esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Arrhythmia Improvement  
In order to assess the detection of errors caused by arrhythmia, all of the labels obtained were checked and confirmed. In total, 24 PCG recordings with arrhythmia annotation errors under TCN + Viterbi (Gauss, \(\alpha=1\)) were identified in the two databases. These heart sounds comprised 79 errors due to arrhythmia, with 30 errors in LH-training and 49 errors in PN-training-a, and possibly some of the errors were from the same original recordings. By using TCN + Viterbi (Poisson, \(\alpha=1\)) or TCN + Viterbi (Gauss, \(\alpha=0.2\)), 66 of 79 errors were corrected, with 30/30 errors in LH-training and 36/49 errors in PN-training-a. Two examples are shown in Figure 10. In addition, excessive noise was the main reason for the uncorrectable errors caused by arrhythmia and other problems after applying the state annotation algorithm. These problems need to be solved by collecting more training data, stabilizing the quality of the collection device, improving the quality of the ground truth labels, and other methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & **Database** & **Accuracy 1** & \(P_{+}\) & \(Se\) & \(F_{1}\) \\ \hline
[13] & LH-training & 92.52 \(\pm\) 1.33 & 95.92 \(\pm\) 0.83 & 95.34 \(\pm\) 0.88 & 95.63 \(\pm\) 0.85 \\
[15] & PN-training-all 2 & - & 94.9 & 95.9 & 95.4 \\
[3] & LH-training & 91.7 \(\pm\) 1.0 & 95.7 \(\pm\) 1.0 & 95.8 \(\pm\) 1.0 & 95.7 \(\pm\) 1.5 \\
[18] & PN-training-all 2 & 96.9 \(\pm\) 0.13 & 96.3 \(\pm\) 0.42 & 97.2 \(\pm\) 0.19 & 96.70 \(\pm\) 0.17 \\ LSTM + Viterbi & LH-training & 91.64 \(\pm\) 0.17 & 97.25 \(\pm\) 0.18 & 96.80 \(\pm\) 0.17 & 97.02 \(\pm\) 0.17 \\ (Poisson, \(\alpha=1\)) & PN-training-a & 90.59 \(\pm\) 0.17 & 96.49 \(\pm\) 0.15 & 94.99 \(\pm\) 0.21 & 95.74 \(\pm\) 0.17 \\ \hline \end{tabular}  
* Accuracy of the final result after state annotation. 2 All training sets of the public PhysioNet Computing in Cardiology Challenge 2016.  
\end{table}
Table 4: Comparison of the results of recent several algorithms and TCN + Viterbi (Poisson, \(\alpha=1\)) (%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Combination** & **Database** & **Accuracy 1** & \(P_{+}\) & \(Se\) & \(F_{1}\) \\ \hline \multirow{2}{*}{TCN + LMSA} & LH-training & 91.86 \(\pm\) 0.20 & 96.11 \(\pm\) 0.25 & 95.44 \(\pm\) 0.25 & 95.77 \(\pm\) 0.22 \\ \cline{2-6}  & PN-training-a & 89.65 \(\pm\) 0.23 & 94.81 \(\pm\) 0.27 & 92.46 \(\pm\) 0.3 & 93.62 \(\pm\) 0.26 \\ \hline \multirow{2}{*}{TCN + Viterbi (Gaussian, \(\alpha=1\))} & LH-training & 91.23 \(\pm\) 0.17 & 96.84 \(\pm\) 0.19 & 96.27 \(\pm\) 0.18 & 96.56 \(\pm\) 0.18 \\ \cline{2-6}  & PN-training-a & 90.58 \(\pm\) 0.17 & 96.08 \(\pm\) 0.17 & 94.61 \(\pm\) 0.18 & 95.34 \(\pm\) 0.17 \\ \hline \multirow{2}{*}{TCN + Viterbi (Gaussian, \(\alpha=0.2\))} & LH-training & 91.48 \(\pm\) 0.18 & 96.90 \(\pm\) 0.21 & 96.75 \(\pm\) 0.19 & 96.82 \(\pm\) 0.19 \\ \cline{2-6}  & PN-training-a & 90.58 \(\pm\) 0.18 & 96.25 \(\pm\) 0.18 & 95.42 \(\pm\) 0.23 & 95.83 \(\pm\) 0.19 \\ \hline \multi
# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 2 Materials and Methods  
### Image Collection  
Image collection is a critical step in the research process, as it significantly influences the efficiency and reliability of subsequent analyses. In this study, images were primarily captured using an iPhone 14 (Apple Inc., Cupertino, CA, USA), equipped with a high-resolution 12-megapixel main camera, enabling the detailed documentation of crack features. A total of 65 crack images from cultural heritage sites in Maioli County were collected, representing three distinct types of cracks: brick wall cracks, mottled wall cracks, and plain wall cracks.  
The first category, brick wall cracks, consisted of 21 images. These cracks are commonly found in historical buildings. According to [37], brick structures, composed of materials such as bricks, stones, and limestone bound by mortar, are among the most prevalent architectural forms worldwide and hold substantial cultural heritage significance. Regular maintenance of brick structures is essential to preserve their structural integrity and historical value.  
The second category, mottled wall cracks, comprised 22 images. These cracks typically appear as network-like patterns within flaking areas of the wall, often resulting from moisture exposure or chemical erosion. The third category, plain wall cracks, included 22 images. These cracks, often forming as vertical linear fractures, are usually caused by uneven structural load distribution or seismic activity.  
In addition to crack images, 50 non-crack images were collected, featuring corridors, floors, and walls without visible cracks. These non-crack images served as control samples to enhance the deep learning model's training completeness by facilitating contrast-based learning. The inclusion of contrast samples aims to enhance the model's ability to distinguish cracks from non-cracks, reduce false positive rates, improve adaptability across diverse environments, optimize the training process, and bolster generalization capabilities. Furthermore, it minimizes the risk of overfitting, increases reliability, and ensures the model's effectiveness in real-world applications.  
Due to the limited number of images sourced from cultural heritage sites in Maioli County, it was challenging to achieve satisfactory performance using large-scale deep learning models such as Mask R-CNN or Fast R-CNN. Consequently, this study employs a relatively lightweight architecture by integrating GoogleNet with SegNet. The proposed framework first identifies whether an image contains cracks, then performs crack segmentation, and ultimately calculates the crack length. A total of 21 images of brick wall cracks, 22 images of mottled wall cracks, and 22 images of plain wall cracks were selected to ensure balanced performance across the three crack types in subsequent analyses and applications.
## 3 Metrics for Jamming Attack Detection
### Carrier Sensing Time (CST)  
In Media Access Control (MAC) protocols, such as Carrier Sense Multiple Access (CSMA), each node keeps sensing the time for the carrier to be free so that it can then send its own packets. The average, time period for which the node has to wait for the carrier (channel) to become free and available to it is called the Carrier Sensing Time (CST). It is calculated as the mean of the time duration elapsed between the instant a node is ready to send its packet and the instant at which the carrier is found free by it for sending its packet. The nodes fix a threshold value of the CST, which if exceeded, allows it to infer that there is a jamming attack aimed at capturing the carrier. The threshold can either be fixed, as in case of 1.1.1 MAC, or taken as the minimum value over a given time period, as done in case of BMAC. This metric can be applied to only those networks using a MAC protocol based on carrier sensing. Also, this metric is incapable of indicating a physical layer power attack. It also suffers from the problem of fixing thresholds, which is an imprecise process and is computationally taxing on the scarce resources of the WSN node. We therefore, do not find it suitable for our system.
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 3 Methodology
### Geometric Transformation  
The geometric transformation T between the UAS-acquired image and the reference orthophoto can be estimated using the matching landmarks. The following discussion will explain the relation in pixel coordinates that can be modified to use the geopositions of the reference landmarks. In our implementation, we use a down-looking camera with a stabilizer generating images with landmarks at \(\mathbf{x}_{I}\). Transformation matrix between \(\mathbf{x}_{I}\) and landmark \(\mathbf{x}_{L}\) from the reference orthophoto is  
\[\mathbf{x}_{L}=\texttt{T}\times\mathbf{x}_{I}. \tag{1}\]  
The transformation between the down-looking UAS image and the reference data can be computed using 2D transformations as detailed in [19]. In Section 4 of this paper, we compare the pseudo-perspective, affine, and homography transformations listed in Table 1. The pseudo-perspective transformation approximates a perspective transformation using a simpler model that includes rotation and translation and a scale factor based on the object's distance from the camera. The affine transformation adds shearing and independent scaling in the directions of \(x\) and \(y\). The homography introduces perspectivity to the affine transformation. We should note that since the reference data are \"orthophoto\" theoretically, the pseudo-perspective is a more appropriate choice than homography.  
Figure 7: The effect of changes in the attitude on the matching to orthophoto using the SuperGlue and ORB algorithms. Attitude changes are common during UAS flight, and invariance is critical for successful geopositioning. We note that generally, SuperGlue works well in rotations smaller than 40\({}^{\circ}\). Beyond 40\({}^{\circ}\), the performance significantly reduces. ORB’s landmark count is consistent at around 1000 due to its internal settings as a handcrafted landmark extractor.  
In Figure 8, we show an illustration where the UAS flies over the building and acquires two consecutive images \(CAM_{t}\) and \(CAM_{t+1}\). In Figure 8a,b, the ground landmarks \(B_{t}\) and \(B_{t+1}\) intersect on the ground, and the building landmark \(A\) intersects on the building roof. The difference is that in the projective model, the building landmark \(A\) on the roof was projected on the ground as \(A_{t}\) and \(A_{t+1}\), resulting in shearing and non-parallelism. In the pseudo-perspective model, landmark \(A\) was vertically projected onto the ground, which preserves the shearing effect and keeps lines parallel. Figure 8c presents an orthographic camera model; all lines are parallel in both images. This ideal state does not practically exist unless a digital elevation model is used along with the image to generate the orthographic image.
## 2 Measuring System, Materials and Methods
### Methodology
#### 2.1.5 Filtering and Signal-to-Noise Ratio  
Magnetic tests are sensitive to interference and noise. This problem intensifies as the concrete cover thickness increases. Filtration is a prevalent method for enhancing identification outcomes in such situations. Filters smooth the noisy waveform, which allows for the standard deviation of attributes to be lowered and the accuracy of identification to be improved. Empirical tests have indicated that for waveforms where the impact of noise is not pronounced, the application of a median filter is often adequate (with cover thickness below 35 mm). In instances where the waveform demonstrates slow variation (such as a Gaussian distribution) and noise is stronger, both the Savitzky-Golay and the  
Figure 7: Simplified algorithm of the implemented method for extracting association rules from NDT data.  
Butterworth filter may be employed. The parameter selection for the Savitzky-Golay filter is generally straightforward. However, frequency filters like the Butterworth filter remain the preferred solution due to their superior performance and versatility.  
Investigations into the magnetic method have revealed that in scenarios where an effective approximation function has been successfully fitted (the approximation method for attribute extraction), replacing the original waveforms with their corresponding approximation functions can be a viable alternative to filtering.  
The noise level of the waveforms was studied using the SNR (Signal to Noise Ratio). The parameter is given as follows (4):  
\[\text{SNR}=20\frac{\log\Bigl{(}A_{signal}\Bigr{)}}{\log(A_{noise})} \tag{4}\]
## 3 Results
### Performance of Four-System Integrated SPP with a Priori ISB Constraints
The improvement rates of the positioning accuracy in (m) for the seven four-system SPP cases considering a priori ISB constraints over the one ignoring a priori ISB constraints under different elevation masks are presented in Table 5. The positioning accuracy is taken as the RMS statistics of epochwise positioning errors over all the available epochs (from all the employed stations and days in this section) for each four-system SPP processing case. As we can see, when the cutoff elevations are lower than or equal to 30\({}^{\circ}\), the accuracy improvements that benefit from the a priori ISB constraints are marginal (less than 10%), and the positioning accuracy in the east direction with a time span longer than or equal to four days for ISB prediction even slightly degrades after considering the a priori ISB constraints. This implies that there are sufficient visible satellites for four-system integrated SPP even when the elevation masks are increased to 30\({}^{\circ}\). By contrast, when the elevation  
Figure 11: Epochwise positioning errors of four-system integrated SPP with and without a priori ISB constraints under a cutoff elevation of 50\({}^{\circ}\) at station ABMF on DOY 48 of 2020.  
masks are further increased to 40\({}^{\circ}\) and 50\({}^{\circ}\), the accuracy improvements coming with the a priori ISB constraints become significant. The improvements on the positioning accuracy after introducing the a priori ISB constraints can be 22%, 27%, 24%, and 24% and 25%, 26%, 23%, and 23% at an elevation mask of 40\({}^{\circ}\) and 50\({}^{\circ}\) with a time span of ISB prediction of a day in the east, north, up, and 3D directions, respectively. Under a cutoff elevation of 40\({}^{\circ}\) and 50\({}^{\circ}\), the improvement rates decrease with the increasing time span of ISB prediction, and the corresponding accuracy improvements attributed to the a priori ISB constraints with a time span of ISB prediction of seven days can still be 8%, 20%, 12%, and 13% and 12%, 17%, 6%, and 7% in the four directions, respectively. When the time span of ISB prediction is only a day, the positioning accuracy of four-system SPP with a priori ISB constraints at an elevation mask of 10\({}^{\circ}\) to 30\({}^{\circ}\) does not show an obvious difference, which is confined to 8, 6, 15, and 16 cm in the four directions, respectively, and can be 0.517-0.600, 0.846-0.910, 2.549-2.696, and 2.735-2.897 m in the four directions, respectively. When the cutoff elevations are increased to 40\({}^{\circ}\) and 50\({}^{\circ}\), the positioning accuracy of four-system SPP considering a priori ISB constraints with a time span of ISB prediction of a day significantly degrades, but it can still be 0.856, 1.239, 3.728, and 4.020 m and 1.574, 2.346, 7.128, and 7.667 m in the four directions, respectively.
## 3 Background on YANG and OPC UA
### Yang
#### 3.1.1 Module  
The module statement is identified by the module's name, followed by a block of sub-statements that hold detailed module information [33]. The standard order of sub-element definitions within the module is header information, linkage statements, meta-information, revision history, and data definition statements.  
* Header information includes the YANG version, namespace, and prefix statements. The namespace statement within the module defines the unique qualifier for all of the statements defined within the module. The namespace and prefix statement are used in module definitions to differentiate the modules from each other. If the definitions from different vendors have the same names for YANG elements, naming collisions can be avoided.
* Linkage statements contain import and include statements. The import statement references the definitions from another YANG module within the current module using the prefix with a different namespace. The included statements are used for the definitions from another YANG module within the current module, and the current module namespace is used to reference those.
* Meta-information includes information related to the YANG definition, such as the organization name, description, and contact details.
* Revision history includes the revision statements, which track the history of changes to the YANG module and correlate to the different software versions of the managed device that support the YANG module.
# Progress in Phase Calibration for Liquid Crystal Spatial Light Modulators
## 4 Diffractive Phase Calibration Method
### Polarization Method  
In addition to the radiometric diffraction methods, there are also polarization-based diffraction methods. With the help of the Mueller matrices imaging procedures, the spatial nonuniformity of SLM could be measured with a single pixel resolution (\(\sim\)11.5 \(\upmu\)m) [46]. However, this method is very complicated. Another polarization-based system is simple and easy to align, but only the global grayscale-phase relation could be derived [47].
# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 4 Experimental Results
In comparison, the curves in red, green, and purple in Figure 16a-d represent the results obtained using actual scene-based, block-based, and line-based line-time normalization, which is rather glossy for the stitching error between adjacent sub-CCD images. The purple line is located at the lowest point in most cases, which indicates that the line-based line-time normalization method performs the best. Moreover, Table 4 compares the execution times of different methods. The execution times of the traditional line-time normalization method, scene-based line-time normalization method, and block-based line-time normalization method are almost the same, producing a standard image in about five seconds. In comparison, the computational complexity of the line-based line-time normalization method is about one order of magnitude higher than others, leading to degeneration in the processing time to about 58 s per image. Though the stitching accuracy of the line-based line-time  
normalization method performs the best among all methods, its time consumption is much higher. In comparison, the stitching accuracy of the block-based actual line-time normalization method is similar to the line-based method while being much less time-consuming. Therefore, the block-based line-time normalization method is selected in the processing system to achieve a balance between time and accuracy. Figure 16: Geometric error between adjacent CCDs of different datasets: (**a**) geometric error between adjacent CCDs in image data 1, (**b**) geometric error between adjacent CCDs in image data 2, (**c**) geometric error between adjacent CCDs in image data 3, (**d**) geometric error between adjacent CCDs in image data 4.
## 3 Methodology  
This research proposes a data acquisition model called the construction data hub (CDH). The CDH seeks to collect construction project data from twelve dimensions that impact the project's planning and control. It therefore uses the idea of standardizing the WBS form introduced by the US Department of Defense and expands upon the data acquisition model developed by [PERSON] et al. [31] which integrates the OBS and RBS and collects data from the four dimensions: planning and scheduling, cost estimating, cost control, labor resources at the project level (Level 1). This research proposes a further integration of the four structures: OBS, RBS, PBS, and WBS. It focuses on collecting multidimensional construction project data by adding eight new dimensions to the framework by [PERSON] et al. [31]. The additional dimensions are scope management, materials management, equipment management, progress measurement and performance evaluation, document management, risk management, environmental impact, and social impact. It also enhances the detailed work level aspect by focusing on collecting data related to the work package level (Level 3), not the project level (Level 1), as proposed in the study by [PERSON] et al. [31].  
The research methodology consists of three phases. The first phase includes a comprehensive literature review divided into three sections: screening the background of the WBS concept, using data acquisition models and data warehouses to collect historical data, and identifying research gaps. The second phase identifies the twelve dimensions that impact a project's planning and control processes and explains the importance of each dimension, then recognizes the data fields that should be tracked during the project to be stored in the CDH. This phase introduces three new terms/products in the construction project. The first is the multidimensional work package (MDWP), which is an enhanced version of the traditional work package (WP). This section illustrates the structure of the MDWP and its new data dimensions (attributes), making it more powerful than the conventional work package. The second product is the generic template for WBS, which defines standard levels, phases, subphases, and work packages to establish a comprehensive planning template that collects data consistently and meaningfully. The third product is the progress measurement system that uses predefined weighted progress activities for each of the MDWP types. The proposed progress measurement system introduces consistency in evaluating the performance of projects for more efficient analysis of collected data. The third phase presents a case study using actual project data to show the applicability of the framework in real-life situations. An overview of the methodology is presented in Figure 2.
## 3 Basic Theory
### Frequency Domain Analysis  
#### 3.3.1 Description  
Frequency domain analysis has proven to be a useful tool for condition monitoring of industrial machines [22]. This technique uses a function such as a sign wave or a wavelet to represent the spectral content present in a time domain signal [23]. This approach is particularly useful as it is capable of highlighting the frequency characteristics of a signal that are not easily observed in the time domain. Frequency domain analysis has excelled in performing condition monitoring of rotating machines such as bearings, shafts, and fans, since their motion produce a sine wave of a particular frequency and amplitude [24; 25]. We therefore explore the use of frequency domain analysis for monitoring the lubrication condition of the toggle clamping system. The well-established fast Fourier transform algorithm was used as the medium to transform the vibrational signals generated during the clamping and unclamping periods into their corresponding frequency domain representations.