esa-sceva/satcom-chunk-collection huggingface dataset:
# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
## 2 Material and Methods
### Datasets for Performance Evaluation  
In assessing the effectiveness of the proposed framework, we utilized two benchmark datasets, each tailored to evaluate distinct aspects of the framework's performance in classification and detection. The first dataset, INbreast [35], comprises 410 digital mammograms from 115 different cases. Each image in this dataset is assigned a category based on the 'Breast Imaging-Reporting and Data System (BI-RADS)' by an expert radiologist. The dataset categorizes images into three groups: benign, malignant, and normal. It includes 116 positive case samples (benign and malignant) and 294 negative (normal) mammograms. Post-data augmentation, the dataset expanded to 2870 images, offering a robust base for evaluating the classification capability of the framework. The second dataset, the 'Curated Breast Imaging Subset of the Digital Database for Screening Mammography' (CBIS-DDSM) [36], is a recognized benchmark in digital mammogram-based breast cancer screening, encompassing 6775 cases with 10,239 images. We customized the CBIS-DDSM for our purposes, manually annotating the dataset using LabelImg, which is an open-source image annotation tool. During annotation, the bounding box coordinates were created. The Pascal VOC format was used to save annotations as XML files. This customization helped construct and train the breast cancer detection framework. This dual-dataset approach ensures a detailed assessment of the proposed framework's capabilities in BCD.
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 4 Experimental Studies
### Dataset Description
#### 4.1.10 Eastwest Dataset  
The eastwest dataset is designed to predict the direction of a train based on its label. The dataset contains 20 data points, with 10 positive samples representing eastbound trains and 10 negative samples representing westbound trains. The dataset includes 24 features, which may include speed, time, and other train-related metrics. The goal is to determine whether a train is heading east or west, a task relevant to automated train systems, transportation logistics, and predictive maintenance.
## 5 Conclusions  
Considering the key limitations of the previous works in representation learning and interaction information modeling for interaction recognition, we propose a new representation for skeleton data, named Interactive information Embedding Graph (IE-Graph). With the help of IE-Graph, the model can use graph convolution to represent the intra-subject spatial information and inter-subject interaction information in a uniform manner, which allows us to generalize the single-subject action recognition methods to interaction recognition easily. In addition, we propose a contrastive loss as well as an unsupervised pre-training framework for skeletal-based tasks. Our proposed IE-Graph and pre-training pretext tasks achieve impressive experimental results, but there are still some limitations: (1) compared to data-driven representations, IE-Graph may ignore a few joint dependencies; (2) Limited improvement in model performance from pre-training. Our future works will focus on (1) more flexible and efficient representation of skeleton data and (2) more effective pre-training pretext tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **CS acc (\%)** & **Decrease (\%)** \\ \hline GLIA(3-A) & 93.7 & - \\ GLIA(2-A) & 93.1 & 0.6 \\ GLIA(1-A) & 91.7 & 1.4 \\ GLI(non-A) & 89.0 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison in terms of interaaction recognition accuracy of Joint Attention Module.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Joint** & **Bone** & **CS acc (\%)** \\ \hline JointContrast (joints) & yes & - & 93.3 \\ JointContrast (bones) & - & yes & 92.8 \\ JointContrast (both) & yes & yes & 94.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison in terms of interaction recognition accuracy of joint-, bone- and both-stream.  
Figure 7: Attention visualization for the handshake action, where the top ones corresponds to the shallow layer of the network. The attention weights are normalized.  
**Author Contributions:** Conceptualization, [PERSON]; methodology, [PERSON] and X.J.; software, [PERSON] and Z.W.; validation, [PERSON], X.J. and Z.W.; formal analysis, [PERSON]; investigation, F.C.; resources, G.Y. and L.Z.; data curation, J.Z. and X.J.; writing--original draft preparation, X.J., [PERSON] and Z.W.; writing--review and editing, All Authors; visualization, [PERSON] and X.J.; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is partially supported by National Science Foundation of China (No. 62172372, 62272006, 61972438), and Zhejiang Provincial Natural Science Foundation (No. LZ21F030001).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiments and Results
### Local Causal Network Construction  
According to the steps in Section 3.2.2, a third-order adjacent local causal network of the target variable was constructed, as shown in Figure 5.  
In this third-order adjacent local causal network, there are 32 direct and indirect causal nodes of the target variable. There are five direct cause nodes whose shortest causal pathway length with the target variable is 1 (Markov boundary of the target variable). There are 13 indirect cause nodes whose shortest causal pathway length with the target variable is 2. There are 14 indirect cause nodes whose shortest causal pathway length with the target variable is 3.
# Recent Advances in Positive-Instance Driven Graph Searching +
## 4 A Unifying Take on Positive-Instance Driven Graph Searching
### Entering the Arena and the Colosseum  
Our primary goal is to determine whether the searchers have a winning strategy. A folklore algorithm for this task is to construct an alternating graph called _the arena_: \(\operatorname{arena}(G,k)=((V_{s}\cup V_{f}),E_{\operatorname{arena}})\) that contains for each position of the searchers (\(S\subseteq V\) with \(|S|\leq k\)) and each position of the fugitive (\(f\in V\)) two copies of the vertex \((S,f)\), one in \(V_{s}\) and one in \(V_{f}\) (see, for instance, Section 7.4 in [2]). Vertices in \(V_{s}\) correspond to a configuration in which the searchers perform the next move (they are existential)and vertices in \(V_{f}\) correspond to fugitive moves (they are universal). The edges \(E_{\text{arena}}\) are constructed according to all possible moves. The question is now whether there is an alternating path from a start configuration to some configuration in which the fugitive is caught. Since alternating paths can be computed in linear time ([79], Section 3.4), we immediately obtain an \(O(n^{k+1})\) algorithm.  
Modeling a configuration of the game as tuple \((S,f)\) comes, however, with a major drawback: The size of the arena does directly depend on \(n\) and \(k\) and does _not_ depend on some further structure of the input. For instance, the arena of a path of length \(n\) and any other graph on \(n\) vertices will have the same size for any fixed value \(k\). As the major goal of parameterized complexity is to gain insight into structural parameters beyond the input size \(n\), such a fixed-size approach is not what we are looking for.  
A bit counter intuitive, we will tackle this problem by, firstly, defining an alternating graph that is _larger_ than the arena: _the colosseum_. As it befits for any good colosseum, it is not only larger but, in particular, \"prettier\" than the arena (which here means that it adapts to the input structure of the graph).  
Once we can capture the structure in the colosseum, we will introduce yet another alternating graph that, finally, is actually _small_. This graph, which will be a subset of the colosseum, is called _the pit_--where only true champions can survive!
## 3 System Design
### An End-to-End Automatic Speech Recognition Algorithm Based on Conformer
The quadratic complexity of the attentional mechanism increases its computational overhead dramatically over long sequences, causing networks of different depths to process completely different amounts of data, especially those that have been downsampled, impairing overall efficiency. The Grouped MHSA Module reduces the attentional complexity from \(O\big{(}n^{2}.d\big{)}\) to \(O\big{(}n^{2}.d\big{/}g\big{)}\) by first grouping long sequences and then performing the attentional operation, and applies it to networks of different depths so that the processed data is approximately the same in different networks, which improves the overall efficiency of SAformerNet. In the MHSA Module, a number of heads \(H\) for a hidden sequence \(X\ \in\ \mathbb{R}^{n\times d}\) is computed as:  
\[H_{h}=softmax\Bigg{(}\frac{Q_{h}K_{h}^{T}+S_{h}^{rel}}{\sqrt{d_{h}}}\Bigg{)}V_ {h} \tag{9}\]  
And in the Grouped MHSA Module, the header \(H\) in Equation (9) transforms into:  
Figure 7: The Convolution Downsampling Module. The residual module contains a pointwise projection layer and a 1D SE-Net. The strided depth-wise convolution is responsible for the downsampling function; The 1D SE-Net utilizes a convolutional layer to extract features, which are pooled to compress the convolutional result into a 1D vector and applied to the convolutional output using pointwise multiplications. \[H_{h}^{grp}=softmax\left(\frac{Q_{h}^{grp}K_{h}^{grpT}+S_{h}^{rel}}{\sqrt{d_{h}^{ \prime}}}\right)V_{h}^{grp} \tag{10}\]  
Equation (10) is the encoder output after the Grouped MHSA Module. Where Attention queries, keys, values are reshaped from \(Q\), \(K\), \(V\in\mathbb{R}^{n\times d}\) to \(Q^{grp}\), \(K^{grp}\), \(V^{grp}\in\ \mathbb{R}^{n^{\prime}\times d^{\prime}}\). \(S^{rel}\in\ \mathbb{R}^{n\times n}\) is a relative position score matrix. \(n^{\prime}=n/g\) and \(d^{\prime}=d\times g\). And concatenated Grouped attention output \(H^{grp}\in\ \mathbb{R}^{n^{\prime}\times d^{\prime}}\) is reshaped to \(H\in\mathbb{R}^{n\times d}\) before the output projection layer. The Grouped attention is applied at the network layer, where the coding sequences are the longest, and the study shows that this approach reduces the computational overhead and improves the efficiency of the model.
## 1 Introduction
### Contributions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Cluster** & **User Distribution** & **Optimization Techniques** & **Optimization Goals** \\ \hline
[30] & ✗ & ✗ & Swarm intelligence algorithms & resource scheduling, parameter optimization \\
[31] & ✗ & ✗ & Fuzzy C-means & resources optimization, path planning \\
[29] & ✗ & Equitably \& randomly & Deep learning & interference coordination \\
[23] & ✗ & Randomly & Reinforcement learning & network coverage, optimal UAV placement \\
[21] & ✗ & Uniformly & Numerical & coverage, interference coordination \\
[5] & ✗ & Poisson point process (PPP) & Brute-Force, Genetic Algorithm & spectral efficiency, energy efficiency, interference coordination \\
[4] & ✗ & PPP & Brute-Force, eHSGA, Genetic Algorithm & spectral efficiency, energy efficiency, interference coordination \\
[32] & ✗ & PPP & Brute-Force, Genetic approach & spectral efficiency, coverage \\
[17] & ✗ & PPP & Q-learning, Deep Q-learning, Brute-force, Sequential algorithm & spectral efficiency, energy efficiency, interference coordination \\
[20] & ✓ & Fast K-means & Numerical & power optimization, resource allocation \\
[15] & ✓ & Poisson cluster process (PCP) & Stochastic geometry & coverage probability and downlink analysis \\
[33] & ✓ & TCP & Closed-form bounds & CDF of the nearest neighbor and contact distance distributions of clusters \\
[34] & ✓ & MCP & Closed-form bounds & contact distance distributions of clusters \\
[35] & ✓ & PCP & Stochastic geometry to find correlation between base-station cell locations & resource block management, coverage probability, throughput \\
[36] & ✓ & PPP, PCP & Geometry-based analysis & downlink coverage probability, interference coordination \\ Our Work & ✓ & TCP and MCP & Brute-Force & spectral efficiency, energy efficiency, interference coordination \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review on whether clustered users were considered and type of distributions used for modeling the user clusters, optimization techniques used, and the optimization goals of the system model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** \\ \hline \(\mathbf{L}_{\text{mbs}}^{\text{3D}},\mathbf{L}_{\text{ue}}\) & Locations of MBS and UE. \\ \(\Delta_{\text{mbs}}\)/\(\Lambda_{\text{gue}}\) & Distribution intensities of the MBS and UE nodes \\ \(\text{Pow}_{\text{mbs}},\text{Pow}_{\text{uabs}}\) & Maximum transmit power of MBS and UABS \\ \(A_{E}(\phi,\theta)\) & Transmitter antenna’s 3 DBF element of antennas for all base stations \\ \(F\) & Account for Nakagami fading \\ \(\varphi(d_{on})\), \(\varphi(d_{un})\) & Respective path loss from MBS and UABS in dB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notations and symbols used in the system model.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
###### Abstract  
In the field of remote sensing, super-resolution methods based on deep learning have made significant progress. However, redundant feature extraction and inefficient feature fusion can, respectively, result in excessive parameters and restrict the precise reconstruction of features, making the model difficult to deploy in practical remote-sensing tasks. To address this issue, we propose a lightweight Dual Attention Fusion Enhancement Network (DAFEN) for remote-sensing image super-resolution. Firstly, we design a lightweight Channel-Spatial Lattice Block (CSLB), which consists of Group Residual Shuffle Blocks (GRSB) and a Channel-Spatial Attention Interaction Module (CSAIM). The GRSB improves the efficiency of redundant convolution operations, while the CSAIM enhances interactive learning. Secondly, to achieve superior feature fusion and enhancement, we design a Forward Fusion Enhancement Module (FFEM). Through the forward fusion strategy, more high-level feature details are retained for better adaptation to remote-sensing tasks. In addition, the fused features are further refined and rescaled by Self-Calibrated Group Convolution (SCGC) and Contrast-aware Channel Attention (CCA), respectively. Extensive experiments demonstrate that DAFEN achieves better or comparable performance compared with state-of-the-art lightweight super-resolution models while reducing complexity by approximately 10\(\sim\)48%.  
c 2025 17 1078
# A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing
## 5 Algorithm Description for Multi-Observation Point Detection System  
Denote \(S(k,m)\) as the slow-time range matrix obtained at a single observation point, where \(k\) = 0, 1,..., \(K-1\) is the range cell index, and \(m\) = 0, 1,..., \(M-1\) is the slow-time index. For quasi-static trapped human beings with quasi-periodic but weak respiration, we denote \(S_{i}(k,m)\) and \(S_{j}(k,m)\) as the output slow-time range matrixes obtained from the \(i\)th and \(j\)th (\(i\
eq j\)) observation points after eliminating the time-invariant clutter/interference, respectively. Due to the cross-correlation function of the non-periodic noise being prone to zeroing, the cross-correlation between \(S_{i}(k,m)\) and \(S_{j}(k,m)\) is applied to improve the low signal-to-noise ratio (SNR). However, the quasi-periodic component and its harmonic components contained in the slow-time signal \(S_{i}(k,m)\) and \(S_{j}(k,m)\) are still preserved. Assume that the size of \(S_{i}(k,m)\) and \(S_{j}(k,m)\) are \(K_{a}\times M_{a}\) and \(K_{v}\times M_{b}\), respectively. The cross-correlation function \(R_{ij}(u,v,m)\) is defined as  
\[R_{ij}(u,v,m)=\textbf{E}[S_{i}(k_{u},m_{a})\cdot S_{j}(k_{v},m_{b})] \tag{3}\]  
Figure 5: The workflow timing.  
\[R^{AN}{}_{ij}(u,v,m)=\mathbf{max}_{m}[R_{ij}(u,v,m)]\cdot\mathbf{AN}_{m}[R_{ij}(u,v,m)] \tag{4}\]  
where the range cell index \(k_{u}\in[0,\,K_{u}-1]\) and \(k_{v}\in[0,\,K_{v}-1]\), the slow-time cell index \(m_{a}\in[0,\,M_{a}-1]\), \(m_{b}\in[0,\,M_{b}-1]\) and \(m=m_{a}-m_{b}\). Then, the advance normalization (AN) method is applied to \(R_{ij}(u,v,m)\) so that the weak quasi-periodic component of the quasi-static trapped human being can be further enhanced. We denote the output result of the AN method as \(R^{AN}{}_{ij}(u,v,m)\). The Fourier transform of \(R^{AN}{}_{ij}(u,v,m)\) is taken in each slow-time dimension, and the maximum corresponds to the quasi-static trapped human being; i.e., \((u_{i}\text{max},\,v_{i}\text{max})\) indicates the possible range location of the quasi-static trapped human being depending on the different \(i\)th and \(j\)th observation points.  
\[(u_{i}\text{max},v_{i}\text{max})=\mathbf{argmax}_{uv}[\text{FFT}_{m}[R^{AN}{}_ {ij}(u,v,m)]],\,u\
eq v \tag{5}\]
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
A PICOS (participants, intervention, comparator, study outcomes, and study design) strategy was used to rate the studies for eligibility [34]. Adulthood was defined as the age range of 15-65 years [35], although all the eligible studies were conducted on subjects within the age range of 18-50 years. The inclusion criteria were as follows: (1) participants--healthy adult individuals; (2) intervention--e.g., foot or feet wedges, and change in ankle position; (3) comparator--crossover studies comparing neutral position (non-wedge) with various foot position changes; (4) study outcome--studies that used electromyography to assess the activity (mean or peak) level of at least one of the thigh muscles, including the vastus medialis (VM), vastus lateralis (VL), rectus femoris (RF), semitendinosus (ST), and biceps femoris (BF); and (5) study design--within-group repeated-measures design.
# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 4 Dataset and Preprocessing  
For our experiments, we use historical closing price data from two stock market indices, i.e., the KOSPI 200 and the S&P 500, and included all stocks in each index from 31 July 2012 to 31 July 2018 on a working-day basis. These data were obtained from the Yahoo financial website ([[https://finance.yahoo.com/](https://finance.yahoo.com/)]([https://finance.yahoo.com/](https://finance.yahoo.com/))). Let \(n\) denote the number of stocks we collected.  
During the entire time period from 2012 to 2018, this study considered three sub-study periods, as shown in Figure 2.  
Each study period included stock data from four years, which were partitioned into three parts: the first three years for the training set, next six months for the validation set, and final six months for the test set.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Transfer Learning Settings** & **Source Data Labels** & **Target Data Labels** \\ \hline \multirow{2}{*}{Inductive transfer learning} & Available & \multirow{2}{*}{Available} \\  & Unavailable & \\ \hline Transductive transfer learning & Available & Unavailable \\ \hline Unsupervised transfer learning & Unavailable & Unavailable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different settings of transfer learning.  
Figure 2: Study periods.  
Let \(P_{t}^{s}\) denote the closing price of stock \(s\) on day \(t\). In our context, \(s\in\{s_{1},s_{2},...,s_{n}\}\) corresponds to the position of stocks in descending order of market capitalization. Similar to [20], we define \(r_{t,p}^{s}\) as the return of stock \(s\) on day \(t\) over the previous \(p\) days (i.e., the difference in closing price between day \(t\) and day \(t-p\)) as follows:  
\[r_{t,p}^{s}=\frac{P_{t}^{s}-P_{t-p}^{s}}{P_{t-p}^{s}}. \tag{6}\]  
For all stock data, we first calculate one-day (\(p=1\)) returns, \(r_{t,1}^{s}\), for each day \(t\) of each stock \(s\). Then, 20 one-day returns of the most recent 20 days with day \(t+1\) are stacked into one feature vector, \(R_{t}^{s}=\{r_{t-19,1}^{s},r_{t-18,1}^{s},...,r_{t,1}^{s}\}\), on day \(t\). In total, in each study period, the numbers of samples for the training set, the validation set, and the testing set of each stock are described in Table 2.  
Meanwhile, \(y_{t}^{s}\) is used to indicate the output of the estimation model, which shows the change in the closing price of each stock \(s\) between two consecutive days (day \(t\) and day \(t+1\)). For example, \(y_{t}^{s}=1\) indicates that an increase in the closing price on day \(t+1\), compared to the previous day, and \(y_{t}^{s}=0\) means a decrease in the closing price.
## 6 Evaluation and Validation
The primary reasons for the undetected oil pixels include the inhomogeneity of oil objects and the large variations in oil pixel \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Sensor** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{27–28 March 2021} & Sentinel-2 & 0.978 & 0.578 & 0.726 \\ \cline{2-5}  & Sentinel-3 & 0.917 & 0.538 & 0.735 \\ \cline{2-5}  & Sentinel-1 & 0.980 & 0.476 & 0.641 \\ \hline \multirow{4}{*}{5–6 July 2021} & Sentinel-1 & 0.92 & 0.87 & 0.894 \\ \cline{2-5}  & Sentinel-2 & 0.658 & 0.588 & 0.621 \\ \cline{2-5}  & Landsat-8 & 0.95 & 0.378 & 0.529 \\ \hline \multirow{4}{*}{3 September 2021 (three scenes)} & Sentinel-1 & 0.98 & 0.90 & 0.938 \\ \cline{2-5}  & Sentinel-2 & 0.878 & 0.743 & 0.805 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation of the proposed algorithm’s performance (HSBA, combined with oil contour determination) in the Persian Gulf vs. manually segmented ground truth data. Table 4 presents the Precision, Recall, and F1-score values of the proposed algorithm, compared to ground truth data, for oil spill detection off the coast of Mauritius. With the exception of Sentinel-1 EW, the Precision scores for Sentinel-1 IW and ICEYE-X are quite high and closely align with those in Table 3. The Recall values are also good in this case, leading to satisfactory F1-scores. The lower Precision rate for Sentinel-1 EW is likely due to the algorithm's overestimation, which may result from the coarse spatial resolution of this image mode (30 m pixel-size) compared to the 10 m pixel-size of Sentinel-1 IW and the 3 m pixel-size of ICEYE-X.
## 3 Materials and Methods
### Holt-Winters Model  
Holt-Winters is a classic time series method commonly used for short-term forecasting [14]. Developed to predict future values of variables in time series that include both trend and seasonal effects, this method has a structure designed for such predictions. The approach employs three smoothing parameters to forecast the average level (\(L_{t}\)), slope (\(T_{t}\)), and seasonal component (\(S_{t}\)) of the time series [29]. These components are calculated as shown in Equations (6)-(8), and forecasting is performed using them, as indicated in Equation (9).  
\[L_{t}= \propto(y_{t}-S_{t-s})+(1-\infty)(L_{t-1}-T_{t-1}) \tag{6}\] \[T_{t}=\beta(L_{t}-L_{t-1})+(1-\beta)\ T_{t-1}\] (7) \[S_{t}=\gamma(y_{t}-L_{t})+(1-\gamma)S_{t-s}\] (8) \[F_{t+k}=L_{t}+kT_{t}+S_{t+k-s} \tag{9}\]  
Here, \(\alpha\), \(\beta\), and \(\gamma\) are the smoothing parameters for the level, trend, and seasonal components, respectively.