esa-sceva/satcom-chunk-collection huggingface dataset:
# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 2 Materials and Methods  
### Materials  
This research employed a dataset comprising data extracted from a cohort of 20 patients who had undergone coronary angiography procedures at Hokkaido University Hospital. Notably, all the angiographic images and video streams derived from these patients underwent meticulous and precise diagnosis by experienced radiologists, ensuring the accuracy and reliability of the clinical data underpinning our study. To facilitate subsequent image analysis, the continuous video streams of each patient were discretized into individual images, with a consistent frame rate of 15 frames per second maintained throughout this process. For the purpose of our experiment, a total of 250 contrast images were obtained and classified manually by right coronary artery (RCA) and left coronary artery (LCA) (each artery category encompassed three distinct imaging angles, namely the left anterior oblique (LAO), right anterior oblique (RAO), and the ZERO angle).
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Technique** & **Accuracy** & **Study** \\ \hline \multirow{3}{*}{SchizoGoogLeNet} & CNN with & & \\  & GoogLeNet & 99.02\% & [PERSON] et al. [51] \\  & architecture & & \\   Recurrence Plot + & CNN-based spatial & & \\   Gramian Angular & representations & 93.2\% & [PERSON] and [PERSON] [17] \\   Field & CNN for non-linear & & \\ CNN-based EEG & EEG signal & 92\% & [PERSON] et al. [52] \\   model & classification & & \\  & Multi-Scale Spectral & & \\   MSST-Bi-CNN & Transformation with & 84.42\% & [PERSON] et al. [53] \\  & BiLSTM-CNN & & \\   SchizoNET & Time-frequency & 99.74\% & [PERSON] et al. [54] \\   Proposed & CNN-BiLSTM on & & \\ CNN-BiLSTM (TE & transfer entropy & 99.94\% & Present Study \\   matrices) & matrices & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of other AI-based methods for schizophrenia detection using EEG data. The proposed model shows comparable performance to SchizoGoogLeNet and SchizoNET, which have demonstrated some of the highest accuracies in the field. Importantly, by focusing on TE matrices, our model shares a novel approach to capturing both spatial and temporal neural connectivity patterns, which may offer additional clinical relevance in understanding and managing schizophrenia. The high performance of the CNN-BiLSTM model, combined with the potential for real-time TE matrix generation, presents significant opportunities for integration into the NeuroPredict platform. The NeuroPredict platform is designed for continuous mental health monitoring, integrating EEG data with other physiological and behavioral metrics. Using the proposed approach, the platform could be enhanced to provide real-time assessments of connectivity patterns, which are crucial for early detection of schizophrenia relapse or cognitive decline. Moreover, since the Muse 2 headband's electrodes cover critical frontal and temporal brain regions, the NeuroPredict platform could adopt a similar TE-based analysis pipeline to capture disruptions in connectivity that are indicative of schizophrenia progression. This would allow for a more proactive approach to mental health management, offering clinicians real-time insights into their patients' cognitive states and enabling timely interventions.
## 3 Performance Analysis
### Ergodic Capacity  
The ergodic capacity of a dual-hop cooperative communication system employing best relay selection is computed as  
\[C_{e}=\frac{\log_{2}(e)}{2}\mathbb{E}[\log_{e}(1+\Psi_{c2e})]\quad\mathrm{ bits}/\mathrm{sec}/\mathrm{Hz}, \tag{21}\]  
where \(\mathbb{E}[\cdot]\) is the statistical average operator, and \(e=2.7183\). Equation (21) can be evaluated by solving the following integral:  
\[C_{e}=\frac{\log_{2}(e)}{2}\int_{0}^{\infty}\log_{e}(1+\Psi)f_{\Psi_{c2e}}( \Psi)d\Psi, \tag{22}\]  
where \(f_{\Psi_{c2e}}(\Psi)\) is the probability density function (pdf) of random variable \(\Psi_{c2e}\), which can be obtained by differentiating (20) with respect to \(\Psi\) as follows (note that, for notational simplicity, symbol \(\Psi\) is used in place of \(\Psi_{th}\)):\[f_{\Psi_{G\infty}}(\Psi) = M\Big{[}1-\mathcal{Q}\bigg{(}\frac{\ln(\Psi)-\mu_{\Psi_{SR}}}{ \sigma_{\Psi_{SR}}}\bigg{)}\mathcal{Q}\bigg{(}\frac{\ln(\Psi)-\mu_{\Psi_{RD}}}{ \sigma_{\Psi_{RD}}}\bigg{)}\Big{]}^{M-1} \tag{23}\] \[\times\Big{[}\mathcal{Q}\bigg{(}\frac{\ln(\Psi)-\mu_{\Psi_{RD}}}{ \sigma_{\Psi_{RD}}}\bigg{)}f_{\Psi_{SR}}+\mathcal{Q}\bigg{(}\frac{\ln(\Psi)- \mu_{\Psi_{SR}}}{\sigma_{\Psi_{SR}}}\bigg{)}f_{\Psi_{RD}}\Big{]},\]  
where \(f_{\Psi_{j}}(\cdot),\ j\in\{\mathcal{S}\mathcal{R}_{m},\mathcal{R}_{m} \mathcal{D}\}\) is the PDF of \(\Psi_{j}\) given by  
\[f_{\Psi_{j}}(\Psi)=\frac{1}{\sqrt{2\pi}\ \sigma_{\Psi_{j}}\Psi}\exp\bigg{[} \frac{(\ln(\Psi)-\mu_{\Psi_{j}})^{2}}{2\sigma_{\Psi_{j}}^{2}}\bigg{]}. \tag{24}\]  
On substituting (24) and (23) into (22) and rearranging the resulting expression, we have  
\[C_{e}=\frac{M\log_{2}(e)}{2}[\mathrm{Y}_{SR}+\mathrm{Y}_{RD}], \tag{25}\]  
\[\mathrm{Y}_{SR}=\frac{1}{\sqrt{2\pi}\ \sigma_{\Psi_{j}}\Psi}\int_{0}^{ \infty}\Bigg{[}1 - \mathcal{Q}\bigg{(}\frac{\ln(\Psi)-\mu_{  
where \(\mathrm{Y}_{SR}\) and \(\mathrm{Y}_{RD}\) are the integral expressions given in (26) and (27), respectively. On substituting \(x=\frac{\ln(\
abla)-\mu_{\mathrm{y}_{RD}}}{\sqrt{2}\
u_{\mathrm{Y}_{SR}}}\) and \(y=\frac{\ln(\
abla)-\mu_{\mathrm{y}_{RD}}}{\sqrt{2}\sigma_{\mathrm{y}_{SR}}}\), respectively, into Equations (26) and (27) and after rearranging the resulting expressions, we obtain (28) and (29). Equations (28) and (29) match Gauss-Hermite quadrature integral form \(\int_{-\infty}^{\infty}g(\zeta)\exp(-\zeta^{2})d\zeta\) and can be solved using the numerical integration technique as stated in [40] ([Table 25.10]).  
On applying the Gauss-Hermite quadrature integration technique in (28) and (29), and substituting the resulting expression into (25), we obtain the final closed-form expression of the ergodic capacity as given in (30). Note that, in (30), \(w_{i}\) are the weights of the \(N\)th order Hermite polynomial, and \(x_{i}\) and \(y_{i}\) are the corresponding zeros of the Hermite polynomial.
## 8 Summary and Future work
### Damage Diagnosis  
It can be concluded that there is no outright consensus among researchers regarding which vibration-based damage indicator or detection method is the most suitable: (1) The response-based methods are straightforward, easy for application, and not computationally intensive, but at the same time they do not give a quantitative assessment of damage. It is a valuable tool for damage identification in the preliminary stage. (2) The model-based methods assume that a detailed well-correlated numerical model of the structure is available for damage identification. Nevertheless, the construction of such models usually results in modeling errors from simplified assumptions. In order to detect damage rather than the artificial errors from the model construction, a good quality model that could accurately predict the behavior of the structure is required, but is usually a quite difficult challenge.  
Although new techniques and extensive studies have been undertaken in the recent decades, there are still many challenges for the future research in structural damage identification to increase the reliability and safety of structures in civil engineering.  
1. Most current damage detection techniques are based on linear theory and measurements, which can be used as a rapid estimation of the damage presence if the damage is not very small. However, if the intact structure is nonlinear by nature or a nonlinear behavior is presented due to damage accumulation, the development of nonlinear based damage indicators or detection methods could be more effective for robust damage detection.
2. The effect of various factors on the vibrational measurements needs to be considered for damage detection, for an instance, the ambient conditions (e.g., temperature, traffic, etc.), the evolution of boundary conditions, and the variations in the mass and stiffness in real structures. All these variations can introduce uncertainties in both the linear and nonlinear vibrational measurements [94].
3. The development of detection/monitoring techniques that can take into account the modeling errors and uncertainties is desirable to improve the correlation between experimental and numerical results.
4. It is desirable to develop more effective structural models, sensitive damage indicators, and efficient optimization algorithms for the damage detection.
# Crouch Gait Recognition in the Anatomical Space Using Synthetic Gait Data
## 3 Results  
The experimental results for each classification algorithm are calculated using a k-fold cross-validation with \(k=10\). Tables 7 and 8 present the results of accuracy (Acc), recall (R), specificity (SP), precision (P), and F-measure (FM) using the pelvic (\(q_{1}\), \(q_{2}\)) body section with K-nearest neighbors (kNN), Naive Bayes (NB), discriminant analysis (DA), decision trees (DT), and an artititial neural network (ANN), respectively. These metrics are calculated using a multiclass confusion matrix. The algorithm with the best accuracy to correctly predict the outcome of the pelvic section for N, C1, C2, and C3 is DA, except that for C4 it is ANN. Sensitivity or recall (R) is the probability of correctly identifying the gait in people characterized by this type of gait. The best performance on the recall for the five gaits is DA. Precision is the rate of relevant instances among retrieved instances, which implies the probability of correctly classifying specific crouch individuals as general subjects with this condition. Specificity, on the other hand, is the true negative rate. These are the negative cases that the algorithm has classified as corresponding to other gaits. Precision refers to the distance from the measurement result to the true value. The algorithm with the best precision is DA for the five gaits. The F-measure is a measure of predictive performance and the DA is the best algorithm in this way, while KNN has the lowest performance. Tables 9 and 10 show the performance using a similar approach, but using the body section of the right lower limb. As we can see in Tables 9 and 10 related to the right lower limb body section, the performance for the five gait types with the five metrics is better. This is because now it is considered a joint function more and before the feature selection stage derived from the feature relevance there are four more predictors, which improves the classification performance. For normal, crouch-1, crouch-3, and crouch-4 gaits, the best performance is achieved by NB, while for C2 it is achieved by KNN. In the same sense, to evaluate the classification performance by regions, Tables 11 and 12 summarize the results for the five gaits and the five metrics using the left lower limb body region. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**DT**} & \multicolumn{4}{c}{**ANN**} \\ \hline
**Metric** & **N** & **C1** & **C2** & **C3** & **C4** & **N** & **C1** & **C2** & **C3** & **C4** \\ \hline Acc & 99.40 & 99.00 & 99.20 & 99.00 & 99.40 & 98.00 & 97.00 & 97.56 & 99.40 & 96.20 \\ R & 98.09 & 97.63 & 98.00 & 99.41 & 98.04 & 96.33 & 99.17 & 100.00 & 99.17 & 98.20 \\ SP & 99.74 & 99.50 & 99.50 & 98.92 & 99.77 & 98.52 & 96.63 & 97.09 & 99.52 & 97.14 \\ P & 99.23 & 98.18 & 98.00 & 96.98 & 98.57 & 94.57 & 91.35 & 93.57 & 98.00 & 92.43 \\ FM & 98.60 & 97.81 & 98.00 & 98.14 & 98.19 & 95.03 & 93.95 & 95.47 & 98.45 & 94.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification performance of 5 gaits using right lower limb body section with DT and ANN.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 2 CO2 Observation, Datasets, Global Related Research
### Section Summary  
This section summarizes ground observation and space carbon satellite observation methods. The part gives a detailed introduction to the development of satellite remote sensing sensors and techniques. Additionally, this subsection introduces relevant datasets, incorporating those from carbon satellites and anthropogenic emissions. Finally, the section employs the Citespace visualization tool to showcase recent papers on carbon-emission research in terms of regions, keywords, and topic clustering. Section 3 spotlights the retrieval algorithm of the column concentration of atmospheric CO\({}_{2}\) in satellite observations.
## Nomenclature  
AB annual benefit AC annual cost AEC annual equivalent cost BCR Benefit-cost ratio CHP combined heat and power GHG greenhouse gas HC hydrocarbon HFC hydrofluorocarbon HC investment cost IR interest rate IR interest rate IRR internal rate of return NPV net present value ORC organic Rankine cycle O and M operation and maintenance R134a 1,1,2-Tetrafluoroethane (CF\({}_{3}\)CH\({}_{2}\)F) R152a 1,1-Difluoroethane (C\({}_{2}\)H\({}_{4}\)F\({}_{2}\)) (replacement for R134a) R245 fa 1,1,1,3,3-Pentafluoropropane is a hydrofluorocarbon (C\({}_{3}\)H\({}_{3}\)F\({}_{5}\)) R290 n-propane (C\({}_{3}\)H\({}_{8}\)) R600 n-butane(C\({}_{4}\)H\({}_{10}\)) R600a isobutane (C\({}_{4}\)H\({}_{10}\)) R601 n-pentane (C\({}_{5}\)H\({}_{12}\)) R601a isopentane (C\({}_{5}\)H\({}_{12}\))
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 2 Materials and Methods  
### Study Area  
The Indus River has its origins in the Tibetan Plateau; it flows through western China, the Ladakh region of India, and then enters Pakistan through the Gilgit-Baltistan region [4]. It continues its journey across the country's entire length in a southerly direction to empty into the Arabian Sea near Sindh, the port of Karachi [26]. The river passes through the Muzaffargarh District of the Punjab region (Figure 1), which forms the focus of this study. This district comprises four subregions, with one of them bearing the same name as the district. To the north of the district is Taunsa Barrage, which was built to control the flow of the Indus River for irrigating agricultural land and for flood mitigation [27].
# Real-Time Spatial-Division Multiplexing Transmission with Commercial 400 Gb/s Transponders
###### Abstract  
As single-mode-fiber transmission systems are reaching their capacity limits, spatial-division multiplexing (SDM) techniques have been investigated to increase the per-fiber capacity. However, the compatibility with current single-mode transponders severely hinders the near-term deployment of SDM systems. In this paper, we experimentally propose two real-time SDM transmission schemes using commercial single-mode 400 G dual-polarized 16 quadrature amplitude modulation equipment. In the first experiment, 60 km weakly coupled single-mode 7-core fiber with a pair of fan-in and fan-out devices are adopted. In the second experiment, the fiber link consists of 60 km/150 km weakly coupled few-mode fiber (FMF) and low-modal-crosstalk mode multiplexers, in which only non-degenerate LP\({}_{01}\) and LP\({}_{02}\) modes are utilized. In order to investigate the effect of splice on SDM fiber links, 20-roll, 3 km multicore fibers (MCFs) and FMFs are spliced and tested in the experiments. The bit error rates of all SDM experiments are all below \(4.75\times 10^{-2}\) forward-error-correction threshold of the 400 G transponders. The experimental results prove that the near-term deployment of SDM systems could be accelerated by utilizing weakly coupled MCFs or non-degenerate modes of weakly coupled FMFs which are compatible with commercial single-mode transponders without any software or hardware modifications.  
spatial-division multiplexing; weakly coupled FMF; 400 G transponders +
Footnote †: journal: _Photonics_
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 6 Geo-Localization
### Position Estimation  
After successfully extracting the depth information, the subsequent task of calculating the object's relative position to the camera became relatively straightforward, owing to the availability of inverse perspective projection equations as outlined in the earlier sections. The triangulation equations depended on the used camera model. In the case of the pinhole camera model, the equations can be represented as follows.  
\[X=\frac{(u-c_{x})\cdot Z}{f_{x}}\]  
\[Y=\frac{(v-c_{y})\cdot Z}{f_{y}}\]  
Figure 8: Distance extraction.  
Figure 7: Fire detected in the images adding bounding boxes.  
where  
* (\(u\), \(v\)) are the 2D pixel coordinates in the image.
* (\(c_{x}\), \(c_{y}\)) are the principal point coordinates (intrinsic parameters).
* (\(y_{x}\), \(f_{y}\)) are the focal lengths along the \(x\) and \(y\) axes (intrinsic parameters).
* \(Z\) is the depth or distance of the point from the camera (the required value).  
To obtain the depth or distance (\(Z\)) of the point from the camera, the triangulation equations were rearranged and solved for \(Z\) using the known values of (\(u\), \(v\)), (\(c_{x}\), \(c_{y}\)), (\(f_{x}\), \(f_{y}\)), and the calculated values of (\(X\), \(Y\)).  
Once the depth \(Z\) was determined, the world coordinates (\(X\), \(Y\), \(Z\)) of the point were derived. These coordinates represented the position of the point in the world coordinate system.
## 3 Experiment and Results  
As illustrated in Figure 3, this paper first captures polarized images under foggy conditions across various scenes using a polarization camera as raw data for processing. Subsequently, threshold segmentation and fusion are applied to images at different scales to distinguish fog-affected areas from fog-free regions. The scope of the light intensity region at infinity is then determined to calculate both the light intensity value and atmospheric polarization degree at infinity. By leveraging the extreme values of target light in fog-free regions for boundary constraints, the transmittance is estimated, ultimately achieving image dehazing. Next, this section will provide a detailed explanation of the algorithm proposed in this article.  
Figure 2: DoP Map under clear conditions.  
To conduct data collection across multiple scenarios under foggy conditions, this study employs a polarization camera model OR-250 CNC-P for image acquisition. As depicted in Figure 4a, the polarization camera outputs images with a resolution of 2448 \(\times\) 2048 pixels. Within the camera sensor, adjacent pixels are arranged in a 2 \(\times\) 2 configuration, corresponding to the output polarization image information. The resulting images for different polarization components have a resolution of 1224 \(\times\) 1024 pixels. The lens used for this data collection was a standard 50 mm fixed-focus lens, shown in Figure 4b. The camera was secured using a tripod and pan-tilt head, constructing a stable imaging platform illustrated in Figure 5, ensuring maximum precision in data acquisition under foggy conditions. The IDE used in this study is Microsoft Visual Studio, with C++ as the programming language, utilizing OpenCV for image processing operations.  
Figure 4: Polarization camera and fixed focus lens. (**a**): Polarization camera; (**b**): focus lens.  
Figure 5: Data Acquisition Setup and Pixel Arrangement Diagram.  
Figure 3: Flow chat of dehazing algorithm based on joint polarimetric transmittance estimation via multi-scale segmentation and fusion.
# Multi-Dimensional Attention ```
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{\begin{tabular}{c} **Bayesian** \\ **Approach** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **BP Neural** \\ **Network** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Meta-Heuristic** \\ **Method Evolves** \\ **CNN-LSTM** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Pharmaceutical** \\ **Supply Chain** \\ **Model** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MMCW-** \\ **BiLSTM** \\ \end{tabular} } \\ \hline \multirow{4}{*}{\begin{tabular}{c} AV Demand \\ Forecasting \\ Dataset \\ \end{tabular} } & MAE & 2.78 & 3.46 & 2.57 & 2.09 & 2.09 & 1.75 \\ \cline{2-7}  & MAPE & 12.11 & 9.85 & 4.8 & 5.88 & 7.44 & 2.89 \\ \cline{2-7}  & MSE & 12.97 & 16.13 & 10.29 & 8.3 & 8.88 & 6.76 \\ \cline{2-7}  & RMSE & 3.6 & 4.02 & 3.21 & 2.88 & 2.98 & 2.6 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Forecast for \\ Product \\ Demand \\ \end{tabular} } & MAE & 2.39 & 4.06 & 3.64 & 2.48 & 2.77 & 1.97 \\ \cline{2-7}  & MAPE & 8.34 & 24.20 & 6.74 & 9.39 & 4.02 & 3.91 \\ \cline{2-7}  & MSE & 11.02 & 25.17 & 14.13 & 10.39 & 11.10 & 8.76 \\ \cline{2-7}  & RMSE & 3.32 & 5.02 & 3.76 & 3.22 & 3.33 & 2.96 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Dairy Goods \\ Sales Dataset \\ \end{tabular} } & MAE & 4.74 & 3.79 & 3.03 & 2.70 & 3.54 & 2.54 \\ \cline{2-7}  & MAPE & 27.48 & 5.15 & 8.10 & 11.33 & 5.81 & 3.69 \\ \cline{1-1} \cline{2-7}  & MSE & 32.33 & 20.23 & 14.98 & 11.79 & 17.61 & 10.39 \\ \cline{1-1} \cline{2-7}  & RMSE & 5.69 & 4.50 & 3.87 & 3.43 & 4.20 & 3.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparative discussion table for AV demand forecasting dataset, forecast for product demand dataset, and dairy goods sales dataset.
###### Abstract
In particular, the proposed approach automatically detects, counts, and calculates the average number of squamous nuclei in images from liquid-based cytology (LBC) samples and consequently classifies it as adequate or inadequate based on the cellularity threshold established in The Bethesda System (TBS)--a minimum of 5000 well-preserved squamous nuclei (3.8 per microscopic field at 40\(\times\) magnification) to consider a specimen as adequate for diagnosis [5]. Therefore, this paper aims to study the impact and feasibility of using a nucleus-based deep learning approach to detect different TBS classes of cervical lesions in mobile-acquired microscopic images of LBC samples. In particular, this work starts by contributing with a new annotated dataset of images from LBC samples digitalized with a portable smartphone-based microscope, which supported the development of a novel approach to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples. Several experiments were conducted to optimize the performance of the developed lesion detection network, namely (i) hyperparameter optimizations, namely learning rate (LR) and batch size (BS); (ii) transfer learning optimizations through weight initialization from networks pre-trained on closer and distant application domains; (iii) detected class optimizations through the inclusion of normal squamous nuclei as a class detected by the model; and (iv) per-class tuning of post-processing parameters, like score threshold. A comparison between the performance achieved by the proposed nucleus-based methodology and a previous region-based work (which considered entire cells and cell aggregates as regions to detect) [6] that used the same dataset for cervical lesion detection is provided, thus supporting the contributions of this work. This paper is structured as follows: Section 1 summarizes the motivation and objectives of the work; Section 2 outlines the relevant related work present in the literature; in Section 3, the datasets used are described; Section 4 presents the methodology, including the system overview and the proposed approach to expand its capabilities from sample adequacy assessment to nucleus-based detection and classification of cervical lesions; and throughout Section 5, results are drawn alongside the discussion. Finally, Section 6 summarizes the developed work, followed by a conclusion and future work.