esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety
## 3 Home Fire Data Analysis  
To understand the importance of the study, we did some analysis by using different fire datasets. We took these datasets from very reliable sources [36; 37]. The datasets include fire statistics containing: (i) a percentage of homes that have fire alarms in England & Wales, (ii) causes of the fires, (iii) fire alarms with respect to sensors type, and (iv) death data per million population because of dwelling fires. While analyzing the house fire data initially, we examined the trend of using alarm during the years from 1988 to 2015. Figure 1 shows the graph of this trend. It shows that almost 93.4% of houses in English countries are now using fire alarms.  
As this analysis shows, almost 93% of houses are equipped with fire alarms but the study indicates that there are still many deaths despite the fire detection systems. Figure 2 shows the pictorial representation of the analysis of the death dataset taken from [37]. The graph shows the number of deaths per million people in each year. The study shows that more than ten deaths per million population are still occurring every year. The loss of lives or property caused by fire is due to many reasons, such as alarm failure, false alarm, no response, and many other unspecified reasons. We performed another analysis of fire-caused losses where fire detection systems were installed. This analysis was made on the data taken from England between 2010 and 2016. Figure 3 shows the study of fire-caused losses per year. We analyzed fire losses due to three causes: (1) Fire damages due to a false alarm, (2) Fire losses due to no person response, (3) Other/unspecified reasons. The analysis shows that most of the losses were caused by false alarms. As we described earlier, most of the fires are not detected because of sensors' failure and thus progress. Most of the houses examined were equipped with WSN-based fire alarm systems.  
Most of the time, alarm failures are due to the breakdown of the WSN. In the WSN, the association between sensors is essential. Therefore, the communication between sensors cannot be performed if the network is disconnected. So, the failure of a single sensor may result in many harsh events. Most of the sensors are battery-powered and might not have the possibility to recharge. Most of the energy is spent in communication, and, if the sensor energy is exhausted during communication, sensor failure occurs, and this can sometimes lead to network failure. We performed analyses on alarm's failure in the presence of a fire detector sensor. The analysis results are shown in Figure 4. The analysis graph displays the percentage of true alarm generation and sensors failure with respect to the different categories of sensors from 2010 to 2016.  
Figure 1: Percentage of houses owing fire alarms per year.
## 3 Results and Discussion
### Matric Suction Determined Using the Filter Paper Method
#### 3.1.2. Matric Suction of Different Compacted Specim Types with a Constant Initial Void Ratio
The three-way analysis of variance conducted, shown in Table 6, determined that varying the different factors, such as moisture content, polyurethane content, and number of wet-dry cycles, had a significant effect on the obtained matric suction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Degrees of Freedom** & **Mean Square** & **F-Value** & \(p\)**-Value** & \\ \hline Model & 480.09 & 10 & 48.01 & 202.12 & \textless{}0.0001 & Significant \\ Moisture content & 459.97 & 1 & 459.97 & 1936.52 & \textless{}0.0001 & Significant \\ Polyurethane content & 1.95 & 1 & 1.95 & 8.23 & 0.0048 & Significant \\ Cycle & 3.32 & 2 & 1.66 & 7.00 & 0.0013 & Significant \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of variance of CPCL and CL matric suction. Figure 19: SEM image of (**a**) loose polyurethane–clay, (**b**) CPCL before the wet–dry cycles, and (c) CPCL after three wet–dry cycles. Figure 18: Changes in CPCL at different stages in the three wet–dry cycles. The quadratic regression model, which can be used to predict the suction of CPCL for three wet-dry cycles and a wide range of moisture contents is expressed in Equations (14)-(16). The high coefficient of determination, \(R^{2}\), which has a value of 0.9383, indicates that 93.83% of the actual data fits the model. Figure 20 shows the plot of the predicted and actual values of the matrix suction, which shows a linear relationship indicating the regression model obtained is a good model to predict the matric suction at a large variety of moisture contents. \[log\psi_{CL,C1}=5.21665-2.994\times 10^{-2}w-9.921\times 10^{-4}w^{2} \tag{11}\]  
\[log\psi_{CL,C2}=5.36433-4.141\times 10^{-2}w-9.921\times 10^{-4}w^{2} \tag{12}\]  
\[log\psi_{CL,C3}=5.26486-4.051\times 10^{-2}w-9.921\times 10^{-4}w^{2} \tag{13}\]  
\[log\psi_{CPCL,C1}=5.53189-4.520\times 10^{-2}w-9.921\times 10^{-4}w^{2} \tag{14}\]  
\[log\psi_{CPCL,C2}=5.42783-5.667\times 10^{-2}w-9.921\times 10^{-4}w^{2} \tag{15}\]  
\[log\psi_{CPCL,C3}=5.37784-5.577\times 10^{-2}w-9.921\times 10^{-4}w^{2} \tag{16}\]  
where \(\psi_{CL,C1}\) is the matric suction of CL at the first wet-dry cycle in kPa, \(\psi_{CL,C2}\) is the matric suction of CL at the second wet-dry cycle in kPa, \(\psi_{CL,C3}\) is the matric suction of CL at the third wet-dry cycle in kPa, \(\psi_{CPCL,C1}\) is the matric suction of CPCL at the first wet-dry cycle in kPa, \(\psi_{CPCL,C2}\) is the matric suction of CPCL at the second wet-dry cycle in kPa, \(\psi_{CPCL,C3}\) is the matric suction of CPCL at the third wet-dry cycle in kPa, and \(w\) is the moisture content in percentage.
# Optimal Attitude Determination for the CR200 Underwater Walking Robot
## 3 Results  
In this section, the process of identifying the optimal posture that induces the maximum downforce is described. The posture is defined by two parameters: the pitch angle \(\alpha\) and the seafloor clearance \(\hat{\kappa}\). The variation range of each parameter was selected considering the degree of change in the downforce. The CFD simulations were conducted accordingly, and a comparison was made between the optimal case, where downforce is maximized, and the suboptimal case, where downforce is minimized.
## 3 Results
### Correlations of 20S proteasome and UCH-L1  
Correlation analysis between 20S proteasome and UCH-L1 concentrations was performed using the Pearson test (for normally distributed data) or the Spearman test (for non-normally distributed data). The levels of urine 20S proteasome correlated positively with the levels of 20S proteasome in serum (r = 0.64; Pearson test) (Figure 4A). No significant correlation was found between urine UCH-L1 levels and serum UCH-L1 levels (r = 0.08; Spearman test).  
Figure 4: [PERSON]’s correlation analysis between urine and serum 20S proteasome concentration (r = 0.64) (**A**), and [PERSON]’s correlation analysis between serum UCH-L1 and serum 20S proteasome concentration (r = 0.47) (**B**).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AUC** & \(\mathbf{p}\) & **Sensitivity** & **Specificity** & **PPV** & **NPV** & **Cut-Off** \\ \hline Proteasome 20S  Muscle-invasive (Figure 2A) & 0.64 & 0.029 & 46 & 82 & 61 & 71 & 21.79 \\ \hline Proteasome 20S  High-grade (Figure 2B) & 0.65 & 0.019 & 82 & 42 & 66 & 63 & 13.54 \\ \hline Proteasome 20S  Recurrence (Figure 2C) & 0.62 & 0.49 & 67 & 63 & 57 & 72 & 17.48 \\ \hline UCH-L1  Muscle-invasive (Figure 2D) & 0.74 & 0.0003 & 75 & 77 & 66 & 84 & 6.27 \\ \hline UCH-L1  High-grade (Figure 2E) & 0.64 & 0.02 & 100 & 25 & 65 & 100 & 2.74 \\ \hline UCH-L1  Recurrence (Figure 2F) & 0.53 & 0.62 & 76 & 42 & 65 & 56 & 4.51 \\ \hline \hline \end{tabular} AUC—area under the curve, PPV—positive predictive value, NPV—negative predictive value.  
\end{table}
Table 5: Diagnostic efficiency of serum 20S proteasome and UCH-L1.  
Spearman tests were also performed for linear correlation between UCH-L1 and 20S proteasome concentrations in serum and between UCH-L1 and 20S proteasome concentrations in urine. A positive correlation (r = 0.47) was observed between UCH-L1 and 20S proteasome in serum only (Figure 4B).
## 9 Specific RID Algorithm Examples
### Gadras  
Development of the \(\gamma\) detector response and analysis software (GADRAS) at Sandia National Laboratory began in 1986 [24, 26] focusing on DRF estimation using relatively simple 1-dimensional geometric model. GADRAS currently includes several RID algorithms, and perhaps the most extensive uses multiple linear regression with backward variable elimination to find the best fitting subset of library isotopes. A key requirement is to characterize each detector and measurement scenario with measured spectra that are used to form the spectral library described in Section 5.  
Backward elimination is a search strategy that in this case begins by fitting a large model that contains all isotopes in the library and then successively omitting isotopes whose presence in the model does not significantly improve the fit. The best fitting subset is then the predicted isotope subset.  
Multiple linear regression is a standard technique [10] that we will not fully describe here. See, for example, [10, 15]. Briefly, using the notation from the Data Model section, with \(\mu_{i}=\sum_{j}R_{ij}(\rho)a_{j}\), if the measured counts satisfy \(C_{i}\sim\) Poisson(\(\mu_{i}\)) and \(\mu_{i}\) is large enough (20 or larger) for the Poisson distribution to be well approximated by the Gaussian, then the weighted least squares (WLS) solution might be competitive with a more correct maximum likelihood approach that relies on the Poisson likelihood. The WLS solution is \(\hat{a}=(R^{T}\hat{\Sigma}R)^{-1}R^{T}\hat{\Sigma}^{-1}C\), where \(\hat{\Sigma}\) is the estimated variance-covariance matrix of the count vector, which would be estimated using the fact that variance of \(C_{i}\) is \(\mu_{i}\). The WLS approach is nearly the optimal method and is more easily implemented than a technically more correct approach that uses the Poisson likelihood. Because there will be measurement errors in the \(R\) matrix, the most appropriate approach requires both Poisson likelihood for the measured counts and a measurement error model for \(R\) (see the Future Directions section).
## 3 Results and Discussion
### Effect of EGR on the Dual-Fuel Engine
#### 3.3.1 Performance Characteristics  
Figure 7 shows the effects of EGR on the ITE and IMEP of the dual-fuel engine. It is evident that as the EGR rate increases from 0% to 20%, the ITE decreases from 42.3% to 39.4%, and the IMEP decreases from 2.33 MPa to 2.14 MPa. The results further reveal that, although EGR exhibits an adverse influence on both ITE and IMEP, the power output and fuel economy of the dual-fuel engine remain superior to those of the original engine as long as the EGR rate is below 20%.
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
## 3 Results
### Hologram Realized by the Array of Identical Point Sources
The distance between grid nodes is \(D=\lambda_{0}\), and there are 50 grid nodes in the horizontal and vertical directions, so the size of the hologram is \(50\lambda_{0}\times 50\lambda_{0}\), which is the same as the size of the desired image. The BEM-based numerical simulation is used to confirm the performance of the PS array. Figure 2c shows the simulation results, which show a clear image that closely matches the desired image. The RMSE between this holographic image and the desired image shown in Figure 2a is 0.1737.
# A Novel OFDM Format and a Machine Learning Based Dimming Control for LiFi
## 3 System Description of DPO
### DPO Receiver  
The DPO receiver is presented in Figure 2. In the receiver, a reverse operation is performed. A photodetector detects the transmitted signal, and the signal is converted to the electrical domain, which is specified by \(\hat{z}\) as follows.  
\[\hat{z}=z+n_{AWGN} \tag{6}\]  
In (6), \(n_{AWGN}\) is the additive white Gaussian noise (AWGN). The received elements are \(\hat{x}_{DCO}\) for the transmitted DCO signal elements \(x_{DCO}\), and \(\hat{y}_{PAM}\) for the transmitted PAM-DMT signal element. The combined received signal is converted from analog to digital by an A/D converter, and the cyclic prefix is removed and converted from serial to parallel. Finally, it takes the following form:  
\[\hat{z}=\hat{x}_{DCO}+\hat{y}_{PAM}+n_{DCO}+n_{PAM}+n_{AWGN} \tag{7}\]  
The signal is then sent to a fast Fourier transform (FFT) operator to generate frequency domain signal, \(\hat{Z}\).  
\[\hat{Z}=\hat{X}_{DCO}+\hat{Y}_{PAM}+N_{DCO}+N_{PAM}+N_{AWGN} \tag{8}\]  
On the receiver side, photodetectors convert the incoming optical signal to the electrical domain. The DCO symbols carried by the odd subcarriers are recovered first. After removing Hermitian symmetry, it is demodulated using an M-QAM demodulator. Note that the DCO signal may contain clipping noise, but it can be negligible only by applying an appropriate DC bias. In the same process, PAM-DMT is recovered using an M-PAM demodulator. It can be noted that noise due to clipping in PAM-DMT affects each even subcarrier on its real part. Therefore, it does not have any effect on the signal. Finally, the even parts of imaginary subcarriers are extracted.
## 2 Materials and Methods
### Automated Satellite-Based Surface Water Mapping  
In this research, the satellite-based surface water mapping assessment was performed using the modified toolsets from Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods: [[https://github.com/Servir-Mekong/hydra-floods/tree/master/hydrafloods](https://github.com/Servir-Mekong/hydra-floods/tree/master/hydrafloods)]([https://github.com/Servir-Mekong/hydra-floods/tree/master/hydrafloods](https://github.com/Servir-Mekong/hydra-floods/tree/master/hydrafloods)), accessed on 1 May 2022). HYDRAFloods is an open source Python framework for obtaining, processing, and generating surface water maps assessed from both optical (e.g., Landsat-7/8/9) and SAR (Sentinel-1) satellite remote sensing images. The framework is developed and deployed on the GEE and Google Cloud Platform (GCP), which provide automated, rapid, and high-quality surface water mapping products to the users with minimal effort. In this study, the data downloading, image processing, water surface detection, data visualization, and analysis were carried out on top of Earth Engine Python API. The results were computed in GCP and evaluated in the GEE integrative interface. Figure 4 outlines the workflow of the water surface detection using HYDRAFloods, where details of image preprocessing and water mapping algorithms are introduced in the following sections.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Provider** & **Resolution** & **Reference** \\ \hline ALOS & JAXA & 30 m & [49] \\ HydroSHEDS & WWF & 93 m & [50] \\ HydroSHEDS Hydrologically Conditioned & WWF & 93 m & [50] \\ MERIT & Dai Yamazaki & 93 m & [51] \\ GMTED 2010 & USGS & 232 m & [52] \\ SRTM & NASA/USGS & 30 m & [53] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characters of the DEM data used in this study.  
Figure 4: The workflow of automated satellite-based surface water mapping.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 3 Background  
In this section, we present some definitions related to dynamic graphs, and review the personalized PageRank algorithm. Additionally, we outline the problem formulation of our anomaly detection method. The complete set of notations is provided in Table 2.  
**Definition 1**.: _A **graph**, \(G=(V,E)\), is defined as a pair consisting of a set of nodes, \(V=\{v_{1},\ldots,v_{n}\}\), and a set of edges, \(E\), which is a subset of the Cartesian product \(E\subseteq V\times V\) and defines the connections between the nodes._  
**Definition 2**.: _A **weighted graph**, \(G=(V,E,W)\), is a graph where each edge \(e\in E\) has an associated weight, \(W(e)\in\mathbb{R}\)._  
**Definition 3**.: _A **graph snapshot** at a specific timestamp, \(t\), is denoted as \(G_{t}=(V_{t},E_{t},W_{t})\), where \(V_{t}=\{v\in V\mid i_{v}=t\}\) is the set of nodes present at time \(t\), and \(E_{t}=\{e\in E\mid i_{e}=t\}\) is the set of edges existing between the nodes in \(V_{t}\) at time \(t\); \(W_{t}\) represents the weights assigned to the edges in \(E_{t}\), which may consist of plain or labeled edges._  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Property** & **Seden-Spot** & **MIDAS-R** & **F-RADE** & **DNNNACTG** & **Dyn-Neuron** & **Multi-AD** & **AneEdge** & **DecayRank** & **Our** \\  & **CQENS** & **GQENS** & **(2021)** & **(2021)** & **(2022)** & **(2021)** & **(2021)** & **(2021)** & **(2021)** & **(2021)** & **Method** \\ \hline Edge Anomaly & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Node Anomaly & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Benchmark & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Sudden Delay Changes & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Adaptive Bayesian updating & & & & & & & & & ✓ \\ \hline \hline \end{tabular} Note: The checkmarks (✓) indicate that the corresponding method supports the listed property. A dash (✓) means the feature is not applicable or not reported in the respective study.  
\end{table}
Table 1: Comparison of relevant anomaly detection methods in dynamic graphs.  
**Definition 4**.: _We define **dynamic graph**, \(\mathcal{G}=G_{t=1}^{T}\), as a sequence of ordered sets of graph snapshots at different timestamps, \(t\), where \(T\) is the total time steps, and each \(G_{t}\) is a static graph representing the state of the graph at timestamp \(t\)._
## 5 Water Treatment Employing Bioadsorbents  
### Bioadsorbents Based on Grains and Seeds  
#### 5.1.1 Nutrients and Heavy Metals  
Well-known contaminants in water that need to be continuously monitored include the nutrients nitrogen and phosphorus and the various heavy metals. [PERSON] et al. [82] studied a bioadsorbent based on peanut husk functionalized with zirconium, iminodiaceae acid, and Fe\({}_{3}\)O\({}_{4}\) that was able to remove up to 88.5% of phosphates in six hours at a temperature of 30 \({}^{\circ}\)C and pH 3. The addition of zirconium was crucial due to its interaction with hydroxyl, amines, and carboxyl groups; this enhanced PO\({}_{4}\)\({}^{3-}\) sequestration at the maximum sorption capacity of 13.2 mg/g. Rubber pod husk is another byproduct without commercial value that can serve as an adsorbent to remove phosphates dissolved in water (0.1-0.5 g/L), showing favorable results (q\({}_{\text{m}}\) = 39.9 mg/g) after six hours (contact time) at pH 7 and 29 \({}^{\circ}\)C [50]. Raw tamarind seeds are a residue of interest due to their high content of lignocellulosic and surface groups; these change under chemical (H\({}_{2}\)SO\({}_{4}\)) and physical (ultrasonic waves) treatment, resulting in q\({}_{\text{m}}\) values around 18 mg/g and a 50% reduction in the reaction time to adsorb Pb(II) [32]. Ultrasound-modified residues present a large surface area with large pores and cavities. Additionally, variations in pH charge the hydroxyl groups, increasing their affinity for positive ions and achieving 99.5% removal of Pb(II) at pH 6 and 30 \({}^{\circ}\)C [32]. [PERSON] et al. [28] analyzed Pb(II) adsorption by employing a modified magnetic-coffee membrane. The assays of sorption capacity showed better results with 0.025 g of the nanocomposite at 25 \({}^{\circ}\)C and pH 7.3, removing 99.5% of the contaminant in 30 min. On the other hand, the non-activated exhausted coffee was able to remove 97.6% of Hg(II), adjusting the adsorbent and mercury concentrations to 0.4 and 77.9 mg/L, respectively, with a contact time of 192.4 min at 33 \({}^{\circ}\)C and pH 7 [55]. Another metal that contaminates water is silver. According to [PERSON] et al. [56], it is possible to remove up to 99% (q\({}_{\text{m}}\) = 23.13 mg/g) of silver using moringa powder thanks to its high protein content which, depending on pH, provides a high amount of charged amine and carboxyl groups. The optimal conditions for Ag retention were obtained usingan adsorbent with a <500 mm particle size and 20 min of contact time at pH 6.5. [PERSON] and [PERSON] [89] demonstrated the feasible removal of Fe, Mn, Zn, Ni, Cu, and Cd from aqueous solutions by implementing raw and modified walnut shells. In general, the maximum sorption capacity for the six metals was 44.1 \(\pm\) 18.5 mg/g, while a removal efficiency of 96.5% was observed only for Mn at pH 8. Table 3 summarizes the phosphorus and heavy metals removal efficiencies previously described and the isothermal models that fitted the experimental data; the Langmuir, Freundlich, and pseudo-second order models best explained the adsorption behavior in the biomaterials.
## 4 Results and Analysis
### Effect of Transverse Bulkheads on Bend Damage
When the transverse bulkhead is located directly above the explosive, the side plates at the corresponding position are firmly fixed, and it is difficult to produce a transverse deformation. Therefore, it is difficult for the two ends of the SHG to rotate with the transverse bulkhead as the center; therefore, the bend position is offset to both sides of the transverse bulkhead. Depending on the load conditions, there can be one or two bends, as shown in Figure 11c,d. As for Figure 11d, because the explosive is too close to the water surface, the air is sucked in during the bubble contraction, which destroys the internal pressure state, leading to an early recovery time for sagging amplitude. In general, the relative longitudinal position of the explosive and the transverse bulkhead will affect the bend position and size, and the load strength will affect the bend quantity. In the results of this paper, all the side plate creases are W-shaped. However, more crease patterns may appear under different conditions, which requires further study. Table 6 lists the damage results for SHGs in different cases. The ratio of transverse bulkhead position to half of the bend zone length was used to characterize the parameter of the relative position of the transverse bulkhead and bend zone, i.e., \(x_{W}\). The calculation formula is  
\[x_{W}=\frac{2|x|}{L_{W}}. \tag{10}\]  
In cases 1 and 2, the SHGs move in whipping mode, so there is no bend zone. The large error in case 3 is due to the influence of the transverse bulkhead, which increases the length of the bend zone by about 12%, indicating that a \(1\leq x_{W}\leq 1.23\) transverse bulkhead will slightly increase the length of the bend zone. The error in case 9 is large because  
Figure 11: Local creases evolution process: (**a**) 5 ms, (**b**) 13 ms, (**c**) 30 ms, (**d**) 52 ms.  
the transverse bulkheads are located within the predicted bend zone, which restricts the outward development of plastic hinge lines. The same SHG was used in cases 10 and 11. However, the error in case 10 is larger because the load input energy is smaller than in case 11. Part of the load energy is absorbed by the warping deformation of the left hull plate, resulting in a smaller bend zone length. It can be seen from case 11 that when the load is strong enough, the length of the off-center bend zone with only one constrained side can also be well predicted. It can be seen from cases 4-8 that the transverse bulkheads with \(x_{W}\geq 2.47\) have little influence on the length of the bend zone. Thus, when the bend zone is less affected by the transverse bulkheads, the prediction error of the formula is less than 5%, which can reflect the potential bend zone length and provide a reference for the anti-impact work of ships.