esa-sceva/satcom-chunk-collection huggingface dataset:
# Generation of Flat Optical Frequency Comb Using Cascaded Electro-Optic Modulators
## 3 Methods and Results
This uniform frequency interval between the optical carriers ensured they met the requirements for subsequent modulation. These separated optical carriers were modulated and transmitted as information-bearing signals in the following steps. IQ modulation processed each optical carrier to apply a 16-QAM modulation. This modulation type allows for transmitting more data simultaneously on the same carrier, enhancing the signal's spectrum utilization. After modulation, we combined the signal using a multiplexer and transmitted it through a 1200 km single-mode fiber. Next, the signal was amplified by an Erbium-Doped Fiber Amplifier (EDFA), and then the carrier signal of each channel was filtered out using a tunable filter. Each optical carrier was mixed with a local oscillator light source at the receiver to demodulate the input signal. A coherent receiver converted the mixed signal into an electrical signal to enable the transition from the optical to the electrical domain. Ultimately, the electrical signal was sampled and processed through digital signal processing (DSP) operations to recover the transmitted data information. Figure 7 displays the optical spectrum before and after the fiber transmission, where we can observe that the channels appear flat. Figure 8 displays the bit error rate (BER) for the eight channels after the demodulation, along with the constellation diagram for the channel operating at a frequency of 193.15 THz. The results indicate that the BER  
Figure 5: Simulation result for the optical frequency comb with an optimal flatness of 0.5508 dB. Figure 6: Scheme of the optical transmission system. values of the eight channels were similar. The inset shows precise and closely clustered constellation point distributions. This indicates that the optical signal retained good phase and amplitude characteristics during the transmission, which resulted in a low bit error rate and high transmission quality. Consequently, the signal integrity and stability of the system were effectively verified. Figure 8: The result of the bit error rate (BER) for the eight channels. The inset shows the constellation diagram for the channel at 193.15 THz. Figure 7: The optical spectrum (**a**) before and (**b**) after the fiber transmission (resolution is 0.1 nm).
## 3 Results and Discussion
### Identification of Metallic/Bimetallic Nanoparticles
#### 3.1.4 Zeta Potential Analyses  
Figure 4 displays the zeta potential analyses of the Ag-mNPs, Ag-SeO\({}_{2}\) bmNPs, and Ag-TiO\({}_{2}\) bmNPs, providing evidence concerning the charge and stability of the biosynthesized nanoparticles in the suspension. The small nanoparticles have a large surface area relative to their volume, and this surface area can be charged. The charge on the surface of the nanoparticles is counterbalanced by the presence of ions of an opposite charge in the liquid. This creates an electrical double layer around the nanoparticles. The zeta potential is the potential difference between the bulk of the liquid and the stationary layer of fluid attached to the surface of the particle. It is a measure of the strength of the electrical double layer. It was documented that zeta potential analyses are the factor controlling the estimation of the stability of the nanoparticles in the solution [51]. The results specified that Ag-mNPs revealed a mean zeta potential at \(-27.6\) mV with a mean electrophoretic mobility of \(-0.000213\) cm\({}^{2}\)/Vs. The Ag-SeO\({}_{2}\) bmNP solution displayed a mean zeta potential at \(-12.7\) mV with a mean electrophoretic mobility \(=-0.000098\) cm\({}^{2}\)/Vs, while the Ag-TiO\({}_{2}\) bmNPs revealed a mean zeta potential at \(-30.3\) mV with a mean electrophoretic mobility \(=-0.000234\) cm\({}^{2}\)/Vs. The results of all of the samples indicated the good stability of the biosynthesized metallic/bimetallic nanoparticles prepared from the aqueous extract of _Beta vulgaris_ L.  
A high value of negative charge in zeta potential analysis indicates that the particles in suspension have a strong electrostatic repulsion between them. This repulsion prevents the particles from aggregating or clumping together, which can improve the stability of the colloidal system. In general, particles with a zeta potential of \(\pm 30\) mV or more are considered to be stable. Thus, the Ag-TiO\({}_{2}\) bmNPs exhibited good stability with a zeta potential at \(-30.3\) mV, followed by Ag mNPs (zeta potential \(=-27.6\) mV), and Ag-SeO\({}_{2}\) bmNPs (zeta potential \(=-12.7\) mV) had the least stability in the colloidal system.
## 4 Results and Discussion
### Simulation of the Sun  
Single-axis solar tracker systems rotate on an axis to track the sun, facing east in the morning and west in the afternoon. This type of follower needs seasonal tilt angle adjustment.  
The meteorological parameters such as temperature and sunshine of the city of Khemis Miliana were extracted using a PV-system site in real-time and the data of the meteorological station from the university.  
Figure 10: Step responses of a position-control servo motor.  
Figure 9: Block model of a voltage-fed motor using SimScape’s Simulink.  
The temporal variation of different sun parameters like sun radiation, temperature, and sun angle \(\theta\) are shown in Figures 11a,b and 12. The simulation was carried out for 16 h from 4:00 a.m. to 8:00 p.m. in May. Where the value of the irradiance used in the study area during the days of the simulation was:  
* Equal to 0 from 00:45 a.m. to 4:45 a.m. and from 8:45 p.m. to 11:45 p.m.;
* Starts increasing by 11 and 1030 from 5:45 a.m. to 13:45 p.m., then decreases from 1030 to 8 from 2:45 p.m. to 7:45 p.m.  
As long as the light reappears each morning at sunrise and the temperature rises and begins to disappear at sunset, there is a variation in irradiance during the day, regardless of the season. It can be seen that the irradiance reaches its maximum value at 2:00 p.m. and decreases at sunset when it is colder than during the day. Global irradiation corresponds to the solar energy received in one day; this irradiation is linked to irradiance. It is noted that the temperature and the irradiance vary during the year gradually; they increase from winter until summer and decrease from summer until winter. This is explained by the height of the sun, which varies according to the seasons: the sun is higher at noon in a summer sky than in a winter sky.  
Figure 11: (**a**) Temporal variation of irradiance during a day. (**b**) Temporal variation of temperature during a day.  
The position of the sun is given by the zenith angle (\(\gamma\)s) and the azimuth (\(\alpha\)s). The zenith angle is the angle between the local vertical and the line that connects the observer to the sun. The sun's azimuth is the deviation of the sun's position from the south. The zenith and azimuth depend on the local time of day (t), the day of the year (d), and the latitude (\(\lambda\)) of the observer (h) [4]. Figure 12 shows that the sun's angle is equal to 90\({}^{\circ}\) almost at noon, so the tracker is perpendicular. At 180\({}^{\circ}\) at 8:00 p.m., the tracker is at a horizontal position and returns to its starting position.
# Study on Blasting Vibration Control of Brick-Concrete Structure under Subway Tunnel
## 2 Blasting Scheme and Parameters
### Distribution Law of Blasting Vibration Frequency  
According to the data in Table 2, the law of blasting vibration frequency changing with the position of the working face is drawn, as shown in Figure 10.  
Figure 10: The situation of the frequency changing with working face location.  
Figure 9: The situation of the blasting vibration velocity changes with measuring point location.  
Before the optimization of the blasting scheme, during the blasting construction, the working face mileage is located at DK44+192.54.200-DK44+214.04, and the maximum single section charge is 7-10 kg. During the blasting construction, 7 sets of data were monitored, 28 blasting vibration signals were measured, and the blasting vibration frequency measured at the measuring point was within the range of 1.53-3.66 Hz. After the optimization of the blasting scheme, during the blasting construction, the working face mileage is located at DK44+214.04-DK44+224.04, and the maximum single section charge is 2.4-3.2 kg. During the blasting construction, six times of monitoring were conducted and six groups of measured data were obtained, with a total of 24 blasting vibration signals. The measuring points were arranged in the brick concrete structure building, and the measured blasting vibration frequency was 1.53-6.41 Hz, mainly concentrated in 1.53-5.00 Hz. By comparing the monitored blasting vibration frequencies before and after the optimization of the blasting scheme in Table 2 and the distribution of blasting vibration before and after the optimization of the blasting scheme in Figure 8, the optimization of the blasting scheme can be obtained, which significantly increases the blasting vibration frequency. The reference [37] stipulates that the allowable safety standards for blasting vibration in China consider both blasting vibration frequency and blasting vibration speed. For the same object, when the blasting vibration frequency is low, the safety allowable vibration speed is small. Since the natural vibration frequency of the brick concrete structure building is low, the blasting vibration frequency of the brick concrete structure building after the blasting scheme optimization becomes larger, which makes it more difficult to form resonance phenomenon, and the building is safer.
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
###### Abstract  
Crowd management and monitoring is crucial for maintaining public safety and is an important research topic. Developing a robust crowd monitoring system (CMS) is a challenging task as it involves addressing many key issues such as density variation, irregular distribution of objects, occlusions, pose estimation, etc. Crowd gathering at various places like hospitals, parks, stadiums, airports, cultural and religious points are usually monitored by Close Circuit Television (CCTV) cameras. The drawbacks of CCTV cameras are: limited area coverage, installation problems, movability, high power consumption and constant monitoring by the operators. Therefore, many researchers have turned towards computer vision and machine learning that have overcome these issues by minimizing the need of human involvement. This review is aimed to categorize, analyze as well as provide the latest development and performance evolution in crowd monitoring using different machine learning techniques and methods that are published in journals and conferences over the past five years.  
crowd monitoring; crowd counting; crowd density estimation; deep convolutional neural networks (DCNN); crowd behavior
## 4 Implementation
### Simulation Results
#### 4.2.3 String Formation When Moving Along a Straight Trajectory  
This platform in Figure 10 forms up a string when moving along a straight trajectory. Both the adaptive law and the controller parameters were kept unchanged. The state variables and the control inputs are not illustrated because they are similar to Figure 3. The initial postures of the three robots are set by  
\[\mathbf{q}_{1}=\left[\begin{array}{cc}0.5\text{m}&0\text{m}&\frac{1}{2}\pi \text{rad}\end{array}\right]^{T},\ \mathbf{q}_{2}=\left[\begin{array}{cc}1.2\text{m}&0.5\text{m}&0\text{rad} \end{array}\right]^{T},\ \mathbf{q}_{3}=\left[\begin{array}{cc}2\text{m}&1\text{m}&\pi\text{rad} \end{array}\right]^{T} \tag{64}\]  
Figure 9: String formation of the platform when moving along an S-shape trajectory.  
According to this control task and the initial postures, the initial states of the formation dynamics can be calculated by  
\[\mathbf{x}_{12}(0)=\left[\begin{array}{cc}0.9\text{m}&0\text{m/s}&1.69\pi\text {rad}&0\text{rad/s}\end{array}\right]^{T}\text{, }\mathbf{x}_{13}(0)=\left[\begin{array}{cc}1.7\text{m}&0\text{m/s}&1.65\pi \text{rad}&0\text{rad/s}\end{array}\right]^{T} \tag{65}\]  
Similarly, the desired states can be obtained by (59) on account of the leader's trajectory.  
\[\mathbf{x}_{12}^{d}=\left[\begin{array}{cc}0.3\text{m}&0\text{m/s}&1.2\pi \text{rad}&0\text{rad/s}\end{array}\right]^{T}\text{, }\mathbf{x}_{13}^{d}=\left[\begin{array}{cc}0.6\text{m}&0\text{m/s}&1.8\pi \text{rad}&0\text{rad/s}\end{array}\right]^{T} \tag{66}\]  
The simulation results in Figures 9 and 10 indicate that the presented control method is available for various formation patterns in spite of the adverse effects of uncertainties and disturbances. This fact means such an adaptive-gain super-twisting sliding mode control method is an alternative solution for the formation maneuvers of uncertain multi-robot systems under the mild assumption that the uncertainties and disturbances are bounded by an unknown boundary.
# High-\(Q\) TeO\({}_{2}\)-Si Hybrid Microring Resonators
###### Abstract  
We present the design and experimental measurement of tellurium oxide-clad silicon microring resonators with internal \(Q\) factors of up to \(1.5\times 10^{6}\), corresponding to a propagation loss of 0.42 dB/cm at wavelengths around 1550 nm. This compares to a propagation loss of 3.4 dB/cm for unclad waveguides and 0.97 dB/cm for waveguides clad with SiO\({}_{2}\). We compared our experimental results with the Payne-Lacey model describing propagation dominated by sidewall scattering. We conclude that the relative increase in the refractive index of TeO\({}_{2}\) reduces scattering sufficiently to account for the low propagation loss. These results, in combination with the promising optical properties of TeO\({}_{2}\), provide a further step towards realizing compact, monolithic, and low-loss passive, nonlinear, and rare-earth-doped active integrated photonic devices on a silicon photonic platform.  
silicon-on-insulator (SOI); silicon photonics; optical microrings; tellurium oxide +
Footnote †: journal: Physics Letters B
# A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
## 2 Basic Steps in Identification of Diseases from Leaf Images
### Data Collection  
The first step in plant disease identification is the collection of image data. Several standard plant diseases datasets are available online such as the PlantVillage dataset [23], Cassava dataset [24; 25], Hops dataset [26], Cotton disease dataset [27] and Rice disease dataset [28; 29]. The PlantVillage dataset consists of 38 different classes of 14 different plant species (vegetable and fruits) such as apple, blueberry, cherry, corn, grape, orange, peach, pepper, raspberry, potato, soybean, squash, strawberry and tomato. The diseases include apple scalp, apple black rot, cedar apple rust, apple healthy, blueberry healthy, cherry healthy, cherry healthy, cherry powdery mildew, corn gray leaf spot, corn healthy, corn northern leaf blight, grape black rot, grape black measles, grape healthy, grape leaf blight, orange huanglongbing, peach bacterial spot, peach healthy, pepper/bell bacterial spot, pepper/bell healthy, potato early blight, potato healthy, potato late blight, raspberry healthy, soybean healthy, squash powdery mildew, strawberry healthy, strawberry leaf scorch, tomato bacterial spot, tomato early blight, tomato healthy, tomato late blight, tomato leaf mold, tomato septoria leaf spot, tomato spider mites, tomato target spot, tomato mosaic virus and tomato yellow leaf curl virus. All the images were taken in a laboratory setup condition. The Cassava disease dataset consist of five different classes of diseases and the images are real-time field-captured images. Diseases in the Cassava dataset includes cassava mosaic disease, cassava bacteria blight, cassava brown streak disease, cassava green mite and cassava healthy. The Hops dataset consists of five different classes of diseases with nonuniform background conditions. Diseases include downy, powdery, healthy, nutrient and pest diseases. The Cotton dataset consists of healthy and diseased cotton leaves and plants. The Rice disease dataset consists of four different classes of diseases captured in field conditions. Diseases in the Rice disease dataset are bacterial blight, blast, brown spot and tungro. Some of the researchers built their own diseases dataset in their work. Table 1 shows the available standard datasets of images of plant diseases along with image environment.
## 3 The Proposed MR Mount  
### Conceptual Design of Mount  
The proposed mount is composed of two main parts: elastic and damping parts (Figure 2). Firstly, the elastic part functions to receive, transmit, and absorb the vibration source from the rail surface. The rubber generates the damping force, which is relatively small, compared to that of the damping part. Secondly, the damping part generates and adjusts the damping force by supplying the current into the coil and changing its magnitude. This part includes six steel cores, six copper coils, two steel discs, two aluminum cases, and bolts. In addition, the mount also has a body component that supports and links the mount to the car frame and a diaphragm component that help to compensate volume for MRF by moving down and up. As the mount receives the excitation source, the elastic part receives vibrations and changes from hard vibrations of source to soft vibrations of the elastic part, then pumps (when the sprung part moves downwards relative to the un-sprung part) and sucks (when the sprung part moves upwards relative to the un-sprung part) the MRF flow into the radial channel. When the MRF goes through the radial channel, it is impacted by the external magnetic field. The yield magnetic field will magnetize MRF particles. The shear stress appears to resist MRF flow, increasing the pressure drop of MRF. Therefore, as the external magnetic flux changes by changing the input current magnitude into the mount, the damping force of the mount also changes.  
Figure 1: The proposed configuration of the RSD inspection car.  
Figure 2: The proposed MR mount: **(a)** isometric view; **(b)** 3D and **(c)** 2D cross-sectional views.
# On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel
## Appendix B Approximate Message Passing (AMP)  
In this section, we provide implementation details for the AMP algorithm used in the numeric comparisons of Section 3.  
Introduced by [PERSON] et al. [73], AMP is a state-of-the-art recovery algorithm for solving linear inverse problems \(\mathbf{y}=Ax+\mathbf{z}\). Originally developed for sparse vector recovery (compressed sensing), AMP and its extensions have been shown to yield state-of-the-art algorithms for several other linear inverse problems, with Gaussian or \"Gaussian-like\" (for example, orthogonally invariant or with some spatially coupled structure) random sensing matrices. For a very partial, selective list of references, see, for example, [73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84]. In unsourced random access, AMP was first used by [PERSON] et al. [9] and extended by [PERSON] et al. [11].  
The AMP algorithm of [PERSON] et al. [73] uses an iteration of the following form, starting from \(\mathbf{x}^{(0)}=\mathbf{0}\), \(\mathbf{r}^{(0)}=\mathbf{0}\):  
\[\begin{split}\mathbf{b}^{(t)}&=A^{\top}\mathbf{r}^{(t)}+ \mathbf{x}^{(t)}\\ \mathbf{x}^{(t+1)}&=f_{t}\Big{(}\mathbf{b}^{(t)}\Big{)}\\ \mathbf{r}^{(t+1)}&=\mathbf{y}-A\mathbf{x}^{(t+1)}+\frac{1}{n} \sum_{i=1}^{n}f_{t}^{\prime}\Big{(}\mathbf{b}^{(t)}\Big{)}\,.\end{split} \tag{10}\]  
Here, \(f_{t}:\mathbb{R}\to\mathbb{R}\) is a sequence of univariate functions (for a vector \(\mathbf{b}\), \(f(\mathbf{b})\) stands for applying \(f\) separately to each coordinate). The main idea of AMP (\"State Evolution\") is that in the large-dimensional limit (and under certain technical assumptions), the iterates \(\mathbf{b}^{(t)}\) behave as \(\mathbf{b}^{(t)}\approx\mathbf{x}+\sigma_{t}\mathcal{N}(0,I)\); that is, additively corrupted measurements of the true signal \(\mathbf{x}\). The variance \(\sigma_{t}^{2}\) can be estimated from \(\mathbf{r}^{(t)}\), which approximately has the law \(\mathcal{N}(0,\sigma_{t}^{2}I)\); conversely, it can be tracked via an explicit recursive formula. For our experiments, we use the proposal of [PERSON] et al. [74], and use the robust estimator \(\hat{\sigma}_{t}=\mathrm{median}(|\mathbf{r}^{(t)}|)/\Phi^{-1}(0.75)\), where \(\Phi^{-1}(\cdot)\) is the inverse Gaussian CDF.  
The details of the AMP algorithm (10) rely on the specific choice of functions \(f_{t}\). In compressed sensing of binary signals (1), a natural choice is the MSE-optimal estimator for \(\mathbf{x}\) from \(\mathbf{b}=\mathbf{x}+\sigma_{t}\mathcal{N}(0,I)\), namely,  
\[f_{t}(\mathbf{b})_{i} =\mathbb{E}\big{[}x_{i}\,\big{|}\,b_{i}\big{]}=\frac{\frac{k}{M} \cdot e^{-\frac{1}{2\sigma_{t}^{2}}(b_{i}-1)^{2}}}{\Big{(}1-\frac{k}{M}\cdot \Big{)}e^{-\frac{1}{2\sigma_{t}^{2}}b_{i}^{2}}+\frac{k}{M}\cdot e^{-\frac{1}{ 2\sigma_{t}^{2}}(b_{i}-1)^{2}}}\,.\]  
This is what we used for the experiments presented in Section 3.
## 3 The Dynamic Harmonic Compensation Sliding Mode Control Method
### Theoretical Analysis of QPR-Cascaded SMC  
The principle of harmonic suppression based on QPR is illustrated in Figure 2, where the red amplitude-frequency curve corresponds to the harmonic frequency \(W_{\rm C}\). To increase the gain at \(W_{\rm C}\) without altering the loop gain of the original system, a proportional-resonant (PR) controller can be cascaded with the original system. This method effectively enhances the gain at \(W_{\rm C}\), thereby suppressing disturbances at this frequency. The synthesized loop amplitude-frequency curve, as shown by the blue curve in the figure, demonstrates a significant increase in gain at \(W_{\rm C}\), effectively mitigating disturbances at that frequency. Additionally, this approach ensures that the amplitude and phase characteristics across other frequency bands remain consistent with the original system, except in the vicinity of \(W_{\rm C}\). This indicates that cascading the QPR controller does not negatively impact the overall performance of the original vector control system.  
To gain a deeper understanding of the implementation of this control method, it is necessary to further analyze the system's dynamic behavior under sliding mode control.  
Figure 2: Principles of harmonic suppression based on QPR.  
Considering the switching function \(z\), under the condition that the system stably enters the sliding mode surface and only moves within the boundary layer, the expression for \(z\) is given by:  
\[z_{d,q}=k_{\mathrm{s}}\Big{(}\hat{t}_{d,q}-i_{d,q}\Big{)} \tag{18}\]  
From Equations (9), (11), and (12), the transfer function between \(\hat{t}_{d,q}\) and \(i_{d,q}\) can be derived as Equation (19):  
\[\frac{\hat{t}_{d,q}}{\hat{t}_{d,q}}=\frac{k_{\mathrm{s}}}{L_{d,q}s+R_{\mathrm{ s}}+k_{\mathrm{s}}} \tag{19}\]  
Considering the transfer function of the proportional resonant filter in the closed-loop control system, the transfer function of the proposed dynamic harmonic compensation sliding mode control method can be obtained as follows:  
\[G(s)=\frac{k_{\mathrm{s}}(1+G_{PR})}{L_{d,q}s+R_{\mathrm{s}}+k_{\mathrm{s}}(1+ G_{PR})} \tag{20}\]  
This method effectively increases the gain at specific frequencies without compromising the performance of the system in other frequency bands, thereby achieving efficient harmonic suppression. It not only enhances the system's harmonic suppression capability in the target frequency range but also ensures stability and responsiveness across other frequency bands.