esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 3 Results  
### Study Selection  
The study selection process is illustrated in Figure 1. The initial search across relevant databases yielded 6276 studies. After removing duplicates, 6114 studies were screened based on title and abstract. Subsequently, 6089 studies were excluded due to irrelevance to the research question. A full-text assessment was conducted for the remaining 84 reports. A further 64 reports were excluded for various reasons: unavailability of full text (\(n=6\)), duplicate publication (\(n=7\)), mismatched intervention (\(n=11\)), irrelevant outcome (\(n=14\)), or inappropriate population (\(n=15\)). Ultimately, 11 studies met the inclusion criteria and were included in the systematic review and meta-analysis. All included studies were retrospective cohorts (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
The included studies were conducted over a broad period, from 2012 to 2023, with data collection spanning from 1995 to 2019. A variety of ML models were investigated across  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Study** & **Design** & **County** & **Period** & **Sample Size** & **ML Models** & **Summary** \\ \hline [PERSON] (2012) [34] & Retrospective & US & 200–2009 & 2326 & Logistic Regression & Suggested potential for prediction models to evolve into alert systems for timely intervention in high-risk patients. \\ \hline [PERSON] (2022) [35] & Retrospective & US & 2008–2017 & 345,305 & XGBost & Developed an XGBost model with excellent calibration for early mortality prediction post-dialysis initiation. \\ \hline [PERSON] (2021) [36] & Retrospective & China & 2007–2016 & 1200 & Multiple ML Models (e.g., SVM, LSTM) & Employed anomaly detection with LSTM autoencoder, effectively identifying high-risk patients using longitudinal HD data. \\ \hline [PERSON] (2021) [37] & Retrospective & Iran & 2007–2017 & 758 & Decision Tree, SVM, Logistic Regression & Logistic Regression & Logistic Regression outperformed other models; key predictors included gender, age, iron levels, CRP status, and URR. \\ \hline [PERSON] (2020) [38] & Retrospective & US & 2004–2016 & 363 & Random Forest & Identified key predictors (e.g., LDH, RDW) in pediatric HD patients, emphasizing the need for tailored management strategies. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Flowchart of the study selection process. ** Records excluded** (n = 6089) due to irrelevance at title and abstract screening.  
these studies, including Logistic Regression, Adaptive Boosting, Decision Tree, Gradient Boosting, K-Nearest Neighbor, Random Forest, XGBoost, and Support Vector Machine.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles
### Linewidth Calculation Method Based on Frequency Noise
#### 2.5.3 Optical Coherent Reception Method Based on a 120\({}^{\circ}\) Interferometer  
The optical coherent reception method based on a 120\({}^{\circ}\) interferometer utilizes the principle of phase reconstruction to characterize phase/frequency noise [79; 80; 81]. The tested laser passes through a phase difference interferometer system with a 3 \(\times\) 3 coupler to measure the differential phase information, as shown in Figure 5d. The measured laser is adjusted for power through a variable optical attenuator and is divided into two parts by a ring-shaped device. One part is directly connected to the data acquisition card as the reference light, and the other part is connected to a 3 \(\times\) 3 coupler. The light passing through output ports 1 and 2 of the coupler is reflected back to the coupler for mixing interference by the FRMs, and output port 3 is unloaded. The three output terminals are split beam lasers with the same amplitude and a phase difference of 120\({}^{\circ}\) from each other. The frequency/phase characteristics that deviate from linear scanning from the linear fitting curve are analyzed after the output data are collected through analog cards [82; 83]. The phase noise PSD is obtained by demodulating the phase information [84; 85].  
The linewidth calculation based on phase/frequency noise measurements does not require excessively long fibers, avoiding Gaussian broadening and additional losses. The Lorentz linewidth and integral linewidth calculations are based on the \(\beta\) isolation line algorithm, which is suitable for any type of noise in the tested laser and does not require subsequent fitting of the beat frequency curve. The linewidth is analyzed and calculated by measuring the frequency noise power spectrum. Considering the main contributions of 1/_f_ noise and white noise to the laser linewidth, the contributions of phase/frequency noise to the laser linewidth can be observed, and the complete distribution characteristics of noise and linewidth in the Fourier frequency domain can be obtained.
## 1 Introduction
Wigner-Ville distribution (WVD) is an ideal approach for a three dimensional representation of non-stationary signals in the time-frequency-amplitude domain, which is effective for monitoring machinery under non-stationary conditions [21]. [PERSON] et al. [22] presented a mixed approach of adaptive noise cancelling (ANC) and time-frequency signal analysis application for detection of wheel flat and rail surface defect. The experimental results show that the combination of ANC technique and WVD can extract weak signals and effectively suppress noises for fault diagnosis. Although the WVD is a valuable tool for time-frequency signal analysis, the cross-term interference problem is the main limitation for its application [23]. Based on the above discussion, a joint time-frequency method based on improved EMD (called the \"ensemble EMD\", or EEMD) and the WVD is proposed to extract characteristics from axle-box acceleration signals. This approach minimizes cross-term interferences and permits an adaptive time-frequency-amplitude representation. The aim of this paper is thus to present an analysis of axle-box vibration acceleration using the joint time-frequency method to determine the essential information relating to polygon wheel defects. The first step of this study is to provide quantitative theoretical analysis of the relation between axle-box accelerations and polygonal wheel defects. Then follows the simulation of dynamic responses of axle-box acceleration due to wheel polygons. Based on the theory of vehicle-track coupling dynamics, a dynamic numerical train-track coupling model is developed that considers both elastic wheelsets and elastic tracks implemented in Universal Mechanism (UM) and finite-element analysis (ANSYS) software. The results form a basis for detecting various polygon wheel defects for high-speed railways. Finally, the signal processing of axle-box acceleration signals is performed based on improved EMD and WVD joint time-frequency approaches.
## 2 Related Works  
### Identification of Traffic Signal Information  
Prior to propagating traffic signal information through C-ITS such as V2I communication, a number of studies on the identification of traffic signal, localization, and state classification (i.e., traffic signal color) have mainly counted on the vision-based methods up to recently, conceding its problems due to occlusions, illumination, scale variations, and so on [4; 5]. Nevertheless, most studies on it have made greater efforts to improve their detection performance, aiming for utilization in autonomous vehicle applications.  
[PERSON] (1999), [PERSON] et al. (2020), and [PERSON] et al. (2021) proposed and evaluated a vision-based traffic lights recognition algorithm under a variety of lighting conditions using a public data set on the actual city road at night with YOLOv3, YOLOv4, and convolutional neural network (CNN) models, concluding that it has a relatively high accuracy rate in complex scenarios and can promote the engineering application of autonomous vehicles with some limitations, such as the distance from vehicles to the traffic signal [4; 5; 6]. [PERSON] et al. (2020) employed the improved YOLOv4 algorithm and investigated its effectiveness in enhancing the detection and recognition precision of traffic signals, resulting in a precision that was 2.86% higher than the mean average precision of the original YOLOv4 algorithm and introducing it as a practical method for autonomous vehicle applications [7]. Also, [PERSON] et al. (2003) implemented the adaptive thresholding and deep learning method  
Figure 1: Status of greenhouse gas emissions of the transportation sector and roads in Korea.  
to recognize the traffic signal based on images taken by a camera mounted on a vehicle, revealing an 89.60% true detection rate [8].  
Despite these research efforts, there are still inherent limitations with the vision-based traffic signal recognition methods, which is that approaching vehicles must recognize the traffic signal and state without any latency due to traffic safety issues. Hence, a more robust and solid method (e.g., V2I communication) to recognize the traffic signal information compared with the vision-based one should be developed and implemented for the diverse autonomous vehicle applications.
## 4 Methodology to Define the Haptic Scales
### Experiment 1: Obtaining a Proper Amplitude for the Recognition of Each Wave
#### 4.1.1 Experimental Design  
We designed an adaptive method based on the user responses. An initial sequence of waves was initially presented to the subject, consisting of three shapes to be recognised, starting with the lowest amplitude perceived in previous tests (4 mm), and a frequency equal to the average value within the range obtained in [PERSON] et al. [32]. This value is equal to 10.21 Hz. The procedure in this experiment is based on the theory that the user must not only recognise the different shapes but also indicate how sure he or she feels when haptically differentiating between the three shapes (sine, sawtooth and square). Users indicate the level of confidence with which they recognise these shapes using a 5-point Likert scale, where 5 represents \"very high confidence in the response\" and 1 represents \"very low confidence in the response\". The different shapes are randomly displayed. While the user answers correctly, with a maintained level of confidence, the amplitude will be taking a greater value. The increase in amplitude obeys Weber's Law [16], starting with a base stimulus in which the amplitude is 4 mm (lower amplitudes were not perceived in preliminary tests). Then, based on [PERSON] et al., the just-noticeable difference (JND) increment is equal to 1/5 of the base stimulus. The experiment stops when the user fails to identify the shape or if the confidence level in the response decreases (even when the answer is correct). The optimum amplitude is then set to the previous amplitude value that provided the correct identification of the shape with the highest degree of confidence.
## 4 Discussion
### Soil pH, Organic Matter Content, and Cation Exchange Capacity  
Soil pH measures hydronium ion (H\({}_{3}\)O\({}^{+}\), or more commonly the H\({}^{+}\)) activity in the soil solution [36] which influences nutrient solubility and availability in the soil [37]. The ion exchange reaction may have contributed to the increase of soil pH in PL treated soils when OH\({}^{-}\) of Al or Fe\({}^{2+}\) hydroxyl oxides are replaced by organic anions [38]. The observed increase in soil pH from acidic (6.36) to alkaline pH (8.24) indicates the potential limiting effect of poultry litter and its biochar as observed by [39]. The soil alkalinity of biochars may have been attributed to ash content which elevates soil pH [33]. Basic cations are released during microbial decarboxylation [40], and reduced cations leaching increased soil pH. Increased base cations released in the soil may explain the increased soil pH in PL and BC350 with the increasing application rates [41]. Furthermore, higher organic matter content produces more alkali oxides, which hydrolyze and generate OH groups, increasing pH values [42], as shown in Figure 1a. The deacidifying effect findings agree with [43] which indicates a liming effect after applying 1% and 2% of biochars to the soil. [PERSON] et al. [44] also demonstrated an increase in soil pH following the incorporation of organic amendments into the soil.  
Cation exchange capacity (CEC) is a measurement of the number of negatively charged sites on soil surfaces to retain positively charged ions (cations) Akca & Namli [45]. It is directly affected by soil pH and indicates the soil's ability to retain and supply nutrients to a crop. The additions of organic material increased soil's CEC over time [45] due to the influence of soil pH (Figure 1a). Besides soil pH, an increase in organic matter content may have contributed to an increase in CEC because more cations bind to organic matter particles. Furthermore, the findings agree with those of [46], who discovered an increase in CEC with the incorporation of poultry litter into soil. The Ross & Ketterings [47] study found that rice-husk biochar increased soil pH and cation exchange capacity in tropical Alfisols which agrees with this study. CEC values are low for clay-poor soils, i.e., control soils and soils amended with low application rates had the lowest CEC.  
The increased soil pH increases soil organic matter solubility by increasing the dissolution of acid functional groups [48]. The incorporation of organic amendments may also improve soil drainage, which reduces aeration and, as a result, the decreased rate of decomposition leads to increased organic matter content [33].
## 3 Results
### Ocean Modeling  
Seaglider(r), Sailbuoy, and Wave Glider observations were assimilated into the NorShelf-2.4 km model daily by ingesting data from the past two days to produce improved initial conditions for the ocean forecast and the nested high-resolution ocean model of the LoVe  
Figure 7: Spectrogram showing the narrow frequency of a humpback whale (red) and a fin whale (orange) vocalization (**A**) and airgun shots from a seismic survey (**B**). Color represents sound from low (dark blue) to high intensity (yellow–red). Data collected with the Seaglider® equipped with an Observer/AMAR G4 from JASCO.  
region. The assimilation of glider observations had a significant impact on the model fields. While the maximum analysis increments occurred near the glider's locations, glider data assimilation also had a considerable effect on adjusting the remote hydrographical field (Figure 8).  
The difference between the two models was mainly dominated by salinity (Figure 9). NorShelf-2.4 km had a more realistic description of the low-saline water masses associated with the Norwegian Coastal Current and better overall agreement with the observations. Our results indicated that even with relatively limited incorporation of glider observations, the model performance was improved over a larger geographical extent. This functionality of Gliders, in turn, provided a significant gain in adjusting the simulations and may help to constrain small-scale circulation features, such as mesoscale eddies. Gliders can thus be an important complement to the existing observations [57]. The previous ocean model simulation for the LoVe region (METreport-04/2018 ISSN 2387-4201 Oceanography) showed a strong negative bias both in salinity and temperature fields. This could be an indication of an inaccurate freshwater in-flow budget. In the LoVe region, the NwAC meets a narrow continental shelf, where large-scale flow dynamics directly affect the oceanic micro-processes. The multiscale high-resolution setup was helpful in better resolving those processes (for more details, see [57]).  
Figure 8: Snapshots (left column: 20 March 2018, right column: 3 April 2018) of glider data assimilation influence on the NorShelf-2.4 km model salinity and temperature fields. Upper panel: topography and gliders’ positions, middle panel: salinity difference at 20 m depth, lower panel: temperature difference at 20 m depth [57].
## 4 Fault Diagnosis Results
### Fault Diagnosis Results
This bias caused the 1D CNN model to diagnose the fault state improperly, showing inferior fault diagnosis performance. The test result for synthetic data is depicted in Figure 10b. Compared to the results from the experimental data, the performance for the healthy state decreased slightly. However, the classification results for faulty states increased dramatically; 23 cases were predicted correctly out of 24 cases. From this, it could be concluded that the proposed data augmentation technique increased the overall fault diagnosis performance. At first glance, the accuracy seems to show only a negligible improvement, rising from 89.29% to 91.96%. However, the other metrics, precision and sensitivity, showed much-improved outcomes. The precision surged from 29.76% to 74.10%, while the sensitivity soared from 33.33% to 94.39%. These differences in results across the metrics derive from the imbalanced nature of the dataset. Imbalanced datasets can often lead to misleading accuracy metrics, as the model might perform very well on the majority class but poorly on the minority class. In such cases, precision and sensitivity provide a more accurate understanding of the model's performance, especially in identifying and classifying the underrepresented class.
## 2 Literature Review
### MCDM Approaches  
Recognizing the human factors that affect decision-making when establishing action plans is relevant. Multicriteria decision-making (MCDM) methods help with this task. MCDM is a decision theory methodology used across disciplines [41; 42; 43; 44; 45; 46; 47; 48; 49]. The use of MCDM to address evacuation-related decision-making problems has been appreciated in recent years [14; 15; 18; 21; 22; 50; 51].  
For instance, the Analytic Hierarchy Process (AHP) was applied in [14], to prioritize the evaluation indices involved in expert decisions. In [15], AHP and TOPSIS were used to evaluate criteria weights and the suitability of locations in emergency medical service facilities. These techniques were combined with specific technical tools such as Building Information Modeling (BIM) [51].  
As shown in Table 1, some studies have used MCDM for emergency evacuation management. However, its potential benefits remain underexplored, with a significant gap in synthesizing advances from various disciplines such as the social sciences, psychology, and engineering. For example, in the review conducted in [21], the contribution of artificial intelligence to natural disaster management was identified, and its interaction with MCDM models was studied. However, their scope was primarily limited to engineering aspects. Additionally, in [22], a model was employed that integrates certain aspects related to the human factor; however, their approach is more oriented towards general aspects, with MCDM being used for aspects unrelated to the human factor's incidence from a socioeconomic or psychological perspective.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author(s)** & **Human Factor** & **Evacuation** & **MCDM Methods** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Comparison of papers related to emergency evacuation management. (‘x’ indicates that the respective aspect is addressed in a study.)Several MCDM techniques have been used for facing various applied problems. As presented in Table 2, there are not any works using a combined CRITIC, WASPAS, and TOPSIS MCDM technique.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Author(s)** & **Human Factor** & **Evacuation** & **MCDM Methods** \\ \hline  
[15] & & x & x \\
[17] & x & x & \\
[64] & & x & x \\
[20] & x & x & x \\
[65] & & & x \\
[48] & & & x \\
[23] & & x & \\
[42] & & & x \\
[46] & & & x \\
[41] & & & x \\
[66] & & x & x \\
[45] & & & x \\
[44] & & & x \\
[47] & & & x \\
[14] & x & x & x \\
[21] & & x & x \\ Our study & x & x & x \\ \hline \end{tabular}
\end{table}
Table 2: MCDM techniques used in different related works. (‘x’ indicates that the respective method was used in a study.)
# Scale-BOSS-MR: Scalable Time Series Classification Using Multiple Symbolic Representations +
## 4 Evaluation
### Evaluation of SBMR with the Ridge Regression with Cross-Validation Classifier  
As part of the evaluation, we used a Ridge Regression with a Cross-Validation classifier denoted by RidgeCV. We chose to run experiments using RidgeCV because it is the classifier used in ROCKET, miniRocket, and WEASEL 2.0.  
The results with the RidgeCV and W0 configuration can be found in Table 7, showing, at a great extent, consistency with the results reported for RF.  
From Table 8 and Figure 5, we can see the following:  
1. The best configuration in terms of accuracy is SBMR-RidgeCV-W8-trend-D6-BG, which achieves an accuracy of 0.892 with 517.5 s of mean total execution time.
2. Other window configurations can improve the execution time, but at the expense of lower accuracy: * SBMR-RidgeCV-W14-trend-D6-BG achieves an accuracy of 0.882 with 160.4 s of mean total execution time; * SBMR-RidgeCV-W11-trend-D6-BG achieves an accuracy of 0.881 with 129 s of mean total execution time; * SBMR-RidgeCV-W0-trend-D6-BG achieves an accuracy of 0.844 with 70.2 s of mean total execution time.
3. Opting for no dilation but with different window configurations can further improve the total execution time, but at the expense of even lower accuracy: * SBMR-RidgeCV-W11-trend-D0-BG achieves an accuracy of 0.844 with 63.5 s of mean total execution time; * SBMR-RidgeCV-W15-trend-D0-BG achieves an accuracy of 0.831 with 40.6 s of mean total execution time; * SBMR-RidgeCV-W11-trend-D0-UG achieves an accuracy of 0.827 with 43.7 s of mean total execution time.  
Figure 4: Average total execution time (in seconds) and accuracy for multiple window configurations.  
From the above, we can see that using RidgeCV SCALE-BOSS-MR gives better results in terms of accuracy when using both multiple windows representation and dilation, compared to RF, without a significant increase in total execution time.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Accuracy\_Mean** & **Accuracy\_std** & **Total\_Time\_Mean** & **Total\_Time\_std** \\ \hline SBMR-RidgeCV-W0-trend-D1-BG & 0.858 & 0.095 & 135.9 & 66.4 \\ SBMR-RidgeCV-W0-trend-D2-BG & 0.855 & 0.094 & 100.2 & 48.8 \\ SBMR-RidgeCV-W0-trend-D3-BG & 0.852 & 0.090 & 78.2 & 38.0 \\ SBMR-RidgeCV-W0-trend-D6-BG & 0.844 & 0.087 & 59.6 & 37.4 \\ SBMR-RidgeCV-W0-trend-D5-BG & 0.841 & 0.095 & 59.8 & 33.8 \\ SBMR-RidgeCV-W0-trend-D4-BG & 0.840 & 0.089 & 60.2 & 36.4 \\ SBMR-RidgeCV-W0-trend-D6-UG & 0.818 & 0.101 & 38.7 & 21.9 \\ SBMR-RidgeCV-W0-trend-D0-UG & 0.780 & 0.101 & 21.5 & 11.4 \\ SBMR-RidgeCV-W0-noTrend-D0-UG & 0.743 & 0.100 & 11.2 & 6.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy and execution time of RidgeCV classifier without multiple windows.  
Figure 5: Average total execution time (in seconds) and accuracy using ridge regression.