esa-sceva/satcom-chunk-collection huggingface dataset:
# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
###### Abstract
Therefore, we introduce a novel image segmentation method, a transformer-based explainable model for breast cancer lesion segmentation (TEBLS). This method uses a multi-scale information fusion approach centred on a swin transformer, allowing for the effective integration of spatial information across various scales. Additionally, it incorporates visualization techniques to monitor and track the segmentation results. The main contributions of this study are as follows:  
* A novel image segmentation method, TEBLS, for breast cancer lesion segmentation. TEBLS combines a multi-scale information fusion approach with a swin transformer, which allows for the effective integration of spatial information across multiple scales. * TEBLS employs a self-attention mechanism within the transformer architecture to enhance its ability to capture both long-range dependencies and local features, addressing the limitations of traditional CNN-based methods in segmenting complex medical images. * TEBLS incorporates explainability methods to provide insight into the segmentation results, improving interpretability and offering clinicians the ability to better understand and trust the model's decisions, which is crucial for clinical settings. * This study overcomes the shortcomings of traditional deep learning models, such as the inability to capture fine-grained semantic information and global context within multi-scale semantics, thereby advancing the state of the art in breast cancer lesion segmentation. * Extensive experiments confirm the effectiveness of the proposed TEBLS over other state-of-the-art methods.
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
###### Abstract  
In recent years there has been an increasing interest in the use of autonomous underwater vehicles (AUVs) for ocean interventions. Typical operations imply the pre-loading of a pre-generated mission plan into the AUV before being launched. Once deployed, the AUV waits for a start command to begin the execution of the plan. An onboard mission manager is responsible for handling the events that may prevent the AUV from following the plan. This approach considers the management of the mission only at the vehicle level. However, the use of a mission-level manager in coordination with the onboard mission manager could improve the handling of exogenous events that cannot be handled fully at the vehicle level. Moreover, the use of vehicle virtualization by the mission-level manager can ease the use of older AUVs. In this paper, we propose a new mission-level manager to be run at a control station. The proposed mission manager, named Missions and Task Register and Reporter (MTRR), follows a decentralized hierarchical control pattern for self-adaptive systems, and provides a basic virtualization in regard to the AUV's planning capabilities. The MTRR has been validated as part of the SWARMs European project. During the final trials we assessed its effectiveness and measured its performance. As a result, we have identified a strong correlation between the length of mission plan and the time required to start a mission (\(\rho_{s}=0.79,\ n=45,\ p\ 0.001\)). We have also identified a possible bottleneck when accessing the repositories for storing the information from the mission. Specifically, the average time for storing the received state vectors in the relational database represented only 18.50% of the average time required for doing so in the semantic repository.  
cooperative robotics; autonomous underwater vehicles; automated mission planning; automated mission management; control station +
Footnote †: journal: Applied  
Article
# Estimation of Structural Deformed Configuration for Bridges Using Multi-Response Measurement Data
###### Abstract  
The structural deformed shape (SDS) is considered an important factor for evaluating structural conditions owing to its direct relationship with structural stiffness. Recently, an SDS estimation method based on displacement data from a limited number of data points was developed. Although the method showed good performance with a sufficient number of measured data points, application of the SDS estimation method for on-site structures has been quite limited because collecting sufficient displacement data measured from a Global Navigation Satellite System (GNSS) can be quite expensive. Thus, the development of an affordable SDS estimation method with a certain level of accuracy is essential for field application of the SDS estimation technique. This paper proposes an improved SDS estimation method using displacement data combined with additional slope and strain data that can improve the accuracy of the SDS estimation method and reduce the required number of GNSSs. The estimation algorithm was established based on shape superposition with various combined response data (displacement, slope, and strain) and the least-squares method. The proposed SDS estimation method was verified using a finite element method model. In the validation process, three important issues that may affect the estimation accuracy were analyzed: effect of shape function type, sensor placement method, and effectiveness of using multi-response data. Then, the improved SDS estimation method developed in this study was compared with existing SDS estimation methods from the literature. Consequently, it was found that the proposed method can reduce the number of displacement data required to estimate rational SDS by using additional slope and strain data. It is expected that cost-effective structural health monitoring (SHM) can be established using the proposed estimation method.  
Keywords:SHM; estimation method; SDS; multi-response data +
Footnote †: journal: Applied
## 3 Results
### Profiles across Measures
#### 3.4.2 Profiling at 3 Months after Stroke  
At 3 months after stroke, the MoCA, MADRS, mRS, RAPA WSAS and SIS scores were reported. Based on these data, the AI module generated three significant profiles indicating different impairments among 35.6% of mild stroke patients, as shown in Figure 7 and Table 5.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Profile 1: Low Cognition** & & **Other Participants** \\ \hline
**Day 3–7 MOCA Score** & **Profile 1** & **Other Participants** \\ Mean & 19.000 & 25.767 \\ Variance & 13.500 & 8.250 \\ Observations & 13 (17.8\%) & 60 (82.2\%) \\  & \(p\) = 0.0000 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of impairment profiles on day 3–7 after stroke.  
Figure 6: Impairment profiles on day 3–7 after stroke.  
Participants in profile 1 (9.6%) demonstrated a higher level of depression based on their scores for the MADRS assessment (MADRS mean: profile 1 = 12.71, other participants = 5.47, \(p<0.05\)). Apart from their higher level of depression, they did not exhibit significant impairment in other assessments.  
Another notable exemption is profile 2 (12.3%), where participants recorded higher scores for the mRS assessment (mRS mean: profile 2 = 1.66, other participants = 0.85, \(p<0.05\)), which evaluates the degree of disability following stroke. Participants in this cluster also showed comparatively lower scores on the SIS, which assesses the other dimensions of health-related quality of life: emotion, communication, memory and thinking, and social role function (SIS mean: profile 2 = 64.44, other participants = 84.32, \(p<0.05\)). It could be determined that in contrast to the other groupings among survivors of mild stroke  
Figure 7: Impairment profiles at 3 months after stroke.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Profile 1: Higher Depression Level** & \\ \hline
**MADRS** & **Profile 1** & **Other Participants** \\ Mean & 12.714 & 5.469 \\ Variance & 49.238 & 48.855 \\ Observations & 7 (9.6\%) & 66 (90.4\%) \\  & \(p=0.017\) & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of impairment profiles at 3 months after stroke.  
at 3 months after stroke, profile 2 participants displayed more impairment and impact due to their increased disability and poor quality of life.  
The AI algorithm separated another group of participants in profile 3 (13.7%), who scored higher on the WSAS assessment (WSAS mean: profile 3 = 15.15, other participants = 3.79, \(p<0.05\)), which indicates low work and social adjustment in daily life. These participants did not show a significant difference in the other assessments.
# Application of a MEMS-Based TRNG in a Chaotic Stream Cipher
## 1 Introduction
The whole stream cipher block has been implemented and tested in a Field Programmable Gate Array (FPGA), obtaining a throughput of 1.072 Gbps. Regarding the seed generation, an in-depth analysis of the viability of the system has been made. This analysis includes the capture of the accelerometer signal at different sample rates and a study of its randomness properties. Furthermore, a conditioning stage based on Secure Hash Algorithm 512 (SHA-512) [10] has been implemented in order to whitening the raw sensor data. The paper is organized as follows: Section 2 explains the stream cipher algorithm in detail, as well as its implementation results and a comparison with other chaos-based stream ciphers; Section 3 includes an analysis of the noise signal generated by a commercial accelerometer at rest as well as an explanation of the conditioning stage; Section 4 presents a cryptanalysis of the encryption system; finally, conclusions are drawn in Section 5.
# Performance Comparisons of Broadband Power Line Communication Technologies
###### Abstract  
Broadband power line communication (PLC) is used as a communication technique for advanced metering infrastructure (AMI) in Korea. High-speed (HS) PLC specified in ISO/IEC12139-1 and HomePlug Green PHY (HPGP) are deployed for remote metering. Recently, internet of things (IoT) PLC has been proposed for reliable communications on harsh power line channels. In this paper, the physical layer performance of IoT PLC, HPGP, and HS PLC is evaluated and compared. Three aspects of the performance are evaluated: the bit rate, power spectrum, and bit error rate (BER). An expression for the bit rate for IoT PLC and HPGP is derived while taking the padding bits and number of tones in use into consideration. The power spectrum is obtained through computer simulations. For the BER performance comparisons, the upper bound of the BER for each PLC standard is evaluated through computer simulations.  
advanced metering infrastructure; power line communication; IoT PLC; HPGP; ISO/IEC 12139-1; HS PLC; bit error rate +
Footnote †: journal: Applied Sciences
## 5 Dipole Projections  
From [PERSON] et al. [14] the absorption in a particular direction for a biaxial crystal is given as:  
\[A=\sum_{\alpha}3\epsilon_{\alpha}cd\frac{1}{m}\sum_{j=1}^{m}\left(\hat{\mu}_{aj} \cdot\hat{\epsilon}\right)^{2} \tag{6}\]  
In orthorhombic space groups, the two-fold symmetry in all three principal directions results in the coincidence of the principal optic axes being with the crystal axes, and also the value of \(\left(\hat{\mu}_{aj}\cdot\hat{\epsilon}\right)^{2}\) being invariant in the two-fold rotation such that the transition dipole orientations can be described with respect to the crystallographic axes [14]:  
\[\hat{\mu}_{aj}=\left(\sin\theta_{aj}\cos\phi_{aj},\sin\theta_{aj}\sin\phi_{aj},\cos\theta_{aj}\right) \tag{7}\]  
where \(\theta\) and \(\phi\) are the polar and azimuthal angles, measured with respect to the chosen crystal axis. In monoclinic space groups there is only one two-fold symmetry direction that results in the alignment of one optic axis with the crystallographic \(b\)-axis. However, in triclinic space groups the absence of symmetry elements causes there to be no relationship between the crystallographic and optical directions. Therefore, we present the treatment for biaxial crystals only in relationship to the optical directions. In this work, we define these angles with respect to the high refractive index axis (Figure 1 right). This results in the dipole projection being simply the square projection of these two vectors summed over all dipoles within the asymmetric unit, \(\langle(\hat{\mu}\cdot\hat{\epsilon})^{2}\rangle\).  
In the uniaxial case, the value of \(\langle(\hat{\mu}\cdot\hat{\epsilon})^{2}\rangle\) is strongly impacted by the rotational symmetry of the system [12; 14], resulting in:  
\[\langle(\hat{\mu}\cdot\hat{\epsilon}_{3})^{2}\rangle=\cos^{2}\theta_{d} \tag{8}\]  
\[\langle(\hat{\mu}\cdot\hat{\epsilon}_{1})^{2}\rangle=\langle(\hat{\mu}\cdot \hat{\epsilon}_{2})^{2}\rangle=\sin\theta_{d} \tag{9}\]  
where \(\hat{\epsilon}_{3}\) is the high-symmetry axis. Combining (8) & (9) with \(\hat{k}\) in spherical coordinates gives the total value:  
\[\langle(\hat{\mu}\cdot\hat{\epsilon})^{2}\rangle=\left[cos^{2}\theta_{k}\cos ^{2}\theta_{d},\sin^{2}\theta_{k}\sin^{2}\theta_{d},\ \sin\theta_{d}\right]\left[\begin{array}{c}x\\ y\\ z\end{array}\right] \tag{10}\]  
The first observation is that this value is invariant in \(\phi_{d}\). Secondly, when looking at the distributions over all values of \(\hat{k}\) (see Supplementary Materials and [12]) it can be seen that dipoles aligned parallel to the high-symmetry axis result in a larger variation of \(\langle(\hat{\mu}\cdot\hat{\epsilon})^{2}\rangle\), with a significant number of orientations where the term is actually suppressed. Conversely, when the dipole is oriented perpendicular to the high-symmetry axis, the maximum value that \(\langle(\hat{\mu}\cdot\hat{\epsilon})^{2}\rangle\) is suppressed by is a factor of 2, though it is also noted that non-zero values are obtained over a larger number of orientations [12].
## 4 Instrument Simulation
### SIMION Results for Protons  
We simulate three different types of solar wind: slow, intermediate and fast (for parameters, see Table 4). The SIMION results are presented in Figures 9 and 10 for Maxwellian distributions and Figures 11 and 12 for \(\kappa\)-distributions. Figures 9 and 11 present the count numbers recorded on each pixel as a function of hit distance. We then use Equation (28) to obtain the associated VDFs and show the results in Figures 10 and 12. The fit results of the estimated distribution functions are summarized in Tables 5 and 6. MPA resolves the plasma parameters accurately over its 5 ms acquisition time. This outcome supports the feasibility of MPA to give precise one-dimensional VDF measurements.  
Figure 11: Three different types of solar wind simulated with SIMION. We present the number of counts per pixel (1 mm wide) as a function of hit distance on the position-sensitive sensor along the \(z\)-axis in our instrument design. In all three examples, the velocities of the simulated particles follow a \(\kappa\)-distribution function with \(\kappa=3\).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Solar Wind** & **Density (cm\({}^{-3}\))** & **Thermal Speed (m/s)** & **Bulk Speed (m/s)** & \(\kappa\) \\ \hline Slow & \(5.43\pm 0.16\) & 34,538 \(\pm 1468\) & 399,981 \(\pm 68\) & 3.44 \(\pm 0.34\) \\ Intermediate & \(4.99\pm 0.2\) & 39,447 \(\pm 2361\) & 499,994 \(\pm 101\) & 3.26 \(\pm 0.41\) \\ Fast & \(2.96\pm 0.12\) & 56,492 \(\pm 3533\) & 699,963 \(\pm 138\) & 3.10 \(\pm 0.37\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Estimation of the plasma moments after fitting for our three \(\kappa\)-distributions.  
Figure 10: Estimated (red) and model-input (blue) distribution functions. The estimation of the distribution function is based on the counting results shown in Figure 9. In all three examples, the velocities of the simulated particles follow a Maxwellian distribution function.
## 1 Introduction
proposed that under the CAVs environment, the increase in road capacity is estimated to be between 40% to 273% [29]. [PERSON] et al. proposed a new algorithm to solve bus route optimization under uncertain conditions [30]. The relevant works about how mixed traffic flows affect the capacity of bus stops are few, which mainly focus on analyzing the benefits of CAVs and how to optimize bus routes under the CAVs environment [31; 32]. Although traffic flow analysis for mixed traffic with CAVs and HVs has attracted considerable research efforts in the last few years, even in the context of traditional traffic, many studies have explored the evaluation method of traffic capacity of the bus stop. For example, [PERSON] et al. incorporate the influence of the traffic volume of adjacent lanes on the traffic capacity of bus stops in the middle section [33]. [PERSON] et al. considered the signal timing and the distance between stop and signal factors, which were ignored in the previous literature, by using time-space diagrams of bus trajectories and probabilistic methods [34]. The correlation impact analysis and evaluation of bus stop capacity under the CAVs environment is very lacking, and the analysis of its traffic capacity can facilitate the management and control of traffic. To fill the research gap, this paper proposes simulation analyses of the capacity of bus stops under a CAVs environment. The rest of this paper is organized as follows. In Section 2, two important evaluation indexes called failure duration (FD) and forced lane-changing rate (FLR) are explored by analyzing the traffic capacity of bus stops. Section 3 proposes the method and experimental schemes of traffic flow simulation near a bus stop under a CAVs environment. A correlation between FD, FLR, and traffic flow rate is discussed in Section 4. The relationship function model is fitted in Section 5 to analyze the effectiveness of FD and FLR in evaluating traffic capacity and the effectiveness of FD and FLR in predicting the traffic capacity of bus stops under the CAVs environment. In Section 6, the experimental results and the differences with related methods are discussed. Section 7 summarizes this paper. Figure 1 illustrates the research process of this study. Figure 1: The flowchart of logical methodology of the study.
## 4 Results and Discussion
### Regular Waves
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Load** & **Platform Motions** & **Statistics** & **3 Columns** & **4 Columns** & **6 Columns** \\ \hline \multirow{6}{*}{LC1} & \multirow{3}{*}{Surge (m)} & Max & 5.46 & 5.14 & 4.51 \\  & & Min & 4.08 & 3.86 & 3.41 \\  & & Mean & 4.79 & 4.53 & 3.96 \\ \cline{2-6}  & \multirow{3}{*}{Heave (m)} & Max & 0.04 & 0.03 & 0.22 \\  & & Min & \(-\)0.49 & \(-\)0.50 & \(-\)0.31 \\  & & Mean & \(-\)0.22 & \(-\)0.24 & \(-\)0.04 \\ \cline{2-6}  & \multirow{3}{*}{Pitch (deg)} & Max & 2.19 & 2.20 & 2.21 \\  & & Min & 1.28 & 1.31 & 1.33 \\  & & Mean & 1.72 & 1.75 & 1.76 \\ \hline \multirow{6}{*}{LC2} & \multirow{3}{*}{Surge (m)} & Max & 6.16 & 5.88 & 5.26 \\  & & Min & 3.17 & 2.94 & 2.49 \\  & & Mean & 4.70 & 4.44 & 3.87 \\ \cline{2-6}  & \multirow{3}{*}{Heave (m)} & Max & 0.44 & 0.43 & 0.62 \\  & & Min & \(-\)0.86 & \(-\)0.88 & \(-\)0.69 \\  & & Mean & \(-\)0.21 & \(-\)0.22 & \(-\)0.03 \\ \cline{2-6}  & \multirow{3}{*}{Pitch (deg)} & Max & 2.49 & 2.49 & 2.51 \\  & & Min & 0.97 & 1.01 & 1.03 \\  & & Mean & 1.73 & 1.75 & 1.76 \\ \hline \multirow{6}{*}{LC3} & \multirow{3}{*}{Surge (m)} & Max & 7.19 & 6.91 & 6.31 \\  & & Min & 1.89 & 1.69 & 1.25 \\ \cline{1-1}  & & Mean & 4.58 & 4.34 & 3.79 \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{Heave (m)} & Max & 0.82 & 0.83 & 1.01 \\ \cline{1-1}  & & Min & \(-\)1.20 & \(-\)1.23 & \(-\)1.04 \\ \cline{1-1}  & & Mean & \(-\)0.17 & \(-\)0.18 & 0.01 \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{Pitch (deg)} & Max & 2.66 & 2.62 & 2.64 \\ \cline{1-1}  & & Min & 0.79 & 0.87 & 0.88 \\ \cline{1-1}  & & Mean & 1.73 & 1.75 & 1.76 \\ \hline \end{tabular}
\end{table} 
Table 5: Statistics of the motions of the platform in the time domain for LC1–LC3. the surge response, the minimum of the heave response is negative. For the mean value, the same trend as the surge motion is found. As shown in Figure 11, although the pitch amplitudes of the three models are different, the average values are relatively stable. The average pitch is around 1.73 deg for the 3-offset columns platform, while the average pitch of the 4-offset columns platform and 6-offset columns platform are, respectively, about 1.75 deg and 1.76 deg, indicating that the number of the offset columns has little effect on the pitch motion.
## 5 Discussion: Future Work
### Development of Advanced Data-Driven Predictive Models  
Conventional statistical methods have limitations in accurately capturing nonlinear factors and complex environmental interactions. In particular, a key point is how to take into account the high variability of fishing vessels, including factors such as operating areas, weather conditions, types of fishing activities, the condition of the crew, and loading status. Consequently, there has been a growing trend in research utilizing advanced algorithms, such as BN, machine learning, and RF, to improve the predictive accuracy of fishing vessel accidents. Studies employing BN have demonstrated the capability to analyze various accident scenarios and risk factors, enabling quantitative assessments ([PERSON] et al., 2020 [20]; [PERSON] et al., 2023 [58]; [PERSON] et al., 2023 [3]; [PERSON] et al., 2024 [49]; [PERSON] et al., 2021, 2023 [37; 45]; [PERSON] et al., 2024 [46]; [PERSON] et al., 2024 [42]). In particular, recent studies have integrated algorithms, such as RF, DST, and Chi-square tests, alongside BN. These methods are employed for variable selection, handling uncertain conditions, and qualitative relationship analysis, thereby complementing the limitations of BN and enhancing its applicability ([PERSON] et al., 2023 [3]; [PERSON] et al., 2024 [49]; [PERSON] et al., 2020 [20]).  
Future research should aim to establish a balance between quantitative and qualitative analyses through approaches such as BN or methods capable of quantifying accident risk. However, this process is often complex, involving high initial configuration costs and computational overhead. Integrating artificial intelligence (AI) technologies, such as machine learning or automated processes, can potentially overcome the static characteristics of con ventional models, enabling real-time risk prediction. Moreover, employing hybrid models based on deep learning can significantly enhance predictive performance by automatically learning the nonlinear relationships between variables. For instance, combining BN with tree-based models, such as Gradient Boosting Decision Trees (GBDT), can facilitate visual analysis of nonlinear interactions between variables, which aids in identifying key causes of accidents and understanding complex interdependencies ([PERSON] et al., 2018 [61]).  
Additionally, sensitivity analysis and Shapley values can be used to quantitatively evaluate the impact of each risk factor on accident probability, thereby enhancing model transparency and providing decision-makers with more reliable insights ([PERSON] et al., 2016 [62]). Consequently, this approach enables the detection of hidden risk factors that may not be identifiable through traditional BN and facilitates the integration of diverse environmental data to better predict accident occurrence and improve overall risk assessment. This approach will be helpful in addressing the diverse situations encountered by fishing vessels.