esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The estimation accuracy, the number of pixel candidates and the final PSI displacement maps retrieved are deeply discussed and compared with the classical approaches described above. The test site selected to demonstrate the feasibility of the TSC approach corresponds to the slow-moving landslide of El Forn de Canillo, located in the Andorran Pyrenees. Applying PSI algorithms over this type of rural scenario, mostly vegetated, constitutes an extra challenge, in contrast with urban areas, where PSI techniques typically perform better. For this purpose, two datasets are employed. On the one hand, 10 sliding spotlight TerraSAR-X images covering the period from October 2010, to November 2011, are used. On the other hand, \(10\) X-band images collected during the same period with a frequency modulated continuous wave (FMCW) GB-SAR sensor are employed to complete the study. The inclinometric results available for the maximum displacement area of the landslide, known as Cal Boro-Cal Ponet, are used as ground-truth in order to validate the PSI results obtained. The paper is structured as follows. Section 2 presents a description of the test site and the datasets employed for the study in order to introduce the reader to the context of this work. In Section 3, the sublook generation and TSC estimation processes are briefly reviewed. A comparison between the TSC and the traditional phase quality estimators is presented at this stage. Section 4 shows the relationship between the TSC metrics and the pixels' phase dispersion. A comparison in terms of pixels selected between the TSC and the classical approaches is presented. Section 5 shows the final displacement maps retrieved by means of the TSC approach. These results are deeply discussed and compared with the ones obtained with the classical approaches. In addition, some ground-truth is shown in order to validate the displacement results retrieved. The main conclusions and major remarks of the manuscript are given in Section 6.
# Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on [PERSON] Shi
## 6 Conclusions  
We propose a photonic deep neural network based on the use of WDM input signals, and an SOA-based matrix multiplication unit. The integrated photonic neural network is employed as a weighted addition, thereafter combined with an offline hyperbolic tangent function as a nonlinear function is demonstrated on the simulation platform _VPIphotonics_. We study the weight calibration and weighted addition with different crosstalk of photonic integrated AWGs and SOA-based cross-connects. The error from the weighted addition is found to decrease when the numbers of input channels increase, so that a high number of input channels is beneficial for the implementation of the PDNN. A trained 3-layer DNN is implemented by reconfiguring the weight setting on the subnetwork and feeding the layer output to the next layer. The performance is simulated with different values of crosstalk, energy consumption per operation, and waveguide loss. The experimental results are in agreement with the simulation results, meaning that the implemented simulation offers a solid base for further study of scalability for this kind of network architecture. The results show that the photonic DNN is robust to the noise added during the signal processing. The error induced by the first layer is greater than the next two layers, due to the higher resolution multilevel encoding at the input layer with respect to the resolution at the 2 nd and 3 rd layer, but the error is not necessarily degrading the performance for a maximum allowed error. The performance analysis as a function of the path losses suggests the photonic neural network could be further optimized for lower power consumption. These results provide enough insights for the design of scalable photonic neural networks to a higher dimension for solving higher complexity problems. Finally, in future, a combination of the weighted addition function with on-chip non-linearities holds the promise to enable further acceleration for computation.  
Conceptualization, B.S., N.C. and R.S.; formal analysis, B.S. and R.S.; investigation, B.S.; methodology, B.S., N.C. and R.S.; validation, B.S.; visualization, B.S.; writing--review & editing, B.S., N.C. and R.S. All authors have read and agreed to the published version of the manuscript. This work is financially supported by the Netherlands Organization of Scientific Research (NWO) under the Gravitation program (Zwaartekracht programm), Research Centre for Integrated Nanophotonics'. The authors thank the technical support from VPIphotonics. The authors declare no conflict of interest.
## 2 Methodology and Data
### Methodology  
#### 2.1.1 Scale Variance Decomposition  
The scale analysis of variance can quantitatively analyze the economic differences at different spatial scales in the region to identify the optimal spatial scale, so it has been widely used in multi-space scale analysis [32; 33]. Therefore, this paper uses the scale variance decomposition method to study the carbon emissions in the Yangtze River Delta region and to identify the optimal spatial scale.  
The scale variance analysis method first needs to divide the research area into different levels of spatial hierarchy and layer the research units of different scales according to certain criteria. According to the administrative division, the Yangtze River Delta region is divided into three spatial scales and the county scale is the minimum spatial scale. The regional nesting includes the city scale according to the provincial scale, the city scale includes the county scale standard, and the three levels of spatial scale, defined as the provincial, municipal, and county domains, are represented by \(\alpha\), \(\beta\), and \(\gamma\) respectively, and they indicate that the statistical model of the variance within the region is as follows.  
\[X_{ijk}=\mu+\alpha_{i}+\beta_{ij}+\gamma_{ijk} \tag{1}\]  
\(X_{ijk}\) represents the carbon emissions of the (\(k\))th county in the (\(j\))th city of the (\(i\))th province and \(\mu=\overline{X}\), \(\alpha_{i}=\overline{X}-\overline{X}_{i}\), \(\beta_{ij}=\overline{X}_{ij}-\overline{X}_{i}\), \(\gamma_{ijk}=X_{ijk}-\overline{X}_{ij}\), and \(\overline{X}=\frac{\Sigma_{i}\Sigma_{j}\Sigma_{k}X_{ijk}}{N}\)\(\overline{X}_{i}=\frac{\Sigma_{j}\Sigma_{k}X_{ijk}}{n_{i}}\), \(\overline{X}_{ij}=\frac{\Sigma_{k}X_{ijk}}{n_{ij}}\). \(N\) represents the number of counties in the Yangtze River Delta; \(n_{i}\) indicates the number of counties in  
Figure 2: A flow chart of methodology.  
the (_i_)th province; and \(n_{ij}\) indicates the number of counties in the (_j_)th city of the (_i_)th province. Table 1 shows the composition of the scale variance at different spatial scales.
# High-Density 3D Printable Chipless RFID Tag with Structure of Passive Slot Rings
## The Principle of the Design
### Chipless Tag of the Rectangular Slot Rings  
The above analysis is suitable for the printed single slot. To create a multi-resonant structure for the purpose of multi-bits coding, the multiple slots need to be printed in a compact manner. Here a concentric contour is designed to include multiple rectangular slots with different sizes while keeping the highest compact density, as shown in Figure 4a. The key parameters include the width of slot \(W_{slot}\) and the gap distance of adjacent slots d. For the structure with n-bit resonant slots, the size of the tag can be expressed as:  
\[a_{n}=a_{1}+2(d+W_{slot})\times n \tag{2}\]  
where \(a_{1}\) is the size of primary resonant slot.  
The macro-model of the tag is shown in Figure 4b. It shows the equivalent circuit of the proposed resonant slot-patch RFID, in which LC coupled resonator is used to establish the resonant frequency. R indicates the attenuation of the UWB signal. C represents the capacitance of the sealed slot. Its value is proportional to the total length of the slot while being inversely proportional to the width of the slot. L is used to describe the inductance of the metallic strips and its value is also proportional to the total length of the metallic strips while being inversely proportional to the width of the strips. In our design, the equivalent capacitance and inductance are increased significantly because of the convoluted structure that makes both the slot dipole and the metallic strips longer.  
For the designed concentric structure in Figure 4a, one important parameter is the gap between two adjacent slots, d. The gap here corresponds to the convex part on the substrate. To investigate the influence of the gap on the resonant property, a simulation is conducted on the designed tags with gap values varied from 0.1 mm to 0.9 mm while the slot width fixed to be 0.5 mm. The results are shown in Figure 5. It shows that the coding results are sensitive to the gaps. For the designed structure, the coding of resonant frequencies can be distinguished clearly when the gap is larger than 0.5 mm, as shown in Figure 5c,d, while obvious data missing occurs when the gap is smaller than 0.5 mm, as shown in Figure 5a,b. The phenomenon can be explained by the coupling effect, in which the effect of the adjacent slot rings is more obvious at smaller gap because the interference appears stronger.  
So, based on above analysis, the thumb rules for the 3D printed chipless tag based on the designed concentric slots on insulator substrate include:  
(1) The upper resonant frequency is limited by the 5 th harmonic frequency of the fundamental frequency.  
(2) The slot width should be larger than 0.3 mm.  
(3) The gap among slots should be larger than 0.5 mm to guarantee the coding quality.  
Figure 4: (**a**) Diagram of the designed concentric rectangular slots printed on substrate; (**b**) Equivalent circuits of rectangular slot ring resonators. Each rectangular slot ring is depicted as a LC shunt circuit.
## 3 Results and Discussion
### Ion Distributions in EDL
This suggests a loose accumulation of \([\text{EMIM}]^{+}\) cations owing to their great steric effect and a greater distance between the two stacked layers of \([\text{EMIM}]^{+}\) cations in a pair. On the other hand, the RDF of \([\text{BF}_{4}]^{-}/[\text{BF}_{4}]^{-}\) exhibits a medium first peak at a value of \(r\) similar to that of \([\text{EMIM}]^{+}/[\text{EMIM}]^{+}\)which is attributed to the easier packing of \([\mathrm{BF_{4}}]^{-}\) anions due to the smaller steric effect compared to the \([\mathrm{EMIM}]^{+}\) cations. In addition, this also shows that the distance between the stacked layers of \([\mathrm{BF_{4}}]^{-}\) anions in the pair approximates that between the stacked layers of \([\mathrm{EMIM}]^{+}\) cations. Especially, it can be seen that the crests on the RDF curves of \([\mathrm{EMIM}]^{+}/[\mathrm{EMIM}]^{+}\) and \([\mathrm{BF_{4}}]^{-}/[\mathrm{BF_{4}}]^{-}\) appear to correspond to the valley on the RDF curves of \([\mathrm{EMIM}]^{+}/[\mathrm{BF_{4}}]^{-}\) and vice versa, which further supports the alternate distribution of stacked layers of cations and anions near the electrode surface. Additionally, it is worth noting from Figures 4 and 5 that even on the neutral electrode surface (\(\sigma=0\) e/atom) there also exists an obvious first layer of ions in the EDL, wh  
Figure 6: Ion–ion radial distribution functions (RDFs) for the \([\mathrm{EMIM}]^{+}/[\mathrm{BF_{4}}]^{-}\) electrolyte on the non-rough neutral electrode surface. potential on the uncharged electrode surface (as discussed in the following Section 3.2). In order to clarify this phenomenon, Figure 7 depicts the variations in potential energies of the [EMIM]\({}^{+}\) cation and [BF\({}_{4}\)]\({}^{-}\) anion near the neutral electrode, where \(E\) is the potential energy of the ion and \(D\) represents the distance between the ions and the electrode. As shown, potential energy valleys are observed for both the [EMIM]\({}^{+}\) cation and the [BF\({}_{4}\)]\({}^{-}\) anion, which establishes the potential energy difference to compensate the energy cost of the accumulation of ions in the location of the valleys. Therefore, the [EMIM]\({}^{+}\) cations and [BF\({}_{4}\)]\({}^{-}\) anions are accumulated in the location of the valleys, forming the first layers on the neutral electrode. In addition, it can be seen that the potential energy valley of the [EMIM]\({}^{+}\) cation appears a little closer to the electrode than the [BF\({}_{4}\)]\({}^{-}\) anion, which explains the occurrence of the first peak at the smaller \(z\) for the [EMIM]\({}^{+}\) cation on the neutral electrode surface (\(\sigma\) = 0 e/atom) (see insets (a) and (b) in Figures 4 and 5).
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 2 Model Development
### Coordinate Change  
To calculate the scattering properties of different geophysical parameters using GNSS-R requires consideration of bistatic scattering. Therefore, the forward scattering alignment  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**Valide Conditions**} & \multicolumn{3}{c}{**Recommended Conditions**} \\
**Name** & & & & & \\ \hline GO & \(s\geq\lambda/3\) & \(l_{b}\geq\lambda\) & \(l_{b}^{2}>2.76s\lambda\) & \(0.4\leq m\leq 0.7\) \\ \hline PO & \(0.05\lambda\leq s\leq 0.15\lambda\) & \(l_{b}\geq\lambda\) & \(m\leq 0.25\) & \(l=\lambda/6,l_{b}\geq 6l\) & \(0.05\lambda\leq s\leq 0.15\lambda\) \\ \hline SPM & \(s\leq 0.05\lambda\) & \(m\leq 0.3\) & \(l_{s}\leq 0.5\lambda\) & \(l_{b}\leq 0.25\lambda\) & \(s\leq 0.05\lambda\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Application range of the GO model, PO model, and SPM [4].  
(FSA) coordinate system should be employed (Figure 1) because FSA mainly applies to the bistatic scattering of nonuniform media [26; 27; 28; 29; 30].  
Vertical and horizontal vectors in FSA are defined along the direction of wave propagation. The coordinate system is consistent with the standard spherical coordinate system. The definitions of an incident wave and a scattering wave are as follows:  
\[n_{i}=\hat{x}sin\theta_{i}cos\phi_{i}+\hat{y}sin\theta_{i}sin\phi_{i}+\hat{z} cos\theta_{i} \tag{1}\]  
\[\hat{h}_{i}=-\hat{x}sin\phi_{i}+ycos\phi_{i} \tag{2}\]  
\[\hat{v}_{i}=\hat{h}_{i}\times\hat{n}_{i}=\hat{x}cos\theta_{i}cos\phi_{i}+\hat{ y}cos\theta_{i}sin\phi_{i}-\hat{z}sin\theta_{i} \tag{3}\]  
\[\hat{n}_{s}=\hat{\gamma}sin\theta_{s}cos\phi_{s}+\beta sin\theta_{s}sin\phi_{ s}+\hat{z}cos\theta_{s} \tag{4}\]  
\[\hat{h}_{s}=-\hat{x}sin\phi_{s}+\hat{y}cos\phi_{s} \tag{5}\]  
\[\hat{v}_{s}=\hat{h}_{s}\times\hat{n}_{s}=\hat{x}cos\theta_{s}cos\phi_{s}+\hat{ y}cos\theta_{s}sin\phi_{s}-\hat{z}sin\theta_{s} \tag{6}\]
## 4 Propagation Model  
In this section, we parameterize the model by providing first the average attenuation coefficient due to absorption in the atmosphere, \(L_{A}(f,d)\), and then the overall losses due to cloud blockage, \(L_{C}(f,d)\).
# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
## 4 Materials and Methods
### Datasets
#### 4.1.1 Dataset 1  
This dataset consists of 7896 images of vehicles with damage and 2464 images of vehicles without damage. The damage has been divided into eight types: scratches, deformations, breakage--crack, breakage--loss of material, paint defect--peeling, paint defect--lacquering, missing part, and special damage.  
The main types of damage are defined as follows:  
* Scratch: scratch without deformation produced on some elements of the vehicle's exterior bodywork. This category includes damage to the exterior paintwork, usually by detachment, mainly suffered by plastic bodywork elements.
* Deformation: Dent in any of the parts of the exterior bodywork of a vehicle.
* Breakage: Cracks, indentations, and detachment/lack of material from any of the bodywork parts. They usually appear on plastic parts, although they can be found in the form of a hole in metal parts. It takes the form of peeling of the lacquer or veiling of the paint.
* Paint damage: Damage not resulting from an accident but from the attack of atmospheric or chemical agents, or even from defects in previous repairs. It takes the form of peeling of the lacquer or veiling of the paintwork.
* Special damage: This includes all damage that has not been considered above and indicates the existence of damage, but the type of damage is unknown. This type of damage is also affected by occlusions and usually requires further examination.  
To ensure the consistency of the annotated data, a labeling protocol is specified, which includes non-linear damage. We address this by using small overlapping rectangles to cover the entire damage. Parallel scratches are labeled individually, even if they belong to the same area of damage.  
The types of damage that have been labeled can be visualized in the Figure 4, where only damage of the corresponding type is displayed.  
We limited the damage to be detected from eight categories to the two most common: scratches and deformations. This is because they tend to be more recurrent in damage assessment than the other types.
# Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels +
## 3 Method
### Hidden State Filtering
#### 3.3.1 Forward-Backward Procedure  
The forward-backward procedure comprises the iterative calculation of forward and backward variables given the observed sequence and model parameter \(\Omega\). Forward variable \(\alpha_{j}(t)\) is defined as the probability of a state at time \(t\) given the observed sequence up to and including time \(t\):  
\[\alpha_{j}(t)\triangleq P(s(t)=j|z(1:t),\Omega),j=0,1. \tag{7}\]  
Solving for \(\alpha_{j}(t)\) consists of the following steps [41]:  
1. Initialization: \[\alpha_{i}(1)=\pi_{i}b_{i}(z(1)),i=0,1.\] (8)
2. Induction: \[\alpha_{j}(t+1)=\Bigg{[}\sum_{i\in\{0,1\}}\alpha_{i}(t)a_{ij} \Bigg{]}b_{j}(z(t)),\] \[\qquad\qquad\qquad j=0,1,0\leq t\leq T-1.\] (9)3. Termination: \[P(z(1:T)|\Omega)=\sum\limits_{i=0,1}\alpha_{i}(T).\] (10)  
Backward variable \(\beta_{i}(t)\) is defined as the probability of the observation sequence from \(t+1\) to the end of the sequence, conditioned on state \(s(t)=i\)  
\[\beta_{i}(t)\triangleq P(z(t+1:T)|s(t)=i,\Omega),i=0,1. \tag{11}\]  
Solving for \(\beta_{i}(t)\) consists of the following steps [41]:  
1. Initialization: \[\beta_{i}(T)=1,\ i=0,1.\] (12)
2. Induction \[\beta_{i}(t)=\sum\limits_{j\in\{0,1\}}a_{ij}b_{j}(z(t+1))\beta_{j} (t+1),\] \[i=0,1,t=T-1,T-2,\ldots,1.\] (13)  
HSF methods discussed next use the forward and backward variables.
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 2 The DED-RL Algorithm
### Field Computation  
Another important feature that makes the DED-RL algorithm computationally efficient is that, by being environment-driven, the tiles are both \"bouncing-tiles\" and the \"points of arrival\" where the field is computed. In other terms, the ray tubes are launched from the transmitter towards the visible tiles, and the incident field on them is computed and recorded, in order to achieve a complete field prediction map on all surfaces. Then, bounced ray tubes are generated from all of them towards other visible tiles using the visibility matrix, which stores all the tiles visible from the incident tiles, and then applying the proper reflection, diffraction, or scattering coefficient. Reflected and diffracted fields are computed with the usual Geometrical Optics (GO) [1] and Uniform Theory of Diffraction (UTD) [2] coefficients, while diffuse scattering is computed according to the Effective Roughness (ER) model [27], whose main parameter is the scattering coefficient S, corresponding to the percentage of power scattered in all directions at expenses of specular reflection. Diffraction is computed only for the \"edge tiles\". Every time a tile is hit by a new ray tube, the computed field on its center is updated; if the field strength contribution on a tile falls below a given minimum threshold, the incident field is summed with the one already present on the tile, but the ray tube is no longer propagated with additional bounces. Fields are computed and summed in a fully coherent way, taking into account phases and polarization, and also cross-polarization introduced by some interaction mechanisms such as diffuse scattering [28].  
In such a way, DED-RL allows a full 3D prediction on all the surfaces of the environment (i.e., ground, building walls, and roofs). An example of such 3D prediction is depicted in Figure 2 for a dense urban area located in the center of San Francisco.  
Figure 1: Automatic ray splitting using discrete ray launching.
## 7 Conclusions  
This work proposes an on--board monitoring method based on the axlebox acceleration using the VMD--ES method. A three--dimensional vehicle--track coupled dynamic model verified by field test was established to simulate the axlebox acceleration of a high--speed railway. The simulation and field test results show that the VMD-ES method accurately extracts the impact characteristics of the wheel flat and can quantitatively identify the wheel flat faults at various speeds. Based on the results, the following conclusions can be drawn:  
Figure 12: The placement of the axlebox acceleration sensor.  
Figure 13: Field test: (**a**) the measured data; (**b**) The results of the measured data based on the VMD-ES method.  
1. The axlebox acceleration includes strong interference signal excited by the rail and wheel which influences the accurate identification of the wheel flat. The results show that the proposed VMD-ES method accurately extracts the characteristic frequency of the wheel flat from complex frequency components. Meanwhile, the mean amplitude of the first four multiple frequency increases monotonically with the length of flat in ES. Therefore, the VMD-ES method can be applied to quantitatively identify the wheel flat faults at various speeds.
2. In the case of the wheel eccentricity, only the dominant frequency (33.6 Hz) is presented in the results of ES. At the co-existence of the wheel eccentricity and flat, the VMD-ES method can accurately extract the multiple frequency of the wheel flat. The method has good adaptability in wheel flat identification; wheel eccentricity has no influence on the detection.
3. The field test is carried out to verify the accuracy of the method. The results of the field test are consistent with the simulation results, which reflect the correctness of the established vehicle-track coupled dynamics model, and the feasibility of the wheel flat identification by using this method.  
In this paper, the wheel flat detection is investigated in detail by using the VMD-ED method. The results show that the method can quantitatively identify the wheel flat faults at various speeds. Machine learning methods will be combined with advanced signal processing techniques to identify the wheel flat in our future work.  
X.L.: Conceptualization, Methodology, Writing original draft, Software. Z.H.: Supervision, Resources, Writing--review and editing. Y.W., L.Y. and H.W.: Validation. L.C.: Field test instruction. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China, grant numbers 52162047, 52062028, the Natural Science Foundation of Gansu Province, grant number 20 JR5 RA393, and the opening foundation of the State Key Laboratory of Traction Power in Southwest Jiaotong University, grant number TPL1902.  
Not applicable.  
The authors declare no conflict of interest.