esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Conversion from Arithmetic to Boolean Masking for Embedded IoT Processor
### Our Contribution  
Masking conversion algorithms are mainly used to mask ARX ciphers. Most of the ARX ciphers, which is used in the IoT environments, are consisted of 32-bit unit operations. These ciphers aim to achieve fast encryption and a small code size. However, even if the ARX cipher is masked by [PERSON]'s method, memory usage is still of concern. To overcome this problem, we propose an extremely low-memory algorithm that converts from arithmetic masking to Boolean masking.  
The contributions of this paper are as follows:  
* Reducing the memory usage: We further reduce the memory usage of the pre-computation table from the \((2^{(k+1)})(k+1)\)-bit achieved by [PERSON]'s method [22] to \((2^{k})(k)\)-bit. The main idea is that the pre-computation table can be reduced by one bit based on the fact that the XOR operation is the same as the subtraction on \(\mathbb{F}_{2}\); this is the so-called _LSB trick_. The LSB trick has been mentioned in previous papers, but we apply this trick specifically to the A2B algorithms. Furthermore, we validate this intuitive fact using mathematical induction. As a result, our proposal can save one bit for each table. Our proposal also allows for compact, optimized memory usage in the real world. For example, if \(k\) is 8-bit, our algorithm can be constructed using a char data type.
* Reducing the execution time: We reduce the execution time of the pre-computation phase to approximately half of that achieved by [PERSON]'s method. When measuring the execution time of the algorithms using a pre-computation table, some researchers have focused only on the encryption parts.  
However, in the real world, the execution time of the pre-computation phase cannot be disregarded. We design a new pre-computation algorithm to minimize the number of **for** loops and the number of operators in each **for** loop. Our evaluation shows that our proposal requires approximately half of the operations of [PERSON]'s pre-computation algorithm. In additional, we simulate 8-bit, 16-bit, and 32-bit environments. On average, the results of the simulation show improvements in speed of 87% in the pre-computation phase and of 23% in the conversion, compared to that in [22].
* Verifying the security and the soundness: We verify the security against side-channel attacks and the soundness of the proposed algorithm using mathematical induction. That is, we show that all intermediate values of the proposed algorithm are random, and the soundness is reflected by the fact that the proposed algorithm always returns the correct output for an arbitrary input.
## 5 Experiments and Results
### Effect of Leaders Ratio
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Ratio of Leaders** & **Accuracy** & **Recall** & **FPR** & **TNR** & **Precision** & **G-mean** & **AUC** \\ \cline{2-7}  & **avg \(\pm\) std** & **avg \(\pm\) std** & **avg \(\pm\) std** & **avg \(\pm\) std** & **avg \(\pm\) std** & **avg \(\pm\) std** & **avg \(\pm\) std** & **avg \(\pm\) std** \\ \hline
80 \% & 0.976 \(\pm\) 0.018 & **0.984**\(\pm\) 0.021 & 0.032 \(\pm\) 0.023 & 0.968 \(\pm\) 0.023 & 0.969 \(\pm\) 0.023 & 0.976 \(\pm\) 0.018 & 0.976 \(\pm\) 0.018 \\
66 \% & 0.969 \(\pm\) 0.018 & 0.970 \(\pm\) 0.019 & 0.032 \(\pm\) 0.036 & 0.968 \(\pm\) 0.036 & 0.969 \(\pm\) 0.032 & 0.969 \(\pm\) 0.018 & 0.969 \(\pm\) 0.018 \\
50 \% & **0.980**\(\pm\) 0.022 & 0.980 \(\pm\) 0.021 & **0.020**\(\pm\) 0.028 & **0.980**\(\pm\) 0.028 & **0.980**\(\pm\) 0.027 & **0.980**\(\pm\) 0.022 & **0.980**\(\pm\) 0.022 \\
32 \% & 0.972 \(\pm\) 0.015 & 0.978 \(\pm\) 0.018 & 0.034 \(\pm\) 0.025 & 0.966 \(\pm\) 0.025 & 0.967 \(\pm\) 0.024 & 0.972 \(\pm\) 0.016 & 0.972 \(\pm\) 0.015 \\
24 \% & 0.971 \(\pm\) 0.016 & 0.966 \(\pm\) 0.025 & 0.024 \(\pm\) 0.021 & 0.976 \(\pm\) 0.021 & 0.976 \(\pm\) 0.020 & 0.971 \(\pm\) 0.016 & 0.971 \(\pm\) 0.016 \\
20 \% & 0.976 \(\pm\) 0.016 & 0.980 \(\pm\) 0.016 & 0.028 \(\pm\) 0.029 & 0.972 \(\pm\) 0.029 & 0.973 \(\pm\) 0.027 & 0.976 \(\pm\) 0.017 & 0.976 \(\pm\) 0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance evaluation of the SSA algorithm at different ratios of leaders, in terms of average accuracy, recall, FPR, TNR, precision, g-mean, and AUC. Figure 8: Box plots presentation of the SSA algorithm at different ratios of leaders.
## 3 Application
The conditional probability \(Pr(C|[A_{i}])\) for each alternative is calculated as follows: \(Pr(C|[A_{1}])=0.4688\); \(Pr(C|[A_{2}])=0.4369\); \(Pr(C|[A_{3}])=0.4761\); \(Pr(C|[A_{4}])=0.5083\); \(Pr(C|[A_{5}])=0.6555\); \(Pr(C|[A_{6}])=0.4926\); \(Pr(C|[A_{6}])=0.4926\); The results of the multi-attribute three-way decisions are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{\(c_{1}\)} & \multicolumn{3}{c}{\(c_{2}\)} & \multicolumn{3}{c}{\(c_{3}\)} & \multicolumn{3}{c}{\(c_{4}\)} & \multicolumn{3}{c}{\(c_{6}\)} & \multicolumn{3}{c}{\(c_{7}\)} \\ \cline{3-14}  & & \(C_{1}\) & \(\
eg C_{1}\) & \(C_{2}\) & \(\
eg C_{2}\) & \(C_{3}\) & \(\
eg C_{3}\) & \(C_{4}\) & \(\
eg C_{4}\) & \(C_{6}\) & \(\
eg C_{6}\) & \(C_{7}\) & \(\
eg C_{7}\) \\ \hline \multirow{3}{*}{\(A_{1}\)} & \(a_{P}\) & 0 & 0.4991 & 0 & 0.5237 & 0 & 0.412 & 0 & 0.4589 & 0 & 0.7238 & 0 & 0.3607 \\  & \(a_{B}\) & 0.1753 & 0.1747 & 0.1905 & 0.2095 & 0.2352 & 0.1648 & 0.2164 & 0.1836 & 0.1381 & 0.3619 & 0.2238 & 0.1262 \\  & \(a_{N}\) & 0.5009 & 0 & 0.4763 & 0 & 0.588 & 0 & 0.5411 & 0 & 0.2762 & 0 & 0.6393 & 0 \\ \hline \multirow{3}{*}{\(A_{2}\)} & \(a_{P}\) & 0 & 0.833 & 0 & 0.8113 & 0 & 0.3914 & 0 & 0.8402 & 0 & 0.5397 & 0 & 0.588 \\  & \(a_{B}\) & 0.0585 & 0.2916 & 0.0755 & 0.3245 & 0.2434 & 0.1566 & 0.0639 & 0.3361 & 0.2302 & 0.2699 & 0.1442 & 0.2058 \\  & \(a_{N}\) & 0.167 & 0 & 0.1887 & 0 & 0.6086 & 0 & 0.1598 & 0 & 0.4603 & 0 & 0.412 & 0 \\ \hline \multirow{3}{*}{\(A_{3}\)} & \(a_{P}\) & 0 & 0.602 & 0 & 0.5507 & 0 & 0.412 & 0 & 0.3609 & 0 & 0.5397 & 0 & 0.1476 \\  & \(a_{B}\) & 0.1393 & 0.2107 & 0.1797 & 0.2203 & 0.2352 & 0.1648 & 0.2556 & 0.1444 & 0.2302 & 0.2699 & 0.2983 & 0.0517 \\  & \(a_{N}\) & 0.398 & 0 & 0.4493 & 0 & 0.588 & 0 & 0.6391 & 0 & 0.4603 & 0 & 0.8524 & 0 \\ \hline \multirow{3}{*}{\(A_{4}\)} & \(a_{P}\) & 0 & 0.5261 & 0 & 0.5237 & 0 & 0.412 & 0 & 0.5942 & 0 & 0.7238 & 0 & 0.0713 \\  & \(a_{B}\) & 0.1659 & 0.1841 & 0.1905 & 0.2095 & 0.2352 & 0.1648 & 0.1623 & 0.2377 & 0.1381 & 0.3619 & 0.325 & 0.025 \\  & \(a_{N}\) & 0.4739 & 0 & 0.4763 & 0 & 0.588 & 0 & 0.4058 & 0 & 0.2762 & 0 & 0.9287 & 0 \\ \hline \multirow{3}{*}{\(A_{5}\)} & \(a_{P}\) & 0 & 0.4434 & 0 & 0.4608 & 0 & 0.4099 & 0 & 0.9201 & 0 & 0.5397 & 0 & 0.3892 \\  & \(a_{B}\) & 0.1948 & 0.1552 & 0.2157 & 0.1843 & 0.236 & 0.164 & 0.032 & 0.368 & 0.2302 & 0.2699 & 0.2138 & 0.1362 \\  & \(a_{N}\) & 0.5566 & 0 & 0.5392 & 0 & 0.5901 & 0 & 0.0799 & 0 & 0.4603 & 0 & 0.6108 & 0 \\ \hline \multirow{3}{*}{\(A_{6}\)} & \(a_{P}\) & 0 & 0.8314 & 0 & 0.8652 & 0 & 0.412 & 0 & 0.6804 & 0 & 0.5397 & 0 & 0.588 \\  & \(a_{B}\) & 0.059 & 0.291 & 0.0539 & 0.3461 & 0.2352 & 0.1648 & 0.1278 & 0.2722 & 0.2302 & 0.2699 & 0.1442 & 0.2058 \\ \cline{1-1}  & \(a_{N}\) & 0.1686 & 0 & 0.1348 & 0 & 0.588 & 0 & 0.3196 & 0 & 0.4603 & 0 & 0.412 & 0
## 3 Results and Discussion  
### Stress-Strain Curves  
The stress-strain curves of cracked specimens with different crack dip angles under uniaxial compression tests are shown in Figure 6. The uniaxial compression of rocks typically undergoes initial compaction, elastic deformation, plastic-yielding, and brittle failure stages. The presence of pre-existing cracks leads to stress concentration, resulting in variations in the stress-strain curves of the rock specimens. The pre-existing cracks impact the plastic-yielding stage more than the initial and elastic deformation stages of the stress-strain curves. In the initial compaction and elastic deformation stages, the stress-strain curves of the cracked specimens with different crack dip angles are roughly similar. At these two stages, the rock has not yet formed new microcracks, and the influence of pre-existing cracks is limited to the crack tips, exerting little effect on the overall stress-strain curve and mechanical properties of the specimens.
###### Abstract  
Our recent study detected the expression of a tissue renin-angiotensin system (tRAS) in human intervertebral discs (IVDs). The present study sought to investigate the impact of the angiotensin II receptor type 1 (AGTR1) antagonist losartan on human nucleus pulpous (NP) cell inflammation and degeneration induced by tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)). Human NP cells (4 donors; Pfirrmann grade 2-3; 30-37-years-old; male) were isolated and expanded. TNF-\(\alpha\) (10 ng/mL) was used to induce inflammation and degeneration. We examined the impact of losartan supplementation and measured gene expression of tRAS, anabolic, catabolic, and inflammatory markers in NP cells after 24 and 72 h of exposure. T0070907, a PPAR gamma antagonist, was applied to examine the regulatory pathway of losartan. Losartan (1 mM) significantly impaired the TNF-\(\alpha\)-induced increase of pro-inflammatory (nitric oxide and TNF-\(\alpha\)), catabolic (matrix metalloproteinases), and tRAS (AGTR1a and angiotensin-converting enzyme) markers. Further, losartan maintained the NP cell phenotype by upregulating aggrecan and downregulating collagen type I expression. In summary, losartan showed anti-inflammatory, anti-catabolic, and positive phenotype-modulating effects on human NP cells. These results indicate that tRAS signaling plays an important role in IVD degeneration, and tRAS modulation with losartan could represent a novel therapeutic approach.  
intervertebral disc; renin-angiotensin system; degeneration; regeneration; spine; inflammation 4.0/. +
Footnote †: journal: Medical Cancer  
Article  
Angiotensin II Type 1 Receptor Antagonist Losartan Inhibits TNF-\(\alpha\)-Induced Inflammation and Degeneration Processes in Human Nucleus Pulpous Cells  
[PERSON]  
1 AO Research Institute Davos, Clavadelerstrasse 8, 7270 Davos, Switzerland; [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (J.P.); [EMAIL_ADDRESS] (S.G.); [EMAIL_ADDRESS] (M.A.); [EMAIL_ADDRESS] (R.G.K)  
2 Department of Orthopedics and Trauma Surgery, Medical Centre--Albert-Ludwig-University of Freiburg, Faculty of Medicine, Albert-Ludwig-University of Freiburg, Hugtetterstrasse 55, 79106 Freiburg, Germany; [EMAIL_ADDRESS] (L.W.); [EMAIL_ADDRESS] (N.S.); [EMAIL_ADDRESS] (H.S.)  
3 Department of Orthopaedic Surgery and Traumatology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland; [PERSON]S] (S.H.); [PERSON](C.E.A)  
4 Department of Cardiology and Angiology I, Faculty of Medicine, Heart Center Freiburg University, University of Freiburg, Hugtetter Str. 55, 79106 Freiburg, Germany; [PERSON]Department of Medicine III (Interdisciplinary Medical Intensive Care), Medical Center, Faculty of Medicine, University of Freiburg, Hugtetterstr. 55, 79106 Freiburg, Germany  
5 Correspondence: [EMAIL_ADDRESS]  
6 These authors contributed equally to this manuscript.
## 2 Proposed Method
### Prediction Model  
Next for classification, each matrix (i.e., \(\mathbf{C}_{N_{\max}}\), \(\mathbf{C}_{N_{\min}}\), \(\mathbf{C}_{N_{\epsilon}}\), and \(\mathbf{C}_{N_{\mu}}\)) is divided into training and testing observations and utilized to create a prediction model based on Support Vector Machine (SVM). SVM has proven to perform well in many classification tasks although its hyper-parameters and regularization term must be tuned carefully during the training process to achieve the highest accuracy resulting in high computational  
Figure 3: The modified connections of residual units showing each block are densely connected to previous blocks. (**a**) Residual block, (**b**) dense block, and (**c**) the modified residual blocks densely connected.  
cost [38]. Therefore, Bayesian optimization is used to find the best hyper-parameters and thus improving the prediction model [39].  
Given \(N\) observations \(\{\mathbf{c}_{n},\mathbf{y}_{n}\}_{n=1}^{N}\) where \(\mathbf{c}_{n}\in\mathbb{R}^{N}\) and \(\mathbf{y}_{n}\) is the corresponding category vector for each \(\mathbf{c}_{n}\), the optimal score function \(f\) is found by SVM through solving the regularized risk minimization objective using hinge loss [40; 41] as an optimization problem such:  
\[\arg\min_{f(x)}\sum_{n=1}^{N}\max(0,1-\mathbf{y}_{n}f(\mathbf{c}_{n}))+\gamma R (f) \tag{3}\]  
where \(\max(0,1-\mathbf{y}_{n}f(\mathbf{c}_{n}))\) is the hinge loss for the classification function \(f(\mathbf{c}_{n})\), \(\gamma\) is the hyper-parameter to fine-tune \(f\) training error versus complexity, and \(R\) is the regularize function. For nonlinear classifier, a kernel method is used in SVM which transform \(f(\mathbf{c}_{n})\) into higher transformed data-point function \(\varphi(\mathbf{c}_{n})\) using a kernel function \(k(\mathbf{c}_{n},\mathbf{c}_{m})=\varphi(\mathbf{c}_{n}).\varphi(\mathbf{c}_ {m})\). The hyper-parameters are obtained by maximizing:  
\[\max_{a_{n}=1:N}=\sum_{n=1}^{N}\alpha_{n}-\frac{1}{2}\sum_{m=1}^{N}\mathbf{c}_{ n}\mathbf{y}_{n}k(\mathbf{c}_{n},\mathbf{c}_{m})\mathbf{c}_{m}y_{m}. \tag{4}\]  
Various kernel functions, scales, and constrained are examined such as linear, quadratic, cubic, and Gaussian kernels.
## 2 Adaptive Streaming Algorithm for Internet of Autonomous Vehicles
### Real-Time Driving Speed and Time Calculation for an Autonomous Vehicle
\[att_{p_{i+1}^{\sigma}}=att_{p_{i}^{\sigma}}+\frac{sl_{p_{i}^{\sigma},p_{i+1}^{ \sigma}}}{sp_{t_{i}^{\sigma},p_{i+1}^{\sigma}}^{\prime}\left(att_{p_{i}^{ \sigma}}\right)},\kappa^{\sigma}\leq j<h^{\sigma}\] (11) \[att_{p_{i}^{\sigma}}=ct^{\sigma}+\frac{rsl_{p_{i}^{\sigma},-1,p_{i}^{\sigma} }}{sp_{t_{i}^{\sigma},-1,p_{i}^{\sigma}}^{\prime}\left(att_{p_{i}^{\sigma},-1} \right)}\] (12) \[p_{1}^{\sigma}<p_{i}^{\sigma},\] (13) where \(ct^{\sigma}\) represents the current time, \(att_{p_{i}^{\sigma}}\) denotes the recalculated arrival time of \(\sigma\) at \(p_{i}^{\sigma}\), and \(\rho^{\prime}t_{p_{i}^{\sigma},p_{i+1}^{\sigma}}^{\prime}\left(att_{p_{i}^{ \sigma}}\right)\) stands for the traffic flow of the segment connecting \(p_{j}^{\sigma}\) and \(p_{j+1}^{\sigma}\) at time \(att_{p_{i}^{\sigma}}\). Significantly, \(\kappa^{\sigma}\) designates the initiation point of the subsequent road segment that \(\sigma\) will be approaching. \(sp_{t_{i}^{\sigma},-1,p_{i}^{\sigma},p_{i}^{\sigma}}\left(att_{p_{i}^{\sigma},-1}\right)\) denotes the driving speed of \(\sigma\) on the current segment, and \(sp_{t_{i}^{\sigma},p_{i}^{\sigma},p_{i+1}^{\sigma}}\left(att_{p_{i}^{\sigma}}\right)\) represents the average vehicle speed of \(\sigma\) passing through the segment connecting \(p_{i}^{\sigma}\) and \(p_{i+1}^{\sigma}\) after reaching \(att_{p_{i}^{\sigma}}\). In this context, \(rsl_{p_{i}^{\sigma},-1,p_{i}^{\sigma},p_{i}^{\sigma}}\) represents the remaining length of the current route segment that \(\sigma\) is currently traversing. * Compute whether the arrival time of the autonomous vehicle at each segment surpasses each time slot interval set by the system: \[dat_{p_{i}^{\sigma}}^{\sigma}= \begin{cases}1&\text{if }\left|att_{p_{i}^{\sigma}}-at_{p_{i}^{\sigma}}\right|\geq\Delta,\; \kappa^{\sigma}\leq j<h^{\sigma}\\ 0&else\end{cases} \] (14) where \(\Delta\) represents each time slot interval. * If the previously computed \(dat_{p_{i}^{\sigma}}^{\sigma}\) is equal to 1, the relevant RSU overseeing the segment is notified about the updated arrival time for the segment traversal. * Before the autonomous vehicle reaches its intended destination, we return to Step 4 and proceed with the execution. The primary computational overhead in this module occurs during Step 1, which executes the Dijkstra algorithm [35]. The computational complexity of the Dijkstra algorithm, as explained in [35], is \(\text{O}(\left|E\right|\;\log\;\left|V\right|)\), where \(\left|E\right|\) and \(\left|V\right|\) represent the total number of road segments and intersections in this module, respectively.
# Extended L-Band InAs/InP Quantum-Dash Laser in Millimeter-Wave Applications
###### Abstract  
We report the generation and transmission of a millimeter-wave (MMW) signal with a frequency of 28 GHz by employing an InAs/InP quantum-dash dual-wavelength laser diode (QD-DWL) emitting in the \(\sim\)1610 nm extended L-band window. The self-injection locking (SIL) technique has been engaged to improve the linewidth and reduce the noise of the optical tone. Besides, the transmission of a 2 Gbits/s quadrature phase-shift keying (QPSK)-modulated 28-GHz MMW beat tone over a hybrid 20-km radio-over-fiber combined with 5-m radio-over-free-space-optics and up to 6-m radio frequency wireless link has been demonstrated. Moreover, comparing the proposed QD-DWL with a commercial laser showcased similar performance characteristics, making the QD-DWL a candidate source for MMW applications.  
quantum-dash lasers; L-band wavelength region; millimeter waves; radio-over-fiber +
Footnote †: journal: _Photonics_  
**2021**, \(8\), 167. [[https://doi.org/10.3390/photonics8050167](https://doi.org/10.3390/photonics8050167)]([https://doi.org/10.3390/photonics8050167](https://doi.org/10.3390/photonics8050167))
###### Abstract  
In this paper, a direct-quadrature (D-Q) synchronous min-max coordinate system is proposed (as a new method) for diagnosing the occurrence of inter-turn short circuits (ITSC) of three-phase induction motors, and it was found that this method can linearly diagnose such short circuits using only the maximum value of the d-axis current component from the heavy load to the full load. In the diagnosis of ITSC, a method to perform linear discriminant analysis (LDA) efficiently was applied owing to the difficulty of linear separation under light load conditions. In the aforementioned method, time burden is generated because operations are performed for the entire data and between classes. However, the proposed method is useful even when it is applied to the entire load with only the LDA eigenvector of the minimum light load. This is proved by the graphical evaluation of the interaction between the false acceptance rate (FAR) and false recognition rate (FRR), and the results demonstrate that the proposed method is more efficient than existing LDA application methods.  
3-phase induction motor; fault diagnosis; inter-turn short circuit (ITSC); linear discriminant analysis (LDA); D-Q synchronous min-max coordinate
# Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning
## 4 Implementation Details  
In this section, we present in detail the machine-learning techniques used for the _heartbeat-detection_ and the _arrhythmia-classification_ modules. The preliminary analysis of the dataset for the fine tuning of the modules and the training of the classifiers was conducted on a workstation using an Intel i5 processor with 8 GB of RAM, under a generic linux kernel v4.4.0-130. The initial models were developed using Python version 3.5.2 in combination with scikit-learn ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/))) library v0.18.1 and keras [[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/))) library v1.2.2. For the evaluation of the performance using a hardware-assisted core, an Arduino/Genuino 101 development board [[https://store.arduino.cc/genuino-101](https://store.arduino.cc/genuino-101)]([https://store.arduino.cc/genuino-101](https://store.arduino.cc/genuino-101))) was used using the Intel\({}^{\textregistered}\) Curie\({}^{\textregistered}\) ODK Z tree firmware ([[https://github.com/0](https://github.com/0) long/CODK-Z]([https://github.com/0](https://github.com/0) long/CODK-Z)).
## 4 Method and Discussion
### Evaluation and Mapping
The area of this region is about 8706 square kilometers. Region B, which is close to the coastline and encompasses a few rivers, many cities, villages, farmlands, and roads, was also hit by the flood. The area of this region is about 9742 square kilometers. Region C, in close proximity to the region A, includes a river, an inland lake, and the cities Zaboli and Suran, which were affected. The area of this flooded region is about 1196 square kilometers. Therefore, based on the estimates from the CYGNSS observations, about 19644 square kilometers were affected by the flood in the south and middle parts of the Sistan and Baluchestan province. More severe impacts were seen in the regions close to the coastlines and nearby rivers. Figure 14: Map of the flooded regions laid over Google Maps. The blue-colored polygons show the boundary of the flooded areas, yellow lines are roads, and names of the cities are written on the map. Figure 13: The georeferenced optical satellite imagery of the flood over the cities of Zaboli and Suran (region C in Figure 10) from MODIS (13 January 2020) overlaid by the corrected SNR derived from CYGNSS observations (13 January to 15 January 2020). The region includes a river and an inland water body.
## 4 Coordinate System Conversion Algorithm-based Depth Estimation
### Camera Calibration  
To derive the conversion formula between the camera and world coordinate systems, several internal parameters of the camera must be known in advance. They are the focal length (\(f_{x}\), \(f_{y}\)) and the principal point (\(c_{x}\), \(c_{y}\)) related to lens and image sensor information, and radial (\(k_{1}\), \(k_{2}\)) and tangential (\(p_{1}\), \(p_{2}\)) distortion coefficients related to distortion. Obtaining these parameters is called camera calibration.  
Camera calibration is performed by geometrically interpreting an image taken from a 3D or 2D calibration object having a large number of prominent feature points. The calibration object mainly used is a chess board in which black and white squares appear alternately. The [GML Camera Calibration] Toolbox provided by the 'Graphics and Media Lab' provides calibration results by applying OpenCV, which implements [PERSON]'s thesis [24], to multi-view images of the same chess board.  
Figure 9 shows a chessboard image for the internal calibration of the SNO-6084R CCTV camera. The square of the chess board forms a pattern with a square of 3 cm in width and length, and corner points with a size of 9 \(\times\) 6 can be detected.  
Figure 8: Measured information of floor plan using the world coordinate system.  
Figure 7: Floor plan using the world coordinate system.  
Table 3 lists the internal factors and distortion coefficients of the SNO-6084R CCTV camera obtained by internal calibration using chess board images. The internal factors \(f_{x}\), \(f_{y}\), \(c_{x}\) and \(c_{y}\) are measured in pixels and the distortion coefficients \(k_{1}\), \(k_{2}\), \(p_{1}\) and \(p_{2}\) are constants.