esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The optimization and evaluation of 3D-printed polylactic acid (PLA) materials for reinforcing concrete elements present a promising avenue for advancing sustainable construction methods. This study addresses the challenges associated with PLA's dual nature--biodegradable yet mechanically limited for long-term applications--while leveraging its potential to enhance concrete reinforcement. The research identifies gaps in understanding PLA's mechanical and chemical behavior in alkaline environments, particularly its interactions with concrete matrices. To bridge this gap, four distinct PLA variants (high-impact PLA, engineering PLA, electrical ESD PLA, and gypsum PLA) and ABS (acrylonitrile butadiene styrene) were subjected to dissolution tests in NaOH solutions (pH 12 and 12.55) and mechanical evaluation under three-point bending using digital image correlation (DIC) technology. Test specimens were prepared using optimized 3D printing strategies to ensure structural consistency and were embedded in concrete beams to analyze their reinforcement potential. Force-displacement data and GOM ARAMIS measurements revealed significant differences in mechanical responses, with peak loads ranging from 0.812 kN (high-impact PLA) to 1.021 kN (electrical ESD PLA). Notably, electrical ESD PLA exhibited post-failure load-bearing capacity, highlighting its reinforcement capability. Chemical dissolution tests revealed material-specific degradation patterns, with high-impact and Gypsum PLA showing accelerated surface changes and precipitation phenomena. Observations indicated white crystalline precipitates, likely lime (calcium hydroxide--Ca(OH)\({}_{2}\)), residue from the dissolution tests (sodium hydroxide--NaOH), or material-derived residues formed on and near PLA elements, suggesting potential chemical interactions. These findings underline the critical role of material selection and optimization in achieving effective PLA-concrete integration. While PLA's environmental sustainability aligns with industry goals, its structural reliability under long-term exposure remains a challenge. The study concludes that electrical ESD PLA demonstrates the highest potential for application in reinforced concrete, provided its chemical stability is managed, as its peak value (1.021 kN) showed 25.7% higher load-bearing capacity than high-impact PLA (0.812 kN) and did not lose any of its structural stability in the dissolution tests. This work advances the understanding of PLA as a sustainable alternative in construction, offering insights for future material innovations and applications.  
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Footnote †: journal: Applied Sciences  
_Article_
## 3 Field Test
### Image Analysis Results
#### 3.2.2 Image Analysis Results According to the Sampling Depth  
Abrasion according to sampling depth was investigated comparing with the TQI and the number of maintenance works at each sampling location. As described in Section 3.2.1, roundness exhibited a high correlation with TQI and sphericity did so with the number of maintenance works; thus, roundness was compared with the TQI and sphericity was compared with the number of maintenance works.  
As shown in Figures 10 and 11, both roundness and sphericity had a relatively low correlation with TQI and the number of maintenance works in the case of using only the aggregates from surface or middle layer. The aggregates collected from the middle layer may be insufficient to reflect the severity of settlement.  
When sphericity was calculated for the samples collected from the surface, it was distributed within 2% in most sections, regardless of the number of tamping works performed. It can be implied that aggregates on the surface are mainly abraded by factors other than tamping, such as weathering or train load.
## 2 Methodology
### Ensemble Approach to Anomaly Detection Using Spherical Detectors
#### 2.5.3 Ensemble Construction  
1. Train \(K\) anomaly detection models, \(\{D_{k}\}_{k=1}^{K}\), each using one of the previously described spherical detector generation algorithms. Due to the randomness inherent in these algorithms, each model, \(D_{k}\), will produce a different set of detectors.  
Figure 4: Example result of negative selection algorithm on two-dimensional data. Yellow area is marked as containing anomalies, while the examples from the training set assessed as anomalies are marked with ‘+’ (for the threshold value of detected neighbours equal to 70%).  
2. Each model, \(D_{k}\), consists of a set of spherical detectors: \[D_{k}=\{(c_{k,j},r_{k,j})\mid j=1,2,\ldots,M_{k}\},\] where \(c_{k,j}\in\mathbb{R}^{n}\) is the center and \(r_{k,j}\) is the radius of the \(j\)-th detector in model \(D_{k}\).
## 2 Materials and Methods
### Methods  
We adopted a method consisting of four main steps. First, we obtained scientific evidence and generated models highlighting wildland fire-induced risk zonation and mapping [1] from Earth Observation for Environment Laboratory (EOEL) at the University of Calgary (UofC). Second, we identified the stakeholders and rightsholders in the RMWB area, prepared the questionnaire, decided on the survey format, and received ethical approval from the Research Ethics Board (REB) of UofC. During the process, we selected both stakeholders and rightsholders according to the recommended protocols suggested by OCAP (Ownership, Control, Access, and Possession) [42] and TCPS (Tri-Council Policy Statement) [43]. Note that the principles of OCAP uphold that the First Nations have control on data collection processes, and they own and control the use of this information; and TCPS promote the ethical conduct for research involving humans in the fields of health, natural sciences and engineering, and social sciences and humanities. Third, we directed  
Figure 1: The study area is the communities of Fort McMurray urban service area in RMWB. Here, the service area and community boundaries are demarcated as solid and dotted lines, respectively over a WorldView-2 satellite image acquired on 6 June 2016. Dark greenish to gray colors in the image represent burned vegetation, and bright red to reddish colors as healthy vegetation. (adopted from [PERSON] et al. [1] licensed under CC BY 4.0).  
an online questionnaire survey. Finally, we performed data analysis and sent it to the participants for their further feedbacks.
## 4 Anomaly Detection  
### One-Class Support Vector Machine  
With the evolution of support vector machines, two distinct algorithms have emerged: the support vector data description (SVDD) algorithm, often referred to as the hypersphere method, and the one-class support vector machine (OCSVM) algorithm, commonly known as the hyperplane method. The underlying principle of the OCSVM algorithm is as follows [17]: a hyperplane is found in a higher-dimensional space to perform the linear separation of the original data samples mapped in this space. This process involves the initial establishment of a hyperplane \(\mathbf{w}\cdot\Phi(\mathbf{x})-\rho\ =\ 0\) and the subsequent maximization of the distance \(\rho/\parallel\mathbf{w}\parallel\) between the target data sample and the origin. This approach determines the optimal hyperplane, as shown in Figure 7.  
In the hyperplane method, the optimization problem is solved as follows [17]:  
\[\begin{split}\min_{\mathbf{w}\rho,\xi_{i}}\frac{1}{2}\parallel\mathbf{w }\parallel^{2}+\frac{1}{\
u}\sum\limits_{i\ =\ 1}^{l}\xi_{i}-\rho\\ s.t.\Phi(\mathbf{x}_{i})\mathbf{w}\geq\rho-\xi_{i},\xi_{i}\geq 0,i\ =\ 1,2, \ldots,l\end{split} \tag{9}\]  
where \(\mathbf{x}_{i}\) is the training sample; \(\Phi:\ X\to H\) signifies the mapping relationship between the original space and the high-dimensional feature space; \(\mathbf{w}\) and \(\rho\) are the parameters of the hyperplane; \(\xi\) represents a slack variable; \(\
u\) is a balancing parameter used to adjust  
Figure 6: Schematic diagram of pooling: (**a**) average pooling; (**b**) maximum pooling.  
the degree of relaxation; and \(l\) is the number of training samples. The dual form of the optimization problem is as follows [17]:  
\[\begin{split}\min\limits_{\alpha}\ \frac{1}{2}\sum\limits_{i} \frac{l}{j}\alpha_{i}x_{i}f(x_{i},x_{j})\\ s.t.\ 0\leq\alpha_{i}\leq\frac{1}{i!}\sum\limits_{i}\sum\limits_{j} \alpha_{i}\ =\ 1,i\ =\ 1,\ldots,l\end{split} \tag{10}\]  
where \(\alpha_{i}\) is the Lagrange factor. The final decision function is [17]  
\[f(x)\ =\ \text{sign}(\sum\limits_{i}^{l}\alpha_{i}^{*}K(x_{i},x)-\rho) \tag{11}\]  
where \(\alpha_{i}^{*}\) corresponds to the vector, and \(x_{i}\) is the support vector. A sample point was considered normal when it was \(f(x)>0\); otherwise, it was considered abnormal.
## 2 Review of the PlanetScope (PS) Sensors
Planet provides an xml file with coefficients to transform the sensor radiance values from the newer generation instruments (PS2.SD and PSB.SD) to match the older PS2 instrument. However, it is important to note that in matching the spectral response of the older instrument, image quality from the newer sensors is downgraded during the transformation. Researchers seeking to leverage the improved spectral response of the newer generation instruments should not expect to include older images (pre 2018) in their mosaics. Figure 2: Spectral responses for PlanetScope: (**a**) Dove Classic ’PS2’, (**b**) Dove-R ’PS2.SD’, and (**c**) SuperDove ’PSB.SD’ sensors. Adapted from [15; 16]. Alongside Planet's coefficients, the remote sensing community has also developed regression-based transformation functions so users can adjust the RGB-NIR surface reflectance from first- and second-generation PS sensors to provide more consistent time series. Comparing thousands of images across 500 locations in Africa, [PERSON] and [PERSON] [10] quantified the spectral differences between PS2 (Dove Classic) and PS2.SD (Dove-R) sensors using reduced major axis regression. They found that spectral variation between the two sensor generations varied as much as 20.1%. The authors transformed RGB-NIR values using Theil-Sen regression to render the two generations compatible. These coefficients are provided by the authors to the user community [10].
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
## 6 Discussion  
The achieved results pave the way to multiple discussion points, starting from the fact that a univocal emissivity value for each rock type could not be detected, but rather a range of values (Figure 4a). This is likely due to the effect of some features, which lead to an uneven thermal behavior of the framed rock surface. In fact, by moving the measuring area during the image processing, slight temperature and emissivity variations were experienced (Figure 5a). This suggests the need to define a representative \(\varepsilon\) value for IRT applications on rocks, whose approach is focused on the framing of the whole rock specimen surface, rather than on a small portion (e.g., voids or crystals). Therefore, it is reasonable to consider the mean value of the ranges found herein for each rock type as the representative emissivity. This choice is quite objective, also thanks to the narrow \(\varepsilon\) range extension of each rock type, which generally involves two or maximum three consecutive values (Figure 4a).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Rock Type** & **Acronym** & **Emissivity Range** & **Average Emissivity** \\ \hline Basalt (massive) & Bm & 0.93–0.95 & 0.94 \\ Basalt (vesicular) & Bv & 0.96–0.97 & 0.965 \\ Calcarenite & C & 0.98–0.99 & 0.985 \\ Calcisilite & CS & 0.94–0.96 & 0.95 \\ Dolostone & D & 0.94–0.95 & 0.945 \\ Granite (polished) & WGP-KGP & 0.83–0.85 & 0.84 \\ Granite (smooth) & WGs-KGs & 0.91–0.93 & 0.92 \\ Granitoid plutonite & GP & 0.89–0.91 & 0.90 \\ Limestone & L & 0.94–0.96 & 0.95 \\ Marble (polished) & MBp & 0.95–0.96 & 0.956 \\ Marble (smooth) & MBs & 0.97–0.99 & 0.98 \\ Migmatite & M & 0.89–0.91 & 0.90 \\ Quartzarenite & QA & 0.91–0.93 & 0.92 \\ Red granite (smooth) & AKs & 0.92–0.95 & 0.935 \\ Red granite (polished) & AKp & 0.83–0.86 & 0.845 \\ Sandstone & S & 0.92–0.93 & 0.925 \\ Serpentinite & SP & 0.92–0.95 & 0.935 \\ Slate & ST & 0.89–0.90 & 0.895 \\ Travertine & T & 0.96–0.97 & 0.965 \\ Tuff & TF & 0.97–0.98 & 0.975 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Emissivity values estimated for the tested rock types.  
Based on the above consideration, a discussion on the effects causing such slight emissivity variations deserves to be proposed. According to achieved results, two main macroscopic features can be, indeed, indicated as \"emissivity controllers\", i.e., the presence of pores and mineral composition.
## 3 Results
### Results and Evaluation of Trajectory Estimation  
#### 3.4.1 Trajectory Estimation in Environments with Minimal Disturbances  
The evaluation trajectories consisted of a rectangular trajectory with \((x,y)=(5,10)\) [m] and a circular trajectory with a radius of 3 m. These are shown in the figures below. This environment is on a ground floor with good visibility, providing an environment with minimal magnetic interference. The building is constructed with reinforced concrete.  
The evaluation is conducted using the estimated trajectory during walking along the same rectangular and circular trajectories, as shown in Figure 8. The evaluation is based on the difference between the starting point and the endpoint. This difference is calculated as the distance between the final point of the estimated trajectory and the origin, as the  
\begin{table}
\begin{tabular}{c c c} \hline  & **Absolute Error When Not Used [deg]** & **Absolute Error When Used [deg]** \\ \hline
1 & 10.51 & 0.47 \\
2 & 15.21 & 0.65 \\
3 & 11.42 & 0.13 \\
4 & 10.15 & 0.35 \\
5 & 13.32 & 0.08 \\
6 & 14.23 & 0.12 \\
7 & 10.57 & 0.88 \\
8 & 10.91 & 0.43 \\
9 & 11.25 & 0.12 \\
10 & 12.89 & 0.78 \\ \hline Ave. & 12.04 & 0.40 \\ \hline \end{tabular}
\end{table}
Table 8: Error in _Yaw_ axis estimation with and without geomagnetic sensor.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Absolute Error at Fixed \(\beta\) [deg]** & **Absolute Error When \(\beta\) Changes Dynamically [deg]** \\ \hline
1 & 96.26 & 1.78 \\
2 & 73.12 & 0.63 \\
3 & 102.66 & 0.82 \\
4 & 80.29 & 0.91 \\
5 & 161.81 & 1.21 \\
6 & 89.11 & 1.98 \\
7 & 133.12 & 1.03 \\
8 & 148.99 & 0.91 \\
9 & 103.31 & 1.87 \\
10 & 129.73 & 0.22 \\ \hline Average & 111.84 & 1.13 \\ \hline \end{tabular}
\end{table}
Table 9: Error in _Yaw_ axis estimation with and without change in \(\beta\).
# GA-PSO Algorithm for Microseismic Source Location
## 3 Experiments and Results
### Simulation Validation of Microseismic Location Algorithms
#### 3.1.2 Analysis of the Simulation Results for Microseismic Source Location
Among these, the errors of PSO-ACO, NM-PSO, and GA-PSO are minimal. To enhance clarity, Figure 3b provides an enlarged view of Figure 3a, focusing on the results of the three hybrid algorithms. From Figure 3a, it can be seen that due to the tendency of the PSO algorithm to become trapped in local optima and the random search nature of the GA, both the PSO algorithm and GA show significant errors. In contrast, the GA-PSO algorithm, by optimizing the PSO algorithm and combining the strengths of both PSO and GA, demonstrates the smallest absolute error and improved precision. Figure 3b presents a comparison among the GA-PSO, PSO-ACO, and NM-PSO algorithms. The results indicate that the errors of all three hybrid algorithms are confined within 0.03 m, demonstrating strong performance. Among these, the PSO-ACO algorithm performs slightly worse than the other two hybrid algorithms, while the NM-PSO and GA-PSO algorithms yield comparable results. However, the GA-PSO algorithm performs slightly better than the NM-PSO algorithm and exhibits a certain degree of robustness. From Figure 3c, it can be seen that among the five algorithms, the search time of the GA-PSO algorithm is shorter and stable, ranging between 0.16 s and 0.17 s. In contrast, the  
Figure 3: (**a**) Comparison of location results of the five algorithms; (**b**) enlarged view of (**a**), focusing on the hybrid algorithms; (**c**) comparison of the search time of the five algorithms. GA requires breeding and selection in each generation of the population, which increases its operational complexity, usually leading to a greater need for computational resources and longer execution time. The PSO algorithm, on the other hand, typically finds the optimal solution in fewer iterations, thus reducing its computation time compared to the GA. The GA-PSO algorithm combines the advantages of both algorithms, enhancing local search efficiency while ensuring a global search, thereby reducing the complexity of each iteration and overall execution time. The PSO-ACO algorithm integrates the features of both PSO and ACO, including operations such as pheromone updates. Consequently, its higher computational complexity results in longer execution times. In contrast, the NM-PSO and GA-PSO algorithms demonstrate shorter runtimes. Notably, the GA-PSO algorithm reduces computation time by improving the PSO algorithm and integrating the advantages of the GA.
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Anomaly Types  
Anomalies are observations that deviate significantly from other observations as to arouse suspicion that it was generated by a different mechanism [47]. Anomalies can be categorized as follows.  
\begin{table}
**Techniques** & **Nature of the Data** & **Types of Anomaly** & **Anomaly Detection Types** & **Windowing** & **Dataset** & **Evaluation Criteria** \\ \hline LSTMs [40] & Time-Series & Point anomaly & Supervised learning using deep learning & Sliding window & Yahoo Webscope & Confusion matrix. \\ \hline Autoencoder & Data Stream (evolving) & Point anomaly & Unsupervised learning based on Ensembles neural networks & & HTTP, SMTP, SMTP+HTTP, COVERTYPE, SHUTTLE, Weather & AUC \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c} \hline \hline (OFAT) Deep neural network [42] & Time series & Point anomaly & Supervised learning & Window-based & Web traffic dataset, Avocado dataset, Temperature dataset & Statistical tests (average Rank), Mean Average Score (MAS) \\ \hline Evolving network [43] & Data Stream (evolving) & Point anomaly & Unsupervised learning & Sliding window & \
\begin{tabular}{c} 3 Benchmark dataset \\ \end{tabular} & Accuracy \\ \hline ISTL [44] & Data Stream (evolving) & Point anomaly & Unsupervised learning based on deep learning & & UCDS &
\begin{tabular}{c} Pedestrian \\ datasets, Ped 1 \\ and Ped 2) and \\ CUHK Avenue \\ dataset \\ \end{tabular} & Accuracy \\ \hline (e-SREBOM) & Data Stream (evolving) & Point anomaly & Unsupervised learning using Spiking Neural Networks (eSNN) & Window-based & Water\_tower\_dataset, Accuracy, gas\_dataset, Speed, Time to electric\_dataset & learn \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of deep learning techniques for data stream anomaly detection.
## 4 Experiments and Discussion
### Experiment Results and Discussion in RSDD Dataset  
In order to further exhibit the superiority of the proposed YOLO-DSD based on YOLOv4 in optical remote sensing object detection application, another comparison experiment between YOLO-DSD and YOLOv4 was conducted in a four-category dataset, RSDD [50], which contained aircraft, oil tank, playground and overpass. The experiment result is shown in Table 10. It can be seen that YOLO-DSD outperforms in accuracy and inference time compared with YOLOv4 under different input sizes, including 416 \(\times\) 416, 512 \(\times\) 512 and 608 \(\times\) 608. Specifically, YOLO-DSD increases \(\text{mAP}^{0.5}\), \(\text{mAP}^{0.50.95}\) and FPS by 2.6%, 0.8% and 50.2%, respectively, under the input size 416 \(\times\) 416, while, under the input size 512 \(\times\) 512, \(\text{mAP}^{0.5}\), \(\text{mAP}^{0.50.95}\) and FPS are improved by 2.1%, 1.9% and 54.9%, respectively. In terms of the input size 608 \(\times\) 608, the \(\text{mAP}^{0.5}\), \(\text{mAP}^{0.50.95}\) and FPS of YOLO-DSD are 1.5%, 1.2% and 59.3% higher than those of YOLOv4.  
However, it is noteworthy that the overpass AP of YOLO-DSD is higher than that of YOLOv4 in RSDD, whereas it is the opposite in DIOR. One possible reason for this is that 'bridge' and 'overpass' possess a significant inter-class similarity and thus interfere with the detection performance of YOLO-DSD in these two categories in DIOR. Therefore, how to overcome the inter-class similarity between 'bridge' and 'overpass' for a better detection accuracy while keeping its deployability and inference speed is one of our future works.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Detector**} & \multirow{2}{*}{**Input Size**} & \multirow{2}{*}{**FPS**} & \multicolumn{5}{c}{**AP\({}^{0.5}\)**} & \multirow{2}{*}{**mAP\({}^{0.50.95}\)**} \\ \cline{3-3} \cline{5-8}  & & & **Aircraft** & & **Oil Tank** & **Playground** & **Overpass** & \\ \hline YOLOv4 & \multirow{2}{*}{416 \(\times\) 416} & 40.2 & 97.8 & 95.7 & 99.4 & 67.2 & 90.0 & 52.1 \\ YOLO-DSD & & 60.7 & 98.0 & 98.2 & 99.6 & 74.4 & 92.6 & 52.9 \\ YOLOv4 & \multirow{2}{*}{512 \(\times\) 512} & 37.1 & 98.1 & 97.5 & 99.5 & 73.5 & 92.2 & 55.1 \\ YOLO-DSD & & 57.5 & 98.5 & 98.6 & 99.8 & 80.1 & 94.3 & 57.0 \\ YOLOv4 & \multirow{2}{*}{608 \(\times\) 608} & 34.9 & 98.2 & 98.5 & 99.9 & 79.2 & 94.0 & 58.5 \\ YOLO-DSD & & 55.6 & 99.1 & 98.9 & 99.9 & 84.2 & 95.5 & 59.7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of comparative experiment for YOLOv4 and YOLO-DSD in RSDD.  
Figure 18: The precision–recall curves and AP (IOU = 0.5) of YOLOv4 and YOLO-DSD in each category.
## 3 Results and Discussion
### Structural Characterisation Using AFM
The average roughness, calculated from the AFM images at each consequent immobilisation step, is presented in Table 1. The clean polished SiO\({}_{2}\) surface (sample ID1) had an average roughness of _Ra_ = 0.12 \(\pm\) 0.04 nm, with no significant change following the silanisation step (ID2, _Ra_ = 0.15 \(\pm\) 0.05 nm). The average roughness increased to _Ra_ = 0.60 \(\pm\) 0.16 nm after Con A immobilisation (ID3), and increasing further to _Ra_ = 0.77 \(\pm\) 0.04 nm following incubation with Alexa 488 dextran (ID6), and _Ra_ = 1.14 \(\pm\) 0.35 nm with FITC dextran (ID4). The roughness observed within the AFM topographs were not changed, _Ra_ = 0.76 \(\pm\) 0.22 nm (Alexa 488 dextran, ID7) and _Ra_ = 1.12 \(\pm\) 0.50 nm (FITC dextran, ID5) after incubation in a MOPS buffer containing 40 mM glucose. This data confirms that the addition of new layers (immobilisation and dextran incubation) increases the relative surface roughness. The subsequent removal of layers (competitive displacement of dextran by glucose immersion) was not yielding a straightforward reduction in the roughness parameter, possibly due to a rather large standard deviation of the roughness. The average roughness may depend on the AFM tip radius provided that the height variations are within the same size range.