esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed Scheme
#### 2.5.4 Authentication Phase
* Upon receiving \(M_{5}\), the \(SN_{i}\) decrypts the message \(CT_{f}=E_{A_{i}}[HGW_{ID},FGW_{ID},SN_{ID},\)\(PID_{i},N_{f},V_{i},TS_{4}]\), using the key \(A_{j}\) to obtain information. Then, it checks the freshness of the timestamp \(|TS_{4}^{*}-TS_{4}^{{}^{\prime}}|\leq\triangle T\), and checks \(SN_{1D}^{*}\
eq SN_{ID}\). If holds, \(SN_{i}\) calculates \(V_{i}^{\prime}=h(TID_{i}\parallel N_{f}^{*}\parallel TS_{4}^{*})\) and verifies \(V_{i}^{\prime}\
eq V_{i}^{*}\), if does not hold,  
Figure 8: Login phase. ends session. Otherwise; \(SN_{i}\) generates a random nonce \(Nc_{f}\), and computes \(F_{i}=h(PID_{i}\oplus N_{f}\oplus Nc_{f})\), \(Q_{i}=h(FGW_{ID}^{*}\parallel Nc_{f}\parallel TS_{5})\) and sends \(M_{6}=SN_{ID},F_{i},Q_{i},TS_{5}\) to the FGWN. * The FGWN receives \(M_{6}\), it checks the freshness of the timestamp \(TS_{5}\), and computes \(Nc_{f}^{*}=F_{i}\oplus h(PID_{i}\oplus N_{f})\), and \(Q_{i}^{*}=h(FGW_{ID}\parallel SN_{ID}\parallel PID_{i}\parallel Nf\parallel Nc _{f}^{*}\parallel TS_{5})\). Then, it validates \(Q_{i}^{*}\
eq Q_{i}\), if holds, the FGWN calculates \(SK_{ef}=h(FGW_{ID}\parallel SN_{ID}\parallel PID_{i}\parallel Nf\parallel Nc _{f}^{*}\parallel TS_{5})\), and \(CT_{if}=E_{SK_{ef}}[FGW_{ID},SN_{ID},PID_{i},N_{f},Nc_{f}^{*},T\) \(S_{6}]\). Finally, FGWN prepares and sends the \(M_{7}=SN_{ID},CT_{if}\) to the \(U_{i}\). * After receiving \(M_{7}\), the \(U_{i}\) calculates key \(SK_{u}=A_{if}^{*}\oplus h(PID_{i}^{*}\parallel PWR_{i}^{*}\parallel TID_{i}) \oplus h(\sigma_{i}^{*}\parallel k_{i}^{*})=h(FGW_{ID}\parallel PID_{i} \parallel FGW_{MSK})\), and decrypts \(CT_{if}=D_{SK_{u}}[FGW_{ID},S\) \(N_{ID},PID_{i},N_{f},Nc_{f}^{*},TS_{6}]\) to obtain information. After retrieving the data, it checks the freshness of the timestamps \(TS_{6}\). Furthermore, the sensor identity is \(SN_{ID}\). If it holds, \(U_{i}\) generates a random nonce \(Nc_{u}\) and computes \(D_{i}=h(PID_{i}\parallel N_{f}\parallel Nc_{f}^{*}\parallel Nc_{u})\), and also computes shared session key as \(SK_{U\to SN}=h(HGW_{ID}\parallel FGW_{ID}^{*}\parallel SN_{ID}\parallel PID _{i}^{*}\parallel Nc_{f}\parallel N_{j}\parallel N_{j}^{*})\), and\(J_{i}=h(SK_{U\to SN}\parallel TS_{7})\). Finally, \(U_{i}\) sends \(M_{8}=SN_{ID},D_{i},J_{i},TS_{7}\) to the sensor node. * The \(SN_{i}\) receives \(M_{8}\) and starts checking the freshness of the timestamp \(TS_{7}\), then calculates \(Nc_{u}^{*}=D_{i}\oplus h(PID_{i}\parallel N_{f}\parallel N_{f})\) and session key \(SK_{U\to SN}^{*}=h(HGW_{ID}^{*}\parallel FGW_{ID}^{*}\parallel SN_{ID}\parallel PID _{i}\parallel N_{f}\parallel Nc_{f}^{*}\parallel Nc_{u})\), and \(J_{i}^{*}=h(SK_{U\to SN}^{*}\parallel TS_{7})\). After that, the \(SN_{i}\) checks the condition \(J_{i}^{*}\
eq J_{i}\), and if it holds, the agriculture professional and sensor node are successfully and mutually authenticated.
## 2 Literature Review
### Smart Healthcare
#### 2.5.2 Countermeasures
The authors stored records using the interplanetary file system (IPFS), which has the benefit of being dispersed and assuring record immutability. The suggested methodology also keeps illness data without invading any patient's privacy. The rising availability of healthcare data necessitates the precise analysis of illness diagnosis, progression, and real-time monitoring to enhance patients' therapies. Machine learning (ML) models are used in this context to extract significant characteristics and insights from high-dimensional and heterogeneous healthcare data to identify various illnesses and patient behaviours in a SHeS. However, recent studies reveal that ML models employed in different application areas are susceptible to adversarial assaults. This work [116] describes a novel adversarial method for exploiting the ML classifiers used in an SHeS. An attacker with a rudimentary understanding of data distribution, the SHeS model, and an ML algorithm may launch both targeted and untargeted assaults. Their attack employs five different adversarial ML algorithms to carry out various malicious behaviours on an SHeS (e.g., data poisoning, misclassifying outputs, and so on). Using these adversarial capabilities, the authors modify medical device readings resulting from the SHS to change the patient status (disease-affected, normal condition, activities, etc.). Furthermore, according to an adversary's training and testing phase capabilities, the system undertakes white and black box attacks on an SHeS. They also assess the effectiveness of their work in various SHeS settings and medical equipment. Their rigorous study demonstrates that the suggested adversarial approach may severely reduce the effectiveness of an ML-based SHeS in properly recognizing illnesses and normal patient behaviours, resulting in incorrect treatment.
## 4 Conclusions  
To conclude, VO\({}_{2}\) thin films with monoclinic crystal phase are here successfully grown on glass substrates by PLD (Plused Laser Deposition) techniques. Cu\({}_{50}\)Zr\({}_{50}\) buffer layers of various thicknesses were introduced to modulate the MIT and optical properties. It is observed that a pronounced smaller \(\Delta H\) across the MIT is achieved in the optimized VO\({}_{2}\) (60 nm)/Cu\({}_{50}\)Zr\({}_{50}\)(40 nm)/glass film. The \(\Delta T_{sol}\) value is as high as \(\sim\)14.3%, with the \(T_{vis}\) value up to 52.3%. A \(\Delta T_{2500\rm nm}\) value of up to 60.2% is achieved in the VO\({}_{2}\) (60 nm)/Cu\({}_{50}\)Zr\({}_{50}\)(40 nm)/glass film. The present study suggests that the introduction of a Cu\({}_{50}\)Zr\({}_{50}\) buffer layer of appropriate thickness on a glass substrate is beneficial to the improvement of crystalline quality and thermochromic properties of VO\({}_{2}\) thin films, which makes VO\({}_{2}\) a candidate material for use in photoelectronic devices and smart windows.  
Figure 6: Summarized optical properties of the VO\({}_{2}\)/Cu\({}_{50}\)Zr\({}_{50}\)/glass thin films with different buffer layer thicknesses.  
**Author Contributions:** [PERSON] and [PERSON] conceived this paper based on previous work. [PERSON], [PERSON] and [PERSON] contributed to the experimental work. [PERSON] wrote the paper with input from all authors. [PERSON], [PERSON] and [PERSON] discussed this work and commented on this manuscript.  
**Funding:** This research was funded by by the National Natural Science Foundation of China (No.11405045, 51402281, 11775224 and 11304081), the Program for Innovative Research Team of Henan Polytechnic University, China (No. T2017-2) and the Fundamental Research Funds for the Central Universities of Hefei University of Technology, China (No. 108-4115100092).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Samples  
The binary system studied in this work consists of palmitic acid (PA) and stearic acid (SA). These materials belong to the vegetable oils considered as waste disposal that could even replace petroleum-based polymers, as explained in Reference [12]. Information regarding those materials and their properties is given in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Palmitic Acid** & **Stearic Acid** \\ \hline CAS number & 57-10-3 & 57-11-4 \\ Formula & C\({}_{16}\)H\({}_{32}\)O\({}_{2}\) & C\({}_{18}\)H\({}_{36}\)O\({}_{2}\) \\ Molar mass (g/mol) & 256.43 & 284.48 \\ \(T_{m}\) (\({}^{\circ}\)C) in this work & 61.5 & 68.9 \\ \(T_{m}\) (\({}^{\circ}\)C) from [13; 14; 15] & 624.6, 62.4, 62.3 & 70, 69, 70.8 \\ \(\Delta_{m}H\) (J/g) & 204 & 222, 214 \\ \(\Delta_{m}h\) (J/mol) & 52 312 & 63 155, 60 879 \\ Purity & 99\% & 98,5\% \\ Price (USD/kg) [16] & 0.7–0.83 & 0.72–0.87 \\ \hline \end{tabular}
\end{table}
Table 1: General information about the fatty acids used in this study and provided by Sigma-Aldrich (St. Louis, MO, USA).  
Droplets of the PA-SA binary system, each with a specific molar fraction of both components, are deposited on an aluminum plate and regularly spaced so that the heat released/absorbed by a droplet does not interfere with the others. The droplet composition ranges from 0 to a 100% of PA with a step increment of 1% in order to have a significant amount of data. Those samples were prepared by weighing each component (~3 mg) using a Mettler Toledo scale with a weighing accuracy of \(\pm\)0.03 mg.
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 2 The Importance of Order
### Aperiodic Ordered Structures
New designs based on the aperiodic ordered photonic structures were proposed for promising technological applications as well. [PERSON] et al. have studied the emission in aperiodically-ordered structures, considering a Penrose-type quasicrystal laser [35]. [PERSON] et al. demonstrated a quasi-periodic distributed feedback laser based on Fibonacci mirrors enabling directional output independent of the emission frequency and multicolor operation [36]. Nanoscale aperiodic surfaces were proposed for label-free biological sensing applications. The photon trapping arising from higher-order multiple scattering processes leads to a dense spectrum of highly complex structural resonances, i.e., colorimetric fingerprints, like those shown in Figure 1d, which can be captured with conventional dark-field microscopy and exploited to detect surrounding refractive index variations by means of imaging analysis techniques [37]. In particular, both the peak wavelength shifts of the scattered radiation as well as the environment-dependent spatial structure of the colorimetric fingerprints of aperiodic surfaces have already been utilized to detect the presence of nanoscale protein layers [38]. Quasicrystals were investigated for solar energy harvesting too. Light-trapping optimization by nanopatterning is in fact a crucial point to enhance the amount of light that can be absorbed by the device, especially in thin-film silicon solar cells, where the light absorbance can be a bottleneck of the photovoltaic process [39]. For example, an established approach to increase light trapping in thin-film silicon solar cells is the growth of transparent zinc oxide electrodes with a random pyramidal texture by means of chemical vapor deposition. This texture has already demonstrated exceptional light-trapping capabilities and is essential in overtaking the 10% conversion efficiency limit for single junction amorphous silicon solar cells [40]. [PERSON] et al. made a comparison between the light-trapping efficacy of random nanopyramids and of periodic nanocavities with similar dimensional features (Figure 1e). By performing theoretical calculations on the basis of the two real geometries they predicted that periodic structures should outperform random textures, as they avoid scattering into lossy radiation channels. The external quantum efficiency of the two devices, however, was exactly the same, leading to the conclusion that the performance of thin-film silicon solar cells is currently limited by the trade-off between optical and electrical performance [41]. In a more recent study, [PERSON] et al. showed that aperiodic tiling of photonic quasicrystals could potentially offer specific advantages to enhance light trapping capability through aperiodic patterned back reflectors in thin film solar cells by fully exploiting the additional degree of freedom and the richness of states offered by quasicrystals [42].
## 3 The SOMM Model
### Achieving Flexibility via Virtual Machine
#### 3.3.2 Routing and Network Configuration
After servicing the client, server node informs the service registry that it can serve new clients and then service registry again refers to its table and marks the corresponding service as available.
## 4 Surface Characterizations Based on Surface Susceptibilities
### Example of Surface Characterizations Based on Surface Susceptibilities  
An example of surface characterization based on surface susceptibilities is given here. Configuration of the discussed surface's unit cell is shown in Figure 11, which is composed of dual-layer PEC Jerusalem cross-shaped patches with four metallized vertical vias. Its scattering properties at 10 GHz will be characterized through the procedure introduced above. At 10 GHz, the wavelength \(\lambda\) is equal to 30 mm, which is much larger than unit cell's period \(p\), which makes the assumption of homogeneous effective valid. Also, its thickness \(h\) is much smaller than \(p\), so it can be regarded as a single-layer structure. The unit cell has the same effect on EM fields along \(\hat{x}\) and \(\hat{y}\) directions, leading to isotropy of the discussed surface.  
In order to determine its surface susceptibilities, three simulations of the unit cell are done at 10 GHz using ANSYS HFSS with PBCs, where the value of \(\theta_{0}\) is selected as \(60^{\circ}\). The extracted surface susceptibilities based on Equation (32) with variation of parameter \(a\) are shown in Figure 12. The fluctuation of \(\chi^{zz}_{mm}\) should be due to the numerical errors from simulation software.  
With \(\overline{\overline{\chi}}_{ee}\) and \(\overline{\overline{\chi}}_{mm}\) determined, \(\Gamma\) and \(T\) of this surface for arbitrary \(\theta\) and polarizations can be readily computed. For verification purpose, computations with \(\theta=45^{\circ}\) for both TE and TM polarizations are selected as examples. Figure 13 shows the computed transmission coefficients at 10 GHz through Equations (21) and (22), compared with simulated results from ANSYS HFSS. Good agreement between computation and simulation is observed, which validates the effectiveness of the method. Although only one example is given here, this characterization method can be utilized for arbitrary single-layer isotropic metafilms.  
Figure 11: Unit cell of a single-layer isotropic metafilm with \(p=3\) mm, \(w=0.2\) mm, \(h=1\) mm, radius of vias equals 0.05 mm and relative permittivity \(\varepsilon_{r}=4\): (**a**) perspective view, (**b**) top view.
## 2 Materials and Methods
### Methods
#### 2.2.4 Evaluation Method of Calibration Results  
First, we calculated the comprehensive gain corresponding to a certain integration stage individually, assuming the offset \(B_{i}\) in Equation (2) was zero. The standard deviation (SD) of the differences between the comprehensive gains on different dates and the corresponding mean value was calculated, and then, the relative bias (RB) was computed by using the SD divided by the mean comprehensive gain. The RB value was used as the characterization of the stability of calibration results at different sites and different integration stages in the same year. The formulas of SD and RB are shown in the following Equations (7) and (8), respectively:  
\[\sigma=\sqrt{\frac{\sum_{j=1}^{N}\left(x_{j}-\overline{x}\right)^{2}}{N-1}} \tag{7}\]  
\[U=\frac{\sigma}{\overline{x}}\times 100\% \tag{8}\]  
where \(j=\left(1,2,\ldots,N\right)\) represents the index of the calibration results at different sites and different integration stages, \(x_{j}\) represents the \(j\)-th comprehensive gain obtained by the \(j\)-th calibration, and \(\overline{x}\) represents the mean value of multiple comprehensive gains.  
The fitting comprehensive gain for a given year was solved through Equation (6) using satellite data and RadCalNet data at different sites and different integration stages. Assuming the comprehensive gain was applicable to all of the integration stages of the CMOS sensor, in this paper, we evaluated the accuracy of the fitting comprehensive gain. The fitting comprehensive gain was applied to the satellite data to obtain the satellite radiance, and the corresponding simulated radiance was calculated using the RadCalNet data. In this paper, we used the average relative difference %\(RE\) and root mean square error %\(RMSE\), which are shown in Equations (9) and (10), respectively.  
\[\%RE=\frac{1}{N}\sum_{k=1}^{N}\left|\frac{\hat{L}_{k,i}-\overline{L}_{k,i}}{ \overline{L}_{k,i}}\right|\times 100 \tag{9}\]  
\[\%RMSE=\frac{\sqrt{\frac{1}{N}\sum_{k=1}^{N}\left(\hat{L}_{k,i}-\overline{L} _{k,i}\right)^{2}}}{\frac{1}{N}\sum_{k=1}^{N}\overline{L}_{k,i}}\times 100 \tag{10}\]  
where \(\overline{L}_{k,i}\) is the simulated radiance calculated using the RadCalNet data, \(\hat{L}_{k,i}\) is the satellite radiance calculated using the fitted comprehensive gain, \(N\) represents the number of samples, \(k\) represents different dates, and \(i\) represents different bands.  
In addition, the measured surface reflectance in the field was used to evaluate the fitted comprehensive gains. The radiometric calibration of the satellite image was carried out first, and the atmospheric correction on the radiance image using the 6S radiative transfer model was then carried out to obtain the surface reflectance image. Finally, the reflectance retrieved from the satellite image was compared with ground measured surface reflectance.
# Road Vehicles Surroundings Supervision:
## 1 Introduction  
New assistance systems, cooperative services and autonomous driving of road vehicles imply an accurate knowledge of vehicle surroundings. This knowledge can come from different sources, such as onboard sensors, sensors in the infrastructure, and communications.  
Among onboard sensors, short- and long-range sensors can be distinguished. In the first case, ultrasonic, infrared, and capacitive sensors can be cited. Among the second group, laser scanners and computer vision technologies appear to provide the best performance, although there are many others that can complement the information using data fusion processes. In any case, the goal is to have a representation of vehicle surroundings that is as complete as possible. Sensor fusion algorithms are a common solution to overcome the sensors' individual limitations.  
Vehicle positioning is also essential. For this purpose, satellite positioning is commonly used, but when it is not sufficiently reliable or the signal is lost, the same technologies as those used for the recognition of the surroundings can provide an acceptable solution. In this regard, the SLAM (Simultaneous Localization and Mapping) problem should be noted, which tries to build or update the map of an environment that is not known a priori, and position the vehicle on that map simultaneously. This technique, widely used and proven in robotics, has also been implemented in the vehicular field for the perception of the environment in real time, for support and accuracy improvements in autonomous global navigation satellite systems (GNSS) navigation, and for generation of digital maps or calculation of trajectories followed when no GPS (Global Positioning System) signal is received.  
Vehicle-to-vehicle (V2V) communications and vehicle-to-infrastructure (V2I) communications allow the vehicle to have greater knowledge of surroundings and to obtain information that is far away from the onboard sensors. These communications provide additional data that could be used for driver information purposes or for decision modules in autonomous driving, for example. In this sense, connected and cooperative driving appears as a catalyst of autonomous driving, because it enables real deployment under complex driving situations.
# U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data
## 4 Numerical Results  
### Synthetic Data  
To test the reliability of the trained ACB-UNet, we use it to attenuate the traffic noise in the synthetic data. Figure 8a shows synthetic data without noise, the number of data traces was 360, and the record length was 7 s. Figure 8b shows the noise we extracted from the real data, which mainly includes two types of typical traffic noise: noise distributed in the fan-shaped and thin strip. The synthetic data containing noise is shown in Figure 8c, from which we can see that the road noise covers the reflection and affects the analysis and utilization of the effective signal. We further analyze the characteristics in f-k spectra, as shown in Figure 8d. The reflection is mainly distributed in the range of 0-20 Hz. Noise is primarily distributed in the range of 3-60 Hz, coupled with reflection in the 3-20 Hz range.  
We use the proposed ACB-UNet method to attenuate traffic noise in synthetic seismograms and compare it with other methods. Figure 9a,b show the denoising results and noise section obtained by f-k filtering. It can be seen from Figure 9a that there are still noise residues in the denoised records, and the reflection at the noise location has been distorted. From Figure 9b, we can see serious artifacts in the noise section caused by the F-K algorithm mechanism. Figure 9c shows the denoised result obtained by the UNet. The thin strip noise has been well removed, but the fan-shaped noise (near the 60 th trace) removal is not ideal, and there is still noise residue. The result after the ACB-UNet attenuates the traffic noise is shown in Figure 9e, and there is no longer visible noise residue in the denoised result.  
Figure 10 shows the f-k spectra of Figures 8a and 9e. Comparing Figures 8d and 10a, it can be found that there is no aliasing in the f-k spectra of clean records. As can be seen in Figure 10a,b, there is no significant difference between the f-k spectra of the two, and the noise is effectively attenuated. The trained ACB-UNet method can recover the f-k spectra. To quantitatively evaluate the performance of the ACB_UNet method in synthetic data, we plotted the amplitude spectra, as shown in Figure 11. The blue line corresponds to the synthetic data with traffic noise, the black line represents the synthetic data, and the red line denotes the results obtained by the ACB_UNet. Compared to the lines, the energy of traffic noise is successfully attenuated by the ACB_UNet. Figure 11b shows the enlarged rectangular area. The result obtained by the ACB_UNet is very close to the synthetic data, with no significant loss of effective signal.  
Figure 8: (**a**) Synthetic data without noise; (**b**) traffic noise; (**c**) synthetic data after adding noise to (**a**); (**d**) the f-k spectra of (**c**).
## 7 Conclusions  
In this work, a novel energy-efficient routing protocol named LEACH-PRO was proposed to make WSNs more sustainable and optimize their operation in many smart cities use cases. This protocol was designed to overcome the limitations of LEACH by introducing the probabilistic cluster head selection based on the residual energy and the distance to the base station. Performance evaluation results highlighted that LEACH-PRO outperforms both LEACH and direct transmission protocol in terms of the achieved network lifetime, node death rate speed, and residual energy depletion speed, in addition to the generated traffic load on the network. However, the results show also that as the network size increases the performance of LEACH-PRO degrades to some extent. Future works will include implementing LEACH-PRO on a hardware platform and testing its performance at the campus of QUEST in Pakistan. The promising performance shown by LEACH-PRO suggests that it can efficiently support several application scenarios in future smart cities.  
Conceptualization, Z.Y., I.H. and S.D.; methodology, Z.Y., I.H. and S.D.; software, Z.Y.; validation, Z.Y., I.H., S.D. and Y.H.-A.; formal analysis, S.D. and Y.H.-A.; investigation, Z.Y. and I.H.; resources, I.H.; data curation, Z.Y. and I.H.; writing--original draft preparation, Z.Y., I.H. and S.D.; writing--review and editing, S.D. and Y.H.-A.; visualization, Z.Y.; supervision, I.H. and S.D.; project administration, I.H.; funding acquisition, I.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available yet as it is still being used in an ongoing project.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 15: Traffic load evolution under varying network densities. Network of **(a)** 50, **(b)** 100, **(c)** 200, **(d)** 500 nodes.