esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology
###### Abstract  
The straight-through magneto-optical glass current sensor has desirable temperature properties, but it is vulnerable to magnetic interference. In contrast, a polarization-type fiber optic current sensor has poor temperature performance, but the magnetic anti-interference characteristic is very good. Aiming at the problem that the accuracy of a fiber optic current sensor is susceptible to external disturbances and temperature fluctuations, we present an adaptive technology of a fiber optic current sensor that uses the magneto-optical output signal to correct the fiber output signal. The working principle of the improved method is introduced in this paper. The structure of the specific optical system and the signal processing system are presented. Temperature fluctuation and magnetic change detection units are included in the design in order to provide signal selection under different environmental fluctuations, thus stabilizing the output current data. The signal processing system was proved to be effective by building an experimental platform.  
adaptive; temperature drift; external disturbances; fiber optic current sensor; magneto-optical glass Article
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 2 Materials and Methods
### NFC Potentiostat Design  
The NFC potentiostat is designed in the form of a compact credit card having dimensions of 5.5 cm \(\times\) 8.6 cm, consisting of a planar antenna, NFC microchip (SIC4341) and connector for electrode interface. The dimension of the NFC potentiostat is designed to ensure stable operation during tapping the mobile, while leaving some space for electrode insertion and solution drop. The NFC microchip harvests energy from the magnetic field emitted from a smartphone or RFID reader through a loop antenna realized from 1 oz. (35 \(\upmu\)m) copper trace on FR4-PCB. The printed circuit board was processed by PCBway (Shenzhen, China). The antenna consists of a 4-turn loop with a size of 4.5-by-3.0 cm maximum perimeter and 250 \(\upmu\)m conductor width and spacing. The antenna has an inductance of 2.04 \(\upmu\)H with a quality factor of 130, measured by LA-19-13-02 VNA from LA technique Ltd. (Surrey, UK) and couples to a microchip having a 50 pF internal capacitance. This creates a parallel resonance circuit at a peak resonance frequency of 15.7 MHz, based on the \(fr=1/2\pi\sqrt{\text{LC}}\) relationship. The resonance frequency of the antenna is set a bit higher than the operating frequency of 13.56 MHz in order to compensate coupling effects from near-field operation. The maximum operating read distance from the antenna is 2 cm, over which the potentiostat can still operate stably. The energy is regulated to the internal 1.8 V power supply for the internal potentiostat to cover a maximum operation range of \(\pm\)20 \(\upmu\)A, an analog-to-digital data converter, a digital signal processor to convert raw data into measurement current information and an NFC analog front-end communication circuit to communicate with the smartphone or RFID reader.
###### Abstract  
Keeping an overview of all ongoing processes on construction sites is almost unfeasible, especially for the construction workers executing their tasks. It is difficult for workers to concentrate on their work while paying attention to other processes. If their workflows in hazardous areas do not run properly, this can lead to dangerous accidents. Tracking pedestrian workers could improve the productivity and safety management on construction sites. For this, vision-based tracking approaches are suitable, but the training and evaluation of such a system requires a large amount of data originating from construction sites. These are rarely available, which complicates deep learning approaches. Thus, we use a small generic dataset and juxtapose a deep learning detector with an approach based on classical machine learning techniques. We identify workers using a YOLOv3 detector and compare its performance with an approach based on a soft cascaded classifier. Afterwards, tracking is done by a Kalman filter. In our experiments, the classical approach outperforms YOLOv3 on the detection task given a small training dataset. However, the Kalman filter is sufficiently robust to compensate for the drawbacks of YOLOv3. We found that both approaches generally yield a satisfying tracking performances but feature different characteristics.  
c 2020, 10, 8466; doi:10.3390/app10238466
# Exposure Bracketing Techniques for Camera Document Image Enhancement
###### Abstract  
Capturing document images using digital cameras in uneven lighting conditions is challenging, leading to poorly captured images, which hinders the processing that follows, such as Optical Character Recognition (OCR). In this paper, we propose the use of exposure bracketing techniques to solve this problem. Instead of capturing one image, we used several images that were captured with different exposure settings and used the exposure bracketing technique to generate a high-quality image that incorporates useful information from each image. We found that this technique can enhance image quality and provides an effective way of improving OCR accuracy. Our contributions in this paper are two-fold: (1) a preprocessing chain that uses exposure bracketing techniques for document images is discussed, and an automatic registration method is proposed to find the geometric disparity between multiple document images, which lays the foundation for exposure bracketing; (2) several representative exposure bracketing algorithms are incorporated in the processing chain and their performances are evaluated and compared.  
Keywords:tone mapping; exposure fusion; document image registration; Optical Character Recognition +
Footnote †: journal: Applied Sciences
# Study on Blasting Vibration Control of Brick-Concrete Structure under Subway Tunnel
## 4 Spectrum and Energy Analysis of Blasting Vibration Signal
### Spectrum and Energy Analysis
#### 4.2.2 Energy Analysis Based on Wavelet Transform  
Firstly, db8 is selected as the wavelet base. Then, the blasting vibration signals before and after blasting scheme optimization are transformed by the wavelet transform. Finally, the frequency band energy distribution diagram of the blasting vibration signal is obtained, as shown in Figure 17.  
For the signal before optimization, only the first three frequency bands have energy. The energy in the first frequency band accounts for about 95% of the total energy, the energy in the second frequency band accounts for about 5% of the total energy, and the energy in the third frequency band is negligible. The energy contained in the monitored blasting vibration signal before the optimization of the blasting scheme is mainly concentrated in the 0-62.5 Hz frequency band; For the optimized signal, there is energy in the 1 st, 2 nd, 3 rd, 4 th, 6 th, and 8 th frequency bands, of which the energy in the 1 st frequency band accounts for about 70% of the total energy, the energy in the 2 nd frequency band accounts for about 20% of the total energy, the energy in the 4 th frequency band accounts for about 10% of the total energy, and the energy in the 3 rd, 6 th, and 8 th frequency bands is relatively small, which can be ignored. The energy contained in the blasting vibration signal monitored before the blasting scheme optimization is mainly concentrated in the 0-125 Hz frequency band. It can be seen from Figure 17 that after the optimization of the blasting scheme, the energy contained in the blasting vibration signal is more widely distributed in the frequency band and less distributed in the low frequency. The less energy distributed in the low frequency is more conducive to the safety of brick concrete structure buildings, which shows that the optimized scheme has achieved a good blasting construction effect.  
The blasting vibration signals before and after the blasting scheme optimization are wavelet transformed to obtain the three-dimensional energy spectrum of the blasting vibration signal. The yellow part indicates the higher relative energy, as shown in Figure 18. The maximum relative energy amplitude before the optimization is about 4, and the maximum relative energy amplitude after optimization is about 0.6. Compared with the Hilbert three-dimensional energy spectrum of blasting vibration signals before and after optimization in Figure 16, the maximum relative energy amplitude is about 5 and 0.5, respectively. The difference between the two results is small, and the results are in good agreement.  
Figure 17: Band energy distribution diagram of blasting vibration signal (**a**) before optimization and (**b**) after optimization.
## 3 Analytical Results
### Influence of the Length of the Transition Zone  
In practice, a transition tunnel is often included to mitigate the potential adverse effects of the sharp change of ground properties. To investigate the effect of the length of the transition zone, four cases with different length of the transition zone are investigated, with values of 0 m, 24 m, 48 m and 96 m, considering that the length of the concrete trolley in tunnel construction is generally 9 m or 12 m at present. Note that 0 m indicates that no Region 2 is considered. Figure 8 shows the maximum bending moment and shear force of the tunnel structure under the different scenarios discussed. The minimum value of bending moment occurs when the length of Region 2 is 36 m, followed by the length of Region 2 being 24 m and 48 m, where the maximum bending moments are very close to each other. In terms of the shear force, the Region 2 of 24 m has the minimum value, and the one with 36 m is 2.55% larger than that with 24 m. Therefore, the length of Region 2 with 24 m or 36 m can be taken as the optimal length.
# Multi-Objective Evolutionary Neural Architecture Search with Weight-Sharing Supernet
## 4 Multi-Objective Evolutionary Neural Architecture Search Algorithms
### Bi-Population MOEA/D-Based Neural Architecture Search Algorithm
In addition, an inter-population communication stage is introduced to enhance the algorithm's exploratory capability after the two sub-populations' optimizations. Specifically, the exchange parameter \(N_{\text{comm}}\) is set to control the number of individuals exchanged between the two sub-populations in each period. In sub-population \(A_{1}\), some sub-problems will obtain individuals with larger model sizes, while the corresponding sub-problems in sub-population \(A_{2}\) will optimize individuals with a larger (\(1-model\_size\)), i.e., smaller model sizes, as illustrated by the red dashed boxes in Figure 5. Therefore, during the communication, the algorithm exchanges the solutions to these \(N_{\text{comm}}\) sub-problems between the two sub-populations. In other words, the large models in sub-population \(A_{1}\) will be exchanged with the small models in sub-population \(A_{2}\). In this way, the algorithm is able to explore the intermediate region between the two sub-populations to some extent, avoiding the situation where each sub-population locally converges independently. Furthermore, the sub-populations will share and update the error rate recorded in their own reference point, which enables the sub-populations to perceive the global information. By introducing the inter-population communication stage, the optimization information can be shared between two sub-populations and prevent them from blindly converging  
Figure 4: An example of population distribution and the corresponding Pareto fronts. Figure 5: An example of weight vectors and sub-populations. towards their own reference points, in which case the algorithm may output two completely unrelated sub-populations.
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Evaluation Methodology  
The effect of image fusion can be judged by visual and quantitative evaluation. Visual evaluation refers to judging the image after fusion through visual interpretation and combining it with operating experience, to draw a conclusion. Visual evaluation has the advantage of being more intuitive and easygoing but it is difficult to describe precisely. The quantitative evaluation is more objective and comparative to describe and judge the characteristics of the fusion images with numerical values. The quantitative evaluation in this study includes statistical parameters and the \(CQ_{max}\) index.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data Type** & **Band (\(\mu\)m)** & **Spatial Resolution** & **Image Date** \\ \hline \multirow{6}{*}{GF-1} & Band1—blue 0.45–0.52 & \multirow{6}{*}{Panchromatic (2 m) Multi-spectral (8 m)} & \multirow{6}{*}{15 February 2016} \\  & Band 2—green 0.52–0.59 & & \\  & Band 3—blue 0.63–0.69 & & \\  & Band 4—near infrared & & \\  & Band 0.77–0.89 & & \\  & Band 1—blue 0.45–0.52 & & \\  & Band 2—green 0.52–0.59 & & \\  & Band 3—blue 0.63–0.69 & & \\  & Band 4—near infrared & & \\  & Band 0.77–0.89 & & \\  & Band 1—coast 0.40–0.50 & & \\  & Band 2—blue 0.45–0.51 & & \\  & Band 3—green 0.51–0.58 & & \\  & Band 4—yellow 0.58–0.62 & & \\  & Band 5—red 0.63–0.69 & & \\  & Band 6—red edge 0.70–0.74 & & \\  & Band 7—near infrared & & \\  & 0.77–0.89 & & \\  & Band 8—near infrared 2 & & \\  & 0.86–1.04 & & \\ \hline \end{tabular}
\end{table}
Table 1: The sample image data description of GF-1, GF-2, and WV-2.
## 5 RFI Measurements  
### RFI Related Capabilities  
One of the main objectives of instrument MERITXELL is to be a flexible platform to perform experiments related to the detection, characterization, and localization of RFI signals present at the frequency bands used for passive microwave radiometry. In order to avoid self-interference, data buses have been check to not emit any electromagnetic emission in the working bands. This was done during the assembly process of MERITXELL.  
According to the designed structure of its back-end, MERITXELL allows to perform real-time RFI signal detection and characterization in time, frequency and statistical domains. The power measurements allow to retrieve the spectrum of the signal under analysis, and thus, to detect RFI signal whose power is more concentrated around some frequency points of the receiving bandwidth. Moreover, thanks to the spectrogram capabilities, RFI signals can be analyzed both in time and frequency domains by identifying temporal changes in the signal spectrum.  
Furthermore, the back-end of MERITXELL also allows to perform a statistical analysis of the received signals. As shown in [18], several normality tests can be used to detect the presence of RFI signals, although the combination of the Kurtosis and the Anderson-Darling tests leads to an optimum combo test without blind spots. MERITXELL allows to perform this analysis using the I/Q measurement mode, and then applying such normality tests on post-processing in the host computer. In [21], a statistical analysis of RFI signals was already performed using MERITXELL.
## 1 Introduction
Probability density functions (PDFs) of output step-responses and steady-state responses affected by the various noises are derived. Output responses are derived from repetitive step-response simulations using HSPICE. For each response, occurrence probability is calculated to derive the PDFs. In this step, it is possible to consider the bit pattern impacts mathematically. From the derived  
Figure 1: The limitation of conventional transient eye-diagram simulation/estimation methods is depicted. (**a**) Factors increasing complexity in transient simulations. (**b**) Power/ground noise due to SSO buffers sharing the PDN. (**c**) Discrepancy between an actual eye-diagram and simulated eye-diagram. output response PDFs, main-cursor (MC) PDFs and inter-symbol interference (ISI) PDFs are defined based on the data rate. Statistical output responses are derived based on recursive convolution between the MC-PDFs and ISI-PDFs. Finally, a statistical eye-diagram is formulated by superposing the derived statistical output responses and expanding to two unit intervals (UIs). The accuracy and computational efficiency of the proposed method are validated by comparing the result with HSPICE transient simulation result. The proposed method is also compared with conventional statistical methods such as peak distortion analysis (PDA) and statistical channel simulation in the transient simulator. Lastly, the proposed method is applied to SI/PI co-design and co-analysis in the high bandwidth memory (HBM) interposer channel. The impacts of decoupling capacitors on hierarchical PDN impedance, statistical eye-diagram of the HBM channel, and bit error rate (BER) Bathtub curves are summarized. Finally, the BER eye-diagram is derived from the estimated statistical eye-diagram for timing and voltage analysis. The impacts of hierarchical PDN design and bit-patterns on SI/PI are discussed. Statistical Eye-Diagram Estimation Method Considering Power/Ground Noise Due to SSO Buffers and Bit-Patterns  
In this section, a new statistical method, which is capable of estimating an accurate statistical eye-diagram of the high-speed interconnect, is proposed. From the estimated statistical eye-diagram, various SI/PI indicators, such as BER bathtub curves and BER eye-diagrams, can be derived. In the derived/estimated results, the impacts of various power/ground noise, hierarchical PDN, channel factors, and bit-patterns are included.
# Efficient Detection of Pre-Proinsulin by Double Antibody Sandwich ELISA
## 3 Results
### Double Antibody Sandwich ELISA Screening for Paired Antibodies  
P1, P2, P3, P4, P5 and P6 were successively used as solid-phase antibody packages in the enzyme-labeled plate, PPI was added as an antigen, and 1% BSA as blank control. A biotin-labeled monoclonal antibody was added for double-antibody sandwich ELISA detection. The antibody in the detection hole with an absorbance value 2.1 times greater than that of the blank hole was used as the positive paired antibody (Table 1). The results showed that when P5 was used as the solid-phase antibody, biotin-labeled P1, P2, P3, P4 and P6 could be used as detection antibodies. P2 was not successfully paired as a solid-phase antibody. P1, P3, P4 and P6 could only be detected with biotin-labeled P5 as solid-phase antibodies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** \\ \hline P1-bio & - & - & - & - & + & - \\ P2-bio & - & - & - & - & + & - \\ P3-bio & - & - & - & - & + & - \\ P4-bio & - & - & - & - & + & - \\ P5-bio & + & - & + & + & - & + \\ P6-bio & - & - & - & - & + & - \\ \hline \end{tabular}
\end{table}
Table 1: Screening of paired antibodies.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 3 Dynamic Model of DFIG and Wind Turbine
### Model of DFIG Generator  
Figure 2 shows the widely used structure of the DFIG generator for stability analysis with the following assumptions:  
1. the dynamics of the DC capacitor are neglected;
2. the active powers of the rotor side converter (RSC) and grid side converter (GSC) are considered as equal;
3. GSC is assumed to be ideal in that there is no reactive power exchanged with the grid during the transient and the total reactive power is supported only by the stator [22].  
To protect the power electronic converters and rotor circuit from high transient current in the rotor, a protection device named as a crowbar is used. A crowbar is the external rotor impedance that is coupled with the sliding ring of the generator rotor. This device is triggered and blocks the RSC when the current in the rotor exceeds the rated current of the crowbar.  
By ignoring the dynamics of the stator current, the DFIG model could be described as  
\[\frac{1}{\omega_{\mathrm{b}}}\frac{\mathrm{d}e_{\mathrm{d}}}{\mathrm{d}t} =-\frac{1}{\Gamma_{0}}\big{[}e_{\mathrm{d}}-\big{(}X-X^{\prime} \big{)}i_{\mathrm{qs}}\big{]}+s\omega_{\mathrm{s}}e_{\mathrm{q}}-\omega_{ \mathrm{s}}\frac{L_{\mathrm{m}}}{L_{\mathrm{r}}+L_{\mathrm{m}}}v_{\mathrm{qr}} \tag{16}\] \[\frac{1}{\omega_{\mathrm{b}}}\frac{\mathrm{d}e_{\mathrm{q}}}{ \mathrm{d}t} =-\frac{1}{\Gamma_{0}}\big{[}e_{\mathrm{q}}+\big{(}X-X^{\prime} \big{)}i_{\mathrm{ds}}\big{]}-s\omega_{\mathrm{s}}e_{\mathrm{d}}+\omega_{ \mathrm{s}}\frac{L_{m}}{L_{r}+L_{m}}v_{\mathrm{dr}}\] (17) \[\frac{\mathrm{d}\omega_{\mathrm{r}}}{\mathrm{d}t} =\frac{1}{2H_{\mathrm{g}}}\big{[}K_{\mathrm{tw}}\theta_{\mathrm{tw }}+D_{\mathrm{tw}}(\omega_{\mathrm{t}}-\omega_{\mathrm{r}})-(e_{\mathrm{d}}i_{ \mathrm{ds}}+e_{\mathrm{q}}i_{\mathrm{qs}})\big{]}\] (18) \[v_{\mathrm{ds}} =-r_{\mathrm{s}}i_{\mathrm{ds}}+X^{\prime}i_{\mathrm{qs}}+e_{ \mathrm{d}} \tag{19}\]\[v_{\rm qs}=-r_{\rm s}i_{\rm qs}-X^{\prime}i_{\rm ds}+e_{\rm q} \tag{20}\]  
\[P_{w}=v_{\rm ds}i_{\rm ds}+v_{\rm qs}i_{\rm qs}-v_{\rm dr}i_{\rm dr}-v_{\rm qr}i_{ \rmqr} \tag{21}\]  
\[Q_{w}=v_{\rm qs}i_{\rm ds}-v_{\rm ds}i_{\rm qs} \tag{22}\]  
where \(e_{d}\) and \(e_{q}\) are \(d\) and \(q\) components of internal voltage; \(P_{w}\) and \(Q_{w}\) are active and reactive power of DFIG absorbed by network; \(X\) and \(X^{\prime}\) are open-circuit and short-circuit reactance; \(T_{0}\) is the transient open-circuit time constant; \(H_{\rm g}\) is the generator inertia constant.
## 2 Synthetic DAS Signal Generation Methodology
### Synthetic Dataset Creation  
The synthetic strain-rate signals simulating DAS measurements are generated by applying Equations (1) and (2) for each wheel of each vehicle trajectory obtained from the VISSIM simulations (Section 2.1). The contributions from all wheels are summed at each DAS channel location and time step.  
This process yields time-series data representing the strain rate along the virtual fiber for the simulated traffic scenario. To enhance realism for machine learning training, controlled noise (e.g., white noise with a specified standard deviation) can be added to the generated synthetic signals. This procedure allows the creation of large-scale, labeled datasets where the ground truth (e.g., vehicle type, direction, position, ideal signal from an alternative layout) is precisely known, facilitating the development and evaluation of DAS signal processing algorithms. Throughout this paper, the term \"synthetic DAS signal\" or \"synthetic strain-rate signal\" refers to this simulated output.