esa-sceva/satcom-chunk-collection huggingface dataset:
# Drive-by Bridge Damage Detection Using Continuous Wavelet Transform
###### Abstract  
Bridges serve as vital engineering structures crafted to facilitate secure and effective transportation networks. Throughout their life-cycle, they withstand various factors, including diverse environmental conditions, natural hazards, and substantial loads. Recent bridge failures underscore the significant risks posed to the structural integrity of bridges. Damage detection techniques, being core components of structural health monitoring, play a crucial role in objectively assessing bridge conditions. This article introduces a novel framework for identifying damage in bridges utilizing continuous wavelet analysis of accelerations recorded using two sensors mounted on a vehicle traversing the bridge. The proposed method leverages changes in the static response of the bridge, which has proven to be more sensitive to damage than its dynamic counterpart. By doing so, the method eliminates the reliance on modal parameters for damage identification, addressing a significant challenge in the field. The proposed framework also addresses key challenges encountered by drive-by monitoring methods. It mitigates the adverse effects of road roughness by utilizing residual accelerations and efficiently detects and locates damage even in the absence of corresponding data from an undamaged bridge. Numerical investigations demonstrate the robustness of the proposed method against various parameters, including damage location and extent, vehicle speeds, road roughness levels, different boundary conditions, and multi-damage scenarios.  
damage detection; vehicle-scanning methods; wavelets; drive-by monitoring Article  
Drive-by Bridge Damage Detection  
Using Continuous Wavelet Transform.  
_Appl. Sci._**2024**, _14_, _2969_. [[https://doi.org/10.3390/app14072969](https://doi.org/10.3390/app14072969)]([https://doi.org/10.3390/app14072969](https://doi.org/10.3390/app14072969))  
Academic Editors [PERSON] and [PERSON]. Received: 15 March 2024  
Revised: 27 March 2024  
Accepted: 29 March 2024  
Published: 31 March 2024
## 2 ITS Applications and Communication Technologies, Critical Design Requirements, and Related Works
### Low-Cost GNSS Receivers
The feasibility and successful implementation of the proposed solution met the accuracy specifications of critical ITS services.
## 3 Results and Discussion
### Some Visual Results
YOLOv10 showed a greater tendency towards false positives in empty areas, erroneously detecting non-existent elements, which can be attributed to its sensitivity to noise or variations in the page background; in addition, some images were detected in a fragmented way, suggesting difficulties in generalizing to illustrations of different formats. YOLOv11, on the other hand, had the highest rate of omission of graphics and images, possibly because it prioritizes textual patterns over more varied visual elements, although it was more conservative in detecting text, avoiding undue groupings, but resulting in some under-detections. These shortcomings highlight common challenges in detecting the layout of historical documents, especially in separating dense text, accurately identifying images and graphics, and minimizing false positives in empty areas. It is believed that improvements can be achieved through pre-processing techniques such as contrast enhancement and noise removal.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 1 Introduction
### Martian Lava Tubes  
Lava tubes and related flow features on the surface of Mars were first recognized by analyzing images from the Viking orbiter. More recently, lava tube entrances have been discovered using the Thermal Emission Imaging System (THEMIS) onboard the 2001 Mars Odyssey orbiter. Other discoveries have been made by the Mars Orbiter Camera (MOC) onboard the Mars Global Surveyor, the High-Resolution Stereo Camera (HRSC) onboard the Mars Express orbiter, and the High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter (Figure 2). On Pavonis and Ascraeus Montes, lava tubes appear to have collapsed, leaving linear, sometimes discontinuous, channel-like features. Several lava tubes have been identified on the northern portion of the Northern shield Alba Patera between 241-2451 W and 44-511 N [11]. In the Tartarus Colles, a partially roofed lava channel was imaged by the HiRISE camera, indicating this feature was a lava tube with a hollow conduit-like channel [12]. Several lava tubes and flow channels were identified on Olympus Mons based on MOC images [13]. Tube-fed flows and raised ridges (interpreted as uncollapsed lava tubes) cover as much as 8% of the flanks of Olympus Mons based on HRSC images [14]. Caves have also been identified mainly by the presence of \"skylights\", which can originate as openings in partially collapsed ceilings of lava tubes [15]. If life exists on Mars, one compelling place to search for it would be within a lava tube or cave [16; 17]. Here, living organisms could have been sheltered from dangerous ultraviolet radiation, while volcanic minerals might have provided a rich source of nutrients for chemosynthesis (similar to living colonies near Earth-based volcanic events) [18]. Even more intriguing is the possibility the caves were partially or wholly sealed, permitting the build-up of a high-pressure atmosphere and the existence of liquid water. Minerals commonly formed in Earth caves may also be found in Martian caves and may represent important records of past environmental conditions or even past life through preservation of biosignatures [19].
## 3 Results and Discussion
### HE in the Weld Metal  
Figure 8 shows the SSRT results for the different subsets of notched WM samples. A reduction in elongation with increasing pressure is observed, but the contribution of the WM is not straightforward to obtain since deformation in these samples is not localised to the notch area. The regions of the WM specimens outside of the notch also deform plastically since the blunt notch does not fully compensate for the higher strength of the WM compared to that of the BM. This is represented in the tensile data by the emergence of secondary and ternary yield points at 0.4 and 0.8 mm of elongation, respectively, as shown in Figure 8. The yield points have been enlarged in the insert in Figure 8 and can be related to yielding of the Heat Affected Zone (HAZ) and WM, respectively. Because of the lack of strain localisation, reduction in elongation data for strictly the WM were not obtained. However, %RA data can be used to quantify the extent of HE of the WM instead. Weld samples at all hydrogen pressures show an \(I\) of approximately 11%. The data as listed in Table 3 suggests a trend of decreasing \(I\) with increasing H\({}_{2}\) pressure, but the measurement error for results with large pressures is too large to make any conclusive statement about this.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Test Matrix and Results**} \\ \hline
**Material** & **Geometry** & **Pressure** & **Gas** & **\%RA** & **Embrittlement Index \(I\) **[\%]** \\ \hline \multirow{4}{*}{Base metal} & \multirow{2}{*}{Smooth} & 0 barg & Air & 75.5 \(\pm\) 0.6 & 0 \\ \cline{3-6}  & & 100 barg & N\({}_{2}\) & 76.6 \(\pm\) 0.9 & \(-\)1.5 \(\pm\) 2.1 \\ \cline{3-6}  & & 100 barg & H\({}_{2}\) & 55.2 \(\pm\) 2.6 & 27.9 \(\pm\) 4.6 \\ \cline{3-6}  & & 100 barg & N\({}_{2}\) & 72.1 \(\pm\) 1.0 & 0 \\ \cline{3-6}  & & 100 barg & H\({}_{2}\) & 52.0 \(\pm\) 2.0 & 28.0 \(\pm\) 4.7 \\ \hline \multirow{4}{*}{Weld metal} & \multirow{4}{*}{Notched} & 100 barg & N\({}_{2}\) & 48.2 \(\pm\) 1.5 & 0 \\ \cline{3-6}  & & 30 barg & H\({}_{2}\) & 42.9 \(\pm\) 0.6 & 11.0 \(\pm\) 4.6 \\ \cline{3-6}  & & 70 barg & H\({}_{2}\) & 43.0 \(\pm\) 0.8 & 10.9 \(\pm\) 5.1 \\ \cline{3-6}  & & 100 barg & H\({}_{2}\) & 43.0 \(\pm\) 1.8 & 10.8 \(\pm\) 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: %RA and embrittlement index \(I\) values for all sample sets. Errors are standard deviation.  
Figure 8: SSRT results from representative samples of each subset of notched weld metal specimens.
## 3 Results
### Test on OECD Soils
#### 3.1.1 Environmental Availability  
The in vitro assay performed on artificial soils demonstrated the efficiency of the amendment strategy to reduce the NDL-PCBs leaching from soil. Indeed, for PCB 101, 138, 153 and 180, environmental availabilities concentrations from amended soils were significantly lower than those from the non-amended control group (Figure 1), excluding biochar-amended groups for PCB 180. PCB 101 appeared the most susceptible compound to this strategy. Indeed, contrarily to other congeners, even biochar amended soils presented over 50% reduction (62 to 80%, mean) of environmental availability concentrations (Figure 1). Such amendment led to significant but lower reduction for the three most lipophilic compounds: 35 to 42% (mean) for PCB 138, 37 to 42% for PCB 153 and 27 to 33% for PCB 180. As expected, NDL-PCBs were far more sequestrated when AC was amended but contrasted results were obtained from granular AC1 compared to powdered AC2, AC3. Indeed, AC1 displayed the lowest reduction potential among ACs as no significant distinction was found between this AC and BC amendment (\(p>0.10\)). In contrast, AC2 and AC3 amendment presented the most intense sequestration for all congeners as environmental availability levels were similar to the control levels, i.e., the analytical quantitation limit for each congener. Due to these analytical limits, only minimal reduction factors could be derived from this assay: 79% for PCB 101, 91% for PCB 138, 88% for PCB 153 and 45% for PCB 180. Thus, from this environmental availability assay, the following efficiency hierarchy (reduction of transfer potential) was found between the used sequestration materials: BC1 = BC2 \(\leq\) BC3 = AC1 \(<<\) AC2 = AC3.
## 3 Materials and Methods
### Description of the Studied Patients
#### 3.1.4 Depression Level as an Important Factor  
The Yesavage Scale, also known as the Geriatric Depression Scale (GDS), is a diagnostic tool used to assess depression levels in elderly individuals.  
The scale consists of 15 questions covering various aspects of daily life and emotional well-being, with \"yes\" or \"no\" answers assigned points, resulting in a final score between 0 and 15. Higher scores indicate more severe depression symptoms.  
Interpretation of scores is as follows:  
* 0-4 points: no or minimal depression;
* 5-9 points: moderate depression;
* 10-15 points: severe depression.  
The level of depression assessed using the 15-point Yesavage Scale is a key element in analyzing rehabilitation outcomes. The data shows varying levels of depression among patients before starting therapy.  
Paying attention to patients with different levels of depression before rehabilitation and tracking if these levels correlate with therapy outcomes is valuable. This, in turn, will enable a more personalized approach to therapy, tailored to the specific needs of patients.
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 4 Accuracy Enhancing Methods
### Signal Processing System
#### 4.2.1 Signal Processing Methods  
The center of the zero-order fringe in white light interference means the position of the surface. The methods to obtain the center from the positioning signal are studied. There are many kinds of methods for line scale signal processing. Generally, they are classified as two classes. One is determined by a few points to the left and right edges of the signal [12], and the other is determined by multiple points to the left and right edges of the line scale signal [13]. Since the positioning signal of the surface is familiar to the positioning signal of the line scale, the signal processing method used for line scale is suitable for surface positioning. Due to the advantage of reliability, the multiple point method was adopted in this paper. For the multiple point method, one important step is to ensure the section of signal for processing in an appropriate section, beneficial to enhance the positioning accuracy Figures 15 and 16 show the positioning signal of the first and second surface of the step tested, respectively. The section of signal to be processed was the rectangular area below the peak of signal, determined by the value V\({}_{1}\) and V\({}_{2}\). As showed in Figure 16, when V\({}_{1}\) and V\({}_{2}\) were appropriate, the area A1 was a good area, and when V\({}_{1}\) and V\({}_{2}\) were not appropriate, the area was the bad area A\({}_{2}\). In the case of A\({}_{1}\), the computed result was accurate to the center, while in the case of A\({}_{2}\), the computed result was left to the center, causing the positioning error. In fact, there are many cases that were worse than the case of A\({}_{2}\), so the parameters V\({}_{1}\) and V\({}_{2}\) should be automatically adjusted to fit different poor positioning signals. Furthermore, for more complex cases, when it is difficult to select automatically, the processing section should be decided by the system operator. After confirming the processing section of signal, the result is automatically computed as Equation (6).  
\[C(l)=\frac{\int_{l_{1}}^{l_{2}}s(l)ldl}{\int_{l_{1}}^{l_{2}}s(l)dl} \tag{6}\]  
where \(l\) is the length; \(C(l)\) is the center computed; \(l_{1}\) is the length \(l\) when \(s(l)\) is V\({}_{1}\); and \(l_{12}\) is the length \(l\) when \(s(l)\) is V\({}_{2}\).  
Figure 14: The shaping of white light interference signal.  
Considering the importance of the values V\({}_{1}\) and V\({}_{2}\), the measurement repeatability affected with value V\({}_{1}\) and V\({}_{2}\) were also done. The experiment result was that if the signal was good in uniformity, the optimal values of V\({}_{1}\) and V\({}_{2}\) were 20% and 40% of the peak-to-peak value, respectively. However, if the signal was bad in uniformity, the suitable value of V\({}_{1}\) and V\({}_{2}\) were those as close as possible to the peak value of the signals.
## 3 Results
### The Effect of Compressing or Stretching the Original Mosaic Block Duration (OMBD)  
#### 3.2.1 Native-English Listeners  
The intelligibility of English mosaic speech for the native-English listeners was highest at the MBD of 20 ms after preserving and the MBD of 40 ms after preserving or stretching, and decreased as the MBD was shorter or longer. When the OMBDs were compressed, the intelligibility decreased by about 20%. When the OMBDs were stretched into an MBD of 80 ms, the intelligibility decreased sharply. For an MBD of 320 ms after stretching, the intelligibility was close to zero.  
A [PERSON] two-way analysis of variance by ranks [39] was performed to analyze the main effects of compressing or stretching the OMBD. There was a significant difference in intelligibility between mosaic speech types (\(n=19\), \(k=19\), \(\chi^{2}\)= 188.004; \(p<0.001\)). Multiple comparisons with the Wilcoxon Signed-rank test [39] were used to follow up this finding, and a Holm-Bonferroni correction [40; 41] was performed after that. In detail, compressing the OMBD deteriorated intelligibility (OMBD of 20 ms: \(p=0.010\); OMBD of 40 ms: \(p=0.009\)). The intelligibility of the MBD after preserving was significantly higher than that of the MBD after stretching (80, 160, or 320 ms; \(p<0.05\)). Stretching the OMBD by a factor of 2 led to significantly higher intelligibility than compressing the OMBD by half for the OMBD of 20 ms (\(p=0.010\)), but not for the OMBD of 40 ms (\(p=0.075\)). Moreover, stretching the OMBD by a factor of 2 induced higher intelligibility than stretching the OMBD by a factor of 4 or 8 (\(p<0.01\)).  
Figure 3: English word identification accuracy (intelligibility) for mosaic speech as functions of MBD after compressing/preserving/stretching (English, \(n=19\); Indonesian, \(n=19\); Chinese, \(n=20\)). The data for half-phase and whole-phase types are collapsed. Error bars indicate standard errors.
## 2 Materials and Methods
### SEM and EDX Analysis  
Sample morphology was investigated by Scanning Electron Microscopy (SEM), using a ZEISS 1530 SEM, equipped with a Schottky emitter, with two different secondary electrons (SE) detectors (the in-lens and the Everhart-Thornley), operating at ten keV, coupled with an energy dispersive microanalysis (EDX).
# Facial Expression Recognition Based on Discrete Separable Shearlet Transform and Feature Selection
## 4 Results and Discussion
### Recognition Rates of the Proposed Method  
The recognition rates of the proposed method were evaluated for each dataset separately under the settings, as mentioned above. The 3D feature plots of the proposed method for the six expressions after applying LDA on four datasets are shown in Figure 3a-d, and the recognition rates are shown in Table 1.  
In the legend in Figure 3, H represents happiness, Su represents surprise, Sa represents sadness, D represents disgust, A represents anger, and F represents fear. It is observed that the algorithm clearly classifies the features of six kinds of expressions, which provides a powerful tool for subsequent classification and recognition. It is clear from Table 1 that the proposed FER system achieved a high recognition rate on the four datasets. Moreover, the recognition rate decreased significantly without the feature selection method. The experimental results show that the improved feature selection method proposed in this paper plays an important role in the high recognition of the FER system.  
Figure 3: 3D feature plots of the proposed method for recognizing the expressions on the four datasets: (**a**) Japanese Female Facial Expression (JAFFE) dataset, (**b**) extended Cohn-Kanade (CK+) dataset, (**c**) MMI Facial Expression (MMI) dataset, and (**d**) Psychological Image Collection at Stirling (PICS) dataset.  
To further verify that this algorithm is independent of the dataset and has strong robustness, we employed 4-fold cross-validation on the dataset. Out of the four datasets, one was utilized as the training data and the others were used as the testing data. This process was repeated four times. The weighted average recognition rates of the proposed method are shown in Table 2.  
As seen from Table 2, the proposed algorithm not only has a high recognition rate for a single database, but also has a high recognition rate when training one database and testing the other three databases. This proves that the algorithm has strong robustness.
# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 4 Experimental Study
### Synthetic and Real Experimental Data  
As mentioned above, we first generated synthetic seismic waves and fault images. From the total of 500 seismic wave images, two patch sets of 'Test1' and 'Train1' were constructed as follows: Test1 is a set of 76,038 patches extracted from the first 100 images; and Train1 is a set of 284,850 patches extracted from 400 seismic images from the remaining 101 to 500 images. Train1 (and Test1) was used to pretrain the CNN models needed to extract the CNN-features.  
For real-world seismic images, we considered the real seismic wave image data, cited from the Project Netherlands Offshore F3 Block--Complete [42]. We first asked some experienced experts to manually mark the fault lines according to the structural information (the work of assigning fault lines to real seismic wave images should be done by human labelers using appropriate tools, as was performed in ImageNet [28]). Next, for a simple comparative analysis, only ten seismic images along with fault lines were selected. Then, a total of 52,026 (26,013 for each class) fault and nonfault patches were extracted from the ten seismic images. When extracting the patches, the number of patches on the larger side was adjusted to the smaller side by randomly selecting them in order to prevent an imbalance between the classes. Hereafter, this real-world patch data is referred to as \"RealPatch\".
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 5 Performance Evaluation
### MANET with RandomWaypoint
#### 5.2.2 Throughput vs. Speed of Nodes  
In this case, we consider a fixed density scenario of 36 nodes and speeds of: 5, 10 and 20 km/h. Figure 7 shows the average results obtained for the test with 5 km/h.  
Figure 6: Analysis of average transmission power. DPCM 1 and DPCM 2 uses an upper bound of five and six respectively. Both DPCM use a lower bound of four.  
Figure 7: Throughput vs speed of nodes. DPCM 1 uses as upper bound, five. DPCM 2 uses an upper bound of six. Both DPCM configurations use a lower bound of four.  
These results analyze throughput at different speeds. The use of the power control mechanism is favorable since it allows a higher level of successful transmissions. The throughput level is higher when the speed at which the nodes move is lower, that is, at 5 km/h. For 10 and 20 km/h, it is necessary to increase the power a little more to get more lifetime of the routes.  
Also, the power control mechanism allows to improve the forwarding rates for almost all cases and present a lower number of retransmissions (the number of jumps was reduced) which indicates that the mechanism designed increased the power to such a point of having longer links and therefore reducing the number of jumps. This is beneficial for the network because reduces the retransmission load, collisions, queue times and transmission time.  
The evaluation of the variation of the transmission power is shown in Figure 8.  
The average transmission power, as in the previous tests, is lower than the power predefined by the simulator for ad-hoc networks with IEEE 802.11.  
We can conclude that our power control mechanism for a MANET is beneficial in most cases. This is verified in simulations where the mechanism allows us to increase the performance. In addition, we can see that the system loses stability and the results are less favorable when the upper bound of neighbors is set above six. Moreover, the nodes' speed has a direct relation with the transmission power. As the speed of a node increases, the transmission power must increase as well to maintain a stable link to avoid fast changes in the topology of the network.
## 4 Applications in Photonic Devices  
Photonic devices manipulate and control light through various optical phenomena, enabling applications in communications, sensing, computing, and display technologies. These devices can be broadly categorized into passive and active components based on their interaction with light. Passive components guide, filter, or scatter light without external energy input, while active components require energy to modify, generate, or detect light signals. Polymer materials offer unique advantages in both categories, including solution processability, mechanical flexibility, and tunable optical properties.