esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Calibration and Square Lattice Surrogate Models
### Diffusivities for the Square Lattice Surrogate Models
#### 4.2.2 Multisize Obstacles Model  
In the multisize obstacles model, the particle has different diffusivities in the vertical and horizontal directions. Yet, the large majority of lattice-based models in computational biology employ a square lattice. Thus, while our derivation below allows for distinct values of \(\Delta x\) and \(\Delta y\), we ultimately set these quantities to be equal in order to reconcile the two viewpoints.  
We follow the same procedure as in Section 4.2.1, but for this case, note the following: (i) \(q_{1}\) is the probability that the particle stays at its current lattice point; (ii) \(q_{x}\) is the probability that the particle moves to a neighboring lattice point in the horizontal direction; (iii) \(q_{y}\) is the probability that the particle moves to a neighboring lattice point in the vertical direction. Note that \(q_{1}+q_{x}+q_{y}=1\). We can then write the following relation for the particle density:  
\[\begin{split} u(x,y,t+\Delta t)=& q_{1}u(x,y,t)+ \frac{q_{x}}{2}u(x+h,y,t)+\frac{q_{x}}{2}u(x-h,y,t)\\ &+\frac{q_{y}}{2}u(x,y+k,t)+\frac{q_{y}}{2}u(x,y-k,t).\end{split} \tag{27}\]  
Via the Taylor series expansions in (20)-(22), the above equation can be expanded and simplified to  
\[\begin{split} u+u_{t}(\Delta t)&+\mathcal{O}(( \Delta t)^{2})=q_{1}u+q_{x}u+q_{y}u+\frac{q_{x}}{2}\Big{(}u_{xx}(\Delta x)^{2} +\mathcal{O}((\Delta x)^{4})\Big{)}\\ &+\frac{q_{y}}{2}\Big{(}u_{yy}(\Delta y)^{2}+\mathcal{O}((\Delta y )^{4})\Big{)}.\end{split} \tag{28}\]  
Since \(q_{1}+q_{x}+q_{y}=1\), upon dividing by \(\Delta t\) we can rewrite (28) as  
\[u_{t}+\mathcal{O}(\Delta t)=\frac{q_{x}}{2}u_{xx}\frac{(\Delta x)^{2}}{(\Delta t )}+\frac{q_{y}}{2}u_{yy}\frac{(\Delta y)^{2}}{(\Delta t)}+\frac{q_{x}}{2} \mathcal{O}\bigg{(}\frac{(\Delta x)^{4}}{(\Delta t)}\bigg{)}+\frac{q_{y}}{2} \mathcal{O}\bigg{(}\frac{(\Delta y)^{4}}{(\Delta t)}\bigg{)}. \tag{29}\]  
Imposing our restriction to a uniform 2D lattice (\(\Delta x=\Delta y\)), the simplified diffusion equation for the multisize obstacles case is thus (assuming \(q_{x}(\Delta x)^{4}\ll\Delta t\) and \(q_{y}(\Delta x)^{4}\ll\Delta t\))  
\[u_{t}\approx\frac{(\Delta x)^{2}}{2(\Delta t)}\big{(}q_{x}u_{xx}+q_{y}u_{yy} \big{)}. \tag{30}\]  
Hence, when a 2D square lattice is used as a surrogate model for the multisize obstacles case, there are two theoretical diffusivities that we refer to as the horizontal diffusivity (\(D_{H}\)) and the vertical diffusivity (\(D_{V}\)), which are given by  
\[D_{H}=\frac{q_{x}(\Delta x)^{2}}{2(\Delta t)},\ \ \ D_{V}=\frac{q_{y}(\Delta x)^{2}}{2( \Delta t)}. \tag{31}\]  
We note that in the case \(q_{x}=q_{y}\), the diffusion equation for the multisize obstacles model (30) reduces to the diffusion equation for the equally spaced obstacles model (25).
# From Mirrors to Free-Space Optical Communication--Historical Aspects in Data Transmission
## 6 FSO Advantages and Disadvantages
FSO links have a wide range of applications in different fields, such as connections between Base Transceiver Stations (BTS) of mobile telephony, Local Area Network (LAN-LAN) connections between office buildings with Fast Ethernet or Gigabit Ethernet speeds [103], confidential communication networks (i.e., financial, medical, and military networks [104]), temporary connections (e.g., HD image transmission from cameras, large screen connection, Internet access, e.g., during the Olympic Games). Due to its stability and flexibility, FSO is a good solution for building-to-building connectivity. Its use can help solve the last mile problem for connections at fiber speeds [105; 106]. FSO enables the quick rebuilding of connections after damage and disasters. Moreover, it can serve as an additional backup for fiber-optic networks, and it can complement other wireless technologies. FSO systems also have their drawbacks and limitations. As was already mentioned, the spreading loss is high, and there is atmospheric loss resulting from water and carbon dioxide molecules. The power consumption is higher than for typical RF links. The transmitter and receiver should be in the line of sight, so there may be significant restrictions on the location of their installation because the transmitter could get blocked due to trees, buildings, etc. Furthermore, birds and scintillation cause beam interruptions. High-powered lasers may not be safe for bystanders, and the installation must be performed by qualified personnel. The costs of the system and its installation are also a big limitation, as the cost of equipment could be between $3000 and $10,000. It is also worth mentioning that the laser diodes used in FSO transceivers have a limited life before they fail, typically about 7-8 years, in comparison to radio links, which can last much longer.
###### Abstract  
Objectives: This study aimed to create a three-dimensional histological reconstruction through the AI-assisted classification of tissues and the alignment of serial sections. The secondary aim was to evaluate if the novel technique for histological reconstruction accurately replicated the trabecular microarchitecture of bone. This was performed by conducting micromorphometric measurements on the reconstruction and comparing the results obtained with those of microCT reconstructions. **Methods:** A bone biopsy sample was harvested upon re-entry following sinus floor augmentation. Following microCT scanning and histological processing, a modified version of the U-Net architecture was trained to categorize tissues on the sections. Detector-free local feature matching with transformers was used to create the histological reconstruction. The micromorphometric parameters were calculated using Bruker's CTAn software (version 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets. **Results:** Correlation coefficients calculated between the micromorphometric parameters measured on the microCT and histological reconstruction suggest a strong linear relationship between the two with \(p\)-values of 0.777, 0.717, 0.705, 0.666, and 0.687 for BV/TV, BS/TV, Tb.Pf Tb.Th, and Tb.Sp, respectively. Bland-Altman and mountain plots suggest good agreement between BV/TV measurements on the two reconstruction methods. **Conclusions:** This novel method for three-dimensional histological reconstruction provides researchers with a tool that enables the assessment of accurate trabecular microarchitecture and histological information simultaneously.  
artificial intelligence (AI); microCT; histomorphometry; three-dimensional histological reconstruction; bone augmentation; dental implant
## 6 Experimental Verification of the Mirror Alignment Method
### Experimental Alignment of the Plane Unit
#### 6.2.2 Results and Discussion  
The experimental results by the three-rotation alignment method and two-rotation alignment method are as shown in Tables 3 and 4. For the axis vector alignment of the metal plane, using the three-rotation and two-rotation alignment method can greatly improve facet alignment accuracy. Using \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metal Plane**} & \multirow{2}{*}{\begin{tabular}{c} **Before Alignment** \\ **(Measurement Value)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **OneT–Two Method** \\ **(Section 3.2) **(theoretical Value)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Two–Rotation** \\ **Alignment Method** \\ **(theoretical Value)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **After Alignment** \\ **(Measurement Value)** \\ \end{tabular} } \\ \hline \multirow{4}{*}{Plate 1} & \(\mathbf{N_{\text{II}}}=[-0.0142,0.0227,1.0]\) & \(\mathbf{N_{\text{I}}}=[-0.0143,0.0285,0.9995]\) & & \(\mathbf{N_{\text{A}\text{II}}}=[-0.0009,-0.0062,1.0]\) \\ \cline{2-5}  & \(\theta=26.75\) mrad & \(\theta=31.79\) mrad & \(\theta=0.45\) mrad & \(\theta=6.26\) mrad \\ \cline{2-5}  & \(\mathbf{T_{\text{II}}}=[-460.00,287.10,-38.21]\) & \(\mathbf{C}\text{err}=5.82\) mrad & \(d_{\text{A}}=-11.99\) mrad & \\ \cline{2-5}  & \(\mathbf{T_{\text{II}}}=[-182.00,289.80,-32.99]\) & & \(d_{\text{B}}=-7.99\) mrad & \\ \cline{2-5}  & \(\mathbf{T_{\text{II}}}=[177.70,12.20,-26.35]\) & \(\mathbf{C}\text{err}=5.08\) mrad & \(\theta=0.82\) mrad & \(\theta=2.05\) mrad \\ \cline{2-5}  & \(\mathbf{T_{\text{II}}}=[-0.0403,0.0162,1.0]\) & \(\mathbf{N_{\text{I}}}=[-0.0465,0.0127,0.9988]\) & & \(\mathbf{N_{\text{A}\text{II}}}=[0.0019,0.0008,1.0]\) \\ \cline{2-5}  & \(\mathbf{\sigma}=6.11\) mrad & \(\theta=49.68\) mrad & \(\theta=0.32\) mrad & \(\theta=2.05\) mrad \\ \cline{2-5}  & \(\mathbf{T_{\text{II}}}=[-176.70,704.20,-26.65]\) & \(\theta=3.52\) mrad & \(d_{\text{A}}=8.12\) mrad & \\ \cline{2-5}  & \(\mathbf{T_{\text{II}}}=[-182.70,428.40,-23.09]\) & & \(d_{\text{B}}=13.01\) mrad & \\ \cline{2-5}  & \(\mathbf{T_{\text{II}}}=[-460.00,433.60,-34.71]\) & & \(d_{\text{C}}=0\) mrad & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the facet alignment of the metal plane using the two-rotation alignment method.
# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
## 4 Experimental Results
### Structure Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & **SCD** & **Macenko** & **ACD** & **Reinhard** & **StainGan** & **SCAN** & **Proposed** \\  & [16] & [20] & [26] & [17] & [28] & [23] & **Method** \\ \hline MITOS-ATYPIA-14 & 26.01 & 25.03 & 23.01 & 29.03 & 30.01 & 29.7 & 33.02 \\ TUPAC 2016 & 26.04 & 28.04 & 24.02 & 30.04 & 31.04 & 30.2 & 34.08 \\ ICIAR 2018 & 29.07 & 27.07 & 25.05 & 31.05 & 29.09 & 32.6 & 33.07 \\ MICCAI’16 & 28.02 & 28.06 & 22.04 & 31.08 & 31.02 & 31.1 & 35.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of PSNR of different color normalization methods using various histopathology datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & **SCD** [16] & **Macenko** [20] & **ACD** [26] & **Reinhard** [17] & **StainGan** [28] & **SCAN** [23] & \multicolumn{2}{c}{**Proposed**} \\  & **PCC** & **SSIM** & **PCC** & **SSIM** & **PCC** & **SSIM** & **PCC** & **SSIM** & **PCC** & **SSIM** & **PCC** & **SSIM** \\ \hline MITOS-ATYPIA-14 & 0.868 & 0.750 & 0.830 & 0.854 & 0.902 & 0.891 & 0.932 & 0.957 & 0.882 & 0.891 & 0.861 & 0.912 & 0.960 & 0.940 \\ TUPAC 2016 & 0.868 & 0.750 & 0.885 & 0.931 & 0.921 & 0.892 & 0.970 & 0.931 & 0.941 & 0.952 & 0.894 & 0.887 & 0.951 & 0.910 \\ ICIAR 2018 & 0.851 & 0.742 & 0.871 & 0.902 & 0.953 & 0.862 & 0.954 & 0.961 & 0.970 & 0.915 & 0.910 & 0.892 & 0.981 & 0.992 \\ MICCAI’16 & 0.932 & 0.925 & 0.892 & 0.897 & 0.912 & 0.910 & 0.891 & 0.942 & 0.932 & 0.921 & 0.889 & 0.910 & 0.982 & 0.972 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of PCC and SSIM for different color normalization methods.
# A Low-Cost Electronic System for Human-Body Communication
## 3 System Description
### Sensing Amplifier  
This circuit block recovers the signal injected in the body through the differential current driver. The signal is represented by a voltage that can be identified by the receiving electrodes located a short distance from the source. The receiver device equipped with a sensing amplifier makes it possible to read and decode data using analog measuring devices, such as oscilloscopes, data acquisition boards, or microcontrollers.  
Figure 2 shows the design diagram for the sensing amplifier component. This circuit detects the very low differential voltage at the sensing electrodes produced by ionic currents generated at the driver end. The differential voltage is immersed in a relatively strong common-mode interference voltage, which is present due to the proximity to power cords. The sensing amplifier suppresses the DC voltage from the electrode-electrolyte interface. It rejects the common-mode interference and amplifies the differential voltage to a level that can be registered by a measurement instrument, such as an oscilloscope. This circuit is powered by a 9V battery.  
The sensing amplifier comprises the front-end, DC suppression, main amplifier, low-pass filter, and active common-mode rejection.  
Front-end: it includes an integrated instrumentation amplifier, and it features low noise, low offset, and high input impedance. We limit the gain to 20 \(V/V\), thus avoiding saturation due to the DC voltage produced by the electrode-electrolyte interface.  
DC suppression: it is an active high-pass filter for suppressing the DC voltage produced by the electrode-electrolyte interface, amplified by the front-end. The cutoff frequency is 1 Hz to prevent the deformation of square-shaped pulses.  
Main amplifier: it is responsible for reaching the high-gain amplification, making the low differential voltages in the electrodes detectable by the other circuit blocks. This amplifier's gain is 50 \(V/V\); the global gain is 1000 \(V/V\).  
Low-pass filter: this stage limits the amplifier bandwidth to prevent aliasing at the sampling stage. The frequency response can be freely modified to satisfy codification/modulation restrictions. For testing purposes, we set for a 10 kHz Chebyshev response with a 0.5 dB ripple.  
Active common-mode rejection: the high impedance featured by the electrode-electrolyte interface allows easy coupling of electrical interference to the conducting medium. The power-line interference usually coupled to the electrolyte medium can be as high as 1 V. Thus, retrieving the 0.5 mV pulses from such a hostile medium is not a simple task, and to reduce the electrical pollution, we include a driven common-mode rejection (or right-leg [23]) circuit in our design, which requires a third electrode.
## 1 Introduction  
Nano technology has been improved in the field of energy, environment, electricity and medical science for several decades. A manufacturing technology to produce fibers under several hundred nanometers has been used--for example, by filtering, battery or fuel cell separators, tissue engineering, and drug delivery system [1; 2; 3; 4; 5; 6]. Mechanical property assessments of the nanofibers, such as yield strength or strain, should be required for optimal use of them. It is not easy to prepare mechanical test specimens from nanofibers under a few hundred nanometers, because they are very weak at very small forces. Thus, most of the mechanical test specimens should be obtained from fibrous mats. Each nanofiber property was evaluated by quantitative analysis comparison with nanofiber mat mechanical properties [7; 8; 9; 10; 11]. [PERSON] et al. introduced mechanical models of nonwoven fabrics with unit cells and fiber directions [12]. [PERSON] and [PERSON] proposed a theory to evaluate the elastic modulus of nonwoven fabrics by fiber direction effect on fabric strain [13]. Different mechanical test results may be caused by mat fiber density, porosity, and specimen dimensions. Therefore, the displacement and strain of the mat should be measured through testing the surface of the specimen for the mechanical property evaluation of nanofiber mats.  
The DIC method is a non-contact measurement technique which demonstrates displacement and strain distributions of a test specimen with time by acquiring a reference image and deformed image and then analyzing the correlation between the two images. The precision level in the measurement was low and it took a long time to compute, due to the poor computer and camera capacities in the first step of DIC development [14]. However, there has been a remarkable enhancement in computer and camera technologies over recent decades, so the DIC method has become one an alternative systems to contact-type measurement systems. In addition, portable measurement devices with DIC systems is possible because of its simple constitution with only a digital camera, computer for control, and operating algorithm [15; 16]. It is possible to apply this system to video or image recording systems, such as Computed Tomography (CT), an X-ray, or a Scanning Electron Microscope (SEM). Therefore, a new non-contact displacement and strain measurement system with DIC technique was proposed to evaluate the mechanical properties of such a non-uniform material as nanofiber mat in this research.
# Period-1 Motions and Bifurcations of a 3D Brushless DC Motor System with Voltage Disturbance
## Appendix A  
\(F_{0}^{(m)},F_{k/m^{\prime}}^{(c)}F_{k/m}^{(s)}\) are expressed as follows:  
\[\begin{split} F_{01}^{(m)}&=\frac{1}{mT}\int_{0}^{mT }\left(v_{q}-x_{1}-x_{2}x_{3}+\rho x_{3}+Q_{0}\cos\Omega t\right)dt\\ &=v_{q}-a_{01}^{(m)}-a_{02}^{(m)}a_{03}^{(m)}-\sum\limits_{i=1}^{N }\frac{1}{2}(b_{i/m2}b_{i/m3}+c_{i/m2}c_{i/m3})+\rho a_{03}^{(m)}\\ & F_{02}^{(m)}&=\frac{1}{mT}\int_{0}^{mT}\left(v_{d}- \delta x_{2}+x_{1}x_{3}\right)dt\\ &=v_{d}-\delta a_{02}^{(m)}+a_{01}^{(m)}a_{03}^{(m)}+\sum\limits_ {i=1}^{N}\frac{1}{2}(b_{i/m1}b_{i/m3}+c_{i/m1}c_{i/m3})\\ & F_{03}^{(m)}&=\frac{1}{mT}\int_{0}^{mT}\left( \sigma(x_{1}-x_{3})+\eta x_{1}x_{2}-T_{L}\right)dt\\ &=\sigma a_{01}^{(m)}-\sigma a_{03}^{(m)}+\eta a_{01}^{(m)}a_{02} ^{(m)}+\eta\sum\limits_{i=1}^{N}\frac{1}{2}(b_{i/m1}b_{i/m2}+c_{i/m1}c_{i/m2} )-T_{L}\\ \end{split} \tag{18}\]  
\[\begin{split} F_{1k1}^{(m)}&=\frac{2}{mT}\int_{0}^{mT }\left(v_{q}-x_{1}-x_{2}x_{3}+\rho x_{3}+Q_{0}\cos\Omega t\right)\cos(\frac{k} {m}\Omega t)dt\\ &=-b_{k/m1}-(a_{02}^{(m)}b_{k/m3}+a_{03}^{(m)}b_{k/m2})-\sum \limits_{i=1}^{N}\sum\limits_{j=1}^{N}\frac{1}{2}(b_{i/m2}b_{j/m3}\delta_{2c1 }+c_{i/m2}c_{j/m3}\delta_{2c2})+\rho b_{k/m3}+Q_{0}\delta_{m}^{k}\\ \end{split} \tag{19}\]  
\[\begin{split} F_{1k2}^{(m)}&=\frac{1}{mT}\int_{0}^{mT }\left(v_{d}-\delta x_{2}+x_{1}x_{3}\right)\cos(\frac{k}{m}\Omega t)dt\\ &=-\delta b_{k/m2}+a_{01}^{(m)}b_{k/m3}+a_{03}^{(m)}b_{k/m1}+\sum \limits_{i=1}^{N}\sum\limits_{j=1}^{N}\frac{1}{2}(b_{i/m1}b_{j/m3}\delta_{2c1 }+c_{i/m1}c_{j/m3}\delta_{2c2})\\ \end{split} \tag{20}\]  
\[\begin{split} F_{1k3}^{(m)}&=\frac{1}{mT}\int_{0}^{mT }\left(\sigma(x_{1}-x_{3})+\eta x_{1}x_{2}-T_{L}\right)\cos(\frac{k}{m}\Omega t )dt\\ &=\sigma(b_{k/m1}-b_{k/m3})+\eta(a_{01}^{(m)}b_{k/m2}+a_{02}^{(m) }b_{k/m1})+\eta\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\frac{1}{2}(b_{i/m1}b _{j/m2}\delta_{2c1}+c_{i/m1}c_{j/m2}\delta_{2c2})\\ \end{split} \tag{21}\]  
\[\begin{split} F_{2k1}^{(m)}&=\frac{2}{mT}\int_{0}^{mT }\left(v_{q}-x_{1}-x_{2}x_{3}+\rho x_{3}+Q_{0}\cos\Omega t\right)\sin(\frac{k} {m}\Omega t)dt\\ &=-c_{k/m1}-(a_{02}^{(m)}c_{k/m3}+a_{03}^{(m)}c_{k/m2})-\sum \limits_{i=1}^{N}\sum\limits_{j=1}^{N}\frac{1}{2}(b_{i/m2}c_{j/m3}+b_{i/m3}c_ {j/m2})\delta_{2s1}+\rho c_{k/m3}\\ \end{split} \tag{22}\]  
\[\begin{split} F_{2k2}^{(m)}&=\frac{1}{mT}\int_{0}^{mT }\left(v_{d}-\delta x_{2}+x_{1}x_{3}\right)\sin(\frac{k}{m}\Omega t)dt\\ &=-\delta c_{k/m2}+(a_{01}^{(m)}c_{k/m3}+a_{03}^{(m)}c_{k/m1})+ \sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\frac{1}{2}(b_{i/m1}c_{j/m3}+b_{i/m3} c_{j/m1})\delta_{2s1}\\ \end{split} \tag{23}\]  
\[\begin{split} F_{2k3}^{(m)}&=\frac{1}{mT}\int_{0}^{mT }\left(\sigma(x_{1}-x_{3})+\eta x_{1}x_{2}-T_{L}\right)\sin(\frac{k}{m}\Omega t )dt\\ &=\sigma(c_{k/m1}-c_{k/m3})+\eta(a_{01}^{(m)}c_{k/m2}+a_{02}^{(m) }c_{k/m1})+\eta\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\frac{1}{2}(b_{i/m1}c_ {j/m2}+b_{i/m2}c_{j/m1})\delta_{2s1}\\ \end{split} \tag{24}\]
## Appendix A DASH7 CRC16 Validation  
A cyclic redundancy check (CRC) is an error-detecting code commonly used in digital networks and storage devices to detect accidental changes to raw data. Blocks of data entering these systems receive a short check value attached, which is based on the remainder of a polynomial division of their contents. On retrieval, the calculation is repeated. When the check values do not match, corrective action can be taken against data corruption. A frame is always terminated by a 16-bit CRC field. Only when the CRC calculation of a received frame matches the supplied value will it be sent to the Data Link Layer, which continues processing the frame. The calculation of the field includes all previous bytes of the frame. For DASH7, the CRC16/CCITT_FALSE polynomial is used. This is expressed as \(x^{16}+x^{12}+x^{5}+x^{0}\) (0x1021 or 1000100000100001B) with an initial value of 0 xFFFF. Using this algorithm correctly, the value 0x29B1 will result from the computation of the nine-character ASCII reference string \"123456789\" (note: the numbers of the reference string are ASCII-encoded, not binary). To compute an n-bit binary CRC, line the bits representing the input in a row, and position the (n + 1)-bit pattern representing the CRC's divisor (called a \"polynomial\") underneath the left-hand end of the row.  
To make the operation straightforward, we encode a one-byte message with a 3-bit CRC, which uses the polynomial \(x^{3}+x+1\). The polynomial is written in binary as the coefficients; a third-order polynomial has four coefficients (\(1x^{3}+0x^{2}+1x+1\)). In this case, the coefficients are 1, 0, 1, and 1. The result of the calculation is 3 bits long.  
We start with the message to be encoded: 11001100. This is first padded with zeros corresponding to the bit length (n) of the CRC (add 0 0 0 at the end of the message), and an initial value of n bits is added at the beginning of the message (add 1 1 1 at the beginning of the message). The first calculation for computing a 3-bit CRC is as follows:  
\(111\) 11001100 000  
\(101\) 1 \(\leftarrow\) divisor (4 bits) = \(x^{3}+x+1\)  
The algorithm acts on the bits directly above the divisor in each step. The result for that iteration is the bitwise XOR of the polynomial divisor with the bits above it. The bits not above the divisor are simply copied directly below for that step. The divisor is then shifted one bit to the right, and the process is repeated until the divisor reaches the right-hand end of the input row. The entire calculation is written out below.
## 2 Results  
### Participant Characteristics  
A total of 351 individuals representing 15 different countries participated in this study, with the vast majority (80.1%, n = 281) living in high-income countries (HICs) and the remaining 19.9% (n = 70) from low- and middle-income countries (LMICs). Most participants came from the Netherlands (48.7%, n = 171), followed by Spain (27.6%, n = 97) and Myanmar (9.7%, n = 34). All represented countries are shown in Figure 1.  
Among the participants, 51.9% (n = 182) were female, 46.4% (n = 163) were male, and 1.7% (n = 6) did not enclose their gender. Half of the study sample (51.0%, n = 179) were between 40 and 60 years-of-age, 26.8% (n = 94) were younger than 40, and 22.2% (n = 78) were older than 60. Regarding occupational background, the vast majority (86.3%, n = 303) held positions at the government, while 12.8% (n = 45) worked for non-governmental organizations. Regarding participants working at a governmental body, 52.1% (n = 183) were working at a municipal or regional level, 18.2% (n = 64) held positions at provincial levels, and 7.1% (n = 25) were working on a national level. Educational backgrounds were mostly master/doctoral degrees (44.7%, n = 157), followed by bachelor degrees (40.7%, n = 143), and lower levels of education (14.0%, n = 49). Regarding field of expertise, 46.4% (n = 163) had a scientific background (i.e., medicine or life sciences), whereas the other half (53.6%, n = 188) had an expertise in other backgrounds. Demographic characteristics, except gender, were significantly different between HIC and LMIC subsets (\(p\) < 0.05). A detailed overview of all demographic characteristics is shown in Table 1.