esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion  
### The Spectral Profile of Almond and Apricot Kernels  
Figure 4a presents the raw spectral data and mean raw data, and b presents the SNV pre-processed spectral data obtained from the kernels in the range of 950-1622 nm based on the ROI selection.  
The spectral data before 950 nm and after 1622 nm were omitted due to the high noise level, which was mainly caused by the low signal response of the camera. Based on the mean of spectral data, the apricot kernel reflectance spectral pattern seems to be higher than that of the almond kernels. This is because of the chemical composition content in both kernels. Both patterns are the same, but the almonds show a different reflectance due to their high amounts of proteins and amino acids, saturated fatty acid, and carbohydrates compared to apricot kernels, based on a nutritional comparison of almonds and apricot kernels [29]. Therefore, more light is absorbed by the almond kernels, and more light is reflected by the apricot kernels.  
The differences in chemical composition in both seed kernels can be observed in the spectral regions representing the lipid, protein, and carbohydrates [30; 31; 32]. The SNV preprocessed spectra of two different kernels in Figure 4b showed distinctive spectral differences at four different wavebands: 950-1120, 1180-1220, 1260-1340, and 1400-1500 nm. The range between 950 and 1220 nm and the peak of 995 nm were caused by the N-H second overtone associated with peptides and proteins [33]. The range between 1180 and 1220 nm is repressing two significant peaks--1165 and 1200 nm associated with lipids [34] and proteins [35], respectively. The spectral range 1260-1340 nm is related to the combination of C-H stretching in the peak 1335 nm, which is representative of the starch content [36]. The spectral range between 1400 and 1500 nm corresponds to the first overtone of the hydroxyl group representing the hydrogen bonding in the starch molecules [37].  
The spectral patterns based on the SNV (b) pre-processed mean data show the important bands between 1435 and 1450 nm, corresponding to the first O-H stretching overtone, which can also be assigned to the starch. The spectral fluctuations from 1400 to 1600 nm correspond to the first overtone of the hydroxyl group. The precise position of these bands is very sensitive to hydrogen bonding in the starch molecule [36]. The absorption bands between 1165 and 1238 nm are associated with lipids, which shows the main differences between almonds and apricot seeds, and the bands include the C-H (-CH) second overtone stretching band (1200-1214 nm) [38].
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 3 System Model and Assumption  
This paper addresses the problem of developing an efficient healthcare monitoring system using NB-IoT that necessitates access prioritization of UEs to communicate with others through a base station (eNB) and proficient channel allocation with different time slots. This section presents the network environment and application model followed by assumptions.
# Accumulative Roll Bonding--A Review
## 2 Basics and Mechanisms  
Bonding quality is significantly affected by ARB parameters and underling mechanisms. Ample studies have been conducted to find out the bonding mechanisms during roll bonding process. The following are four major theories proposed to explain the bonding mechanisms:  
* Film theory;
* Energy barrier;
* Recrystallization
* Diffusion bonding.  
Film theory proposes that if two clean metal surfaces are placed in very close contact, a bond will create. The film theory is a dominant mechanism of low temperature ARB and bonding occurs when metal surfaces are exposed and deformed to a sufficiently large value. The fracture of surface layers and extrusion of virgin metals through the cracks have the main roles in a real contact.  
The energy barrier theory proposes that even if the fresh surfaces are in contact firmly, no bond creates; bonding precondition is to overcome an energy barrier. [PERSON] concluded that the energy barrier is recrystallization; however, [PERSON] believed that energy barrier is diffusion [26]. [PERSON] [27] suggested that the energy barrier comes from the crystal mis-orientation at the contact surfaces. His findings showed that bonding occurred for Al, Cu and silver (Ag) at the temperature of liquid nitrogen, hence it cannot be attributed to diffusion or recrystallization. [PERSON] et al. [28] and [PERSON] and [PERSON] [29] expressed that the film theory is the utmost mechanism during rolling at low temperatures. An appropriate surface preparation technique removes oxides and contaminant layers, creates a hardened layer, and increases surface roughness. It was expressed that brittle surface layers of both metals are broken up during the rolling operation, and the underlying layers are then exposed. The unveiled islands of metals are extruded and bonded together through opened cracks owing to rolling pressure from both sides. This mechanism has been confirmed by optical and scanning electron microscopes (SEM) [30; 31]. Figure 1b depicts a simplified illustration of the film theory wherein it is showed that at the outset, fracture of the brittle top layer is ongoing when the extrusion of virgin metal has just been started through the cracks and no bonds are visible yet. Continuing the rolling operation and increasing the pressure establishes a contact between the highest asperities of the uncovered material of the two opposing surfaces to form a metallic bond. In the meantime, some other areas are uncovered and innumerable bonds are created [32]. The investigations are still ongoing to find a better understanding about the correct mechanism(s).  
It should be noted that the aforesaid bonding mechanisms are metallurgical, however, it is thought that an initial mechanical bonding is first originated due to a physical contact and afterwards a strong metallurgical bond develops at the interface during ARB that may/may not be followed by a heat-treatment process [33].
# Single Trace Analysis on Constant Time CDT Sampler and Its Countermeasure
## 3 Single Trace Analysis on a CDT Sampler  
STA is a statistical power analysis attack using a single power consumption trace. The STA performs by statistically analyzing the distribution of an operation related to a secret key. For example, [PERSON] et al. recovered the secret exponent in RSA by exploiting the average Hamming weight difference between squaring and multiplication operations [23]. Since STA uses a single power consumption trace, blinded exponent or randomization is meaningless. More examples of these kinds of power analysis attacks include Horizontal Correlation Analysis (HCA) [24], Horizontal Collision Correlation Analysis (HCCA) [25], Recovery of Secret Exponent by Triangular Trace Analysis (ROSETTA) [26], and [PERSON] et al.'s attack [27]. In this section, we propose that the constant-time CDT sampler is not secure against STA. We theoretically and experimentally analyzed that an attacker can reveal every value of an error by exploiting a single power consumption trace. This indicates that lattice-based cryptosystems that use a constant-time CDT sampler are vulnerable since an attacker can reveal the secret key by using the recovered error vectors from STA.  
This section is organized as follows: first, we justify our attack by examining raw traces of a CDT sampler. Then, we show that our attack works on the simulated CDT sampler. Finally, we show how to recover the secret key given the sampled values. We demonstrated that any other lattice-based cryptosystems that use the constant-time CDT sampler as in Algorithm 2 is vulnerable to STA.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 3 LoRa, Channel and Noise
### Channel and Noise
#### 3.2.1 Channel  
Signal transmission over a wireless communication channel is affected by both large-scale and small-scale propagation effects. This propagation is influenced by four fundamental mechanisms, namely spatial dispersion, reflection, refraction, and scattering. A generalized channel model used for wireless communication systems is defined as follows [40]:  
\[y(t)=h(t)*x(t)+n(t), \tag{9}\]  
where \(y(t)\), \(h(t)\), \(n(t)\), and \(x(t)\) represent the received signal, channel impulse response, random noise, and transmitted signal, respectively. Typically, in industrial environments, nodes are arranged in a LOS configuration. The authors in [22, 39] carried out several channel propagation measurements in different industrial environments. Their measurements showed that in an industrial environment, the Rice distribution, Rayleigh, and normal Log characteristics are tracked by the time envelope of the signal received at a fixed point. Our  
Figure 2: Example of two-symbol demodulation.  
wireless transceiver, based on LoRa technology, is evaluated using a Rician distribution when the configuration is LOS, and a Rayleigh distribution in the opposite case NLOS.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 3 Results
### Application of Synthetic Observations to Study the Synergy between TEMPO and GOES-R
#### 3.2.2 Case Study 1: Smoke Detection
Some missed smoke detections, such as the thin to moderately dense parts of the Washington state smoke plume that was transported away from the fire and mixed with other aerosol, occurred even over dark surfaces because the AAI was too low. As we know the true aerosol composition in each pixel, we can examine how the algorithm performs as a function of the contribution of smoke to the total AOD. The diurnal variation of the accuracy of the algorithm is shown in Figure 16. Accuracy is defined as:  
\[Accuracy=\frac{T_{positive}+T_{negative}}{T_{positive}+T_{negative}+F_{ positive}+F_{negative}} \tag{25}\]  
where \(T_{positive}\) (true positive) is the number of pixels where the algorithm correctly detects smoke, \(T_{negative}\) (true negative) is the number of pixels where the algorithm correctly detects there is no smoke, \(F_{positive}\) (false positive) is the number of pixels where the algorithm falsely detects smoke, and \(F_{negative}\) is the number of pixels where smoke is present but the algorithm fails to detect it. Figure 16 indicates that the algorithm has the highest accuracy when only pixels with pure smoke (>95% of total AOD coming from smoke) are considered as \"true smoke\". For this case, the accuracy of the retrieval is greater than 80%, in agreement with the results from the validation of the algorithm using real observations [58]. The diurnal variability in the accuracy of the algorithm is due to the fact that fire activity increases in the afternoon, which increases the number of missed detections. Figure 16: Accuracy of smoke detection on 27 July 2006 (solid lines). The different colored lines represent the accuracy when a “true smoke” pixel is defined by different thresholds of the contribution of smoke to the total AOD. The highest accuracy occurs when only pixels with pure smoke (>95% of total AOD coming from smoke) are considered as “true smoke”.
# Unmanned Surface Vehicle Using a Leader-Follower Swarm Control Algorithm
## 4 System Structure of the USV
### Power System of the USV  
The power system of the USV consists of a battery, a coulometer, an indicator, a DC/DC converter, etc. The battery is the most important component that determines the USV operating time. Therefore, in this study, the battery with the most suitable capacity for the USV was selected according to the battery-energy amount calculation formula (Equation (19)).  
\[W_{h}=\frac{\left(W_{USV}\times L_{f}\times\mathrm{H}\,\times O_{P}\right)}{ \left(B_{a}\times S_{f}\right)} \tag{19}\]  
Here, \(w_{h}\) is the total uptime of the platform, \(w_{USV}\) is the total power consumption of the entire system, \(L_{f}\) is the average load factor of the system, H is the operating time with one charge, \(O_{P}\) is the operating rate of the system, \(B_{a}\) is the usable capacity compared to the total capacity of the battery, and \(S_{f}\) is the safety factor. Information on the related equations and variables can be found in [25].  
In this study, the total power consumption of the entire system was the sum of the thruster and CPU capacity, and the average load ratio and operation rate were limited to 40%. The usable capacity compared to the total capacity of the battery was set to 80%, the value recommended by the battery manufacturer, and the battery was selected with a safety factor of 1.5.  
Figure 11 shows the configuration diagram of the developed USV power system. Two Li-ion type batteries with a wattage of 25.9 V and 75 Ah were connected in parallel. The Li-ion type battery is used in this study because it is lighter in weight to volume and has high energy density compared to Li-polymer or LiFePo\({}_{4}\) batteries. However, Li-ion batteries have a fatal flaw of the memory phenomenon and a high risk of explosion. Therefore, in this study, the circuit of the power system was configured in such a way that a coulometer indicator and an electronic switch were installed to monitor the voltage input/output to the battery in real time and to cut off the power in the case of a problem. In addition, a battery management system (BMS) was additionally installed in the battery to handle the memory phenomenon, overcharge, and overdischarge.  
\begin{table}
\begin{tabular}{c c} \hline
**Size (L \(\times\) W \(\times\) H)** & **1500 \(\times\) 920 \(\times\) 920 (mm)** \\ \hline Weight & 60 (kgf) \\ Navigation sensor & Marineinnotc K-STAR \\ Battery & 25.9 V, 75 Ah \\ Camera & Hanwha QN0-6012R \\ Main controller & MSI cup 5 10M \\ AP bridge & GT-Wave 860S \\ Thruster & CiLab T80-60 \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of the leader and follower unmanned surface vehicles.  
Figure 10: Leader and follower unmanned surface vehicles manufactured in this study.
# Magnetic Resonance-Electrical Properties Tomography by Directly Solving Maxwell's Curl Equations
## 4 Discussion  
### Advantages of the Proposed Method  
It has been well recognized that most EPT methods developed so far are based on the second-order differential wave equation. These methods are theoretically sound but practically un-robust in real imaging applications. In particular, these methods all rely on the accurate phase information of the \(B_{1}^{+}\) data, which is particularly challenging to obtain in ultra-high field MRI. To find EPT solutions, assumptions and simplifications have to be made and cause system errors. By directly solving [PERSON]'s equations related to EPT (in which first-order differential operation only is involved) and probing dielectric properties via analysis of the \(\left|B_{1}^{+}\right|\) data, the proposed method inversely finds solutions [38, 39] with improved robustness and practicability for ultra-high field applications. This has been demonstrated in both simulation studies and phantom-based experimental testing at 9.4T. Results indicated that the proposed EPT offers a significant enhancement in the reconstruction quality compared with existing EPT approaches. Specifically, lower tissue boundary artefacts and less sensitive to measurement noise are observed, and acceptable EP reconstruction can be achieved with less than a 0.2 RMSE value when the SNR level reached 15 dB.  
As shown in the experimental results, the proposed method can provide EPT solutions, as well as export full components of the magnetic and electrical fields. With the available \(B_{z}\) profiles, one can see that near the birdcage center, the EPT reconstruction should be quite accurate without using the \(B_{z}\) information. However, in the regions close to the birdcage end rings, neglecting the \(B_{z}\) component would introduce system errors as the intensity of \(B_{z}\) component becomes comparable with that of \(B_{x}\) and \(B_{y}\). This \(B_{z}\) issue will be more prominent in array coil systems because of the larger value of \(\left|B_{2}\right|\)/\(\left|B_{x,y}\right|\) in such cases, and it is therefore expected that the new EPT method can provide much better solutions when surface coils are used. Furthermore, the proposed EPT method can also export the electric fields, which cannot be measured inside patients. The availability of these field components will be particularly useful to the estimation of patient-specific SAR and also new development of imaging methods in ultra-high field MRI.
# Advanced Metamaterial-Integrated Dipole Array Antenna for Enhanced Gain in 5G Millimeter-Wave Bands
## 3 Antenna Results and Discussion  
The performance parameters of the proposed metamaterial-based array dipole antenna are presented in this section. All of the antenna performance parameters, including the reflection coefficient, radiation characteristics, radiation efficiency, and gain, are suitable for wideband 5G NR millimeter-wave applications. The fabricated antenna prototype is shown in Figure 9a, while Figure 9b contains the measurement setup in an anechoic chamber [37] used for the antenna's radiation characterization. The S-parameters of the proposed metamaterial-based dipole antenna are measured in a normal environment using the Keysight E8364B PNA network analyzer. The far field is measured in an anechoic chamber, as shown in Figure 9b, while a horn antenna with standard performance is used as the transmitting antenna, and the proposed antenna receives the signal as a receiver.  
Figure 8: The gain response of the dipole antenna varies with different numbers of metamaterial cells (**a**) horizontally and (**b**) vertically.  
Figure 9: _Cont._
## 2 Materials and Methods
### Implant Position Planning Procedure in Three Different Software Programs  
The 90 duplicated models were randomly divided into 3 groups according to the 3 different implant planning software programs. The STL file of each model derived from an extra-oral scanner (D900, three shapes, Copenhagen, Denmark) was transferred into three different implant planning software programs; namely, Implant Studio (3 Shape(r) Copenhagen, Denmark), coDiagnostiX(r) (Straumann(r), Basal, Switzerland), and Blue Sky Plan (_Blue Sky Bio(r)_, LLC, Libertyville, IL, USA). For the remainder of this report, the alphabet is used to represent these implant planning software programs: Program A for Implant Studio, Program B for coDiagnostiX(r), and Program C for Blue Sky Plan. In each program, a virtual implant with a diameter of 4.1 mm and length of 10.0 mm of a bone level taper (BLT) Straumann implant system was selected from the existing library. For the determination of the planned implant position, the center axis line of the virtual implant was positioned on the same line of the center axis line of the cylinder-shaped box. Therefore, two-dimensional cross-sectioned images of each study model at 0 degrees (0\({}^{\circ}\)) or the meso-distal aspect and 90 degrees (90\({}^{\circ}\)) or the buccolingual aspect adjusted and confirmed the virtual implant axis line and the center axis line of the cylinder-shaped box of the base of the study model that was placed on the same line. With assistance from each implant planning program, a \"Ruler tool\" or \"Measure tool\" was used to confirm the virtual implant position and the two-dimensional images at 0 degrees; they also ensured that the distance of the mesial side and the distal side of the virtual implant had equal values (Figure 2). At 90 degrees, the distance of the buccal side and the lingual side of the virtual implant should also have had equal values (Figure 3).
## 2 Materials and Methods
### Surface Characterization
#### 2.3.2 SEM-EDS Analysis  
One sample for each surface was analyzed by Scanning Electron Microscopy / Energy Dispersive X-ray Spectrometry (SEM/EDS) carried out by an SEM (Quanta 200, FEI Europe Company, The Netherlands) at 25 kV coupled with an EDS analyzer (TEAM(tm) EDS, EDAX Inc., Mahwah, NJ, USA). Samples were not metalized, to keep surface properties undamaged. Measurements were performed in quadruplicate of each examined particle and area of the surface.
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Authentication Delegation
#### 6.2.2 Integrity  
In this domain, the number of systems secure in terms of confidentiality is equal to those secure in terms of integrity across all types of adversaries. It can be associated with the absence of any monitoring mechanisms in the evaluated systems. As a result, a violation of confidentiality typically leads to a violation of integrity. For example, if \(\mathcal{A}\) manages to capture the user's credentials and authenticate on their behalf to gain access to resources, \(\mathcal{A}\) can often also manipulate those resources by impersonating the user. The same logic applies in reverse. If \(\mathcal{A}\) cannot illegally authenticate and gain access to resources intended for the user (i.e., cannot violate confidentiality), then they cannot manipulate these resources to compromise integrity either.  
Considering the influence of \(\mathcal{A}_{weak}\) on system integrity, it can be concluded that \(\mathcal{A}_{weak}\) is not able to violate it, as they can only observe the communication without the capability to alter or corrupt it.  
Against \(\mathcal{A}_{middle}\), integrity remains secure for most of the protocols because they use digital signatures, message authentication codes and hashes to ensure that any unauthorised modifications to data can be detected [41; 47; 48]. However, some protocols [43; 40; 42; 39] may be vulnerable, because internal \(\mathcal{A}_{middle}\)'s accomplices could have access to sensitive tokens, session data, or authentication credentials, allowing them to tamper with the data or compromise their integrity.  
For \(\mathcal{A}_{strong}\), integrity is compromised in most protocols, as this adversary can exploit internal access to directly tamper with or modify data. The ability to manipulate keys, authentication data, or communication channels from within makes it difficult for these protocols to ensure data integrity. However, protocols employing a decentralised system architecture [45; 46] demonstrate resistance even against \(\mathcal{A}_{strong}\) due to their distributed nature.
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
## 2 Algorithm Design  
When pyramid LK algorithm is used for velocity calculation, local moving pixels may sometimes lead to wrong optical value. Therefore, large singular values would occur during calculation. Further, it affects the precision of UAV velocity in vision navigation system. So, Mask-R-CNN is introduced to perform object instance segmentation, in order to recognize and cover the objects with local movements. Additionally, K-Means clustering method can eliminate the singular optical values calculated by pyramid LK algorithm. The process is shown in Figure 1.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
## 2 Network Layout
### Chromatic Dispersion  
In OCN, each wavelength consists of ON/OFF flashes of light to perform the modulation of data. Spreading of optical pulses phenomenon occurs after traveling through several km in optical medium and this effect is known as dispersion [25]. Different wavelengths travel at slightly different speeds in OCNs. The dispersion which is caused due to variation of core refractive index is known as material dispersion [25]. However, some optical pulses end up in propagating through the cladding of the fiber, where the speed of pulse is different from the speed of light signal in the core. This effect is called wave-guide dispersion. The combined effect of wave-guide and material dispersions gives a common effect known as CD. Pulse broadening problem rises due to CD, as shown in Figure 3. Hence, in this paper, we mitigate CD losses using DEF/FFE techniques.  
Figure 2: The electronic equalizer framework in multi-channel OCN.