esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes
## 1 Introduction
Additionally, when using deep learning networks [31; 32] for map building, an unavoidable problem is the poor real-time performance of the system, and the processing time per image frame increases significantly. When the above methods build a map in a dynamic environment, they have the disadvantages of inaccurate detection of dynamic feature points and low accuracy of pose estimation, as well as the large computational power of semantic networks leading to the slow running of algorithms and the phenomenon of lost frames. These problems will affect the mapping and pose estimation accuracy of the SLAM system, leading to the failure of mapping and serious deviations of positioning and navigation. To solve the interference of dynamic objects in the environment to the SLAM system and to improve the accuracy of SLAM mapping and pose estimation, a new SLAM mapping method in a dynamic environment is proposed in this paper. In this paper, a dynamic feature detection module is added to the ORB-SLAM2 algorithm. In the feature point recognition and rejection module, the LK optical flow method is used to detect and reject dynamic feature points. The YOLOv5 (You Only Look Once) target detection network can detect most objects with dynamic features. Afterward, the detected images are detected and rejected twice using the optical flow method. By detecting and rejecting the dynamic features twice, the proposed algorithm can reject most of the dynamic features well and ensure the accuracy of map building and pose estimation. Moreover, the YOLOv5 network has a small scale and small amount of calculation, which ensures the running speed of the algorithm, effectively solving the problem of lost frames in the algorithm. Compared with SLAM algorithms of the same type, the running speed of the algorithm in this paper is much faster than that of DynaSLAM and DS-SLAM algorithms in the literature. The main contributions of this paper are as follows: 1. Applying the deep learning target detection network YOLOv5 to the SLAM mapping system, which enables the SLAM system to perform fast target detection and segmentation; 2. A secondary detection of dynamic targets based on the deep learning network YOLOv5 combined with the LK optical flow method is proposed, which can effectively remove dynamic features in the dynamic feature region of points; 3. Based on the ORB-SLAM2 system, a new SLAM mapping system is successfully constructed. The structure of this paper is as follows: In the Section 1, the principle of the ORB-SLAM2 algorithm and YOLOv5 network structure, as well as the function of each module, are introduced in detail. In the Section 2, an improvement to the ORB-SLAM2 algorithm is proposed, and the principle of eliminating dynamic feature points is described in detail. The Section 3 uses the TUM dataset to verify the proposed algorithm, and provides the experimental results based on the TUM dataset. Finally, the Section 4 summarizes and considers future developments that may arise from the work in this paper.
## 4 Conclusions  
In conclusion, we would like to report that the obtained limit of detection (LoD) was 29.5 mg/kg for lead (LoDPb = 29.5 mg/kg), 95.5 mg/kg for arsenic (LoDAs = 95.5 mg/kg), and 327 mg/kg for manganese (LoDMn = 327 mg/kg). We achieved these results due to the modification of the LIBS optical collection, which provided better coupling to the spectrometer. We also measured As and Pb in an argon atmosphere under ambient pressure. This modified the ablation plasma characteristics, further improving the signal-to-noise  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **R2 Value** & **F Value** & **P > F Value** \\ \hline As & 0.977 & 83.342 & 0.012 \\ Pb & 0.992 & 372.629 & \(3.036\times 10^{-4}\) \\ Mn & 0.929 & 13.034 & 0.172 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical parameters for calibration lines.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & **Media** & **Level** & **Agency and Reference** & **LoD** & **Max Measured** \\  & & & **Reference** & **Concentration in** & **Studied Samples** \\ \hline Arsenic (AS) & Soil & 40 mg/kg & USS EPA [31] & 95.5 mg/kg & 174 mg/kg \\ Led (Pb) & Soil & 200 mg/kg & USS EPA [32] & 29.5 mg/kg & 234 mg/kg \\ Manganese (Mn) & Soil & 1800 mg/kg & USS EPA [33] & 327 mg/kg & 2100 mg/kg \\ \hline \hline \end{tabular}
\end{table}
Table 2: Permissible amounts of As, Pb, and Mn in different media published by several agencies.  
ratio. We could measure Mn and Pb within the safety range established by the US EPA in all soil samples from the affected area of the Superfund site. Surprisingly, most soil samples from the affected area contained cadmium (Cd) below the detection limit of ICP-MS, and only a few had Cd concentrations in the single digits (mg/kg). Therefore, we are not reporting Cd detection in this manuscript.  
Conceptualization, V.A. and S.M.; data curation, N.A., D.M. and V.F.; methodology, N.A., D.M., V.F., D.D. and S.M.; writing--review and editing, N.A., D.M., V.F., D.D., V.A. and S.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Institute of Environmental Health Sciences (1P42 ES027723-01A1).  
Not applicable.  
The original contributions presented in the study are included in the article, further in-quiries can be directed to the corresponding author.  
We thank the UAB Superfund Research Center members for useful discussions.  
The authors declare no conflicts of interest.
## 4 Case-Study Building
### Modal-DBD Design Steps
#### 4.2.10 Modal-DBD Application and Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Initial Estimation**} & \multicolumn{4}{c}{**Sbase-Wall Assessment**} & \multicolumn{4}{c}{**Design Verification**} \\ \hline  & \multicolumn{2}{c}{**Initial**} & \multicolumn{2}{c}{**Default**} & \multicolumn{2}{c}{**Required**} & \multicolumn{2}{c}{**Required**} & \multicolumn{2}{c}{**Actual Jr./o AN(Num)**} & \multicolumn{2}{c}{**Actual Jr./o AN(Num)**} & \multicolumn{2}{c}{**Actual Jr./o AN(Num)**} & \multicolumn{2}{c}{**Actual Jr./o AN(Num)**} & \multicolumn{2}{c}{**Actual Jr./o AN(Num)**} & \multicolumn{2}{c}{**Actual Jr./o AN(Num)**} \\ \cline{2-13}
**IS** & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) & \(\mathbf{S_{\text{P}}}\) \\ \hline
1 & 1 & 1 & 2.50 & 4.50 & 0.75 & 1.83 & CF & 1.90 & CF & 3.72 & 1 & 0.75 & 4.56 & 0.82 \\
2 & 0.50 & 0.58 & 4.25 & 0.50 & 1.77 & PF & 1.71 & PF & 3.48 & 0.90 & 0.56 & 4.25 & 0.82 \\
3 & 3.10 & 0.10 & 14.16 & 0.5 & 5.85 & SF & 5.73 & SF & 11.58 & 3.10 & 0.34 & 7.00 & 1.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: IO level analysis (\(\gamma\)-direction), accounting for the EP shear-wall stiffnesses. As shown, the CP level is largely verified for the case-study Blockhaus system. The stiffness of the building in the X-direction (i.e., east and west log-walls) is in fact up to +27% higher than the minimum required. Regarding the X-direction (i.e., south and north), the estimated stiffness is up to +64% larger than required. Similar considerations can be derived from the LS level. The stiffness of the actual lateral force resisting system, parallel to the east and west log-walls, is about 40% higher than the minimum required. In the direction parallel to south and north log-walls, such a ratio is up to +65%. The same tables, finally, emphasize that the fixed drift limit of 0.75% for the IO configuration is extremely conservative for Blockhaus systems, given that the intrinsic flexibility of log-walls turns out in stiffness contributions that hardly match the actual required estimates.
# Dynamic Behavior of an Embedded Foundation under Horizontal Vibration in a Poroelastic Half-Space
## 3 Results and Discussion
### Effect of the Footing Embedment Ratio  
The embedment of a foundation is crucial to the stiffness of the foundation because the stiffness can significantly increase with the embedment. The foundation stiffness increases because part of the load is transmitted into the soil through shear and normal traction acting on the side-walls [5]. With uniformly distributed soil around the foundation and under the foundation, the effect of embedment ratio L/R = 0, 0.5, 1, 2 is investigated. The dynamic response of the depth ratio under horizontal excitations is presented by varying the nondimensional frequency of excitation. Figure 4 indicates the real part (\(k_{\rm h}\)) and the imaginary part (\(c_{\rm h}\)) of \(K_{\rm h}\), respectively. As we can see from Figure 4, the dynamic impedance increases significantly with increasing foundation embedment ratio. However, the variation of \(k_{\rm h}\) and \(c_{\rm h}\) with the frequency \(a_{0}\) shows a different trend for all the values of foundation embedment. In the case of the real part, the stiffness of the foundation for horizontal vibration increases with increasing \(a_{0}\). The spring coefficient increases remarkably with the embedment ratio at a higher frequency. For the imaginary part, the damping of foundation decreases \(a_{0}<2\) and reach a minimum value at about \(a_{0}=1.5\) and then increases with increasing frequency. It is noted that both components of the dynamic impedance vary for different embedment ratios. The real part of dynamic impedance is small at low frequency and then increases linearly with frequency thereafter which result from little wave propagates at these low frequencies. Different from this trend, the imaginary part decreases with the interested frequency range and then increases slowly, indicating wave propagation throughout the frequency [32]. The magnitudes of the real part (\(k_{\rm h}\)) and the imaginary part(\(c_{\rm h}\)) of \(K_{\rm h}\) increases  
Figure 3: Verification of the foundation side.  
with increasing embedment. The influence of embedment has a relatively smaller influence on the imaginary part when compared to the real part.  
Figure 5 indicates the effect of the depth ratio on the dynamic response factor. It is clear that the embedment of the foundation has a significant effect on the magnitude and shape of the dynamic response factor. It is noticed that the foundation becomes stiffer as the dynamic response factor decreases with increasing embedment ratio. This effect can be explained with the restriction of the movement of the embedded foundation by the normal stress and shear stress from the overlying soil. The foundation's response factor also decreases with increasing \(a_{0}\), as expected. However, the dynamic response factor is dependent on high frequency (\(a_{0}>2\)). In addition, the larger the embedment ratio, the steeper is the corresponding response factor curve. Therefore, at high frequencies away from 2, the dynamic response factor keeps steady.
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 2 Beamforming Imaging Method  
### DAS Method  
In the DAS beamforming imaging algorithm, the imaging region is usually divided into grids, and the pixel value of each grid point \(P(x,y)\) corresponds to an imaging index. For different imaging points, combined with the array layout, the imaging index can be obtained by applying appropriate delay to the received signals, and the delay of each array element can be calculated by the following equation:  
\[t_{n}(x,y)=t_{0}+\frac{\sqrt{\left(x-x_{0}\right)^{2}+\left(y-y_{0}\right)^{2} }+\sqrt{\left(x-x_{n}\right)^{2}+\left(y-y_{n}\right)^{2}}}{c_{g}} \tag{1}\]  
where \(t_{0}\) is the time point corresponding to the maximum amplitude of the excitation signal, \(x_{0}\) and \(y_{0}\) are the coordinates of the target imaging point, \(x_{n}\) and \(y_{n}\) are the coordinates of the excitation array elements and are the coordinates of the receiving array elements, \(n=1\), \(2\), \(\ldots\), \(N\), \(N\) is the number of elements in the array, and \(c_{g}\) is the group velocity of the Lamb wave. For different imaging points in the imaging region, the signal amplitude after delay is as follows:  
\[z_{n}(x,y)=s_{n}(t_{n}(x,y)) \tag{2}\]  
where \(s_{n}(t_{n})\) denotes the damage scattering signal received by the nth array element of the array. To optimize the resolution and contrast of the imaging results, a preset apodization weight vector \(\mathbf{w}(x,\,y)\), such as the Hanning window, is introduced to weight and sum the signal vectors \(z(x,y)\) of the received channels. Apodization--the process of applying spatially varying weights to sensor channels--adjusts the amplitude distribution of signals during beamforming. This technique enhances imaging performance by suppressing sidelobe interference and sharpening the main lobe, thereby improving noise reduction and spatial resolution. The image \(I_{\text{DAS}}(x,y)\) generated by the DAS method can be obtained as follows:  
\[I_{\text{DAS}}(x,y)=\mathbf{w}^{\text{T}}(x,y)\cdot\mathbf{z}(x,y) \tag{3}\]
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 4 Estimation of Traffic Accident Reduction Effect and Application Method
### Field Application Method  
The steps followed by working officials for the estimation and implementation of speed limits in specific road sections are presented in Table 14. In addition, when applying estimated speed limits, the speed limits of nearby road sections and the proximity of sections with high accident rates should be considered.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Step** & **Data Required** & **Notes** \\ \hline \multirow{2}{*}{Target segment setting} & Directional urban road with two or more lanes (except two lanes & Minimum length 800 m \\  & both sides or free flow roads) & \\ \hline \multirow{4}{*}{Collecting data} & Road function, whether there is an absence of a median strip & \\  & (50\% or more facility separation), average lane width over 3.3 m), & \\ \cline{1-1}  & (50\% or more radius), driverways and entrances to buildings, the number of signal or no & \\ \cline{1-1}  & signal intersections and crossings & \\ \hline Setting speed limits & Model application calculation & Rounding (10 km/h) \\ \hline \multirow{2}{*}{Field application} & Connection road speed limit, geometry & Multiple \\  & accident roads & \\ \hline \hline \end{tabular}
\end{table}
Table 14: Application steps and details.  
Figure 20: Simple linear relationship between the relative change in speed and the corresponding change in accident frequency.  
If it is difficult to collect data for each branch, the results interpreted based on the data applied in this study can be used. Table 15 shows the classification of road functions and the proposed speed limits (10 km unit) into optimal groups.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 6 Conclusions  
In this paper, an optimal cluster size interval was calculated. The lower bound of this interval was calculated based on the density of nodes in the network. To determine the maximum value of interval and the exact value of the angle, a two-stage GA was proposed. Since one objective of employing clustering is to decrease the energy consumption of nodes, the power expenditure of sensors before applying clustering was also considered in our model. As a result, the energy consumption of MNs is remarkably reduced by using the method proposed in this work. We also proposed the CMS2V2 algorithm in order to mitigate the energy hole problem and extend the network lifespan. Finally, simulation results show that compared with other methods, our strategy can achieve better performance in terms of network lifespan. In addition, CMS2V2 could balance the energy consumption ratio of CHs in different time periods. In the future, we plan to determine the optimal sojourn time of MSs at different sites to solve the problem of the unbalanced energy consumption of sensor nodes.
## 1 Introduction
Furthermore, thermodynamic parameters such as enthalpy (\(\Delta H\)), entropy (\(\Delta S\)), and Gibbs free energy (\(\Delta G\)) can be used to explain the drug kinetic release profiles [31]. Even though there has been extensive research on drug delivery systems, relating the drug release parameters with their thermodynamic parameters is still in its infancy [31; 32]. In an effort address this unmet need, polymeric microspheres were developed using the W/O/W emulsion technique [33; 34] in order to explore the kinetics and thermodynamics of anticancer drug release. The polymers of choice are poly (lactic-co-glycolic acid) (PLGA), chitosan, and polyethylene glycol (PEG). PLGA and PEG are biocompatible polymers that have been approved by the Food and Drug Administration (FDA) for use in the field of drug delivery [35; 36; 37]. The drug release characteristics of PLGA are tunable by altering the ratios of polylactic acid and polyglycolic acid [35]. PEG decreases interactions with blood components [36]. Chitosan is a naturally abundant polymer and is useful in medicine due to its biodegradability, biocompatibility, mucoadhesive, and nonimmunogenic properties, together with its ability to enhance the penetration of large moleculesacross mucosal surfaces [38]. These polymers have been used in several investigations related to drug delivery applications in cancer treatment [34; 39; 40; 41]. The physicochemical and morphological properties of synthesized PLGA-CS-PEG microspheres were characterized using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Mathematical models were used to analyze the kinetics and thermodynamics of the in vitro drug release from the microspheres at hyper-thermic and human body temperatures [11; 12]. Alamar blue assay was used to evaluate the cell viability and drug-induced cytotoxicity on triple negative breast cancer cells [11; 12]. The implications of the results are then discussed as they relate to the development of drug-encapsulating microspheres for controlled release of cancer drugs in triple negative breast cancer treatment.
# Power Allocation and User Grouping for NOMA Downlink Systems
## 4 Numerical Results
We assume that there are 20 randomly distributed users in a cell, the number of subcarriers is 10, and the transmit power of the BS is between 5W and 10W. We can see that DLPA-CFE achieves the largest system sum rate compared with the other three methods, and the improvement is about 10% compared to the FTPA-FTPA method. This is because DLPA performs better than FTPA in power allocation among subcarriers. When power is allocated to users within subcarriers, the user power calculated by CFE can maximize the sum rate under the condition of a minimum transmission rate. FTPA only allocates more power to users  
Figure 5: Sum rate comparison under different transmit powers. Figure 6: Sum rate comparison under different numbers of users. with poor channel conditions. Although FTPA ensures certain fairness, it cannot achieve the optimal global solution. Figure 8 verifies that the DLPA-CFE power allocation scheme is the scheme that achieves the maximum system sum rate. The transmit power of the BS is 10 W and the number of users ranges from 20 to 40.
# Comparison Study of the PSO and SBPSO on Universal Robot Trajectory Planning
## 2 Materials and Methods
### Serendipity-Based Particle Swarm Optimization
Figure 4:0 In the HappyCat (30 particles) function, the traditional PSO has stalled close to iteration 1560 and the SBPSO remains active close to iteration 2000. Figure 4:1 Task contour of universal manipulator robot UR5 with SBPSO algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Functions** & **Iteration** & **PSO** & **SBPSO** \\ \hline
**Sphere function** & 1000 & \(3.1403\times 10^{-22}\) & \(3.7821\times 10^{-156}\) \\  & 2000 & \(1.4253\times 10^{-6}\) & \(3.6721\times 10^{-81}\) \\ \hline
**Rosenbrock function** & 1000 & \(57.7381\) & \(1.4324\times 10^{-4}\) \\  & 2000 & \(150.7192\) & \(2.8091\times 10^{-2}\) \\ \hline
**Griewank function** & 1000 & \(0.1692\) & \(0\) \\  & 2000 & \(0.0185\) & \(0\) \\ \hline
**Rastrigin function** & 1000 & \(3.9673\) & \(0\) \\  & 2000 & \(48.7790\) & \(0\) \\ \hline
**HappyCat function** & 1000 & \(4.6100\) & \(0\) \\  & \(1500\) & \(19.6670\) & \(0\) \\ \hline
**Ackley function** & 1000 & \(4.1558\times 10^{-11}\) & \(0\) \\  & 2000 & \(4.8007\times 10^{-11}\) & \(0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between standard deviation of the traditional PSO and SBPSO with a populations of 20 particles.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 5 Methodology
### Structural Analysis  
#### 5.2.1 Initial Static Analysis  
Structural analysis begins with an initial static equilibrium assessment, where gravity loads are applied to establish the baseline stress state before seismic excitation [49].
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 3 System Description
### System Model
#### 3.1.2 Physical Proximity Layer  
The PPL describes the physical and network constraints of DUEs. It can also be modeled using the directional weighted graph GPPL = VPPL \(\epsilon\)PPL [14], where the vertices set, VPPL, denotes the DUEs (from a to h) while the edges set, \(\epsilon\)PPL, denotes the physical link between the DUEs as shown (with black double arrows) in Figure 1. The existence of the physical edge \(\epsilon\)PPL between DUEs validates the feasibility of a D2D link between them. As we assume that the SCBS knows the locations of the users, it can determine if the two users are in physical proximity to each other or not. There are four possible D2D scenarios based on the locations of \(Tx\) and \(Rx\):  
1. \(Tx\) and \(Rx\) are connected to the same SCBS.
2. \(Tx\) and \(Rx\) are connected to different SCBSs.
3. \(Tx\) is connected to the SCBS and Rx to the MCBS.
4. \(Tx\) and \(Rx\) are connected to MCBS.  
As D2D refers to the communication between the UEs located in proximity, for simplicity, we assume that the \(Tx\) and \(Rx\) DUEs are connected to the same SCBS.