esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
Nomenclature  
AM Air mass  
CI Cloud index  
C\({}_{sat}\) Raw satellite pixel value  
C\({}_{0}\) Total atmospheric air mass correction, backscattering effect correction, and satellite sensor system correction  
CC Normalized actual satellite pixel value  
C\({}_{\min}\) Normalized minimum satellite pixel value  
C\({}_{\max}\) Normalized maximum satellite pixel value  
G\({}_{ext}\) Extraterrestrial irradiance on a horizontal plane (W/m\({}^{2}\))  
I\({}_{0}\) Solar constant of 1367 W/m\({}^{2}\)  
I\({}_{0m+t}\) Total irradiance in the visible sensor of the satellite system (W/m\({}^{2}\))  
k\({}_{c}\) clear-sky index  
k\({}_{T}\) clearness index  
T Atmospheric path transmittance from the sun to the earth's surface  
T\({}_{L}\) Linke turbidity  
T\({}_{sat}\) Atmospheric path transmittance from the earth's surface to the visible sensor of satellite  
T\({}_{rd}\) Diffuse irradiance transmission function  
w
Precipitable water vapor  
z
Site elevation (m)  
Greek Symbols  
\(\theta_{2}\) Solar zenith angle (deg)  
\(\gamma\) Solar elevation angle (deg)  
\(\psi\) Backscatter angle (deg)  
\(\varphi\) Satellite zenith angle (deg)  
\(\epsilon\) Normalized variation of the sun-to-earth distance from its mean value  
\(\delta_{R}\) Rayleigh optical thickness  
\(\beta\) Angstrom turbidity  
Subscripts  
c Clear sky  
B Heliosat-1 Beyer model  
R Heliosat-2 Rigollier model  
Heliosat-2 Hammer model  
P [PERSON] model  
M Hybrid model  
Abbreviations  
COMS Communication, ocean, and meteorological satellite system  
DNI Direct normal irradiance (W/m\({}^{2}\))  
DHI Diffuse horizontal irradiance (W/m\({}^{2}\))  
GHI Global horizontal irradiance (W/m\({}^{2}\))  
KMA Korean Meteorological Administration  
MBE Mean bias error (W/m\({}^{2}\))  
RMSE Root-mean-square error (W/m\({}^{2}\))  
rRMSE Relative root-mean-square error (%)  
rMBE Relative mean bias error (%)  
PV Photovoltaic
## 2 Plasma  
Plasma is considered the fourth state of matter after solid, liquid and gas. It can be defined as a partially or fully ionized quasi-neutral substance, composed of electrons, ions, neutral particles, molecules in the ground or excited state, radical species and quanta of electromagnetic radiation (UV photons and visible light). These particles exhibit collective behavior [9]. Plasma exists in many forms in nature and constitutes more than 99% of the matter in the visible universe (stars, interstellar and interplanetary media, solar wind, tail of a comet, Aurora Borealis and Australis, quark-gluon plasma, etc.). Man-madeplasma systems use heat, apply high voltage or inject electromagnetic waves to a gas for plasma generation. Plasmas are classified as equilibrium and nonequilibrium according to the relative temperature of electrons, ions and neutrals [9,12]. Equilibrium forms (e.g., torch, plasma spraying, arc jet) are known as thermal (hot) plasmas, since the temperatures of neutrals, ions and electrons are approximately of the same order maintaining a thermal equilibrium [12,13]. Nonequilibrium plasmas are known as nonthermal plasmas (cold), with the temperature of the particles varying from each other [1,14]. The electron (light particles) temperature (\(\approx\)10,000 K) is much higher compared to the temperature (\(\approx\)300-1000 K) of heavy species (ions and neutrals) [15,16]. Cold plasmas are weakly ionized gases, which can be generated at low as well as atmospheric pressures. They are usually excited and sustained electrically by applying radio frequency (RF) power, microwave (MW) power, alternating current (AC) and direct current (DC) [12,17,18].
# Cross-Cycled Uplink Resource Allocation over NB-IoT
## 4 Performance Evaluation  
### Simulation Setups  
The simulation setups are based on 3 GPP specifications [1; 14]. We compare the performance of the proposed algorithm (_cross-NP uplink resource allocation (CNPUlRA)_) with two baselines. The first baseline is the _link adaptation and uplink resource allocation_ (LAURA) algorithm [12]. The _LAURA_ algorithm first uses a dynamic programming algorithm to determine which UEs should be allocated in an NP and decide the used unit type and modulation scheme for each chosen UE without considering NPRACHs. The dynamic programming algorithm can maximize the transmission bits in an NP. Then, _LAURA_ considers NPRACHs to adjust the used unit type, modulation, and allocated uplink radio resources for each scheduled UE. The second baseline is _energy saving (ES)_[11]. The \(ES\) algorithm schedules the UE in order from having the fewest usable combinations of unit types and modulations to the most usable pairs. Then, because the algorithm considers energy efficiency, it picks the unit type with the modulation for the currently scheduled UE requiring the fewest subframes for transmitting data. Because the \(ES\) scheme does not consider uplink resource allocation, it adopts the two functions for the uplink resource allocation of the _LAURA_ algorithm to find DCIs and delay values and to decide the resource assignment for each UE.  
We use MATLAB to build our simulation. The simulation considers a cell serving UEs varying from 400 to 2000. The transmission power of the cell and each UE are set to 32 and 23 dBm, respectively [11]. The distance \(\varphi\) of each UE from the cell is distributed randomly between [1, 5000] meters, considering only one CE level. The demand of each UE is randomly set from 20 to 200 bytes [8]. Our channel model considers the path loss model as \(120.9+30.76\log(\theta)\) dB, where the unit of \(\theta\) is kilometers [11]. The thermal noise density is \(-173\) dBm/Hz. There are four types of resource unit. The SNR of each UE can be estimated using the path loss model, unit type, and distance \(\varphi\) of the UE from the cell.  
The set of resource unit numbers that can be assigned to a UE in an NP is \(I_{RU}=\{1,2,3,4,5,6,8,10\}\)[1]. There are 14 modulation and coding scheme indices. The uplink TBS, comprising the adopted modulation-coding scheme index and the assigned resource unit number, is shown in Table 2. The selectable \(R_{max}\) values are 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, and 2048. The selectable \(G\) values are 1.5, 2, 4, 8, 16, 32, 48, and 64. The DCI repetition number is set as \(R_{max}/8\) for all the UEs. The scheduling delay values for the uplink are 8, 16, 32, and 64. The repetition number for a UE to deliver data can be chosen from: 1, 2, 4, 8, 16, 32, 64, or 128 [1]. The selected \(R_{max}\) and \(G\) values should satisfy the DCI and data repetition requirements of all UEs. In the simulation, we consider three different NPRACHs configured according to [12].
## 1 Introduction
With these goals, in this paper, we focus on answering two research questions to get user perspectives and understanding on identifying and using security metadata. * RQ1. What aspects of security metadata are useful to end-users in an IoT-Health architecture? * RQ2. How can they help empower users' cybersecurity situational awareness and design future systems? The aim of this study is to identify a set of tangible security metadata that provide significant security evidence in a typical, idealized, realistic IoT-Health scenario (as depicted in Figure 1). It also covers pertinent issues and suggestions from the system design viewpoint, while a technical analysis of healthcare scenarios can identify the security controls used, for the anticipated focus on the end-user, it is important to include human perceptions and people's estimation of risks. Therefore, we undertook table-top exercises [17] with ten security, health, and information and communication technologies (ICT) experts to get their perceptions of the IoT-Health system's security status and methods for better risk estimation and security awareness for end-users. We conducted the table-top exercises by asking domain experts to work on three tasks to get their understanding and insights on different attacks, security solutions and security metadata in a designed IoT-Health architecture. Moreover, leveraging the security metadata identified during our table-top exercises, we simulated three IoT-Health use cases to highlight the critical role of this metadata in uncovering security risks. This demonstration underscores its significance in enhancing our security posture. While the security of Internet of Things applications has been subject to long-term research and many approaches to improve security have been developed, this study contributes by adding users' perspectives to security by identifying what kind of security metadata can be made available and can potentially improve the users' cybersecurity situational awareness. Further, it highlights experts' concerns and proposals for important design decisions to be made in the development of IoT applications for digital health. In the following sections, we will first provide some details on the study background, then illustrate the table-top exercises with experts and present the findings. Further, we analyze the findings to identify security metadata currently available but often not used and suggest potential additional ones to be used for better risk estimation and system security awareness. Finally, the paper concludes with relevant discussions and future implications.
# A Survey of Structure of Atmospheric Turbulence in Atmosphere and Related Turbulent Effects
## 1 Introduction  
It is well known that the Earth's atmosphere is a typical random inhomogeneous medium that contains two kinds of medium: one is a discrete turbid atmospheric medium composed of particles, and the other is a \"continuous\" turbulent atmospheric medium composed of thermally moving molecules. The first medium causes the energy attenuation of optical waves, namely the attenuation effect. The second medium causes atmospheric turbulence effects such as intensity scintillation, angle-of-arrival fluctuations, beam wander, beam spread, spot distortion, and so on [1]. The study of optical wave propagation in atmospheric turbulence began in [PERSON]'s time, however, systematic research began in the mid-20 th century. The lower part of the Earth's atmosphere shows Kolmogorov statistic characteristics, as even in 1960 there were studies that showed that spectra deformated significantly in the lower atmosphere (in different stratifications). The free atmosphere often demonstrates \"non Kolmogorov fluctuations\" but at least turbulence spectrum contains \(-3\), \(-5/3\), and other ranges. The early theoretical research on optical wave propagation in atmospheric turbulence was mainly about Kolmogorov turbulence. However, withfurther measurements of the Earth's atmosphere and further development of theoretical study on the statistical characteristics of atmospheric turbulence, it was confirmed that Kolmogorov turbulence is only a part of the statistical characteristics of the atmosphere, and non-Kolmogorov statistical features are exhibited in some parts of the Earth's atmosphere. This has also prompted experts and scholars at home and abroad to carry out the study of optical wave propagation in non-Kolmogorov turbulence. Currently, it is necessary and significant to conclude the structure of turbulence in the Earth's atmosphere and review the research progress of optical wave propagation theory in atmospheric turbulence.  
In this paper, we summarize the experimental measurements and related theoretical results on statistical characteristics of atmospheric turbulence and further conclude the turbulent structure in Earth's atmosphere. On this basis, we summarize the development status and latest progress of propagation theory in the atmospheric turbulence, mainly including propagation theory in conventional Kolmogorov, turbulence, and one in non-Kolmogorov atmospheric turbulence. Finally, the research progress on the combined effects of non-Kolmogorov and Kolmogorov atmospheric turbulence in the Earth's atmosphere is also summarized. The structural block diagram is shown in Figure 1. This study will help researchers in transforming their ideas into shape and in obtaining clear direction for future research.
# Experimental Study on Dynamics of Wooden House Wall Panels with Different Thermal Isolation
## 4 Dynamic Tests on Wall Panels  
In the final stage of investigation, dynamic tests for real size wall panels of the wooden frame house were carried out for two variants of skeleton filling.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency [Hz]** & **Storage Modulus [kPa]** & **Loss Modulus [kPa]** & **Tan Delta** \\ \hline
1 & 2.10 & 1.81 & 0.86 \\
10 & 7.65 & 0.65 & 0.10 \\
20 & 8.45 & 0.25 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of DMA tests results for mineral wool for temperature of 20 \({}^{\circ}\)C.  
Figure 14: Results of DMA tests for polyurethane foam; comparison for all frequencies analyzed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency [Hz]** & **Storage Modulus [kPa]** & **Loss Modulus [kPa]** & **Tan Delta** \\ \hline
1 & 25.55 & 4.70 & 0.175 \\
10 & 33.00 & 2.55 & 0.07 \\
20 & 37.50 & 2.15 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of DMA tests results for polyurethane foam for temperature of 20 \({}^{\circ}\)C.  
Figure 13: Results of DMA tests for polyurethane foam for frequency 20 Hz.  
For the purpose of this study, a typical wooden frame house set of wall panels were prepared (see Figure 17). Using a modulus of 60 cm for the frame elements, each panel was constructed with C24 wood, filled either with mineral wool or polyurethane foam, and covered on both sides by OSB3 (Oriented Strand Board) sheaths. The dimensions of each panel were 129 cm in length and 60 cm in width. Wooden skeleton elements (posts and cap) were connected to each other using standard screws. Screws were also applied to connect the OSB3 sheaths and the skeleton itself. In this case, the connectors were used at the corners and along the length of the board at intervals of 10 cm. Since the skeleton was covered by the single OSB3 boards on both sides (see Figure 17), there was no need to apply any connectors in the middle of the element. Material properties of different elements used in the panels are shown in Table 4.  
One side of each of the tested panel was fixed to the experimental stand using 10 mm bolts, while the movement of the second side was allowed in the horizontal direction (buckling was prevented by special blocking system; see Figure 17).
## 3 Self-intersection Penalty Term
### Gradients Calculation
\right)=\frac{1}{3}\sum_{l =1}^{n}S_{l}\mathbf{n}_{l},p_{t}\in V_{out} \tag{8}\]  
For the last case \(p_{t}\in V_{in}\), the derivation process of partial derivative is similar to the process described in the second case, and the results are same in magnitude but opposite in sign. The gradient in this case can be obtained as:  
\[\
abla E_{SPT}(V)=-\frac{1}{3}\sum_{l=1}^{n}S_{l}\mathbf{n}_{l},p_{t}\in V_{in} \tag{9}\]  
In summary, the gradient of self-intersection penalty term with respect to the coordinate of vertex \(p_{t}\) can be represented as:  
\[\
abla E_{SPT}(V)= \begin{cases}\vec{0}&p_{t}\in V_{0}\\ \frac{1}{3}\sum_{l=1}^{n}S_{l}\mathbf{n}_{l}&p_{t}\in V_{out}\\ -\frac{1}{3}\sum_{l=1}^{n}S_{l}\mathbf{n}_{l}&p_{t}\in V_{in}\end{cases}  \tag{10}\]  
Employing the equation above for a gradient-based optimization algorithm to remove self-intersection works well in most cases. But when there are great differences between the areas of triangles, the process of optimization tends to be unstable. To solve this problem, a modified version of the gradient formula is presented:  
\[\
abla E_{SPT}(V)= \begin{cases}\vec{0}&p_{t}\in V_{0}\\ \frac{\sum_{l=1}^{n}S_{l}\mathbf{n}_{l}}{\left\|\sum_{l=1}^{n}S_{l}\mathbf{n}_{l}\right\| _{2}}&p_{t}\in V_{out}\\ -\frac{\sum_{l=1}^{n}S_{l}\mathbf{n}_{l}}{\left\|\sum_{l=1}^{n}S_{l}\mathbf{n}_{l}\right\| _{2}}&p_{t}\in V_{in}\end{cases}  \tag{11}\]  
For the value of \(E_{SPT}(V)\), it is difficult and unnecessary to calculate the exact volume of the intersection region by coordinates of vertices. Instead, \(E_{SPT}(V)\) is assigned with the ratio of the number of vertices in self-intersection to the number of vertices not in self-intersection. This ratio is easy to calculate and significant for indicating the degree of self-intersection. The expression of \(E_{SPT}(V)\) is presented as:  
\[E_{SPT}(V) =\frac{|V_{out}|+|V_{in}|}{|V_{out}|+|V_{in}|+|V_{0}|} \tag{12}\] \[=\frac{|V_{out}|+|V_{in}|}{N}\]  
where \(|\cdot|\) denotes the number of elements in a set. So far, the forward and backward processes of our proposed self-intersection penalty term have been defined.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 5 A Taxonomy for Gait, Balance, RoM Analysis
### Analysis of Joint Range of Motion
For joint coordinate systems illustrations, refer to studies by [PERSON] et al. [141; 142]. The location of the sensors depends on which RoM angles are intended to be measured. One study introduced the use of a smartphone mounted to subject's back to measure back inclination while subjects stood up and sat down [52]. Other researchers mounted IMUs to the thighs and shanks to extract Euler angles and measure knee flexion [25]. [PERSON] et al. mounted an IMU on a shoe while subjects performed walking exercises to measure ankle joint angles using quaternions [39].
## 8 Technical Challenges for 6G Development
### Terahertz Frequency
#### 8.1.2 Coverage and Directional Communication  
The characteristics of electromagnetic wave propagation reveal that fading in free space increases with the frequency squared. Consequently, the lower part of the terahertz frequency band undergoes more significant free space loss. Communication over terahertz frequencies depends on the propagation of signals through highly focused beams, requiring the mechanisms involved in this direction-specific signal propagation to be rethought and optimized [42].
## 2 Brief Background of USV Technology
### Size-Based Classification of USVs  
In this work, USVs were categorized as small, medium, or large based on their size (Figure 1). Small USVs have a 2 m long upper limit, which was defined based on the observations during the preparation of this work. There is a huge number of unmanned surface vessels below this value, the majority being designed for nearshore monitoring, survey missions, and environmental assessments. They offer agility and ease of deployment, making them ideal for scientific data collection and security applications in confined waters.  
Medium-sized USVs are up to 24 m in length, which is a significant threshold mentioned in different regulations ([PERSON], 2013; [PERSON], 2014). This value defines requirements for vessels under and above it, commonly called length of rule \(L\). In general, these USVs offer extended endurance and enhanced payload capabilities, making them suitable for hydrographic surveys, offshore inspections, and security operations. These vessels are commonly equipped with advanced sensor suites, enabling precise data acquisition for industries such as oil and gas, fisheries, and environmental research.  
Figure 1: Size classification of USVs. \(L\) stands for length.  
Finally, above 24 m in length, we classify USVs as large-sized USVs. For the present work, the only USV we found to be operative and with available specifications was the Armada 78 (A78) from Ocean Infinity [31]. There are also other projects with limited available information, such as the Norwegian YARA Birkeland, an unmanned electric 80 m long commercial ship [32].
## 3 Distribution of Si and Ge Atomic Fractions  
The atomic fractions of Si and Ge were measured by Rutherford backscattering spectrometry (RBS), as shown in Figure 2. The RBS analysis was performed in a scattering chamber with a two-axis goniometer connected to the custom-made 5-MV EG-2R Van [PERSON] accelerator of the Wigner RMI Institute of the Hungarian Academy of Sciences. The 2-MeV \({}^{4}\)He\({}^{+}\) analyzing ion beam was collimated with two sets of four-sector slits to the spot size of 0.5 mm \(\times\) 0.5 mm, while the beam divergence was kept below 0.06\({}^{\circ}\). The beam current was measured by a transmission Faraday cup [23]. Backscattered He\({}^{+}\) ions were detected using an ORTEC surface barrier detector (ORTEC, Illinois, USA). The energy resolution of the detection system was 20 keV. Spectra were recorded in Cornell geometry at scattering angle of \(\Theta=165^{\circ}\) for two different sample tilt angles of 7\({}^{\circ}\) and 60\({}^{\circ}\). For quantitative compositional analysis, both axial and planar channeling of the He\({}^{+}\) projectiles in the single-crystalline Si substrate were avoided. The measured data were evaluated with the spectrum simulation code named RBX [24].  
Figure 2: Atomic fraction as a function of position along the center line of the combinatorial sample measured by Rutherford backscattering spectrometry. The black dotted line shows the distribution of the atomic fraction based on the calibration using energy dispersive spectrometry (EDS) in a scanning electron microscope.  
The crystalline quality of single crystalline samples can be qualified by RBS/channeling experiments if the sample is oriented along a major crystalline axis and the yield of the oriented spectrum is compared to the yield of the random, i.e., not oriented spectrum. In our case, oriented (channeling) RBS spectra were not measured, only random spectra were recorded. In the evaluation, we supposed random orientation of the SiGe layer. In this case, a quantitative analysis for the Si/Ge atomic fraction can be performed. On the other hand, selected area electron diffraction pattern represents an amorphous structure (see Figure 3).  
The RBS spectra have been recorded from edge to edge in the center line of the sample. Figure 2 reveals that the atomic fraction shows an exactly linear dependence on the position. The distribution is in a good agreement with the calibration based on electron microscopy measurement on a test sample as shown by the black dotted line in the figure.