esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Limitations and Future Work  
Some cautions should be paid attention to in the study findings. First, due to equipment and venue limitations, the frequency of immersion exercises performed by one participant was only once and not representative of long-term feelings and acceptance. Second, facilitating conditions and social influence were not measured in this study, and additional factors will be added in subsequent experiments with multi-user testing. Third, during this experiment, the participants were not alone in the immersion exercise during the experiment but were surrounded by other older adults who were not participating or had finished participating. Therefore, the surrounding physical environment and the crowd may have had some influence on the results of the experiment. For example, in terms of perceived safety, sound localization may have an impact on perceived physical safety due to the continuous presence of sounds from other people in the surrounding physical environment. Secondly, in terms of the results of willingness to use, the crowd and friends around the participants making them excited may have been the reason for the generally higher scores. The sounds of the physical environment, as well as the surrounding crowd, should be controlled during subsequent experiments.
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 2 Background
### Related Literature  
To derive a stable key from a string of bits where a few locations in the string are unreliable, a number of technology and algorithmic solutions have been proposed to improve the reliability of keys produced from SRAM PUFs. Since an ideal accuracy of 100% would require a large cost, typically, a low error rate such as an error rate of \(10^{-6}\) is used as the design target [9; 10]. The solutions are discussed below.
## 3 Numerical Modeling of the Xulong High Arch Dam
### Numerical Model and Analysis Cases  
Figure 6 illustrates the 3D numerical model of the Xulong high arch dam and foundation, and the distribution of main faults including F1, f3, f10, f11, f26, f57, f74, and f75. In this 3D model, the simulation range is 840 m \(\times\) 800 m \(\times\) 553 m (length \(\times\) width \(\times\) height). The numerical model adopts 8-node hexahedral elements, with the total number of 129,241 elements and 147,331 nodes. There are 34,284 elements and 41,204 nodes for the dam.  
Based on laboratory testing, the main physical-mechanical parameters of the rock masses and dam concrete are listed in Table 2. Considering the influence of temperature and complex geological conditions on the cracking of arch dams, this study uses the overloading method to judge the overall stability of the arch dam and foundation. Temperature load, self-weights of the dam and foundation, water pressure, and silt pressure are considered in the ten analysis cases as follows.  
In order to compare the effect of temperature rise and drop loading on the stress and displacement of the arch dam during the construction period after arch closure, temperature drop loading is applied in cases 1 and 10, and the other loads in cases 1 and 10 are the same. In order to obtain the three safety factors to evaluate the dam overall stability, cases 1-9 correspond to 1-9 times of overloading, respectively, and the other loads in cases 1 and 9 are the same.  
Figure 4: Surface fault statistics in dam site area. **(a)** Rose of the strike; **(b)** rose of dip; **(c)** dip angle histogram.  
Figure 5: Bedrock distribution of the foundation surface.  
The influence of temperature loading on cracking of the arch dam during operation period is analyzed. Under the long-term external temperature variation, the temperature loading can be divided into mean and linear temperature difference. The mean and linear temperature difference under normal water level is illustrated in Table 3.
## 3 Results and Analysis
### Satellite Laser Ranging Validation
Meanwhile, the accuracy of SLR validation for reduced dynamic orbits is higher than the accuracy for kinematic orbits as we need to interpolate the ephemeris of LEO satellite orbits recorded in integer seconds (for example, 2021-06-13T09:48:40) to match the non-integer second-time (for example, 2021-06-13T09:48:40.487596) points recorded in the SLR observations when performing the SLR validation. Since no orbital dynamics were taken \begin{table}
\begin{tabular}{c c} \hline \hline  & **Models/References** \\ \hline Station coordinates & SLRF2014 \\ Solid earth and pole tides & IERS2010 [29] \\ Sea tide loading & FES2004 [30] \\ Tropospheric refraction & Marini-Murry models [39] \\ Relativity & IERS2010 [29] \\ Eccentric correction of Swarm & Provided by ESA \\ Eccentric correction of GRACE-FO & Provided by ILRS \\ Elevation cutoff & 25\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SLR data processing strategies. into account in the kinematic orbits' calculation, some jumping phenomena may occur at certain moments, and the frequency and intensity of these phenomena affect the accuracy of the interpolation and thus reduce the accuracy of the SLR validation. For example, GRACE-D has a worse accuracy of undifferenced kinematic orbits than its reduced dynamic orbits due to more jumping phenomena, and these phenomena will become more evident in the next section.
## 4 Results and Discussion
### Baseline Particle Classification Methods
#### 4.2.2 Random Forest  
Using the same metrics as for NN models, the RF classifier with default settings gives the results presented in Table 7.  
The hyperparameter optimization gives the next combination of tuned hyperparameters: _criterion:_ 'gini', _max_depth:_ 40, _max_features_: 'auto', _n_estimators_: 200. The achieved metrics are almost the same as those obtained with the predefined settings, which are shown in Table 7. Even though the achieved accuracy is similar to the NN model's case, both FNR and FPR are slightly higher. The next attempt is to run a grid search to optimize the recall score, but with no improvement (maximizing recall, FNR is minimized).
# Inkjet-Printed and Paper-Based Electrochemical Sensors
## 5 Conclusions
Among the reviewed electrochemical sensors, carbon-based materials were the most common. In particular, carbon-based materials included carbon powder, carbon nanotubes, and graphene. Metallic nanowires and nanoparticles were also used in some cases, but it is important to remember that gold and silver--the most commonly used metallic nanomaterials--can significantly drive up the production cost. All of the devices discussed thus far were tested in various environments to demonstrate the sensing abilities of paper-based electrochemical devices. In some simple cases, basic chemicals were used as the analyte, but in other cases, the authors provided real-world scenarios to establish the validity of these sensors. Potential applications for these sensors include water and air pollution monitoring, cancer screening, and biomarker detection for countless conditions. Traditional laboratories are absolutely required in certain circumstances, but low-cost point-of-care devices are preferred in many cases. Going forward, there is still much to investigate and many challenges to overcome. For example, inkjet printers can be enhanced to provide better control and greater accuracy during material deposition. The FUJIFILM Dimatix Materials Printer certainly pushes the limits with respect to modern inkjet printing, but its resolution and control is still far inferior to existing semiconductor fabrication processes. Furthermore, most advanced inkjet printing systems are not designed for mass production. As a result, it will be important to continue improving inkjet printing technology in order to produce consistent and reliable sensors on a large scale. With respect to materials, there are many commercially available types of ink from which to select. Although ink diversity is not necessary an issue at this point, it is still very important to choose the right ink to ensure high conductivity electrodes and high-resolution printing. It is also essential to continue investigating various electrode treatments to improve selectivity and sensitivity. Furthermore, paper selection and treatment has also been shown to drastically affect printing. At this point, there is very little literature on paper treatment, but the initial results indicate that paper substrate selection and potential treatment methods may significantly enhance device performance. As further progress is made in the fields of paper-based analytic devices and printed electronics, the production of low-cost point-of-care sensors will not be far behind.
# Laser-Based Additive Manufacturing of Zirconium
## 2 Experimental Procedure
### LENS(tm) Processing of Zirconium  
Zirconium powder with a size distribution between 44 um and 149 um (+325-100 Mesh) and purity of 99.98% was used for LENS(tm) processing (CERAC Specialty Materials, Milwaukee, WI, USA). The substrate or base plate material used was Ti6 Al4V alloy (President Titanium, Hanson, MA, USA) of 99.999% purity. The substrate Ti6 Al4V alloy plate was 3 mm thick. LENS(tm) processing was done at different machine parameters that are tabulated in Table 1. Various problems and processing issues associated with each processing attempt have also been described. From the various attempts carried out, the optimum laser power of approximately 450 W was used at a raster scan speed of 80 cm/min and powder feed rate of 16 g/min. Two different geometries were processed. To study the interface with Ti6 Al4V alloy for X-ray diffraction, SEM-EDS analysis, and hardness testing, square-shaped samples with side length of 14 mm were processed. Each of these LENS(tm)-deposited coatings was between 1.0 to 1.5 mm thick. In addition to this, some samples were laser scanned at 450W with no powder addition to remelt and solidify the initially deposited material. To demonstrate the 3D near net shape manufacturing capabilities of the LENS(tm) system, a cylindrical component with inner diameter 15.24 mm (0.50 inch), outer diameter 25.40 mm (1.00 inch) and height of 25.40 mm (1.00 inch) was designed. This part was built on Ti6 Al4V alloy base plate with the same LENS(tm) parameters as other Zr processed parts. The glove box of the LENS(tm) system was kept purged with high purity argon and oxygen was kept below 10 ppm during the processing of the Zr components.
## 3 Results
### Change in Floor Area with Same Aspect Ratio  
To further analyze the impact of building finishing materials based on the area of the fire room, we assumed that only the area was expanded without changing the aspect ratio. The aspect ratio was fixed at 1, and the length was extended by 2 m per case for additional simulations. Figure 4 depicts the conditions for the simulation with a fixed aspect ratio, and Table 7 details the sizes applied to the simulation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{6}{c}{**Aspect ratio**} \\ \cline{2-7}  & & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{3}{*}{Polyurethane foam} & Temperature & 3.20 s & 4.80 s & 6.00 s & 7.60 s & 9.21 s \\ \cline{2-7}  & Visibility & 3.20 s & 4.80 s & 6.00 s & 7.60 s & 9.21 s \\ \cline{2-7}  & FED & 8.40 s & 8.80 s & 10.40 s & 11.61 s & 12.80 s \\ \hline \multirow{3}{*}{MDF} & Temperature & 3.20 s & 4.80 s & 6.40 s & 8.40 s & 10.80 s \\ \cline{2-7}  & Visibility & 3.20 s & 4.80 s & 6.40 s & 8.40 s & 11.20 s \\ \cline{2-7}  & FED & 14.40 s & 16.41 s & 18.81 s & 21.21 s & 22.81 s \\ \hline \multirow{3}{*}{CFRBFM} & Temperature & 3.60 s & 5.20 s & 7.20 s & 9.61 s & 11.61 s \\ \cline{2-7}  & Visibility & 4.00 s & 5.20 s & 7.20 s & 10.00 s & 11.61 s \\ \cline{2-7}  & FED & 68.81 s & 101.22 s & 135.64 s & 159.62 s & 186.45 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: ASET for each building finishing material by aspect ratio change.  
Figure 4: Simulation condition of same aspect ratio.  
The simulation results showed varying ASET differences between temperature, visibility, and FED criteria in Cases 1 and 5. For polyurethane foam, these delays ranged from 10.0 s (temperature) to 10.4 s (visibility) and 20.4 s (FED). Similarly, MDF exhibited delays of 21.2 s (temperature), 20.4 s (visibility), and 27.2 s (FED). However, CFRBFM displayed a significantly larger disparity. The time differences for CFRBFM were 164.0 s (temperature), 52.8 s (visibility), and a substantial 331.2 s (FED). The average increase in the ASET for temperature, visibility, and FED with the increase in area was 2.5 s, 2.6 s, and 5.11 s, respectively, for polyurethane foam and 5.3 s, 5.1 s, and 6.8 s for MDF. In contrast, CFRBFM exhibited increases of 41.0 s, 13.2 s, and 82.8 s, respectively, demonstrating superior performance in temperature and FED compared to the building finishing material of the control group. These results indicate enhanced safety in indoor fire diffusion suppression and occupant breathing areas.
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 3 Materials and Methods
### Evaluation  
In order to observe the performance using different image conditions, three images are chosen from each of the selected datasets. The different image conditions include varying illumination characteristics, the variety of objects, and the amount of shadow in the images. The image in the testing set is then cropped into 444 patches and evaluated using qualitative and quantitative analysis.  
The predicted shadow mask obtained with the implementation of the proposed post-processing methods will be compared with the corresponding ground truth image. Different evaluation metrics are used to quantitatively examine the performance of the applied methods. To monitor the overall performance, overall accuracy (OA) is calculated based on Equation (6). F1-score is calculated as the weighted average of precision and recall, as given in Equation (9). Precision and recall are calculated using Equation (7) and Equation (8), respectively. Intersection Over Union (IoU), as in Equation (10), which is also called the Jaccard index, is used to measure the percentage of overlap between the ground truth and output predicted mask.  
\[\textit{Overall Accuracy}=\frac{\textit{True Positive}+\textit{True Negative}}{\textit{True Positive}+\textit{True Negative}+\textit{False Negative}+\textit{False Positive}} \tag{6}\]  
\[\textit{Precision}=\frac{\textit{True Positive}}{(\textit{True Positive}+ \textit{False Positive})} \tag{7}\]  
\[\textit{Recall}=\frac{\textit{True Positive}}{(\textit{True Positive}+ \textit{False Negative})} \tag{8}\]  
\[\textit{F1 Score}=\frac{\textit{(Recall}\times\textit{Precision)}}{( \textit{Recall}+\textit{Precision)}} \tag{9}\]  
\[\textit{IoU}=\frac{\textit{True Positive}}{(\textit{True Positive}+ \textit{False Positive}+\textit{False Negative})} \tag{10}\]
## 2 Foundational Concepts
### Federated Learning
Decentralized FL (DFL) employs peer-to-peer communication, potentially increasing robustness and scalability for certain network topologies (like mesh networks common in IoT scenarios) but adding complexity in coordination and convergence analysis [43]. * Non-IID Data: A central challenge in FL stems from heterogeneous data distributions across clients, commonly referred to as Non-Independent and Identically Distributed (Non-IID) data [44]. This means the statistical properties of data significantly vary between clients; for instance, clients might hold data with different label distributions (label skew) or different feature characteristics for the same label (feature skew). Such heterogeneity can substantially degrade the performance of standard algorithms like FedAvg, as the single global model aggregated from diverse local models may not generalize well to each client's specific data distribution [7].
# Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel
## 3 Method
Furthermore, if the value of \(k\) is too small, it cannot achieve the desired denoising effect. Inspired by the edge-aware weighted guided image-filtering method [13], we introduce the edge-aware mechanism to reduce the impact of edge loss. We use \(\zeta=\lambda/\Gamma_{g}\) to replace \(\epsilon\) in Equation (8), and \(\Gamma_{g}\) is given by  
\[\Gamma_{g}=\frac{\sigma_{g}^{2}(p^{\prime})+\zeta}{\frac{1}{n}\sum\limits_{p= 1}^{n}\sigma_{g}^{2}(p)+\zeta}, \tag{13}\]  
where \(\lambda\) is a regularization parameter that penalizes large \(a_{i}\). \(\zeta\) is a small constant and its value is empirically set as \((0.001\times d)^{2}\), while \(d\) is the farthest Euclidean distance between two points in the point-cloud model. \(n\) is the number of points in the point-cloud model. \(\sigma_{g}^{2}(p^{\prime})\) represents the variance of the point in its k-nearest neighbors and the value of \(k^{\prime}\) here is selected as 10. When the point is located at a sharp edge, the value of \(\Gamma_{g}\) is usually larger than 1 and \(\zeta\) becomes small. That is, the punishment for \(a_{i}\) in Equation (8) is small and \(\overline{a}_{i}\) in Equation (12), it is large. Therefore, the fine edge features generated from the guidance point cloud can be maintained well while smoothing noise points. The results of unsharp mask guided filtering are directly related to the selection of the guidance point-cloud model. Ideally, the ground-truth model without noise should be chosen as the guidance point-cloud model, but this may not be obtained in practical applications. Therefore, we use the raw noisy point cloud as the guidance point cloud. Compared with classical guided filtering, which needs to calculate two parameters \((a,b)\), our novel filtering method needs to estimate only one coefficient \(a\). At the same time, it can maintain the edge features while smoothing the point-cloud model.
## 3 Operation and Models of the VANET Testbed
### Network Models
#### 3.2.1.2 RSU and Vehicle Communication Model  
The RSU and the vehicles are the major communication nodes in VANET. Our RSU is a WiMAX BS. Each RSU is non-application-sensitive so that a wide range of information can be sent and received. Each vehicle has WiMAX receiving and transmitting capabilities to enable vehicle to vehicle and vehicle to infrastructure (RSU in our case) interaction. Table 2 shows the basic essential characteristics of our model along with some typical settings.  
Figure 4: Driving video VANET OPNET Model structure.
## 2 Materials and Methods
### Experimental Design  
Figure 2 shows the accelerated corrosion test method used in this study. The top and bottom surfaces of the RC specimens were coated with epoxy resin after being air-cured for 28 days. After the epoxy coating had hardened, the specimens were immersed in a 3% NaCl aqueous solution. After 24 h, a current was applied and the accelerated corrosion tests begun.  
As corrosion cracking progresses in RC, the amount of rebar corrosion is proportional to the concentration of chloride ions [12]. In in-situ RC columns, there are differences in the concentrations of chloride ions and the amount of corrosion in individual rebar bars because the cover thickness varies between longitudinal and transverse bars. Therefore, different current application times were used for the longitudinal and transverse rebar to simulate such differences in chloride ion concentrations. To control the current application times independently, individual parallel lead wires were connected to each rebar, and a switch was installed on the lead wires at the longitudinal bar side. To determine the chloride ion concentrations in the cover layers of the longitudinal and transverse rebar, and the current application times, Equation (1) was employed, which uses the relationship between the concentration of chlorides on the concrete surface and the ion diffusion factor for concrete according to [PERSON]'s second law of diffusion [12]:  
\[C(x,t)=C_{0}\left\{1-erf\left(\frac{x}{2\sqrt{D_{ap}t}}\right)\right\} \tag{1}\]  
where \(C\) (\(x\),\(t\)) is the chloride ion concentration (kg/m\({}^{3}\)) for length \(x\) (cm) and time \(t\) (years), \(C_{0}\) is the surface chloride ion concentration (kg/m\({}^{3}\)), \(D_{ap}\) is the apparent diffusion coefficient for chloride ions (cm\({}^{2}\)/year) where the water cement ratio (W/C) is used in Equation (2), and \(erf\) (s) is the error function (Equation (3)) [13].  
\[\log_{10}D=\ -3.9(W/C)^{2}+7.2(W/C)-2.5 \tag{2}\]  
\[erf(s)=\ \frac{2}{\sqrt{\pi}}\int_{0}^{s}e^{-\eta^{2}}d\eta \tag{3}\]  
No current was applied to the longitudinal bar for approximately 50% of the overall test time for the standard specimen (12-Fc48) and 25% of the time for specimen 12-Fc24, which had a large concrete ion diffusion coefficient. After corrosion cracking occurred in the test specimens, a current was applied continuously to the longitudinal bar.  
For the hourly integrated current amount, 2 \(\Omega\) resistors were connected to wires of each rebar and the voltage between these resistors was measured hourly using a data logger. The current applied to  
Figure 2: Accelerated corrosion test method.  
each rebar was then calculated based on the voltage data, and the ampere-hours of the accelerated test period (approximately three months) were determined. The transverse bar strain was measured and logged hourly. In addition, a digital microscope with a resolution of 1/100 mm was used to measure the widths of surface cracks.
## 5 Experiment Analysis
### Experimental Results Analysis
#### 5.2.1 Win Rate Analysis
The test results and rankings are shown in Table 6.  
In Figure 10, in the 8m environment, our DE-COMA algorithm performs second only to the QMIX algorithm, maintaining an advantage in standard deviation compared to other algorithms. As indicated in Table 3, for the 8m scenario, DE-COMA shows a 12.84% improvement in win rate compared to COMA. In this environment, we conducted a Friedman test on the recorded win rate data. The test results and rankings are shown in Table 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Average Ranking** & **Final Rank** \\ \hline DE-COMA & 3.19 & 4 \\ COMA & 4.74 & 6 \\ QMIX & 3.07 & 2 \\ VDN & 3.46 & 5 \\ QTRAN & 3.17 & 3 \\ MAVEN & 3.02 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average ranking of DE-COMA, COMA, QMIX, VDN, QTRAN, MAVEN according to the Friedman test on 3m win rate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Average Ranking** & **Final Rank** \\ \hline DE-COMA & 3.87 & 4 \\ COMA & 4.63 & 6 \\ QMIX & 3.36 & 3 \\ VDN & 2.25 & 1 \\ QTRAN & 2.53 & 2 \\ MAVEN & 3.96 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average ranking of DE-COMA, COMA, QMIX, VDN, QTRAN, MAVEN according to the Friedman test on 2s_vs_1 sc win rate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Average Ranking** & **Final Rank** \\ \hline DE-COMA & 4.19 & 5 \\ COMA & 5.66 & 6 \\ QMIX & 1.63 & 1 \\ VDN & 2.06 & 2 \\ QTRAN & 3.86 & 4 \\ MAVEN & 3.59 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average ranking of DE-COMA, COMA, QMIX, VDN, QTRAN, MAVEN according to the Friedman test on 2s3z win rate. Overall, the introduction of the differential evolution algorithm to optimize the COMA policy network has proven to be effective, outperforming many multi-agent reinforcement learning algorithms. However, since the COMA algorithm itself is policy-based, there is still a gap compared to other advanced value-based algorithms.