esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 3 System Model
### Computation Model  
There are four alternative decisions for task execution in our dynamic offloading decision algorithm: (a) local execution on the UE, (b) remote execution on a helper UH, or (c) remote execution on the MEC server, whereby a task can be transmitted directly by the UE to the MEC server or via a relay (UH). We proceed to define each possible decision in detail.  
Figure 2: Example illustration of general task dependency graph specifying the required task execution order for an application with a total of \(K=10\) tasks. Tasks \(k=2\), \(3\), and \(4\) depend on the prior completion of task \(k=1\). The last task \(K=10\) has to be completed by the deadline \(T_{d}^{\text{max}}\).
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 4 Deceptive Jamming Extraction
### SAR Image Co-Registration  
Generally, SAR image registration consists of geometric registration and image co-registration. The geometric registration based on imaging geometry and orbit parameters is not necessary unless there is obvious offset or angulation between master and slave image. The co-registration of InSAR images, including coarse co-registration and fine co-registration, is one of the key steps of interferometric processing [24]. Normally, the registration offsets in coarse or fine co-registration depend on the peak position of the correlation function. When calculating offsets, the real and complex correlation functions simplified by Fast Fourier Transform (FFT) can be written as [25]  
\[\rho_{r}=norm(\left|\mathrm{FFT}^{-1}(\mathrm{FFT}(|S_{M}(m,n)|)\mathrm{FFT}(|S _{S}(m,n)|)^{*})\right|) \tag{15}\]  
\[\rho_{c}=norm(\left|\mathrm{FFT}^{-1}(\mathrm{FFT}(|S_{M}(m,n)|)\mathrm{FFT}( S_{S}(m,n)))^{*}\right|) \tag{16}\]  
Figure 4: Detailed steps of the proposed SAR deception detection method.  
where \(norm(\cdot)\) denotes the normalization operator, and \(S_{M}(m,n)\) and \(S_{S}(m,n)\) represent the master and slave images in the estimated window, respectively.  
The research in [26] suggests that the real correlation function is more robust than the complex one in practice, especially in low-coherence regions. Therefore, we choose the real correlation function in coarse co-registration. However, when the two areas have strong coherence and similar scattering properties, the registration error of using real correlation function is approximately \(\sqrt{2}\) times that of using the complex correlation function [27]. Meanwhile, after coarse co-registration, the corresponding small grids on the two images are more coherent and have similar scattering properties. As a result, the complex correlation function is selected to fine co-registration.
# Study of Green Synthesis of Ultrasmall Gold Nanoparticles Using _Citrus Sinnensis_ [PERSON]
## 3 Results and Discussion
### Hydrodynamic Diameter Analysis of AuNPs by DLS  
The DLS measurement demonstrates (Figure 6) that the average size of the AuNPs formed from the _C.sinensis_ peel at pH = 11 adjusted after the reaction in colloidal solution was 2.11 \(\pm\) 1.01 nm (1.75 \(\pm\) 0.86 by ImageJ). The reasons for the difference in size distribution between DLS and ImageJ software based on TEM are as follow: (1) DLS measures the hydrodynamic size rather than the bare particle as in TEM; (2) the plant extracts used in this study contain a large number of plant fibers, protein and other substance, which will surround or adhere to the surface of the particles during the formation of AuNPs, resulting in larger DLS size than TEM size; (3) metal crystals have completely different colors from organic matter in TEM images, the biological material on the AuNPs surface disturbs the DLS measurement which could be ignored during manual label the particles size before measurement by ImageJ software. Based on these results, the nanoparticles sizes were evaluated by ImageJ software in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bond** & **Type of Vibration** & **Control Spectrum** & **Adjust pH before** & **Adjust pH after** \\  & & **(cm\({}^{-1}\))** & **Reaction (cm\({}^{-1}\))** & **Reaction (cm\({}^{-1}\))** \\ \hline O-H & Alcohol, phenols, Carboxylic acids & 3363 & 3434 & 3436 \\ N-H & Amines & 2929 & 2969, 2944 & 2931 \\ C-H & Alkanes & 1724 & 1737 & 1724 \\ C=O & Amide I bond of proteins & 1608 & 1635 & 1631 \\ CH\({}_{2}\) & Alkanes & 1367 & 1363 & 1346 \\ C-O & Carboxylic acid, ester, or ether & 1203 & 1216 & 1203 \\ C-N & Aliphatic amines or alcohol/phenol & 1058 & 1070 & 1058 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the peak wavelength (\(\lambda_{\text{max}}\)), Intensity, size of AuNPs obtained from _C. sinensis_ peel at different experiment conditions.  
Figure 5: (**a–d**) Illustrates the transmission electron microscopy (TEM) images of nanoparticles synthesized using the _C. sinensis_ peel extract at different pHs which are adjusted after the reaction.
## 1 Introduction  
Bluetooth low-energy (BLE) applications are wireless, home area network (WHAN)-based technologies for communication with a short transmission distance in 5G/Internet of Things (IoT) wireless technology [1]. These systems require a low-power, all-digital phase locked loop (ADPLL) circuit, with many studies demonstrating improved low power and low phase noise performance using these circuits [2, 3, 4, 5]. The performance of all-digital PLLs, including frequency resolution, low phase noise, and low power consumption, is determined by the digitally controlled oscillator (DCO) [2], because the DCO block is a sub-block circuit in the ADPLL and is important in terms of power consumption, phase noise, and oscillation frequency range. Recently, DCOs were widely employed because of their fast design time, efficient chip area, and low cost [2]. The layout of a typical DCO requires a design that reflects on/off parasitic capacitance switching in the capacitor bank, high-Q inductor in the LC tank, parasitic components on the layout, and the burden of a large control circuit area, which were previously studied in the literature [2, 3, 4, 5].  
A low-power, high-resolution DCO was designed using a current reuse structure and a multistage capacitor reduction technique. However, in a multistage capacitor using a unit variable capacitor, it is difficult to implement a small capacitor for high resolution and it is sensitive to parasitic effects [4]. Previous studies [5, 6, 7, 8] on DCOs focused on optimizing the circuit or structures. The performance-enhancing technologies of circuit and structure optimization require a large die area due to the large inductor and capacitor array (LC tank), and a control circuit to ensure a wide oscillation frequency range and excellent phase noise performance. Additional parasitic components were generated due to the connecting metal lines required in the layout, resulting in poor performance of the DCO. The g\({}_{\text{m}}\) cell required to compensate for these parasitic components is a design consideration to improve the DCO performance. However, the g\({}_{\text{m}}\) cell location was designed inconsistently (theDCO connected to the tail of the LC tank or the DCO at the neck of the inductor) and did not present optimum conditions for excellent performance.  
In this paper, we present a DCO design layout based on the measurement results for layout optimization using \(\mathrm{g_{m}}\) cell locations and an Al metal layer. When the \(\mathrm{g_{m}}\) cell is located after the main inductor and the capacitor bank, it is difficult to predict the performance of the DCO due to the parasitic line inductance generated between the main inductor and the capacitor bank. The rest of this paper is organized as follows: Section 2 describes the advantages and disadvantages of each \(\mathrm{g_{m}}\) cell location. Section 3 covers the LC-based DCO design and configuration of the capacitor bank with the optimum layout. Section 4 describes the tests of the DCOs used to verify the key idea of this paper, and discusses measurement results and considerations. Finally, Section 5 is the conclusion of this paper.
## 3 Results and Discussion
### Characterization of AC@SCF Activated Carbon
For instance, the position of G band of pure crystalline graphite (-1580 cm\({}^{-1}\)) can be correlated with the presence of surface graphene layers or graphitic molecular nanostructures with strained geometry [50; 53]. The ratio of relative intensities of D and G bands I\({}_{\text{D}}\)/I\({}_{\text{G}}\) is also related to the structural disorder degree and hence with the size of the graphitic domains [50; 51; 52; 53]. In case of AC@SCF, the I\({}_{\text{D}}\)/I\({}_{\text{G}}\) ratio (0.88) as well as the shape and position of G and D bands, indicate the existence of mainly amorphous carbon structure and amorphous regions with negligible graphitic ordering. Finally, the crystallite size (La) of AC@SCF carbon was calculated using the Tuinstra and Koenig law [52; 53; 54; 55] and was found to be 21.85 nm. An amorphous carbon structure was also revealed from the X-ray diffraction (XRD) pattern of AC@SCF sample in Figure 2c. The XRD pattern exhibited two broad reflection peaks at 20 = 25\({}^{\circ}\) and 43\({}^{\circ}\), these peaks can be attributed to the (002) and the (100) reflections of graphite, respectively, and originate from the stacking of the (002) graphene layers and the (100) in-plane structure of graphitic crystallites. The broadening of these peaks compared with the XRD pattern of graphitic carbon expressed a more amorphous carbon structure. Figure 2: (**a**) Fourier-transformed infrared (FT-IR) spectra, (**b**) Raman spectra, (**c**) X-ray diffraction (XRD) pattern, and (**d**) differential thermal analysis (DTA)/TG% (Thermo Gravimetric) curves of AC@SCF. Figure 2d shows DTA and %TG (Thermo Gravimetric) curves of AC@SCF in air. The inset image of Figure 2d shows the corresponding curves of spent coffee before the pyrolysis and chemical activation. The DTA curve of spent coffee consisted of three exothermic peaks at 310, 420, and 472 \({}^{\circ}\)C accompanied with 100% weight loss, which correspond to the combustion of the organic ingredients of the wasted coffee. After pyrolysis and chemical activation, the DTA curve of AC@SCF exhibited two exothermic peaks at 394 and 422 \({}^{\circ}\)C, which correspond to the carbon matrix combustion, and were accompanied with 76% weight loss. At lower temperature (<100 \({}^{\circ}\)C), there was an endothermic peak, which corresponds to the removal of physically adsorbed water molecules. The TGA curve exhibited some remaining mass (~4%), which was composed when the TGA temperature was raised to values >450 \({}^{\circ}\)C. This observation could be attributed to K impurities from KOH that have been used for the chemical activation and trapped in carbon's pores. The TGA curve of spent coffee did not show remaining mass after combustion of the organic phases. Figure 3 shows SEM (a,b) and TEM (c,d) images of the AC@SCF material. From SEM images, the material seemed to have a \"sponge-like\" morphology consisting of macropores with 1 \(\upmu\)m diameter, whereas TEM images also showed the existence of micropores.
# HORSIC+: An Efficient Post-Quantum Few-Time Signature Scheme
## 4 Analysis
### Security Analysis  
In this subsection, we analyze the security of HORSIC+. We prove HORSIC+ is existentially unforgeable under chosen message attacks, if the used function family \(\mathcal{F}_{n}\) is a second-preimage resistant family of undetectable one-way functions and \(H\) and \(G\) are cryptographic hash functions in the random oracle model.  
**Theorem 1**.: _Suppose \(\mathcal{F}_{n}=\{f_{k}:\{0,1\}^{n}\rightarrow\{0,1\}^{n}\mid\kappa\in\mathcal{ K}\}\) is a second-preimage resistant, undetectable one-way function family and \(H\) and \(G\) are cryptographic hash functions in the random oracle model. Then the insecurity of HORSIC+ against an EU-CMA attack is bounded by_  
\[\begin{split}&\mathrm{InSec}^{\mathrm{EU-CMA}}(\mathrm{HORSIC+}(1^{n},t,k,z,w);T,1) \\ &\leq\max\{T\cdot\frac{k!(k-1)!(z-k)!}{t^{k}(z-1)!},\\ &\quad w\cdot\mathrm{InSec}^{\mathrm{UD}}(\mathcal{F}_{n};T^{ *})+wt\cdot\max\{\mathrm{InSec}^{\mathrm{OW}}(\mathcal{F}_{n};T^{ *}),w\cdot\mathrm{InSec}^{\mathrm{SPR}}(\mathcal{F}_{n};T^{*})\}\}\end{split} \tag{30}\]  
_with the time \(T^{\prime}=T+(t+2k)w\) and \(T^{*}=T+(t+2k+1)w-1\)._  
Proof of Theorem 1.: The proof is provided in Appendix A.
# An Approach for Setting Parameters for Two-Degree-of-Freedom PID Controllers
## Appendix  
From Equation (5), the following equation is obtained:  
\[u=-(\xi+k_{2})/l-\dot{\xi}/kl.\] (A1)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Methods** & \
\begin{tabular}{c} **Settling Time** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Overshooting** \\ **(\%)** \\ \end{tabular} & **IAE** & \(M_{s}\) \\ \hline \multirow{2}{*}{\(G_{1}\)} & DDE\(-\)GFM & 16.952 & 1.56 & 11.96 & 2 \\  & Panagopoulos\({}^{\prime}\) & 28.678 & 16.77 & 9.252 & 2 \\ \hline \multirow{2}{*}{\(G_{2}\)} & DDE\(-\)GFM & 24.232 & 6.89 & 8.6902 & 2 \\  & Panagopoulos\({}^{\prime}\) & 39.392 & 21.68 & 9.1035 & 2 \\ \hline \multirow{2}{*}{\(G_{3}\)} & DDE\(-\)GFM & 1.4 & 0 & 0.0113 & 2 \\  & Panagopoulos\({}^{\prime}\) & 3.344 & 0 & 0.0058 & 2 \\ \hline \multirow{2}{*}{\(G_{4}\)} & DDE\(-\)GFM & 11.368 & 0.57 & 1.441 & 2 \\  & [PERSON]\({}^{\prime}\) & 13.022 & 23.47 & 1.3511 & 2 \\ \hline \multirow{2}{*}{\(G_{5}\)} & DDE\(-\)GFM & 15.468 & 0 & 2.4522 & 2 \\  & [PERSON]\({}^{\prime}\) & 19.429 & 27.24 & 2.749 & 2 \\ \hline \multirow{2}{*}{\(G_{6}\)} & DDE\(-\)GFM & 20.829 & 0 & 3.8433 & 2 \\  & [PERSON]\({}^{\prime}\) & 25.273 & 27.39 & 4.1654 & 2 \\ \hline \multirow{2}{*}{\(G_{7}\)} & DDE\(-\)GFM & 24.521 & 2.81 & 5.2316 & 2 \\  & Panagopoulos\({}^{\prime}\) & 30.931 & 26.72 & 5.5291 & 2 \\ \hline \multirow{2}{*}{\(G_{8}\)} & DDE\(-\)GFM & 11.003 & 0 & 5.67 & 2 \\  & Panagopoulos\({}^{\prime}\) & 17.027 & 10.19 & 6.1111 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance for the DDE\(-\)GFM and [PERSON] method.  
Substituting \(\hat{f}=\xi+kz_{2}\) into Equation (4), and the following equation is obtained:  
\[u=-\frac{\xi+kz_{2}}{l}-\frac{h_{0}(z_{1}-y_{r})+h_{1}z_{2}}{l}. \tag{11}\]  
Compare Equation (10) with Equation (11); the following equation is obtained:  
\[\dot{\xi}=kh_{0}(z_{1}-y_{r})+kh_{1}z_{2}. \tag{12}\]  
Integrate Equation (12), and the following equation is obtained:  
\[\xi=kh_{0}\int{(z_{1}-y_{r})dt}+kh_{1}z_{1}. \tag{13}\]  
Substitute Equation (13) into Equation (11), and define \(e=y_{r}-z_{1}\); the following equation is obtained:  
\[\begin{array}{ll}u&=-\frac{kh_{0}\int{(z_{1}-y_{r})dt}+kh_{1}z_{1}+kz_{2}}{l }-\frac{h_{0}(z_{1}-y_{r})+h_{1}z_{2}}{l}\\ &=\frac{(h_{0}+kh_{1})e+kh_{0}\int{dt}+(h_{1}+k)e-kh_{1}y_{r}}{l}\end{array} \tag{14}\]  
## Appendix  
Take the derivative of the first equation in Equation (5), and the following equation is obtained:  
\[\dot{f}=\dot{\xi}+k\dot{z}_{2} \tag{15}\]  
Substitute the second equation in Equation (5) and the second equation in Equation (2) into Equation (15), and the following equation is obtained:  
\[\dot{f}=-k(\dot{\xi}+kz_{2})+kf \tag{16}\]  
Substitute the second equation in Equation (5) into Equation (16), and the following equation is obtained:  
\[\dot{f}=-k\dot{f}+kf \tag{17}\]  
Take the Laplace transform of Equation (17), and the following equation is obtained:  
\[\dot{f}=\frac{k}{s+k}f \tag{18}\]
## 3 Conventional and Proposed Methods
### Proposed Method
#### 3.2.1 CSI-Based Nonlinear Allocation Method
The phase shift for each element, \(\theta_{m,n}\), is expressed as follows:\[\theta_{m,n}=\frac{2\pi d}{\lambda}(m\sin\theta\cos\varphi+n\sin\theta\sin\varphi). \tag{26}\]  
In this context, \(d\), \(\lambda\), \(\theta\), and \(\varphi\) represent the distance between antenna elements, the signal wavelength, the azimuth angle, and the zenith angle, respectively.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Rocket Launches
#### 5.4.2 Ariane 5 Launch  
On 25 July 2018, an Ariane 5 ES rocket was successfully launched into orbit from the Guiana Space Centre, near Kourourou, French Guiana, aboard with four navigation satellites  
Figure 9: Distribution of _Swarm_ A/B/C VFM magnetic field fluctuations after wavelet analysis grouped in individual frequency bands for the SpaceX Falcon 9 launch, Vandenberg Air Force Base, California, USA. The spatial box size is \(\pm\)5.5 deg, date 21 November 2020 (payload Sentinel-6 Michael Freilich satellite), and time span \(\pm\)1 day. For _Swarm_ B VFM (middle column) the individual crossing segments relative to the take off moment are \(\Delta t\) = {-0.40, +0.11, +0.59, +1.10} days. All three components for the first crossing after the launch (\(\Delta t\) = {+0.11} days) are enhanced (bars in red colour). The colour coding is the same as in Figure 2, the geomagnetic activity is \(|\)Dst\(|\)\(\leq\) 38 nT for the selected time span.  
from Galileo GNSS. We investigate this rocket ascent (VA-244, and further launches) from this site near the equator with tropical climate, because the defined sequences repeatedly occur and, therefore, enable the possibility of launch event comparisons several times per year. The fluctuation statistics for a _Swarm_ A/B/C VFM scalogram investigation is shown in Figure 10. _Swarm_ A VFM has an attenuation in the Y- and Z-components, _Swarm_ B/C VFM measurements are similar in all components.
# Review of Automatic Estimation of Emotions in Speech
## 2 Emotions
### Physical Measures of Emotion in Speech  
In terms of the acoustics of speech, emotion is often linked to intonation (also called suprasegmentals or prosody), which embodies changes in the fundamental frequency (F0) of the vibration of vocal cords (in quasi-periodic voiced sounds), the intensity of speech waveforms, and durations of linguistic sound units (phonemes, syllables, and words). All three physical acoustic measures vary with emotion, but in complex ways [23].  
In humans, the sympathetic nervous system becomes aroused when one experiences strong emotions such as anger, joy, or fear. This may include a higher heart rate and blood pressure, changes in respiratory motion, more sub-glottal pressure, mouth dryness, and muscle tremor [24]. The speech that results may become more intense, faster, and with more energy at higher frequency, higher mean F0, and larger F0 range. On the other hand, arousal of the parasympathetic nervous system occurs with sadness, and blood pressure and heart rate decrease, with speech that is slower, with lower F0, and with less high-frequency energy.  
From speech, listeners (or machines) can judge emotion from either (or both of) linguistic information (the lexical content of what is said) or paralinguistic information (acoustics of how it is said) [25]. Linguistic data, obtained using ASR and natural language processing (NLP), may be more helpful to estimate valence, but such data are language-specific [26]. Paralinguistic data are more useful for arousal and dominance, and generalize more readily across languages and emotions; such data normally refer to acoustic analysis of spoken words, but also nonverbal sounds as well [27]. Some SER systems are multi-modal, integrating audio, textual, and/or visual data [28]. This review paper focuses on speech applications, and does not consider text-based or multi-modal emotion recognition [29; 30].
## 6 Numerical Experiments
### Prediction Error in the Entire Design Space  
Since the number of initial samples is limited, there will be prediction errors when the current established surrogate model is used to predict new samples, which will affect the accuracy and efficiency of prediction. Therefore, it is important to evaluate the prediction error of the established Kriging model first and to select the proper sample plan. The distribution of prediction error in the entire crack parameters space from the Kriging model of measurement point A is shown in Figure 8, under different sample plans.  
Figure 8a corresponds to the prediction error by the Kriging model established by the initial 20 samples, as shown in Figure 7. From Figure 8a, one can see that the RMSE (the ratio between MSE and the corresponding amplitude of 2\(\times\) component) is quite large at the boundary when the crack depth is zero, which means there is a large error when the Kriging model is used to predict sample cases without a crack. In order to improve the prediction performance for no-crack cases, the samples with crack depth zero and crack location ranging from the 10 th element to the 52 th with a step of 2 elements were added to the initial samples (it should be noted that the added samples are the same case with no crack, however different in mathematic form). The prediction error by the Kriging model re-established is shown in Figure 8b. As one can see from Figure 8b, the prediction error was reduced after adding the samples. However, the error was still large at the area where the crack location was near the 52 th element. Then, nine new samples with crack location in the 52 th element with crack depth varying in the range [0.05D: 0.05D: 0.45D] were added to improve the Kriging model, leading to results in Figure 8c. As shown in Figure 8c, the RMSE in the entire crack parameter space had been much reduced, and the error at the boundary had also dropped considerably. Though the error at the boundary remains large relatively to the inside area, the Kriging model is thought to be acceptable as a reasonable model for crack identification. The prediction error is the major source of identification error, which can be reduced by a bigger sample size. Therefore, there will be a trade-off between the cost and accuracy.  
Figure 8: Distribution of RMSE by the established Kriging surrogate model using various sample sets (**a**) using initial samples; (**b**) after adding the samples near boundary the first time; (**c**) after adding the samples near boundary the second time.