esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparative Analysis
### RMS Delay Spread  
Table 2 indicates a minimal difference in the RMS delay spread for the two frequency bands. The statistical results are consistent with the above theoretical prediction. This phenomena has also been observed in the measurements of [26; 27], where the RMS delay spreads from 2.4 GHz to 28 GHz are similar for the indoor LOS and non-LOS (NLOS) channels. However, some papers present different results. For example, in [10], for LOS, the RMS delay spread is around 8 and 4 ns for 2-10 and 57-66 GHz, respectively. The measurements were performed in a laboratory furnished with several closets, shelves, desk, and chairs. In this scenario, there are not only free space propagation and reflection but also the diffraction. The diffraction gain is frequency-dependent [24]. This may be the reason for the difference of the RMS delay spread in this scenario.  
Because the reflection coefficient is insensitive to the frequency with the large frequency range for many typical building materials, it is reasonable to hypothesize that the RMS delay spread is also independent of the frequency in the scenario with the typical building materials where free space propagation and/or reflection are the main mechanisms. To verify this hypothesis, we conducted some measurements in the National Institute of Metrology (NIM) RC (see the Verification in Reverberation Chamber section).
## 5 Process of Optimal Foil Width Determination and Results
### TP2 Results
Based on Tables 18-22 and Figure 22, it can be concluded that function F4 was also the best among those selected in the case of TP2. In the case of TP2, the specific resistance of the upper layer of the earth was 1005.3 \(\Omega\)m, which can be seen in Table 6 for the four-layer soil model. Using the same procedure for determining the limit value of the step voltage, as shown in Section 5.1.3, we established that \(U_{\text{Slimit}}=2535\) V. At _fw_ = 2 m, the maximum step voltage was 1737.7 V, which was less than the limit value. If protective foil is used in this case, a width of 2 m would be sufficient. Even in this case, it is necessary to determine the potential difference between the potential above and below the foil in order to choose the foil with the appropriate breakthrough strength. Figure 23 shows the potential above and below the protective foil as well as the potential difference. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Function F4**} & \multicolumn{3}{c}{**Used Foil Width**} \\ \cline{2-4}  & **2 m, 4 m, 8 m** & **2 m, 5 m, 8 m** & **2 m, 6 m, 8 m** \\ \hline OF three foil widths & 9.3215 \(\times\) 10\({}^{-31}\) & 1.4493 \(\times\) 10\({}^{-31}\) & 5.0728 \(\times\) 10\({}^{-30}\) \\ \(a\) & 4364.05 & 4322.29 & 4564.87 \\ \(b\) & 0.8561 & 0.8354 & 0.9547 \\ \(c\) & 209.73 & 213.30 & 192.72 \\ \hline OF all seven foil widths & 1.2286 \(\times\) 10\({}^{-4}\) & 1.0906 \(\times\) 10\({}^{-4}\) & 2.8286 \(\times\) 10\({}^{-4}\) & \(\sum\)5.1478 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Calculated parameters and OF for three foil widths and for all foil widths based on three foil widths for F4 using TP2 data. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Function F5**} & \multicolumn{3}{c}{**Used Foil Width**} \\ \cline{2-4}  & **2 m, 4 m, 8 m** & **2 m, 5 m, 8 m** & **2 m, 6 m, 8 m** \\ \hline OF three foil widths & 0 & 0 & 0 \\ \(a\) & 1653.35 & 1602.19 & 1564.09 \\ \(b\) & 0.4781 & 0.4447 & 0.4119 \\ \(c\) & 630.38 & 611.24 & 586.70 \\ \hline OF all seven foil widths & 3.5331 \(\times\) 10\({}^{-3}\) & 3.9778 \(\times\) 10\({}^{-3}\) & 8.6763 \(\times\) 10\({}^{-3}\) & \(\sum\)1.6200 \(\times\) 10\({}^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Calculated parameters and OF for three foil widths and for all foil widths based on three foil widths for F5 using TP2 data. Figure 23 shows that the potential was higher below the protective foil than above it. The protective foil lowers the potential on the soil surface and consequently the step voltage. The maximum calculated value was 23,624 V, which means that the breaking strength of the foil must be higher.
###### Abstract  
With developments in maritime trade, the need to increase port capacities has emerged. It is important that the reconstruction of such port structures be carried out without disrupting the activities of the existing port and without damaging the old structures. However, the reconstruction and strengthening of these structures require significant engineering work. Especially in the design of such structures, geotechnical and structural engineering theories and practices need to be considered together. The most important challenge in this type of engineering design is to ensure harmony in geotechnical and structural analyses. In this study, the strengthening of a port structure in Guinea is taken as a case study. First, the existing quay wall was evaluated, and then the geotechnical and structural design of the new structure to be built for capacity increase was discussed. A geotechnical design was made with the Plaxis 2D program, and the structural design was made with the Sap2000 program. The soil behavior was analyzed nonlinearly with the Plaxis program and was modeled with the same accuracy as the Sap2000 program. In addition, pile capacities were confirmed by loading tests performed on the piles. The results show that complex hybrid solutions consisting of reinforced and unreinforced piles can be modeled with the finite element method. As seen from this case study, the capacities of existing port structures can be safely increased, provided that necessary geotechnical precautions are taken.  
quay wall; piles; port; finite element analysis; strengthening Article
###### Abstract  
Sending-or-not sending twin-field quantum key distribution (SNS TF-QKD) has the advantage of tolerating large amounts of misalignment errors, and its key rate can exceed the linear bound of repeaterless quantum key distribution. However, the weak randomness in a practical QKD system may lower the secret key rate and limit its achievable communication distance, thus compromising its performance. In this paper, we analyze the effects of the weak randomness on the SNS TF-QKD. The numerical simulation shows that SNS TF-QKD can still have an excellent performance under the weak random condition: the secret key rate can exceed the PLOB boundary and achieve long transmission distances. Furthermore, our simulation results also show that SNS TF-QKD is more robust to the weak randomness loopholes than the BB84 protocol and the measurement-device-independent QKD (MDI-QKD). Our results emphasize that keeping the randomness of the states is significant to the protection of state preparation devices.  
twin-field quantum key distribution; weak randomness; asymptotic cases; finite-key Article
# Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy
## 3 Results
### Human Skin Tissue
#### 3.2.2 SORS Measurements
Figure 8: PC3 loading (0.4%) of eyelid SORS dataset and PC2 loading (1%) of scalp tissue SORS dataset subplotted with the reference Raman spectrum of the subcutaneous fat layer. Figure 6: PCA scatter plots for Raman spectra collected from scalp skin of (**a**) different sample areas and donors and (**b**) different offsets. Figure 7: PCA scatter plots for Raman spectra collected from eyelid skin of (**a**) different sample areas and donors and (**b**) different offsets. To further explore PCA separation, score plots were additionally labelled by offset. In Figure 7a,b we observe that for all samples, except for Sample 3, the zero offset measurements are grouped on the negative values of PC2 (scalp tissue) and PC3 (eyelid tissue) score, indicating a strong association with the negative peaks of the respective loadings. Those peaks are mostly assigned to all skin layers, however some of them are specifically assigned to ceramides of the stratum corneum (1671 cm\({}^{-1}\)), and the C-S cysteine vibration of dermis and epidermis layer around 700 cm\({}^{-1}\). On the contrary, bands that are positively associated with both loadings for PC2 (scalp tissue) and PC3 (eyelid tissue), include 1063, 1083 and 1123 cm\({}^{-1}\) assigned to C-C bending modes of the lipid skeleton, as well as 1301, 1437, 1656 and 1744 cm\({}^{-1}\). From spectra comparison in Figure 8, it becomes obvious that these are all bands assigned to the transcutaneous fat layer of skin. Since positive scores result from bands associated with positive loading features, while negative scores result from bands associated with negative loading features [49], we can conclude that increasing offset in measurements is associated with increasing fat contribution in the recorded signal. This is expected as Raman photons from deeper tissue layers (such as the transcutaneous tissue) are being collected with increasing offset. Our PCA results indicate that for sample thickness up to 3.5 mm and epidermis thickness up to 1 mm (Table 1), the Raman signal from the fat layer is not present in the zero offset recorded measurements in the given conditions of the study. This does not necessarily rule out the presence of fat in the measuring volume, as the measurement sensitivity may be insufficient to allow detection, but it certainly allows for a more accurate data analysis and therefore diagnosis in the absence of additional overlapping signals from the fat, which is not included in the measuring volume of interest (since the melanoma and non-melanoma lesions initiate from the epidermis) [3,50].
## 3 Validation  
After the modeling of the pantograph-catenary interaction system, the present model is validated by the EN50318:2018 standard [11], where the stitched catenary and its corresponding double pantograph are considered, and the pantograph distance is 200 m. The total length of the catenary is 1800 m (30 spans), and the number of modes used in the calculation is \(n=1200\). Two vehicle operation velocities, \(V=275\) km/h and 320 km/h, are considered for simulation. The time histories of the pantograph-catenary contact force with respect to different pantographs and vehicle velocities are shown in Figure 3, and their corresponding statistical results are shown in Table 1, which are compared with those from the EN50318:2018 standard. It can be seen from Figure 3 that the calculation results from the present model are reasonable, and their corresponding statistical results show good agreement with those listed in the EN 50318-2018 standard, which means that the present model can accurately simulate the dynamic responses of the pantograph-catenary interaction in a stitched catenary system.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline Speed [km/h] & 275 & & 320 \\ \hline Pantograph & Leading & Trailing & Leading & Trailing \\ \hline \(F_{m}\) [N] & 143.5 (143–144) & 143.5 (142–144) & 169 (169) & 169 (169) \\ \hline \(\sigma\) [N] & 23.8 (20.2–14.7) & 28.2 (24.4–36.2) & 22.8 (20.5–24.7) & 34.9 (30.4–38.3) \\ \hline \(\sigma\) (0 Hz-5 Hz) [N] & 14.2 (11.7–15.2) & 17.4 (17.0–18.2) & 12.8 (11.8–13.3) & 22.4 (20.4–24.2) \\ \hline \(\sigma\) (5 Hz–20 Hz) [N] & 18.3 (16.5–19) & 24.9 (16.4–27.4) & 18.1 (15.2–20.9) & 27.8 (21.5–29.8) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical results of the pantograph–catenary interaction with respect to different pantographs at \(V=275\) and 320 km/h and comparison with the values in EN50318:2018 (in parentheses).  
Figure 3: Time histories of pantograph–catenary contact forces with respect to different pantographs and velocities: (**a**) \(V=275\) km/h and (**b**) \(V=320\) km/h.
# Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model
## 4 Experimental Results  
### Experimental Environment and Settings  
The MIRFLICKR [23] dataset is used to evaluate the performance of the proposed algorithm ScreenNet. In the experiments, 20,000 images are selected for training, and the image size is adjusted to 400 \(\times\) 400 pixels. The graphics card used is NVIDIA RTX 2080 ti GPU training. The code is written in tensorflow framework. From the MIRFLICKR dataset, 2000 images which have no intersection with the training set are selected to form the verification set to evaluate the robustness under common noise attacks.  
Watermarking information is a random sequence composed of 0 s and 1 s. Through experiments on different capacities of watermarking bits, the final choice is to embed 100 bits of watermarking information in the cover image. The distance similarity measures PSNR and SSIM are used to measure the quality of watermarked images. From Table 1, if the watermarking information bits increase, the value of PSNR and SSIM decrease. Consequently, the image quality will be poor, whereas the watermark capacity will be improved. Based on the comprehensive consideration of watermark capacity and image quality, 100 bits of watermarking information is selected to embed into the cover image. In this paper, the accuracy of extracting watermark information is used to evaluate the robustness of the proposed algorithm against a screenshot attack. The formula of the accuracy is shown in Equation (6).  
\[Accuracy=1-BER \tag{6}\]
## 2 Materials and Methods
### Chip and Package Strucure and \(T_{j}\) Measurement Method  
Due to nanoscale dimensions of the junction, \(T_{j}\) is hard to determine directly and accurately, especially at low temperatures [11; 12]. In the recent years, many researchers have sought to develop methods for accurate \(T_{j}\) measurements at high temperatures [12; 13]. [PERSON] and [PERSON] reported an advanced direct measurement technique using the nematic liquid crystals thermography in 2004, the measurement accuracy was within \(\pm\)1 K [14]. [PERSON] et al. had developed a theoretical model for the relationship between the forward voltage (\(V_{F}\)) of LED and the \(T_{j}\), the theory was verified by experimental data, and they had accurately measured the \(T_{j}\) of ultraviolet LEDs using the \(V_{F}\)[15; 16; 17].  
In this work, \(T_{j}\) was accurately measured in wide range with a built-in \(T_{j}\) sensor unit. The structure and dimensions of the LED with the \(T_{j}\) sensor unit have been described elsewhere [18; 19]. The designed chip was composed of a single light-emitting unit with the dimensions of 750 x 430 \(\upmu\)m\({}^{2}\), and a single sensor unit. The chips were packaged without phosphor using a four-lead high-power lead frame, and the packages were mounted on the metal-core printed circuit board (MCPCB).  
Since the sensor unit is smaller and is located next to the LED unit, the \(T_{j}\) of the LED unit was approximately equal to that of the sensor unit. In the experiment described below, the forward voltage (\(V_{F}\)) of the sensor diode was used to characterize the \(T_{j}\)[15; 17]. In the ambient temperature range from 77 K to room temperature, the temperature was controlled by placing the samples in a liquid nitrogen tank at different distances above the liquid nitrogen level. For the ambient temperatures in the range from room temperature to \(\sim\)350 K, the temperature was controlled by placing the samples into an oven whose temperature resolution was 0.1 K and temperature fluctuation within \(\pm\)0.5 K.  
First, the \(V_{F}\)-\(T_{j}\) relationship of the sensor unit was calibrated. A Keithley 2636B SourceMeter was used as the current source and voltage/current meter. The package temperature was measured using a thermocouple closely attached to the heat slug of the package. To avoid the generation of a temperature difference between the heat slug and the p-n junction caused by the self-heating during the calibration, a weak testing current of 500 \(\upmu\)A was used. The \(V_{F}\) of the sensor unit was measured after the reading of the thermocouple was stable for at least 15 min.
## 3 Results and Discussion
### IPS Parameters Effect
#### 3.2.2 Coating Range  
Type (3) layout was tested in case 9, where all the heating sheets A/B/C/D/E were turned on and covered with superhydrophobic coatings. Each heating sheet was provided with the same power density which decreased gradually during the test until ice formation occurred. Heating power details are shown in Table 6. On the first attempt, a power density of 5 kW/m\({}^{2}\) was chosen based on the minimum power density obtained from the pre-tests described above. This was evidently sufficient to prevent ice formation due to the additional sheets D and E. However, when the provided power density fell to 4 kW/m\({}^{2}\), ice appeared on the leading edge, as shown in Figure 16. The ice protection results are shown in Table 6. At a power density of 4.5 kW/m\({}^{2}\), the total power of sheets A/B/C/D/E was 466 W, whereas the total power at a power density of 5 kW/m\({}^{2}\) with sheets A/B/C on was 396 W. Increased coating range led to a decrease in heating power density but not in the total power.  
It can be seen from this part of the study that increasing the range of superhydrophobic coating and heating, while decreasing the heating power density of heating sheets, cannot lead to a reduction in total heating power consumption. Efficient distribution of heating power remains the key consideration in IPS design, even with a superhydrophobic coating.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Heating Power**} & \multicolumn{1}{c}{**Total Power**} & \multicolumn{1}{c}{**Heating Power Density**} & \multicolumn{1}{c}{**Valid or Invalid**} \\ \cline{2-9}  & **Sheet** & **Sheet** & **Sheet** & **Sheet** & **Sheet** & **W** & **kW/m\({}^{2}\)** & **Valid or Invalid** \\  & **A** & **B** & **C** & **D** & **E** & & & \\ \hline
1 st try & 36 & 105 & 255 & 60 & 60 & 516 & 5 & Valid \\
2 nd try & 33 & 95 & 230 & 54 & 54 & 466 & 4.5 & Valid \\
3 rd try & 29 & 84 & 204 & 48 & 48 & 413 & 4 & Invalid \\ Case 1 & 36 & 105 & 255 & \(\times\) & \(\times\) & 396 & 5 & Valid \\ \hline \hline \end{tabular}
\end{table}
Table 6: Heating power details in case 9.  
Figure 15: Comparison of surface temperature with sheet A on and sheets A/B/C all on.
## Appendix A Generalized Linear Astigmatism Compensation  
Equation (9) compensates for linear astigmatism only for the first stability limit, \(\delta_{0}\). This is because the above expression for mirror astigmatism, \(\Delta f(f,\theta)\), assumes a collimated beam in both arms of the cavity, which is only true at \(\delta_{0}\), as seen in Figure 3. For any other stability limit, one must use a generalized expression for the curved mirror astigmatism that takes into account non-collimated beams, focused on the relevant end mirror. By considering the curved mirror as an imaging lens, one can calculate the distance of the point of the image relative to the curved mirror:  
\[v_{s,t}(f,\theta,D)=\frac{Df_{s,t}}{D-f_{s,t}} \tag{17}\]  
where \(D\) represents the distance of the \"object\". For a collimated beam, \(D=\infty\) and \(v_{s,t}=f_{s,t}\). For a non-collimated beam, the \"object\" is effectively located at the end mirrors (see Figure 2); hence, \(D\) equals the length of the corresponding cavity arm. Using Equation (17), one can compensate for linear astigmatism for each stability limit, \(\delta_{i}\), separately, by solving:  
\[\Delta v(f_{1},\theta_{1},D1_{i})+\Delta v(f_{2},\theta_{2},D2_{i})=\Delta L \tag{18}\]  
The corresponding values of \(D1_{i}\) and \(D2_{i}\) are given in Table 1. As an example, we can examine typical cavity parameters: curved mirrors radius of curvature \(R\) = 10 cm, short arm length \(L_{1}\) = 30 cm, long arm length \(L_{2}\) = 60 cm and crystal length \(3\) mm. Compensating linear astigmatism for the first stability limit, \(\delta_{0}\), results in \(\theta_{1}=\theta_{2}=6.15^{0}\). In order to compensate for the second stability limit, \(\delta_{1}\), the angle, \(\theta_{2}\), must be reduced to \(5.53^{0}\).  
Astigmatism compensation can be further generalized to compensate for \(\delta\) values within the stability zones of the cavity (not necessarily at the stability limits) by using a complex representation of the imaging expression given in Equation (17), which simulates a Gaussian mode (in contrast to the stability limits where the mode can be represented using geometrical optics, as illustrated in Figure 2). However, it is easier to set the values of the folding angles, \(\theta_{1}\) and \(\theta_{2}\), to compensate for the nearest stability limit and then to fine-tune one of the folding angles until a circular mode is achieved.
## 5 Conclusions  
It has been shown that PECVD graphene nanowalls synthesized on G substrate, especially at 200 W, exhibited stronger antibacterial activity against _E. coli_. and _S. aureus_. It can also be said that the antibacterial activities of PECVD grown graphene samples are more effective against _E. coli_ and _S. aureus_ than CVD grown graphene samples. With the increase in RF power, we can expect more tightly packed 3D structures and defects on the graphene surfaces. This may create a better antimicrobial effect. It may also be thought that PECVD grown graphene against _S. aureus_ is more effective than against _E.coli_. It is also concluded that the cell viability (%) of _E. coli_ CVD monolayer GrNs synthesized onto Ni substrate was better than CVD monolayer GrNs synthesized onto G substrate. It was observed that the cell viability (%) against _S. aureus_ was extremely weak for the samples grown on Ni substrate.  
As a result, it has been deduced that substrate materials, deposition method, and the number of layers of graphene affect the antibacterial properties of the samples. We have concluded that each sample created from graphene has unique properties and displays different results. To fully clarify the antibacterial mechanism of GrNs, detailed research is needed. To the best of our knowledge, such interactions of bacteria with GrNs synthesized onto various substrates (G and Ni) via different deposition methods (CVD and PECVD), and the evaluation according to the number of layers of graphene, have not been comparatively reported previously, and the findings may offer new viewpoints both for the better interpreting of the antibacterial activity of GrNs and for the better designing of graphene-based food-packing and biomedical device applications.  
Conceptualization, E.O.; methodology, E.O. and L.A.; validation, L.A. and B.A.; formal analysis, E.O., L.A. and B.A.; investigation, E.O.; resources, E.O.; writing--original draft preparation, E.O., F.O. and T.V.; writing--review and editing, E.O., F.O. and T.V.; visualization, E.O., F.O. and T.V.; supervision, F.O. and T.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Gazi University, grant number 18/2015-03.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions privacy and ethical.  
**Acknowledgments:** The authors would like to thank the Gazi University Electro-Optic Research Laboratory and Gazi University Molecular Biology Laboratory.  
**Conflicts of Interest:** The authors declare no conflict of interest.