern recognition, and signal processing [51; 52; 53].  
DNNs have a structure in which several hidden layers are added to an MLP, which has only one or two hidden layers. Figure 3 shows the DNN structure. The left side of the DNN has an input layer. The center has \(L-1\) hidden layers. The right side has an output layer. Hence, there are a total of \(L\) layers. \(X=\left(1,x_{1},x_{2},\ldots,x_{d}\right)^{T}\) is input to the input layer, and the output layer outputs \(\mathsf{o}=\left(0_{1},0_{2},\ldots,0_{c}\right)^{T}\). Therefore, there are \(d\) nodes, except for the bias node in the input layer and \(c\) nodes in the output layer. The number of nodes, excluding the bias node, in layer \(l\) is denoted as \(n_{j}\). The 0 th layer corresponds to the input layer, and \(n_{0}\) is equal to \(d\). The \(L\)th layer corresponds to the output layer, and \(n_{l}\) is equal to \(c\)  
Figure 2: An average weekly power consumption pattern company T for three years.  
The weights connecting layers \(l-1\) and \(l\) are computed as \((n_{l-1}+1)*n_{l}\). The total weight may be represented by the matrix \(U^{l}\) in Equation (1).  
\[U^{l}=\left(\begin{array}{cccc}u_{10}^{l}&u_{11}^{l}&\dots&u_{1n_{l-1}}^{l}\\ u_{20}^{l}&u_{21}^{l}&\dots&u_{2n_{l-1}}^{l}\\ \vdots&\vdots&\ddots&\vdots\\ u_{n0}^{l}&u_{n_{l}1}^{l}&\dots&u_{n_{l}n_{l}-1}^{l}\end{array}\right)l=1,2, \dots,L \tag{1}\]
## 2 Numerical Modeling
### Governing Equations and Solution Strategy
Specific dissipation rate equation:  
\[\frac{\partial(\rho u_{i}\omega)}{\partial x_{i}}=\frac{\partial}{\partial x_{j}} \left(\Gamma_{\omega}\frac{\partial\omega}{\partial x_{j}}\right)+G_{\omega}- \Upsilon_{\omega}+D_{\omega} \tag{11}\]  
where \(k\) is the turbulent kinetic energy; \(\omega\) is the specific dissipation; \(\Gamma_{k}=\mu+u_{t}/\sigma_{k}\) and \(\Gamma_{\omega}=\mu+u_{t}/\sigma_{\omega}\) are the effective diffusivities of \(k\) and \(\omega\), respectively; \(\sigma_{k}\) and \(\sigma_{\omega}\) are the inverse of the turbulent Prandtl numbers for \(k\) and \(\omega\), respectively; \(G_{k}\) is the generation of turbulent kinetic energy caused by the mean velocity gradient; \(G_{\omega}\) is the generation of the specific dissipation; \(Y_{k}\) and \(Y_{\omega}\) are the dissipations of \(k\) and \(\omega\) due to turbulence, respectively; \(D_{\omega}\) is the cross-diffusion term. For the solid domain, the temperature is governed by the heat conduction equation, as follows:  
\[\frac{\partial}{\partial x_{i}}\left(\frac{\lambda_{s}}{\rho_{s}c_{p,s}}\frac {\partial T}{\partial x_{i}}\right)=0 \tag{12}\]  
where \(\lambda_{s}\), \(\rho_{s}\), and \(c_{p,s}\) are the thermal conductivity, density, and specific heat of the solid material, respectively. Thermal stresses can arise due to temperature differences in the cooling channels. For brevity, the fundamental equations related to the linear elasticity problem are not provided here. The governing equations for flow and heat transfer were solved using the commercial software ANSYS Fluent 21.0. The coupling of pressure and velocity was achieved through the implementation of the SIMPLEC (Semi-Implicit Method for Pressure-Linked Equations--Consistent) algorithm. In the spatial discretization module, the Green-Gauss node-based method was employed to solve the gradients, while the pressure was determined using the PRESTO! approach. A second-order upwind scheme was utilized to solve the momentum, turbulent kinetic energy, and specific dissipation rate equations. The calculations were terminated when all the equation residuals fell below \(10^{-5}\), ensuring mass and energy conservation preservation. Additionally, ANSYS Mechanical was employed to determine the deformation and stress distribution for thermal-mechanical problems.
# A Comparative Analysis of Active Learning for Biomedical Text Mining
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BERT** & **HoC** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & F1 & 83.60 & 83.46 & 89.26 & 89.33 & 89.26 & 89.63 \\ \cline{2-7}  & F1 & 85.96 & 82.87 & 84.20 & 84.51 & 84.20 & 78.80 \\ \hline \multirow{3}{*}{KNN} & F1 & 82.81 & 82.22 & 81.96 & 81.56 & 81.96 & 81.86 \\ \cline{2-7}  & F1 & 86.80 & 85.24 & 86.40 & 85.94 & 86.40 & 86.29 \\ \hline \multirow{3}{*}{Random forest} & F1 & 83.69 & 83.43 & 83.69 & 82.40 & 83.69 & 84.17 \\ \cline{2-7}  & F1 & 94.65 & 86.64 & 95.67 & 91.50 & 95.67 & 91.87 \\ \hline \multirow{3}{*}{CatBoost} & F1 & 85.72 & 85.24 & 86.28 & 85.95 & 86.28 & 86.68 \\ \cline{2-7}  & F1 & 88.85 & 88.73 & 88.11 & 88.99 & 88.41 & 88.21 \\ \hline \multirow{3}{*}{Random forest} & Accuracy & 75.89 & 78.93 & 79.43 & 78.25 & 79.90 & 79.43 \\ \cline{2-7}  & F1 & 77.80 & 82.30 & 83.64 & 81.87 & 84.37 & 82.78 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 82.59 & 74.85 & 81.74 & 81.93 & 81.53 & 79.76 \\ \cline{2-7}  & F1 & 89.80 & 76.37 & 87.41 & 88.39 & 87.91 & 84.87 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 81.11 & 81.03 & 81.88 & 82.16 & 82.45 & 81.91 \\ \cline{2-7}  & F1 & 85.85 & 85.14 & 86.59 & 87.80 & 87.79 & 86.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of results with and without AL methods (BERT) for HoC dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BERT** & **ChemProt** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & Accuracy & 79.59 & 79.58 & 78.68 & 79.20 & 79.44 & 79.50 \\ \cline{2-7}  & F1 & 88.63 & 88.63 & 84.80 & 87.00 & 87.41 & 88.01 \\ \hline \multirow{3}{*}{NB} & Accuracy & 69.71 & 67.61 & 67.45 & 67.00 & 60.44 & 55.39 \\ \cline{2-7}  & F1 & 73.09 & 70.10 & 69.60 & 69.15 & 59.19 & 51.79 \\ \hline \multirow{3}{*}{KNN} & Accuracy & 65.92 & 68.42 & 64.70 & 64.12 & 64.73 & 64.69 \\ \cline{2-7}  & F1 & 65.87 & 69.73 & 64.36 & 63.58 & 64.36 & 64.33 \\ \hline \multirow{3}{*}{XGBoost} & Accuracy & 79.57 & 79.44 & 79.44 & 79.38 & 79.40 & 79.35 \\ \cline{2-7}  & F1 & 88.49 & 88.19 & 88.01 & 87.61 & 87.84 & 87.68 \\ \hline \multirow{3}{*}{Random forest} & Accuracy & 76.62 & 76.11 & 76.73 & 76.18 & 77.01 & 76.26 \\ \cline{2-7}  & F1 & 82.88 & 82.31 & 83.21 & 82.18 & 83.62 & 82.27 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 79.17 & 68.12 & 73.86 & 71.79 & 76.62 & 74.45 \\ \cline{2-7}  & F1 & 87.93 & 70.18 & 79.46 & 76.09 & 83.04 & 80.28 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 78.19 & 77.81 & 77.77 & 77.67 & 77.31 & 76.61 \\ \cline{2-7}  & F1 & 85.53 & 84.72 & 84.82 & 84.31 & 84.22 & 82.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of results with and without AL methods (BERT) for ChemProt dataset.
# Performance Comparisons of Broadband Power Line Communication Technologies
## 4 BER Comparisons and Discussion
### Simulation Results and Discussion
Figure 12:3 BER results for HPGP: (**a**) MINI mode; (**b**) STD mode; and (**c**) HS mode. Now, performance comparisons of the three PLC technologies are provided. Since there are many modes of PLC technologies to compare, a plot would be very complex and difficult to read if all the BER curves for all the modes were shown in the same plot. Instead of comparing all the BER curves, the SNR value required to obtain a target BER at each mode of the PLC is compared. In Figure 21, the SNR values required to obtain a BER of \(10^{-3}\) are shown for all modes of the PLC technologies. The horizontal axis is the diversity number (the number of copies for the coded bits) and the vertical axis represents the required SNR. Therefore, a lower point means better BER performance. The three dotted lines represent the results for IoT PLC. IoT PLC in QPSK mode has better BER performance than any other PLC technologies at each diversity number. The QPSK mode of IoT PLC with \(DV_{n}=15\) is the best in terms of the BER performance. The Mini DF of IoT PLC has an SNR value similar to the QPSK mode with \(DV_{n}=11\), but this SNR value cannot be achieved with the other PLC technologies. On the other hand, the STD mode of HPGP outperforms the other two modes of HPGP and provides similar performance to the QPSK mode of IoT PLC with \(DV_{n}=4\). The HS mode of HPGP has a slightly worse performance than the QPSK mode of IoT PLC with \(DV_{n}=2\). The DV mode of HS PLC has worse performance than \(\pi/4\)-DQPSK mode with \(DV_{n}=12\) and has similar performance to \(\pi/4\)-DQPSK mode with \(DV_{n}=9\). The performance of the DV mode of HS PLC is worse than that of the STD and MINI mode of HPGP. However, the DV mode of HS PLC has better performance than the HS mode of HPGP. The NORMAL mode of HS PLC does not appear in this figure since it cannot achieve a BER of \(10^{-3}\) on the PLC channel. This also applies to the 16 QAM mode of IoT PLC with \(DV_{n}=1\). Figure 12:4 BER results for HS PLC: (**a**) NORMAL mode; and (**b**) DV mode. Figure 12:5 BER results of HPGP and HS PLC.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 3 Materials and Methods
### YOLOv5 Network Model
#### 3.1.2 Neck Module  
The main function of the neck module is to generate a feature pyramid and transfer the features of the image to the prediction layer. The feature pyramid can be used to optimize the network model's detection of target objects of different scales, and then to identify the same target objects at different sizes and scales. Before the PANet [35] structure came out, FPN was always the preferred structure for the feature aggregation layer of the object detection framework. In the research on YOLOv4, it has been found that the most suitable feature fusion network for YOLO is PANet. Therefore, both YOLOv4 and YOLOv5 use PANet as the neck to aggregate features.  
PANet is based on the Mask R-CNN and FPN frameworks, and on this basis, the dissemination of information is optimized [36; 37]. The feature extractor of the network uses a bottom-up path FPN structure, thereby optimizing the propagation of low-level features. The feature map of the previous stage is used as the input of each stage of the third path, and a 3 \(\times\) 3 convolutional layer is applied to process it at the same time. The output is added to the feature map of the same stage of the top-down path through the horizontal connection, and these feature maps provide information for the next stage. At the same time, adaptive feature pooling is used to restore the damaged information paths between all feature levels and each candidate area and aggregate each candidate area on each feature level in order to prevent arbitrary allocation [38; 39].