esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Vehicle SPN Emissions  
The SPN emissions ranged from 10\({}^{8}\) #/kWh up to 2 \(\times\) 10\({}^{13}\) #/kWh. Note that the high emissions are from the cold start parts (first 900 s) of the cycles. As mentioned in the experimental part, the tests are from different projects, some of them challenging the diesel particulate filters (DPFs) and some at low ambient temperatures. Furthermore, the analysis did not follow the regulation, but only parts of the cycles were considered here to compare the instruments on a wide range of emission levels. The lowest emissions, practically in the background of the instruments and setup, were from the CNG vehicle equipped with a particulate filter. The majority of the emissions were around 10\({}^{11}\) #/kWh. Although the absolute emission levels of the vehicles are of no interest to this study, the relative difference between 23 nm and 10 nm concentrations can help understand possible deviations from the instruments. Figure 3 plots the excess particles below 23 nm, that is, (\(\text{(SPN}_{10}-\text{SPN}_{23})/\text{SPN}_{23}\)), as a function of the \(\text{SPN}_{23}\) emission levels.  
For vehicles CNG and D1, the percentage is \(<\)50%, a number typical for light-duty vehicles [17] and some heavy-duty diesel engines [17,65,66]. CNG engines have a typically high percentage of sub-23 nm particles [17,19], but in our study, a particulate filter was installed, and thus the sub-23 nm particles were also captured efficiently [18,67,68,69]. For D2 and D3, the percentages are very high (\(>\)50%) in the range of 7 \(\times\) 10\({}^{10}\) #/kWh to 3 \(\times\) 10\({}^{11}\) #/kWh. Such high levels have also been reported by other studies [21,22]. This range is at or above the Commission's proposed \"hot\" limit for Euro 7 (2 \(\times\) 10\({}^{11}\) #/kWh for \(\text{SPN}_{10}\)), but within the Council's proposal of 9 \(\times\) 10\({}^{11}\) #/kWh [70]. However, for the current \(\text{SPN}_{23}\) limit of 6 \(\times\) 10\({}^{11}\) #/kWh, this high percentage is not so relevant because it is equivalent to 10-25% of the \(\text{SPN}_{23}\) limit, and most importantly, most of these particles are below 23 nm. At high concentrations, typically during cold starts or regenerations, the percentages are very low, indicating that the nature of the particles is different. Based on the literature, the particles during cold start at high concentrations have fuel combustion origins (i.e., soot), while those in the 10\({}^{11}\) #/kWh range during hot vehicle operation are formed by urea origins [23]. At the high-speed part of the cycle, high soot emissions due to passive regeneration can also be measured [17,71,72,73].  
Figure 3: Particle concentration below 23 nm in function of emission levels for various vehicles. The dotted line is the 6 \(\times\) 10\({}^{11}\) #/kWh limit for \(>\)10 nm particles. SPN = solid particle number.
### Problem Statement  
Figure 2 depicts the investigated nanofluid hydrodynamic problem.  
It is a rectangular enclosure, with a length \(l\) and a height \(h\), filled with Fe\({}_{3}\)O\({}_{4}\)-Water nanofluid. The top wall of the enclosure is a finned absorber plate. This research work investigated various heights for the fins, such as \(\delta=0\), \(h/8\), \(h/4\), \(h/2\). A fin height \(\delta=0\) means obviously that the considered absorber plate is without fins. The absorber, kept at a constant hot temperature \(T_{h}\), is heated by solar energy (solar irradiance \(I\)), and it transfers heat by convection to the nanofluid. Fins permit to enhance the heat transfer from the heated absorber plate to the Fe\({}_{3}\)O\({}_{4}\)-Water nanofluid. Parts of right and left walls are maintained at a constant cold temperature \(T_{c}\). The remaining wall parts of the enclosure are insulated.  
The studied enclosure presents the specificity of having an imposed hot temperature coming from an absorber plate (top wall of the enclosure), heated at the top by solar energy. The inclination angle of the plate facing the sun's ray affects considerably the amount of solar energy kept by the absorber plate, and consequently the hot temperature imposed to the enclosure. Thus, it makes sense to consider several inclination angles for the enclosure in order to vary the imposed hot temperature. It is to note that the optimum inclination angle for a flat plate solar collector varies from \(0^{\circ}\) to \(80^{\circ}\) depending on the latitude of the location and the day number of the year [14]. The investigated nanofluid enclosure was, hence, inclined relative to the horizontal by several angles: \(\gamma=0^{\circ}\), \(\gamma=15^{\circ}\), \(\gamma=30^{\circ}\) and \(\gamma=45^{\circ}\). The investigated inclination angle (\([0^{\circ},45^{\circ}]\)) was within this optimum inclination angle range (\([0^{\circ},80^{\circ}]\)), and it permitted to ensure a maximum solar energy collection during a typical day time in the region of Gafsa, Tunisia (\(34^{\circ}25^{\circ}52.1076^{\prime\prime}\) N and \(8^{\circ}46^{\circ}32.3616^{\prime\prime}\) E).  
The solid volume fraction of the investigated nanofluid is around \(\phi=0.05\). It is to note that we used a solid volume fraction not exceeding a threshold of 0.1 in order to avoid nanoparticles precipitation problems that may occur in nanofluids. The Prandtl  
Figure 2: Schematic of the Fe\({}_{3}\)O\({}_{4}\)-Water nanofluid filled 2D rectangular enclosure heated by solar energy using finned absorber plate.  
number of the Fe\({}_{3}\)O\({}_{4}\)-water nanofluid was the same as the one associated to its base fluid (water). Hence, the investigated Prandtl number was around Pr = 7. The Rayleigh number of the investigated nanofluid varied as Ra = 10\({}^{3}\), Ra = 10\({}^{4}\), and Ra = 10\({}^{5}\). These relatively low values of Rayleigh number allow the nanofluid to remain in a laminar flow. This avoids chaotic changes in pressure and flow velocity that characterize turbulent flows. Thus, the simulation numerical results will be more significant.
# BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
## 3 The Principle of Laser Chaos Decision-Maker
By arranging pipelined thresholds, we can choose one among multiple slot machines. Figure 3 illustrates the scalable decision-making utilizing laser chaos time-series. More concretely, scalable decision-making follows the rule below. At step \(t_{1}\), the level of chaotic output \(s(t_{1})\) is compared to the threshold \(TH_{1}\). At step \(t_{2}\), the level of chaotic output \(s(t_{2})\) is compared to the threshold \(TH_{2,1}\), if \(s(t_{1})\) is greater than or equal to \(TH_{1}\). Otherwise, \(s(t_{2})\) is compared to the threshold \(TH_{2,2}\). Similarly, at step \(t_{3}\), \(s(t_{3})\) is compared with either \(TH_{3,0,0}\), \(TH_{3,0,1}\), \(TH_{3,1,0}\), or \(TH_{3,1,1}\). In this manner, comparisons with the chaotic signals and multiple threshold continue until a slot machine is selected. Thus, comparisons with the chaotic signals and multiple thresholds allow scalable decision making. Figure 3: Scalable decision-making by a hierarchical architecture [31].
## 3 Results and Discussions
### Dynamic Mechanical Analysis
#### 3.3.3 Frequency Sweep Isotherms  
Frequency was swept from 0.01 to 30 Hz for each test temperature and complex shear modulus (G*) and phase angle (8) were measured. Figure 13 demonstrates the effect of frequency changes on stiffness of base and rejuvenated asphalt binder in the unaged condition. For brevity, G* and Phase angle data for only three testing temperatures (\(-\)5, 25, 55 and 95 \({}^{\circ}\)C) were shown. It is clear from the figures that with the increase of frequency, G* tended to increase while increasing temperature had an opposite effect on G*. An abrupt behavior in G* at \(-\)5 \({}^{\circ}\)C was depicted by UFO binder, which happened due to partial slippage or breakage of asphalt material sandwiched between the parallel plate geometry at low temperatures. However, FO samples did not show any breakage of material under the same testing conditions which can be regarded to the presence of metal traces hindering the molecular bonding and causing cracking of binder.  
Phase angle, ratio of permanent deformation to elastic deformation, increased with increasing temperatures until 70 \({}^{\circ}\)C, after which it tended to decrease. At lower temperatures, asphalt binder behaved more elastically which resulted in low phase angle values and higher storage modulus. On the other hand, at higher temperatures, viscous behavior is more prominent and asphalt binder started to behave like a Newtonian fluid and hence resulted in higher phase angle values where almost all the response to applied loading was dissipated within the binder.  
Figure 12: %Recovery and Jnr at different temperatures.  
Figure 13: Isotherms of complex shear modulus and phase angle for (**a**) BB and (**b**) F.O.
## 2 Calculation Model and Solution Method
### \(p\)-\(y\) Curve Characteristics and Selection  
The relationship between pile side resistance, \(p\), and displacement, \(y\), directly affects the working characteristics of the pile. The current Chinese pile foundation design code assumes elastic resistance that is divided into the \"m method\", \"k method\", and \"C method\", according to different assumptions about how the proportional coefficient of the foundation, \(c_{z}\), varies with depth. When the soil displacement at the pile side is large, the nonlinear relationship between resistance, \(p\), and displacement, \(y\), is often best described by a \(p\)-\(y\) curve.
## 4 Case Study
### Analysis of Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Number** & **Departure Task Chain** & **Deadhead Traveling Path (m)** & **Vehicle Size** \\ \hline
1 & 11-48-58-173-41-45-147-63 & 853 & Medium \\
2 & 46-52-20-149-72-148-19 & 0 & Small \\
3 & 53-1-175-12-55-89-22-138-106 & 5042 & Small \\
4 & 88-17-123-77-155-76-54 & 3343 & Small \\
5 & 44-172-140-4178-5-38 & 2996 & Medium \\
6 & 96-80-23-37-109-164 & 3583 & Small \\
7 & 8-158-176-87-107-65 & 2317 & Small \\
8 & 157-77-122-39-166-9-170 & 1985 & Medium \\
9 & 36-135-156-151-146-26-2-16 & 2101 & Medium \\
10 & 162-127-131-116-137-154-121-145 & 6696 & Medium \\
11 & 141-124-161-129-29-125-3-120 & 1761 & Small \\
12 & 30-78-163-174-56-24-50-86-167 & 9401 & Medium \\
13 & 117-7-44-34-171-43-59 & 13,273 & Small \\
14 & 90-130-177-133-28-126-103 & 13,259 & Small \\
15 & 168-64 & 1121 & Small \\
16 & 73-114-70-6-108-180-100-51-160 & 2185 & Large \\
17 & 33-10-42-95-139-105-35-136 & 9293 & Medium \\
18 & 113-69-57-81-118-104-128 & 4878 & Medium \\
19 & 61 & 0 & Small \\
20 & 98 & 0 & Small \\
21 & 27-84-179-31 & 1742 & Medium \\
22 & 68-93-110-92-82-67-102 & 2306 & Large \\
23 & 75-119 & 6062 & Medium \\
24 & 159-142-85-13-144-15-152 & 2264 & Large \\
25 & 60-111-14-21-97-143-91 & 10,714 & Large \\
26 & 40-79-112-169-66-18-132 & 6595 & Large \\
27 & 94-25-83-62-153 & 9919 & Large \\
28 & 32-150-115-99 & 6044 & Large \\
29 & 101-49 & 0 & Large \\
30 & 134-165 & 2215 & Large \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The scheduling scheme corresponding to the optimal target value. If only the unilateral factor of vehicle number is considered without the deadhead traveling path factor, the results for the same case are compared in Table 6. The results show that compared with the scheduling solution that only considers the vehicle size unilaterally, the scheduling solution that integrates the vehicle size and mileage factors can reduce the total empty mileage of the vehicle chain by 23.34%, effectively reducing invalid empty trips and thus reducing the empty trip cost, while reducing the vehicle size to the same level. The above results prove the feasibility and effectiveness of this model algorithm for solving the DRT-based reservation scheduling problem.
# A Study of Landfast Ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea
## 4 Results and Discussion
### Coherence Magnitude Analyses
#### 4.1.1 VH and VV Polarization Mode Comparison  
The IW SLC acquisitions are in two VH and VV polarization modes. Both polarizations were studied over the area of interest. VV polarization coherence is much higher than VH polarization in interferometric coherence histograms (shown in Figure 8). It can be seen that VV coherence in the areas of landfast ice varies from 0.2 to 0.44, while VH coherence magnitude does not reach higher than 0.35.  
Figure 7: The degree of interferometric coherence for image pair on 6 and 18 February 2015 over the study area: (**a**) Sentinel-2 image showing an optical overview of the study area on 29 May 2015, where green areas are land and islands, and black areas are water; (**b**) interferometric coherence magnitude image; and (**c**) a polygon extracted from the InSAR coherence image, variation of coherence is 0.2–0.46.  
Thus, in the absence of any other structural differences between polarizations, our further analysis concentrated on the VV polarization mode.
## 3 Results and Discussion
### Response of the System in Presence of Buffer Ions  
To investigate the influence of the buffer ions on the electrochemical double layer and the column resistance, experiments with 10 mM phosphate buffer, conventionally employed in biotechnology, were carried out. Additionally, DI water experiments were run again for the direct comparison of results. These experiments confirmed the reproducibility of the current response behavior of the PCC column described in Section 3.5 as well as the aging effect, which is present for buffer and DI-water (after more than 250 working hours). The current/potential diagram in Figure 4 presents the relationship between the applied potential, the current, the electrode aging and the effect of the buffer ions. It shows that the mean current value increases in the phosphate buffer environment and the packed bed resistance decreases, caused by the higher ion concentration in the system. In addition, the EDL rearrangement time is longer (see Figure S11), and a higher concentration gradient is expected between the MWCNT surface and the bulk solution.  
The aging of the electrode is caused by repeated use, which leads to structural changes in the CNT-K matrix (see Figure S12 for tracer experiments to demonstrate the aging of the MWCNT matrix). Consequently, the mean current decreases and the EDL rearrangement time changes. In the DI-water system, the mean current profile decreases, especially at high negative potentials when aging takes place, whereas this development is substantially less pronounced for the buffer. This non-linear behavior in the DI-water environment indicates that a detectable increase in electron transfer is present when fewer ions are available. Regarding the packed bed resistance, the ion rich buffer seems to be the more promising medium for an electrochemical cell, resulting in \(6.47\times 10^{5}\) mm\({}^{2}\)/m at the beginning and in \(1.32\times 10^{6}\)\(\Omega\) mm\({}^{2}\)/m after aging. In comparison, the DI-water system reaches packed bed resistances between \(1.26\times 10^{6}\)\(\Omega\) mm\({}^{2}\)/m and \(1.09\times 10^{9}\)\(\Omega\) mm\({}^{2}\)/m. The electric charge of the working electrode correlates with the ion concentration (Figure S13): if more ions are available in the system, the electric charge is higher, up to \(\pm 1000\) mC, while it is around \(\pm 500\) mC in DI-water, independent of the potential-switch order of chronoamperometry experiments (Figure S14). A loss of electric charge occurs when aging effects occur, as caused by structural changes in the CNT matrix and redox reactions on the surface.
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 3 Experiments  
The research experiments are performed on the processing dataset and the public Massachusetts Road Dataset [39], respectively. The deep learning framework selects TensorFlow as the platform to train and test the relative networks. In addition, two sets of NVIDIA GTX 2080 Ti GPU are bridged to guarantee computing power.
## 3 Data Description and Data Preprocessing
### Data Preprocessing
#### 3.2.2 One-Hot Encoding  
The original data contain categorical variables that cannot be directly input into the model, such as the bit type and formation lithology. Categorical variables need to be processed. One-hot encoding can convert categorical variables into a numerical form that can be used in machine learning algorithms, and it can enable algorithms to better understand categorical variables.  
However, it has the potential to greatly increase the data dimensionality, especially for categorical variables with large numbers of categories. To avoid such problems, this paper only uses one-hot encoding for the bit types.  
The bits mentioned in this article are all PDC bits, but they have different specifications, sizes, and nomenclatures. They need to be encoded, and the encoded forms are as follows:  
\[\begin{array}{rl}\text{T1665R}&\rightarrow[1,0,0,0,0]\\ \text{KM1662}&\rightarrow[0,1,0,0,0]\\ \text{M1665S}&\rightarrow[0,0,1,0,0]\\ \text{ST915}&\rightarrow[0,0,0,1,0]\\ \text{ST615 RS}&\rightarrow[0,0,0,0,1]\end{array} \tag{11}\]  
Figure 5: Flowchart of data preprocessing.  
where these bits are named differently by the IADC. They come from different manufacturers and are named according to the manufacturers' own rules. The first (or first two letters) in the bit code refer to the manufacturer, and the rest refer to the bit properties, such as the cutter size and number of blades.
## 5 Conclusions  
In this paper, a novel pre-production THz spectrometer was tested and calibrated. The T-Sweeper is a fast-scanning frequency-domain spectrometer designed for industrial applications. It was calibrated, tested and compared with commercially available TDS and FDS instruments, a TeraFlash pro (TDS) and a TeraScan (FDS).  
To specify the operation of the T-Sweeper, its operational range of frequencies and the frequency dependent dynamic range have been measured and shown to be 0.1-3 THz and  
Figure 8: Absorption coefficient (**a**) and refractive index (**b**) of UHMWPE measured by TeraFlash, T-Sweeper, and the TeraScan. The narrow range of values measured reveals the noise in the extracted parameters.  
94 dB, respectively, when using 10,000 measurement averages. Frequency calibration was performed using atmospheric water vapour and a silicon wafer etalon, confirming that the T-Sweeper has a linear frequency axis with no offsets or drift. Amplitude linearity was tested using a set of silicon plates, confirming that the amplitude axis of the T-Sweeper is linear up to the limits of its dynamic range.  
In addition, spectroscopy measurements were carried out comparing the performance of the T-Sweeper with TeraFlash and TeraScan, using several types of material samples (resonant mesh filters, lactose monohydrate and UHMWPE). The T-Sweeper performance was observed to be repeatable and in good agreement with other devices, within its range of operation and within measurement uncertainty.  
T-Sweeper was confirmed as an accurate, reliable instrument, with good repeatability, suitable for industrial use case scenarios.  
Conceptualization, L.L., N.V., M.N., J.K. and K.W.; methodology, J.K.; writing--original draft preparation, J.K. and M.N.; writing--review and editing, J.K., M.N., L.L., N.V., K.W. and R.K.; visualization, J.K.; supervision, M.N. and N.V.; project administration, M.N.; funding acquisition, M.N. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 765426 (TeraApps).  
The data that support the findings of this study are available from the authors on reasonable request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 2 Background Motivation and State of the Art  
There are two main approaches to ensure a high-security level of medical image transmission systems [2]: First, watermarking which is defined as a technique of embedding certain information into a medical image [11]. Digital watermarking targets are data-hiding, integrity control, and authenticity [12]. Second, metadata which is defined in this context as the attached data to a medical image. Here, the digital signature is one of the famous techniques of metadata that ensures the integrity and the authenticity of the medical image.  
Figure 1 illustrates the previous two techniques to provide medical image transmission systems with a high level of security. In Figure 1a, a digital signature is generated by an asymmetric algorithm after signing a hashed value of the original medical image. The concatenation operator links the original medical image and the digital signature to generate a signed image. On the receiver side, the verification of the validity of the resulting signed image requires the corresponding public key to retrieve the received hashed value and compare it with the computed hashed value from the original medical image. The presented digital signature scheme deploys a hash function and asymmetric encryption. However, the asymmetric encryption algorithms are considered as computationally intensive techniques, relatively slow, and a certificate authority is required to manage the public keys [13].  
In Figure 1b, a watermarking system is presented. A watermark (WM) is, in particular, extracted from the original medical image by a watermark generator. Then, the extracted watermark is embedded in the original medical image. Moreover, the medical information such as the patient's information, hospital logo, and Doctor ID can be embedded into the original image as a watermark for authentication, tamper-proofing, and copyright protection as well [14, 15]. In [13], a technical discussion about watermarking for medical images and other security techniques was reviewed. The results showed that watermarking techniques are still not accepted yet for modern applications, where the current watermarking techniques suffer from some weaknesses; for instance, the sensitivity of the bit error is very low, and the possibility of detecting a valid watermark image as an invalid watermark image or vice versa is very high [13]. As a solution to such vulnerabilities, several medical image security approaches that merge watermarking and cryptographic techniques for medical image systems were proposed in the literature such as [16, 17, 18]. In the following, we briefly present some related works to the combination of watermarking and cryptographic primitives.