esa-sceva/satcom-chunk-collection huggingface dataset:
# Demonstration of High-Speed Optical Transmission at 2 \(\upmu\)m in Titanium Dioxide Waveguides
## 1 Introduction  
Nowadays, optical communication traffic continuously increases, inexorably approaching a \"capacity crunch\" [1; 2; 3]: the conventional C-band around 1.55 \(\upmu\)m will not be sufficient anymore and alternative approaches have to be adopted [4]. Recently, the 2 \(\upmu\)m spectral region has been suggested as a new transmission window [5], benefiting from the emergence of thulium-doped fiber amplifiers (TDFA) with broadband and high gain spanning from 1900 to 2100 nm [6]. This has stimulated studies of dedicated photonic components such as InP-based modulators [7; 8] or arrayed waveguide gratings [9]. High bit rate communications over distances exceeding one hundred meters have already been successfully demonstrated [10; 11; 12; 13] in low-loss hollow core bandgap photonic fibers designed to present minimal losses around 2000 nm [14] or in solid-core single mode fibers [15].  
Optical transmissions over much shorter distances (typically a few hundred micrometers) also deserve interest in the context of on-board connections and photonic routing operations. Therefore, this open issue requires further experimental investigations to evaluate the potential of various materials transparent in this new spectral band. Optimally, such a material should be also transparent in other telecommunication bands ranging from O-band (1310 nm) to C-band (1550 nm) and eventually also in the 850-nm band. Regarding the recent research, a natural choice could be silicon nitride (Si\({}_{3}\)N\({}_{4}\)), which has already stimulated many works in the visible but also in the mid-infrared range, mainly in the context of frequency combs [16]. In this new contribution, we are interested in another material which remains to date relatively unexplored: titanium dioxide (TiO\({}_{2}\)). This cost-efficient material is indeed transparent from visible to mid-infrared wavelengths [17] and can be considered as a complementary metal-oxide semiconductor (CMOS) compatible material [18]. Compared to Si\({}_{3}\)N\({}_{4}\), TiO\({}_{2}\) presents several advantages. Among them, it presents lower stress constraints for thicknesses beyond 250 nm and has an easier deposition process possible at lower temperatures. Note also that it exhibits a higher linear refractive index, which is critical for a stronger confinement [19]. Up to now, detailed studies have taken advantage of its negative thermo-optic coefficient [18; 20] and its transparency in the visible range [21; 22], or have reported on its linear and nonlinear properties inthe C-band [19; 23; 24]. In this work, we experimentally explore, for the first time, TiO\({}_{2}\) as an efficient medium for a photonic component operating at 2 \(\upmu\)m. After describing the design and fabrication of our TiO\({}_{2}\) waveguides, we detail the experimental setup under use and validate the device for error-free transmission of a 10 Gbit/s on-off keying signal for both a subwavelength single mode waveguide and a multimode waveguide.
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 2 Data and Methods
### GEO-LEO Satellite Image Fusion Algorithm
#### 2.3.3 Pixel Matching  
The physical products of the VISSR are the input features, and AMSU-A provides the \(SI\) as a machine learning label. We need to fuse the VISSR and AMSU-A pixels to construct training sets. The spatial resolution of the AMSU-A pixels (45 km) is much lower than that of the VISSR pixels (5 km), and the pixel deformation caused by the Earth's curvature is ignored under the constraints of the zenith angle of the satellite. When the center distance between two pixels is less than the empirical distance threshold \(dmax\), a VISSR pixel is considered an AMSU-A pixel in the footprint [20].  
\[d=2R\sin^{-1}\sqrt{\left(\sin\frac{x2-x1}{2}\right)^{2}+\cos x1*\cos x2*(\sin \frac{y2-y1}{2})^{2}} \tag{7}\]  
\[d<d_{\max} \tag{8}\]where x1 and y1 are the latitude and longitude of the AMSU-A pixel, respectively; x2 and y2 represent the latitude and longitude of the VISSR data, respectively; R is the radius of the Earth; and _dmax_ is set as a threshold of 30 km. After the VISSR pixels within the AMSU-A footprint that satisfied the distance criteria are identified, the average value of these VISSR pixels is utilized as input information. The schematic diagram of the pixel-matching process is shown in Figure 3.
# Scattering of Plane Waves by Cylindrical Cavity in Unsaturated Poroelastic Medium +
## 1 Introduction
According to the analysis, saturation has a significant influence on the responses of the radial displacements, hoop stresses, and pore fluid pressures at the cavity surface. In considering the scattering problem of plane P wave disturbed by a lined tunnel embedded in an infinite unsaturated poroelastic medium, [PERSON] et al. [16] also showed that the degree of saturation plays an important role in the dynamic response of the lined tunnel in the unsaturated porous medium. All of these reminded us that the effects of saturation on dynamic responses of underground cavities in unsaturated porous media may need to be carefully considered. The authors presented the analytical solution of the scattering of plane waves by a cylindrical cavity with an impermeable surface and analyzed the effects of saturation on the dynamic responses around the cavity in the paper submitted to the Special Issue of PBD-IV [17]. In this study, the scattering of plane P\({}_{1}\) waves and SV waves by a cylindrical cavity with a permeable surface in an infinite unsaturated porous medium is solved based on Ref. [17]. The dynamic stress concentration of the cavity surface is investigated according to the solutions. A detailed parametric study is presented to illustrate the influence of the degree of saturation, porosity, and Poisson's ratio on the dynamic stress concentration of the hoop stresses at the cavity surface under different conditions.
## 4 Flux Probe Design
### Electronics Unit  
The electronics unit of the flux gate sensor primarily consists of an excitation circuit and a detection circuit.  
The excitation circuit comprises a waveform generation circuit, frequency dividers, power amplification circuit, and matching network. Its main function is to provide periodic square wave current signals to ensure that the magnetic core is periodically deeply saturated. The waveform generation circuit uses a crystal oscillator and inverter to produce a 1 MHz oscillation signal, which is then divided by 32 to generate a 31.25 kHz double-frequency signal for the demodulation circuit reference and divided by 64 to generate a 15.625 kHz fundamental frequency signal for the excitation coils. The power amplification circuit further amplifies the power of the fundamental frequency excitation signal to drive the excitation coils, creating a large excitation magnetic field by generating a high current in the excitation coils. The matching circuit uses an impedance network to tune the frequency of the excitation coils and the excitation signal.  
The detection circuit demodulates the signal from the induction coils and outputs a voltage signal proportional to the measured strength of the target magnetic field, thus completing the detection of magnetic field signal. Our system employs the commonly used second harmonic detection method. The induced signal and double-frequency signal pass through an analog multiplier to demodulate the signal. The demodulated signal then passes through a low-pass filter circuit, which isolates the useful signal proportional to the strength of the magnetic field. The low-pass filter circuit performs both amplification and filtering. It utilizes a low-noise amplifier (with noise less than 10 nV Hz\({}^{-1/2}\)) and adjusts  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Structure** & **Design Indicators** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Magnetic core} & Material & Cobalt-based amorphous ribbons \\  & & Length & 21 mm \(\pm\) 0.2 mm \\  & & Diameter & 0.2 mm \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Excitation coil} & Wire diameter & 0.035 mm \\  & & Number of turns & 500 \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Induction coil} & Wire diameter & 0.2 mm \\  & & Number of turns & 800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The design parameters of the flux gate probe.  
Figure 12: The structure of the single-axis flux gate probe.  
the flux gate bandwidth using resistor-capacitor (RC) parameters. The filter bandwidth was set to 1 kHz in this study. The feedback resistor network compensates the flux gate sensor via the feedback coil and adjusts the sensor output response. The circuit diagram is shown in Figure 13.
## 2 Methodology
### Energy Station Site Optimization Based on an Improved P-Median Model  
The location of the energy station is not only related to the distance from the load point, but also to the load of users in the region. Users with larger load require a shorter distance to the energy stations to minimize energy loss and investments. However, the initial station locations based on the hollow circle characteristic of the Voronoi diagram in Section 2.2 may not be located in the load distribution centers, so this section will establish an improved P-median model to optimize the station locations.  
The P-median problem is to study how to locate and set up P service points, so that the average weighting distance from all demand points to service points is the shortest. The energy station location problem can be simply described as follows: give an undirected network graph \(G(V,E)\), then find a subset \(V_{p}\) from the road node collection \(V\), so that the sum of the distances from the road node collection \(\big{\{}V-V_{p}\big{\}}\) to the nearest vertex in \(V_{p}\) is the minimum.  
The traditional P-median model takes the linear distance between the load point and the energy station as the shortest energy supply distance [21]. However, in order to make full use of resources and reduce the construction cost of energy supply pipelines, the energy supply routes in practice are usually laid along the road edge [18]. Therefore, this paper uses an improved P-median model to select energy station locations. The model is shown as follows:  
\[\min Z=\sum_{m\in M}\sum_{n\in N}a_{mn}d_{mn}Q_{m} \tag{3}\]  
\[d_{mn}=|x_{m}-x_{n}|+|y_{m}-y_{n}| \tag{4}\]  
In the formula, M is the collection of load points and N is the collection of energy stations. \(Q_{m}\) is the load value of th load point m. \(d_{mn}\) represents the distance from the energy station n to the load point m, considering that the realistic energy supply routes are generally laid along the roads. The Manhattan distance, which is closer to the actual route, is adopted, as shown in Formula (4), where \(x_{m}\) and \(y_{m}\) represent the spatial location  
Figure 3: Initial site selection.  
properties of the load point m and \(x_{n}\) and \(y_{n}\) represent the spatial location properties of the energy station n. \(\alpha_{mn}\) represents whether the energy station n will serve the load point m; if so, \(\alpha_{mn}=1\), or \(\alpha_{mn}\)= 0.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Image Encryption
#### 5.5.2 Encryption  
Neural networks see much use in image encryption. [PERSON] and [PERSON] [239] proposed a novel Wavelet-based Chaotic Neural Network (WCNN) used for image encryption. A differ ent proposal by [PERSON] et al. [240] also uses neural networks for encryption, but additionally adds impurities to confuse analysts. Other utilized techniques include multi-layer [241] and multi-image [242].
# A Lightweight YOLOv5 Optimization of Coordinate Attention
## 3 Proposal Model
### Modified Network Model  
Our proposed YOLOv5-RC network is obtained after modifying the network structure by the previously mentioned method, and the network model changes as shown in Figure 2.  
In this paper we have made four improvements:  
1. In order to extract different resolution feature maps,we reduced the number of channels of YOLOv5.
2. Then added a matching detection on the head model.
3. Finally by addition of Coordinated Attention module, it is to get our optimized model YOLOv5-RC.  
Figure 2: (**a**) YOLOv5, (**b**) YOLOv5-RC, the added structure is marked with light color box.  
In Figure 2, Conv module is a basic convolution module, which includes a normal convolution layer, a batch normalization layer and an activation function. In particular, the first Conv structure is to split the input image into four copies as a way to improve the receptive field and reduce the loss of original information. C3 is a simplified version of BottleneckCSP that serves to fuse features and reduce computational effort. SPPF serially uses three convolutions of the same convolution kernel and is designed to fuse more features of different resolutions. Relative to Figure 2a, the marked box portion of Figure 2b shows an additional detection head, and the network structure matches the additional detection head.  
YOLOv5-RC in Figure 2b adds a new set of Conv and C3 structures in Backbone, which can obtain additional feature maps of different resolutions for input to the feature pyramid composed of FPN+PAN structures. Due to the input of this different set of feature maps, the number of layers of the feature pyramid in Neck is increased from 3 to 4, and the feature maps of the new layer are input into Head. Finally, a new Detect Head with a different object detection size range is added. The best location of attention insertion will be discussed in the next section. In summary, it is obvious that to reduce the number of channels and then add a detection head later seems to be an optimization for lightweight.
## 6 Simulation and Analysis
### Cross-RIES Optimization Results and Analysis
#### 6.4.1 Green-Certificate-Trading Mechanism  
The green-certificate-trading mechanism provides external subsidies to renewable energy through market-based transactions, alleviating the financial pressure of renewable energy price subsidies for achieving carbon-peaking targets. In the optimization of the cross-RIES system, excess renewable energy beyond the quota can be converted to green certificates and sold, while RIES systems that fail to meet the quota need to purchase green certificates to avoid penalties. The penalty price for a renewable energy quota shortfall (\(punish_{gen}\)) is set at 10,000 CNY per unit.  
There are two key parameters in the green-certificate-trading mechanism: the renewable energy quota and the green certificate price. To support the carbon-peaking targets, the renewable energy quota for each RIES system is set at 10% of the total electricity generation. Based on the first green certificate transaction of China's parity project in 2021, the green certificate price is set at 50 CNY per unit, equivalent to 50 CNY per 1000 kWh of green electricity.
## 3 Methodology
### Tested Models  
The sequence of cells traversed by a visitor is modelled using four types of models, namely a Recursive Neural Network with a Gated Recurrent Unit (RNN-GRU), a shallow neural network and a first-order Markov chain. Underneath the design and training of the four models is elaborated upon briefly.  
The RNN-GRU is a neural network which loops information through a cell, which allows information to persist. In case of a GRU, an update and reset gate determine what information is passed to the output. The RNN-GRU used in this research consists of 150 hidden nodes. The model is trained in batches of 100 records and 10 epochs. The Adam optimization algorithm is used to train the RNN-GRU [52].  
Besides the RNN-GRU three simple neural network are trained, with no memory (NN-1), a memory of order one (NN-2) and a memory of order two (NN-3). This model consists of an input layer of one, two or three times the number of cells as nodes, one hidden layer of 150 nodes and one output layer with a length of the number of cells. The input consists of one, two or three one-hot variables pasted behind each other. The output is a one-hot variable. These pattern recognition networks are all trained in Matlab using a resilient backpropagation algorithm with a learning rate of 0.01.  
Similar to the shallow neural networks, three Markov chains are estimated that incorporate incrementally more memory, namely a first-order (MC-1), second-order (MC-2) and third-order (MC-3) Markov chain. Depending on the order only the last \(m\) visited cells are used to determine the transition matrix. Accordingly, the cell with the highest probability is adopted as the next cell in the sequence.
## 2 Research Methods
### Torsional Stress Analysis  
#### 2.2.1 Free Torsional Shear Stress  
When a box girder is free to twist, the longitudinal deformation of each fiber in the section is free; therefore, there is no longitudinal normal stress in the section since there is only free torsional shear stress. As shown in Figure 2, the torque acting on the closed  
Figure 1: Geometric shape of a corrugated steel web.  
section of a box girder is \(M_{\text{so}}\), and each cell has the same twist rate, \(\theta^{\prime}\), in the free torsion, and the relationship between the shear flow and twist rate in each cell is as follows [23]:  
\[q_{\text{si}}\oint_{i}\frac{\text{ds}}{t}-\sum q_{\text{sk}}\int_{i,k}\frac{ \text{ds}}{t}=G_{\text{s}}\theta^{\prime}\Omega_{i} \tag{7}\]  
where \(\oint_{i}\) is the integral around cell \(i\); \(\int_{i,k}\) is the integral within the web shared by cells \(i\) and \(k\); \(\Omega_{i}\) is twice the area enclosed by the midline of the wall of cell \(i\); \(k\) is the adjacent cell of cell \(i\); \(k=i\pm 1\); \(q_{\text{si}}\) is the free torsional shear flow of cell \(i\); \(t\) is the converted thickness of the corresponding top plate, bottom plate, and web.  
By balancing the internal and external forces, the sum of the torque generated by the shear flow in each cell is equal to the total torque of the section. The specific relationship is as follows:  
\[\sum_{i=1}^{n}q_{\text{si}}\Omega_{i}=M_{\text{so}} \tag{8}\]  
According to Equations (7) and (8), the free torsional shear flow and twist rate of each cell in a box girder can be obtained.  
Therefore, the free torsional shear stress of a box girder section is given by:  
\[\tau_{\text{s}}=\frac{\overline{q}_{\text{s}}}{t} \tag{9}\]  
where \(\overline{q}_{\text{s}}\) is the actual free torsional shear flow, \(\overline{q}_{\text{s}}=\begin{cases}q_{\text{si}}-q_{\text{sk}}&(\text{Common cell wall})\\ q_{\text{si}}&(\text{Independent cell wall})\\ 0&(\text{Cantilever plate})\end{cases}\)  
For a cantilever plate, the free torsional shear stress in the center line of the cantilever plate is zero, because the free torsional shear flow cannot be formed on the center line of the open section, and there is only secondary shear flow.  
According to the relationship between torque and twist rates [23], the torsional moment of inertia of the closed section of a box girder can be obtained as:  
\[I_{\text{do}}=\sum_{i=1}^{n}q_{\text{si}}\Omega_{i}/(G_{\text{s}}\theta^{ \prime}) \tag{10}\]  
The torsional moment of inertia of the cantilever plate is given by [24]:  
\[I_{\text{dc}}=\frac{1}{3}\sum_{j=1}^{2}ct_{\text{so}}^{3} \tag{11}\]  
where \(c\) is the length of the cantilever plate on one side.  
The total torque in a box girder-free torsion can be written as:  
\[M_{\text{z}}=G_{\text{s}}I_{\text{d}}\theta^{\prime} \tag{12}\]  
where \(I_{\text{d}}\) is torsion moment of the inertia of the box girder section, \(I_{\text{d}}=I_{\text{do}}+I_{\text{dc}}\).  
Figure 2: Free torsion of multi-cell closed section.
## 4 Analysis and Suppression Method of Short Circuit Current in MMC-Based MTDC Power Grid
### Short Circuit Current Calculation Method
#### 4.1.2 Short-Circuit Current Calculation Considering MMC Control  
A short-circuit current calculation method that considers MMC control for MMC-based MTDC power grids is proposed in [40]. When compared with the previous calculation method in [13; 35], the MMC is modeled as an RLC series circuit in parallel with a time-dependent controlled current source \(I_{\text{x}}\) based on the AVM, as shown in Figure 9.  
The equivalent model for an MMC-based MTDC power grid is established by combing the equivalent MMC model with the equivalent resistance-inductance series circuits of the DC lines. The post-fault branch currents and node voltages are obtained by solving the equivalent model of the DC grid through a companion circuit method. The proposed short circuit current calculation method that considers MMC control can be flexibly accommodated for all kinds of DC faults by updating the conductance matrix, history current vector, and DC current source vector [40].  
The calculation of DC short circuit current calculation considering MMC control in [42] is performed in the frequency domain. The MMC is represented by an AVM and the effect of fault-generated traveling waves (FTWs) is fully considered. Therefore, the proposed method avoids the shortcoming that the \(RL\) line model-based methods cannot solve the nodal voltage, and the effective time window is much longer than the EDM-based method. The proposed method satisfies the accuracy requirement with a small computational burden. Therefore, it can be applied to a large number of fault calculations requirement scenes, such as parameter design and the protection of DC power grids.  
When compared with the equivalent circuit method in [13,34] ignoring the MMC control, the short calculation proposed in [40, 41, 42] consider the dynamics of the control system in the short circuit current calculation, which can provide guidance for fault current suppression through MMC control.