esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 7 Outing and Visiting Detection
### Outing Detection  
As described above, a segment between adjacent door activities is considered to be an outing event, if no sensor activity was detected during this time period. Due to calibration errors, sensors may trigger events with different delays which could result in signals detected right after the beginning or shortly before the end of the segment, even in the case of an actual outing event. To account for this error, we introduce a tolerance buffer before and after any door event. Within the buffer time any sensor activity is neglected. In practice, we found one minute to be a reasonable buffer to be used in our system. Additionally, sensors may trigger short and isolated events by accident. Thus, we employ a noise cut-off threshold to eliminate this type of noise. In practice, we take \(0.5\%\times D^{O}\) as the cut-off threshold, with \(D^{O}\) representing the segment duration.  
Figure 12 shows outings detected at a household. We can see that outings happened quite often in the morning or around 4 p.m. in the afternoon. However, since March 2020, barely any outings were detected, except for a few cases in April. This is another behavioral change due to the COVID-19 lockdown. We found similar outing reductions across the entire cohort.
## 3 Results
### Spatial Autocorrelation of Displacements  
In the case of displacements, hot spot analysis was used to study the spatial relationship of velocity and acceleration of displacements in each year. The Moran scatterplot for displacement velocity and acceleration is shown in Figure 3. In the case of velocity, it can be seen that the spatial correlation is greater. This is also shown by the global Moran's I statistic, which for velocity was on average around 0.4, and for acceleration -0.2.  
The visualization of hot spot analysis on the map will display areas with highly correlated values, indicating potential danger zones. This information reveals high velocities or accelerations of displacement at multiple points in a specific area. Mining experts can evaluate the size of this area and the levels of displacements to assess the degree of danger. Conversely, areas classified as cold spots indicate lower probabilities of threat. These areas are characterized by low correlated values of velocity or acceleration of displacements. Additionally, areas with a negative correlation may also be significant. An increase in displacement at one point, without correlation with others, may signify a measurement error or a new phenomenon affecting the neighborhood over time, and thus can be used for anomaly detection. Figure 4 shows some results from the hot spot analysis by location. The black line marks the primary dam. It can be seen that there are both hot spots and cold spots of different sizes. When analyzing the acceleration of benchmark displacement, more statistically insignificant points were observed due to the smaller spatial correlation for this parameter.
## 3 Methodology
### Prediction Accuracy Measures  
In this paper, the mean absolute error (MAE) and root mean square error (RMSE) are used as the evaluation indexes of deterministic point prediction results, and the prediction interval coverage (PICP) and Mean prediction interval width (MPIW) are used as the reliability and clarity evaluation indexes of probabilistic prediction results [38].  
1. Mean absolute error (MAE) \[MAE=\frac{1}{n}\sum_{i=1}^{n}|Y_{i}-Y_{i}^{P}|\]
2. Root mean square error (RMSE) \[RMSE=\frac{\sum_{i=1}^{n}(Y_{i}-Y_{i}^{P})^{2}}{\sum_{i=1}^{n}Y_{i}^{2}},\] \(n\) is the number of prediction points, \(Y_{i}\) is the real RUL of the sample, \(Y_{i}^{P}\) is the predicted value.
3. Reliability Prediction interval coverage probability (PICP) is usually used to evaluate the accuracy of prediction interval. It is composed of upper bound and lower bound of coverage target value. \[PICP=\frac{1}{n}\sum_{i=1}^{n}C_{i},\] A larger \(PICP\) value means that more target values \(Y_{i}^{P}\) fall within the constructed prediction interval. \(n\) is the number of prediction points, \(C_{i}\) is the coverage of the prediction interval, \(C_{i}=1\), if \(Y_{i}^{P}\in[l_{i},u_{i}],\textit{otherwise},C_{i}=0\). \(u_{i}\) and \(l_{i}\) are the upper and lower bounds of the target value \(Y_{i}^{P}\) respectively, and the optimal value of PICP is 100%, which means that all the target values \(Y_{i}^{P}\) fall within the prediction interval, that is, the coverage rate is 100%.
4. Clarity Mean prediction interval width (MPIW) shows the average width of prediction interval [39]. \[MPIW=\frac{1}{nD}\sum_{i=1}^{n}(u_{i}-l_{i}).\] where \(D\) is the difference between the maximum value and the minimum value of the target values \(Y_{i}^{P}\), and the target with \(D\) is used to normalize the average width. The bandwidth of the prediction interval should be as small as possible, which determines the amount of information in the prediction interval. Therefore, the width of prediction interval can be used as a standard to compare the prediction results.
## 3 Development of the Virtual and Digital Laboratory
### Space and Equipment  
The VR/AR laboratory was established in two spaces:  
* _A_: This space was equipped with adequate lighting, Ethernet connection points, and VR sensors. Its main purpose is to provide support for collaborative VR/AR projects, including digital design and manufacturing of automotive parts and simulation. The space is distributed in accordance with the safety guidelines of the ISO/IEC TR 23842-1 standard. Figure 2 presents the general layout of space \(A\), and Table 1 presents the details of the planned VR/AR equipment.
* _B_: This space is located in a building shared with other engineering laboratories. It features an industrial KUKA robot, which was adapted for testing automotive designs. It also provides support for collaborative VR/AR projects in mechatronics and industrial engineering. Figure 3 presents the general equipment installed in this room.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment** & **Description** \\ \hline VR & VR Headset HTC VIVE Pro (\(\times 3\)) \\ \hline AR & AR Headset Moverio BT-300 Developer Edition \\ \hline Digital Technologies & HP Z6 G4 Workstation \\ (CAD/CAM/CAE/Additive Manufacturing) & HP ZBook 15 G5 \\  & 3D Printer Markforged \\  & 3D Printer Formlabs High Resolution \\ \hline Collaborative Activities and Projects & Samsung Flip 55” Touch Screen \\  & Samsung 65 “Smart TV Ultra HD 4K (\(\times 2\)) \\  & Kanto MTM65 PL Mobile support for TV (\(\times 2\)) \\  & Blackboard ALFRA 3213 Pintarron Star (\(\times 4\)) \\  & Neewer Portable Aluminum Alloy Camera \\  & Tripod Monopod \\  & Pepper Sacks Armchair Puff Drop Red (\(\times 6\)) \\  & Globa AL-1085 mobile table (\(\times 3\)) \\  & COSMO AL-430 VERSA chair \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equipment and resources in the VR/AR laboratory at UPAEP: room \(A\).  
Figure 2: Distribution of work spaces, Ethernet connections, and VR sensors in the VR/AR laboratory at UPAEP: room \(A\).  
The advantage of considering the ISO/IEC TR 23842-1 standard (Sections 5.2, 5.3, and 6.0) during the design process is that the laboratory consists of fully-open reconfigurable and obstacle-free spaces with safety and hygiene guidelines for VR/AR interactions. These features were valuable assets during the COVID-19 pandemic, which required social distancing protocols [20].
## 4 Device Characterization
### I-V Characteristics and Frequency Response
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. Year**} & \multirow{2}{*}{\begin{tabular}{c} **Wavelength** \\ **(nm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ge** \\ **THK** \\ **(nm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ge** \\ **Diameter** \\ **(\(\mu\)m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Optical** \\ **Apperture** \\ **(\(\mu\)m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Responsivity** \\ **(A/W)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **QE** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Current** \\ **Density** \\ **(mA/cm\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Bandwidth** \\ **(GHz)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Reverse** \\ **Bias** \\ **(C)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **QE-BW** \\ **(GHz)** \\ \end{tabular} } \\ \hline
[27] & 2005 &
\begin{tabular}{c} 1310 \\ 1550 \\ 1620 \\ \end{tabular} & 2350 & 300 & 300 & 0.65 & 56.79 & \multirow{4}{*}{12 (\(\text{\#}-1\text{\text{\text{V}}}\))} & \multirow{4}{*}{-} & \multirow{4}{*}{-} \\
[27] & 2005 &
\begin{tabular}{c} 850 \\ 1298 \\ 1552 \\ \end{tabular} & 2350 & 300 & 300 & 0.16 & 23 & \multirow{4}{*}{100 (\(\text{\#}-1\text{\text{\text{V}}}\))} & \multirow{4}{*}{38.9} & \multirow{4}{*}{-2} & 8.95 \\
[9] & 2005 &
\begin{tabular}{c} 1298 \\ 1552 \\ \end{tabular} & 300 & 10 & 10 & 0.17 & 16 & 100 (\(\text{\#}-1\text{\text{\text{V}}}\)) & 38.9 & \multirow{4}{*}{-2} & 8.95 \\
[8] & 2009 &
\begin{tabular}{c} 1550 \\ 1550 \\ \end{tabular} & 330 & 10 & 10 & 0.05 & 4 & 600 (\(\text{\#}-1\text{\text{\text{V}}}\)) & 39 & \multirow{4}{*}{-2} & 1.56 \\
[15] & 2010 &
\begin{tabular}{c} 1310 \\ 1550 \\ \end{tabular} & 800 & 30 & 30 & 0.65 & 61.53 & \multirow{4}{*}{14.8} & \multirow{4}{*}{14.8} & \multirow{4}{*}{12.6} & \multirow{4}{*}{-3} & 7.75 \\
[14] & 2020 &
\begin{tabular}{c} 1550 \\ \end{tabular} & 350 & 14 & 14 & 0.62 & 49.6 & 33 (\(\text{\#}-1\text{\text{\text{V}}}\)) & 33 & \multirow{4}{*}{-3} & 16.37 \\ \hline This work & 850 & 1000 & 29 & 54 & 0.6 & 87.53 & 20 (\(\text{\#}-3\text{\text{\text{V}}}\)) & 21.4 & \multirow{4}{*}{-3} & 18.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of vertical Ge p-i-n PD. Figure 10: (**a**) Typical dark and photo I-V curves of the fabricated 29 \(\upmu\)m diameter device. (**b**) Statistics of the dark currents at \(-3\text{\text{\text{V}}}\) from three batches of the fabricated Ge p-i-n PDs. Figure 11: (**a**) Measured S21 frequency response. (**b**) Statistics of the 3 dB bandwidth from three batches of the fabricated Ge p-i-n PDs.
## 1 Introduction  
Radiation is an essential part of life and despite all its benefits; it can cause damage to humans either directly or indirectly through deformation and inheritance to future generations because of its ability to penetrate human bodies [1; 2; 3; 4]. This is the reason why researchers are focusing on finding suitable materials that have the ability to shield radiation in order to reduce its damage on humans. Radiation materials used in medical applications should be environmentally friendly, transparent, durable, and easy to shape and construct [5; 6; 7; 8; 9; 10].  
Researchers have been focusing on studies related to radiation shielding material, as radiation plays an essential part in medicine, for the sake of finding the most suitable materials for different applications. Glass has been considered an excellent radiation shield due to its properties [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Many studies focused on investigating the capabilities of glass to shield against different radiation for many industrial and medical applications [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28].  
Glass offers excellent shielding properties as well as good transparency to visible light which is important in many medical-related applications [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28].  
The ZnO-Bi\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\) anti-radiation glass is one of the most commonly used lead-free glasses in radiation protection [28]. Heavy metal oxide modifiers such as PbO enhance the shielding properties of glass when added at certain percentages [29; 30; 31; 32; 33; 34; 35; 36]. However, research interest has shifted towards environmentally friendly materials such as Na\({}_{2}\)O, Al\({}_{2}\)O\({}_{3}\), MgO, TiO\({}_{2}\), SrO, Sb\({}_{2}\)O\({}_{3}\), and BaO instead of PbO. Those modifiers influence many desirable properties such as high density, transparency to visible light, stability in air, and water, high interaction cross section, high infrared transparency, and good absorption of radiation [37; 38; 39]. Glasses with heavy metal oxide modifiers can shield against both photons and neutrons since they are of high content of both heavy and light elements [40]. However, the neutron-induced damage to these heavy metal oxide modifiers due to the transfer of incident neutrons energy via elastic collision with the primary knock-on atoms, should be considered [41; 42; 43; 44; 45; 46; 47; 48].  
In this work, the photon- and neutron-shielding capabilities of the ZnO-Bi\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\) anti-radiation glass are investigated with different weight fractions of heavy metal oxides at wide photon and neutron energy ranges. Geant4, which is a Monte Carlo-based powerful toolkit, is used to find the mass attenuation coefficients (\(\mu_{m}\)) of photons, as well as the effective removal cross sections (\(\Sigma_{R}\)) of neutrons, of all the investigated samples in the studied energy range.
# Sustainability Enhancement and Evaluation of a Concrete Dam Using Recycling
## 2 Methodology  
### Design of Concrete Gravity Dams  
Concrete gravity dams are designed to resist external forces primarily through their weight, ensuring stability under various loading conditions. This fundamental design approach involves analyzing the forces acting on the dam, computing the stresses induced in the structure, and ensuring that safety factors against failure are met. The stability of these dams is governed by well-established engineering principles, including the equilibrium of forces and moments.  
These dams rely on their own weight to resist external forces such as hydrostatic pressure, silt pressure, uplift pressure, and seismic loads. Key considerations in dam design include evaluating stability against sliding and overturning, assessing the effects of uplift pressure on structural integrity, and ensuring adequate safety factors under normal, flood, and seismic conditions. Additionally, material properties, foundation conditions, and load combinations must be carefully analyzed to optimize the dam's performance. International design standards, such as IS 6512:2019 [29] and other regulatory codes, provide guidelines for assessing these factors, ensuring that dams are designed to withstand extreme environmental conditions and long-term operational stresses.
## 5 Rate-Compatible IR-HARQ Transmission Scheme Based on PCPP Codes
### Parameter Optimization  
a. Rate difference \(\Delta R\) and the variation range of the time-varying channel \([I_{min},I_{max}]\)  
In Section 5.2, we analyzed the optimal initial code-rate for the PCPP coding block in our RC IR-HARQ transmission scheme over a time-varying channel in three different cases, where the key parameters of our design method are dependent on the rate difference \(\Delta R\) and the variation range of the time-varying channel \([I_{min},I_{max}]\). The complete characterization of the optimal initial code-rate with these two parameters in Section 5.2 are given in Table 2.  
b. Number of punctured bits \(P\)  
Recall the Case 3 in Section 5.2, where we need to construct a punctured polar code \(C_{n+1}(N_{1},I_{max},\mathcal{A}_{1}^{(1)})\) with initial code-rate \(R_{1}^{(n+1)}=I_{max}\) from the mother code \(C_{n}(N_{k},R_{k},\mathcal{A}_{k}^{(k)})\) with code-rate \(R_{k}^{(n)}=R_{U}\) to transmit the \((n+1)\)-th data block. Let \(C_{n}([N_{k}]_{P},R_{j},\mathcal{A}_{j}^{(k)},\mathbb{P}_{j}^{N_{k}})_{j=k}^{J}\) be a sequence of nested punctured polar codes, where \(\mathbb{p}_{j}^{N_{k}}=(p_{1},p_{2},...,p_{N_{k}})\in\{0,1\}^{N_{k}}\) is the \(j\)-th puncturing pattern. Let \(P=|\bar{\mathbb{p}}|\) denote the number of punctured bits and meet the conditions that \([N_{k}]_{P}=N_{k}-P<N_{k}\) and \(R_{U}<R_{j}\leq 1\).  
Our IRP algorithm has limited the puncturing pattern to avoid selecting from the information bits set, such that we have \(R_{j}[N_{M}]_{P}=R_{k}N_{k}\) and the number of punctured bits \(P\) is calculated as  
\[P=N_{k}-\frac{R_{k}N_{k}}{R_{j}},\text{and }\ 0<P\leq N_{k}-R_{k}N_{k}. \tag{9}\]
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 6 Evaluation
### Completeness of the CFGs (RQ3)  
In the above experiments, we evaluate the effectiveness of two key techniques in our approach. In this section, we further evaluate our prototype against existing approaches and attempt to answer the research question: how complete is the CFG constructed by our approach?  
We compare DGF-Constructor with the following tools:  
**Static-CFGConstructor.** It is a pure static tool implemented according to LLVM's builtin APIs.  
**CGF-CFGConstructor.** It is a hybrid tool based on the approach proposed in [26]. The tool has been used in evaluating the test case generation in Section 6.2.  
There is not appraisal index to evaluate the completeness of CFGs in the previous work. In many practical program analyses, such as taint analysis and symbolic execution, analysts usually pay more attention to the code that is reachable from the entry of program on the CFG. Therefore, we adopt _reachable code scale_ as an important factor to measure the completeness of CFGs.  
More specifically, we present two appraisal indexes \((R_{f},R_{b})\) to quantify the scale of the reachable code. The first index \(R_{f}\) is the number of functions that can be reachable from the program entry on the iCFG. The second index \(R_{b}\) is the number of reachable basic blocks. On the same eight benchmarks, we use three approaches to conduct the experiment: the static analysis (Static-CFGConstructor), the CGF-based approach (CGF-CFGConstructor), and our DGF-based approach (DGF-CFGConstructor). The experiment lasted for 20 h.  
Figure 11 shows the reachable code scale of the CFGs constructed by the three approaches. On most benchmarks, DGF-CFGConstructor can discover more reachable functions and basic blocks than the other two tools. Precisely, our approach discovers 62.9% more reachable functions and 94% more reachable basic blocks than Static-CFGConstructor. Compared with CGF-CFGConstructor, our approach discovers 23.9% more reachable functions and 34.9% more reachable basic blocks.  
Overall, the analysis indicates that the answer to RQ3 is definite. Our approach is able to construct more complete CFGs than other approaches.
## 3 Results and Discussions
### Effect of pH  
The adsorption of Pb\({}^{2+}\) and NR dye over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite at different pH values were studied, as shown in Figure 6. The studied range was from 2.0 to 6.0 and from 1.5 to 13.0 for Pb\({}^{2+}\) and NR dye, respectively. According to Figure 6, the removal efficiency for Pb\({}^{2+}\) ions and NR dye was decreased by the decrease in pH value as predicted. This minimum removal efficiency of Pb\({}^{2+}\) ions at a low pH value was attributed to the existence of hydronium ions H\({}_{3}\)O\({}^{+}\) that compete with the positively charged metal ions on the adsorption sites over adsorbent surfaces [51]. While the increased value of pH enhanced the ionization of carboxylic and hydroxyl groups over the nanocomposite surfaces, increasing the adsorption of metal ions by ionized functional groups [52]. Furthermore, the hydroxides and oxides of lead ions precipitate at a pH of more than 6. The pH increased from 2.0 to 6.0, leading to an increase of Pb\({}^{2+}\) adsorption capacities over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite from 155.70 to 463.0 mg/g.  
Also, for cationic NR dye, at low pH values, the protonation of nanocomposite functional groups and the existing hydronium ions negatively affected the dye adsorption. So, the NR adsorption capacities over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite are increased from 17.0 to 24.0 mg/g by pH increase from 1.5 to 3.5, and the adsorption capacity reached its maximum by pH increase to 6.0 then reached the equilibrium and not affected by the further pH increase. Thus, the optimum pH value for Pb\({}^{2+}\) and NR dye adsorption over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite was 6.0.
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 1 Introduction
Among ANN and ANFIS techniques, which one is better for AF classification for local patient datasets? 3. What are the best parameter settings for ANN and ANFIS for constructing the AF classification models? 4. How is the performance of the proposed models based on ANN and ANFIS?
# Seepage Characteristics Study of Single Rough Fracture Based on Numerical Simulation
## 3 Results and Discussion
### Effect of Rough Element Shape and Density on Permeability Coefficient
The roughness elements with different shapes have similar variation trends. In the same shape of roughness element fracture medium, with the increase of roughness element density, the influence of roughness element density on the permeability coefficient \(K\) becomes greater. It can also be seen from Table 4 that, in the data corresponding to the triangle roughness element fracture, when the density value \(A\) is 6, the correlation coefficient is 0.417, when \(A\) is 5, the coefficient increases to 0.577, and when \(A\) is 4, the coefficient decreases to 0.404; in the rectangular roughness element, when \(A\) is 6, the correlation coefficient is 0.542, and when \(A\) is 5 and 4, the corresponding correlation coefficients are 0.562 and 0.458, respectively. In the sinusoidal roughness element fracture, the correlation coefficient corresponding to \(A\) of 6 is 0.623, and the correlation coefficients corresponding to \(A\) of 5 and 4 are 0.790 and 0.708, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Roughness** & **Density** & **Average Fracture** & **Linear Regression** & **Correlation** & **Number of** \\
**Elements Shape** & \(A\) & **Aperture (mm)** & **Equation** & **Coefficient** & **Sampling Points** \\ \hline \multirow{3}{*}{triangular} & 6 & 2.33 & \(K=8.95\,L\,-\,0.207\) & 0.417 & 10 \\  & 5 & 2.24 & \(K=11.14\,L\,-\,0.249\) & 0.577 & 10 \\  & 4 & 2.06 & \(K=13.02\,L\,-\,0.319\) & 0.404 & 10 \\ \hline \multirow{3}{*}{rectangular} & 6 & 1.73 & \(K=7.77\,L\,-\,0.155\) & 0.542 & 10 \\  & 5 & 1.57 & \(K=14.94\,L\,-\,0.337\) & 0.562 & 10 \\  & 4 & 1.33 & \(K=22.05\,L\,-\,0.521\) & 0.458 & 10 \\ \hline \multirow{3}{*}{sinusoidal} & 6 & 1.82 & \(K=9.67\,L\,-\,0.204\) & 0.623 & 9 \\  & 5 & 1.65 & \(K=6.86\,L\,-\,0.137\) & 0.790 & 9 \\ \cline{1-1}  & 4 & 1.38 & \(K=5.99\,L\,-\,0.126\) & 0.708 & 9 \\ \hline \end{tabular}
\end{table} 
Table 4: Correlation between permeability coefficient \(K\) and horizontal distance \(L\) from the sampling point to inlet. With the increase of roughness elements distribution density \(A\), the correlation coefficient between permeability coefficient \(K\) and corresponding horizontal distance \(L\) first increases and then decreases, which may be due to the coupling influence of fracture aperture, fracture roughness element height, and roughness element distribution density on the scale effect of permeability coefficient \(K\). Under the condition that the fracture aperture and roughness element height remain unchanged, the influence of roughness element distribution density \(A\) on the correlation between \(K\) and \(L\) also increases first and then decreases. This is because the fracture wall tends to be smooth when the distribution of rough elements is very dense or sparse, while the influence of the distribution of roughness elements on the flow in the real case is between the two.