esa-sceva/satcom-chunk-collection huggingface dataset:
# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
## 3 Methodology
### Transformer-Based Dense Nested Feature Fusion Network  
Based on the Swin-Unet [16] and Unet++ [37] models, we propose a transformer-based densely nested feature fusion image model for medical image segmentation (Swin-Unet++). This model is built with an encoder, a bottleneck, a decoder, and skip connections based on the swin transformer module. \(B^{i,j}\) represents the output of feature learning through the swin block layer, where \(i\) indexes the downsampling layers of the encoder, and the swin block layers are indexed along the skip connection path. Each swin block layer contains two swin transformer blocks. The upsampling output of \(B^{0,0}\) and \(B^{1,0}\) is fused to obtain \(B^{0,1}\); then, \(B^{1,0}\) is fused with the upsampling output of \(B^{2,0}\) to obtain \(B^{1,1}\). The upsampling output of \(B^{1,1}\) is fused with \(B^{0,0}\) and \(B^{0,1}\) through skip connections to obtain \(B^{0,2}\). Similarly, through layer-wise upsampling and feature fusion, \(B^{2,1}\) and \(B^{1,2}\) are sequentially obtained, ultimately resulting in the final output, which is the fusion of the upsampling output of \(B^{0,3}\); \(B^{0,3}\) is the fusion of the upsampling output of \(B^{1,2}\), \(B^{0,0}\), \(B^{0,1}\), and \(B^{0,2}\). Table 1 below shows the detailed output information corresponding to each swin block layer unit in the transformer-based densely nested feature fusion network.
## 4 Methodology
### Pre-Processing of Speech Signals
#### 4.1.1 Signal Normalization  
Audio signal normalization refers to the change in overall volume to equalize within the same speech file and for all speech files considered for training and testing subsets. It differs from compression and does not affect the sound and language content and identities of the speaker. The normalization changes the amplitude, which limits the signal range from \(-1\) to 1 [22]. We express Equation (1) in the audio signal, the original signal samples divided by the max of the absolute value of the signal:  
\[\text{Normalized Signal}=\frac{\text{original signal}}{\text{max}(\text{abs}( \text{whole original signal}))}. \tag{1}\]
## 2 Materials and Methods
### Extracted Building Characteristics and Accuracy Assessment  
#### 2.3.1 Building Polygons  
Building footprints were extracted from the orthorectified pansharpened WorldView-2 and Pleiades satellite imagery using a Deep Learning workflow applied in ArcGIS Pro 2.8. We also applied the workflow to a \(\sim\)0.5 m resolution Google Satellite basemap. Here, we masked building detections from the Google Satellite Basemap classifications to the Sentinel-2 2021 built-up area classification. For each dataset, the deep learning workflow involved manually digitizing building polygons within randomly selected 200 m square cells covering built-up areas. All buildings intersecting each cell were digitized, with a total of \(\sim\)2000 building polygons used for each dataset. Additionally, we digitized a further 1000 validation buildings for each dataset following the same random cell selection method. Training data were exported with a tile size of 400 \(\times\) 400 pixels and a stride of 200 pixels. A Region-based Convolutional Neural Network Mask R-CNN deep learning model was trained to detect buildings in the high-resolution satellite imagery. Mask R-CNN is an instance segmentation model that provides a segmentation mask and output polygon for each instance of a building detection. Mask R-CNN was chosen owing to its efficient building extraction capabilities applied to high-resolution satellite imagery [30; 67; 68]. We trained the Mask R-CNN to 20 epochs using an unfrozen ResNet-50 backbone model and 20% of the data used for validation. Buildings were detected from the satellite imagery using the trained Mask R-CNN models with 100-pixel padding. We first ran the deep learning model using the initial 2000 buildings for each dataset and subsequently digitized a further 500 buildings through manual inspection of buildings that were not detected by the first run. Then, 2500 buildings were used to train the final deep learning model for each dataset. Buildings less than 5 m\({}^{2}\) were removed. We report commonly used accuracy assessment metrics for binary classification including precision, recall, and the F1 score for buildings with an intersection over union (IoU) of \(\geq\)0.5:  
\[Precision=\frac{True\ Positive}{(True\ Positive+False\ Positive)} \tag{2}\]  
\[Recall=\frac{True\ Positive}{(True\ Positive+False\ Negative)} \tag{3}\]  
\[F1\ score=\frac{(Precision\times Recall)}{\left(\frac{(Precision+Recall)} {2}\right)} \tag{4}\]  
For comparison with open access data, we used OpenStreetMap [69] and GlobalML-BuildingFootprints datasets ([[https://github.com/microsoft/GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints)]([https://github.com/microsoft/GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints)) (accessed on 19 May 2022)) of building polygons intersecting our AOI. OpenStreetMap data is of unknown date, whereas the GlobalMLBuildingFootprints specified that it was derived from a basemap mosaic containing imagery from 2014-2021 (we estimated the date was 2016 for our study area).
## 1 Introduction
Conventional queuing models, including Markovian processes and Poisson distributions, typically depend on rigid assumptions regarding arrival and service rates, which fail to reflect the fluctuations of patient influx in actual situations. ANN-PSO, conversely, adjusts its parameters in real time according to live data, which enables healthcare systems to stay flexible and adaptable to evolving circumstances [16]. Beyond queue management, ANN-PSO has also been utilized in diagnostic and monitoring applications. In telemedicine, ANN-PSO models have been integrated with IoT-based cloud networks to enhance the efficiency of remote patient monitoring. For example, a study by [PERSON] et al. [16] introduced an improved PSO approach for physiological sensor data fusion in IoT healthcare frameworks. This model improved EEG-based epilepsy diagnosis by optimizing the propagation of neural networks, which achieved a 4.6%improvement in execution time compared to Genetic Algorithm-based models [16]. Recent studies further underscore ANN-PSO's suitability for telemedicine applications. [PERSON], [PERSON] & [PERSON] [18] demonstrated that ANN-PSO accelerates neural network training, enabling telemedicine systems to make rapid adjustments for service optimization and thereby greatly enhancing system scalability and adaptability in large-scale deployments. [PERSON] et al. [16] confirmed that, compared to traditional backpropagation methods, ANN-PSO achieves faster convergence rates while maintaining high prediction accuracy, a critical advantage for real-time telemedicine platforms. Moreover, ANN-PSO's integration with IoT devices supports continuous remote patient tracking and predictive health analytics, facilitating early identification of potential health issues in chronic disease management [19]. For instance, in remote cardiovascular risk assessment and diabetes-monitoring systems, ANN-PSO-enhanced models delivered more reliable prognosis due to their ability to process and learn from high-frequency data streams [16]. ANN-PSO also excels in multi-modal optimization tasks--leveraging a population-based search to navigate complex, non-differentiable solution spaces--making it ideal for balancing competing telemedicine objectives (e.g., minimizing patient wait times, optimizing resource use, and maximizing service quality) without the need for gradient information. Finally, by reducing computational overhead and enabling lightweight model updates, ANN-PSO allows cloud-based telemedicine platforms to sustain uninterrupted service during peaks in demand, delivering cost-effective, scalable solutions without performance degradation [16].
## 2 Preliminaries and Problem Description
### Platoon Formation Control with Output Constraints  
We can see a graphic description about the platoon formation control performing in a real-time strait in Figure 3. When USVs moves in a formation from a wide passage to a constrained channel, they have to change the pattern while keeping connectivity and avoiding collision. With our proposed method, the desired performance can be achieved. In this paper, we consider a control strategy to achieve the satisfactory desired distance and angle among USVs with the intervehicle communications. Under the designed control for each vehicle of a string of \(n+1\) USVs, the distance and angle errors can be constrained in a certain range. The line-of-sight (LOS) strategy [36] is used to define the distance and angle between the two USVs,  
\[d_{i}=\sqrt{\left(x_{i-1}-x_{i}\right)^{2}+\left(y_{i-1}-y_{i}\right)^{2}}, \tag{4}\]  
\[\varphi_{i}=\arctan 2(y_{i-1}-y_{i},x_{i-1}-x_{i}). \tag{5}\]  
In order to avoid collision among vehicles, the desired distance during the whole moving process must satisfy the following equations:  
\[d_{i,\min col}<d_{i}\leq d_{i,\max com}, \tag{6}\]  
where \(0<d_{i,\min col}<d_{i,\max com}\). The minimum safety distance is denoted by \(d_{i,\max com}\) and the maximum effective communication distance is denoted by \(d_{i,\max com}\). Next, the formation errors of the USVs are designed [21]:  
\[e_{di}=d_{i}-d_{i,des}, \tag{7}\] \[e_{\varphi i}=\psi_{i-1}-\psi_{i},\]  
where \(d_{i,des}\) is a desired distance between the two USVs, and \(d_{i,des}\) satisfies the inequality \(0<d_{i,\min col}<d_{i,des}<d_{i,\max com}\). For convenience, we define \(\underline{e}_{di}(t)=d_{i,col\min}-d_{i,des}\) as the minimum error distance  
Figure 3: Platoon formation control graphic description in a real-time strait.  
and \(\tilde{e}_{di}(t)=d_{i,con\max}-d_{i,des}\) as the maximum error distance. To guarantee the output constraints, we need to ensure that all signals are bounded and that the errors defined in Equation (8) are not violated, i.e.,  
\[\begin{array}{l}\text{e}_{di}<e_{di}<\tilde{e}_{di},\\ \text{e}_{\psi i}<e_{\psi i}<\tilde{e}_{\psi i}.\end{array} \tag{8}\]
###### Abstract  
Existing short-circuit calculation methods for distribution networks with renewable energy sources ignore the fluctuation of renewable sources and cannot reflect the impact of renewable sources and load changes on short-circuit current in real time at all times of the day and in extreme scenarios. A real-time short-circuit current calculation method is proposed to take into account the stochastic nature of distributed generators (DGs) and electricity loads. Firstly, the continuous power flow of distribution networks is calculated based on the real-time renewable energy output and electricity loads. And then, equivalent DG models with low-voltage ride through (LVRT) strategies are substituted into the iterative calculation method to obtain the short-circuit currents of all main branches in real time. The effects of different renewable energy output curves on distribution network short-circuit currents are quantitatively analyzed during the fluctuation in distributed power output, which can provide an important basis for the setting calculation of distribution network relay protection and the study of new principles of protection.  
renewable energy; electrical distribution systems; short-circuit current; relay protection; real-time calculation
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 3 Novel Pooling Methods
### Methods to Preserve Critical Information When Pooling  
Improper pooling techniques can lead to information loss, especially in the early stages of the network. This loss of information can limit learning and reduce model quality [60,61]. Detail-preserving clustering (DPP) [62] and local importance-based clustering (LIP) [63] minimize potential information loss by preserving key features during pooling operations. These approaches can also be known as soft approaches. Large networks require a lot of memory and cannot be started on devices with limited resources. One way to solve this problem is to quickly down sample to reduce the number of layers in the network. Poor performance may be the result of information loss due to the large and rapid reduction of the feature maps. RNNPool [64,65] attempts to solve this problem using a recursive down sampling network. The first recurrent network highlights feature maps and the second recurrent network summarizes its results as pooling output.
# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 4 Results and Analysis
### Ablation Experiment of Improved Module  
To demonstrate the effectiveness of each improvement module, ablation experiments were conducted. The network was trained and evaluated using the corresponding datasets. The training parameters and loss functions of each module were consistent. The comparison of the experimental results is shown in Table 3.  
After replacing the backbone network, the mIoU value decreased from 89.66% to 88.53%, and the precision and recall values decreased slightly, mainly because the replaced simpler backbone network reduced the complexity of the model and resulted in a significant reduction in the number of parameters. When an efficient channel attention module was introduced to improve the model's sensitivity to rebar information, The mIoU value increased from 88.53% to 92.98%, the precision value increased from 96.16% to 97.32%, the recall value increased from 90.43% to 95.29%, and the F1 score value increased from 93.21% to 96.29%. Finally, when the atrous convolution was adjusted the mIoU value reached 94.62%, which was 4.96% higher than that of the original model, and the precision, recall and F1 score also had some improvement, with the values reaching 97.42%, 96.95%, and 97.18%, respectively, while the number of parameters decreased to 27.4 M. The experimental results showed that the improved DeeplabV3+ model could effectively improve the detection efficiency of the rebar images.
# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 2 Data and Methodology  
### Study Area and Engineering Background  
The Bayan Obo Main Pit is located in central Inner Mongolia, 106 km south of the Mongolian People's Republic and 149 km north of Baotou City. It covers a total area of 48 square kilometers, with the current slope height exceeding 200 m. The southern slope of the main pit features well-developed joint fissures and fault structures and contains a large amount of water-sensitive minerals such as mica and montmorillonite. These minerals remain relatively stable within the rock mass under dry conditions, but once exposed to water, they undergo disintegration and volume expansion. This results in additional tensile stress within the internal fissures of the rock mass, potentially leading to matrix dilatancy, joint opening, propagation, and interconnection--posing significant risks to the stability of the southern slope and threatening the safe mining operations at the Bayan Obo Iron Mine. The geological cross-section is shown in Figure 1.  
Figure 1: Cross-sectional view at the middle part of the landslide.  
Under the influence of intense rainfall, a major landslide occurred on the southern slope of the main pit on 29 August 2020. The location of the landslide is shown in Figure 2, situated between elevations 1626 m and 1570 m, with the collapsed material extending down to the 1458 m level. The sliding body had an approximate thickness of 40 m. As shown in the geological cross-section, the southern side of the main pit is primarily composed of north-dipping slate. The landslide was not only affected by fault zones but also mainly controlled by anti-dip structural planes. The landslide mode is identified as a toppling and fracturing failure induced by the combined effects of fault fracture zones and structural planes. The rear of the sliding mass was controlled by the dip-aligned F17 fault fracture zone, while the front was primarily affected by the F18 fault fracture zone. The anti-dip structural planes within the rock mass are the key factors causing the toppling-type failure of the landslide [35; 36].  
A ground-based synthetic aperture radar (GB-SAR) was deployed on the northern slope of the main pit. Monitoring was conducted by aligning differential interferometry measurements with point cloud models obtained via drone photogrammetry. Displacement cloud maps of the landslide area at different time intervals during the failure process are shown in Figure 3. The maps reveal that as time progressed, the displacement within the landslide zone continuously increased, which corresponds well with the actual landslide behavior.  
Figure 3: Radar cloud maps at different time periods.  
Figure 2: Current status of the southern slope landslide.
## 2 Background and Related Works
### Background  
In XAI, generating an explanation can be performed intrinsically if the model is transparent (such as decision tree and a rule-based system) or after the AI model has produced its output (post hoc methods). Several post hoc methods such as SHAP [4] and LIME [5] are model-agnostic, which means that they are applicable to any black-box model such as neural networks (NN), ensemble models, etc. One type of XAI methods focuses on feature importance where knowing the contribution of different input variables is important to understand the characteristics of certain input variables. It may also indicate which factors/variables are not important and can be changed without affecting the output or which ones are important and must be retained to keep the same output. In the RL problem, this information is closely related to the input data used, specifically the states of the environment during the training phase. Deeplift [6], SHAP [4], and LIME [5] are methods that can be used to explain the feature importance of the DRL agent with respect to the actions. However, a method to measure the contribution of input features to the components of the RL reward function is lacking in the literature.  
In RL, the reward function is designed to quantify the desired situation as feedback to update the agent's parameters. Designing and obtaining feedback on the formulation of the reward function is challenging. Its calculation generally considers several factors, but without a reward decomposition method, such as Hybrid Reward Architecture (HRA) [7], only a single value is given to the agent. HRA uses several reward values (respective of the components) to update the RL agent's parameters. The HRA authors mentioned that the reward component typically depends on a subset of features without explaining this dependency further. [PERSON] et al. [8] used HRA to explain the output layer of a DRL agent, specifically the RL reward components. However, it only presents the values of reward components without showing the bias and effect of different weights for each component.  
A correlation of input and output explanations is lacking in the literature. With the available methods, the contributions of the input features are not reflected in the explanation of the reward component. Conversely, the feature importance does not inform us about the reward component to which the state variable contributes. In this work, we present a method for expanding these techniques not only to correlate input and output explanations but also to expose the effect of the reward weight and to identify and mitigate the bias on different reward components.
## 5 Conclusions  
Misdetected and false alarm precipitation generated in the delineation of rain areas is the first-order error in the satellite precipitation retrieval algorithm, which can be inherited and exacerbated in the following process. In this study, we proposed a new rain area delineation method based on PNN using rain flags derived from rain gauges and multi-channel microwave brightness temperature observed by TMI. Independent validation indicated that the false alarm precipitation of PNN is 30% and 55% less than that generated by the TRMM 2A12 satellite precipitation dataset and Kmean method, while the misdetected precipitation of PNN is 19% and 26% less than them. Compared with the threshold method used in traditional SI algorithm, the PNN method can reduce 41% of the false alarm precipitation, and their misdetected precipitation is very similar. Furthermore, with this new rain area delineation method proposed by this study, the precipitation volumes retrieved by the SI algorithm could be improved with a 19% and 10% decrease of MAE and RMSE and a 29% increase of \(R^{2}\). The results revealed the efficiency of the PNN in the delineation of the rain areas with microwave observations and the significance of reducing the misclassified rain/no rain areas in the retrieval of precipitation based on satellite observations. This research pointed out a new idea to reduce the errors in high-resolution microwave-based precipitation datasets, which has great significance for monitoring the precipitation over ungauged areas. Compared with the threshold method used in the SI algorithm, the PNN-based rain area delineation method could not significantly reduce the misdetected precipitation, which limited the performance of this new method. In the future, we will attempt to add more information (such as topography, soil moisture and infrared brightness temperature) into the PNN to provide more accurate rain area delineation.  
This work was funded by National Natural Science Foundation of China (Grant No. 41371013), the Research Fund for International Young Scientists to [PERSON]. (Grant No. 41450110076), the One-Hundred Talent Program to [PERSON], and the Free Investigation and Yong Scientist Projection of the State Key Laboratory of Remote Sensing (Y4Y00200 KZ).  
The idea of this research is proposed by the first author, [PERSON]. The experiments of this research are conducted by [PERSON] and [PERSON]. [PERSON] made some improvement for the algorithm, and edit the English of this manuscript. [PERSON] processed and provided the ground observations.  
The authors declare no conflict of interest.