esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 2 Materials and Methods
### Slam-Based Semi-Autonomous Data Acquisition
#### 2.2.3 Point Cloud Preprocessing  
The point cloud data are labeled using category-defined labels, which include two types of object labels as follows: background and wall spall. It is worth noting that although there are only two types of labels, some of the building components, such as windows, doors, and pipes, are all categorized as background, which means there is a great deal of variation among these scenes. While the diversity of backgrounds may pose a challenge in using the dataset, it also shows that the proposed segmentation model is highly generalizable if it can be successfully applied to segmentation test scenes.  
In this study, CloudCompare [34] was utilized for point cloud segmentation and labeling to create a dataset of wall spall in civil buildings. The point cloud dataset of wall spall in civil buildings created for this study includes 10 complete scenes and 231 corresponding point cloud files, divided into training, validation, and test sets in an 8:1:1 ratio. Figure 7 shows an example of the structure of the prepared dataset, including annotated fragment samples, where a black background indicates no defects, white areas indicate spalls, red boxes highlight significant spalls, and green circles indicate easily overlooked spalls.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Time** & **Quality** & **Reconstructed Length (m)** & **Ground Truth (m)** & **Error (m)** \\ \hline SLAM-based & Simultaneous & Medium & 0.501 & 0.513 & 0.012 \\ \hline SfM-based & 21 min 10 s & High & 0.504 & 0.513 & 0.009 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data comparison of different methods.  
Figure 6: Comparison of dense reconstruction.
## 3 Results
### Logistic Regression Analysis on Musculoskeletal Pain
#### 3.3.3 Logistic Regression Analysis on Lower Limb Pain  
Table 8 shows the logistic regression analysis results on lower limb pain. The model exhibited satisfactory explanatory power with a 0.167 Nagelkerke value. Furthermore, the result regarding model fit was considered suitable, as evidenced by the 5.151 chi-square value and 0.741 significance value. The model's prediction accuracy was 77.4%.  
The key factors affecting lower limb pain were gender (_p_ < 0.001), work experience (_p_ = 0.001), weekly working hours (_p_ = 0.039), low temperature (_p_ = 0.013), and handling heavy loads (_p_ < 0.001). Females were 2.667 times more likely to report lower limb pain than males (_p_ < 0.001), and workers with over 10 years of work experience were 2.936 times more likely than those under 3 years (_p_ < 0.001). Workers exposed to low temperatures for 2 to 4 h are 2.795 times more likely to report lower limb pain than those exposed for less than 2 h (_p_ = 0.011). Additionally, workers who handle heavy loads for 2 to 4 h are 1.759 times more likely than those under 2 h (_p_ = 0.035), while workers handling heavy loads for 4 h or more are 3.582 times more likely than those under 2 h (_p_ < 0.001). Conversely, contrary to general results, workers who worked less than 40 h were 1.618 (1/0.618) times more likely than those over 40 h (_p_ = 0.039).
# Designing an Adaptive Underwater Visible Light Communication System +
## 6 Receiver Design
### Receiving Preamble  
Before receiving the actual message, the receiver receives a preamble. Time synchronization is crucial in this process. The program is designed to ensure that MATLAB has enough time to calculate the power at each angle before receiving the next frame for processing. The details of this process can be visualized in Figure 11.  
Figure 10: Adaptive receiver design.  
When Li-Fi Rx receives the preamble, it is processed at the USRP and then fed to MATLAB as shown in Step 1 highlighted in Figure 11. Since the comparison is made at three different angular positions, the angular position \(N\) is 3. For \(n<=3\), the preamble is detected, and power is calculated, which is stored in an array. When \(n=4\), the program exits the loop and the maximum power (\(Pmax\)) is selected from \(P1,P2\), and \(P3\) (as shown in Figure 10).The angle information corresponding to \(Pmax\) is then sent to the Arduino, which controls the direction of the Li-Fi Rx through the positional servo motor.
## 2 Theoretical Background, Related Work, and Hypotheses
### Foundations of Automation Trust in ATS Systems
Operators' awareness of the system's capabilities and safety redundancies plays a crucial role in building trust. For instance, a study on seafloor visual inspection found that explicitly informing operators of the system's false alarm rate increased their trust in automation [41]. Similarly, in a virtual decision-making experiment, the reliability of automated agents strongly influenced trust, particularly under conditions involving personal safety and economic costs [22]. [PERSON] et al. further demonstrated that system dependability is the most reliable predictor of initial trust [42]. However, operators often underestimate system reliability [43]. While subway operation systems maintain high reliability through redundant automation designs, this very redundancy can also disrupt trust in automation, forming the basis of Hypothesis 3 in this study.
# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 6 Conclusions and Future Works  
In this paper, we proposed a dynamic channel allocation method which is composed of a base module and an adaptive module. The base module consists of two parts. The first part of the base module groups a set of WBANs that will severely interfere with each other if they use the same channel. Then, for each cluster, the second part of the base module allocates different channels to the WBANs in the same cluster. An adaptive module is designed to adjust to the gradual topology changes quickly by avoiding the graph partitioning process. We compare the performance of our method with those of the conventional methods through extensive simulation studies. We show that our method outperforms the conventional centralized method by 2.2 times in terms of the average data rate of a WBAN when the density of the coexisting WBANs is 0.5 WBANs per square meter. Our method further increases the average data rate by 5.4 times when the density of the coexisting WBANs is 2 WBANs per square meter. Compared with a distributed method involving an iterative process, our method reduces the channel decision time by 94.6%.  
As future work, we will optimize the channel allocation method when the number of colors required to color the WBANs in a cluster is larger than the number of channels available for WBANs. The data rate obtained by a WBAN located at the edge of cluster is relatively smaller than that of a WBAN close to the center of a cluster. To reduce the edge effect, we are working on developing a deep learning model that uses not only the location information of a WBAN, but also other information related with the received signal strength. When the number of coexisting WBANs becomes larger than some point, the data rate of a WBAN cannot be improved by a single channel allocation strategy. Overcoming such a limit by jointly using the channel allocation strategy, transmission power control, and transmission time control is one of our important future works.  
Software, W.S.; Data curation, W.S.; Conceptualization, J.P.; writing--original draft, J.P.; Investigation, J.P. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Comparison with the SLA in terms of the average data rate of the coexisting WBANs.  
**Funding:** This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1F1A1065371). This research was supported by the MSIT(Ministry of Science and ICT), under the National Program for Excellence in SW (2017-0-00096), supervised by the IITP(Institute of Information & Communications Technology Planning & Evaluation).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results and Discussion
### Scattering Parameters  
The S-parameters of the projected antenna design are measured using an Agilent N5247A PNA-X vector network analyzer. The numerical and experimental findings of the S-parameters are depicted in Figure 16. The measured reflection coefficient is below \(-10\) dB from 3.4 to 11.9 GHz, except for the notched frequency bands 4.5-5.3 GHz, 7.2-9 GHz WLAN, and X-band. Throughout the operating frequency of the projected antenna, isolation is better than the 16 dB.
## 1 Introduction
[21] notes that, in the early 2000s, engineers found that the Sydney Harbor Bridge was experiencing excessive wind-induced vibrations and installed tuned mass dampers to address the issue. These devices reduce vibrations by absorbing energy [21]. The Millau Viaduct in France, with a main span of 2460 m, underwent extensive wind tunnel testing and numerical simulations to evaluate its aerodynamic performance. The Tower Bridge in London recently underwent a renovation, which included the installation of wind sensors and a weather station to monitor wind conditions and optimize the bridge's operation and maintenance. The Akashi Kaikyo Bridge in Japan also underwent full aeroelastic model testing using a big boundary layer wind tunnel, which helped engineers develop design considerations for wind effects on the bridge [22]. These examples indicate a strong need for comprehensive aerodynamic model testing especially when additive manufacturing is prevailing in the bridge industry [23]. The Great Belt Bridge, which connects the Danish islands of Funen and Zealand, is a three-span suspension bridge and one of the longest bridges in the world. Suspension bridges are the most efficient form of long-span bridges because of their low stiffness, but they also become more flexible with increasing span and have prominent dynamic problems [24]. Wind tunnel testing is the best option for checking the aerodynamics and aeroelasticity of long-span bridges and can be classified into three types: sectional model testing, taut strip model testing, and full aeroelastic model testing. In this research, a section of the Great Belt Bridge has been selected as a reference to perform wind tunnel testing due to its streamlined aerodynamic shape. A novel testing setup is designed, which facilitates a single-degree-of-freedom (heave and pitch) and coupled two-degree-of-freedom motion to a wind tunnel section model. The direct and cross-flutter derivatives are obtained from the measured data using several system identification methods, including the Hilbert transform method, the logarithmic decrement method, the iterative least squares method, and the modified Ibrahim time domain method. Furthermore, an analytical study along with numerical computational fluid dynamic simulations are also performed to validate the experimental results.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 6 Performance Evaluations
### Simulation Model  
A series of experiments are implemented in the MATLAB tool to validate our proposed algorithms comprehensively. The experiments are carried out in an Intel Core i5 dual-core CPU with a frequency of 2.3 GHz, 4 GB of memory, and a Windows 7 operating system. The simulation environment is composed of a mobile sink and \(N\) sensor nodes in a squared area of 1000 \(\times\) 1000 m. After deployment, all sensor nodes are believed to be stationary. The centroid is supposed to be the starting point for the mobile sink to gather data from all cluster heads. The mobile sink should traverse all cluster heads and return to the starting position. In addition, all the later experiments are performed assuming that the network is affected by some environmental events such as wildfire and rainstorms.  
To simulate the wildfires, we employed the same radiation model in [13]. A Poisson process is used to generate the data traffic with a mean parameter \(\lambda\). In addition, The WSN lossy links model used in this paper is referred to [34]. Table 4 summarizes the simulation parameters.  
We adopted the energy consumption model of [34] to be used in our experiments. This model considers that the total energy consumption by a node mainly includes the energy consumed in transmission and reception of data packets.
## Materials and Methods
### Assessment of Shelfife Analysis  
Shelf life can be defined as the length of time during which the food retains the required level of quality that appeals to the consumer [29; 30]. The main specific markers, color, total soluble solids, and apple flesh firmness for fresh-cut apple quality, were monitored in the first hours after preparation at room temperature and during a short period of cold storage.  
\begin{table}
\begin{tabular}{c c c} \hline
**Code** & **Composition of Solutions** & **pH of Solutions** \\ \hline DW & distilled water, control & 7.00 \\ BHT * & 1\% butylated hydroxytoluene & 7.75 \\ CA * & 1.0\% citric acid & 2.01 \\ AA * & 0.5\% ascorbic acid & 2.66 \\ AA-CA * & 1.5\% ascorbic acid + citric acid & 2.00 \\ PE ** & water solution of E10 1:1 & 6.10 \\ PE-CA ** & PE +1\% citric acid & 2.18 \\ PW ** & water solution of W 1:1 & 6.10 \\ PW-CA ** & PW +1\% citric acid & 2.17 \\ \hline \end{tabular}  
* Conventional preserving compounds, ** Novel preservative solutions.  
\end{table}
Table 1: Different preservative media used for dipping of fresh-cut apple cubes: - standard compounds; - novel compounds: water (W) and ethanol 10% (E10) extracts from potato peels (P) and control.  
Color of apple surface was quantified using a benchtop tristimulus colorimeter (Eoptis, Mod. CLM-196 Benchtop, Trento, Italy) supplied with its own white reference standard. Color was evaluated on the basis of CIE L*a*b* color System accepted by the Commission International Elairage, where L* is the lightness, a* and b* are the red-greenness and blue-yellowness components, respectively.  
According to [31], the results were expressed as \(\Delta\) Browning Index (\(\Delta\)BI) values by the following equation:  
\[\text{BI}=100^{\ast}\;(\text{x}-0.31)/0.172 \tag{1}\]  
where  
\[\text{x}=(\text{a}^{\ast}+1.75\text{L}^{\ast})/(5.645\text{L}^{\ast}+\text{a} ^{\ast}-3.012\text{b}^{\ast}) \tag{2}\]  
and  
\[\Delta\text{BI}=\text{BI}_{\text{f}}-\text{BI}_{\text{s}} \tag{3}\]  
where: \(\text{BI}_{\text{f}}=\text{BI}\) at the end of each observation time, \(\text{BI}_{\text{s}}=\text{BI}\) at the start of each experiment.  
Total soluble solids (TSS) obtained from the fresh-tissue sap of apple flesh cubes were measured using a hand refracometer (Mod. 2369-Bertuzzi, Milan, Italy) and expressed as \({}^{\circ}\)Brix. Flesh firmness or texture of apple flesh cubes was evaluated by a manual penetrometer (Mod. 53205, TR Turoni & C. snc Forli, Italy), measuring the resistance of flesh to the entrance of a metal probe (8 mm diameter and depth). The force needed to break parenchyma cells in the cortex was expressed in kilogram-force (kg/0.5 cm\({}^{2}\)).  
Moreover, at every storage time the water loss from the apple cubes was gravimetrically monitored computing the weight loss percentage.
###### Abstract
Flooding tests were carried out at reservoir conditions to estimate the oil recovery during GLDA, HEDTA, and EDTA flooding. In addition, the impact of chemical injection on the rock porosity system was studied using computed tomography (CT) scans and NMR analyses. HEDTA showed the highest impact on the rock properties for dolomite and sandstone; the rock porosity and permeability were increased significantly after the HEDTA flooding. [PERSON] and [PERSON] [18; 21] investigated the alterations in carbonate rocks during chelating agent flooding. The impacts of chemical concentration, injected volume, system pH, and brine salinity on the hydrocarbon system were studied. Zeta potential, ions concentration, spontaneous imbibition, coreflooding, and NMR experiments were conducted. After the treatment, the carbonate wettability was changed to less oil-wet status, and more oil was produced consequently. In addition, the capillary pressure was reduced considerably due to the chemical flooding, resulting in better flow conditions for oil. Among all tested chemicals, the highest oil recovery was obtained using GLDA as the chelating agent. Indicating that GLDA can outperform other chelating agents for enhancing the oil recovery at the same injected volume and concentration. Chelating agents can be used as standalone fluids for enhancing the oil recovery from carbonate and sandstone formations [15; 16; 17]. However, the effectiveness of chelating agent solutions depends on different factors such as acid concentration, salt type, and injected volume. Extensive work was conducted to assess the performance of several chelating agents under different reservoir and treatment conditions [18; 21; 22]. The impacts of chemical concentration, chelating agent type, solution pH, and rock type were investigated [13; 17; 20; 21]. However, there is a gap in the literature about the influence of salt type on the hydrocarbon recovery from carbonate and sandstone reservoirs. Therefore, the objective of this work is to study the influence of the salt type of chelating agent on the oil recovery. The performances of the calcium-based GLDA chelating agent (Ca\({}_{2}\)GLDA), the potassium-based chelating agent (K\({}_{4}\)GLDA), and the sodium-based chelating agent (Na\({}_{4}\)GLDA) in recovering oil from sandstone and carbonate rocks are examined. The three salts used in this study are readily available in the market and can be secured easily compared to other chelating agents types. In addition, GLDA was selected in this work, because it is the most environmentally friendly chelating agent compared to all chelating agents. Results of coreflooding, dynamic adsorption, interfacial tension, and contact angle measurements are discussed in this paper.
## 4 Proposed Alternating Optimization Algorithm to Minimize MSE
### Optimizations of Precoding Matrix  
When the IRS auxiliary matrix \(\mathbf{V}^{(r)}\) is specified at the \(r\)-th iteration, the problem \(\mathcal{P}2\) can be simplified to the following form:  
\[\mathcal{P}2-a:\min_{\mathbf{W}} \text{MSE}\Big{(}\mathbf{W},\mathbf{V}^{(r)}\Big{)}\] (35) s.t. \[\text{C1}:\sum_{k=1}^{K}\mathbf{w}_{k}u_{k}+\mathbf{D}\geq \mathbf{0},\] \[\text{C2}:\sum_{k=1}^{K}\mathbf{w}_{k}u_{k}+\mathbf{D}\leq\frac{P _{\max}\tau}{h\
u}\mathbf{1}.\]  
From (33), it is evident that \(\text{MSE}\Big{(}\mathbf{W},\mathbf{V}^{(r)}\Big{)}\) is a quadratic function, indicating that \(\text{MSE}\Big{(}\mathbf{W},\mathbf{V}^{(l)}\Big{)}\) is a convex in terms of \(\mathbf{W}\). However, the two constraints of problem \(\mathcal{P}2-a\) do not satisfy convexity yet since it becomes uncertain, depending on the transmitted symbol vector \(\mathbf{u}\). To eliminate this uncertainty, we consider two extreme cases, where \(u_{k}=-1,\forall k\in\mathcal{K}\) and \(u_{k}=1,\forall k\in\mathcal{K}\). Constraint C1 turns into \(\sum_{k=1}^{K}\mathbf{w}_{k}+\mathbf{D}\geq\mathbf{0}\) and \(\sum_{k=1}^{K}(-\mathbf{w}_{k})+\mathbf{D}\geq\mathbf{0}\) in these two extreme cases, respectively. Considering these two cases together, we can arrive at a more general expression:  
\[\sum_{k=1}^{K}|\mathbf{w}_{k}|\leq\mathbf{D}. \tag{36}\]  
Similarly, constraint C2 can be updated as  
\[\sum_{k=1}^{K}|\mathbf{w}_{k}|\leq\frac{P_{\max}\tau}{h\
u}\mathbf{1}-\mathbf{ D}. \tag{37}\]  
As a result, problem \(\mathcal{P}2-a\) is transformed into  
\[\mathcal{P}3-a:\min_{\mathbf{W}} \text{MSE}\Big{(}\mathbf{W},\mathbf{V}^{(r)}\Big{)}\] (38) s.t. \[\eqref{eq:p3},\eqref{eq:p3}.\]  
For the optimization problem \(\mathcal{P}3-a\), the objective function is convex, and constraints (36) and (37) are also convex (specifically, affine). Therefore, \(\mathcal{P}3-a\) constitutes a standard convex problem, which can be directly solved using the CVX toolbox [40] in MATLAB (2023b).
## 3 Results
086\;^{\mathrm{H}}\mathrm{M}^{\mathrm{v}}-0.012\;^{\mathrm{S}}\mathrm{M}_{ \mathrm{E}}\) (4) \\  & \((0.03,0.01,0.02,0.01,0.02,0.01,0.02)\) \\  & N(TR) = 45, \(\mathrm{r}^{2}=0.957\), \(\mathrm{s}=0.04\); N(+14 EV) = 59, \(\mathrm{r}^{2}=0.951\), \(\mathrm{s}=0.03\) \\  & Excluded outliers: McCl \& MeOH \(\in\) [EV] \\ \hline \(\delta^{\mathrm{v}}_{\mathrm{ppo}}(-0.5)\) & \(\mathrm{\gamma}=8.683+0.386\;\mathrm{D}^{\mathrm{v}}+397.6\;^{1}\chi^{\mathrm{v}} \mathrm{s}+151.9\;^{\mathrm{T}}\mathrm{b}_{\mathrm{I}}-502.4\;^{1}\mathrm{b} _{\mathrm{I}}+3.347\;\Delta\) (5) \\  & \((2.3,0.05,57,36,90,0.7)\) \\  & N(TR) = 29, \(\mathrm{r}^{2}=0.835\), \(\mathrm{s}=3.1\); N(+10 EV) = 39, \(\mathrm{r}^{2}=0.792\), \(\mathrm{s}=3.1\) \\  & Excluded outlier: formamide, nitromethane \(\in\) [EV] \\ \hline \(\delta^{\mathrm{v}}_{\mathrm{ppo}}(0.5)\) & \(\mathrm{FP}=387.1+26.99\;^{\mathrm{H}}\mathrm{M}-94.38\;^{\mathrm{H}}\mathrm{M} _{\mathrm{I}}+33.03\;^{\mathrm{G}}\mathrm{M}_{\mathrm{E}}+114.5\;^{\mathrm{U}} \mathrm{M}_{\mathrm{I}}-83.10\;^{\mathrm{H}}\mathrm{M}_{\mathrm{E}}\) (6) \\  & \((26,\,6.2,\,12,\,5.2,\,13,\,11)\) \\  & N(TR) = 29, \(\mathrm{r}^{2}=0.829\), \(\mathrm{s}=16\); N(+11 EV)= 40, \(\mathrm{r}^{2}=0.764\), \(\mathrm{s}=17\) \\  & Excluded outliers: Acetone \(\in\) [EV] \\ \hline \(\delta^{\mathrm{v}}_{\mathrm{ppo}}(-0.5)\) & \(\mathrm{\eta}=-0.216+0.001\;^{1}\chi_{\mathrm{d}}+0.486\;^{1}\mathrm{b}_{ \mathrm{I}}+2.20\cdot 10^{-5}1\mathrm{b}_{\mathrm{I}}-3.83\cdot 10^{-6}\;^{0} \mathrm{b}_{\mathrm{F}}\mathrm{d}+0.098\;\Sigma\) (7) \\  & \((0.2,\,0.0003,0.1,7\cdot 10^{-6},10^{-7},0.01)\) \\  & N(TR) = 28, \(\mathrm{r}^{2}=0.969\), \(\mathrm{s}=0.4\); N(+10 EV) = 38, \(\mathrm{r}^{2}=0.939\), \(\mathrm{s}=0.4\) \\  & Excluded outlier: MeOH \(\in\) [EV] \\ \hline \(\delta^{\mathrm{v}}_{\mathrm{ppo}}(5)\) & \(\mathrm{UV}=299.1+50.54\;^{5}\mathrm{M}^{\mathrm{v}}-37.34\;^{1}\mathrm{M}^{ \mathrm{v}}-9.048\;^{\mathrm{H}}\mathrm{M}_{\mathrm{E}}+1.310\;^{5}\mathrm{M} _{\mathrm{E}}\) (9) \\ \(\delta^{\mathrm{v}}_{\mathrm{ppo}}(50)\) & \((13,\,4.9,\,3.8,\,1.1,\,0.2)\) \\  & N(TR) = 25, \(\mathrm{r}^{2}=0.893\), \(\mathrm{s}=15\); N(+8 EV) = 33, \(\mathrm{r}^{2}=0.826\), \(\mathrm{s}=21\) \\  & Excluded outlier: 4-Me-2-pentanone \(\in\) [TR]; 2-butanone, MeOH, acetonitrile \(\in\) [EV] \\ \hline \(\delta^{\mathrm{v}}_{\mathrm{ppo}}(50)\) & \(-\chi\cdot 10^{6}=0.617+0.044\;^{0}\chi_{\mathrm{d}}+2.208\;^{1}\chi^{\mathrm{v}} \mathrm{s}-2.212\;^{1}\mathrm{b}_{\mathrm{I}}+0.070\;\Delta-0.016\;\Sigma\) (10) \\  & \((0.02,\,0.01,\,0.4,\,0.5,\,0.008,\,0.003)\) \\  & N(TR) = 23, \(\mathrm{r}^{2}=0.876\), \(\mathrm{s}=0.04\); N(+7 EV) = 30, \(\mathrm{r}^{2}=0.875\), \(\mathrm{s}=0.04\) \\  & Excluded outlier: nitromethane \& MeOH \(\in\) [EV] \\ \hline \(\delta^{\mathrm{v}}_{\mathrm{ppo}}(1)\) & \(\mathrm{EI}=0.018+0.181\;\times 10^{-3}\;^{1}\chi_{\mathrm{d}}-0.675\cdot 10^{-6}\; ^{1}\chi^{\mathrm{v}}_{\mathrm{d}}+0.003\;^{0}\mathrm{b}_{\mathrm{I}}+140.8\; \mathrm{T}_{\Sigma/\mathrm{M}}\) (11) \\  & \((0.02,\,0.00006,10^{-7},0.