esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In principle, a structural system can be idealised and modelled in two ways (e.g., [8; 9; 10; 11; 12; 13]): (1) a series system, also known as the weakest link system, which is said to fail if at least one component (e.g., structural element, section, or point in the structure where yielding occurs) fails or the conditions are met to fail in one of several mutually exclusive modes [10], and (2) a parallel system, which is said to fail if all components have failed or all originally redundant structural configurations or paths to carry the load have been eliminated [10]. In theory, a third type of structural system, a so-called hybrid or mixed system, is also possible to mechanically idealise and model by combining the concepts of series and parallel systems (e.g., [11]). Figure 1: Scheme of the typical crack formations of punching shear failure modes: (**i**) inside and (**ii**) outside the zones of punching shear reinforcement, (**iii**) at maximum punching shear capacity and (**iv**) without punching shear reinforcement (adapted from [2]). By using the theory of structural systems reliability and focusing on the structural code DIN EN 1992-1-1+NA(D) [4; 5; 6; 7], this study explores how the punching shear failure modes affect the safety level of a RC flat slab with punching shear reinforcement, and ultimately, the variation of partial factors for concrete. This study was undertaken in the context of a research initiative promoted by the German Federal Ministry for Economic Affairs and Energy (in German: _Bundesministerium fur Wirtschaft und Energie_). One of the goals of this research project was the development of an open-source software package (developed in the software language \(R\)[14]) for structural reliability computations. Named as _TesiproV_ (Hochschule Biberach/RWTH Aachen) [15], the ultimate goal of this package is to facilitate and promote the use of reliability-based approaches among structural engineering practitioners and scientific communities [16; 17]. It should be also noted that this investigation is in line with previous reliability assessments of punching shear resistance models for flat slabs (e.g., [18]). Yet, the parametric study addressed in this paper is original and not addressed in any other publication. This paper is organised in the following manner: Section 2 presents the design provisions for punching shear with shear reinforcement as described in DIN EN 1992-1-1+NA(D) [4; 5; 6; 7]. Section 3 offers a brief overview of the theory of systems reliability and Section 4 describes the considerations adopted for the design and the reliability-based analyses of the structural system addressed in this investigation. Then, the respective results are presented in Section 5 and discussed in Section 6. Finally, Section 7 summarises the main conclusions of this investigation, alongside the main limitations and recommendations for further research developments.
## 4 Experimental Results
### Experimental Setup  
The algorithm was implemented with C++ and PCL on Ubuntu 18.04. The experiment was performed on a computer with a 3.5 GHz Intel Core i7-7800X processor, 32 GB RAM, and a RTX3080 GPU.  
The autonomous truck was equipped with an oyster OS1-64 LiDAR on the front to acquire 3D point clouds, as shown in Figure 8. The LiDAR consisted of 64 channels in the vertical resolution and was configurable in the horizontal resolution of 512, 1024, or 2048 with output frequency of 10 or 20 Hz. To meet the requirements of detection accuracy and computational efficiency, 1024 horizontal resolution with 10 Hz output was configured. The field of view in the horizontal plane was 360\({}^{\circ}\), and \(-16.6^{\circ}\)\(\sim\)16.6\({}^{\circ}\) in the vertical plane.  
Because the autonomous truck was large, the installation angle of the LiDAR had to consider both the nearest blind area and the farthest detection requirement. For the location of installation, the effective horizon field of view was from 0 to 180 degrees in the front direction. To reduce the computational burden, the point clouds from 180 to 360 degrees were filtered in the rear direction.  
The truck front was the y axis, and the vertical ground was the z axis. The x axis was determined with the right-hand-side coordinate system. The raw points of the LiDAR were in polar coordinates. According to the LiDAR characteristics, the polar coordinate data were converted into a Cartesian coordinate system.  
The configuration parameters are presented in Table 1. The parameters of \(H\), \(\theta y\), \(\theta p\), \(\theta r\) were associated with the LiDAR installation.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 2 Light Field Camera and Imaging Modeling
### Light Field Camera  
[PERSON] et al. [20] first defined the concept of the \"Plenoptic Camera\" in 1992. Then, [PERSON] et al. [6] designed the first hand-held light field camera with a Plenoptic 1.0 structure in 2005. As shown in Figure 1, the consumer light field cameras in the past decades include the Lytro Illum in the Plenoptic 1.0 structure and the Raytrix in the Plenoptic 2.0 structure. Moreover, in 2019, VOMMA (Shanghai) [21] produced consumer light field acquisition chips for 3D measurement and detection applications. By setting the relative positions of the main lens, micro-lens array, and photosensor, the imaging structure corresponds to Plenoptic 1.0 and 2.0, and the captured light fields exhibit significant variations in spatial and angular resolution, requiring specialized decoding and post-processing methods.  
As shown in Table 1, the lab-designed camera follows the Plenoptic 1.0 structure, similar to the Lytro Illum, but with more advanced features. In contrast to the Raytrix camera, although the camera lens is replaceable, challenges arise from the secondary development of the SDK (Software Development Kit) due to the third-party hardware and its limited openness. Additionally, adjusting imaging parameters according to varying lighting environments has proven to be difficult. The lab-designed light field camera can capture both images (.raw) and videos (.vraw) to generate 5D data \(L(u,v,x,y,t)\) (where \(t\) is the time dimension). Moreover, for ease of use, we have made significant improvements in both the hardware and software. Before capturing objects, a series of white images are captured for each lighting environment. Then the entire processing framework, which includes decoding, calibration, devignetting, enhancement, depth estimation, and 3D display, takes less than one second, thanks to acceleration with GPU computing.
## 4 Challenges and Limitations
### Integration Challenges
This approach minimizes initial costs while allowing companies to realize immediate operational benefits, such as reduced downtime and fuel savings [87]. In summary, while recent advancements in communication technologies, IoT, and cybersecurity have greatly enhanced the integration of AI systems in maritime operations, significant challenges remain. The need to retrofit older vessels, ensure robust cybersecurity, and manage the high costs associated with upgrading infrastructure are critical issues that must be addressed to fully realize the benefits of AI in this sector. Nevertheless, the transformative potential of these technologies, especially when implemented through scalable and modular solutions, offers a path forward for creating a safer, more efficient maritime industry.
# Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder
###### Abstract  
Ultrasonic testing (UT) is one of the most popular non-destructive evaluation (NDE) techniques used in many industries to evaluate structural integrity. The commonly used NDE techniques are basic inspection techniques, such as visual testing (VT), penetration testing (PT), and magnetic testing (MT), and advanced inspection techniques, such as UT, radiography testing (RT), eddy current testing (ECT), and phased array ultrasonic testing (PAUT). Among the numerous advanced techniques, ultrasonic testing (UT) is usually used for the inspection of welds in various industries. However, the application of UT still has some shortcomings to overcome. One major shortcoming that reduces the precision of UT is the extra signals from the geometrical interface of a specimen. UT uses the reflection indications of the ultrasonic beam. However, the reflection signals from the welding interface and geometry along with the target flaw signal produce mixed signals. The inspectors use a 1-D reflection outcome called the ultrasonic A-scan to evaluate the welding integrity. The mixed ultrasonic A-scan signals are often very difficult to analyze because inspectors must distinguish the target flaw signal of welding from the mixed ultrasonic A-scan signal, which includes the flaw indication as well as the background signal. Therefore, a method to distinguish between the flaw signal and the background signal must be developed for the efficiency of UT. Autoencoder is an artificial neural network that is made for feature extraction from the input. Denoising autoencoder (DAE) is one of the derivative models of the autoencoder which adds or eliminates random noise signals to extract the prominent features. DAE is already widely used in the denoising of images and sound data. The characteristics of DAE are used in this research to distinguish the ultrasonic flaw signal from the mixed ultrasonic A-scan signal. For the training, 2463 mixed A-scan signals were obtained from 45 different standard blocks in which 5 different types of flaws were embedded. For testing, we used 1000 mixed A-scan signals. The performance of the network was evaluated using a point-by-point comparison method. The autoencoder was trained to denoise the background signal from the mixed ultrasonic A-scan, and the target flaw signal was extracted from the original A-scan signal.  
ultrasonic testing: flaw signal extraction; denoising autoencoder; nondestructive evaluation; artificial neural networks +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder by Denoising Autoencoder. _Appl. Sci._**2023**, 13, 3534. [[https://doi.org/10.3390/app13063534](https://doi.org/10.3390/app13063534)]([https://doi.org/10.3390/app13063534](https://doi.org/10.3390/app13063534))
# ITOC: An Improved Trie-Based Algorithm for Online Packet Classification
## 3 The Proposed Algorithm  
### Problem and Analyzing: How to Optimize the Update Process  
By updating the trie structure instead of rebuilding it, an online trie-based algorithm will take less time than DPDK ACL library to get a new lookup structure when the ruleset changes. Because in most cases, updating has a smaller time complexity than rebuilding. However, the processing time varies when the input rules are different. The update latency is uncertain as the new rule is unpredictable. Sometimes the update latency could be very high, which case is called update latency exploding.  
Update latency exploding happens when a rule with many wildcards is inserted into a ruleset that already has a large number of rules. Under such circumstances, the update latency can be more than 100 times longer than usual in the worst case. To avoid update latency exploding, we should explore the update process of the online trie-based algorithm in depth.  
The adding update process has only three main steps: rule transforming, trie merging, and trie compressing. The trie merging step takes the longest time. In trie merging, the nodes from the new tries will compare with other nodes in the same level of the old trie, and the node of the old trie will be operated if it intersects with the node from the new tries.  
The number of nodes that will be operated in trie merging is influenced by rule content. For example, we suppose that there are two rules in a ruleset, and we mark the rules as \(R_{a}\) and \(R_{b}\). \(R_{a}\) is a catch-all rule, in which all bytes of the mask are 0x00, meanwhile \(R_{b}\) is an exact match rule, in which all bytes of the mask are 0 xff. To show the difference between the two types of rules, we consider a one-byte long merging of the match fields. The merging processes of the two rules are shown in Figure 3. When inserting \(R_{a}\), all nodes in the same level intersect with the new node. When we insert \(R_{b}\), only one node intersects with the new node.  
In addition to the type of rule content, the status of the target trie also influences the merging process time. Obviously, the number of nodes in the trie will influence the time. The more nodes the trie contains, the longer time the merging process will take. In the offline algorithm, DPDK ACL library, the method will split the trie into smaller ones when there are too many nodes in the trie.  
Another trie status is the node distribution in the trie. Considering \(R_{c}\) is a rule which has catch-all match fields and exact match fields. If the node distribution is average in the  
Figure 3: The sample of merging in update process.  
trie, the number of nodes to be operated will hardly change in the process of \(R_{c}\) insertion when the exact match fields of \(R_{c}\) change. However, if the node distribution is not average, the number of nodes will vary when the content of the exact match fields changes.
## 3 Results
### Results of Triaxial Tests  
The undrained shear strength \(\tau_{fu}\) in cohesive soils from the Ursynow, Bielany, and Stegny sites was determined in CIU triaxial tests on isotropically consolidated samples with shearing in undrained conditions. The CIU triaxial tests were performed in four consecutive stages: Flushing, saturation, consolidation, and shearing in undrained conditions. Saturation of soil samples was performed using the back pressure method. Figure 7 presents the characteristics of the deviator stress \(q\) depending on the vertical strain \(\varepsilon_{1}\) from CIU triaxial tests for the tested cohesive soils. Based on the deviator stress at failure \(q_{f}\) the undrained shear strength was determined as \(\tau_{fu}=q_{f}\)/2 which is shown in Table 4 and was used in statistical analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Values** & \(\begin{matrix}p_{\sigma}\\ \text{(MPa)}\end{matrix}\) & \(\begin{matrix}p_{1}\\ \text{(MPa)}\end{matrix}\) & \(\begin{matrix}V_{s}\\ \text{(m/s)}\end{matrix}\) & \(\begin{matrix}I_{D}\\ \text{(-)}\end{matrix}\) & \(\begin{matrix}\text{\(K_{D}\)}\\ \text{(-)}\end{matrix}\) & \(\begin{matrix}E_{D}\\ \text{(MPa)}\end{matrix}\) \\ \hline Ursynów & Mean value & 1.363 & 2.569 & 395 & 0.93 & 11.1 & 42 \\ Auditorium & Standard Deviation & 0.263 & 0.509 & 47.2 & 0.38 & 2.64 & 14.0 \\  & Coefficient of Variation & 0.19 & 0.20 & 0.12 & 0.41 & 0.24 & 0.33 \\ \hline Ursynów & Mean value & 1.617 & 3.160 & 377 & 0.99 & 12.9 & 54 \\ Building & Standard Deviation & 0.420 & 0.924 & 40.5 & 0.46 & 4.04 & 23.7 \\ No. 37 & Coefficient of Variation & 0.26 & 0.29 & 0.11 & 0.46 & 0.31 & 0.44 \\ \hline Ursynów & Mean value & 1.370 & 2.503 & 397 & 0.84 & 11.3 & 39 \\ Building & Standard Deviation & 0.397 & 0.716 & 41.9 & 0.21 & 1.41 & 13.2 \\ No. 34 & Coefficient of Variation & 0.29 & 0.29 & 0.10 & 0.24 & 0.12 & 0.33 \\ \hline Bielany & Mean value & 0.972 & 1.610 & 320 & 0.78 & 6.1 & 22 \\  & Standard Deviation & 0.343 & 0.509 & 57.0 & 0.62 & 1.04 & 12.8 \\  & Coefficient of Variation & 0.35 & 0.32 & 0.19 & 0.78 & 0.17 & 0.58 \\ \hline Stegny & Mean value & 0.810 & 1.337 & 196 & 0.71 & 5.9 & 18 \\  & Standard Deviation & 0.286 & 0.473 & 23.9 & 0.19 & 0.96 & 7.2 \\  & Coefficient of Variation & 0.36 & 0.36 & 0.12 & 0.28 & 0.16 & 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean value, standard deviation, and coefficient of variation of: The corrected first pressure reading \(p_{o}\) or the corrected second pressure reading \(p_{1}\), shear wave velocity \(V_{s}\), material index \(I_{D}\), horizontal stress index \(K_{D}\), and dilatometer modulus \(E_{D}\). Figure 7: Characteristics of the deviator stress \(q\) depending on the vertical strain \(\varepsilon_{1}\) from CIU triaxial tests for sixteen tested cohesive soils.
# Analysis of Scintillation Effects on Free Space Optical Communication Links in South Africa
## 7 Average Bit Error Rate (BER) Analysis
### Quadrature Amplitude Modulation (SIM-QAM) FSOC Links
Additionally, when turbulence eddies have sizes of zero and infinity in the Kolmogorov model with an infinitely large inertial range, the BERs have better performances based on the zero inner scale and infinite outer scale model, as shown in Figure 9a-d.  
Figure 9: BER of 32-QAM FSOC links vs. average SNR (dB) over weak and moderate to strong atmospheric turbulence channels for various cities in South Africa.
# Simulation Study of Two Torque Optimization Methods for Direct Torque-Controlled Induction Motors
## 2 IM Torque Response Optimization
### System Controller  
A block diagram of the control system of Method (2) is shown in Figure 3. It is the same as that of Method (1) except there is no rotor flux observer and the optimization Equation (13) is replaced by Equation (21). The whole control system is working with digital signals and given sampling time. The controller inputs are the measured currents and DC link voltage. Simple estimators are implemented for the stator flux and torque. However, another estimator for the rotor flux angle is required for Method (1). A detailed flow chart for the control algorithm of optimization Method (2) is shown in Figure 4. The block diagram in Figure 3 represents proposed control Method (1) with the dashed arrow for the rotor angle (\(\theta_{ro}\)). Additionally, the control flow chart of Figure 4 is typical for Method (1) except for the estimator of (\(\theta_{ro}\)) and the optimization Equation (21).  
Figure 4: The flow chart of the Control algorithm for Method (2).  
Figure 3: The block diagram of the proposed DTC utilizing optimization Method (2).
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques
### Range-Based Technique
#### 3.1.2 Time of Arrival  
The time of arrival (ToA) measurement technique uses the propagation time, i.e., the time required for the signal to travel from the unknown node to the anchor nodes. The unknown node is to be in the range of anchor nodes. This technique requires at least three anchor nodes for determining the position of the unknown node. The assessed location of the unknown node falls inside the intersection area of the three circles, as represented in Figure 4. The realistic assessed location can be determined using the least square or weighted least square method [19].  
The technique requires accurate synchronization among the anchor nodes and unknown nodes, impacting the geolocation structure complexity. A system can use ToA measurements to overcome this drawback, built on a sound signal produced by the anchor node and at least four unknown nodes [20]. It improves the localization precision considerably. An enhanced ultrasound-based time of arrival technique [21] permits calculating the position in three dimensions and provides the data orientation. Cricket, a ToA-based localization approach [22], uses ultrasound transmitters with known locations. The anchor node evaluates the location of the unknown node with high accuracy.
# Leakage Detection Method of Coal Mine Power Supply System Based on 3 AC
## 2 Leakage Protection Method Based on 3 AC Detection Circuit
### Mathematical Model Based on 3 AC Detection Method
#### 2.2.3 Three-Phase Leakage  
When an \(A\), \(B\), and \(C\) three-phase leakage fault occurs, the insulation resistance is a three-phase resistance in parallel, which is set to \(R_{A}^{\prime}/3\), \(\dot{U}_{0}\) as follows:  
\[\dot{U}_{0}=\frac{-(\dot{U}_{A}+\dot{U}_{B}+\dot{U}_{C})\frac{1}{R_{A}^{ \prime}}-\dot{\mathrm{j}}\omega C_{2}\dot{U}_{A}}{\frac{3}{R_{A}^{\prime}}+ \dot{\mathrm{j}}\omega 3C+\dot{\mathrm{j}}\omega 4C_{2}} \tag{11}\]  
where \(\dot{U}_{A}+\dot{U}_{B}+\dot{U}_{C}=0\), simplifies the above equation:  
\[\dot{U}_{0}=\frac{-\dot{\mathrm{j}}\omega C_{2}\dot{U}_{A}}{\frac{3}{R_{A}^{ \prime}}+\dot{\mathrm{j}}\omega 3C+\dot{\mathrm{j}}\omega 4C_{2}} \tag{12}\]  
Formulas (6) to (12) define the relationship between the voltage \(\dot{U}_{0}\) and the equivalent insulation resistance under various leakage fault conditions. The value of \(\dot{U}_{0}\) can be determined using the capacitance and \(\dot{U}_{C0}\) measured by a current transformer. By substituting these seven-fault state mathematical models into the calculations, the insulation resistance can be solved and compared with the preset action threshold values to determine whether a leakage fault has occurred.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 3 Methodology
### Phase 2: Short-Term Detection  
Once the baseline model is deployed in real-time (\(t>T_{train}\)), each incoming data point \(\mathbf{z}^{(m)}(t)\) must be quickly classified as normal or anomalous. However, a fixed threshold can be insufficient in a changing production environment, so we adopt a Bayesian threshold [22].  
1. Forward Inference: \[\hat{\mathbf{z}}^{(m)}(t)=f_{\hat{\mathbf{z}}^{*}}\Big{(}\mathbf{z}^{(m)}(t)\Big{)},\ E^{(m)}_{recon}(t)=\Big{\|}\mathbf{z}^{(m)}(t)- \hat{\mathbf{z}}^{(m)}(t)\Big{\|}^{2}.\] (7)
2. Bayesian Threshold: Let \(\tau\) be the threshold with prior \(p(\tau)\). Observing \(E^{(m)}_{recon}(t)\) updates the posterior: \[p\Big{(}\tau\Big{|}E^{(m)}_{recon}(t)\Big{)}\propto p\Big{(}E^{(m)}_{recon}(t )\Big{|}\tau\Big{)}p(\tau).\] (8) We take \(\tau_{t}^{*}\) (e.g., the 95% quantile) from the posterior. If \(E^{(m)}_{recon}(t)>\tau_{t}^{*}\), a short-term drift is flagged: \[y^{(m)}(t)=1.\] (9) A drift score \(E^{(m)}_{recon}(t)\) and binary detection label \(y^{(m)}(t)\in\{0,1\}\). This can be executed at a second- or minute-level timescale for real-time alerts.
# Matched-Filter Thermography
## 5 Future Directions  
To date, most of the research in the field of matched-filter thermography has been focused on exploring the advantages of the matched-filter approach over conventional thermography techniques through theoretical modeling and experimentation on standardized samples. Optimization of excitation and signal detection parameters have also been explored. Future directions desired in the field include: standardization of methodologies developed so far through consensus reports or development of standard procedures by regulating bodies such ISO or ASTM; development of strategies and methodologies for quantification of defect and/or material properties from matched-filter responses; and development of strategies for lowering the cost of thermography systems to enable translation/commercialization to industry and medicine.