esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-GeV Laser Wakefield Electron Acceleration with PW Lasers
## 3 Multi-GeV LWFA with PW-Class Lasers
### Dual-Stage LWFA with PW Laser at UQBF
At this condition, the electron bunch from the first jet could be successfully coupled to the second target because the low plasma density in the second jet enlarged the plasma wavelength to have a higher chance to catch the electron bunch from the first target. In addition, the second target having a lower density than the self-injection threshold can prohibit continuous self-injection that can reduce the acceleration field strength. When the plasma densities of the two jets were independently controlled, the electron energy was over 3 GeV. At this condition, the electron beam has a charge of about 10 pC over 2 GeV energy, energy spread about 50%, and beam divergence of about 4 mrad. The dual-stage acceleration can be a simple solution to obtain a high-energy electron beam. Recently, a dual-stage acceleration with two driving laser pulses was demonstrated by using capillary discharge media [31]. Even though we can expect an energy gain at each stage in a staged acceleration, precise control of each stage for stability is challenging. In the dual gas jet target, the turbulence between the targets can also make the electron beam unstable. Thus, the method to handle the qualities of the accelerated electron beam should be investigated by manipulating the driving laser pulse and plasma medium. In addition, the electron energy with dual-stage LWFA was still much less than the 10 GeV that is expected for PW lasers because the laser propagation is limited to be an order of 1 cm. For increasing the electron energy further, an external guiding structure for PW laser pulses should be applied to keep the laser intensity over 10 cm. In the dual-stage acceleration, the PW laser pulse was stretched to be positively chirped with a duration of 60 fs. It was stretched to control the acceleration gradient of LWFA by manipulating the pulse's spectral phase [32]; such a control method was demonstrated experimentally at UQBF [13; 33]. In particular, positive group-delay dispersion (GDD) enhanced the energy and charge of the electron beam, and third-order dispersion (TOD) improved the energy further and the stability of the beams. The combination of a dual-stage target and careful control of laser pulse properties can be an effective way to shape the electron beams and control the acceleration process.
## 4 Datasets and Experimental Results
### Analysis of the Results  
The accuracy assessment in this study includes the use of two main classification indices [45], overall accuracy (OA) and kappa coefficient, both obtained from the confusion matrix, along with class-wise classification accuracy. These measures provide a comprehensive evaluation of the classifier's performance.  
In order to strike a balance between accuracy and processing time, a 9 \(\times\) 9 window size was chosen as the input patch size around each pixel for the CNN classifier. In our proposed SF-ICNN, we employed a simple CNN architecture with two main layers. In the first layer, 3 \(\times\) 3 convolutional filters are used to generate 32 feature maps, followed by BN and ReLu activation and a 3 \(\times\) 3 pooling layer. In the second layer, 3 \(\times\) 3 convolutional filters are again used to generate 16 feature maps, followed by BN and ReLu activation and a 3 \(\times\) 3 pooling layer. Finally, we employ a single layer of fully connected neurons, succeeded by a SoftMax function, to ascertain the labels of individual pixels. We also use ADAM as the optimizer, with the maximum number of epochs equal to 50. All the tests are performed with MATLAB 2020b on a fourth-generation Core i5 3.3 GHz CPU and 8 GB RAM, with the other CNN parameters set to their default values in MATLAB 2020b.  
The effectiveness of the suggested approach is demonstrated by comparing the SF-ICNN method with a few baseline techniques in the following portion of this section. The next two subsections examine the effects of the training sample ratio and number of iterations on the classification outcomes of the suggested SF-ICNN. In the last part, we compare the performance of the suggested SF-ICNN with several SOTA techniques.
## 4 Bionic Morse Coding Method
### Information Modulation
#### 4.2.2 Step 2: Channel Estimation and Equalization
The VTRM equalized channel is equated as \(\hat{h}(t)=h(t)\otimes h^{\prime}(-t)\). When the estimated channel \(h^{\prime}(t)\) approaches to the real channel \(h(t)\), the multipath signals are overlapped with each other making the communication signal energy strong and efficient. It also restrains inter symbol interference caused by multi path arrivals.
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 1 Introduction
For the security concern, in order to withstand the keyword guessing attack launched by the outsider attacker and the malicious insider, the SPE scheme against the keyword guessing attack was given in Reference [11]. _Our Work._ Considering that SPE scheme supporting conjunctive keywords search (SPE-CKS) without keyword field has a good application in many scenarios, especially, for example, encrypted e-mail system, this paper is to construct an efficient and secure SPE-CKS scheme without the keyword field. More precisely, we make two types of contributions, improving both the efficiency and the security of the recent schemes of SPE-SMKS introduced in References [7; 8; 10]. These contributions are listed as follows. 1. Based on the keyword conversion method introduced in Reference [10], we create a novel keyword conversion method which can change the index and query keyword set into an attribute and a predicate vector, respectively. The dimension of vector generated by using our method is  
Figure 1: An example of searching over encrypted e-mail data. much less than that generated by adopting the previous method. Through applying the existing technique called bilinear map to encrypt the attribute and predicate vectors, our scheme achieves a better performance in time and space complexities than the previous schemes. 2. For security concern, we give a detailed proof to demonstrate that our scheme is secure against chosen keyword attack. Moreover, inspired by the idea introduced in Reference [11], through sharing a secret number between the senders and the receiver, our scheme can limit the capability of index building for adversaries. If the adversaries fail to generate the index, the adversaries cannot launch the keyword guessing attack. Compared with the previous schemes, our scheme can both defend against chosen keyword attack and keyword guessing attack. Besides, we also design a detailed experiment based on a real-world e-mail dataset to show the advantage of our scheme. OrganizationThis paper is organized as follows. Related work is discussed in Section 2. In Section 3, we define model of SPE-CKS and security model of SPE-CKS and also introduce some background related to our work. In Section 4, we introduce the keyword conversion method and the concrete SPE-CKS scheme. The security proof of our scheme is also given. In Section 5, we analyze the time and space complexities of our scheme and give a detailed experiment to verify the efficiency of our scheme. This paper concludes in Section 6.
## 4 Experiments and Performance Evaluation
### Performance on Top-\(k\) Queries  
The naive approach of top-\(k\) queries generally takes much time. However, within the Spark framework, it gives time-efficient results. We have compared it with the MapReduce framework. We can see that Spark is far better than MapReduce. The time comparison is shown in Figure 6.
## 2 Materials and Methods
### Film Deposition and Characterization  
Composite film deposition of CuBi\({}_{2}\)O\({}_{4}\) NPs was carried out in air by spin coating in a Smart Coater 200 instrument (Laurell Technologies Corporation, North Wales, PA, USA). The composite films were deposited onto different substrates: ITO-coated glass slides, high-resistivity monocrystalline n-type silicon wafers (c-Si) and Corning glass. Substrates were previously submitted to a sonication cleaning process and dried in vacuum. The deposition was performed by a spin coating technique that produces thin and uniform films and involves spreading a liquid solution over the center of the substrate, which is subsequently rotated for 7 s at a constant angular velocity of 700 rpm. In order to obtain composite films, the solution to deposit in the current work was composed of PEDOT:PSS dissolved in water (Aldrich, Saint Louis, MO, USA) and the CuBi\({}_{2}\)O\({}_{4}\) NPs in suspension. The NPs added to the polymer PEDOT:PSS correspond to CuBi\({}_{2}\)O\({}_{4}\) nanoparticles with an average diameter of 24 \(\pm\) 2 nm. After the composite films were deposited, annealing at 120 \({}^{\circ}\)C for 10 min took place, with the objective of eliminating the residual water from the polymer active layer, and the thickness of the films was 77.6 \(\pm\) 5 nm. In order to study the morphology of the composite films, ZEISS EVO LS 10 SEM (Zeiss International Inc., Gottingen, Germany) was performed for the films deposited on glass substrates, and AFM measurements of the films on silicon substrates were performed in contact mode with a Nanosurf Naio microscope (Intercovamex, S.A. de C.V., Cuernavaca, Morelos, Mexico). In order to verify their stability, these films on silicon were analyzed using a Nicolet iS5-FT IR spectrometer (ThermoFisher Scientific Inc., Waltham, MA, USA), in the wavenumber range from 4000 to 500 cm\({}^{-1}\). The UV-visible spectra of the films on Corning glass were obtained on a UV-Vis 300 Unicam spectrophotometer (ThermoFisher Scientific Inc., Waltham, MA, USA), and the measurements were conducted in the wavelength range from 200 to 1100 nm. Finally, simple devices of glass/ITO/PEDOT:PSS-CuBi\({}_{2}\)O\({}_{4}\) NP/Ag were manufactured (see Figure 1), and the electrical properties were obtained through current-voltage (I-V) measurements (of area 2.6 cm \(\times\) 2.6 cm). A Keithley 4200-SCS-PK1 auto-ranging picoammeter (Tektronix Inc., Beaverton, OR, USA) was used with the four-point probe method, in a sensing station with lighting and temperature controller circuits from Next Robotix (Comercializadora K Mox, S.A. de C.V., Mexico City, Mexico). In the device, the external quantum efficiency (EQE) was obtained using a QUESA-1200 system (TFSC Instrument Inc, Intercovamex, S.A. de C.V., Cuernavaca, Morelos, Mexico) with an LED light source of 100 mW/cm illumination, AM1.5.
# 3D DNA Nanostructures: The Nanoscale Architect
## 3 Dynamic Mechanical Control and Motion
### Control Sources  
In order to bridge interaction between a human observer and nanomechanical DNA systems, scientists have relied on light, pH, temperature, ion concentration, or externally applied fields to deliberately control activity. Molecules that have photo-cleavable bonds or photo-switchable configurations can be used in DNA nanomechanics to implement light-responsiveness. For example, azobenzene that is inserted into oligonucleotides responds to UV-light irradiation by switching between _trans_ and _cis_ formations that disrupt hybridization via steric hindrance. An azobenzene latch implemented into a DNA origami capsule could be used for the light-responsive release of molecular cargo [67] or modulate the reconfiguration of components in dynamic DNA-templated plasmonic assemblies [56].  
Figure 3: Nanomechanics implemented by DNA nanostructures. (**A**) Controlled angle of a hinged DNA nanostructure via an elastic ssDNA spring (reprinted with permission from ACS Publications [61]); (**B**) Modular deformation of a long DNA origami bundle. Linker strands span piers to induce local bending, which sums to global curvature (reprinted with permission from Wiley-VCH [64]); (**C**) Rotational and sliding gear motions of DNA origami bundles with respect to a central DNA nanorod (reprinted with permission from AAAS [53]); (**D**) DNA antijunction units composed into lattice arrays that transmit cascading changes through the structure (reprinted with permission from AAAS [65]).  
[PERSON] et al. also demonstrated the incorporation of _ortho_-nitrobenzyl, which is photo-cleavable, to actuate the opening of a spherical DNA origami capsule [68] (Figure 4A). The [PERSON] group demonstrated both light-responsiveness using azobenzene and ion-responsiveness using DNAzymes in unlocking latches upon a 2D DNA origami to actively create a nanohole in the structure [69]. DNA nanostructures can further be made sensitive to pH using i-motifs [70] or to temperature by integrating temperature-sensitive polymers into the same positions as elastic ssDNA springs to restrain the relative position of two DNA origami sections [71] (Figure 4C). The [PERSON] group demonstrated control over the rotation of a DNA origami bundle arm using an electrical field, which could align the arm due to the intrinsic charge of DNA [72] (Figure 4B).
# Low-Noise Programmable Voltage Source
###### Abstract  
This paper presents the design and testing of a low-noise programmable voltage source. Such a piece of instrumentation is often required as part of the measurement setup needed to test electronic devices without introducing noise from the power supply (such as photodetectors, resistors or transistors). Although its construction is based on known configurations, here the discussion is focused on the characterization and the minimization of the output noise, especially at very low frequencies. The design relies on a digital-to-analog converter, proper lowpass filters, and a low-noise Junction Field-Effect Transistors (JFET) based voltage follower. Because of the very low level of output noise, in some cases we had to resort to cross-correlation in order to reduce the background noise of the amplifiers used for the characterization of the programmable source. Indeed, when two paralleled IF9030 JFETs are used in the voltage follower, the output noise can be as low as 3 nV/\(\surd\)Hz, 0.6 nV/\(\surd\)Hz and 0.4 nV/\(\surd\)Hz at 1 Hz, 10 Hz and 100 Hz, respectively. The output voltage drift was also characterized and a stability of \(\pm\)25 \(\upmu\)V over 3 h was obtained. In order to better appreciate the performance of the low-noise voltage source that we have designed, its noise performances were compared with those of a set-up based on one of the best low-noise solid-state voltage regulators available on the market. Actual measurements of the current noise in a type-II superlattice photodetector are reported in which the programmable source was used to provide the voltage bias to the device.  
low-noise voltage source; low-frequency noise; cross-correlation; field effect transistors; low-noise instrumentation; noise measurements +
Footnote †: journal: Electronics Letters
# Effect of Wind Direction and Velocity on PV Panels Cooling with Perforated Heat Sinks
## 4 Numerical Model Validation  
### Base Case Validation--Without Heat Sink  
The base case was validated in two steps. The first one consisted in analyzing the behavior of the numerical model in NOCT conditions compared to the expected results according to the producer datasheet and literature. The NOCT conditions suppose a PV panel inclined at 45\({}^{\circ}\), direct solar radiation on front of the PV panel of 800 W/m\({}^{2}\), air temperature of 20 \({}^{\circ}\)C and the wind velocity of 1 m/s towards the backside of the PV panel [31]. According to the producer of the PV panel [32], in these conditions, the average temperature of the PV panel \(t_{NOCT}\) should be 47 \({}^{\circ}\)C. Taking into account that the good matching of the PV temperature from simulation was 47.16 \({}^{\circ}\)C, the model was considered appropriate and the next steps of validation were covered.  
A common Equation (1) from literature [31,38] extrapolates the NOCT results and makes the connection between the PV panel temperature (\(t_{PV}\)) and outside conditions (air temperature \(t_{air}\) and solar radiation \(G\)) for each PV panel, characterized by its own \(t_{NOCT}\). By comparing the numerical results with those obtained with Equation (11), the following tendency of the PV panel temperature was observed (Figure 5).  
\[t_{PV}=t_{air}+\frac{t_{NOCT}-20}{800}\cdot G \tag{11}\]  
The differences between the numerical model and Equation (1) were between 0.28 and 1.98%, which represents a very good correlation between them.  
Also, experimental studies from literature [11,39] were compared with the present numerical simulation on the base case for different input variables. For the PV panel inclined to 45\({}^{\circ}\), wind velocity of \(v\) = 1.5 m/s and solar radiation of \(G\) = 837 W/m\({}^{2}\), the difference between the present study (53.68 \({}^{\circ}\)C) and the experimental data (51.6 \({}^{\circ}\)C) [11] was 3.87%.  
Additional results of simulation and experimental studies [23,39] are compared in Figure 6, and a good correlation can be found.  
Figure 5: Validation of the numerical model using NOCT conditions and Equation (1).
# High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data
## 2 Materials and Methods
### High-Order OC-Seislet Stacking
The workflow of the OC-seislet stack is summarized as follows:  
* First of all, convert the pre-stack seismic data into CMP gathers. NMO is applied to reduce the horizontal time shift, where the correction error caused by inaccurate velocity analysis is allowed. * Second, apply logarithmic stretching to the time axis, followed by Fourier transforms along the time (\(t\)) and space (\(y\)) directions. * Third, perform OC-seislet stacking using the OC operator and high-order CDF 9/7 transform coefficients, normalize the weighting coefficients, and apply soft-thresholding for further denoising. OC-seislet operators are able to correct the NMO error in the first step with the help of high-order coefficients. * Finally, apply the inverse Fourier transform along the \(\Omega\) and \(\mathbf{k}\) directions, followed by the inverse logarithmic transformation along the time axis, and obtain the OC-seislet stacking result in the time-space domain.
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 3 Experiment and Analysis
### Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **FP** & **FN** & **OE** & **PCC** & **Kappa** \\ \hline DD-CNN & 848 & 842 & 1690 & 98.33\% & 0.9375 \\ TransUnet & 881 & 2543 & 3424 & 95.78\% & 0.8474 \\ ESMOFCM & 540 & 1932 & 2472 & 97.56\% & 0.9051 \\ Our Method & 208 & 1163 & 1371 & 98.65\% & 0.9480 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics of different methods on the Ottawa dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **FP** & **FN** & **OE** & **PCC** & **Kappa** \\ \hline DD-CNN & 1076 & 3692 & 4768 & 98.18\% & 0.8918 \\ TransUnet & 1543 & 3674 & 5217 & 98.01\% & 0.8827 \\ ESMOFCM & 3637 & 6670 & 10,307 & 87.93\% & 0.8030 \\ Our Method & 1648 & 2174 & 3822 & 98.54\% & 0.9165 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance metrics of different methods on the Mexico dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **FP** & **FN** & **OE** & **PCC** & **Kappa** \\ \hline DD-CNN & 428 & 393 & 821 & 98.75\% & 0.9060 \\ TransUnet & 321 & 509 & 830 & 98.73\% & 0.9058 \\ ESMOFCM & 295 & 437 & 732 & 98.88\% & 0.9170 \\ Our Method & 359 & 354 & 713 & 98.91\% & 0.9181 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance metrics of different methods on the San Francisco dataset. based on traditional algorithms is suitable for such data. Our method has a significant improvement over the neural network-based comparison method, and achieves better results in terms of generalization ability for multiple datasets. The method proposed in this article demonstrates the best performance in terms of boundary detection accuracy and the missed alarms. As shown in Figure 18, the portion circled in green highlights the superior accuracy of this method in boundary segmentation compared to the other three methods, with more precise edge contours. The HF-CRF network fuses the semantic information of the modal difference maps at multiple levels to ensure the integrity of the changing semantics, and designs a CRF model with a fusion iteration structure based on local boundary entropy to realize the information complementation between the multimodal difference maps and enhance the optimization capability of the CRF model.
## 9 Evaluation Methods for RRE
### Instrumental Measures
#### 9.1.1 Spectral Deviation Measures  
The spectral deviation was first used for the evaluation of the RRE procedure in [76], and was then adopted in many other papers [92; 228]. The spectral deviation, \(S_{\text{D}}\), of a frequency response  
where \(Q_{\rm l}\) and \(Q_{\rm h}\) are the lowest and highest frequency indexes, respectively, of the equalized band.  
Usually, the experimental results provide an initial spectral deviation \(S_{\rm D,in}\), calculated with \(E(e^{i\omega})=H(e^{i\omega})\), and a final spectral deviation \(S_{\rm D,fin}\), computed after equalization by considering \(E(e^{i\omega})=H(e^{i\omega})\cdot H_{\rm inv}(e^{i\omega})\), where \(H_{\rm inv}(e^{i\omega})\) represents the designed equalizer. Figure 11 shows the curves used for the \(S_{\rm D}\) calculation. A Mean Spectral Deviation Measure (MSDM) that represents the mean value of the final spectral deviation measures over the entire set of measured RIRs has also been considered [74; 146].  
In analogy to the mean spectral deviation measure, which gives a measure of the deviation of the magnitude frequency response from a flat one [228], a mean group delay deviation measure was introduced in [147; 229] to quantify the average variation in terms of group delay:  
\[GD_{\rm D}=\frac{1}{M}\sum_{l=1}^{M}\sqrt{\frac{1}{Q_{\rm h}-Q_{\rm l}+1}\sum_ {i=Q_{\rm l}}^{Q_{\rm h}}\left(GD_{l}(i)-K_{l}\right)^{2}}, \tag{7}\]  
where  
\[K_{l}=\frac{1}{Q_{\rm h}-Q_{\rm l}+1}\sum_{i=Q_{\rm l}}^{Q_{\rm h}}GD_{l}(i), \tag{8}\]  
\(Q_{\rm l}\) and \(Q_{\rm h}\) are the lowest and the highest frequency indexes, respectively, of the equalized band, and \(GD_{l}(i)\) is the group delay of the \(M\) RIRs for the \(i\)-th frequency index.  
The objective of mixed-phase equalization is to achieve a linear phase, and therefore the group delay should be as flat as possible: using this parameter it is possible to quantify the distance of the obtained group delay from a constant delay.