esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Modeling
### Two-Port Transmission Line Theory
Firstly, when the wavelength is greater than 5 \(\upmu\)m, the transmittance significantly decreases due to the  
Figure 2: (**a**,**b**) Comparison of cascade calculation and direct simulation transmission spectrum between the materials of type and thickness. Herein, (**a**) in the case of sapphire substrate, compare the cascaded calculation with two layers of 2.5 \(\upmu\)m-thick substrate and the directly simulation with one layer of 5 \(\upmu\)m-thick sapphire; (**b**) in the case of gold thin film, compare the cascaded calculation under 50 nm and 50 nm-thick Au film and the directly simulation under 100 nm-thick Au film. (**c**) The transmittance spectrum of the sapphire substrate used in the experiment with a thickness of 0.5 mm. material characteristics of sapphire: the transmittance range of the sapphire material in the infrared band is between 2-6 \(\upmu\)m. At the same time, the measured transmittance distribution spectrum also shows obvious oscillation characteristics, but, due to the dense oscillation period, the curve distribution is relatively smooth compared to Figure 2a. The difference is that, for metal materials only (see Figure 2b), the cascade calculation results produce a slight discrepancy compared with the direct simulation result, but this will not affect the maximum amplitude and trend of the spectral distribution. This difference is attributed to the strong coupling between electrons and photons in metallic materials. In the previous work [26], cascade calculations were carried out for metal films with different thicknesses. The research results indicate that the number of cascade layers of metal films directly impacts their similarity to direct simulation results. Furthermore, the equivalent circuit model calculation reveals that the deposition of metal film on a transparent substrate results in coupling to some degree, with the strength of this coupling being dependent on the thickness of the substrate. In general, reducing the response frequency results in an improvement in the accuracy of the cascade calculation. As a result, for the subsequent direct simulation, we aim to address the issue of excessive mesh generation leading to lengthy simulation times. To achieve this, the following direct simulation only models and simulates a single metal film that has been etched with micro/nano structures. As shown in Figure 2c, the sapphire substrate has a relatively high transmittance at 3-5 \(\upmu\)m.
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 3 Re-Processing of Original Images  
To compare different conditions of the south wall in 1991 to the current condition, we have digitized and re-processed glass plate negatives of the original photogrammetric survey and created results such as a point cloud and an orthophoto. Those results were possible to compare with results from the new photogrammetric survey carried out in 2017.
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
## 2 Preliminaries
### Energy Consumption Model
and the corresponding receiver node expends \(E_{R}(l)\) (\(0\)) in the process of receiving this message,  
\[E_{R}(l)=lE_{e}, \tag{2}\]  
where \(E_{e}\) = 50 (nJ/bit) represents the energy in the circuitry per bit; \(\varepsilon_{f}\) = 10 (pJ/bit/m\({}^{2}\)) and \(\varepsilon_{m}\) = 0.0013 (pJ/bit/m\({}^{4}\)) denote the energy dissipation in the transmission amplifier needed to achieve an acceptable signal-to-noise ratio according to the free space model (Friss) and the multi-path model, respectively; and \(\delta=\sqrt{\varepsilon_{f}/\varepsilon_{m}}\approx 87.7\) (m). The energy for data aggregation per bit is set as \(E_{\text{DA}}\) = 5 (nJ/bit). Given two nodes communicating with each other, we also assume symmetry of the forward and reverse path power dissipation, i.e., the same amount of energy is consumed in the forward and reverse paths.
## 4 Multi-Input Multi-Output Control Design  
Consider the multi-degree-of-freedom (MDOF) model shown in Figure 12. Our control goal remains to suppress vibrations of the first floor.  
Figure 11: Free vibration response when the controller is turned on in the middle of the time.  
Figure 10: Free vibration experiment.  
The matrix equations of motion can be expressed as:  
\[\mathbf{M}\ddot{\mathbf{x}}+\mathbf{C}\dot{\mathbf{x}}+\mathbf{K}\mathbf{x}= \mathbf{B}_{u}\mathbf{u}+\mathbf{d} \tag{10}\]  
where \(\mathbf{x}=[x_{1}\ x_{2}\ \cdots\ x_{n}]^{\mathsf{T}}\), \(\mathbf{u}=[\ u\ \dot{u}\ ]^{\mathsf{T}}\), \(\mathbf{d}=[d_{1}\ d_{2}\ \cdots\ d_{n}]^{\mathsf{T}}\), and:  
\[\mathbf{M}=\begin{bmatrix}m_{1}&0&\cdots&0\\ 0&m_{2}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&m_{n}\end{bmatrix},\ \mathbf{C}=\begin{bmatrix}c_{1}+c_{2}&-c_{2}& \cdots&0\\ -c_{2}&c_{2}+c_{3}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&c_{n}\end{bmatrix}, \tag{11}\] \[\mathbf{K}=\begin{bmatrix}k_{1}+k_{2}&-k_{2}&\cdots&0\\ -k_{2}&k_{2}+k_{3}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&k_{n}\end{bmatrix},\ \mathbf{B}_{u}=\begin{bmatrix}k_{1}&c_{1}\\ 0&0\\ \vdots&\vdots\\ 0&0\end{bmatrix}\]  
Equation (10) can be rewritten as a first-order matrix differential equation:  
\[\dot{\mathbf{z}}=\mathbf{A}_{xx}\mathbf{z}+\mathbf{A}_{xu}\mathbf{u}+\mathbf{ B}_{d}\mathbf{d} \tag{12}\]  
where, \(\mathbf{z}=[\ \mathbf{x}^{\mathsf{T}}\ \mathbf{x}^{\mathsf{T}}]^{\mathsf{T}}\), and:  
\[\mathbf{A}_{xx}=\begin{bmatrix}\mathbf{0}&\mathbf{I}\\ -\mathbf{M}^{-1}\mathbf{K}&-\mathbf{M}^{-1}\mathbf{C}\end{bmatrix},\ \mathbf{A}_{xu}=\begin{bmatrix}\mathbf{0}\\ \mathbf{M}^{-1}\mathbf{B}_{u}\end{bmatrix},\ \mathbf{B}_{d}=\begin{bmatrix}\mathbf{0}\\ \mathbf{M}^{-1}\end{bmatrix} \tag{13}\]  
Figure 12: Multi-degree-of-freedom model.  
We design each PIPO controller as follows to suppress \(m\) (\(\leq n\)) natural modes:  
\[\begin{split}\ddot{u}_{1}+2\zeta_{f}\omega_{1}\dot{u}_{1}+\omega_{1} ^{2}u_{1}&=g_{1}\Big{(}2\zeta_{f}\omega_{1}\dot{x}_{1}+\omega_{1} ^{2}x_{1}\Big{)}\\ \ddot{u}_{2}+2\zeta_{f}\omega_{2}\dot{u}_{2}+\omega_{2}^{2}u_{2}& =g_{2}\Big{(}2\zeta_{f}\omega_{  
Combining Equations (17) and (22), the closed-loop state space equation can be written as:  
\[\dot{\textbf{p}}=\textbf{A}_{c}\textbf{p}+\textbf{B}_{c}\textbf{d},\;y=x_{1}= \textbf{C}_{c}\textbf{p}+\textbf{D}_{c}\textbf{d} \tag{23}\]  
where \(\textbf{p}=\left[\,\textbf{z}^{\text{T}}\,\textbf{v}^{\text{T}}\,\right]^{ \text{T}}\), and:  
\[\textbf{A}_{c}=\begin{bmatrix}\textbf{A}_{xx}&\textbf{A}_{xu}\textbf{E}_{m}\\ \textbf{A}_{ux}&\textbf{A}_{uu}\end{bmatrix},\;\textbf{B}_{c}=\begin{bmatrix} \textbf{B}_{d}\\ \textbf{0}\end{bmatrix},\;\textbf{C}_{c}=\left[1\;0\;0\;\cdots\;0\;\right],\; \textbf{D}_{c}=\left[\textbf{0}\right] \tag{24}\]  
For the closed-loop state space equation given by Equation (23) to be stable, the real parts of the eigenvalues of matrix \(\textbf{A}_{c}\) must be negative, which must be checked before applying the controller.
## 4 Discussion
This performance compares favorably with other state-of-the-art techniques, such as those based on SVMs or RNNs, which typically achieve accuracies in the range of 80% to 95%. Interestingly, to the best of our knowledge, there are very few LOSO CV studies. LOSO CV offers a more robust evaluation of biomedical applications by accounting for subject-specific variability and ensuring clinical relevance. In addition to its performance, the proposed approach offers several advantages over other techniques. VFCDM stands out from other TF methods in its ability to effectively handle the non-linear and non-stationary characteristics of EEG signals. By using variable frequencies, VFCDM adapts to the changing nature of EEG data, capturing intricate dynamics and preserving instantaneous amplitudes, crucial for identifying and characterizing neural activity. VFCDM's non-parametric nature allows it to handle complex and unpredictable EEG patterns without relying on a priori assumptions, making it a powerful tool for analyzing EEG signals. Additionally, CNNs are powerful classifiers that can learn complex patterns from these features, making them well suited for seizure detection. While the VFCDM and CNN combination demonstrated promising potential for automated epilepsy detection, further research is needed to fully evaluate its clinical utility. Additional studies on larger, more diverse datasets are required to confirm the approach's generalizability. Prospective clinical trials should also be conducted to assess its effectiveness in real-world settings. Additionally, addressing limitations, such as the limited dataset scope, exclusion of the pre-ictal state, focus on single-channel EEG analysis, lack of testing in noisy environments, and the absence of comprehensive comparisons with other methods, will strengthen the proposed approach's clinical applicability. In summary, the proposed approach represents a promising step forward in the development of automated seizure detection systems. The combination of VFCDM and CNN offers a powerful and efficient method for extracting and classifying non-linear features from EEG signals. This approach has the potential to significantly improve patient care by reducing the burden on healthcare professionals and enhancing the overall management of epilepsy.
# PreNNsem: A Heterogeneous Ensemble Learning Framework for Vulnerability Detection in Software
## 4 Experiments and Results
### Ability to Detect Different Vulnerabilities  
As shown in Table 6, the proposed method was applied to the six datasets. We tested our model on the buffer overflow CWE-119 dataset and resource management error CWE-339 dataset in order to evaluate our method's detection ability for different types of vulnerabilities. To validate our approach's generalization capabilities, we selected three different types of vulnerability datasets: Array Usage, API Function Call, and Arithmetic Expression. Each type of dataset contains multiple CWE vulnerabilities. Array Usage (87 CWE IDs) accommodates the vulnerabilities related to arrays (e.g., improper use of array element access, array address arithmetic, and address transfer as a function parameter). API Function Call (106 CWE IDs) accommodates the vulnerabilities related to library/API function calls. Arithmetic Expression (45 CWE IDs) contains the vulnerabilities that are related to improper arithmetic expressions (e.g., integer overflow). Finally, we combined the three to form a hybrid vulnerability dataset, Hybrid Vulnerabilities.  
According to the results, we found that the method for detecting specific vulnerabilities performs well. Resource management error has the best result, F1 Score, at 98.6%. Our approach also performs well in detecting the same type of vulnerability. API Function Call has the lowest F1 score, but the result was still no less than 91.5%. The method performed better on hybrid vulnerability datasets than on the same vulnerability datasets, because having more data can improve the model's indicators. In summary, our approach performs well on a variety of data sets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Vulnerability** & **Vulnerability Dataset** & **FPR (\%)** & **FNR (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline \multirow{2}{*}{Specific CWE ID} & Buffer Overflow & 1.5 & 8.0 & 95.4 & 91.9 & 93.6 \\  & Resource Management Error & 0.3 & 2.2 & 99.5 & 97.7 & 98.6 \\ \hline \multirow{4}{*}{Multiple CWE IDs} & Array Usage & 2.6 & 7.9 & 92.3 & 92.1 & 92.2 \\  & API Function Call & 2.1 & 9.5 & 92.4 & 90.5 & 91.5 \\ \cline{1-1}  & Arithmetic Expression & 1.4 & 5.2 & 92.7 & 94.8 & 93.8 \\ \cline{1-1}  & Hybrid Vulnerabilities & 1.5 & 6.3 & 94.4 & 93.7 & 94.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different classification algorithms.
## 2 Communication System Model and Theory
### Correlation Cost Factor-Based Updates to Matched Filters Reception Method for M-SS (CCF-MSS)
#### 2.3.1 Coarse Doppler Estimation  
Given that the duration of the spreading code in this paper is relatively short, it is reasonable to approximate the Doppler factor as invariant within the time width of a single spreading code. From Section 2.1, the time-domain waveforms of the adjacent repeated codes are \(c_{1,b_{1}}(t)\) and \(c_{2,b_{2}}(t)\), where \(b_{1}\) and \(b_{2}\) are equal, and the Doppler factors for the adjacent spreading codes are \(\beta_{1}\) and \(\beta_{2}\); the corresponding received signal after down-conversion to baseband can be expressed as follows:  
\[r_{symbol1}=a_{1}[0]c_{1,b_{1}}((1+\beta_{1})t)e^{j2\pi f_{c}t} \tag{8}\]  
\[r_{symbol2}=a_{2}[0]c_{2,b_{2}}((1+\beta_{2})t+\frac{L\beta_{1}}{1+\beta_{1}B })e^{j2\pi f_{c}t} \tag{9}\]  
Here, \(L\) denotes the length of a spreading symbol and \(\frac{L\beta_{1}}{1+\beta_{1}B}\) represents the compression and expansion interference experienced by the second symbol due to the first symbol. Taking the absolute value of the correlation of the conjugates of \(r_{symbol1}\) and \(r_{symbol1}\) yields  
\[\begin{split} R&=\int r_{symbol1}(t+\tau)r_{symbol2 }^{*}(t)dt\\ &=|\int c_{1,b_{1}}((1+\beta_{1})(t+\tau))c_{2,b_{2}}((1+\beta_{2 })t+\frac{L\beta_{1}}{(1+\beta_{1})B})dt|\end{split} \tag{10}\]  
Here, let \(t^{*}=(1+\beta_{2})t+\frac{L\beta_{1}}{(1+\beta_{1})B}\) substituting this yields  
\[\begin{split} R&=|\int c_{1,b_{1}}(\frac{1+\beta _{1}}{1+\beta_{2}}t^{*}-\frac{L\beta_{1}}{(1+\beta_{2})B}+(1+\beta_{1})\tau)c_ {2,b_{2}}(t^{*})dt|\end{split} \tag{11}\]  
Since the changes in Doppler factors between adjacent spreading symbols are minimal, it can be assumed that \(\frac{1+\beta_{1}}{1+\beta_{2}}\approx 1\). Therefore, when \(R\) exhibits a correlation peak, \(\tau=\frac{L\beta_{1}}{(1+\beta_{1})(1+\beta_{2})B}\approx\frac{L\beta_{1}}{( 1+\beta_{1})^{2}B}\) thus \(\beta_{1}=\frac{L-2B\tau+\sqrt{L^{2}-2B\tau L}}{2B\tau}\), and when \(\tau=0\), \(\beta_{1}\) is also equal to 0. Consequently, the Doppler factor can be initially calculated based on the repeated code group, which serves as an initial value for subsequent fine Doppler estimation. It  
Figure 3: Frame structure.  
is worth noting that this method entails a certain computational load, and may lead to a reduction in communication rate. However, in this paper, each frame utilizes only one pair of repeated code groups for Doppler estimation, and subsequent processing relies on the correlation cost factors for fine Doppler estimation, thus having a negligible impact on the overall computational load and communication rate.
## 2 Graphene Patch Antenna Design
### Graphene Modeling  
The graphene layer is simulated and optimized using CST Microwave Studio, which is based on the Finite Element Method (FEM). This program has two built-in graphene models, namely \"_graphene_\" and \"_graphene-Eps_\". The first model uses a single layer of graphene with a thickness of 0.345 nm, whereas the second uses a multilayer of graphene sheets with varying thicknesses. In this paper, we proposed an application of a single layer of graphene sheet of 0.34 nm thickness for our presented graphene plasmonic nanoantenna. Figure 1a shows the graphical representation for the surface conductivity of the graphene layer at zero value of chemical potential and the relaxation time is 0.1 p sec at room temperature which is identical to the one presented by [PERSON] et al. [32].  
The real and imaginary parts of our simulated graphene are presented in Figure 1b,c for various relaxation time (\(\tau\)) and chemical potential (\(\mu\)c) values, respectively, using the MATLAB program. This figure demonstrates that the overall conductivity of a graphene slab is strongly reliant on the chemical potential and the relaxation time. Chemical potential is proportional to carrier density, which can be varied via an external gate voltage or chemical doping:  
\[n=\frac{2}{\pi\hbar^{2}v_{f}^{2}}\int_{0}^{\infty}\mathcal{E}[f_{d}(\mathcal{E}- \mu_{c})-f_{d}(\mathcal{E}+\mu_{c})]d\mathcal{E} \tag{13}\]  
The relationship between the chemical potential and gate voltage is explained by [33]. Tunability is required for the wideband operation of the proposed antennas which can prevent path loss and enhance data rates in THz communication systems. Tunable antennas have been considered the best tools for manipulating the interaction of light with electrons present in materials. The fundamental capabilities of graphene antennas are vastly utilized in a broad scope of practices, including high-speed communication with high data rate, terahertz detection energy harvesting, and optical light emission.  
\[V_{g}=\left[\frac{e\mu_{c}{}^{2}h}{\pi\hbar^{2}v_{f}{}^{2}\epsilon_{0}\epsilon_ {r}}\right] \tag{14}\]  
where \(h\) is the substrate thickness and \(\epsilon_{r}\) is the relative permittivity of the substrate material. The conductivity of graphene can be changed with the relaxation time (\(\tau\)) as follows [34].  
\[\tau\simeq\mu_{g}\hbar\sqrt{\frac{n\pi\tau}{ev_{f}}} \tag{15}\]  
where \(\mu_{g}\) is the electron mobility of graphene and \(v_{f}\) is the Fermi velocity. The relaxation time increases with the chemical potential values. Long relaxation time supports the propagation of the SPP wave.
## 3 Sound Source Localization Fusion Algorithm of a Three-Plane Five-Element Microphone Array
### Five-Element Microphone Array Localization Algorithm in the Y0Z Plane  
For the Y0Z plane, M2, M4, M5, M6, and M0 form the third single-plane five-element microphone array, setting the rectangular coordinate parameters of the sound source as \(S_{2}(y_{2},z_{2},r_{2})\), and the spherical coordinates as \(S_{2}(r_{2},\theta_{2},\varphi_{2})\). According to the principle of sound source geometric localization, using the distance formula between S and M0, M2, M4, M5, and M6, \(S_{2}(y_{2},z_{2},r_{2})\) is derived:  
\[\left\{\begin{array}{l}y_{2}\approx\frac{(\tau_{40}-\tau_{20})(4 \mu^{2}-c^{2}\tau_{2})}{4\mu m_{2}}\\ z_{2}\approx\frac{(\tau_{40}-\tau_{50})(4\mu^{2}-c^{2}\tau_{2})}{4\mu m_{2}}\\ r_{2}=\frac{4\mu^{2}-c^{2}\tau_{2}}{2m_{2}c}\end{array}\right. \tag{6}\]  
where \(m_{2}=\tau_{20}+\tau_{40}+\tau_{50}+\tau_{60}\), \(n_{2}={\tau_{20}}^{2}+{\tau_{40}}^{2}+{\tau_{50}}^{2}+{\tau_{60}}^{2}\), and the values of \(y_{2}\), \(z_{2}\), and \(r_{2}\) are related only to the time-delay values \(\tau_{20}\), \(\tau_{40}\), \(\tau_{50}\), and \(\tau_{60}\), the sound velocity c, and the array element spacing a.  
Combining Equations (2) and (6), we obtain \(S_{2}(r_{2},\theta_{2},\varphi_{2})\):  
\[\left\{\begin{array}{l}r_{2}=\frac{4\mu^{2}-c^{2}\tau_{2}}{2m_{2}c}\\ \theta_{2}=arcsin\frac{c(\tau_{40}-\tau_{50})}{2\theta}\\ \varphi_{2}=arcsin\frac{c(\tau_{40}-\tau_{20})}{\sqrt{4\mu^{2}-[c(\tau_{40}- \tau_{50})]^{2}}}\end{array}\right. \tag{7}\]  
According to Equation (7), when the array element spacing a and sound velocity c are fixed, the value of the distance \(r_{2}\) from the sound source S to the coordinate origin, the elevation angle \(\theta_{2}\), and the horizontal angle \(\varphi_{2}\) are related only to \(\tau_{20}\), \(\tau_{40}\), \(\tau_{50}\), and \(\tau_{60}\).
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 3 Importance of Components
### Optical Flow Analysis  
To capture the dynamic aspects of hand gestures, we compute the optical flow around each detected keypoint. Optical flow measures the visible motion of objects between successive video frames and is calculated under the assumption that pixel intensity remains constant over a short time interval. This can be mathematically expressed as follows:  
\[I(x,y,t)=I(x+\delta x,y+\delta y,t+\delta t). \tag{15}\]  
Using a Taylor series expansion and dividing by \(\delta t\), we obtain the motion components:  
\[u=\frac{\delta x}{\delta t},\quad\textit{vs.}=\frac{\delta y}{\delta t}, \tag{16}\]  
where \(u\) and \(v\) represent the horizontal and vertical components of motion, respectively. We define a square region around each keypoint with a size determined by the following:  
\[\text{region\_size}=\text{scale}\times h, \tag{17}\]  
To ensure that optical flow is computed in a localized region around each keypoint, a 10% area of the image height is used to define square regions of interest (ROIs) centered on each point. As previously described, each video frame is processed using YOLOv8-Pose to extract 21 keypoints per hand. These keypoints are normalized and used to define ROIs, whose size is determined by the parameter _scale_ (scale = 0.1 in case of 10% of the frame height), which controls the proportion of the image height, and \(h\) (h = 480 in case of \(640\times 480\) resolution), the height of the frame. The scale value determines how large each ROI will be. Dense optical flow is then computed for each ROI using the Farneback algorithm. The final input to the algorithm is a pair of grayscale patches, extracted from corresponding ROIs in two consecutive frames. At each step, the algorithm computesthe average magnitude and angle of motion within each ROI, producing a motion feature vector of shape (84), per frame--42 motion values per hand.  
This localized optical flow calculation provides critical information about movement, which is essential for accurately recognizing rapid and subtle gestures. As shown (Figures 2 and 3), the red squares denote the initial positions from which the optical flow vectors are computed, while the blue arrows illustrate the direction and magnitude of the motion between consecutive frames, thereby effectively capturing the temporal dynamics of hand movement.  
These figures also demonstrate that static gestures form one category characterized by minimal or no motion, whereas dynamic gestures constitute a separate category distinguished by significant temporal changes. This distinction underscores the necessity of incorporating temporal features when analyzing dynamic gestures, as these cannot be fully captured through spatial information alone. Optical flow thus provides a vital temporal dimension that enhances the discriminative power of gesture recognition systems.
## 2 Impedance Spectroscopy  
### Scopes of Application  
IS has been used in many domains to characterize materials and systems. Impedance Spectroscopy is exceptionally attractive in terms of applications as it uses a non-destructive approach, is easily implemented for the evaluation of systems, and due to the usefulness of the obtained data. Industrial and scientific communities have started to recognize IS' potential due to its wide use in electrocatalysis and energy [24; 25], in the evaluation of coatings quality [26], in corrosion phenomenon surveillance [27; 28], and in sensors evaluation and tuning [29; 30].
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 3 Implementation
### Data Analysis
#### 3.2.1 Calculating Energy Used  
The TC66C energy monitor returned a number of parameters across its USB serial connection to the Windows collector. Its power source was also from the Windows device,  
Figure 3: Photograph of the experimental setup illustrating the Raspberry Pi 5 Device Under Test (DUT) and the TC66C test meter inline with the Power Supply for the Pi, measuring its energy usage characteristics.  
Figure 2: Block diagram of the experimental setup—showing the Pi DUT which performed the key generation, and the Windows machine recording the electric supply characteristics from the TC66C meter. Trigger messages were conveyed using the network illustrated, to START and STOP the measurements in line with the state of each experiment.  
avoiding influencing the passing of power data recorded for the Pi. The software created for this paper used a TC66 software library provided by TheHWCave on GitHub [41] to set up the connection to the meter and periodically recover highly accurate data on voltage, current, wattage, and energy used.  
Cumulative energy consumption was converted from milliwatt-hours (mWh) to Joules (J) as follows:  
\[\text{Joules (J)}=\text{Milliwatt-hours (mWh)}\times 3.6\]  
The 3.6 factor is derived from there being 3600 s in an hour, and 1000 mWh in 1 Wh.