esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Recovery of CB Redispersed from Quartz  
Sodium chloride (p.A., Sigma-Aldrich, St. Louis, MO, USA) and quartz sand (Buchi Labortechnik GmbH, Essen, Germany) were tested for their suitability as dry-food simulant. Both materials exhibit sufficient small grain sizes and hardness to provide a homogeneous friction of the polymer surface. Both simulants are insoluble in 96% ethanol and they are easy to be washed off for extracting the abraded matter.  
To evaluate the influence of the dry simulant on the detectability of the CB particles, a compatibility test was performed. In separate vials, 1.0 g of the dry simulants were added to 100 mL of freshly prepared 100 mg/L CB dispersions and shaken for 15 min on a laboratory shaker. The dispersions were then stored at room temperature. After a few minutes, all of the dispersions with sodium chloride showed extensive flocculation of the CB and eventually sedimentation. In contrast, the CB dispersions spiked with quartz sand remained stable with the sand slowly and completely settled at the bottom of the vials. Therefore, quartz sand was considered to be a suitable abrasion medium for CB, whereas sodium salt was not further considered for this study but it may be suitable for other NMs.  
The preparation with quartz sand was repeated for the four CB grades; 1.0 g in 15 mL polypropylene centrifugal tubes containing 10.0 mL of 75 ng/mL PX 80/PX 85 and 125 ng/mL N550/N772. After 15 min of shaking, the four dispersions were left to rest for 10 min, during which the sand settled completely at the bottom of the vials. Approximately 5 mL of the clear supernatants  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Concentration of**} & \multicolumn{4}{c}{**Total Area by MALLS (mV-min)**} \\ \cline{2-5}  & **PX 80** & **PX 85** & **N550** & **N772** \\ \hline
0 & \(-\)0.003 & 0.003 & 0.000 & 0.000 \\
5 & 0.053 & 0.063 & 0.001 & 0.001 \\
10 & 0.123 & 0.168 & 0.003 & 0.004 \\
25 & 0.323 & 0.288 & 0.070 & 0.043 \\
50 & 0.572 & 0.671 & 0.135 & 0.099 \\
75 & 0.907 & 1.023 & 0.192 & 0.174 \\
100 & 1.149 & 1.405 & 0.328 & 0.238 \\
125 & 1.426 & 1.832 & 0.433 & 0.352 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Total peak areas of the respective CB standards used for the calibration.  
Figure 4: Sum of all MALLS detector angle areas (total area) versus concentration of PX 80 (1 mL injections).  
were carefully transferred into AF4 vials and 1 mL of each was injected for analysis. A blank sample (i.e., sand shaken with 96% ethanol with no CB) was also tested. The recovery was compared to the AF4/MALLS signals of CB dispersions with no contact to sand (references).  
AF4/MALLS fractograms showed slightly lower signal intensities for the dispersions having been in contact with the sand (Figure 5). The recovery rates on triplicate injections were calculated as 87% for PX 80, 82% for PX 85, 75% for N550, and 79% for N772.  
Even if some CB remain adsorbed on the sand surface, the quartz sand is deemed suitable as a dry-food simulant for the purpose of this study.
# Chaos and Stability in a New Iterative Family for Solving Nonlinear Equations
## 4 Numerical Results
### First Experiment: Efficiency Analysis of CMT (\(\alpha\)) Family  
In this experiment, we carried out a comparative study between a stable method of CMT (\(\alpha\)) family and the methods of [PERSON], [PERSON], ABA, CH and AHR, in order to contrast their numerical performances in nonlinear equations. We consider as a stable member of CMT (\(\alpha\)) family the method associated with \(\alpha=1\), that is, CMT(1). Thereby, in Tables 1-3 we show the numerical results of the six known methods, considering close, far, and very far initial estimates. Furthermore, in Figure 6 we show graphics that summarize these results for the number of iterations (iter) and the processing time (tcpu). Therefore, from the results of the first experiment we conclude that CMT(\(\alpha\)) family has an excellent numerical performance considering a stable member (\(\alpha=1\)) as a representative. This conclusion has been made based on the following aspects from Tables 1-3: CMT (1) method has the lowest error and lowest number of iterations (iter). However, the mean of the execution time (tcpu) varies according to the nonlinear test function used and the inherent complexity that the iterative scheme of the method presents on the nonlinear function. In several cases, the tcpu of the CMT (1) method is significantly lower than the 6 th order ABA, CH and AHR methods. The theoretical convergence order is also verified by the ACOC, which is close to 6.
# Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm
## 2 Materials and Methods
### HESS Model
The energy tariff has three zones: the morning peak (from 7:00 until 12:00)--2.53 PLN/kWh, the evening peak (varies between seasons but ranges from 16:00 until 22:00)--3.43 PLN/kWh and the off-peak--1.96 PLN/kWh [25]. Surplus of energy was sold at the flat rate of 0.472 PLN/kWh [26]. The price profiles are depicted in Figure 4.  
The aim was to show that the HESS can be economically justifiable and that it increases the self-consumption of the energy produced from the RES. The aim of the implemented optimization method was solely to minimize the cost of operation, a problem for which a solver was used. A simple control method--energy balancing--was given as a benchmark. Control methods are described in detail in the following sections. We assumed that the usage and production of the energy was not subject to changes. The only thing that changed the balance at the point of coupling with the grid was the operation of the HESS.
## 4 Case Studies
### Dataset Descriptions  
In this case, the proposed approach is performed on the public datasets acquired from the Bearing Data Center of Case Western Reserve University [57]. Extensive studies have been conducted on this rolling bearing fault diagnosis dataset. The vibration data used in this work were collected from the sensor placed on the drive end of the motor, which ran at a constant speed with four rotating speeds, i.e., 1730, 1750, 1772, 1797 r/min, and on four health conditions, i.e., normal (NO), ball fault (BF), inner race fault (IF), outer race fault (OF). The three different types of artificial faults were created with diameters of 7, 14, 21 mils. In addition, the CWRU experimental platform is shown in Figure 7.  
Ten operation patterns (containing one NO and nine faulty conditions) in four rotating speed domains are included in the CWRU datasets. It is worth noting that the CWRU dataset is widely considered as a publicly available dataset for fault detection of rolling bearings due to its better data quality and less noise interference. In this study, seven operation conditions were selected to examine the proposed method according to the experimental requirements.  
In the CWRU dataset, the vibration signal was acquired from the sensor at an operating frequency of 1.2 kHz for about 10 s, and each dataset had about 120,000 data points. Taking into account the above factors, performing data truncation on the original signal is critical. Therefore, in this article, a sliding window truncation method is proposed to generate a  
Figure 7: The CWRU bearing test rig.  
dataset for seven operation patterns. As depicted in Figure 8, a truncation window slides along the vibration signal at a shift length of 64 data points, and the window size is 512 data points. Therefore, the signal is converted into a dataset consisting of 512 \(\times\) 512 RGB images under the action of the sliding window. The converted results are presented in Figure 9. For example, BF-07 indicates a ball fault with a fault diameter of 7 mils, IF-14 indicates an inner race fault with a fault diameter of 14 mils, and the rest follow the same pattern.
## 4 In-Situ Stresses
### Results of Stress Determination  
The World Stress Map reveals the thrust faulting situation in the area; moreover, the horizontal stresses that are due to the tectonic stress are oriented along the N60-65E direction, as shown in Figure 4. In this project, in situ stresses were determined via hydro-fracturing and hydro-jacking at two critical sites: (1) the Jhelum river crossing and (2) the underground powerhouse. The test results varied, owing to the anisotropic nature of rock and high in situ stresses [30]. Subsequently, an overcoring stress measurement method, with hollow inclusion triaxial strain cells, was used for stress determination in the high overburden area along the TBM-excavated tunnels of the project, which were susceptible to rock bursting. The China Institute of Rock and Soil Mechanics executed these tests [36]. Fifteen boreholes were drilled in the periphery of the twin headrace tunnels and the testing locations were positioned sufficiently far away from the periphery of the tunnel for safeguarding against construction disturbance. In a high in situ stress environment, core discing occurred in several tests, and only a few tests were completed successfully [24]. Based on the test results in the high-stress environment, it is presumed that the stress levels are higher in the borehole with core disking than in the successfully completed test bore holes [37]. The test details were collected and summarized from published literature, as listed in Table 3.  
Figure 4: Stress conditions in the project area, obtained using the World Stress Map.  
The in situ stress results are plotted at the tested chainage of the tunnel in terms of K\({}_{0}\) (ratio of horizontal to vertical stress) and the overburden (Figure 5). The results indicate the higher horizontal stresses due to tectonics along the tunnel route with a higher K\({}_{0}\) value [37,38]. The horizontal stress (\(\sigma_{1}\)) is greater than 100 MPa, as observed from Table 3. Higher values of \(\sigma_{1}\) are not completely dependent on the tunnel depth as they are perpendicular to the longitudinal axis of the tunnel when the overburden values range from 800 to 1000 m (as shown in Figure 5). The horizontal stress is lower in the area with maximum overburden heights. \(\sigma_{1}\) and \(\sigma_{2}\) are perpendicular and parallel to the tunnel axis in the horizontal plane, respectively; further, \(\sigma_{3}\) is in the vertical direction. Based on the field testing program, a similar stress trend was also noted in the nearby Kohala hydropower project that was being designed [35]. However, in the powerhouse area, \(\sigma_{1}\) is vertical with a value of 12 MPa, and the magnitudes of the intermediate and minor principal stresses are 5 and 3 MPa, respectively [28].
## 1 Introduction
Chloroeremomycin-containing CSPs (Chloro-E-CSP) demonstrated a superior enantioseparation of aromatic amino acids [30]containing CSP (O-CSP) [31]. Amide derivatives of eremomycin have also been tested as chiral selectors in CSPs for the separation of amino acids and profens [30; 31]. The use of aglycones of macrocyclic glycopeptide antibiotics as chiral selectors has a strong effect on the enantioselectivity and resolution of CPSs. Vancomycin aglycon containing CSP was prepared and used for enantioseparation of chiral sulfoxides [32]. DEE-CSP with immobilised eremosaminyl aglycon (see Figure 2 for corresponding structure) demonstrated better enantioselectivity and resolution for the separation of aliphatic amino acids, but not for aromatic amino acids and profens, as compared with eremomycin-containing E-CSP [33]. The present work is devoted to the investigation of enantioseparation of -blockers by using seven CSPs with eremomycin and its derivatives as chiral selectors. The aim of this work is to identify key structural elements responsible for the separation of enantiomers of this group of pharmaceuticals. The CSPs with immobilised eremomycin aglycon (EAG-CSP) and des-eremosaminyl eremomycin (DEE-CSP) were prepared as originally described by [PERSON] et al. [33]. The structures of these aglycones are presented in Figure 2.  
Figure 1: Structures of eremomycin and its analogues used as chiral selectors in CSPs.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 6 Conclusions  
This article reviewed various approaches for chaotic time series forecasting based on machine learning in multiple areas, such as wind farms, PV systems, hydrological systems, communication signals and systems, oil and gas, and other systems. At the beginning of this paper, the chaotic system/time series and the importance of chaos forecasting were introduced. Next, the various machine learning-based chaotic time series forecasting approaches were presented. These approaches use WNN, FNN, CNN, LSTM, and Markov chain models. Then, a review of the prediction of various parameters in multiple applications using machine learning-based techniques is presented. This review concludes that traditional prediction methods can hardly obtain satisfactory results. Hence, many chaotic time series prediction methods were developed using machine learning-based approaches, which enhanced their efficiency and accuracy.
## 5 Results and Discussion
### Transient Engine Modelling  
#### 5.2.1 Transient Simulation Setup  
One of the main objectives of this paper is to demonstrate an improved transient response of the turbocharger with the new turbine. As an engine model is already set up and WAVE has the functionality to perform transient calculations, it was chosen to perform the transient analysis. To set up a transient model, several modifications need to be made to the original model, these were identified by following the setup specified in the user manual [16].  
The transient model is created by utilising the steady-state model. The first step in creating a transient model requires the setup of two cases. First, a steady-state case is required to initialise the transient simulation. It is necessary to turn off convergence control during the transient modelling as WAVE might stop the simulation part way through if it deems convergence has been reached. The second case is the initial conditions for the start of the transient simulation. To implement a transient simulation, a composition of an actuator to regulate the engine speed, a control switch, and a transient profile is used. The switch is used to change the simulation from the steady-state case to the transient profile, and the setup is shown below in Figure 23. The transient profile is then determined by the user. As the study aims to reduce the time to spool up the turbocharger, a constant engine speed is used from 0-3 s as this is the expected maximum duration based on literature [10]. For low load speeds where the exhaust mass flow rate is not sufficient to drive the turbine, the time is increased accordingly.  
Next, the number of cycles for the transient profile needs to be set up properly; otherwise, the simulation may not run for the specified time. WAVE has a pre-generated Excel worksheet to estimate the number of cycles needed to complete the simulation, using a simple integration shown in Equation (16), where \"ncyc\" is the number of cycles required. The user is only required to enter in their specific transient profile, and the sheet can determine the number of cycles required. In WAVE, the number of cycles in the simulation setup is then set as a parameter for each transient case.  
\[ncyc=\ \frac{1}{120}\int_{0}^{time}N_{E}(t)dt \tag{16}\]  
Figure 22: Baseline engine uHC (Unburned Hydrocarbons) emissions at different engine loads.  
As the engine loading condition does not affect this calculation, the number of cycles for each engine speed is kept constant when simulating part load. The calculated number of cycles required to complete a 3-s transient simulation for a constant engine speed is shown below in Table 7.
# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 5 Description of the Case Study  
The proposed methodology was applied to the Savona Campus of the University of Genoa, Italy, where there is a smart polygeneration microgrid (SPM), an innovative facility used to provide electricity and heat to the campus [24]. The SPM constitutes a good example of the penetration of DERs [67] and a complete living lab for smart grid technology.  
The campus network has various generation systems (two microturbines and PV panels); an electrical storage system; and buildings used for living, research, and teaching, which are mainly consumption loads. The structures of these systems are shown in Figure 5.  
The optimal daily operation of the SPM was determined by an EMS. Among other actions, the EMS decides when and how to charge and discharge the storage system, modulate the energy generation of the microturbines, and even decides when to sell electricity to the national grid (if there is a surplus of energy). There are other elements of  
Figure 5: Electrical schema of the campus and its main systems.  
the campus that could also be used as controllable loads, thereby increasing the capacity that can be provided in flexibility services [68].  
The objective of the present study is to use historical and weather data from the Savona Campus to forecast some considered power variables under a day-ahead approach. The available data refer to three years of SPM operations (2016, 2017 and 2018). All electrical variables were measured with a sampling time of 1 min. The day-ahead weather predictions, which were provided by a meteorological service, are given in 30-min intervals for the whole day.  
The five power variables of interest for the present study are the following:  
* \(ge1\): The power absorbed by the SPM.
* \(ge2\): The power withdrawn from the external distribution network.
* \(ut\): The production of the two cogeneration microturbines.
* \(lc\): The electrical demand of the buildings.
* \(pv\): Photovoltaic generation.  
A representation of a summary of these variables can be seen in Table 3. For each of the five variables, some statistical values are provided: the average value (mean); standard deviation (std), minimum value (min); first, second and third quartiles (25%, 50% and 75%, respectively); and maximum value (max). All these values are expressed in kW.  
The historical data concerning the day-ahead weather forecasts for Savona contain the temperature, humidity, pressure, global radiation, and rainfall variables. A summary of the data can be seen in Table 4, with the interpretations of the given values being similar to those in Table 3. The units are indicated in the head of the table.
###### Abstract  
The scientific community's interest in developing sustainable carbon materials from biomass waste is increasing steadily, responding to the need to reduce dependence on fossil fuels. Every day, different biomass sources are suggested for obtaining porous carbon materials with characteristics for application in different areas. Porous carbon materials with a high specific surface area are a subject of interest for application in energy storage devices. This work reports the use of blue shark chondro sulfate and gelatine as precursors for developing porous carbon materials for energy storage devices. Commercial chondro sulfate was used for comparison. The porous carbons obtained in this study underwent various characterization techniques to assess their properties. A BET surface area analyzer measured the specific surface area and pore size. Additionally, scanning electron microscopy (SEM) coupled with energy dispersive X-ray analysis (EDX), a high resolution-scanning transmission electron microscope (HR-STEM), Raman spectroscopy, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) were employed to examine the morphology, composition, and structure of the carbons. A modified glassy carbon (GC) electrode was used as the working electrode for the electrochemical characterization. Cyclic voltammetry and galvanostatic charge/discharge techniques were employed with ethaline, an environmentally friendly and sustainable electrolyte based on choline chloride, to assess the electrochemical performance. Furthermore, the most promising samples were subjected to ball-milling to investigate the impact of this process on surface area and capacitance. Blue shark chondro sulfate-based carbon presented a specific surface area of 135.2 m\({}^{2}\) g\({}^{-1}\), compared to 76.11 m\({}^{2}\) g\({}^{-1}\) of commercial chondro sulfate, both carbonized for 1 h at 1000 \({}^{\circ}\)C. Blue shark gelatine presented a specific surface area of 30.32 m\({}^{2}\) g\({}^{-1}\). The associated specific capacitance of these three samples is 40 F g\({}^{-1}\), 25 F g\({}^{-1}\), and 7 F g\({}^{-1}\). Ball-milling on these samples increased the specific surface area and capacitance of the three studied samples with different optimal milling times. This study presents the novel utilization of carbon materials derived from blue shark (with and without ball-milling) through a one-step carbonization process. These carbon materials were combined with an environmentally friendly DES electrolyte. The aim was to explore their potential application in energy storage devices, representing the first instance of employing blue shark-based carbon materials in this manner.  
marine biomass; chondro sulfate; gelatine; bio-carbon; carbonization process; deep eutectic solvents; specific capacitance; ball-milling +
Footnote †: journal:
## 4 Feature Extraction
### Sleep Structure Related Features  
As for a healthy subject, the sleep cycle usually starts with the NREM stage, and followed by the REM stage. These stages will alternate throughout the whole night periodically. Particularly, most of the slow-wave NREM stages occur in the first half of the night. For example, \(s3\) and \(s4\) sleep stages occupy much less time in the second cycle than in the first cycle and even disappear in later cycles. Moreover, the REM sleep stage (i.e., \(s5\)) may only last about 1 to 5 min in the first cycle and will gradually become longer through the night.  
Sleep-related problems are very clinical among stroke patients, and even more than 50% of stroke survivors suffer from at least one certain type of sleep problem [54; 55; 56]. Specifically, sleep disorder is a typical sleep problem among stroke patients, which includes  
Figure 3: A general view of the prediction model.  
hypersomnia, insomnia, sleep-disordered breathing, etc. All of these symptoms may lead to dyssommias, parasommias, circadian rhythm sleep disorders etc. [1]. While these symptoms are observed among stroke patients, we have reason to assume that these features also exist among subjects who will suffer from ischemia in a near future. Therefore, in this study, we will attempt to build a stroke prediction model by extracting and leveraging sleep related features.  
To investigate the effects of sleep disorders on stroke prediction, we extract sleep structure related features from two aspects. In particular, the first aspect is sleep quality-related features, as more than one third stroke patients have reduced sleep time, insomnia [57], increased awake times [58], low sleep efficiency [21; 59], shorter rapid eye movement-sleep latency and higher sleep latency [60]. The second aspect is sleep trend-related features, as it has been proven that the deep sleep stage of stroke patients will decrease, while the shallow sleep stage of stroke subjects will increase [21; 59]. Therefore, similar to our previous work [38], we extract a number of sleep structure related features, including features total sleep time (TST), sleep efficiency (SE), wake after sleep onset (WASO), awake times (AT), sleep latency (SL), rapid eye movement-sleep latency, FF Trend 1, TF Trend 1 and SF Trend1.