esa-sceva/satcom-chunk-collection huggingface dataset:
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 3 Results
### Parabolic-Trihedral Reflectors and Reflector Arrays  
#### 3.3.1 Retroreflected Energy Levels  
The best overall performance came from the array of seven large parabolic trihedra (Figure 15a), which had both average and median retroreflected energy levels in the vicinity of 5 dB, and a relatively small 8 dB range. These results are similar to the ideal focusing space-reversal patches with widths of 1.2 to 2.4 m for the 7.2 \(\times\) 7.2 m reflector at 2 m distance (Section 3.1). A single large parabolic trihedron yielded a much larger 20 dB range, including both higher and lower values. It is counterintuitive that increasing the number of trihedra from 1 to 7 would reduce the maximum retroreflected energy level (by 2 dB), but this phenomenon occurs from interference; a similar result is seen for the ideal 2.4 m square patch in Sections 3.1 and 3.2.  
Predictably, the single small parabolic trihedron had the least useful results, with retroreflected energy spanning a large range, including many low values. The effect of arraying seven small parabolic trihedra increased the maximum, mean, median, and minimum, and reduced the range. Results for the seven small parabolic trihedra are somewhat similar in distribution to those of the 0.8 m or 1.2 m focusing space-reversal patches for the 2.4 \(\times\) 2.4 m ideal reflector at a 2 m distance (Section 3.2).  
Energetically averaged and median speech-A-weighted results are all much higher than Equation (5)'s theoretical prediction of ideal maximum incoherent summation. The maxima of the single parabolic trihedra are close to Equation (4)'s theoretical ideal focusing prediction, which indicates that the surfaces focus effectively. The single large parabolic trihedron had a maximum 1 dB greater than the theoretical maximum, which might be explained by a contribution from diffraction at the solid edges of the form.  
Figure 14: Octave-band distribution of retroreflected energy levels for 25 collocated source-receivers (small reflector), showing individual values (thin lines) and energetically averaged values (thick lines): (**a**) space-reversal patches (**b**) focusing space-reversal patches.  
All configurations yielded positive spectral slopes up to the 4 kHz octave band (Figure 15b). The slope of the seven large parabolic trihedron array was 3.6 dB/octave in the bands up to 4 kHz, less steep than the other configurations. The single large parabolic trihedron yielded the greatest slope (4.7 dB/octave). The small parabolic trihedron configurations both had a slope of 4.1 dB/octave.
## 4 Experiments and Results
### Results Analysis
#### 4.3.3 Packet Loss Ratio  
In wireless sensor networks, the packet loss ratio is an important metric that measures the number of received packets in relation to the total number of packets transmitted. Our proposed SLF-RPL mechanism effectively detects and mitigates malicious nodes, thereby reducing unnecessary overheads on the DODAG caused by wormhole packet loss. In comparison to the PCC-RPL mechanism, our SLF-RPL mechanism demonstrates superior performance in terms of the packet loss rate, as evidenced by Figure 11. Our mechanism exhibits a 16% lower packet loss rate than the PCC-RPL, with an average packet loss rate of 0.33 as opposed to the PCC-RPL's 0.38.  
Figure 10: Attack Detection Rate at the Node Level.  
Figure 9: Attack Detection Rate at the Network Level.  
Average packet loss rates for different numbers of nodes (30, 60, and 90) are shown in Figure 12.  
Consequently, our approach offers a better defense against wormhole attacks and a lower packet loss rate.
# A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather
## 2 Related Work
### Deep Learning-Based Obstacle Detection  
Deep learning has been successfully applied to obstacle detection. [PERSON] proposed an intelligent railway surveillance framework using deep learning for object and track recognition [24]. [PERSON] presented a multitask learning framework for railway obstacle intrusion detection using convolutional neural networks [25]. The model integrated obstacle detection and track line classification, employing a shared encoder and task-specific decoders. [PERSON] proposed an efficient rail area detection method using convolutional neural networks (CNN) [26]. The method involved two main stages: extracting the rail area with a CNN and optimizing the contours with polygon fitting. [PERSON] proposed FB-Net, a robust CNN for real-world railway traffic detection, balancing speed and accuracy [27]. [PERSON] proposed an AI-based framework for automatically detecting grade-crossing trespassing near misses using computer vision analysis of surveillance video data [28].  
In summary, current deep learning-based railway traffic obstacle detection algorithms can detect obstacles effectively in clear weather conditions but experience significantly reduced accuracy in adverse weather conditions such as fog. Particularly in dense fog environments, existing algorithms fail to meet the requirements for railway traffic obstacle detection. To address these issues, the MSA-YOLO algorithm is proposed. This algorithm enhances the network architecture to improve feature extraction and modeling capabilities. The multi-scale network significantly enhances robustness to disturbances in adverse environments and improves obstacle perception capabilities.
## 3 Methodology
### STA-Blocks  
This paper proposes STA-Blocks, which comprise a temporal attention layer and a spatial attention layer. These layers work together to extract spatial-temporal correlations. The temporal attention layer models non-linear temporal correlations, while the spatial attention layer captures dynamic spatial dependencies.
# Iterative Interferometric Denoising Filter for Traveltime Picking
## 3 Synthetic Example
This comparison demonstrated that most of the differences were less than a fourth of the source wavelet period (20 ms; Figure 6b); hence, the proposed IDF method can be used in first-arrival traveltime picking, which is essential in near-surface velocity model building.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 2 Related Work
### Alignment and Walking Direction Estimation
Alternatively, some research proposes to find the best linear fit to the data--as a step is assumed to be taken in a straight line direction--through curve fitting methods such as least-squared linear regression [49], and use the arctangent of the fitted line as the heading. In some cases, it is safe to assume a straight line motion of the user, and hence the direction of motion is not changing unless a turn event occurs, and the heading change would be based on the estimated turning event angle [63]. This kind of approach is heuristic and not optimal, while it can work efficiently in corridors, it will not be able to track slight changes in heading in open areas and spaces. Principal component analysis (PCA) is another alternative that has been utilized in many studies, where PCA is applied to the horizontal plane accelerations of the LLF to extract the 1 st and 2 nd principal components of the data. The 1 st component is used as the heading direction [37, 64, 65, 66, 67, 68, 69] but suffers from forward/backward directional ambiguity and requires extra processing to distinguish the forward walking direction. Alternative sensors can be used for the heading estimation such as the gyroscope, where it exhibits relatable measurements to the direction of motion as well, especially when placed in pocket or in swinging use case [70]. In a similar manner, the magnetometer measurements can be used to detect the heading changes but not as an absolute value as the magnetic measurements provide an absolute reference to the surrounding magnetic field [71]. A fusion of both sensors can be used to limit the drift induced from the gyroscope measurement integration while avoiding the errors in heading that can be induced by magnetic disturbances [72, 73]. The lack of absolute attitude correction techniques without imposing constraints or relying on alternative techniques that require infrastructure knowledge makes attitude and heading estimation an open challenge for INS. In the case of smartphone-based PDR systems, the free-motion of the device and its changing attitude with respect to the user makes it even more challenging. Frequent attitude and heading corrections are needed to increase the reliability of smartphone-based PDR systems for a longer runtime until a coordinate update (CUPT) is available. Wi-Fi fingerprinting and magnetic-map matching (MM) have been utilized to aid with the positional correction [74, 75, 76, 77], but require regular surveying and maintenance. Furthermore, they suffer from RF interference and magnetic disturbance that occur regularly indoors due to the environment variability.
## 4 Simulation Results
### Results and Discussion
#### 4.4.2 Channel Parameter Estimation Performance  
Figure 3 shows the NRMSE of channel gains, delays, and Doppler shift as a function of pilot SNR in a channel randomly generated according to channel models 1 and 2. In both low- and high-IPI regimes, the P-IPIC is capable of estimating channel parameters with higher accuracy. It can be noticed that in the high-IPI case, at low PSNR, the NRMSE is high due to the fact that small amplitude paths are missed. On the other side, at high PSNR, the NRMSE for the P-IPIC increases because small errors in the parameter estimates during the _search phase_ lead to higher residual IPI that is interpreted as small-amplitude paths, reducing the overall accuracy of the estimates. However, the P-IPIC still outperforms the DDIPIC algorithm for high PSNR.  
Figure 2: NMSE as a function of pilot SNR.
# Factorial Design Analysis for Localization Algorithms
## 3 \(2^{k}\) Factorial Methodology
### Selection of \(k\) Factors  
This section the study factors and performance metrics for the \(2^{k}\) factorial analysis of the range-free and range-based localization algorithms are selected. The analysis involves two performance metrics, the MSE and the CDF of the range-free localization algorithms CL and REWL \((\lambda=0.15)\), of the range-based algorithm WLS multilateration, and of the Hyperbolic algorithm [19]. MSE and CDF performance metrics are determined by four study factors \((k=4)\), such as the path-loss exponent, the level noise, the node density, and the RNs. This selection was made base on the previous work [19; 22], where the study factors determine the MSE and CDF performance metrics, through which the accuracy and precision of the localization algorithms are evaluated. The accuracy is the MSE of the position estimated and true position of the NOI throughout all the realizations [19]; this is, if \((x,y)\) is the real position of the NOI and \((x_{i},\ y_{i})\) is the estimated position of the NOI in the realization \(i=1,2,\ldots,M\), this metric is given by Equation (19). The precision considers the distance error distribution, while the accuracy considers the average value of those errors [19]. When two techniques are compared, a technique with concentrated distance errors on small values is preferred.  
\[\text{MSE}=\frac{1}{M}\sum_{i=1}^{M}\Bigl{[}(x-x_{i})^{2}+(y-y_{i})^{2}\Bigr{]} \tag{19}\]
## 4 Application in Simulated Coal Mine Environment  
The experiment in a simulated coal mine environment is used to verify the feasibility of the proposed image enhancement algorithm, and the part of facial images with coal ash is shown in Figure 15 below. The miner face dataset contains 840 images from 40 people, of which 756 are used as training sets, and the remaining 84 are used as test sets.  
The experiment is carried out in the underground garage of the School of Mechanical and Electrical Engineering. A surveillance camera is set up above the corner to obtain images, and an LED light source is selected to adjust the light intensity. The equipment involved in the experiment is shown in Figure 16 below.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **Original Image** & **MSR** & **CLAHE** & **HF** & **PSO** & **Proposed Algorithm** \\ \hline ALSD & 27.74 & 33.34 & 41.95 & 49.05 & 39.21 & 59.09 \\ AG & 5.16 & 5.62 & 6.38 & 7.46 & 6.88 & 8.45 \\ IE & 6.19 & 7.05 & 6.98 & 6.97 & 6.91 & 7.60 \\ Contrast & 20,573.5 & 21,367.4 & 23,320.5 & 24,335.1 & 227,426 & 25,523.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Yale face image enhancement algorithms.  
Figure 15: Part of facial images with coal ash.  
After the experimental bench is built, the face image dataset is imported into the face recognition system, and a face recognition test is conducted. The recognition result column will output the number of recognized faces and the corresponding identity. The specific software interface is shown in Figure 17 below.  
The face recognition network, VGG-16, is trained on the original image dataset and the enhanced facial image dataset, respectively. The precision, recall and F1-score of the face recognition system are calculated, and the specific experimental results are shown in Table 4 below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Metrics** & **Precision** & **Recall** & **F1-Score** \\ \hline Original image & 89.89\% & 90.07\% & 89.47\% \\ Enhanced image & 92.44\% & 93.44\% & 92.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of face recognition.  
Figure 16: Experimental facilities.  
Figure 17: Experimental scene test.  
As shown in Table 4, the evaluation indexes of the face recognition system, including accuracy, recall and F1-score, are improved by about 3% after the image enhancement. It can be proved that the proposed image enhancement algorithm is useful for improving the face recognition rate in underground coal mine environments.
## 2 Scalable Video Coding (SVC) an Extension to H.264/Advanced Video Coding (AVC)
Finally, coded video is packetized according to Network Abstraction Layer standards and later used to store or stream over the network. Figure 1: Scalable video layer extraction and delivery.
# Application of Dynamic Time Warping to Determine the Shear Wave Velocity from the Down-Hole Test
Moreover, a signal quality assessment method based on signal similarity was proposed using the DTW algorithm. The study concludes with a presentation of results that demonstrate the accuracy of the proposed method and the significance of signal quality assessment.
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 3 Adaptive Modulation Technique  
The primary aim of implementing the AM technique is to maximize the rate of data transmission over the channel while maintaining constant transmitted power. This technique works by monitoring the channel conditions and measuring the received SNR, then determining the most suitable modulation level, \(M\), that satisfies the target ABER, \(P_{o}\). In particular, the receiver compares the received SNR to \(N\) of the predetermined boundaries set, and chooses the modulation level, which is associated with the range of the specific boundaries. For the adaptive M-ary PSK (M-PSK) system, this process can be expressed mathematically as follows:  
\[M=M_{i}=2^{i}\text{ if }\gamma_{i}\leq\gamma\,<\,\gamma_{i+1},\,\,i=1,\ldots,N. \tag{5}\]  
The region boundaries \(\{\gamma_{i}\}\) are set to satisfy \(P_{o}\) over the AWGN channel, where the conditional bit error rate is given by [28]  
\[P_{b}(M,\gamma)=\alpha_{m}\,Q\Big{(}\sqrt{2\gamma}\beta_{m}\Big{)}, \tag{6}\]  
where \(\alpha_{m}=\{1,\frac{2}{\log_{2}M}\}\) and \(\beta_{m}=\{1,\sin\left(\frac{\pi}{M}\right)\}\) for binary PSK (BPSK) and M-PSK, respectively. Taking the inverse of (6), the region boundaries \(\{\gamma_{i}\}\) are given as  
\[\gamma_{1}=\frac{1}{2}\Big{(}Q^{-1}(P_{0})\Big{)}^{2}, \tag{7}\]  
\[\gamma_{i}=\frac{1}{2\beta_{m}^{2}(i)}\bigg{(}Q^{-1}\bigg{(}\frac{P_{0}}{\alpha _{m}(i)}\bigg{)}\bigg{)}^{2},\,\,i=2,\ldots,N, \tag{8}\]  
\[\gamma_{N+1}\rightarrow\infty, \tag{9}\]  
where \(Q^{-1}(\cdot)\) is the inverse Q function.
# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 7 Conclusions and Future Work
### Future Work  
It should be highlighted that the performance evaluation in this paper is only based on the simulation where LMRSS is only invoked once at the initialization (or restart) of the networks. Hence, in the future, we will attempt to investigate the performance of LMRSS in a dynamic fashion where the proposed relay selection scheme will be invoked periodically to make more precious relay selection for WBANs for more lifetime improvement. The feasibility of periodical invoking relies on the simulation results in this paper that the execution time of LMRSS is much smaller than the typical length of one superframe. This time comparison means that LMRSS can be easily invoked in every superframe if wanted. In addition, in order to cooperate with periodical relay selection, the real-time residual energy and location information of each node, which is assumed to be known by the coordinator in this paper, need to be updated by the coordinator (note that the reason for location information updating is WBAN user mobility). Therefore, a method to update the information such as residual energy and location of each node to the coordinator for relay selection needs to be designed next together with the periodical relay selection mechanism.  
This work was supported in part by National High-tech R&D Program (863 Program) of China under Grant 2011 AA01A106 and in part by National Key Technology R&D Program of China under Grant 2012 BAH02B02.  
[PERSON] researched the literature, conceived the study concepts, designed the calibration method, carried out the simulation, analyzed the simulation results, and took charge of the entire manuscript; [PERSON] and [PERSON] assisted with the integrity of the entire study, provided the crucial intellectual support, and revised the manuscript.  
The authors declare no conflict of interest.
## 3 Monitoring Scheme and Tunnel Construction  
### Layout of the Monitoring System  
The deformation, stress, and joint displacement of the tunnel are continuously monitored according to the mechanical characteristics of large-diameter shield tunnels to enable the adjustment of new tunnel parameters and to secure the security of existing large  
Figure 6: (**a**) Plane layout of the monitoring section along the existing large-diameter tunnel and (**b**) layout of the monitoring points.  
diameter shield tunnels. Figure 6 depicts the monitoring section configuration along the existing tunnel. The distance between the essential monitoring portions was 5 m (McS5-13), while the spacing between other monitoring sections was 6 m (McS14-15, McS1-4). A total of 17 monitoring sections were installed along the existing tunnel's longitudinal direction. Figure 6 depicts the overall structure of the monitoring locations with existing tunnel sections. Two monitoring sites, denoted A and B, were installed on both rails of the up-and-down lines. It was also critical to monitor the increased load and joint displacement generated by the installation of new tunnels in existing tunnels. Given the current condition, the 6 th and 12 th monitoring portions of the existing large-diameter shield were chosen as the strain gauge, reinforcement gauge, and joint gauge monitoring sections. Figure 7 depicts the positions of each monitoring element's measuring points. As can be seen in Figure 7 the reinforcement gauge was arranged symmetrically inside and outside, and the pressure box was arranged one segment apart. The installation direction of the bolt force and joint displacement was circumferential. Because the substructure was prefabricated, the surface seam gauge could not be installed in the substructure. The earth pressure is arranged on the outside of the segment and is poured with the segment. The steel bar stress and concrete stress meter were arranged on the inside and outside of the segment and were poured with the segment. The bolt stress measuring point was first arranged on the bolt and then installed on the segment. The site installation and layout are shown in Figure 8. Detailed information on the sensors is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Instrument** & **Model** & **Producer** & **Accuracy** & **Range** \\ \hline
1 & Joint angle meter & JTM-V7000A & JTM & \(\leq\)1.5\%F-S & 0–50 mm \\ \hline
2 & Concrete strain gauge & JTM-V5000 & JTM & \(\leq\)1.5\%F-S & \(-\)800 \(\mu\)\(\sim\)1200 \(\mu\)\(\epsilon\) \\ \hline
3 & Steel stress gauge & JTM-V1000 & JTM & \(\leq\)2.0\%F-S & \(-\)200 MPa-100 MPa \\ \hline
4 & Earth pressure transducer & JTM-V2000D & JTM & \(\leq\)2.0\%F-S & 0–4 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information on the sensors.  
Figure 7: Schematic layout of component measurement points.
# Generator of Fuzzy Implications
## 2 Theory--New Fuzzy Implication Methods  
### Theoretical Framework of Fuzzy Implication  
When generalizing classical logic [32] in order to determine whether the fuzzy propositions are strongly true, the authors are led to evaluate the implications of fuzzy propositions [33; 34; 35; 36].  
**Definition 1**.: _Researchers define fuzzy implication as a function:_  
\[f:[0,\ 1]\times[0,\ 1]\rightarrow[0,\ 1]\]  
_For the definition of a fuzzy logic implication, a set of axioms has been proposed in the literature that every function has to fulfill in order to be considered as a fuzzy implication function [37; 38; 39; 40]. It must satisfy the maximum number of the following axioms [41; 42; 43]:_  
1. _If_ \(\omega_{1}\leq\omega_{2}\) _then_ \(f(\omega_{1},y)\geq f(\omega_{2},y)\) _(decreasing as to the first variable);_
2. _If_ \(\omega_{1}\leq\omega_{2}\) _then_ \(f(x,\omega_{1})\leq f(x,\omega_{2})\) _(increasing as to the second variable);_
3. \(f(0,\omega_{1})=1\)_;_
4. \(f(1,\omega_{1})=a\)_;_
5. \(f(\omega_{1},\omega_{1})=1\)_;_
6. \(f(\omega_{1},f(\omega_{2},x))=f(\omega_{2},f(\omega_{1},x))\)_;_
7. _If_ \(f(\omega_{1},\omega_{2})=1\) _then_ \(\omega_{1}\leq\omega_{2}\)_;_
8. \(f(\omega_{1},\omega_{2})=f(n(\omega_{2}),n(\omega_{1}))\)_;_
9. _The function_ \(f\) _is continuous._ _A fuzzy implication would ideally satisfy as many as possible of the above axioms._ _A fuzzy negation_ _in_ [1] _is a generalization of the classical supplement._  
**Definition 2**.: _The negation \(n\) in fuzzy logic is a function \(n\): [0, 1]\(\rightarrow\)[0, 1], which meets the following condition [44; 45]:_  
1. \(n(0)=1\) _and_ \(n(1)=0\)_;_
2. \(n(n(x))=(n\circ n)(x)=x\)_,_ \(\forall x\in[0,1]\)_;_
3. _The_ \(n\) _is a genuinely decreasing function._  
_Such a function is \(n(x)=1-x\), which satisfies the above properties. The negation \(n(x)=1-x\) is a strong negation. For a negation to be strong, it must meet all three of the conditions above, while if it satisfies the first and third conditions, it is simply a negation. The inconsistency of the proposal is indicated by its degree of truth. The lower the degree of truth, the more inconsistent the proposal. Thus an expression is inconsistent if and only if its negation is strong._  
**Definition 3**.: _The \"or\" or \(t\)-conorm (denoted by \(\vee\)) in fuzzy logic is a depiction [0, 1] \(\times\) [0, 1]\(\rightarrow\)[0, 1], denoted by \(x\lor y\), that should meet the following properties:_  
1. \(x\lor y=y\lor x\)_,_ \(\forall x,y\in[0,\ 1]\) _(commutativity property);_
2. \(x\lor(y\lor z)=(x\lor y)\lor z\)_,_ \(\forall x,y,z\in[0,\ 1]\) _(associative property);_
3. \(x\lor 0=x\)_,_ \(\forall x\in[0,\ 1]\) _(border condition);_
4. _if_ \(\left\{\begin{array}{l}x\leq y\\ \omega\leq\varphi\end{array}\right\}\Rightarrow x\lor\omega\leq y\lor\varphi\)_,_ \(\forall x,y,\omega,\varphi\in[0,\ 1]\) _(monotonicity);_
5. _Such or satisfying all the above properties is the probor_ \(x\lor y=x+y-xy\)_._