esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Mechanism of the Seismo-Ionospheric Coupling  
At present, the research on the mechanism of seismo-ionospheric coupling is mostly in the qualitative analysis stage, and some numerical simulation theories are still constantly developing. There are three main transmission channels that describe the seismo-ionospheric coupling model, such as the acoustic channel, the geochemical channel, and the electromagnetic channel [27; 35; 36; 37].  
Despite some \"up-down\" evidence regarding solar activity and geomagnetic storms before some strong earthquakes, it is observed that the triggering mechanism can be a result of seismic/tectonic processes at depth, i.e., a \"bottom-up\" process, as defined by [PERSON] et al. [38; 39; 40]. Before strong earthquakes, the igneous and high-grade metamorphic rocks are subjected to defect-positive holes in the oxygen anion sublattice. These charge carriers are highly mobile, able to flow out of stressed rocks into surrounding unstressed rocks. They form electric currents, which emit electromagnetic radiation, sometimes in pulses, sometimes sustained, and may cause ionospheric perturbations and earthquake lights.  
Using numerical simulations, [PERSON] et al. [27; 41] demonstrated that observations of nighttime plasma bubbles within the affected region can be used as precursors for EQ prediction. Pressurized rocks can stimulate charged hole carriers and form currents along the stress gradient direction, causing an increase in atmospheric conductivity and jointly triggering the upward electric current over the seismo-active region, ultimately causing ionospheric plasma density enhancement.  
[PERSON] et al. [42], based on observations of total electron content (TEC) perturbations over the seismic region, reported an enhancement of the electric field detected in the ionosphere over the epicenter a few days prior to the main shock, and the plasma density can either increase or decrease (or both) in the perturbed region. The extra electric field E that is generated in the ionosphere over the seismic preparation zone is considered to be the main source of these ionospheric anomalies. The plasma transfer caused by the extra electric field in the direction of E \(\times\) B drift may produce regions of TEC enhancement (plasma aggregation) and weakening (plasma outflow).  
Based on the previous research results and synchronous perturbations of the ULF electric field waveform data and the plasma multi-parameter data presented in Section 3.1, we suggest that large-scale crustal deformation preceded the strong EQ-induced electric current changes in the subsurface, thus giving rise to the upward low-frequency electromagnetic radiation over the epicenter aera, and the electrodynamic effect of the electric field on the plasma induced the plasma perturbation in the ionosphere. The energy propagated along the direction of the magnetic field line in the ionosphere, making the location of the plasma disturbance center shift in a latitude direction [(43; 44)], which can be detected by CSES-01. The possible plasma perturbation process induced by the seismo-ionospheric coupling is shown in Figure 12.
## 6 Results and Discussion
### Model Validation
The energy wastage and completion time calculated by the three models is shown in Table 6, which shows that the minimum completion time model takes the least time, but the gas waste is the largest. The minimum gas consumption model wastes the least amount of gas but has the longest completion time, resulting in the lowest rolling line operation rate. The mixed model combines the above two objective functions and can achieve the goal of saving gas in a relatively short period of time, making it a relatively optimal scheduling solution. It can be concluded that the scheduling method considering the conversion mode of the reheating furnace is effective for energy savings in the continuous casting-hot rolling stage.
## 4 Environment Perception, Mapping, and Representation for Locomotion
### Survey of Environment Perception for Locomotion in Snake Robots
The concept is demonstrated in a very challenging scenario, which is the Collapsed House Simulation Facility, and adopting the _IRS Souryu_ snake robot which uses actively driven tracks for propulsion. The use of ToF cameras is also demonstrated in [70] in a pipe inspection snake robot. The camera is used to detect key aspects of the pipe geometry, such as bends, junctions, and pipe radius. The snake robot's shape can then be adapted to the pipe's features, and navigate that way efficiently, even through vertical pipes. Laser triangulation is a well-known sensing technology in industry, providing very high resolution and high accuracy measurements. The work in [68] focuses on increasing the snake robot's autonomy, which is demonstrated by autonomous pole climbing. This is a complex behaviour to be achieved by teleoperation. The authors have custom-designed a laser triangulation sensor to fit into the size and power constraints of the snake robot. The robot adopts a stable position with the head raised, and rotates the head to perform environment scanning. The resulting point cloud is filtered and processed to detect pole-like elements in the environment that the snake robot can climb. Once the pole and relative positioning is calculated, the robot pose is estimated by using the forward kinematics and IMU data. For the sake of completeness, we can refer to vision systems _offboard_, which might not be applicable to more realistic applications such as exploration, search-and-rescue, or inspection. In [88], two cameras with a top-down view of the operating area are used to detect the obstacles and calculate the snake robot's pose. The pose estimation is simplified by placing fiducials in the snake robot (10 orange blocks along the snake robot's length). In a similar setup, [89] uses a stereovision system to measure the head's position of the robot and target coordinates.
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 2 Materials and Methods
### Design Methodology
#### 2.2.3 Modelling in MATLAB  
In the initial simulation of the system for storing and transmitting brain signals, a function named delta_en was constructed in MATLAB (MATLAB R2021a). This function takes data as input and calculates the differences between successive values of the EEG signal in a loop of repetition. It then returns a one-dimensional encoded data array, transformed into the binary system to compute the necessary bits. The conversion is performed using the function decimal-to-binary (dec_2_bin), which determines the number of bits required to represent the integer and decimal parts and uses sequential divisions to calculate the binary form of the differences. The system determines the selection of all bits under consideration, and an equal distribution would be preferable (16 bits for integer and 16 bits for decimal).  
A part of the modelling is shown in Listing 1. After reading the data file, four channels are chosen and transformed to binary form. The conversion occurs before encoding to quantify the transitions. The entire algorithm comprises successive phases such as signal reading and channel selection. These channels are then transformed to their binary form (each value is represented by 32 bits) to calculate the initial 0-to-0 (zeros_2_zeros), 1-to-0 (ones_2_zeros), 1-to-1 (ones_2_ones), and 0-to-1 (zeros_2_ones) transitions. Finally, the data is encoded (delta_en), and the new 0-to-0 (zeros_2_zeros), 1-to-0 (ones_2_zeros), 1-to-1 (ones_2_zeros), 1-to-1 (ones  
to-1 (ones_2_ones), and 0-to-1 (zeros_2_ones) transition measurements are computed to compare them to the sets from the signal's decoded state.  
```
clearall; closeall; clc; %%readingtheeggfromedfile file [rccord,hdr]=readEDF(file); %%selectionof4 channels %%withdurationof90s(256*90=23,040 samples) channel_1=record(21,23040:46080); channel_2=record(22,23040:46080); channel_3=record(23,23040:46080); channel_4=record(20,23040:46080); %%decimaltobinary bin_ch_1=[]; bin_ch_2=[]; bin_ch_3=[]; bin_ch_4=[]; forcols=1:length(channel_1) out=[]; out=dec_2_bin(channel_1(cols)); bin_ch_1=[bin_ch_1;out']; out=dec_2_bin(channel_2(cols)); bin_ch_2=[bin_ch_2;out']; out=dec_2_bin(channel_3(cols)); bin_ch_3=[bin_ch_3;out']; out=dec_2_bin(channel_4(cols)); bin_ch_4=[bin_ch_4;out']; end bin_ch_1=bin_ch_1'; bin_ch_2=bin_ch_2'; bin_ch_3=bin_ch_3'; bin_ch_4=bin_ch_4'; %%count0-1 transitions count1\(z\)2_0=zeros_2_ones(bin_ch_1) count2\(z\)2_0=zeros_2_ones(bin_ch_2) count3\(z\)2_0=zeros_2_ones(bin_ch_3) count4\(z\)2_0=zeros_2_ones(bin_ch_4)
```  
Listing 1: MATLAB modelling of decimal to binary conversion and calculation of the total number of 0-to-1 transitions.
## 1 Introduction
Among them, inductive coupling methods allow for non-intrusive (intended as not requiring direct connection to live parts or physical modification to the system) measurements as well as less complex setups since they do not require direct electrical contact with the energized system. The first implementation made use of two probes and was proposed for measuring power line in-circuit impedances [22]. Further applications of such a method were presented in [23,24]. A more recent implementation [25] foresees the use of one instead of two probes, with the advantage of, besides requiring less instrumentation, eliminating possible issues due to mutual coupling between the two probes. This method was applied to measure the in-circuit DM and CM impedances at the AC input of a motor-drive system [26,27]. This paper fits into this line of research with a twofold objective. The first objective is to enhance the single-probe method by simplifying the test setup and the calibration procedure. To this end, a sensitivity analysis is carried out to optimize the characteristics and assess the effectiveness of the measurement and calibration setup. The second objective is to exploit the obtained in-circuit measurements of modal impedances to investigate the effectiveness of the measurement setup foreseen by the standard in providing realistic information on the actual suppression performance (filter IL) of EMI filters when installed in a real system. The proposed methodology is applied to measure the modal impedances of a motor drive system and to assess the effectiveness of two three-phase EMI filters installed in such a motor drive system for different operating conditions. The manuscript is organized as follows. Section 2 presents the enhanced single-probe method and its verification by measuring discrete RLC components. Besides, a sensitivity analysis is carried out to assess the effectiveness of the proposed setup. In Section 3, the procedure to measure the modal impedances of a motor drive system is introduced. InSection 4, the theoretical derivation of CM/DM ILs of three-phase EMI filters is proposed. Additionally, the measured in-circuit modal impedances of a motor drive system and the predicted IL of two three-phase filters are presented and discussed. Finally, Section 5 summarizes the obtained results and draws some conclusions.
## 3 Control Strategy
### Emotional Learning Based Intelligent Controller  
In this paper, in order to set proper values for the coefficient of PAC in the mechanical part model of wind turbine, an intelligent control strategy based on human brain mechanisms of emotional learning (EL) has been applied. Figure 5 demonstrates the schematic of the proposed intelligent controller.  
The computational model consists of amygdala and orbitofrontal Cortex units. Amygdala has the task of emotional assessment of stimuli. Orbitofrontal cortex plays the role of correcting the responses and adverse reactions of amygdala. Based on theories, these two units perform the EL [25; 26]. Computational model output (MO) is calculated applying Equation (21).  
\[\mathrm{MO}\ =\ \mathrm{AO}-\mathrm{OCO}, \tag{21}\]  
AO and OCO represent amygdala and Orbitofrontal outputs respectively and can be calculated applying Equations (22) and (23). \(\mathrm{G_{a}}\) and \(\mathrm{G_{oc}}\) are gains of amygdala and orbitofrontal units, respectively [27].  
\[\mathrm{AO}\ =\ \mathrm{G_{a}SI}, \tag{22}\]  
\[\mathrm{OCO}\ =\ \mathrm{G_{oc}SI}, \tag{23}\]  
The input stimulation signal (SI) is calculated applying Equation (24) where signal e is considered the difference between the WTG rotor speed and its upper limit in the mode of applying PAC during high wind condition.  
\[\mathrm{SI}\ =\ \mathrm{K_{p}e}\ +\ \mathrm{K_{i}}\int_{0}^{t}\mathrm{edt}, \tag{24}\]  
EC stands for emotional signal, which indicates the desirability of the controller performance, and is composed of signal e and MO with their appropriate weighting factors, \(\mathrm{a_{ec1}}\) and \(\mathrm{a_{ec2}}\), respectively. EC is calculated applying Equation (25).  
\[\mathrm{EC}\ =\ \mathrm{a_{ec1}e}\ +\ \mathrm{a_{ec2}MO}, \tag{25}\]  
The learning law is mentioned in Equations (26) and (27). \(\mathrm{K_{1}}\) and \(\mathrm{K_{2}}\) are learning rates in amygdala and orbitofrontal units, respectively.  
\[\mathrm{\Delta G_{a}}{=\ \mathrm{k_{1}max}(0,\mathrm{EC}-\mathrm{AO})\geq 0}, \tag{26}\]  
\[\mathrm{\Delta G_{oc}}{=\ \mathrm{k_{2}(MO-EC)}}, \tag{27}\]  
Totally, the output of the model, MO in the Equation (21), can be rewritten as Equation (28).  
\[\mathrm{MO}\ =\ (\mathrm{G_{a}-G_{oc}})\mathrm{SI}\equiv\mathrm{G(SI,EC,\ldots)SI}, \tag{28}\]  
If the gains of amygdala and orbitofrontal units are set properly, EL based intelligent controller helps in achieving a proper value for the proportional gain of PAC in each individual WTG and thus, the output power will trace the Pcmd with the least oscillations in the frequency during wind oscillations. This results in better frequency stability of the system.  
Figure 5: Emotional learning (EL) process trend. Adapted from [25].
## 1 Introduction  
Free electron lasers (FELs) are widely acknowledged as the most versatile generators of coherent electromagnetic radiation. Since the first studies in the late 1960s [1] and their \"official\" invention in 1977 [2], it has been clear that FELs are capable of bridging the gap between conventional electron-based sources (such as klystrons, magnetrons, travelling wave tubes) that are limited to the high frequency direction, and the lasers that, with some exceptions, generate single frequency radiation with a power that is limited by the nature of the active medium. FELs can be designed, in principle, to operate at any frequency and with a time structure and related power suitable for any kind of experiment. This kind of flexibility comes from the fact that many of the parameters involved, such as the electron energy and the magnetic field, can be adjusted with continuity. Moreover, many dynamical regimes can be exploited for FEL design; from the low-gain regime, suitable for oscillator devices [3], to the high gain regime ideal for the self-amplified spontaneous emission (SASE) scheme [4]. Furthermore, FEL offers significant potential options to combine different schemes in sequence in order to increase the performances of some specific features without overly stressing the project parameters. One of the most relevant FEL structures arrangements, among others, is a modulator at a synchronous frequency followed by a frequency multiplier that exploits the harmonic content in the modulated beam [5, 6]. Such an arrangement may be reproduced several times, creating a kind of cascade [7] that results in final beam degradation.  
This study presents one of the possible arrangements based on a Cerenkov FEL oscillator as a modulator and a magnetic undulator as a radiator: this solution can be considered a hybrid FEL scheme device [8]. The reason why the term hybrid can be ascribed to such a scheme is not only due to the fact that the modulator and radiator present two different mechanisms of radiation generation, but mainly because in Cerenkov-like FEL devices [9] (as is the case for Smith-Purcell gratings [10]), the coupling between electrons and field is longitudinal, while the magnetic undulator induces a transverse motion to the electrons and accordingly, to the coupling. This circumstance, combined with the fact that Cerenkov (and Smith-Purcell) FELs operate with slow-wave guiding structures, has a relevant effect on the differences, for \"synchronism condition\" [9, 11],that links the spontaneous emission wavelength with the most relevant parameters of the experiment, between Cerenkov and undulator FELs.  
The paper is arranged in four sections: after the present Introduction, Section 2 is dedicated to the study of the Cerenkov-based modulator. Section 3 will introduce the hybrid system as a whole, highlighting the value of the main parameters. Section 4 will describe the effects of the modulation on the emission from a magnetic undulator exploited as a radiator; this analysis includes the ballistic effects of a variable drift space. Section 5 is devoted to final conclusions.
## 3 Results and Discussion
### DCF and PARX Adsorption Assays  
#### 3.2.1 Effect of Initial pH  
The effect of the solution pH on the adsorption process is known as an important operating parameter. Figure 4 shows the effect of the solution pH on the adsorption of DCF and PARX.  
It is possible to observe a decrease in the adsorption capacity of DCF with ACK carbon with the increase of pH. According to the pH\({}_{\text{PZC}}\) value of ACK (Table 3), this carbon acquired negative charges for pH values above 7.0, while the DCF molecule is in an anionic form in the pH range studied [42], which means that electrostatic repulsion occurs with the increasing pH. CC carbon presents a positively charged surface below its pH\({}_{\text{PZC}}\) (9.13), attracting the DCF molecule through electrostatic interactions, justifying the higher uptake capacity of this carbon for DCF. However, for the sake of simplicity and comparison purposes, the DCF solution was used without pH adjustment for the next adsorption assays with both carbons.  
The PARX molecule presents a basic nature, and it is protonated in the studied pH range [29], with a positive net charge attributed to the N-H\({}_{2}\) group [43], to observe a decrease in its uptake capacity for ACK carbon being possible, particularly for pH values above the pH\({}_{\text{PZC}}\) value of carbon (7.0). Somehow, the negatively charged surface of ACK presented a decreased affinity to the PARX molecule.  
On the other hand, the adsorption capacity of PARX onto CC carbon was not affected by the solution pH. Although both porous carbon and molecule presented positive charges, electrostatic repulsion was surpassed by another adsorption mechanism.  
Given these results, the PARX solution was used in the subsequent assays without a pH adjustment.
# A Magnetotelluric Signal Acquisition and Monitoring System Based on a Cloud Platform
## 2 Overall Design  
### Method  
The geomagnetic method is a geophysical exploration method based on natural electromagnetic fields; it is mainly used to detect the electrical structure of underground media. Its basic principle can be summarized as using the spatial and temporal variations of the natural electromagnetic field outside the earth, and inverting the electrical characteristics of the underground medium through the electromagnetic field signals observed at the surface.  
The electromagnetic wave generated by the natural electromagnetic field can be assumed to be a plane wave incident vertically, and according to [PERSON]'s system of equations, the propagation equation of the alternating electromagnetic field can be expressed as  
\[\
abla\times\mathbf{E}=-\frac{\partial\mathbf{B}}{\partial t}, \tag{1}\]\[\
abla\times\mathbf{H}=\mathbf{J}+\frac{\partial\mathbf{D}}{\partial t}, \tag{2}\]  
\[\
abla\cdot\mathbf{B}=0, \tag{3}\]  
\[\
abla\cdot\mathbf{D}=q, \tag{4}\]  
where \(\mathbf{\
abla}\) is the Hamiltonian operator, \(\mathbf{E}\) is the electric field strength, \(\mathbf{H}\) is the magnetic field strength, \(\mathbf{B}\) is the magnetic induction strength, \(\mathbf{D}\) is the electric flux density, \(\mathbf{J}\) is the current density, and \(\mathbf{q}\) is the free charge density.  
Assuming that both the electric field strength and the magnetic field strength are harmonic fields, In the free space of vacuum, we can obtain  
\[\
abla\times\mathbf{E}=i\omega\mu\mathbf{H}, \tag{5}\]  
\[\
abla\times\mathbf{H}=\sigma\mathbf{E}, \tag{6}\]  
\[\
abla\cdot\mathbf{E}=0, \tag{7}\]  
\[\
abla\cdot\mathbf{H}=0, \tag{8}\]  
Due to the orthogonality of the electromagnetic field, the following relationship exists for the electromagnetic field components in a symmetric non-isotropic medium:  
\[\mathbf{E}_{x}=\mathbf{Z}_{xx}\mathbf{H}_{x}+\mathbf{Z}_{xy}\mathbf{H}_{y}, \tag{9}\]  
\[\mathbf{E}_{y}=\mathbf{Z}_{yx}\mathbf{H}_{x}+\mathbf{Z}_{yy}\mathbf{H}_{y}, \tag{10}\]  
where \(\mathbf{E}_{x},\mathbf{E}_{y},\mathbf{H}_{x},\) and \(\mathbf{H}_{y}\) are the Fourier transforms of the acquisition timing. When the measurement axis is coincident with the electrical spindle, \(\mathbf{Z}_{xx}=\mathbf{Z}_{yy}=0\), from which it follows that  
\[\mathbf{Z}_{xy}=\frac{\mathbf{E}_{x}}{\mathbf{H}_{y}}=\sqrt{-i\omega\mu\mathbf{\rho}_{xy}}, \tag{11}\]  
\[\mathbf{Z}_{xy}=\frac{\mathbf{E}_{y}}{\mathbf{H}_{x}}=\sqrt{-i\omega\mu\mathbf{\rho}_{yx}}. \tag{12}\]  
This leads to a relationship between the dielectric apparent resistivity \(\mathbf{\rho}\) and the phase \(\mathbf{\varphi}\) with the ground wave impedance \(\mathbf{Z}\):  
\[\mathbf{\rho}_{xy}=\frac{1}{\omega\mu}|\mathbf{z}_{xy}|^{2}=\frac{1}{5f}|\mathbf{z}_{xy} |^{2}, \tag{13}\]  
\[\mathbf{\rho}_{yx}=\frac{1}{\omega\mu}|\mathbf{z}_{yx}|^{2}=\frac{1}{5f}|\mathbf{z}_{yx} |^{2}, \tag{14}\]  
\[\mathbf{\varphi}_{xy}=\frac{180}{\pi}arctan\frac{\mathbf{Z}_{xy}^{l}}{\mathbf{Z}_{xy}^{k }}, \tag{15}\]  
\[\mathbf{\varphi}_{yx}=\frac{180}{\pi}arctan\frac{\mathbf{Z}_{yx}^{l}}{\mathbf{Z}_{yx}^{k }} \tag{16}\]
## 3 Intrinsic and Extrinsic Factors Associated with Hair Graying
### Aging  
Aging is the major factor altering the expression of hair graying-associated genes [80]. Aged transgenic mice carrying the oncogenic _RET_ gene exhibit gray hair due to an accelerated hair cycle, accompanied by the senescence of HFKSCs expressing ET1 and a decrease in follicular MSCs expressing endothelin receptor B (ETRB) [81]. Comparison of human scalp samples from young people aged 27-40 years with less gray hair and senior people aged 61-90 years with more gray hair confirmed the age-dependent increase in senescent follicular HFKSCs, decrease in ET-1 expression, and reduction in the number of functional follicular MSCs [81]. Thus, HFKSC senescence, MSC depletion, and their altered gene expression are considered the cause of age-dependent hair graying.  
Comparison of gene expression between hair follicles of young and elder volunteers, aged below 25 and above 50 years, respectively, indicated that KRT33, KRT34, KRTAP4-4, and KRTAP4-7 became downregulated with age [82]. Lipidomic analysis of follicular tissues of black and white hair obtained from female participants aged 55-65 years indicated that white hair contains reduced total lipid, sphingolipid (particularly glucosylce-ramide and galactosylceramide), phosphatidylinositolserine, and phosphatidic acid levels [83]. These studies suggest that age-dependent hair graying is accompanied or influenced by structural, biochemical, and pathophysiological changes in the hair interior and surrounding microenvironment.