esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrimination of Severity of Alzheimer's Disease with Multiscale Entropy Analysis of EEG Dynamics
## 2 Materials and Methods
### EEG Recordings and Preprocessing  
All participants had received routine EEG recording (Nicolet EEG, Natus Medical, Incorporated, San Carlos, CA, USA) in the EEG examination room at the Neurological Institute of Taipei Veterans General Hospital. The EEG recording protocol began with a 5-min habituation to the examining environment, followed by three sessions of 10 s with the eyes closed and then open, and a session of photo stimulus. Recordings were in accord with the international 10-20 system with linked ear reference, 256 Hz sampling rate, high pass filter of 0.05 Hz, low pass filter of 70 Hz, notch filter of 60 Hz, and impedance below 3 k\(\Omega\). We recorded 19 electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2). The signals from these 19 electrodes were referenced to linked earlobe electrodes. Vigilance was monitored by the EEG technician, who alerted patients when signs of drowsiness appeared in the tracings. Vertical eyeball movement was detected from electrodes placed above and below the right eye, with the horizontal analog detected from electrodes placed at the left outer canthus. The EEG signals were exported in European Data Format and were processed using MATLAB software (Mathworks Inc., Sherborn, MA, USA).  
For each subject, 19 electrodes were used to record the EEG signals of the subject. Each electrode recorded three epochs with each of 10 s for a total of 256 \(\times\) 10 = 2560 data points. The EEG signals length of 10 s is consistent with many other studies with length within 5-12 s [4; 5; 6; 7; 8; 9]. Empirical mode decomposition (EMD) method was used for detrending.
# Quantum Circuit Implementation of Multi-Dimensional Non-Linear Lattice Models
## 7 Detailed Description of Quantum Circuit for \(u^{2}+v^{2}\) Evaluation (Design _EKin2D3_)
### \((2N_{m}+1)=9\)-Qubit Results Register \(|r8|\ldots|r0\rangle\)  
The minimum-width design employs a \((2N_{M}+1)=9\)-qubit results register \(|r08|\ldots|r00\rangle\). The \(SQ4\) operator used in this design is shown in Figure 12 and employs the modified full adders defined in Section 6.3. As can be seen, \(N_{M}\) steps are used, with a controlled addition for \(u\)-velocity and \(v-\)velocity mantissa qubits during each step. \(N_{M}-1\) shift is performed on the output register, as previously for one-dimensional lattice models. Figure 11 shows the overall layout of the quantum circuit for \(N_{M}=4\) and \(N_{E}=3\), including the set-up of the \(SQ4\) operation. The four \(u\)-velocity mantissa qubits are set as \(|1 mu2|mu1|mu0\rangle\) for normalized \(u\)-velocity, and \(|0|mu2|mu1|mu0\rangle\) for sub-normal \(u\). The next four blocks of logical operations (separated by dashed lines) define the four qubits defining the mantissa of \(v\)-velocity, using a shift where needed. For \(|ev2|ev1|ev0\rangle=|000\rangle\) (i.e., \(E_{u}=E_{v}\)), the four qubits \(|qv3|qv2|qv1|qv0\rangle\) are set to \(|1|mv2|mv1|mv0\rangle\) (for normalized inputs) or \(|0|mv2|mv1|mv0\rangle\) for sub-normal \(v\)-velocity input. Next, the following blocksaccount for the situations with \(\Delta E=1\), \(\Delta E=2\) and \(\Delta E=3\) in succession. As can be seen, for a normalized \(v\)-input with \(\Delta E=1\), the circuit defines \(|qv3|qv2|qv1|qv0\rangle=|0|1|mu2|mu1\rangle\). Similarly, \(|qv3|qv2|qv1|qv0\rangle=|0|0|1|mu2\rangle\) for \(\Delta E=2\). Finally, for \(\Delta E=3\), only the most significant mantissa qubit of \(v\)-velocity is used (\(|1\rangle\) for normalized and \(|0\rangle\) for sub-normal \(v\)-input). For \(\Delta E\geq 4\), the contribution from \(v\) to \(u^{2}+v^{2}\) is eliminated fully.
# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
## 4 Experimental Setup and Node Architecture  
In order to evaluate the proposed energy aware algorithm, two different platforms have been used and deployed to collect field data. In the first deployment (I), a custom made platform was developed and used for monitoring the wind speed and direction with harvesting energy from the wind. In the second deployment (II) an off the shelf WSN platform was used for monitoring the gas levels (with an emphasis on measuring the CO\({}_{2}\) levels) in a beehive with solar energy harvesting. The deployments are carried out separately in two different application scenarios, with different durations in order to assess the performance of the proposed algorithm in different conditions (_i.e._, different platform, application space, and sensors).  
A generic block diagram of the systems deployed is presented in Figure 2. In the following subsections, the deployments are described in detail.
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 3 Methods
### Vehicle Detection under Gray and RGB Modes  
As mentioned in Section 2, the counting information is obtained based on the changes of pixel value in multiple loops. Therefore, for vehicle detection, the goal is to extract the vehicle as complete as possible. In view of the problem that the vehicle extracted by the existing motion-based method easily produces holes, a detection method operated under two modes of color, that is gray mode and RGB mode, is proposed to improve the integrity of vehicle.
## 2 Materials and Methods
### Fractionation  
Subcellular fractionation of cells was carried out as previously described [64]. Briefly, cell pellets were resuspended in cytosolic extract buffer (CEB; 250 mM sucrose, 70 mM KCl, 137 mM NaCl, 4.3 mM Na2 HPO4, 1.4 mM KH2 PO4) supplemented with 400 \(\upmu\)g/mL digitonin (Sigma-Aldrich, St. Louis, MO, USA, D141-100 MG), 100 \(\upmu\)M PMSF (Carl Roth GmbH, Karlsruhe, Germany, S367.1), 10 \(\upmu\)g/mL leupeptin (ApplChem, Darmstadt, Germany, A2183,0010) and 2 \(\upmu\)g/mL aprotinin (Carl Roth GmbH, Karlsruhe, Germany, A162.1). The cytoplasmic fraction was removed after centrifugation, and the mitochondrial fraction was generated from the pellets via incubation in a mitochondrial lysis buffer (MLB; 50 mM Tris-HCl pH 7.4, 150 mM NaCl, 2 mM EDTA, 2 mM EGTA, 0.2% Triton X-100, 0.3% NP-40) supplemented with 100 \(\upmu\)M PMSF, 10 \(\upmu\)g/mL leupeptin and 2 \(\upmu\)g/mL aprotinin. Extracts were centrifuged and the mitochondrial fraction was removed, before nucleic lysates were prepared from pellets in RIPA buffer, which contained HALT phosphatase inhibitors (Thermo Fisher Scientific, Waltham, MA, USA) and cOmplete proteinase inhibitors (Roche, Basel, CHE). The nuclear extracts were centrifuged to remove RIPA insoluble debris. Protein concentration in each fraction was quantified by BCA assay. Purity of the fractions was assessed via the presence of GAPDH in cytosolic fractions, HSP60 in mitochondrial fractions and LAMIN-A/C in nuclear extracts via quantitative immunoblots.  
Subcellular fractionation of cerebellar tissue was performed as previously described [65]. In brief, one cerebellum was first homogenised in Buffer A (150 mM NaCl, 50 mM HEPES pH 7.4; 1 M hexylene glycol) supplemented with 400 \(\upmu\)m deligitonin, 100 \(\upmu\)M PMSF, 10 \(\upmu\)g/mL leupeptin and 2 \(\upmu\)g/mL aprotinin using a pestle motor mixer. Samples were further homogenised via centrifugation through a QIAshredder (Qiagen, Venlo, The Netherlands). After a 10 min incubation period, samples were centrifuged to obtain the cytoplasmic fraction. Pellets were resuspended in Buffer B (150 mM NaCl, 50 mM HEPES pH 7.4, 1% NP-40, 1 M hexylene glycol) supplemented with 100 \(\upmu\)M PMSF, 10 \(\upmu\)g/mL leupeptin and 2 \(\upmu\)g/mL aprotinin. Extracts were incubated for 30 min and centrifuged to generate mitochondrial fractions. Finally, pellets were incubated with 500 U benzanase nuclease (Sigma-Aldrich, St. Louis, MO, USA, E1014-25 KU) to digest DNA. Nuclei were lysed via a 10 min incubation with Buffer C (150 mM NaCl, 50 mM HEPES pH 7.4, 1 M hexylene glycol, 0.5% sodium deoxycholate, 0.1% SDS) supplemented with 100 \(\upmu\)M PMSF, 10 \(\upmu\)g/mL leupeptin and 2 \(\upmu\)g/mL aprotinin, and nuclear extracts were harvested as supernatant after centrifugation. The fractions were subjected to BCA assay for determination of protein concentration. Purity of fractions was again assessed via the presence of GAPDH in cytosolic fractions, HSP60 in mitochondrial fractions and LAMIN-A/C in nuclear extracts via immunoblotting.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 3 Results
### Variation of Nominal Refractive Index Structure  
3.1.1. Impact on Refractive Index Structure  
The level of refractive index fluctuation in atmospheric turbulence can be estimated using the refractive index structure parameter. Multiple optical receivers may be positioned at various points on Earth in real-life circumstances. The strength of the turbulence flow may vary depending on the value of \(\text{C}_{\text{n}}^{2}(0)\).  
According to the Hufnagel Valley model, Figure 3 depicts the refractive index structure parameter's variation with respect to altitude under various ground-level refractive index structure values but with a constant wind speed. Since the value of the refractive index structure parameter at the ground is comparably higher than the value at higher altitudes, it is evident that there is a strong turbulence condition there, compared to higher altitudes. If the ground nominal value is higher, the refractive index value will be higher up to about 1000 m in altitude.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation** & **Symbol** & **Values** \\
**Parameters** & & \\ \hline Wavelength & \(\lambda\) & 700 nm, 950 nm, 1250 nm, 1550 nm \\ \hline Satellite Altitude & H & 700,000 m, 900,000 m \\ \hline Ground Station & \(\text{h}_{0}\) & 0 m \\ \hline Ground Station Aperture Diameter & D & 0.1 m, 5 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters used for the uplink and downlink FSO channels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Varied Parameter** & **Parameters Affected** \\ \hline Nominal Refractive Index Structure &
\begin{tabular}{c} Refractive Index Structure, Rytov Variance, \\ Scintillation Index \\ \end{tabular} \\ \hline Zenith Angle & Fried Parameter \\ \hline Fried Parameter & Angle-of-arrival Fluctuations \\ \hline \hline \end{tabular}
\end{table}
Table 2: Varied parameters and the parameters impacted.
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 3 Test Results
### Attenuation Law of Lasers with Different Power Levels  
In order to study the relationship between the laser attenuation coefficient and laser emission power in the complex water environment of a karst cave, we selected several laser emitters with a wavelength of 650 nm and emission power levels of 100 mw, 200 mw, and 300 mw, respectively, and used the same suspended medium in the water. Then, the corresponding laser attenuation coefficients were measured, and the laser attenuation coefficient turbidity curve was drawn, which is shown in Figure 7. According to the curve, when the suspended media are CaCO\({}_{3}\), silt, clay, and fine sand, the attenuation coefficient of a laser of the same power level increases linearly with the increase of turbidity. When the turbidity remains constant, the larger the laser emission power of the same wavelength, the smaller the corresponding attenuation coefficient will be, and when the turbidity is  
Figure 6: Attenuation coefficient—turbidity curve in dissolution medium of SiO\({}_{2}\): (**a**) Two dimension; (**b**) Three dimension.  
relatively high, the differences in the attenuation coefficients of lasers of different power levels are larger. Judging from the attenuation coefficients of lasers of different power levels, the lasers with higher power have lower attenuation coefficients in the water. However, high-power lasers cause harm to the operators. Therefore, the selection principle of laser power is based on ensuring the safety of personnel. It is better to select a laser with higher power to better meet the laser detection distance requirements of large water-filled karst caves.
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 4 Proposed DEOR Algorithm  
To solve the routing problem described in Section 3.3, a Directional-area-forwarding-based Energy-efficient Opportunistic Routing (DEOR) for the post-disaster MIoT network is proposed, aiming to improve the energy efficiency and the data transmission reliability of the post-disaster MIoT network in the planar accident mine. The proposed DEOR algorithm mainly consists of three parts: candidate forwarding set construction, relay node selection, and void routing node recovery. Firstly, a directional-area-forwarding-based candidate forwarding set construction strategy is designed. In the initialization phase, according to the deployment density and communication radius of nodes in the accident roadway, a forwarding zone (FZ) is designed for each node to route packets toward the sink. Then, the CFS is constructed by the nodes within the FZ that satisfy the energy constraint and the neighboring node degree constraint. Subsequently, a relay node selection method based on routing quality evaluation is proposed. In the data transmission phase, we take multiple attributes of nodes into account, such as direction angle, transmission distance, and residual energy. All nodes in the CFS are prioritized based on the routing quality evaluation, and the node with the highest priority is selected as the relay to forward packets. Finally, a recovery mechanism for void nodes is designed. When packets encounter the routing void during forwarding, a recovery mechanism is triggered. By employing the modified routing quality evaluation function, packets can bypass the void region and select available relay nodes to continue forwarding. Details are described below.
# Optimization of Buoy Shape for Wave Energy Converter Based on Particle Swarm Algorithm
## 4 Experimental Validation  
To validate the correctness of the optimization method described above, a wave tank experiment was conducted at the Yantai Research Institute of Harbin Engineering University. The experimental setup is depicted in Figure 15. A high-speed camera was employed to capture the buoy's instantaneous motion characteristics, while displacement sensors were utilized to measure the buoy's motion response. The internal clear space dimensions of the wave tank were 10 m \(\times\) 0.8 m \(\times\) 0.8 m. The wave tank had the capability for active wave generation, with a maximum operational water depth of 0.6 m or more. When generating regular waves, the wave periods ranged from 0.5 to 2 s, and the maximum wave amplitudes ranged from 30 to 200 mm.  
Based on the dimensions of the wave tank, two scaled-down buoys were manufactured with a scale ratio of 0.05. The connection between the buoys and the arm was established through bolts, while the connection between the tower and the arm was facilitated by two bearings. In the experiments, regular waves were generated with a wave period set at 1.03 s and a wave height of 0.06 m. The experimental results are depicted in Figure 16.  
The experimental results from the wave tank under scaled conditions clearly indicate a significant difference in the hydrodynamic performance between the scaled-down optimal buoy and the traditional cone buoy. Once the system reached stability, the motion response exhibited a consistent pattern. The swing amplitude of the optimal buoy reached approximately 40 mm, while that of the cone buoy was only 10 mm. This demonstrates  
Figure 14: Buoy taper and flatness variation curve.  
Figure 13: Iterative curves of characteristic parameters: (**a**) draft volume variation curve; (**b**) wet surface area variation curve.  
that the hydrodynamic performance of the optimal buoy surpasses that of the cone buoy, thereby validating the effectiveness of the optimization method proposed in this study.
## 4 Discussion
### The Practical Significance of the Results of the Study to Guide Grouting  
It can be seen from Sections 4.1 and 4.2 that the pore seepage characteristics of deep-mine sandstone are the result of the comprehensive action of the pore structure of sandstone itself and the external environmental factors (confining pressure, water pressure, temperature) deep underground (see Figure 17). Therefore, during the design and construction of pore grouting, it is necessary to comprehensively analyze various influencing factors to make the correct grouting plan and obtain the best grouting effect. The following illustrates the practical significance of the research results for guiding grouting.  
Understanding the actual effects and mechanisms of the \"three-high\" factors on pore seepage under the current coal mining environment is helpful to guide future grouting construction design. For example, according to the results of this study, under the environmental conditions of coal mining in the next 20 years, the increase in high confining pressure and high temperature will mainly produce elastic deformation in deep-mine sandstone, resulting in a reduction in pore throat volume, which will lead to a reduction in sandstone permeability. Therefore, in the grouting design of deep sandstone with \"large-area sweating\" seepage, the grouting holes should be mainly arranged in the high-permeability sandstones, such as subcategory I\({}_{1}\), I\({}_{2}\) and II\({}_{1}\) sandstones, because the low-permeability sandstones, such as subcategory II\({}_{2}\) sandstone, will have fewer pores, and the seepage flow will decrease under the continuous action of the higher confining pressure and high temperature.  
Understanding the difference in the permeability evolution of sandstone with different pore structures under the influence of the \"three-high\" factors can inform the correct selection of grouting parameters. For example, the permeability changes in sandstone with different pore structures vary greatly with increasing water pressure. When grouting low-permeability sandstone with a small porosity and average pore throat radius, it is not appropriate to apply too high a grouting pressure at the beginning of the grouting treatment. The pressure should be increased incrementally by considering the size of the  
Figure 17: Relationship between pore permeability and various influencing factors.  
pore throat; starting from low-pressure grouting, the grouting pressure should be adjusted continuously according to the change in pore structure, and different grouting parameters should be selected to obtain the best grouting effect.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 3 Results
### Accuracy Analysis and Comparison  
#### 3.3.1 Statistics Analysis  
The residual phase of multi-DOR tones is shown in Figure 14, and the residual delay and delay rate are shown in Figure 15. Random error or noise level are also evaluated.  
Figure 14: Phase of DOR tones signal. Fluctuation of residual phase of all tones signal could be identified, mainly caused by ionosphere fluctuation: (**a**) baseline CEB-NNO; (**b**) baseline CEB-KSH; (**c**) Baseline NNO-KSH.  
It is noticed that the random error of all the baselines has the same noise level, about 0.006 ns for residual delay and 0.3 ps/s for residual delay rate, and only effective number values of observables are reserved in Table 4. Because the C/N of signal tones at all the  
Figure 15: Residual delay and delay rate: (**a**) Baseline CEB-NNO; (**b**) Baseline CEB-KSH. (**c**) Baseline NNO-KSH.  
stations is much higher, 20-30 dB higher than the designed threshold in the observation, there is no obvious difference in the observable noise level.
# Comprehensive Analysis of the Acute Toxicity of Ionic Liquids Using Microtax(r) Bioassays
###### Abstract  
The ecotoxicity of a set of 30 ionic liquids, namely 23 aprotic compounds (APILs) and 7 protic compounds (PILs), was analyzed in this work by monitoring the inhibition of the bioluminescence of the bacteria _Aliinibria fischeri_ with varying concentrations of ILs utilizing the Microtax(r) standard toxicity test. The study covered ILs that have various synthetic natures, PILs and APILs, with a common anion or cation, and different alkyl chain lengths. The results indicate that both moieties, anion and cation, have an influence on toxicity, these being the ILs with the bis((trifluoromethyl)sulfonyl)imide (TFSI) anion and imidazolium cation, which are the most harmful, while those less toxic correspond to ammonium-based ILs. The alkyl chain length seems to have the most significant impact on toxicity, except for tris(pentafluoroethyl) trifluorophosphate (FAP) anion-based ILs, which, interestingly, showed the opposite behavior. A critical alkyl size (CAS) at C = 6 was observed for the rest of the families, resulting in a significant reduction in the effective concentration (EC) values: the connection between this CAS and toxicity has never been observed before, and it indicates a threshold that marks the end of harmlessness (C \(<\) 6) and the start of toxicity (C \(>\) 6).  
ionic liquids; Microtax(r) test; anion effect; cation effect; alkyl chain length effect +
Footnote †: journal: _Sci_  
0000-0001-5000-0001-5000]J.J.S.
## 2 Methods
### Bayer Pattern Position Determination  
When a Bayer pattern is converted to a virtual linear sensor, the smallest imaging unit must be complete and cannot be missing; however, owing to the influence of signal occlusion, interference, or rain and snow in the data receiving process, the Bayer pattern image has the phenomena of packet loss, as well as frame and row loss, which destroys the integrity of the Bayer image. Figure 7a shows that, owing to a loss in line 3, the Bayer pattern changed from a pattern of GBRG in lines 3-4 to one of RGGB, and the last row of Bayer pattern data was incomplete (Figure 7b). Therefore, during the calibration process and before Bayer pattern conversion, it is necessary to locate the specific position of each complete Bayer pattern in the image to avoid confusion between separate band detector imaging data and the misalignment of various detector orders in the same band.  
A positioning method linked by \"lineounters\" is designed to detect the specific position of each Bayer pattern. First, a linecounter is added in front of each row of imaging data according to the following rules: (1) when the row data is the first row of the Bayer pattern, the linecounter value is odd, otherwise it is even; (2) a complete Bayer pattern imaging of two rows of data has a continuous linecounter with a difference of 1 [i.e., \({\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{ \it{\it{{\it{{\it{{\it{{             }}}}}}}}}}}}}}}}}}}}}}}}}  
The process of determining the location of the complete Bayer pattern according to the above rules is as follows:  
1. Calculate the linecounter value \(LineCounter_{i}\) of the \(i\)th row of Bayer pattern data.
2. Determine the first position of a complete Bayer pattern. If \(LineCounter_{i}\) is odd, the following equation holds: \[((LineCounter_{i}+1)\%2)=0,\] (2) where the \(i\)th row data is the starting line of the Bayer pattern. If this is true, then the order can proceed to step (3), otherwise the calculation \(i=i+1\) must be performed before returning to step (1).
3. Determine whether the \(i\)th row Bayer pattern data is continuous with the adjacent imaging data and Bayer pattern according to the following rules: \[|LineCounter_{i}-LineCounter_{i+1}|=1.\] (3) \[|LineCounter_{i-1}-LineCounter_{i}|=1.\] (4) If the \(i\)th iteration meets the above formula, the \(i\)th row is the starting row of the complete Bayer pattern data. Otherwise, the \(i\)th data will be discarded and the calculation \(i=i+1\) must be performed before returning to step (1).
4. Repeat steps 1-3 to determine the locations of all complete Bayer pattern data.