esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization
## 1 Introduction
Next, histogram equalization is used to match the color distribution of the low-resolution RGB image and the high-resolution RGB image, taken from a slightly different viewing angle with the low-resolution HSI. Finally, the spatial endmember abundance of the high-resolution HSI is matched to the spatial information obtained from the high-resolution RGB image. The proposed method can be described as a rearranging of the endmember of the HSI to fit into the pixel location of the high-resolution RGB image. Because there is no constraint on the pixel location of the low-resolution HSI, the proposed method can build a high-resolution HSI only if the endmember combination of both images is similar. The performance of the proposed method is evaluated by field experiments comparing the spectrum of the same material in the low- and high-resolution HSIs using a spectral angle mapper.
## 3 Results and Discussion
### Sensing Performance  
Considering the resonance wavelength difference between the two EIT peaks and the transmittance of the two EIT peaks (not shown here), we chose a structure with \(W_{1}\) = 100 nm, \(W_{2}\) = 230 nm and \(g\) = 30 nm to study the corresponding sensing performance. At the same time, the SiO\({}_{2}\) between GWL\({}_{1}\) and GWL\({}_{2}\) changed to the dielectric sample. In order to make the refractive index change of the dielectric more sensitive, \(d_{2}\) was set to 3740 nm. The performance of the sensor was evaluated by two factors, sensitivity (S) and FOM [29]:  
\[\mathrm{S}=\frac{\Delta\lambda}{\Delta n} \tag{2}\]  
\[\mathrm{FOM}=\frac{\mathrm{S}}{\mathrm{FWHM}} \tag{3}\]  
Here, S refers to the resonance wavelength shift caused by the change in the refractive index unit of the dielectric, and FOM represents the optical resolution of the sensor.  
Figure 6 shows the transmission spectrum with a different refractive index of the dielectric, the insert shows the resonance wavelengths of two EIT peaks at a different refractive index of the dielectric. It is obvious that the resonance wavelengths of the two modes had a linear relationship with the refractive index of the dielectric. The changes in EIT\({}_{1}\) and EIT\({}_{2}\) with the refractive index of the dielectric are referred to as mode 1 and mode 2, respectively. Therefore, the average FWHM of the mode 1 and mode 2 from the refractive index of the dielectric from 1.440 to 1.452 was 0.051 nm and 0.061 nm. The S of mode 1 and mode 2 were 29.166 nm/RIU and 35.833 nm/RIU, respectively, so FOM of mode 1 was 571.88, and FOM of mode 2 was 587.42. Compared with the other sensors, this sensor had ordinary sensitivity due to the narrow line width of two EIT peaks [30]. However, it had higher FOM than other previous sensors [5; 6; 31; 32], as shown in Table 1. So, this sensor had a super high optical resolution.  
Figure 5: The transmission spectra under different \(g\), other parameters are the same as Figure 1. (**a**) \(g\) = 10 nm. (**b**) Transmission spectrum with \(g\) = 20 nm. (**c**) Transmission spectrum with \(g\) = 30 nm. (**d**) Transmission spectrum with \(g\) = 40 nm. (**e**) Transmission spectrum with \(g\) = 50 nm. (**f**) Two resonance wavelengths and two resonance wavelengths difference (\(\lambda_{2}\)-\(\lambda_{1}\)) under different \(g\).
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 3 PIDL for Deterministic Tse
### Training Paradigms
#### 3.3.2 Joint Training  
Physics parameters and hyperparameters of the PUNN and the PICG are updated _simultaneously_ or _iteratively_ in the training process. All of the existing literature on PIDL-TSE employs simultaneous updating of all parameters associated with both the PICG and PUNN together, which will be our focus below. However, we would like to pinpoint that there are increasing interests in training both modules iteratively [75], which could be a future direction to improve the training efficiency of PIDL-TSE.  
Challenges  
The PUNN in PIDL is a typical deep learning component that most training techniques can apply. In contrast, the training challenges incurred by the PICG with unknown physics parameters are nontrivial, and, accordingly, substantial research and additional adaptive efforts are needed.  
First, some traffic flow models may include a large number of physical parameters that need to be discovered in TSE, and it is challenging to train all the parameters at once. For example, the three-parameter LWR model in Section 3.4 involves five parameters, and it is reported that the direct joint training for all the parameters with real-world noisy data leads to unsatisfactory results [31]. For this issue, the alternating direction method of multipliers (ADMM) method [76] is an option to improve training stability, i.e., to train one subset of physical parameters at a time with the rest fixed. The advanced ADMM variant, deep learning ADMM (dIADMM), may further address the global convergence problems in non-convex optimization with a faster learning efficiency [77].  
Second, a highly sophisticated traffic flow model may contain complicated terms that are unfriendly to differentiation-based learning, making the model parameter discovery less satisfactory for real data. In this case, the structural design of the PICG plays animportant role to make the framework trainable. Specifically, additional efforts such as variable conversion, decomposition, and factorization need to be made before encoding to make the structure learnable and for the loss to converge. Alternatively, as will be discussed in ML surrogate, one can use an ML surrogate, such as a small NN, to represent the complicated terms in the PICG to avoid tackling them directly [31].
## 4 Discussion  
The results of the experiment show that SLIM can reach a desired TCP pose with an accuracy of 0.003 m in position and 0.006 rad in attitude. At the same time, SLIM is less accurate in following the desired trajectory between two targets. Indeed, 95% of the time the norm of the tracking error is below 0.109 m, but it locally varies up to 0.225 m. Since the robotic arm has a length up to 4.8 m, such performances are considered acceptable. Furthermore, given the value of the tracking error and the dimensions of the modules section (\(\approx\)0.1 m \(\times\) 0.2 m), the requirement of entering through small apertures (40 cm \(\times\) 40 cm) is fulfilled 95% of the time. To fully satisfy the requirement, some improvements in the design and the control strategy are needed to increase the accuracy in the trajectory tracking.  
Moreover, some aspects of the mechanical design were driven by the available machining techniques. Consequently, the design could be improved in terms of mass reduction and stiffness increase by implementing weldings instead of screws and substituting the tie-rods with a cover in carbon fiber.  
Figure 14: Histogram of the norm of the tracking error. The vertical axis indicates the relative probability of each histogram bin.  
Finally, the chosen materials and components for the robotic arm make SLIM suitable for the inspection of harsh environments characterised by high temperatures. The end-effector, instead, is a prototype that does not respect the temperature requirement. Therefore, a new version of the end-effector able to tolerate temperatures up to 100 \({}^{\circ}\)C is needed to meet all the requirements.
## 4 Predictive Current Control Strategy  
### Predictive Current Control of the BTL  
Comparing with the conventional boost converter, the current ripple of the inductor in the boost-TL converter is lower, which reduces the torque ripple of the generator. The voltage stress on the power unit is small, which is only the voltage stress of the common boost converter. In the same inductor current, the switching frequency is half of the common boost converter, so the switching loss is less, and the boost-TL converter can be in the low-speed. The rectifier output DC voltage boost, therefore, can also make the wind turbine operate in a very wide speed range.  
There is only one switch in a two-level boost converter, so there are only 0 or 1 (\(2^{switchnumbers}=2^{1}\)) conduction modes. As shown in the TLB converter, there are upper and lower arms of the two switches, therefore, the conduction of the switch tube (\(2^{switch\ numbers}=2^{2}\)) has four different modes, namely, (00, 01, 10, 11). The increase of the operating modes increases the number of states of switching-on  
Figure 5: Voltage vectors and switching states of the T-type inverter.  
of the switch, resulting in the need to increase the degree of freedom of control of charging and discharging the DC-side equalizing capacitor.  
For the four operating modes in Figure 6, respectively, the Kirchhoff voltage and current formula can be linear continuous systems for the DC-side inductor current and capacitor voltage, respectively:  
\[\frac{d}{dt}\left[\begin{array}{c}i_{dc}\\ v_{c1}\\ v_{c2}\end{array}\right]=\left[\begin{array}{ccc}-\frac{R_{dc}}{T_{dc}}&- \frac{(1-S_{1t})}{L_{dc}}&-\frac{(1-S_{2t})}{L_{dc}}\\ \frac{(1-S_{1t})}{C_{1}}&0&0\\ \frac{(1-S_{2t})}{C_{2}}&0&0\end{array}\right]\left[\begin{array}{c}i_{dc} \\ v_{c1}\\ v_{c2}\end{array}\right]+\left[\begin{array}{ccc}\frac{1}{L_{dc}}&0&0\\ 0&-\frac{1}{C_{1}}&0\\ 0&0&-\frac{1}{C_{2}}\end{array}\right]\left[\begin{array}{c}v_{in}\\ i_{o1}\\ i_{o2}\end{array}\right] \tag{5}\]  
where \(S_{1t}\), \(S_{2t}\) are the \(t_{k}\) boost-TL circuit power switching signals; and \(i_{o1}\) and \(i_{o2}\) are the flowing currents through the load. The voltages across the capacitor at \(t_{k+1}\) can be obtained, respectively, by:  
\[v_{c1}^{p}(k+1)=\frac{(1-S_{1})T_{s}}{C_{1}}i_{dc}(k)+v_{c1}(k)-\frac{T_{s}}{ C_{1}}i_{o1}(k) \tag{6}\]  
\[v_{c2}^{p}(k+1)=\frac{(1-S_{2})T_{s}}{C_{2}}i_{dc}(k)+v_{c2}(k)+\frac{T_{s}}{ C_{2}}i_{o2}(k) \tag{7}\]  
As can be seen from the equations above, by measuring the moment of the inductor current, the two ends of the voltage divider capacitor, the load test output current, and the two switches show four possible conduction states, and the output voltage at the next moment's predictive value can be obtained:  
\[v_{o}^{p}(k+1)=v_{c1}^{p}(k+1)+v_{c2}^{p}(k+1) \tag{8}\]
# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 1 Introduction
### Objectives of This Work  
The main objective of this work is to create a set of tools, based on off-the-shelf drones equipped with RBG cameras, artificial intelligence, and CHAMELON bundles, to check compliance with the mandatory buffer zones around buildings and roads, as determined by the Portuguese law 10/2018 for rural areas. These tools should be evaluated in a small-scale pilot, using drones to capture high-resolution aerial imagery of designated areas and then automatically determining whether these areas comply with the applicable rules regarding vegetation management inside buffer zones. A designated area is a polygonal area defined by the user in a WebGIS, which contains roads and/or buildings with a potential risk of fire propagation that is worth analysing.  
The tools should be able to identify the type of designated area (isolated building, urban-forest interface, road) and automatically delimit the zones that need to be assessed (buffer zones), according to the type of forest interface and the minimum size of the buffer zone, stipulated by law for each case. The tools should also be able to generate a high-resolution orthophoto and a 3D point cloud of the designated area from a set of aerial photos captured by an off-the-shelf drone. These data (especially the 3D point cloud) are required by the CHAMELON bundles to extract information about trees and vegetation in the area.  
The tools should use two different CHAMELON bundles: BC1 (vegetation monitoring and census) and BC3 (continuity of vegetation), for extracting several different metrics from the data. After receiving the processed data from the bundles, the tool should be able to automatically determine if a designated area complies with the law, and in case of non-compliance, it should display a WebGIS map highlighting the specific zones that do not comply, informing the user of the reason, and suggesting a way to clear the vegetation in order to remove the non-compliances.  
The tool should be developed specifically to assess compliance with Portuguese law but prepared to be easily parameterised to assess different requirements for vegetation density and/or distances, so that it can be used on a European scale.
## 3 Stasa
### State Transition Operators  
In continuous optimization problems, STASA employs four types of state transition operators: rotation, translation, scaling, and axis transformation. Each operator plays a distinct role in improving the search process.  
(a) Rotation Operator  
\[x_{k+1}=x_{k}+\varepsilon_{a}\frac{1}{n\|x_{k}\|_{2}}R_{r}x_{k} \tag{10}\]  
where \(\varepsilon_{a}\) is a positive constant known as the rotation factor, and \(R_{r}\in R^{n\times n}\) is an n-dimensional random matrix with elements uniformly distributed in the range of \([-1,\,1]\). Additionally, \(\left\|\cdot\right\|_{2}\) denotes the Euclidean norm (L2 norm). The rotation operator allows the generated candidate solutions to fall within a hypersphere of radius \(\varepsilon_{a}\).  
(b) Translation Operator\[x_{k+1}=x_{k}+\varepsilon_{\beta}R_{t}\frac{x_{k}-x_{k-1}}{\left\|x_{k}-x_{k-1} \right\|_{2}} \tag{11}\]  
where \(\varepsilon_{\beta}\) is a positive constant known as the scaling factor, and \(R_{t}\) is a random variable defined within the range of [0, 1]. The translation operator starts from the initial point \(\mathbf{x_{k}}\) and searches in the direction from point \(x_{k-1}\) to point \(\mathbf{x_{k}}\), with a maximum search length of \(\varepsilon_{\beta}\). This operator possesses local search capabilities.  
(c) Scaling Operator  
\[\mathrm{x_{k+1}}=\mathrm{x_{k}}+\varepsilon_{\gamma}\mathrm{R_{e}}\mathrm{x_{ k}} \tag{12}\]  
where \(\varepsilon_{\gamma}\) is a positive constant known as the scaling factor, and \(R_{e}\in\mathbf{R}^{n\times n}\) is an n-dimensional random diagonal matrix following a Gaussian distribution with a mean of 0 and a variance of 1. The scaling operator has global search capabilities.  
(d) Axis Transformation Operator  
\[\mathrm{x_{k+1}}=\mathrm{x_{k}}+\varepsilon_{\delta}\mathrm{R_{e}}\mathrm{x_{ k}} \tag{13}\]  
where \(\varepsilon_{\delta}\) is a positive constant known as the axis transformation factor, and \(R_{a}\in\mathbf{R}^{n\times n}\) is an n-dimensional random diagonal matrix following a Gaussian distribution in the range [0, 1], with only one non-zero element. The axis transformation operator enhances the search capability in a single dimension.
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 3 Multi-Frequency Detection Schemes
### Modifications Required for Application Against DVB-T Signals
#### 3.1.2 Typical Causes of Target Echo Misalignment across Channels and Counteracting Strategies
For example, when super-refractive conditions or even ducting occur, the propagation paths of target echoes might differ from frequency channel to frequency channel thus yielding a significant displacement on the range/velocity plane. All the above conditions might severely limit the effectiveness of the proposed MF approaches. Obviously, when the expected displacement is very large, computationally expensive signal processing techniques have to be applied to co-register the resulting maps prior to integrating their results. However, when the target echoes misalignment is limited to a few cells, a simple and effective approach can be devised based on the decentralized integration strategy. Specifically, we propose to extend the integration of the binary results after the application of the first threshold so that a few consecutive range bins are included in the sum. Basically, by defining a proper window in the range dimension composed of W cells, Equation (6) becomes:  
\[\sum_{n=1}^{N}\left(\sum_{p=-W/2}^{W/2}\mathrm{b}_{k}\left[l_{0}+p,m_{0}\right] >0\right) \begin{array}{c}H_{1}\\ \gtrapprox L\\ H_{0}\end{array}  \tag{15}\]  
In other words, the binary maps to be integrated are obtained by employing an \"OR\" condition on the detection results obtained on \(W\) consecutive range cells. By properly setting the window size W, limited range misalignments could be managed. Obviously, this is paid in term of target detection performance against targets that do not yield any displacement of the corresponding echoes. In fact, with this approach, the detection threshold to be adopted at the first stage has to be properly increased in order to maintain the same final rate of false alarms. In fact, after the integration on consecutive range cells, the false alarm rate increases as:  
\[P_{fa}^{SF(W)}=1-\left(1-P_{fa}^{SF}\right)^{W}\cong WP_{fa}^{SF} \tag{16}\]  
Moreover, the proposed solution inherently implies a range resolution degradation by a factor of W; therefore the window size has to be limited as far as possible and the application of the proposed approach has to be restricted to cases where it represents an essential step.
# Effects of Nonlinearity and Network Architecture on the Performance of Supervised Neural Networks
## 2 Background and Objectives
Because of its flexibility, nonlinearity in L-ReLU can be adjusted to affect the information flow and the model accuracy in shallow neural networks, and the effects of nonlinearity in neural network models may depend on the model architecture, feature extraction techniques and the type of input data. In this study we provide our findings about the effects of nonlinearity in neural networks, using widely used activation functions, ReLU and L-ReLU and give justifications to the observations as an insight to the deep learning process. We empirically show that the ReLU is a reliable activation function when there is sufficient number of trainable parameters in the network. However, in the case of transfer learning, we show that the L-ReLU improves the classification accuracy than ReLU when used in classification layers. In addition to that, we investigate the effects of nonlinearity in L-ReLU for different modeling approaches and data domains. We show that the information loss due to the nonlinear activation can be interpreted using entropy. Finally, we show that the entropy of the hidden layer outputs of a neural network has a connection to its optimization process. The entropy change during the training process helps to visualize the transformation of function space from a disordered to a more ordered sate as the training progresses.
## 2 Methods and Data
### D-RAP Model  
The Global D-Region Absorption Prediction (D-RAP) model operated by NOAA's Space Weather Prediction Center (SWPC) predicts the absorption values occurring mainly in the D-region. The model determines the combined absorption caused by X-Ray changes and proton events. The D-RAP is a complex prediction model, nevertheless, it still has several weaknesses according to its validation report [27], as follows: at high-latitude stations, the model occasionally overestimates the peak ionospheric absorption by more than a factor of 2; in the European sector the model appears to systematically underestimate the absorption; absorption at midlatitude appears to be also systematically underestimated; the model does not consider additional absorption due to auroral particle precipitation. Figure 3 shows the picture of an X8.2 flare event observed by the Solar Dynamic Observatory (SDO) and the ionospheric absorption caused by the enhanced X-Ray flux as determined by the D-RAP model at 16:06 UT on 10 September.  
The following program was used to download the D-RAP data: [[https://github.com/hayesla/NOAA-DRAP-Analysis/tree/master](https://github.com/hayesla/NOAA-DRAP-Analysis/tree/master)]([https://github.com/hayesla/NOAA-DRAP-Analysis/tree/master](https://github.com/hayesla/NOAA-DRAP-Analysis/tree/master))[28] (accessed on 30 August 2024). This Python code was modified in order to collect the data from the coordinates closest to the ionosonde and riometer stations for comparison between the modeled and measured absorption values. D-RAP originally shows all the frequencies where the absorption is at least 1 dB. However, for the comparison, we needed the absorption that occurred at certain frequencies (2.5 and 4 MHz for the ionosonde, and 30 or 32 MHz for the riometer measurements). To obtain the absorption for a fixed frequency the following equation was used [4]:  
\[f_{1}=f_{\mathrm{C}}\cdot\left(\frac{A_{\mathrm{C}}}{A_{1}}\right)^{\frac{2}{3}} \tag{2}\]  
where \(f_{1}\) is the original frequency for the 1 dB absorption, while \(A_{1}\) = 1 dB, since the absorption value is equal to 1 dB at that frequency. \(A_{\mathrm{C}}\) and \(f_{\mathrm{C}}\) are the input values. Based on Equation (2), we can arrive to the following formula for \(A_{\mathrm{C}}\):  
\[A_{\mathrm{C}}=\left(\frac{f_{1}}{f_{\mathrm{C}}}\right)^{\frac{3}{2}}\cdot A _{1} \tag{3}\]  
where the input data are \(f_{1}\) and \(A_{1}\) = 1 dB. Therefore, at a certain frequency (\(f_{\mathrm{C}}\)), \(A_{\mathrm{C}}\) is going to be the absorption value for that given frequency. Based on this approach, the time series of the absorption values at the given frequencies (2.5 and 4 MHz, and 30 or 32 MHz) and locations (close to the ionosonde and riometer stations) were calculated and analyzed (compared with the measured values there).
# Reduction of Bias and Light Instability of Mixed Oxide Thin-Film Transistors
## 4 Discussion
### SS Dependency on Channel Thickness  
Total shallow trap densities (\(N_{\rm T}\)) can be estimated from SS values using the relation SS = log\({}_{\rm e}\)10 \(\times\)\(k_{\rm B}T/q\)\(\times\) (1 + \(q(N_{\rm int}+t_{\rm IGZO}\times N_{\rm bulk})/C_{\rm OX}\)), where \(q\) is the elementary electric charge, \(k_{\rm B}\) the Boltzmann constant, and \(T\) the temperature. \(N_{\rm int}\) is the area density of the shallow traps close to the Fermi level (\(E_{\rm F}\)) at the gate-insulator/semiconductor interface and \(N_{\rm bulk}\) is the volume density of shallow traps in the bulk of the a-IGZO semiconductor. The sum \(N_{\rm int}\) + (\(t_{\rm IGZO}\times N_{\rm bulk}\)) gives the \(N_{\rm T}\). By linear regression of \(N_{\rm T}\) = \(N_{\rm int}\) + \(t_{\rm IGZO}\times N_{\rm bulk}\), \(N_{\rm int}\) and \(N_{\rm bulk}\) were estimated to be 1.25 \(\times\)\(10^{11}\) cm\({}^{-2}\) eV\({}^{-1}\) and 1.07 \(\times\)\(10^{17}\) cm\({}^{-3}\)eV\({}^{-1}\), respectively (Figure 7).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{\hat{t}_{IGZO}\) (nm)}} & \multicolumn{2}{c}{\(V_{\rm ON}\) (V)} & \multicolumn{2}{c}{\(\mathbf{\hat{\mu}_{EE}\) (cm\({}^{2}\)/V\(\cdot\)s)}} & \multicolumn{2}{c}{SS (mV/dec)} \\ \cline{2-7}  & **Before NBIS** & **After NBIS** & **Before NBIS** & **After NBIS** & **Before NBIS** & **After NBIS** \\ \hline
**3** & 0.0 & \(-\)1.6 & 1.60 & 1.68 & 100 & 196 \\
**5** & \(-\)1.2 & \(-\)6.0 & 17.05 & 17.43 & 127 & 136 \\
**20** & \(-\)2.8 & \(-\)10.0 & 18.49 & 18.88 & 152 & 171 \\
**50** & \(-\)4.4 & \(-\)13.2 & 19.15 & 19.47