esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### 2-LED \(\mu\)Spectrophotometer for Colorimetric Measurements
#### 3.4.3 Colorimetric Assay  
LAMP is an extremely efficient amplification method (10 \(\upmu\)g in a 25 \(\upmu\)L reaction mixture) [37; 38]. Calibration experiments of these LAMP products with our 2-LED-\(\upmu\)-Spectrophotometer were performed and the obtained data were compared with the results achieved with the conventional UV/vis spectrometer. The assay is based on the observation that DNA-AuNP probes remained dispersed in the presence of specific LAMP amplicons that bind on the functionalized particle, but aggregate without DNA or unspecific DNA after addition of MgCl\({}_{2}\) or HCl at RT. Clear color differences (red vs. blue/purple) were observed after the hybridization of the DNA-AuNP probes with the LAMP amplification product, as shown in Figure 9a. The selectivity of the AuNP probes is crucial. The experimental results show that the probes were highly selective and non-target DNA leads to a color shift. More precisely, the sample containing the non-specific template turned blue (+), just like the sample without templates (-. Moreover, the samples containing only the specific template remained as red solution (+). These differences, the aggregation profiles, and thus the change in absorbance ratio are also clearly visualized and analyzed with the 2-LED-\(\upmu\)-Spectrophotometer and show aggregation for the negative control (AuNP probe + salt) as well as for the no template control. Alternatively, the positive control (AuNP probe) and the specific LAMP product that hybridized with the AuNP probe in the reaction remained red. The addition of salt cannot induce aggregation (Figure 9c). Despite this, the use of HCl as aggregation promoter reveals another finding, which is the reversibility of the coalescence of the AuNP probe by adding sodium hydroxide (NaOH), shown in Figure 9c. The threshold of 0.8 was determined for discrimination between positive (Abs\(\uplambda\)/Abs\(\uplambda\)1 \(\geq\) 0.8) and negative (Abs\(\uplambda\)2/Abs\(\uplambda\)1 \(<\) 0.8) aggregation results.  
Figure 8: LAMP results for \(L\). _pneumophila_, 2% agarose gel, targeting the _dotA_ (**a**) and the _lePB_ (**b,c**) gene; (**a,b**) DNA ladder 100 bp, line 1 = no template control, line 2 = serogroup (SG) 1, line 3 = SG 3, line 4 = SG 6, line 5 = SG 8, line 6 = SG 9, line 7 = SG 15; (c) dilution row. DNA ladder 100 bp, line 1 = no template control, line 2 = 500 pg \(\upmu\)L\({}^{-1}\), line 3 = 0.5 pg \(\upmu\)L\({}^{-1}\) line 4 = 0.05 pg \(\upmu\)L\({}^{-1}\), line 5 = 0.005 pg \(\upmu\)L\({}^{-1}\), line 6 = 0.0005 pg \(\upmu\)L\({}^{-1}\), line 7 = empty.
## 3 Proposed Method
### SSUPS Based Digital Twin Framework
#### 3.1.2 Digital Twin Definitions  
To provide the virtual-real synchronization of consistency, completeness and soundness for the operating logic of behavior of oil and gas station, we provide precise definitions for these three aspects.  
**Definition 1**.: _(Consistency): For \(\forall s\in E_{R}\), exist only one \(m\in E_{DT}\), such that \(G(s,r,v)=G(m,r,v)\). Where \(G\) is the knowledge graph, \(s\) is one of the triples in the knowledge graph. This definition reflects entities in physical space are the same as entities in virtual space._  
**Definition 2**.: _(Completeness): For \(\forall s\in E_{R}\), \(\exists m\in E_{DT}\), such that \(G(s,r,v)=G(m,r,v)\), and for \(s\vdash\varphi\), \(G(\varphi,r,v)=G(m^{\prime},r,v)\) is also satisfied. Where \(s^{\prime}\) is the derivation of \(s\) through different relations in the knowledge graph._  
**Definition 3**.: _(Soundness): If \(\exists m\in E_{DT}\), \(f_{m}:\mu\rightarrow\mu^{\prime}\), then \(G(m)=f_{m}(G(m^{\prime}))\). Where \(m^{\prime}=s|s\vdash,s\in E_{RW}\), \(f\) is the mapping function._  
When the constructed digital twin satisfies the above three confirmatory definitions, it can be called a qualified digital twin that meets the demand of anomaly detection.
# Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
## 5 Measurement Results
### In Vivo Measurement  
The proposed system successfully demonstrated the in vivo measurement of epidural neural signals from a primate using our prototype interface module in the [PERSON]'s lab in Washington University. The epidural electrode array was placed on the surface of dura mater of a primate and the neural activities from 16 channels were recorded simultaneously. Figure 22a shows a 1-second waveform clip of the 16-channel ECoG signal recording. To visualize the frequency contents (\(\alpha\): 8-12 Hz, \(\beta\): 18-26 Hz, low \(\gamma\): 30-50 Hz, high \(\gamma\): 70-100 Hz) of the recorded ECoG signals, a spectrogram from a selected channel with its time-domain representation is also plotted in Figure 22b. Typical motor movements are related to the ECoG neural signal power in the frequency bands of 7-30 Hz and 70-110 Hz. The spectrogram in Figure 22b shows the activity related ECoG neural signal power.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & [S] & [6] & [9] & [11] & [19] [2] & [20] [20] & **This Work** \\ \hline \multirow{5}{*}{Integrated Circuits} & Power/Ch. (\(\mu\)W) & 2.3 & 134 & 10.8 & 3.5 & 5 & 2 & **2.5** \\  & Total Power (\(\mu\)W) & 225 & – & – & 3.5 & 20 & 2 & **365** \\  & Supply (V) & 0.5 & 3 & 1.2 & 1 & 1.4-3.3 & 1.8 & **1.00/5** \\  & N. of channel & 64 & 64 & 1 & 1 & 4 & 1 & **16** \\  & Channel gain (dB) & 30 & 60 & 40-75 & 72 & 54-80 & 41,50.5 & **40–60** \\  & Noise PSD & & & & & & & \\  & (\(\mu\)V\({}_{\rm rms}\)/Hz) & & & & & & & \\  & (\(\mu\)V\({}_{\rm 7}\)/\(\sqrt{\rm kHz}\)) & & & & & & & \\  & NEF & 4.76 & 3.5 & 5.38 & 9.37 & 4.6 & 4.6 & **1.69/5.86** \\  & PEF & 11.3 & 42.9 (\({}^{\rm 0}\)) & 34.7 & 87.8 & 38.1 (\({}^{\rm 1}\)) & 38.1 (\({}^{\rm 1}\)) & **2.86/27.04** \\  & CMRR (dB) & 82 & 51 & 80 & 60 & \(>\)80 & 100 & 35 \\  & PSR (dB) & 75 & 69 & 60 & – & \(>\)80 & – & **48** \\  & Area/Ch. (mm\({}^{2}\)) & 0.025 & 2.69 & 2.8 & 0.6 & 5 & 0.8 & **0.155** \\  & Resolution (bit) & 15 & 12 & 24 (\({}^{\rm 0}\)) & 12 & – & – & **8** \\  & SNDR/SFDR (dB) & \(-\)/52 & 66.17/- & \(-/-\) & 65.3/- & \(-/-\) & \(-/-\) & **45.14/73** \\  & \(f_{\rm s}\) (dS/s) & – & 3 & – & 100 & – & – & **31.25** \\  & Wireless (\(\gamma\)/N) & Yes & Yes & No & No & No & No & Yes \\  & Technology (um) & 0.065 & 0.35 & 0.13 & 0.18 & 0.8 & 0.8 & **0.25** \\ \hline \multirow{5}{*}{
\begin{tabular}{} \end{tabular} } & Fabrication & Yes & Yes & No & No & No & No & Yes \\  & Diameter (\(\mu\)m) & 260 & – & – & – & – & – & **300–500** \\ \cline{1-1}  & Impedance (\(\lambda\)f) & 10 & 6100 Hz & – & – & – & – & **1.2–5.6 01 kHz** \\ \cline{1-1}  & Material for electrode & PI/Au & PI/Ir & – & – & – & – & **PI** \\ \hline \end{tabular}
\end{table}
Table 2: Performance summary and comparison with recent ECoG and EEG works.  
Figure 22: _Cont._
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 7 Simulation Results and Analysis
### Average System Capacity under Ideal CSI  
As shown in Figure 2, the channel capacity of several typical MIMO-BC algorithms is compared in ideal CSI. The proposed algorithm only focuses on intercell interference when designing the receive filter and handles intercell and intracell interference (excluding interference between user data streams) when designing the transmit filter and uses a water-filling scheme to optimize power allocation, and finally uses phase alignment. The interference between data streams is rotated into the signal space of the target receiving data stream, thereby enhancing the receiving power of the target data stream.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No** & **Parameter** & **Symbol** & **Value** \\ \hline
1 & Number of cells & G & 2–7 \\
2 & Number of users per cell & \(K\) & 2–10 \\
3 & Degrees of freedom & \(d\) & 4 \\
4 & Signal-to-noise-ratio & SNR & 20–40 dB \\
5 & Number of BS antennas & \(M\) & 13 \\
6 & Number of receiver antennas & \(N\) & 8 \\
7 & Carrier frequency band & \(f_{\mathrm{c}}\) & 2 GHz \\
8 & User velocity & \(V_{k}\) & 20 km/h \\
9 & Correlation coefficient & \(\rho\) & 0.9966 \\
10 & Channel error variance & \(\sigma_{\mathbf{e}}^{2}\) & 0.001 \\
11 & Number of iterations & \(N_{thr}\) & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed System Simulation Parameters.  
However, the MAX-SINR-SCEK algorithm in the literature [17] focuses on intercell interference, but the transmit filter needs to deal with intercell and intracell interference as well as interference between its own data streams. It needs to process more interference than this algorithm. For this reason, the proposed algorithm is better under the same environment configuration. In addition, the minimum interference leakage (MIN-IL) algorithm in [17] requires more antennas at the receiving end which is not practical in practical environments. Careful analysis shows that under MIMO-BC, the receiver needs to process less interference (from the neighboring base station), and the sender (usually the base station) needs to avoid interference to neighboring cell users and other users in the cell. More interference is handled, so it is more practical and reasonable to configure more antennas at the sending end to handle the interference. For comparison, under the same system configurations, the proposed algorithm is also better than the MIN-IL algorithm of [17].
# Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
## 3 Survey Plan
### Issues Identified in Highway Guide Signs
#### 3.4.3 Information Selection Problems  
The analysis of the collected highway guide signs revealed the following key issues regarding the selection of sign information.  
1. Inconsistent guide information According to standards, the information displayed on exit signs, including advance exit signs, exit markers, and signs at branch points after the exit, must remain consistent throughout. Additionally, the first row of the distance sign should align with the destination shown on the subsequent exit marker [39]. However, the survey found that some exit signs presented conflicting information, and the distance signs lacked correspondence with upcoming exit markers. Furthermore, the guide information on exit markers was inconsistent with that on signs at post-exit branch points, leading to driver confusion.
2. Lack of hierarchical information selection Standards require that distance signs follow a hierarchical structure: the first row should display the destination accessible via the next interchange, while the second and third rows should indicate destinations at increasing distances (Tier B and Tier A information, respectively) [39]. However, the survey identified non-compliance, where signs failed to use this hierarchical format and omitted highway numbers, resulting in inconsistencies with previous signs and creating information overload.
3. Inconsistent reference points for distance calculations The standards specify that distance calculations must use a consistent reference point, such as the beginning of the taper at deceleration lanes when guiding towards exits [39]. However, discrepancies were found in the network, with multiple distance signs using inconsistent reference points. This inconsistency can mislead drivers regarding the actual distance to destinations, reducing the accuracy of navigation systems and compromising driving safety.
# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 2 Discretisation  
The knowledge discovery process plays a vital role in a wide variety of applications. One of the key steps in this process is the preparation of the data, which in turn influences the outcome of the subsequent phases of data exploration [13]. Preparation of the data includes various preprocessing methods [14]. Data cleaning covers filling missing values, smoothing noise, identifying or removing outliers, and resolving inconsistencies. Data integration and transformation involve the integration of elements that come from various sources and making them uniform in format. In addition, data transformation includes normalisation and aggregation, which are important for data mining processes. Data reduction makes it possible to obtain a reduced representation of a dataset, which is smaller in volume but produces the same analytical results. In this latter group, discretisation, attribute subset selection, and dimensionality reduction methods can be distinguished. Detailed information on discretisation approaches is presented in the following.
# Research on the Visual Guidance System of Zoning Casting Ginding Based on Feature Points
## 4 Simulation Experimental Verification
### Analysis of Simulation Result  
Figure 16 depicts the relationship between the distance and the number of points, and Table 1 displays the longitudinal comparison data between template casting and ground casting. There are 22,443 points in total within the PLF ROI, and 243 of those points (or 1.08 percent) are bigger than the free tolerance (0.5 mm) from the template. There are 5418 points in the CFH ROI, and 8 of them, or 0.15 percent, are further away from the template than the free tolerance (0.5 mm). The vast majority of the longitudinal range's points comply with the tolerance and grinding standards.  
Table 2 displays the lateral comparison information between the template casting and the ground casting, and Figure 17 depicts the relationship between the distance and the number of points. There are 16,889 points in the PLF ROI, and 98 of them, or 0.58 percent, are further apart than the free tolerance (0.5 mm). In total, 4817 points make up the CFH ROI, and 7 of those (or 0.14 percent) fall beyond the free tolerance (0.5 mm), indicating a more satisfactory grinding result. The overwhelming majority of transverse range points  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Condition** & **>0.3 mm Dots** & **>0.4 mm Dots** & **>0.5 mm Dots** & **Points in ROI** \\ \hline Vertical PLF & 2505 & 632 & 243 & 22,443 \\ Vertical CFH & 278 & 28 & 8 & 5418 \\ \hline \end{tabular}
\end{table}
Table 1: Vertical comparison data table of castings.  
Figure 16: Vertical comparison picture.  
fall inside the tolerance range as well, demonstrating that the grinding system developed for this study complies with the specifications. This further demonstrates the good consistency of the ground finished castings.  
The bigger part of the gap is mostly caused by the individual variances between the castings while the lesser part of the distance is caused by camera mistakes and positioning errors. According to the statistics for both the horizontal and vertical point clouds, the vertical point cloud has a higher percentage of points with distances greater than the free tolerance, and the effect is also slightly worse than the horizontal. Both, however, can perform the grinding duty and are within the tolerance of 0.5 mm. The system in this study can adapt to the distinctive properties of castings and provide efficient grinding trajectories for custom grinding castings, according to the simulation results.
# Principle and Implementation of Stokes Vector Polarization Imaging Technology
## 3 Implementation of Stokes Vector Polarization Imaging Technology
### Simultaneous Polarization Imaging Mode
#### 3.2.1 Fractional Amplitude Polarization Imaging  
The fractional amplitude polarization imaging system uses beam splitter, polarized beam splitter or Wolston prism to divide the incident light into 3 to 4 channels of polarized beam. By combining the imaging lens and multiple imaging detectors, it forms multiple channels to acquire the intensity image of different polarization components of the same target scene. The target Stokes component graph is obtained by data reduction matrix. The fractional amplitude polarization imaging system has the advantages of good real-time performance and high energy utilization.  
[PERSON] designed the first partial amplitude polarization measuring instrument, that is, through splitting prism, PBS (polarization splitting prism) and other functions, the scene radiation is divided into 4 polarized beams, imaging on 4 CCD, and then through the light intensity linear into electrical signals, output to the workstation for polarization analysis. On this basis, [PERSON] et al. also proposed a variety of amplitude-dividing devices, such as the 4-detector polarimeter [22] based only on Fresnel's law without using any common polarizing elements, grating diffraction [23], optical fiber [24], parallel glass plate [25], etc., as amplitude-dividing devices. The fractional amplitude device divides incident light into multiple polarization channels in proportion to intensity, each of which only detects a specific polarization state [14]. Fractional amplitude polarization imaging can be achieved by a variety of methods. For example, the light to be measured can be distributed into four polarization channels through multiple splitter prisms [26] to achieve single-point polarization measurement or polarization imaging. The polarization splitting prism is the most basic amplitude-splitting device. The incident light is divided into two linear polarization states, such as horizontal and vertical polarization components, and the Stokes parameter S can be measured simultaneously 0, S1; The Stokes parameter S can be simultaneously measured by using two polarization splitting prisms with different orientation0, S1, S2; The incident beam can be divided into 4 beams by using 3 or more splitter elements, and the Stokes parameter S can be simultaneously measured 0, S1, S2, S3. As shown in Figures 6 and 7, Schematic of Full Stokes Imaging Polarimeter by [PERSON] et al. [27] in 2009 and the polarization imaging schematic diagram based on fractional amplitude by [PERSON] et al. [28] in 2013 are quoted.  
Figure 6: Schematic diagram of full polarization state of fractional amplitude measurement [27].
## 4 MSIMP Based on EMOEAs
### Surrogate Models  
The surrogate model approximates the original expensive objective function by collecting and analyzing real data to improve the efficiency and feasibility of the EMOEAs in solving the MSIMP problem. At present, the most commonly used surrogate models can be categorized into statistical models, function approximation models, and machine learning models. Statistical models, which aim to use statistical methods as surrogate models to filter out poor candidate solutions to achieve the goal of reducing the computational cost, include Kriging [89], support vector regression (SVR) [90], and Bayesian optimization [91]. Function approximation models are mathematical functions that approximate the true fitness value of the objective function, thus reducing the number of evaluations, which include the response surface methodology (RSM) [92], radial basis functions (RBFs) [93], and polynomial regression [94]. Machine learning models are analyzed via training on historical data to predict candidate solutions and include artificial neural networks (ANNs) [95], deep learning [96], and decision tree regression [97]. The main ideas, advantages, and disadvantages of the three types of surrogate models are summarized in Table 3.
# Analysis of Acoustic Flow Field Characteristics of Key Components of High-Speed Train Bogies
## 3 Analysis of the Results
### Influence of Train Speed on the Sound Field Distribution
#### 3.1.1 Cloud Distribution of Bogie Motors  
The sound field distribution in the bogie region is depicted in Figure 7 for a vehicle speed of 30 km/h. As the frequency increases, the cloud pattern becomes less distinct. At 50 Hz, the sound pressure level predominantly concentrates around the sound source and radiates outward in a circular manner. With increasing frequency, the sound field exhibits greater chaotic behavior. However, overall, the maximum sound pressure level is situated beneath the sound source and shows more lateral variation than vertical within the pressure level cloud. The maximum value of sound pressure level surrounding the bogie rises from 121.7 dB at 50 Hz to 124.2 dB at 350 Hz for a vehicle speed of 30 km/h, resulting in an increase of approximately 2.5 dB. Conversely, there is a continuous decrease in minimum values for sound pressure levels, with an overall reduction of about 15 dB.  
Figure 7: Sound field distribution of bogie motors at a speed of 30 km/h, with different frequencies.  
To better observe the effect of vehicle speed on the bogie sound field distribution, the corresponding sound pressure level at each vehicle speed is plotted as a line graph in Figure 8. It can be observed from the graph that as the vehicle speed increases from 30 km/h to 120 km/h, the effect on the maximum value of the sound pressure level is not evident. However, the effect on the minimum value of the sound pressure level is more apparent, especially at frequencies above 150 Hz.  
At a speed of 30 km/h, the minimum value of sound pressure level reduces from 89.8 dB to 64.8 dB, resulting in a reduction of 25 dB. At a speed of 60 km/h, the minimum value of sound pressure level decreases from 89.9 dB to 65.5 dB, resulting in a reduction of 24.4 dB. At a speed of 90 km/h, the minimum value of sound pressure level decreases from 89.8 dB to 61.1 dB, resulting in a reduction of 28.7 dB. At a speed of 120 km/h, the minimum value of sound pressure level decreases from 89.6 dB to 57 dB, resulting in a reduction of 32.6 dB. The higher the speed, the more significant the reduction in the minimum value of sound pressure level.  
Overall, the effect of vehicle speed on the change in the sound pressure level magnitude becomes more pronounced after 150 Hz.
## 5 Performance Evaluation
In general, the proposed _EBDC_ mechanism outperforms the _Fixed_ and _RMDC_ schemes in terms of the degree of energy balancing in all cases. Figure 11 further compares the proposed _EBDC_ mechanism with the _Angle-based_ approach in terms of the degree of energy balancing. The fan angle \(g\) of _Angle-based_ approach is set to \(10^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\). Similar to Figure 10, we investigate the average energy difference between the randomly selected boundary and central sensors. In the _Angle-based_ approach, the mobile sink traverses the edges of each fan starting form the center of the monitoring region. As shown in Figure 5(a,b), the central sensor will be visited when mobile sink completes the traverse of the edges of each fan. Therefore, as shown in Figure 11, the three _Angle-based_ curves are increased with the elapsed days most of the time. In particular, the three curves drop only when the boundary sensor is visited by mobile sink. However, since the _Angle-based_ approach does not consider the factor of energy balancing, the three _Angle-based_ curves cannot drop to 0 Joule. On the contrary, the _EBDC_ curve periodically drops to 0 Joule when mobile sink completes the data collection task in each round. In general, the proposed _EBDC_ mechanism outperforms the _Angle-based_ approach in terms of the degree of energy balancing in all cases. Figure 11: Comparison of _Angle-based_ and _EBDC_ in terms of the degree of energy balancing. Figure 10: Comparison of _Fixed_, _RMDC_, and _EBDC_ in terms of the degree of energy balancing.
# Coupling Motion and Energy Harvesting of Two Side-by-Side Flexible Plates in a 3D Uniform Flow
## 3 Results and Discussion
### Flow around Two Flexible Plates in Side-by-Side Arrangement
#### 3.2.2 Force and Energy Conversion
More specifically, the optimized selection for higher bending energy conversion efficiency is the plate flapping in single plate mode, especially the plate with a higher aspect ratio. To predict the actual energy harvesting performance, the conversion of bending energy to other direct useable energy and its interaction with the plate's flapping dynamics should be studied, and this is beyond the scope of the present work.