esa-sceva/satcom-chunk-collection huggingface dataset:
# THz-TDS Techniques of Thickness Measurements in Thin Shim Stock Films and Composite Materials
## 4 Results and Discussion
### Measurement of Thickness Using the Reflection Mode  
The THz-TDS reflection mode was applied to measure the thickness for both the Shim stock films and GFRP composites with the one-side direction. Figure 7 exhibits the T-ray scan images of the thin Shim stock films. The thicknesses of the thin Shim stock films were 0.127 mm, 0.254 mm, 0.381 mm, 0.508 mm, and 0.762 mm. The values of the thicknesses were utilized as the standard samples of the films. Figure 7a exhibits the difference (\(\Delta t\)) in the time-of-flight (TOF), which indicates the difference between the surface and the back of the Shim stock films. Figure 7b represents Figure 7a as the FFT domain, and \(\Delta f\) refers to the resonance frequency, which is correlated with the thickness of the thin Shim stock films. Here, \(\Delta t\) is the difference time in the time-of-flight (TOF). Namely, \(1/\Delta t\) should be \(\Delta f\). Here, the example thickness in the thin Shim stock film was 0.381 mm.  
Table 2 shows the comparison of the TOF difference (\(\Delta t\)) of the thin Shim stock films, resonance frequency (\(\Delta f\)), and T-ray measurement and reference thickness. Figure 8 shows the data after the T-ray scanning GFRP composites. The thicknesses of the GFRP composites were 2.02 mm, 3.08 mm, 5.74 mm, and 5.92 mm. The values of the thicknesses were used as the standard thickness of the samples.  
Figure 8a shows the difference (\(\Delta t\)) in the time-of-flight (TOF), which indicates the difference between the surface and the back of the GFRP composites. Figure 8b represents Figure 8a as the FFT domain, and \(\Delta f\) exhibits the resonance frequency, which is related to the thickness of the GFRP composites. Here, \(\Delta t\) is the TOF difference. Namely, \(1/\Delta t\) should be \(\Delta f\). The thickness of the GFRP composites was 2.02 mm.  
Table 3 shows the comparison of the TOF difference (\(\Delta t\)) of the GFRP composites, resonance frequency (\(\Delta f\)), T-ray measurement, and reference thickness.  
Figure 7: A TOF and FFT image of the thin Shim stock films under the reflection mode (0.381 mm in thickness).
# Scattering Reduction and Resonant Trapping of Flexural Waves: Two Rings to Rule Them
## 1 Introduction
Graded rings of point masses on a thin plate have been considered in [33] to describe new applications of rainbow trapping and wave hybridisation effects. A classical approach [34] can be used to study the scattering of a time-harmonic wave by a cluster of small defects. We go further, and in this paper, we build upon the results developed in [32], constructing a novel asymptotic model, which describes the trapping of flexural waves in an elastic plate by a two-ring cluster of point resonators. Each individual resonator may have positive or negative inertia, as in [12; 32], respectively, and the frequency of a trapped waveform can be controlled by tuning the inertia of the resonators and through the dynamic coupling between the vibrations of two circular clusters. Since, in this case, the frequency of the localised vibrations can be chosen a priori, this model offers more versatility than that described in [32]. Furthermore, in Section 3.1, we demonstrate that the scattering by a point mass \(M\) can be reduced if the latter is replaced by a ring of smaller masses with total mass \(M\). An illustrative example of the capabilities of the asymptotic model presented in this paper to predict the occurrence of localised vibrations is shown in Figure 1. The radian frequency \(\omega\) of the localised mode in the plate is chosen arbitrarily, and the geometry of the rings and the properties of the resonators are determined accordingly, as discussed in Section 3.2. In part (a) and (b), the mode number is \(q=4\) and \(q=10\), respectively. From the figure, it is apparent that vibrations are highly localised between the two rings of spring-mass resonators.
## 2 Materials and Methods
### Image Quality-Qualitative Analysis  
All images were imported into the Syngo.via workstation, and the rs-EPI images with 2 b values were processed using the workstation's software to obtain ADC maps. All images were transferred to the picture archiving and communication system (PACS). Image analysis was independently performed by two physicians (with 6 and 16 years of diagnostic experience in MRI of the rectum, respectively) who were blinded to pathological findings and MRI sequences. Subjective parameters of image quality (lesion conspicuity, overall image quality, and margin sharpness) were evaluated based on a 5-point Likert scale, with higher scores indicating better image quality. The results of the subjective scores were averaged from the scores of the two observers. The subjective parameters were assessed in b = 0 s/mm\({}^{2}\), b = 1000 s/mm\({}^{2}\), and ADC map of three rs-EPI sequences, respectively. Lesions were evaluated as follows: (1) the rectal cancer lesion was difficult to detect; (2) the lesion was minimally recognizable; (3) it was moderately visible; (4) it was well visible; (5) it showed excellent visibility. The overall image quality was evaluated as follows: (1) non-diagnostic; (2) poor; (3) satisfactory; (4) good; (5) excellent. The margin sharpness was evaluated as follows [18]: (1) not sharp; (2) slightly sharp; (3) moderately sharp; (4) good sharpness; (5) excellent sharpness [18].
In our case, the matrix has dimension 2 and it is sufficient to evaluate only the trace to determine the dispersion relation. Such simplification gives the possibility to easily identify the dispersive effects of different structural parameters, which are analysed in detail in the present work. The research extends the previous analyses performed in [41; 45], by considering a problem governed by the Helmholtz operator in which the microstructure is arranged both in series and in parallel. The arrangement in series proposed here is a generalisation of the model proposed in [41]. The continuous and discrete character is not trivial and dates back to the concept of structural or structured interface, which was introduced in a seminal work [40] and applied in the elastostatic regime [54; 55; 56] to denote a structural element that involves non-local interactions. The discrete character is a result of a proper asymptotic approximations that was developed in [57] and a nice application on the homogenisation of damage in an elongated structure can be found in [21]. The scope of the work is the description of the filtering properties and energy distribution for wave propagation in periodic elastic structures having a discrete and continuous character and arranged in series and parallel. The description of the filtering properties is obtained from the inspection of dispersion diagrams, while the final investigation describes the energy distribution within different components of the unit cell. The parametric analysis developed here shows which are the mechanical parameters that govern the band distribution for different arrangements and nature of the phases. Particular attention is devoted to internal resonators, which play an important role in several applications [58; 59]. The parametric analysis also evidences the conditions for which a highly localised effect is obtained and the possibility to have multiple localised resonances. In Section 2, we define the transfer matrices for continuous and discrete units and for different combinations in series and parallel. The single components of the transfer matrices for all cases is reported in Appendix A. Dispersion relations and the corresponding behaviours are analysed in Section 3, where, in normalised form, the parametric analysis in terms of frequency and elastic impedance ratios is detailed. In Section 4, the energy distribution in different units is studied, while concluding remarks are given in the Conclusions, Section 5.
## 3 Results
### Korean Version of the Child and Adolescent Behavior Assessment Scale (K-CBCL)  
Table 4 summarizes the K-CBCL scores of the experimental and control groups. Figure 4 shows the change in K-CBL scores after 14 d and 28 d. The repeated measures ANOVA and baseline-corrected covariate analysis showed no significant difference between the experimental and control groups. A significant decrease in scores was reported on day 28 compared with the baseline values of the K-CBCL total behavioral problems (\(p\) = 0.001), K-CBCL internalizing disorder (\(p\) = 0.001), and K-CBCL excitatory disorders (\(p\) = 0.001) in the experimental group. Significant decreases were confirmed on day 28 when compared with the baseline values of the K-CBCL total behavioral problems (\(p\) = 0.001), K-CBCL internalizing disorder (\(p\) = 0.004), and K-CBCL externalizing disorders (\(p\) = 0.010) in the control group.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Control Group**} & \multicolumn{4}{c}{**Experimental Group**} & \(p\)**-Value** \\ \cline{2-9}  & **N** & **Mean** & **SD** & **Baseline vs.** & **Baseline vs.** & **4 Weeks** & \\  & & & & **_p-Value_** & & & **_p-Value_** & \\ \hline \multicolumn{9}{c}{K-CBCL Total Behavior Problems} \\ \hline Baseline & 14 & 68.00 & 12.24 & & 15 & 64.60 & 10.74 & & 0.433  \\ \hline
2 weeks & 14 & 61.79 & 10.90 & & 16 & 60.50 & 9.13 & & 0.728  \\ \hline
4 weeks & 14 & 59.29 & 9.82 & & 16 & 56.38 & 9.53 & & 0.417  \\ \hline \(p\)-value & & 0.633  & 0.001  \\ \hline \multicolumn{9}{c}{K-CBCL Internalizing Disorder} \\ \hline Baseline & 14 & 64.93 & 12.69 & & 15 & 61.07 & 8.52 & & 0.342  \\ \hline
2 weeks & 14 & 59.07 & 12.82 & & 16 & 58.31 & 9.18 & & 0.852  \\ \hline
4 weeks & 14 & 56.71 & 11.13 & & 16 & 53.63 & 9.48 & & 0.418  \\ \hline \(p\)-value & & 0.623  & 0.004  & & & 0.001  & 0.404  \\ \hline \multicolumn{9}{c}{K-CBCL Externalizing Disorder} \\ \hline Baseline & 14 & 65.71 & 14.14 & & 15 & 61.33 & 11.08 & & 0.359  \\ \hline
2 weeks & 14 & 59.00 & 10.58 & & 16 & 58.19 & 9.42 & & 0.825  \\ \hline
4 weeks & 14 & 57.29 & 11.28 & & 16 & 53.88 & 8.60 & & 0.356  \\ \hline \(p\)-value & & 0.914  & 0.010  & 0.001  & 0.453  \\ \hline \hline \end{tabular}
\end{table}
Table 4: Korean-Child Behavior Checklist.  
Figure 3: Korean version of attention-deficit hyperactivity disorder K-ARS Score, K-ARS Attention Deficit, and K-ARS Hyperactivity.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 4 Framework and Formal Definition of the Problem
### Attenuation and Propagation Delay  
Unlike terrestrial WSNs, attenuation in UWSNs does not only depend on the link distance, but also on signaling frequency \(f\), _i.e._, \(A(d,f)\), where \(A\) is the attenuation function. After attenuation, the signal-to-noise ratio (SNR) of the received signal is \(\rho(d,f)\)[15]. For distance \"\(d\)\" between the source node and the MS (destination) at a frequency \(f\)(kHz) with spreading coefficient \(k\), [PERSON] in [16] defined attenuation equation \(A(d,f)=A_{0}d^{k}d^{d}(f)\), where \(A_{0}\) denotes the normalization constant and \(a(f)\) is the absorption coefficient that is described by the Thorp formula [17] and by Equations (19) and (20):  
\[10 log_{a}(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4200+f}+\frac{2.75f^{2}} {10^{4}}+0.003\quad if\quad f>0.4 \tag{19}\]  
and:  
\[10 log_{a}(f)=0.002+\frac{0.11f}{1+f}+0.011f\quad if\quad f<0.4 \tag{20}\]  
This is calculated in dB/km.  
The end-to-end delay model in [18] is used to calculate the propagation delay in Equation (21), _i.e._,  
\[T_{p}=\frac{s}{v} \tag{21}\]  
where \(s\) is the distance between the sender and receiver node and \(v\) is the speed of the acoustic signal, which is given in Equation (22):  
\[\begin{array}{rl}v=&1449.05+45.7t-5.21t^{2}+0.23t^{3}+\\ &(1.333-0.126t+0.009t^{2})(S-35)+16.3z+0.18z^{2}\end{array} \tag{22}\]  
where \(t=T/10\), \(T\) is the temperature in \({}^{\circ}\)C, \(S\) is salinity in ppt, and \(z\) is the depth in m.  
Figure 7: Throughput: feasible region.
# Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure
## 3 Methodology  
### Dataset  
Some of the datasets that are commonly used for this task are Market-1501 [21], CUHK(01,03) [22], DukeMTMC-reID [23] and VIPER [24]. The previously mentioned datasets do not incorporate cloth variation therefore not suitable for testing the proposed approach. After extensive research in this area, we were able to investigate some of the publicly available datasets such as BIWI [25] and PRCC [26].  
BIWI contains 28 different identities captured doing various motion routines using a Microsoft Kinect for Windows. The Kinect provides images with very high resolution,i.e., 1280 \(\times\) 960 pixels. The Kinect was capable of providing depth images, segmentation maps, skeletal data, and RGB images. The set-up of collecting this dataset did not match the environment envisioned for deploying our system; the size of the dataset and number of subjects is relatively small. Additionally, the Kinect produces high-resolution images compared to the security cameras often installed in security systems.  
In 2020, [PERSON] et al. [26] proposed a new dataset called Person re-identification under moderate Clothing Change (PRCC) dataset, which was used to test our methodology. PRCC contains 221 different identities and a total of 33,698 images which is almost ten times larger than BIWI in terms of the number of identities. The dataset was collected using three cameras. Cameras A and B capture a person wearing the same clothes, camera C captures the same person on a different day with different clothing. Figure 1 contains three images of the same person the first two images on the left show the person wearing the same clothes (pink T-shirt with a colorful logo along with grey pants and backpack), the last image is from camera C where the person is wearing different clothes (black T-shirt with a white logo along with grey pants and a backpack).  
The challenging aspects that these dataset meets are not limited to clothes variation only. It takes pose variation, occlusion, viewpoint variation, and illumination changes into consideration.  
The dataset comprises a total of 221 identities. The experimental setup used 150 identities for training and 71 identities for testing, with no identity overlap between training and testing. Table 1 illustrates how we split the data. For testing, a query set (probe/target images) is needed. The query set evaluates the model's performance according to its ability to match a query image of a person with their pictures from the gallery set (in our setup, query and gallery identities do not overlap). To construct the query set, we sampled two  
Figure 1: Samples of PRCC dataset [26] illustrating clothes variation in images taken of the same subject.  
random images for each identity from each camera, a total of six query images per identity. The size of the query set is 71 \(\times\) 6 = 426 query images.
## 3 Simulation Results and Discussion  
### The Investigated Centrosymmetric Crystals  
In this paper, three centrosymmetric crystals, Alpha Barium Borate (\(\alpha\)-BBO), calcite, and rutile, are investigated considering their transparency, THG conversion efficiency, broad phase-matching range, and availability. Here, it should be clarified that \(\alpha\)-BBO is a typical centrosymmetric crystal, which is different from the non-centrosymmetric Beta Barium Borate (\(\beta\)-BBO) in our previous study, although they have a similar refractive index [26]. \(\alpha\)-BBO and calcite are negative crystals (\(N_{\rm e}<N_{\rm o}\)), and rutile is a positive crystal (\(N_{\rm e}>N_{\rm o}\)). The dispersion of the refractive index can be empirically calculated by the Sellmeier relation as shown in Equation (12). The coefficients are given in Table 1 from the literature. The third-order effective nonlinear coefficient \(\chi_{\rm eff}^{(3)}\) should be considered with the phase-matching type and the formation of \(\chi^{(3)}\) matrix, which is dependent on the crystal class. Here, we discuss the type II phase-matching for \(\alpha\)-BBO and calcite of crystal class \(\overline{3}\)m, and the type I phase matching for rutile of crystal class \(4/\)mmm, because of the high THG  
Figure 5: **(a)** The diagram of phase-matching angle \(\theta_{\rm m}\) dispersion; **(b)** The diagram of spectrum peak shift in the frequency domain.  
conversion efficiency [26; 29; 30]. The calculation of \(\chi^{(3)}_{\text{eff}}\) is given in Equation (12), and the parameters \(\chi^{(3)}_{\text{eff}}(\theta_{\text{m}})\) can be found in the literature [30].  
\[\begin{split} N_{\text{e}}(\lambda),N_{\text{o}}(\lambda)=\sqrt{A +\frac{B}{\lambda^{2}-C}-D\lambda^{2}}\,\alpha-\text{BBO}\\ N_{\text{e}}(\lambda),N_{\text{o}}(\lambda)=\sqrt{A+\frac{B \lambda^{2}}{\lambda^{2}-C}-\frac{D\lambda^{2}}{\lambda^{2}-E}}\,\text{ calcite}\\ N_{\text{e}}(\lambda),N_{\text{o}}(\lambda)=\sqrt{A+\frac{B \lambda^{2}}{\lambda^{2}-C}}\,\text{ rutile}\\ \chi^{(3)}_{\text{eff}}=\frac{1}{3}\chi^{(3)}_{11}\cos^{2} \theta_{\text{m}}+\chi^{(3)}_{16}\sin^{2}\theta_{\text{m}}+\chi^{(3)}_{10}\sin 2 \theta_{\text{m}}\sin 3\varphi\alpha-\text{BBO},\text{ calcite}\\ \chi^{(3)}_{\text{eff}}=\frac{1}{2}\cos^{2}\theta_{\text{m}}[ \chi^{(3)}_{11}\sin^{2}2\varphi+\chi^{(3)}_{18}(3\cos^{2}2\varphi-1)]+\chi^{(3 )}_{16}\sin^{2}\theta_{\text{m}}\text{ rutile}\end{split} \tag{13}\]
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 2 Materials and Methods
### The CYGNSS Observatory
We omitted consideration of the smallest error sources that, when combined as a root sum of squares,contribute to less than 1% of the total error magnitude. The five largest sources of error are explored, as shown in Table 1, representing a 1-sigma error magnitude for a reference wind speed of 10 m/s. The following sections evaluate each of these terms in depth.
## 2 Experimental Implementation
#### Experimental Setup
The final part of each experimental setup is the detection stage: photons are detected by a 2D spatially resolving single-photon detector prototype, i.e., a two-dimensional array of \(32\times 32\) \"smart pixels\", each hosting a SPAD with dedicated front-end electronics [86]. The detection of the idler photon (920 nm) by the Si-SPAD on the heralding arm gates the SPAD array with a 6 ns detection window. Furthermore, an optional quantum tomography [87; 88] apparatus, comprising an HWP, a quarter-wave plate (QWP) and a polarizer, can be inserted just before the SPAD array to reconstruct the density matrix of the single-photon state at the end of each measurement procedure.
# Image-to-Image Translation Using a Cross-Domain Auto-Encoder and Decoder
## 4 Results
### Qualitative Comparisons  
#### 4.3.1 Comparisons with Baselines  
We applied our method on CycleGAN and evaluated our results qualitatively. Figures 6 and 7 demonstrate that our model solves the critical problems found in the mistranslated images generated by the baseline model (CycleGAN). Figure 6 shows that some segmented objects are translated with incorrect textures by the baseline model, but our model translates the segmented images correctly on the cityscapes task. Figure 7 shows that some regions or objects other than horses are painted partly with zebra patterns or poorly translated. In the case of our model, it produces better quality images with the correct translation on the horse2 zebra task.  
Figure 6: _Cont._Figure 6: Translated pictures of CycleGAN and our method for the cityscapes dataset. From left to right: input, ground truth, Cycle-GAN (Baseline), and our method. The results of CycleGAN show unstable translations, including mistranslations between colonnade tree label and the building appearance. Our method helps to avoid these instabilities during the learning stage and produces more correct translation results.  
Figure 7: Comparison of pictures translated by CycleGAN and our method from the horse2 zebra dataset. CycleGAN results show mistranslations such as changing texture of background, or removing detailed characteristics of the source object, or just inverting image colors. The results of our method show such mistranslations can instead be correctly translated.  
Figure 8 shows more details of the resultant translation images. It shows that our model can resolve the instabilities of the baseline model (CycleGAN) effectively. Particularly, our model translates the horses to zebras only, and preserves the original features of the other parts of the image (background and other objects) that should not be translated. Since the horse2 zebra dataset is unpaired between the horse domain and the zebra domain, quantitative comparisons with ground-truth are not possible, and qualitative comparisons are the only indicator of the quality of the results.  
We also applied our method on Pix2 pix and evaluated our results qualitatively. In Figure A1, Pix2 pix shows the mixed textures, faded edges, and blurred objects of translated images on the  
Figure 8: Detailed portions of translated pictures. Enlargements of the results of the baseline (CycleGAN) reveal mistranslation from the horse hair to zebra patterns, elimination of detailed features like eyes and nose, and inverting (or blurring) colors. Our model is more stable than the baseline because it makes translations while preserving the main characteristics of all the objects in the image, including eyes, nose, hair, and colors.  
cityscapes dataset, but they are translated with, apparently, relatively sharp edges, and distinct textures and objects by our method.
## 4 Results and Discussion  
### Implementation Details  
The database was randomly divided into training, testing, and validation datasets, and 60% of the data were used for training, while the remaining data were equally divided into testing and validation sets. Moreover, it was made sure that there would be no overlapping between the datasets. In total, 10 repetitions were carried out to train and evaluate the performance of the proposed framework. The pretrained Inception-ResNet-V2 was used, and each time, the model was fine-tuned using the training dataset.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 3 Proposed Scheme
### Hiding Module  
The hiding module carries out two fundamental operations: scrambling of the input samples and hiding data in a host signal. To implement correctly these two processes, the next blocks are used: scrambling, 8-bit conversion, 16-bit conversion, adaptive LSB, Binary (Bin) to Decimal (Dec) conversion (Figure 1). Inputs of this module correspond to the secret signal (voice), host signal (voice), randomization key and \(BH\) (Bits to Hold). Its output corresponds to the stego signal (voice).  
Next, each block of the transmitter module will be explained.