esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Datasets
#### 2.2.3 Drought Index  
The concept of the Standardized Precipitation Evapotranspirationpiration Index (SPEI) was first introduced by [PERSON] in 2010 [51]. It was developed from the Standardized Precipitation Index. SPEI, in addition to considering the impact of precipitation on drought, further incorporates potential evapotranspiration. SPEI has been proven to be a reliable meteorological drought index for assessing drought conditions, such as severity, duration, and intensity [52]. The calculation of SPEI first utilized the FAO-56 Penman-Monteith formula to estimate potential evapotranspiration. Subsequently, a log-logistic distribution is used to fit the monthly series derived from the difference between precipitation and potential evapotranspiration. The probability density function and cumulative probability of the series are then calculated, and finally, the SPEI at various monthly scales is obtained through normal distribution transformation [51]. SPEI is capable of quantifying water surpluses or deficits relative to monthly water deficits across multiple time scales. Therefore, we obtained the SPEI-12 with a spatial resolution of \(0.5^{\circ}\) for the period 1901-2022 from the SPEIbase (version 2.9) dataset developed by the Consejo Superior de Investigaciones Cientificas ([[https://spei.csic.es/spei_database/](https://spei.csic.es/spei_database/)]([https://spei.csic.es/spei_database/](https://spei.csic.es/spei_database/)), accessed on 26 November 2023). In this study, the SPEI-12 for December is used to assess drought conditions on an annual scale when the WUE is taken as an extremum [53]. An SPEI value less than \(-0.5\) is considered indicative of drought, a SPEI value between \(-0.5\) and \(0.5\) is classified as a normal year, and an SPEI value greater than \(0.5\) is considered indicative of humid conditions [54].
## 1 Introduction
[PERSON] [13] studied the seismic performance of a liquid storage tank's fluid-structure-soil system by embedding the tank in a box structure. [PERSON] et al. [14] combined a VOF model with FEM to predict wave heights, demonstrating the significant impact of liquid-wall coupling on oscillation effects. [PERSON] et al. [15] employed the Smoothed Particle Hydrodynamics-Finite Element Method (SPH-FEM) algorithm to evaluate the seismic response of large LNG tanks under different liquid depths. [PERSON] et al. [16; 17] conducted a multi-scale system analysis of underground LNG tanks that included seismic response analysis and thermal-fluid-structure coupling analysis in order to evaluate the long-term performance of cryogenic LNG tanks. Most of the studies in the literature focus on aboveground tanks, with there being relatively few studies on underground tanks with large capacities. Moreover, previous studies have often simplified the soil layers, inadequately capturing the constraint effect of the soil on tanks' structures. Compared to aboveground storage tanks, underground tanks have numerous advantages. Underground cylindrical tanks can fully leverage the compressive strength of concrete, allowing for a larger capacity [16]. In factory areas with multiple storage tanks, the spacing between tanks can be reduced, thus enhancing land space utilization. Moreover, underground tanks offer superior seismic resilience, making them ideal for areas prone to earthquakes [1]. This paper presents an analysis of the combined effects of fluid, tank structure, and soil and a comparison of the deformation and internal force response of an underground tank and an aboveground LNG tank, each with a capacity of 270,000 m\({}^{3}\), under seismic loads. We utilized the finite element model to simulate the tank structure and soil in Ansys (Mechanical), employed a VOF model in FLUENT to simulate the oscillation of the LNG fluid inside the tank, and then solved the transient dynamic equations in a coupled manner. The remainder of this paper is arranged as follows: Section 2 introduces the primary VOF and FEM model that we utilized. Section 3 presents an analysis of the results and a comparison between aboveground tanks and underground tanks, and finally, Section 4 presents the conclusions that can be drawn from this study.
# Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
## 1 Introduction
While studies have often adopted standard laboratory measurements to predict LOS in the ICU, some have relied on physiological scores, such as the Acute Physiology and Chronic Health Evaluation (APACHE) or the Simplified Acute Physiology Score (SAPS), and achieved higher accuracy [34; 35]. Recent work has also leveraged deep learning methods, such as recurrent neural networks (RNNs) with long short-term memory (LSTM), to incorporate the time-series embedded in electronic health records (EHRs) and provide a more accurate estimate of ICU outcomes [36; 37]. The aim of the present study was to develop predictive models for the LOS of patients admitted to the ICU based on their initial health status. We use the publicly available Medical Information Mart for Intensive Care (MIMIC) database and only consider typical health data routinely collected on the first day of ICU admission, including demographic and administrative data as well as vital signs and laboratory measurements. We perform a binary classification to identify patients at risk for a long LOS and estimate the probability that the patient will have a short or long ICU stay. The dichotomization of LOS into short and long is based on the third quartile of the entire dataset. The unexpectedly long LOS places a significant burden on ICU performance and is associated with poor outcomes, especially in patients with chronic diseases such as diabetes [32]. We also perform regression modeling to estimate the actual LOS in the ICU in a more practical framework. In this approach, only patient data whose LOS were predicted to be short in the initial classification are considered. Structural indicators such as the adequacy of equipment and qualifications of medical personnel are beyond the scope of this study. The remainder of this paper is organized as follows: First, in the methods section, we introduce the dataset used in this study and then describe the data preparation, models, and evaluation metrics. Next, we present the classification and regression results and discuss the key points and challenges in the models. Finally, we summarize the main findings of the study and outline avenues for future work.
# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## 4 Historical Development of Optimization of Ship Energy Systems
### Type of Optimization Performed  
The type of optimization and the number of articles in each type are given in Table 2, where the letters S, D, and O stand for synthesis, design, and operation optimization, respectively. The percentages are depicted also in the pie chart of Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**System**} & \multicolumn{4}{c}{**Optimization**} \\ \cline{5-8}  & & & & **S** & **D** & **O** & **Dynamic** \\ \hline
41 & [74] & 2019 & Reliquefaction systems & & \(\bullet\) & & \\ \hline
42 & [75] & 2019 & ORC: JCW-LNG cold & & \(\bullet\) & & \\ \hline
43 & [76] & 2019 & DF engines + propeller & \(\bullet\) & \(\bullet\) & \(\bullet\) & \\ \hline
44 & [77] & 2019 & DF engines + propeller & \(\bullet\) & \(\bullet\) & \(\bullet\) & \\ \hline
45 & [78] & 2019 & ORC, AR, DSO\({}_{\rm x}\), DNO\({}_{\rm x}\) & & \(\bullet\) & & \\ \hline
46 & [8] & 2019 & GT total, DE, and GT total & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \hline
47 & [79] & 2019 & Total with MCFC & \(\bullet\) & \(\bullet\) & & \\ \hline
48 & [23] & 2019 & Total & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \hline
49 & [80] & 2019 & DE, GT, EES, Ren., etc. & \(\bullet\) & \(\bullet\) & \(\bullet\) & \\ \hline
50 & [81] & 2020 & Diesel or DF engines & & \(\bullet\) & & \\ \hline
51 & [82] & 2020 & FC + electric energy storage & \(\bullet\) & \(\bullet\) & \\ \hline
52 & [83] & 2020 & APC (KC), HDH & \(\bullet\) & & \\ \hline
53 & [84] & 2020 & SCBC, KC & \(\bullet\) & & \\ \hline
54 & [85] & 2020 & FC/battery/cold ironing & \(\bullet\) & \(\bullet\) & \\ \hline
55 & [86] & 2020 & ORC: JW+EG-LNG cold & \(\bullet\) & & \\ \hline
56 & [87] & 2020 & Electronics, cooling, ESS & & \(\bullet\) & \(\bullet\) \\ \hline
57 & [88] & 2020 & FC/battery/shore connection & \(\bullet\) & \(\bullet\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Type of Optimization**} & \multicolumn{2}{c}{**Articles**} \\ \cline{2-3}  & **Number** & **Percentage** \\ \hline SDO & 10 & 17.55 \\ SD & 3 & 5.26 \\ DO & 9 & 15.78 \\ D & 24 & 42.11 \\ O & 11 & 19.30 \\ \hline Total & 57 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Type of optimization and number of articles.  
Even though the first two articles in Table 1 applied triple optimization (SDO), only 10 articles in total have been published since 2008 with SDO optimization, while the highest percentage (42.11%) applied design optimization only.
## 3 Results and Discussion
### Case 1  
Case 1 is a scenario where the power system is rendered nearly unstable using the line trip and increasing the reactive power. A time-domain simulation is performed to assess the voltage stability. Figure 13 shows the variation in the voltage magnitude in the power sending part.  
As shown in Figure 13, the two 4400-6950 circuits that are highly important interface lines were tripped after 5 s. Typically, the voltage stability problem of the Korean power system occurs on the  
Figure 12: Korean power system grid and the major transmission lines.  
Figure 13: Voltage magnitude variation of virtual bus.  
4400-6950 transmission line. Consequently, a PMU is installed on this line in case of an emergency. Subsequently, the reactive power loads increase to \(\sim\)355 MVAR in the metropolitan area in 20 s. After 30 s, the voltage of the metropolitan area decreased rapidly to below 0.7 p.u. To investigate the voltage instability, an index based on the maximal power deliverable theory is used. This index shows whether a power system is stable. From this circuit theory, maximal power transfer occurs when \(|Z_{L}|\) equals \(|Z_{Th}|\). The apparent impedance \(|Z_{L}|\) is the proportion between the voltage and current phasors measured at the load bus. When the loading is normal, \(|Z_{L}|\) is significantly greater than \(|Z_{Th}|\). In other words, the index is approximately zero. When the system suffers from voltage instability, the difference between the two impedances approaches zero, and the index is increased by \(\sim\)1.0. Figure 14 shows this concept applied to case 1.  
As shown in Figure 14, \(Z_{Th}\) increases and \(Z_{L}\) decreases, indicating that the system will become unstable. Subsequently, they are near each other at approximately t = 40 s, suggesting that the Z index approaches 1.0 and the system is nearly unstable. This voltage instability index is a real number, i.e., 0.0-1.0; if the index indicates a number over 1.0, the system will be unstable, theoretically. Similarly, an index under 1.0 indicates that the system is stable.
# Machine Learning-Based Lithium Battery State of Health Prediction Research
## 3 Data Analysis of Lithium Battery Aging and Extraction of Health Factors
### Extraction of Health Indicators
It takes approximately 3600 s for new batteries, while this time shortens to 1800 s for aging batteries. As a result, the charging energy is used as an indicator to characterize the health status of the battery, denoted as HI16. A total of 16 health indicators were extracted from the temperature, current, and voltage profiles. However, the correlation between these indicators and battery capacity varies. Therefore, it is necessary to screen these indicators to remove invalid and redundant information, thereby reducing the computational complexity and training time of the network model. For this purpose, the Pearson correlation coefficient method was used to analyze the correlation of the 16 indicators, as shown in the following formula:  
\[ \begin{split} Pearson=\frac{\sum\limits_{i=1}^{N}{(x_{i}-\overline{ x})(y_{i}-\overline{y})}}{\left[\sum\limits_{i=1}^{N}{(x_{i}-\overline{x})^{2} \sum\limits_{i=1}^{N}{(y_{i}-\overline{y})^{2}}}\right]^{\overline{2}}}\\ \end{split}  \tag{12}\]  
The closer the absolute value of the coefficient obtained from the above equation is to 1, the stronger the correlation; the closer the value is to 0, the weaker the correlation [35]. In this study, health indicators with correlation coefficients less than 0.75 are eliminated, and the remaining indicators are used for model training and testing. The results of the correlation calculations are shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **B0005** & **B0006** & **B0007** \\ \hline HI1 & 0.927298 & 0.939146 & 0.892884 \\ HI2 & \(-\)0.570453 & \(-\)0.245327 & \(-\)0.492198 \\ HI3 & 0.022756 & 0.266828 & 0.0600099 \\ HI4 & 0.999935 & 0.999915 & 0.999725 \\ HI5 & \(-\)0.934350 & \(-\)0.848772 & \(-\)0.750104 \\ HI6 & \(-\)0.677412 & \(-\)0.690275 & \(-\)0.404310 \\ HI7 & \(-\)0.807104 & \(-\)0.830185 & \(-\)0.852246 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of health factor and capacity correlation coefficient values. From the results in Table 2, it can be observed that the correlation coefficients of four health indicators--HI2, HI3, HI6, and HI15--are lower than 0.75, and thus should be eliminated. Ultimately, the remaining 12 health indicators were used as the key indicators for battery SOH prediction.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 4 Methodology
### Rock-Fall Prediction Model
#### 4.3.2 Training Methods  
The SciKit-Learn neural network package was used to create the neural network models that were used in this study. The model was generated with three hidden layers. In our method, the training process used 70% of the entire data, while the validation process used 30% of the remaining data. The training process was operated using the multilayer perceptron (MLP). The MLP was chosen because it provides quick predictions after training. It utilizes a supervised learning technique called backpropagation for training with the rectifier linear unit (ReLU). The learning algorithm performs backpropagation, which calculates the correct gradient for nonlinear multilayer networks to reduce errors (the gap between prediction and actual values) [32].  
In this study, a gradient descent method was used as an optimization model. This method updates the variables iteratively in the opposite direction of the gradients of the objective function. Equation (5) performs the gradient descent algorithm. It updates the weight and bias parameters iteratively in the negative gradient direction to minimize the loss function \(f(\theta)\).  
\[\theta_{i}=\theta_{i}-\alpha*\ \frac{d}{d\theta_{i}}\ f(\theta) \tag{5}\]  
Figure 4: The neuron’s main parts.  
where \(\alpha\) is a learning rate, \(f(\theta)\) is a loss function, and \(\theta_{i}\) is the weight or bias parameter, which we need to update.
# Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine
## 4 Modeling Results and Discussion  
### 0.5\(-\)2.1 GHz GaN Class AB PA  
#### 4.1.1 S21  
The modeling results of S21 of PA are shown in Figure 5, where Figure 5a,b show the modeling results with and without simulation data. Firstly, from the temperature characteristics of S21, S21 decreases with the increase in temperature. The main reason for the degradation of S21 with the temperature rise is that the two-dimensional electron gas (2 DEG) mobility drops. Of course, the increase in temperature will also lead to a decrease in the threshold voltage, which will lead to a rise in S21. However, the gate-source voltage is often much larger than the threshold voltage in the actual circuit to obtain a higher gain. Therefore, the effect of the threshold voltage change on the gain is negligible. It is also consistent with what was reported in the literature [11].  
Secondly, from the perspective of model accuracy, the model results of S21 match well with the actual measurement results if the simulation data are used in the modeling process (as shown in Figure 5a). In contrast, if the simulation data are not used in the modeling process of S21 (Figure 5b), there is a large gap between the model results and the actual measurement results. This is mainly because we first used the simula-tion data in the modeling process to describe the trend in the temperature characteris-tics of S21, which is only a little different from the real situation in terms of magnitude. This gap can then be eliminated by calibrating the measured data. Finally, from the point of view of frequency, the variation in S21 at different frequencies is not the same for the same magnitude of temperature change. This indicates that the 2 DEG mobility is not only temperature-dependent but also frequency-dependent.
## 3 Results
### EEG Data  
#### 3.2.1 Result of Context  
As depicted in Figure 5, a more negative deflection for genuine context was observed in the prefrontal lobes within the time window between 200 and 1000 ms. A repeated measures ANOVA was executed on the time window with factors of channel (60) \(\times\) context (genuine, fake). The findings indicated that the main effect of context was significant [F (1, 11) = 4.941, \(p\) = 0.048 \(<\) 0.05], while the interaction of channel \(\times\) context was also significant [F (59, 649) = 2.832, \(p\) < 0.01]. Further analysis revealed that in the FPZ channel, in comparison to the fake context, the genuine context would evoke a more negative amplitude [F (1, 11) = 11.902, \(p\) = 0.005 \(<\) 0.01], and a comparable waveform also manifested in the FP1 channel [F (1, 11) = 5.274, \(p\) = 0.042] and FP2 channel [F (1, 11) = 10.213, \(p\) = 0.009 \(<\) 0.01].
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 3 Experiments and Results
### Evaluation Measurements with Static Sensor
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Distance [m]** & **R1 [mm] 1** & **R1 Refined [mm] 1** & **R2 [mm] 1** & **R2 Refined [mm] 1** & **Length [mm] 1** & **Length Refined [mm] 1** \\ \hline
1.5 & 50.303 \(\pm\) 0.01 & 50.412 \(\pm\) 0.07 & 50.174 \(\pm\) 0.05 & 50.233 \(\pm\) 0.01 & 497.355 \(\pm\) 0.06 & 497.702 \(\pm\)  
Figure 4: Ball-bar length measurement results depending on object distance. Figure 5: False-color representation of flatness deviation: measurement distance of 1.7 m (**above**), 2.0 m (**middle**), and 2.4 m (**below**).
###### Abstract  
The uncasing quest for a profound comprehension of the Earth system propels the continuous evolution of novel methods for Earth observation. Of these, the Lagrange points situated in the cislunar space profiter noteworthy prospects for space-based Earth observation. Although extant research predominantly centers on Moon-based Earth observation and the L1 point within the Sun-Earth system, the realm of cislunar space remains relatively unexplored. This paper scrutinizes the overarching characteristics of the L1 point within the Earth-Moon system concerning Earth observation. A pivotal enhancement is introduced through the incorporation of the halo orbit. This research comprehensively analyzes the relative motion between the halo orbiter and the Earth, achieved via orbit determination within a rotating coordinate system, followed by a transformation into the Earth coordinate system. Subsequently, numerical simulations employing ephemeris data unveil the observing geometry and Earth observation characteristics, encompassing the distribution of nadir points, viewing angles, and the spatiotemporal ground coverage. As a point of reference, we also present a case study involving a Moon-based platform. Our findings reveal that the motion of the halo orbit, perpendicular to the lunar orbital plane, results in a broader range of nadir point latitudes, which can extend beyond 42\({}^{\circ}\)N/S, contingent upon the orbit's size. Additionally, it manifests a more intricate latitude variation, characterized by the bimodal peaks of the proposed temporal complexity curve. The viewing angles and the spatiotemporal ground coverage closely resemble those of Moon-based platforms, with a marginal enhancement in coverage frequency for polar regions. Consequently, it can be deduced that the Earth observation characteristics of the L1 point within the Earth-Moon system bear a close resemblance to those of Moon-based platforms. Nevertheless, considering the distinct advantages of Moon-based platforms, the lunar surface remains the paramount choice, boasting the highest potential for Earth observation within cislunar space. In summation, this study demonstrates the Earth observation characteristics of the L1 point within the Earth-Moon system, emphasizing the distinctions between this and Moon-based platforms.  
Earth observation; halo orbits; imaging geometry; Lagrange point; Moon-based platform; spatiotemporal coverage; viewing angle 2024
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
###### Abstract  
Disconnectable coupling (DC) joints of steel Bracing in foundation pit engineering are inevitably subjected to eccentric load, but their mechanical properties under eccentric load have not been thoroughly investigated. Based on full-scale test results of DC joints under axial compression, a validated finite element model was established. The bearing capacity and flexural performance of DC joints under eccentric load were studied systematically through a series of numerical simulations. These parameters included the length, width and height of the steel wedge; eccentricity; steel tube wall thickness; channel steel thickness and middle-rib plate height. Based on the numerical results, a modified moment-rotation model was established. The results obtained show that the numerical models accurately reflect the failure mode and the load-displacement curves revealed by the full-scale test. The bearing capacity and flexural performance of DC joints decreases with eccentricity, middle-rib plate height, and steel wedge height. The effect of eccentricity is the most significant. By contrast, the bearing capacity and flexural performance of DC joints increases with steel wedge length, steel wedge width, channel steel thickness and steel tube wall thickness. The modified moment-rotation model can describe the flexural performance of DC joints accurately under eccentric load.  
foundation pit engineering; disconnectable coupling joint; eccentric compression; finite element method; parameter analysis; moment-rotation model +
Footnote †: journal: Applied  
doi: 10.3390/0004-0001-9999
###### Abstract  
Mobile sink is widely used for data collection in wireless sensor networks. It can avoid 'hot spot' problems but energy consumption caused by multihop transmission is still inefficient in real-time application scenarios. In this paper, a greedy scanning data collection strategy (GSDCS) is proposed, and we focus on how to reduce routing energy consumption by shortening total length of routing paths. We propose that the mobile sink adjusts its trajectory dynamically according to the changes of network, instead of predetermined trajectory or random walk. Next, the mobile sink determines which area has more source nodes, then it moves toward this area. The benefit of GSDCS is that most source nodes are no longer needed to upload sensory data for long distances. Especially in event-driven application scenarios, when event area changes, the mobile sink could arrive at the new event area where most source nodes are located currently. Hence energy can be saved. Analytical and simulation results show that compared with existing work, our GSDCS has a better performance in specific application scenarios.  
wireless sensor networks; data collection; mobile sink; greedy scanning Article