esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Coronary artery disease (CAD) is a common global health problem and is the main cause of death [1, 2]. The main purpose of using percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) to treat patients with multivessel CAD is to relieve myocardial ischemia and other symptoms. PCI for multivessel diseases can result in inadequate revascularization or contrast-induced acute kidney injury. There is also a 50% probability of cardiac or noncardiac death associated with this procedure [3, 4].  
Compared to CABG surgery, PCI is relatively less invasive. PCI is increasingly being accepted as a treatment option with improved skills and instruments [5]. This procedure can be used to treat patients who refuse or cannot tolerate CABG and is estimated to gradually become the treatment of choice for CAD [6, 7, 8].  
Although many studies mention PCI as the optimal treatment, patient comorbidities can affect morbidity and mortality [9, 10, 11, 12, 13, 14, 15, 16, 17]. When three vessels are treated simultaneously, the probability of complications, or even death, during or after surgery is higher than when one or two are treated [18]. Many risk factors for mortality after PCI have been identified [19, 20], including clinical and anatomic variables. Clinical variables include age, sex, diabetes mellitus, chronic lung disease, prior myocardial infarction, impairment of left ventricular function, renal dysfunction, cardiogenic shock, left main coronary artery (LMCA), and age are the most important predictors of 1-year mortality postoperatively [21]. Thus, postoperative death remains a major issue for PCI.  
Several studies have only used single machine learning methods to make the prediction model, such focused on the degree of revascularization [22, 23] or logistic regression (LGR) for predicting in-hospital mortality [24]. Nevertheless, some mortality prediction models after PCI have been constructed using the data of significantly older people or clinical factors [25, 26, 27, 28]. No study has focused on mortality risk factors among patients who initially underwent 3-vessel PCI, and there are no machine learning prediction models.  
The present study collected data from the Taiwan National Health Insurance Research Database (NHIRD), one of Asia's largest databases, and aimed to identify variables that affect the survival of patients undergoing 3-vessel primary PCI (PPCI). The patient's risk factors and baseline characteristics were collected the year before surgery. After identifying the optimal features, ensemble learning methods were used to construct a prediction model. Five machine learning methods were applied, and majority voting was used to develop the model. This prediction model can be a basis for clinical decision-making and healthcare management policies.
# A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner
## 3 Experimental Validation  
The experimental validation of the designed microstrip devices, presented in Section 2, was carried out using in-house manufactured printed circuit boards (PCBs) fabricated on a 1.6 mm-thick FR-4 substrate PCB material with a copper thickness of 0.035 mm. Keysight (Colorado Springs, CO, USA) M8195A arbitrary waveform generator (AWG) was used to generate a unipolar Gaussian pulse with a 200 ps duration and 1 V\({}_{\text{P-P}}\) amplitude. A 20 dB attenuator was used at the input of the respective device under testing to remove possible back-reflections. The test waveform and its spectral density after the attenuator can be seen in Figure 12. Keysight DSAZ334A digital storage oscilloscope (DSO) was used for waveform acquisition and export for later analysis.  
Due to the observed signal bandwidth, which is around 3 GHz, as can be seen in Figure 12b, further testing in the frequency domain was conducted up to a frequency of 4 GHz using the Rohde and Schwarz (Munich, Germany) ZVRE vector network analyzer (VNA).
# The Correlation between Users' Cognitive Characteristics and Visualization Literacy
## 5 Results
### Numeracy and Visualization Literacy  
We divided the participants into two groups by applying the median split method on the numeracy variable. Participants with a score higher than the median of DRNT scores (\(Mdn=12\)) were classified as a high-numeracy group. Subsequently, those with a score lower than or equal to the median were classified as a low-numeracy group.  
We found evidence to support the first hypothesis (**H1 confirmed**) that the visualization literacy of the high-numeracy user group would be higher than that of the low-numeracy user group. We ran an independent-sample \(t\)-test to determine whether differences in visualization literacy scores between the high-numeracy group and the low-numeracy group. We found no significant outliers in the data as assessed by the inspection of a boxplot. Based on the Shapiro-Wilk test, visualization literacy scores for each numeracy group were normally distributed (high-numeracy group: \(W=0.979\), \(p=0.199\); low-numeracy group: \(W=0.991\), \(p=0.799\)). The scores of the two groups also had homogeneous variances as assessed by the [PERSON]'s test for equality of variances (\(F=0.995\), \(p=0.032\)). The test results indicated that the visualization literacy scores were higher for the high-numeracy group  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Measures** & **\(N\)** & **Min** & **Max** & **\(M\)** & **SD** \\ \hline VLAT & 178 & \(-2.81\) & 37.67 & 19.25 & 8.32 \\ DRNT & 178 & 3 & 15 & 12.13 & 1.91 \\ NCS & 178 & 18 & 126 & 80.98 & 25.81 \\ VVQ & 178 & \(-33\) & 60 & 4.28 & 12.93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive Statistics of Scores on the Measures.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Measures** & **VLAT** & **DRNT** & **NCS** & **VVQ** \\ \hline VLAT & \(1\) & & & \\ DRNT & 0.565 \({}^{**}\) & \(1\) & & \\ NCS & 0.089 & 0.121 & \(1\) & \\ VVQ & 0.117 & 0.080 & \(-0.334\)\({}^{**}\) & \(1\) \\ \hline \hline \end{tabular} \({}^{**}p<0.01\).  
\end{table}
Table 2: Correlations Between Four Measurement Scores (\(N=178\)).  
(\(M=23.49\), \(SD=7.63\)) than the low-numeracy group (\(M=15.64\), \(SD=7.12\)), and they were significantly different, \(t(176)=7.095\), \(p<0.001\), [PERSON]'s \(d=1.07\). We also confirmed the results through a point-biserial correlation analysis (\(r_{pb}=0.47\), \(N=178\), \(p<0.001\)). The results relevant to H1 are summarized in Figure 3.
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 5 Analysis of Cable Life Prediction Model Based on Autoformer
### The Impact of Iteration Count on the Performance of the Autoformer Model  
The iteration count reflects the decrease in the loss function of deep learning models and their convergence status. Utilizing MSE as the loss function, as demonstrated in Figure 4, the predictive performance of the Autoformer model markedly improves, with an increase in iteration count during parameter adjustment experiments. When the iteration count reaches 4, the model has already converged optimally. However, further training beyond this point not only fails to enhance the model's predictive accuracy but also significantly diminishes it. This phenomenon suggests that the model might be overfitting the training data, losing its ability to generalize to new data. This attests to the Autoformer model's robust parallel computing capacity in feature extraction, facilitating faster convergence. Considering the training time and computational costs, this study sets the iteration count for the model at 4.  
Figure 4: Variation in loss with iteration count.  
Figure 3: Comparison of predicted optical cable length changes in 2023: LSTM, Bi-LSTM, Bi-LSTM + Attention, and Autoformer Models. Actual values are shown in gray. The Autoformer model’s predictions are shown in red. Green, orange, and blue lines represent the predictions of the LSTM, Bi-LSTM, and Bi-LSTM + Attention models, respectively.
# Real-Time Tomography of Gas-Jets with a Wollaston Interferometer
## 3 Data Analysis for Interferometry and Real-Time Tomography
### Real-Time Computation of the Density Reconstruction
For each projection 20 images were acquired, 10 of which were recorded with the gas jet on and the other 10 with the gas jet off, as a reference. The achieved speed-ups on multi-core CPUs and GPU will allow performing reconstruction in real time.
# Design of K-Band Power Amplifier with 180-Degree Phase-Shift Function Using Low-Power CMOS Process
## 4 Conclusions  
In this study, a power amplifier was designed using the 40-nm low-power CMOS process. In order to reduce the overall system size, a 180\({}^{\circ}\) phase-shift function was mounted in the first driver stage of the power amplifier. To mount the 180\({}^{\circ}\) phase-shift function, instead of using two identical amplifiers, the phase-shift function was implemented by modifying the single-differential cascode structure. A common-gate transistor of the cascode structure was divided into two transistors to obtain 180\({}^{\circ}\) phase-shift paths. In addition, wide bandwidth characteristics were secured by varying the optimized operating frequency for each stage. The measured P1 dB for 0\({}^{\circ}\)- and 180\({}^{\circ}\)-phase-shift modes were 15.25 dBm and 14.30 dBm, respectively, in the operating frequency of 25.0 GHz. The measured phase difference between the two modes was 217\({}^{\circ}\) at 25.0 GHz.  
Not applicable.  
**Data Availability Statement:** All the material conducted in the study is mentioned in the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Antimicrobial Peptide Screening from Microbial Genomes in Sludge Based on Deep Learning
## 2 Materials and Methods
### Combined Antimicrobial Peptide Screening Deep Network  
This prediction framework (as shown in Figure 1) is employed for the recognition of antimicrobial peptides and combines LSTM and ALSTM networks to form a composite model. This dual-model architecture aims to leverage the respective strengths of LSTM and ALSTM, resulting in more refined prediction outcomes. The sigmoid function can map any variable to a value between 0 and 1, making it well-suited for generating probability outputs. The linear function is essentially an identity function, aiming to directly output continuous prediction values.  
The final prediction of AMPs is determined through a consensus method, where both the LSTM and ALSTM models must independently identify a peptide as an antimicrobial peptide. This fusion method ensures that only peptides identified as potential AMPs by both models are considered, thereby improving prediction accuracy and minimizing false positives. The validation process confirms that the fusion model is significantly better than individual LSTM or ALSTM models, demonstrating enhanced predictive power and reliability.  
\[P_{consensus}=P_{LSTM}\&\&P_{ALSTM} \tag{13}\]  
Here, \"&\(\&\)\" represents the logical AND operator: it is considered an antimicrobial peptide only when equal to 1, and it is set to 1 only when the probabilities identified by both models are greater than 0.5.
# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 3 Hardware-Based Energy Sustainability in WSNs  
### The Architecture of Wireless Sensor Nodes  
Each sensor node of a WSN is a Micro Electromechanical system (MEMS) [1; 26], which is composed of four main and two optional subsystems, as illustrated in Figure 3. The basic subsystems of a node are:  
* The power unit, of which the battery is the main and most commonly used part. Solar panels could also be used as a secondary energy source to a node [3];
* The sensing unit that contains one or more analog or digital sensors and an analog to digital converter (ADC);
* The central processing unit (CPU), which comprises a microprocessor or microcontroller, along with its memory and its main purpose is to aggregate, store and process the data recorded from sensors;
* The communication unit, which is responsible for the transmission of the produced data to other nodes or to the base station. The communication unit usually contains a wireless radiofrequency (RF) transceiver. Moreover, devices for the communication through optical, or infrared signals may be used.  
A sensor node may also contain, as optional subsystems, a position tracking unit, which monitors the current location of this node, and a mobility unit, which provides the node the ability to be transportable [2].  
Summarily, the sensing unit of a sensor node is triggered by an occurring event in its adjacent environment. The ADC converts the signals to electric signals that are handled by the processing unit. Once the processing procedure is completed, the produced data can be wirelessly transmitted to neighboring nodes or/and the BS.
## 1 Introduction
The pixel values belonging to the background region are removed from the solution process. By reducing the number of unknowns while keeping the number of equations in the inverse problem unchanged, the ill-posedness of the image reconstruction problem is improved, ultimately leading to higher-quality reconstructed images. The structure of this paper is organized as follows: Firstly, Section 2 analyzes the basic principles of EIT imaging, establishes the mathematical model of EIT imaging, and analyzes the process of EIT image reconstruction. Then, in Section 3, image segmentation is applied to extract the region of interest for EIT, utilizing the maximum entropy threshold image segmentation algorithm and the soft-threshold segmentation algorithm. Subsequently, Section 4 proposes an image reconstruction algorithm based on image segmentation. In Section 5, a human chest 16-electrode EIT simulation model is established for simulation verification. Finally, in Section 6, an experimental platform for human lung electrical impedance tomography is constructed for experimental validation. The quantitatively calculated evaluation metrics show that the methods proposed in this paper can achieve better-reconstructed images. The schematic diagram of the paper's structure is shown in Figure 1.  
Figure 1: Article structure schematic.
###### Abstract  
The Greenhouse gases Observing SATellite (GOSAT) launched in January 2009 has provided radiance spectra with a Fourier Transform Spectrometer for more than eight years. The Orbiting Carbon Observatory 2 (OCO-2) launched in July 2014, collects radiance spectra using an imaging grating spectrometer. Both sensors observe sunlight reflected from Earth's surface and retrieve atmospheric carbon dioxide (CO2) concentrations, but use different spectrometer technologies, observing geometries, and ground track repeat cycles. To demonstrate the effectiveness of satellite remote sensing for CO2 monitoring, the GOSAT and OCO-2 teams have worked together pre- and post-launch to cross-calibrate the instruments and cross-validate their retrieval algorithms and products. In this work, we first compare observed radiance spectra within three narrow bands centered at 0.76, 1.60 and 2.06 \(\upmu\)m, at temporally coincident and spatially collocated points from September 2014 to March 2017. We reconciled the differences in observation footprints size, viewing geometry and associated differences in surface bidirectional reflectance distribution function (BRDF). We conclude that the spectral radiances measured by the two instruments agree within 5% for all bands. Second, we estimated mean bias and standard deviation of column-averaged CO2 dry air mole fraction (XCO2) retrieved from GOSAT and OCO-2 from September 2014 to May 2016. GOSAT retrievals used Build 7.3 (V7.3) of the Atmospheric CO2 Observations from Space (ACOS) algorithm while OCO-2 retrievals used Version 7 of the OCO-2 retrieval algorithm. The mean biases and standard deviations are \(-0.57\pm 3.33\) ppm over land with high gain, \(-0.17\pm 1.48\) ppm over ocean with high gain and \(-0.19\pm 2.79\) ppm over land with medium gain. Finally, our study is complemented with an analysis of error sources: retrieved surface pressure (P\({}_{\text{surf}}\)), aerosol optical depth (AOD), BRDF and surface albedo inhomogeneity. We found no change in XCO2 bias or standard deviation with time, demonstrating that both instruments are well calibrated.
## 1 Introduction  
Volatile sulfur compounds (VSC), often having very low odor thresholds, are responsible for many common off-flavors in wine [1]. Such compounds often have offensive odors, imparting notes of cabbage, onion, garlic, or rubber to wines [1; 2; 3; 4; 5; 6]. However, because of their relatively low concentrations, a highly sensitive method is required for accurate analysis.  
Solid-phase microextraction (SPME) is a common method for the extraction of volatiles from food and beverage samples [4; 7; 8]. A coated fiber is extended into the headspace of a sample vial, allowing a finite number of volatiles to adsorb until later thermal desorption. The relatively minute scale of the fiber's capacity compared to the over sample ensures that the removal of a small fraction of the volatile content will not disturb the sample equilibrium. This also allows for very fast equilibration between the air-fiber interface as compared to static headspace analyses [9]. However, due to the limitation of space for volatiles to adhere to the fiber, the presence of other volatiles from the matrix can interfere with analysis [10].  
Ethanol is the major volatile in wine and its concentration often varies greatly among cellars and vintages. This can cause issues with sulfur analysis, as a decrease in analysis sensitivity has been shown in samples with increasing ethanol content [11; 12]. While volatile competition on the fiber may be a factor, some have suggested that ethanol acts as a co-solvent in the sample liquid, affecting transition coefficients at the liquid-air interface [13]. This suggestion was a result of ethanol concentration studies using static headspace techniques. Furthermore, it has been shown that varying sample parameters, such as the temperature, extraction time, and matrix can affect individual compounds differently [13]. This is of major concern for wine analysis, as parameters such as ethanol content and volatile and non-volatile profiles are well-varied. For this reason, a proper internal standard must be found for each analyte, ensuring their behaviors in response to parameter variation are consistent and shared.  
Carboxen-poly(dimethylsiloxane) (Car/PDMS) is the most commonly-used fiber coating for SPME sulfur analysis due to its high sensitivity. Because of the porous structure of Carboxen particles dispersed in the PDMS phase, it exhibits the displacement effects of other compounds, whereas other more-uniform fibers such as PDMS and DVB/PDMS have no displacement effect [9]. In addition, it has also been shown to have less repeatability [14].  
In order to investigate the influence of ethanol on SPME sensitivity, several common sulfur compounds were analyzed on three different fibers. Five internal standards were investigated, including ethyl methyl sulfide (EMS) and diisopropyl disulfide (DIDS). The samples were prepared with identical concentrations of analytes and internal standards, with varying ethanol concentrations.
# 3D Photoacoustic Tomography System Based on Full-View Illumination and Ultrasound Detection
## 2 Methods
### The Reconstruction Algorithm of the 3D-PACT System  
According to the principle of photoacoustic imaging, the homogeneous wave equation can be formulized as:  
\[(\
abla^{2}-\frac{1}{c^{2}}\frac{\partial^{2}}{\partial t^{2}})p(r,t)=-p_{0} (r)\frac{d\delta(t)}{dt} \tag{1}\]  
where \(c\) is the velocity of sound and \(p\) is the initial sound pressure generated by the energy deposition of the pulsed laser. By solving the above wave equation, the photoacoustic signal detected by the ultrasonic transducer at any time \(t\) and any position \(r^{\prime}\) is obtained, which can be expressed as:  
\[p(r,t)=\frac{\partial}{\partial t}[\frac{1}{4\pi c^{3}t}\int dr\cdot p_{0}(r) \cdot\delta(t-\frac{|r-r|}{c})] \tag{2}\]  
The distribution of the original photoacoustic signal can be recovered by using the back-projection reconstruction algorithm [37; 38; 39]. Furthermore, the traditional back-projection reconstruction algorithm is shown in Equation (3):  
\[p_{0}(r)=\frac{1}{4\pi c^{3}}\int dS\frac{1}{t}[\frac{p(r,t)}{t}-\frac{ \partial p(r,t)}{\partial t}]|_{t=|r-r|/c} \tag{3}\]  
With the development of the traditional back-projection reconstruction algorithm, a reconstruction algorithm of a ring-shaped array photoacoustic image based on a sensitivity factor is proposed in this study. The photoacoustic signals collected by the transducer were back-projected to each pixel on the arc established by time in the imaging area, which is illustrated in Figure 3a as a schematic diagram. The aforementioned virtual point ultrasonic transducer has a large signal receiving angle (\(100^{\circ}\), corresponding to the angle \(\Phi\) in the figure), and the transducer is relatively sensitive to the central region of its receiving scope. Therefore, a variable sensitivity factor \(S(\psi)\) was introduced in the algorithm of image reconstruction, and it decreased with the increase of \(\psi\), which is the angle between the connecting line of the pixel and probe and the center line of the detection area. In the process of algorithm implementation, the basic Equation (4) needed to be discretized first:  
\[p_{0}(r)=\sum_{i=1}^{n}\big{[}p(r^{\prime\prime},t)-t\frac{\partial p(r,t)}{ \partial t}\big{]}|_{t=|r^{\prime\prime}-r|/c} \tag{4}\]  
where \(r^{\prime\prime}\) is a distance parameter introduced in the process of discretization. Taking the angle of the receiving signal of the ultrasound probe into account, the back-projection reconstruction algorithm based on sensitivity factor is proposed and can be expressed as:  
\[p_{0}(r)=\sum_{i=1}^{n}S(\psi)_{(r,i)}[p(r^{\prime\prime},t)-t\frac{\partial p (r,t)}{\partial t}]|_{t=|r^{\prime\prime}-r|/c} \tag{5}\]In order to perform the 3D image rendering, a large number of B-scans were loaded into Amira 5.4.3 to be superimposed and processed, as shown in Figure 3b.
## 1 Introduction
This paper suggests a method to determine the frequency-power droop coefficients of multiple HVDCs in MIDC systems. First, we propose an optimization problem for MIDC systems, to determine the operating points of HVDCs to minimize system loss. Using the results of the optimization problem, we propose a droop coefficient design method based on Monte Carlo sampling. The proposed method is verified by MATLAB simulation utilizing IEEE test systems and the actual networks of Jeju Island in Korea. Using our method, a system operator can define the operating points and droop coefficients of an MIDC system more efficiently.