esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Material and Methods
### Isotope Coded Protein Labeling (ICPL)
The samples from the second technical replicates (24 OFFGEL-fractions and non-fractionated sample) were also analyzed after in-solution digestion with the enzymes Glu-C und Trypsin [18] by the group of A. Sickmann (Leibniz-Institut fur Analytische Wissenschaften-ISAS-e.V., Dortmund, Germany) with the LTQ-Orbitrap Velos (Thermo Fisher Scientific, Bremen, Germany). Nano-LC-MS/MS was performed on an LTQ-Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) coupled to an Ultimate 3000 Rapid Separation Liquid Chromatography (RSLC) system (Dionex, Germering, Germany). Briefly, peptides were preconcentrated on a reversed-phase (RP) trapping column (Acclaim PepMap, 75 \(\upmu\)m \(\times\) 2 cm C18, 100 A, Dionex) in 0.1% TFA followed by RP separation (Acclaim PepMap RSLC 75 \(\upmu\)m \(\times\) 15 cm, 2 \(\upmu\)m, 100 A, Dionex) using a binary gradient (solvent A: 0.1% FA, solvent B: 0.1% FA, 84% ACN) from 5% to 50% B at a flow rate of 300 nL/min in 90 min. MS survey scans were acquired within the Orbitrap from 300 to 2000 \(m/z\) at a resolution of 60,000 using polysiloxane \(m/z\) 445.120030 as lock mass. The ten most intense signals were subjected to collision induced dissociation (CID) in the ion trap taking into account a dynamic exclusion of 12 s. CID spectra were acquired with a normalized CE (collision energy) of 35%, a default charge state of 2 and an activation time of 30 ms. AGC target values were set to 104 for ion trap MSn and 106 for Orbitrap MS scans. Since only ICP-samples have been analyzed, these generated MS/MS spectra or MSn spectra were evaluated by the ICPL-ESIQuant software (Max-Planck Institut for Biochemistry, Martinsried, Germany).
###### Abstract  
This paper presents a standalone system that utilizes a capacitive isolated series resonant converter using an extremum-seeking control algorithm to extract the maximum power from PV panels. While resonant converters have been used for battery charging applications, series resonant converters that utilize capacitive galvanic isolation have not been sufficiently explored, and their design considerations for battery charging have not been established. In addition, extremum-seeking control algorithms have been explored for maximum power point tracking using PWM converters, but not using PFM converters such as resonant converters. This paper lays out the advantages of using an extremum-seeking-based control algorithm with resonant converters, specifically series resonant converters, and it presents simulation results of a 200 W standalone battery charging system to validate the stated benefits.  
photovoltaic system; photovoltaic efficiency; power conversion efficiencies; distribution systems; electric power systems; renewable energy; extremum-seeking control; resonant converters
## Appendix C Programming Code in Arduino Software and Visual Studio Code  
_Appendix C.1. Arduino Base Code_  
int sensorIn = A0;  
void setup(){  Serial.begin(115200);  
analogReference(DEFAULT);  
void loop(){ //Lectura de la tension  
int sensorValue = analogRead(sensorIn);  
// La senal analogica es convertida a tension  
float voltage = sensorValue*(5000/1024.0);  
if(voltage == 0)  
{  Serial.println(\"Fault\");  
else if(voltage < 400)  
{  Serial.println(\"preheating\");  
else  
{ //Recta de regresion lineal utilizada para la calibracion del sensor  
float p_cero = 2.9713;  
float p_uno = -1150.3;  
float concentration=voltage*p_cero+p_uno;  
Serial.print(voltage);  
Serial.print(\",\");  
Serial.print(concentration);  
Serial.println(\",\");delay(1000); } ```  
_Appendix C.2. Visual Studio Code-Python Programming to Create Data Storage_  
import serial  
import xlwt  
from datetime import datetime  
class SerialToExcel:  
def__init_(self,port,speed):  
self.port = port  
self.speed = speed  
self.wb = xlwt.Workbook()  
self.ws = self.wb.add_sheet(\"Data from Serial\",cell_overwrite_ok = True)  
self.ws.write(0, 0, \"Data from Serial\")  
self.columns = [\"Date Time\"]  
self.number = 100  
def setColumns(self,col):  
self.columns.extend(col)  
def setRecordsNumber(self,number):  
self.number = number  
def readPort(self):  
ser = serial.Serial(self.port, self.speed, timeout = 1)  
c = 0  
for col in self.columns:  
self.ws.write(1, c, col)  
c = c + 1  
self.fila = 2  
i = 0  
while(i < self.number):  
line = str(ser.readline())  
if(len(line) > 0):  
now = datetime.now()  
date_time = now.strftime(\"%m/%d/%Y,%H:%M:%S\")  
print(date_time,line)  
if(line.find(\",\")):  
c = 1  
self.ws.write(self.fila, 0, date_time)  
columns = line.split(\",\")  
for col in columns:  
self.ws.write(self.fila, c, col)  
c = c + 1  
i = i + 1  
self.fila = self.fila + 1  
def writeFile(self,archivo):  
self.wb.save(archivo)  
_Appendix C.3. Visual Studio Code--Data Storage_  
Define where the data search is performed (Arduino Serial Monitor), number of data to be collected, name of parameters collected according to Arduino programming and time between data storage.  
from serialToExcel import SerialToExcel  
serialToExcel = SerialToExcel(\"COM3\",115200)  
columns = [\"voltage\",\"concentration\"]  
serialToExcel.setColumns([\"voltage\",\"concentration\"])serialToExcel.setRecordsNumber(1800) serialToExcel.readPort() serialToExcel.writeFile(\"archivo2.xls\")
# CvAMoS--Event Abstraction Using Contextual Information
## 4 Approach
### Step 1: Construction of the Set of Motifs
```
Input:\(E_{L}\): the event log \(C\): a set of candidate motifs \(d_{act}\), \(d_{attr}\): the max distance values \(cost_{act}\): the activity cost relation \(R\): the attribute map \(q\): percentage of traces that contain the motif Output: the set of verified motifs \(M\leftarrow\emptyset\)
1\(M\leftarrow\emptyset\)foreach\(c\in C\)do\(k\gets 0\)\(\triangleright\) Counter of traces with the motif foreach\(t\in[L,|t|-|c|]\)doif\(dist_{act}(t[i,|c|],c,cost_{act})\leq d_{act}\)and\(dist_{attr}(t[i,|c|],c,\mathcal{R})\leq d_{attr}\)then\(k\gets k+E_{L}(t)\)\(\triangleright\) Increment by the multiplicity of the trace in the log break\(\triangleright\) The motif has appeared once, the trace count for reaching the quorum, continue with the following trace, line 4 if\(k\geq q\cdot|E_{L}|\)then\(M\gets M\cup\{c\}\)\(\triangleright\) The candidate is verified, hence we can consider it return\(M\)
```  
**Algorithm 1**Verification of candidates motifs. The algorithm has linear complexity, meaning that the amount of time required for the algorithm to complete increases in direct proportion to the size of the input data. In this case, it is given by the number of events in the event log times the size of the set of candidate motifs. The current implementation of \(\mathrm{CvAMoS}\) exports the list of verified motifs in an XES file.
## 7 Experimental Verification and Analysis
### Measurement Accuracy Analysis  
Based on the feasibility analysis above, this subsection will verify the measurement accuracy of the proposed method for cislunar spacecraft, which is illustrated in Figures 23-27, with similar simulation parameters shown in Table 8. The measurement accuracy of the pseudocode ranging (in Figures 23 and 24) and ranging rate (in Figures 25 and 26) from the CEI receiver will be presented, followed by the final measurement accuracy for cislunar spacecraft (in Figure 27).  
Figures 23 and 24 illustrates that when the distance of cislunar space targets to Earth range from \(8\times 10^{4}\) km to \(45\times 10^{4}\) km, the \(\sigma_{DLL}\) of ISL at the CEI receiver will reach from 5.345 m to 6.917 m, and the level will be slightly lower (approximately 0.32 m) from the ISL radiation source on GEO and IGSO compared to that on MEO. Meanwhile, Figures 25 and 26 illustrates that the \(\sigma_{FLL}\) of ISL at the CEI receiver will reach from \(3.125\times 10^{-4}\) m/s to \(4.052\times 10^{-4}\) m/s, and the level will be slightly lower (approximately \(0.025\times 10^{-4}\) m/s) from ISL radiation source on GEO and IGSO compared to that on MEO. When RCS is 9 m\({}^{2}\) and CEI Receiving Antenna Aperture \(D\) is 35 m, the \(\sigma_{DLL}\) and \(\sigma_{FLL}\) can remain at 5.345 m and \(3.475\times 10^{-4}\) m/s, even approaching the distances of \(4.5\times 10^{5}\) km.  
In Figure 27, the final measurement accuracy for cislunar spacecraft through the proposed method has been given, which proves that the accuracy of the proposed method for cislunar spacecraft stays in the order of 75 m. When RCS is 9 m\({}^{2}\) and CEI Receiving Antenna Aperture \(D\) is 35 m, the measurement error remain at 62.5 m, even approaching the distances of \(4.5\times 10^{5}\) km. By contrast, the method proposed in [56] achieved position accuracies of 100 m for the DRO, Earth-orbit, or lunar-orbit, through the Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) technique to analyze satellite-to-satellite tracking (SST) range measurements between a DRO satellite and an  
Figure 26: Pseudocode ranging rate performance of CEI receiver arrays in the case of CEI Receiving Antenna Aperture variations (BD-3 in GEO/IGSO).  
Figure 25: Pseudocode ranging rate performance of CEI receiver arrays in the case of spacecraft RCS variations (BD-3 in GEO/IGSO).  
other satellite in cislunar space to determine their absolute orbital states. Furthermore, due to their method's weak measuring geometry and extended orbital period, the Earth-Moon transfer orbit was less detectable.  
The above simulations and analysis illustrate that the proposed method could achieve effective and reliable tracking and measurement for non-cooperative targets in the Earth-Moon space, which indicates a promising potential in the increasingly critical Earth-Moon space situational awareness missions in the future.
###### Abstract  
The prediction of ship traffic flow is an important fundamental preparation for layout and design of ports as well as management of ship navigation. However, until now, the temporal characteristics and accurate prediction of ship flow sequence in port are rarely studied. Therefore, in this study, we investigated the presence of long-range dependence in container ship flow sequences using the Multifractal Detrended Fluctuation Analysis (MF-DFA). We considered three representative container ports in the world--including Shanghai, Singapore, and Rotterdam container ports--as the study sample, from 1 January 2013 to 31 December 2017. Empirical results suggested that the ship flow sequences are deviated from normal distribution, and the sequences with different time scales exhibited varying degrees of long-range dependence. Furthermore, the ship flow sequences generated a multifractal nature, where the larger the time scale of ship flow time series, the stronger the multifractal characteristics are. The weekly ship flow sequence in the port of Singapore owned the highest degree of multifractality. Furthermore, the multifractality presented in the ship flow sequences of container ports are due to the correlation properties as well as the probability density function of the ship flow sequences. The study outlines the importance of adopting these features for an accurate modeling and prediction for maritime ship flow series.  
Keywords:container ship traffic flow; volatility; generalized Hurst exponents; long-range dependence; multifractality +
Footnote †: journal: Applied
Note that each of the processes listed above is quite complex, even if it is considered in isolation from the others. An advanced mathematical model is required to describe each of them. Figure 1: The drilling process scheme. There are dozens of commercial software packages that simulate one of these processes including Oliasoft WellDesign software for designing the well (trajectory, casing, etc.) [3]; TADPRO software from Pegasus Vertex Inc. for torque and drag calculation; CTEMP for wellbore circulating temperature prediction; CDEx for casing design; HYDPRO for drilling hydraulics modeling [4]; software from Halliburton Landmark [5]; DrillPlan for digital well construction planning and OLGA for prediction of key operational conditions from Schlumberger [6]; and many others. Most scientific works in the field of the mathematical modeling of the process of well drilling are also devoted to modeling one of the processes listed here. At present, there are several reviews devoted to the mathematical modeling of certain processes. First of all, the review by [PERSON] et al. [7] should be noted, which is devoted to modeling the dynamics of the drill string. A review of [PERSON] [8] on modeling the flow of drilling fluid is also worth mentioning. At the same time, as far as we know, currently there is no review that combines the studies on modeling each of the processes listed here that occur during the drilling of oil wells. This review aims to fill this gap. The emphasis is on those few studies where an attempt is made to build the complex model with several processes. Criteria for inclusion of an article in this review were as follows:  
* The main topic of the article is the construction, application, or experimental verification of a mathematical model of at least one of the processes taking place during the well drilling. * The article should be devoted to modeling the processes occurring directly in the well during drilling. The studies devoted to modeling the processes occurring in the reservoir (for example, fluid filtration) and those devoted to the operation of surface equipment were excluded from consideration. * The model considered in the paper is not specific to any particular equipment or material used in drilling. For example, studies devoted to the fatigue analysis of some particular type of drill pipes were not included in the review. The rest of the paper is organized in the following way. Section 2 gives a review of the papers on drill string dynamics. The papers devoted to drilling fluid circulation are presented in Section 3. Next, Section 4 is about the mathematical modeling of rock crushing with a drill bit. Sections 5 and 6 are devoted, respectively, to mass and heat transfer between the drilling fluid and surrounding soil. Section 7 describes the attempt to build the complex model that involves different processes occurring during drilling. The main conclusions are given in the final section.
# A Short-Term Forecast Model of foF2 Based on Elman Neural Network
## 2 Model Principle
### Improved Particle Optimization Algorithm
#### 3.1.2 Solar and Magnetic Activities  
Solar variation causes periodic changes of the ionosphere. As the measurement of solar variation, we chose sunspots number (SSN) and 10.7 cm solar flux F10.7 as input parameters. Sporadic changes in the ionosphere are mostly related to geomagnetic disturbances. Moreover, we chose the Kp index and Dst index to describe magnetic activities [14].  
It is more difficult to forecast foF2 during the geomagnetic disturbances period, and some specialized studies have been carried out [15]. However, in this paper, we did not filter the data from the geomagnetic disturbances, but used all the data to train the model.
## 2 Materials and Methods
### Empirical Mode Decomposition (EMD)  
The EMD is a data-driven method for analyzing non-Gaussian, nonlinear, and non-stationary signals [19]. It is used for decomposing a signal into intrinsic mode functions (IMFs), denoted by m\({}_{\text{k}}\)(t) (k = 1... K), reflecting its natural oscillations where the total number of IMFs generated for the signal is represented by K. The process of \"sifting\" is used to extract IMFs that have two properties: an equal number of extrema as zero crossings or differing by one, and a mean value of zero at any point for the envelope defined by local maxima and minima. The original signal X(t) is decomposed as a linear combination of IMFs and the whole process is repeated until the signal becomes a constant, monotonic function, or with a single extremum. The signal X(t) can be expressed as:  
\[\text{X(t)}=\sum_{\text{k}=1}^{\text{K}}\text{m}_{\text{k}}(\text{t})+\text{ f(t)}; \tag{1}\]  
where m\({}_{\text{k}}\)(t) represents the IMFs and f(t) is the residual component [19].
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### BackCom for Bio-Medical Applications  
Tiny passive BackCom tags, with ultra-low power consumption, are widely used in the field of medical science. BackCom tags enable medical devices to communicate without power constraint, with the help of available ambient WiFi signals. Moreover, Amb-BackCom tags allow medical practitioners to diagnose patients remotely. Figure 43 demonstrates an example of WiFi-enabled tags, which can sense a patient's conditions remotely and transmit data using WiFi signals using BackCom. A WiFi-based gesture recognition for humans uses BackCom to recognize emotions with the reflected WiFi signals [196; 197].  
The reflection of WiFi signals, along with machine learning techniques, is used to identify human emotions and heartbeats [198]. This design helps smart devices to recognize a person's emotions for the purpose of treatment. [PERSON] et al. suggested an RF-ECG design using passive tags [199]. The RF-ECG design utilizes a BackCom tag array to extract inter-beat interval in cardiac pulses. Brain machine interface (BMI) technology also uses BackCom neural tags to monitor the neural activities of patients [200]. Similarly, RFID-based backscattering is expected to be a promising solution to empower small neural sensors used in BMI. This work also presents the measurement and design of a \(1\times 1\times 1\) mm\({}^{3}\) backscattering device, based on a cubic loop attached to an RFID chip. The results of this design show that small loops obtain sufficient energy to energize the hardware IC when such hardware is implanted in the head of a pig. This experiment shows the possibility of tiny implant antennas in BackCom-enabled bio-medical systems.  
Figure 42: UAV-based relay with antenna array in RFLY.
# Design of a Modular Energy Production-Storage System for a Sustainable Bicycle
## 3 Results  
### Description of the Modular Charge-Storage Scheme  
In this section, an analysis of the different solutions on the market is carried out, comparing technical characteristics such as power generation capacity or maximum current delivery.  
A modular scheme consisting of three independent modules is proposed. The first module consists of a USB charger, in charge of transforming and storing the energy produced by the hub dynamo. The second module is the battery pack, designed to increase the capacity of the USB charger to supply power to a greater number of devices connected to it. Finally, the third module consists of a solar panel, the objective of which is to increase the charging current in order to charge the batteries in the shortest possible time and even charge devices when the bicycle is stopped.  
The modular interaction scheme of the different modules appears in Figure 1. Although the first USB charger module can work independently from the rest of the modules, its versatility was increased by allowing its connection to the battery pack and solar panel modules. In this way, it is possible to increase the capacity of the batteries and the charging current supplied by the solar panels.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Aesthetics**} & \multirow{2}{*}{**Capacity**} & \multirow{2}{*}{**Current**} & \multirow{2}{*}{**Weight**} & \multicolumn{2}{c}{**Battery**} & \multicolumn{2}{c}{**Solar**} & \multicolumn{2}{c}{**Stable**} & \multirow{2}{*}{**Dietetooth/App**} & \multirow{2}{*}{**Price**} & \multirow{2}{*}{**Total**} \\  & & & **Output** & & & & & & & & \\ \hline Plug5 Plus [46] & 3 & 1 & 1 & 1 & 0 & 0 & 0 & 1 & 0 \\  & & \(1100\) mAh & 1.2 A & 195 g & & & & & & EUR 259 & 7 \\ \hline Appcon3000 [47] & 2 & 3 & 2 & 3 & 0 & 0 & 0 & 1 & 1 & 1 \\  & & 3000 mAh & 2.0 A & 102 g & & & & & EUR 229 & 12 \\ \hline USB-P5 [48] & 1 & 4 & 1 & 2 & 1 & 0 & 0 & 0 & 3 & 12 \\  & & 5000 mAh & 1.5 A & 150 g & & & & & & EUR 95 & 12 \\ \hline Forumslader V5 & 3 & 2 & 3 & 1 & 1 & 0 & 0 & 1 & 1 & 1 \\  & & 2100 mAh & 2.9 A & 210 g & & & & & & EUR 227 & 12 \\ \hline USB-Werk [50] & 1 & 0 & 0 & 4 & 1 & 0 & 0 & 0 & 4 & 10 \\  & & 300 mAh & 0.5 A & 53 g & & & & & & EUR 80 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Market model benchmarking and evaluation scores.  
As shown in Figure 1, the battery pack module and the solar panel module can be used together to store the solar energy in the battery pack. This joint action offers a great advantage since energy can be needed at any time, and having two different sources of generation reduces its depletion probabilities.  
The design specifications of the different modules of the embedded system are described below.
## 2 Computational Framework
### Specifications of Validation Cases
From the mesh sensitivity study, the larger mesh size showed a reasonably better comparison than the coarse mesh. Therefore, for the propeller-wing-fuselage model presented later in this paper, a similar mesh topology, as shown for the validation case, was chosen and applied on the wing-fuselage part. The mesh for the propeller was integrated with the wing-fuselage mesh. The general topology is that an O-grid mesh was used around the propeller, and an H-type mesh was on the outer zone inside the rest of the rotating domain (details are shown in Figure 1).
## 6 Existing and Potential Industrial Applications  
As an advanced combustion mode, SCHC, which has promising prospects in industrial applications, includes hydrothermal spallation drilling, the thermal recovery of heavy oil, clean conversion and utilization of coal-based fuel, and harmless treatment of pollutants. In view of the above four aspects, this section will introduce the current progress of hydrothermal combustion in relevant fields.