esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 6 Conclusions  
This work introduces a novel approach for signal detection in uplink massive MIMO systems. We have employed a novel PSO to enhance the AOR iterative technique by optimizing its relaxation parameters. The proposed signal detection method reconstructs the transmitted signal without computing complex matrix inversions. The simulation results demonstrate that the OAOR-based approach surpasses the recently published AOR, IGS, and E-SOR-based methods. It achieves performance that is close to that of standard MMSE signal detection while significantly reducing the computational complexity. The superior performance of the OAOR iterative method can assist in addressing additional complex problems related to matrix inversion, such as precoding in large and extremely large-scale MIMO systems. In the future, we anticipate further improvements to PSO and the incorporation of data-driven methodologies, such as deep learning and machine learning, to enhance the performance and optimization of the proposed algorithm. To further reduce computational complexity, we plan to implement a more sophisticated approach by running PSO less frequently; specifically, we will execute it for the \(i_{th}\) number of iterations instead of for all iterations of OAOR. This could potentially reduce the PSO-related complexity by 70%. Additionally, we will utilize a coded environment to test the  
Figure 14: Detection performance of various signal detectors for \(N\times K=126\times 18\) in 32-QAM.  
Figure 15: Detection performance of various signal detectors for \(N\times K=126\times 21\) in 32-QAM.  
capabilities of the proposed method under more challenging channel conditions, aiming to obtain results that are more representative of practical situations.  
**Author Contributions:** [PERSON] provided overall guidance and supervision for the research. [PERSON] was responsible for the formulation of the research question and the design of the study and implementation, and the validation of experimental results. [PERSON] conducted the data analysis and interpretation, while [PERSON] helped in outlining the paper and setting up the simulation environment. All authors contributed equally in writing and revising the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** For this research, we acknowledge the support of [PERSON]'s University of Technology, Thonburi, through the Petchra Pra Jom Klao Research Scholarship (Grant Number 5/2565).  
**Data Availability Statement:** No external dataset has been used for this research. The data of our analysis are contained within the article.  
**Acknowledgments:** We sincerely appreciate the contributions of everyone involved in the completion of this research. Special thanks to [PERSON]'s University of Technology Thonburi for their financial support and funding.  
**Conflicts of Interest:** No conflicts of interest are reported by the authors.
## 1 Introduction  
Cardiopulmonary arrest is a phenomenon in which the heart fails to contract efficiently and the general circulatory system of blood stops. The cardiac arrest is mostly caused by a heart attack that causes blood flow to the heart muscle. The Golden Time of cardiac arrest is 4 min, and after 4 min the survival rate is dramatically lowered. According to the recent \"Joint Survey on Sudden Cardiac Arrest\" by the National Fire Agency and Korea Centers for Disease Control & Prevention, the number of domestic cardiac arrest patients increased from 21,905 in 2008 to 29,262 in 2017, which corresponds to an increase of 33.6% (7357 patients) over 10 years [1; 2; 3]. To address this, the government has mandated the installation of Automated External Defibrillators (AEDs) in public health clinics, firefighting emergency medical services, ambulances, airports, vessels weighing \(>\) 20 tons, public facilities, and apartmentsof more than 500 households under the Emergency Medical Service Act, since 2012. Additionally, measures for the treatment of Sudden Cardiac Arrest (SCA) are available nationwide. As a result, the survival rate of patients with SCA increased from 2.3% in 2006 to 8.7% in 2017, a 4-fold increase, but SCA remains an emergency disease with a low survival rate [4; 5; 6; 7].  
Most of the AEDs currently installed and used by various institutions use 2 pads to separate the output for adults and children while delivering electric shocks. An Electrocardiogram (ECG) analysis is performed using 2 pads to identify the required degree of shock to the heart. However, during basic life support procedures such as artificial respiration and CardioPulmonary Resuscitation (CPR), thoracic impedance may change due to changes in blood flow, thoracic cavity pressure, and shape. CPR is a first aid that supplies blood containing oxygen to the heart and brain on behalf of a stationary heart. CPR is an essential first aid for patients with cardiac arrest, but on the contrary, when chest compression is performed, it causes changes in thoracic impedance along with internal organ damage such as fractures, liver and spleen damage. This change may hinder the proper transmission of electric shock energy by the AED. This is due to a limitation in the amount of energy transferred according to thoracic impedance. The modes are classified into adult and pediatric modes [8].  
Until now, the impedance measurement function of the AED has been used to select adult and pediatric age groups. In connection with this, researches on real-time thoracic impedance measurement method, performance evaluation method, and the effect of CPR on bio-impedance have been conducted. Based on these previous researches, this paper compares the difference in the amount of energy transferred according to the change of thoracic impedance when using an AED installed in public facilities, and the inverse relationship between thoracic impedance and electric energy is verified. Additionally, we aimed to analyze the improvement method to accurately transfer energy to the patient through real-time measurement of thoracic impedance along with electrocardiogram analysis through pads.
# Electromagnetic Induction Imaging with Atomic Magnetometers: Progress and Perspectives
## 3 Application Areas and Limitations of Inductive Coils
### Biomedical Imaging
#### 3.3.2 Cancer  
Cancerous tissues are characterized by a significant variation in conductivity with respect to the surrounding healthy tissues. Specifically, in presence of a cancer, the conductivity of liver and lung tissues increases by more than 30%, and by more than 500% in the case of mammary tissue [13]. The discussion of the physiological processes leading to such a difference is well beyond the scope of this work. One may argue that--as the electrical conductivity in live tissues is largely determined by ion transport [14]--the contribution to the generation of the EMI signal from the structures involved in ion exchange may be dominant. However, other possible contributions cannot be ruled out.  
The anomalous conductivity of cancerous tissue makes it detectable by electromagnetically induction imaging. However, cancer is routinely detected and precisely characterized by CT scans, so one may wonder whether low-resolution EMI may add anything at all to the available toolkit available for cancer diagnostics.  
In the context of lung cancer, it was argued [15] that detection of early stage cancer could be the key to increase the 5-year survival rate. EMI may play a role in this direction and its low cost harmless nature makes it an ideal diagnostic tool for very widespread use.
# CFD-DEM Simulation of Dust Deposition on Solar Panels for Desert Railways
## 3 Numerical Simulation Results and Discussion
### Deposition Characteristics of Dust Particles (Clay) on Solar Panels
Compared with Figures 5 and 6, it can be seen that the movement of dust particles followed strongly with the air flow, that is, all the particles above the solar panel almost followed the upward air flow movement and were rarely deposit on the solar panel surface, while below the solar panel, the particles moved around the lower edge of the solar panel at a large speed. Compared with Figures 5b and 6b, after the solar panel, the trajectories of the dust particles were basically consistent with the movement of the air flow vortex and even played the role of flow-field tracer particles. Figure 7 shows the statistical results of the total amount of dust deposition on the solar panel. It can be seen from the figure that the amount of dust deposition on the solar panel was about 14,000 particles per second, and the deposition rate (the number of particles deposited divided by the release number within the same windward area) was about 4.6%. Our simulation results showed that the amount of dust deposition increased linearly with time. Thus, it was calculated that the actual settlement mass of 10 \(\upmu\)m dust on the solar  
Figure 5: Velocity distribution around solar panel on different contour surfaces when \(u_{*}=0.1\) ms\({}^{-1}\). (**a**) \(z=0\) m (**b**) \(x=0.5\) m.  
panel after 24 h was 4.64 g/m\({}^{2}\), which was comparable to the field measurement result (2.5 g/m\({}^{2}\)/day) of [PERSON] [30]. In addition, considering the results of Figure 3, it was concluded that the 10 um dust particles were easy to deposit on the solar panel when the wind speed was small. Similar results have also been reported by [PERSON] et al. [23]. In addition, because the small wind speed was more conducive to dust accumulation, we also inferred that dusty weather will cause a large number of small dust particles to deposit on the solar panel. However, the negative effect of dust accumulation on the PV panels dedicated to railway power supplies could be reduced by increasing its installation height.
## Discussion
### Uncertainties
In contrast, using coarser resolution remote sensing products often fails to capture the effects of sub-grid heterogeneity and topographic relief, two factors that play an important role in surface energy balance and can significantly interfere with vegetation photosynthesis and thus cause bias in the results of vegetation carbon source/sink evaluation [87]. Based on this consideration, our study still has some limitations. First, the MODIS vegetation index data were generated ignoring the topographic effects [88], and second, the meteorological data used in the study, including the solar radiation at 0.1\({}^{\circ}\) (\(\sim\)10 km) and the evapotranspiration/potential evapotranspiration data at 1/24\({}^{\circ}\) (\(\sim\)4 km), were of coarse resolution, and even if they were resampled to 500 m using a bilinear interpolation method that applies to continuous data, their ability to capture changes in the surface energy balance due to sub-grid heterogeneity and topographic relief remains weaker than the higher spatial resolution products [89]. However, even with the uncertainties associated with using these data to force the model, we were able to assess the results for vegetation carbon source and sink at a spatial resolution of 500 m over an area of more than 2 million km\({}^{2}\). Any attempt to build on this foundation with more detailed simulations would be difficult to achieve due to the sheer volume of data. At present, [PERSON] et al. [89] have developed a method for downscaling grid-scale meteorological data to sub-grid scale based on topography in response to the coarse resolution of meteorological data. Moreover, [PERSON] et al. [90] also demonstrated the feasibility of their proposal to run the model at coarse spatial resolution and then apply a linear or non-linear downscaling process to the coarse estimation results by considering surface heterogeneity to obtain high spatial resolution vegetation productivity at large scales. Therefore, our future work will be able to use these methods to conduct more refined simulations of vegetation carbon sinks and to assess the impact of land cover change, thus making our research more considered.
# Experimental Study of Material Proportioning for Similar Modeling of Brittle Rocks
## 3 Experimental Results and Analysis
### Sensitivity Analysis of Factors
#### 3.2.6 Sensitivity Analysis of the Brittleness Index  
As shown in Figure 10, the brittleness index of the specimens decreased as the concentration of the rosin-alcohol solution increased and also decreased with a reduction in the refined iron powder-barite powder ratio. The brittleness index generally exhibited an initial increase followed by a decrease with an increase in both the quartz sand content and the rosin-alcohol solution dosage.  
The brittleness index of the specimens increased with the increasing gypsum content, reaching its maximum value when the gypsum content was 4.0%, indicating the most pronounced brittle characteristics. Gypsum sets and hardens quickly, with a relatively low strength and slight volumetric expansion during hardening, which positively affects the adjustment of the specimen's brittleness. In contrast, the rosin, as a binder, inhibits the formation of microcracks in the specimen. Therefore, the effects of the gypsum and the rosin-alcohol solution on the brittleness characteristics of the specimen show distinctly opposite trends.  
According to the sensitivity analysis of the brittleness index of the specimens (see Table 9), the concentration of the rosin-alcohol solution had the greatest influence on the brittleness index, with a range of 0.651, while the dosage of the rosin-alcohol solution had the least effect, with a range of 0.220. The sensitivity ranking of the factors influencing the brittleness index of the specimens is as follows: D \(>\) C \(>\) B \(>\) A \(>\) E.
## 3 Results and Discussion
### Plate Mechanical Stiffness  
Biomechanically, the tibia bone and plate will be subjected to compression and bending loads during fixation of a tibia shaft, while the plate is fixed on the tension side of the fracture [43]. Furthermore, four-point bending targets the bone plate to withstand compression on one side and tension on the other, longitudinally. Thus, the plates were investigated mechanically considering a four-point bending test. The change in the equivalent bending stiffness of topology-optimized plates compared to the INT-BP is presented in Table 2. The test showed a clear trend that increasing the volume reduction percentage results in reducing the plate's equivalent bending stiffness. The equivalent bending  
Figure 4: Topology optimization of a custom-fit distal tibia plate under two different screw configurations.  
stiffness decreased around 75% when removing 75% of the INT-BP volume for both screw configurations. The reason for this significant decrease is generally related to the plate's reduced volume. The bone plate's stiffness is one of the major indicators of the implant mechanical behavior during bone healing [44]. Higher plate stiffness means a higher risk of stress being shielded by the plate from the fractured bone, and vice versa. Stress stimuli are one of the key factors to a healthy bone remodeling [45]. Similarly to previous studies, these results confer that the risk of stress shielding phenomenon is minimized [46]. Nevertheless, further investigation is still required which will consider comparing the obtained optimized implants with commercially available implants in terms of their stiffness and biomechanical behavior in a bone-model construct.
# Online Batch Selection for Enhanced Generalization in Imbalanced Datasets
## 3 Methodology
### BSBS with Re-Enters  
The purpose of the variants of \(BSBS\) is to alleviate some shortcomings of the algorithm that might arise while it strives to speed up the convergence. The main obstacle of \(BSBS\) concerns the easy samples of the dataset (i.e., the samples that constantly, at least at the first stages of training, display quite low loss). When a sample has low loss for a certain duration, then the algorithm will remove it from training continuously. If the removal of the same samples continues for many epochs, then this has shown, empirically, that it hurts the generalization performance. Additionally, the absence of a sample from batches results in outdated stored loss values (\(BL\)). The danger is that with many network updates the actual losses of the removed samples might tend to rise. That does not happen, though, with all samples that are swapped temporarily from the trainset. A sample of the \(LL\) set, even though it does not update its loss value, is bound to return to the trainset because later updates will eventually reduce the loss of at least \(k\) samples to a value less than the outdated value of the removed sample. This prevents removed samples from being removed for all epochs. However, it has been observed in some datasets that some samples might display extremely low loss values at some point in time and subsequently removed from the training procedure for many epochs. This phenomenon can be problematic regarding generalization in an imbalanced setting and, for this reason, \(BSBS\) is improved with the implementation of re-enters.  
\(BSBS\) with re-enters revolves around finding the samples that are left the most out of training and putting them back in the next epoch. We are specifically interested in the samples that are continuously left out (i.e., for many consecutive epochs) because these samples have the most outdated stored loss values. To find them, we construct a matrix where the number of the consecutive out-of-training epochs is stored for each sample. Then, a threshold is defined, which controls if samples should be re-entered in the next set of batches. Let the matrix of consecutive out-of-training epochs be \(ST\) (swapped times) and the threshold of a re-enter \(E_{sw}\). When \(ST_{i}\) (of the \(i\)th sample) surpasses the threshold, then we modify the stored loss in \(BL\) in order to prevent the sample \(i\) from getting categorized in the \(LL\) set. There can be various ways to do that. In this implementation, we changed the \(BL\) value of those samples to the mean value of all losses. The reason for that was to guarantee that the re-entered samples will not fall neither in \(LL\) nor in \(HL\). Thus, the samples will be added just once in the next training epoch. The full algorithm of \(BSBS\) with re-enters is displayed in Algorithm 2.
## 5 Discussion
### Future Directions
#### 5.2.1 Grid-Forming Inverter Function  
As described in this paper, the STPC topology has the capability to regulate both voltage and frequency through closed-loop control. This makes it suitable for grid-forming inverter applications, particularly in isolated or weak grids where power converters must establish the voltage reference [28; 29].
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 2 Related Work
### FHM Method  
FHP and FHM share common objectives and are regarded as a specific instance of HSI and MSI fusion. Consequently, there has been extensive study in recent years focused on advancing FHP technology for the purpose of addressing HSI fusion concerns. The CS and MRA methods in FHP are commonly employed for merging HSI and MSI images. In their groundbreaking study, [PERSON] et al. [30] were the first to utilize wavelet decomposition technology to accomplish the fusion of HSI and MSI images. Following this, [PERSON] et al. [31] further enhanced the CS pan sharpening method by taking into account the impact of the spectral response function on the fusion process. The enhanced CS approach is utilized to merge HSI and MSI by creating image sets for various pan sharpening sub issues. The picture sets are composed of a multispectral band and its equivalent hyperspectral band, which are organized together using clustering methods based on correlation. [PERSON] et al. [32] employed MRA to combine HSI with MSI. They generated high-resolution images for each hyperspectral band by synthesizing them as a linear combination of MSI using linear regression. The spectral resolution in pan sharpening is lower than that of HSI and MSI fusion issues. As a result, high spatial resolution HSI achieved using the Pansharpening approach often shows spectral distortion. Similar to FHP, the incorporation of HSI and MSI has also produced substantial advancements in the field of deep learning. [PERSON] et al. [6] proposed MLKAF Net, a multi-scale HSI-MSI fusion method., which efficiently creates fusion features that combine information from several modalities in both the spatial and spectral domains. The MLKAF Net primarily comprises three modules: the Multi scale Large Kernel Attention Module (MLKAM), the Spatial Information Aggregation Module (SIAM), and the Spectral Attention Module (SPAM). MLKAM incorporates multi-scale mechanisms into a large kernel decomposition, effectively capturing both long-range dependencies and local granularity information. SIAM ensures the spatial accuracy of reconstructed HR-HSI by aggregating rich spatial information. SPAM employs channel attention to reduce spectral distortion by preserving valuable spectral information. [PERSON] et al. [33] employed two branches of the network to capture the spectral characteristics of each pixel in HSI and the spatial neighborhood characteristics associated with each pixel in MSI. Next, the retrieved features are combined and transmitted to the fully linked layer, where the final fusion process takes place.
## The Lobe Fissure Tracking by the Modified Ant Colony Optimization Framework in CT Images  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 4 and [PERSON] 1**  
1 Department of Computer Science and Information Engineering, Yuanpei University of Medical Technology, Hsinchu 30015, Taiwan; E-Mail: [EMAIL_ADDRESS]  
2 Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan; E-Mail: [EMAIL_ADDRESS]  
3 Department of Computer Science and Information Engineering, Asia University, Taichung 40402, Taiwan; E-Mail: [EMAIL_ADDRESS]  
4 Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-3-5381183 (ext. 7104).  
External Editor: [PERSON]  
_Received: 16 June 2014; in revised form: 8 November 2014 / Accepted: 17 November 2014 / Published: 24 November 2014_  
**Abstract:** Chest computed tomography (CT) is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO) algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.  
**Keywords:** CT; ant colony optimization algorithm; lung; lobe fissure; segmentation