esa-sceva/satcom-chunk-collection huggingface dataset:
# CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
## 1 Introduction
Compared to the method of spherical sweeping in a global hypothetical spherical space, our method only performs spherical sweeping in the local space with the highest probability of omnidirectional stereo matching, which is more efficient. Meanwhile, our method adaptively adjusts the sweeping range of each pixel in the subsequent stages based on the uncertainty of depth, which is beneficial for improving the accuracy of depth prediction. Figure 1: Omnidirectional depth estimation. (**Left Top**): The structure of the system with fisheye images as input. (**Left Bottom**): Real camera rig. (**Right Top**): Panoramic image. (**Right Middle**): Panoramic inverse depth map. (**Right Bottom**): Point cloud image. The contributions of this paper are summarized as follows:  
1. We pioneer applying the cascade spherical cost volume to multi-view omnidirectional depth estimation, achieving lower GPU memory usage, faster inference time, and higher accuracy than alternative methods;
2. We develop a dynamic spherical sweeping technique where the network learns sweeping intervals and ranges according to the uncertainty;
3. Comprehensive experiments showed that our method outperformed the others, which also demonstrate promising performance on real-world data.
# Improved Performance of High-Voltage Vertical GaN LEDs via Modification of Micro-Cell Geometry
## 4 Conclusions  
In this study, laser lift-off and wafer bonding techniques were used to prepare vertical HV-LEDs connected with 2 \(\times\) 2 micro-cells on Cu substrates. Various cell shapes--such as square, rectangle, triangle, and L shapes--were designed and fabricated on the HV-LEDs. As the HV-LED was connected with four square micro-cells, the electrodes in these micro-cells were all far away from the edges, which induced the severe current-crowding phenomenon. However, when four rectangle, triangle, or L-shaped micro-cells were used to fabricate the HV-LED, the electrodes in these micro-cells were close to the edges, enhancing the current spreading effect. The measured results revealed that the HV-LED with four triangular micro-cells exhibited the higher light extraction and a more uniform light emission than did those with other cell shapes. At injection currents of 20 and 80 mA, the HV-LED with epoxy packaging showed higher light output powers of 102.8 and 353.3 mW, and its WPEs were 41.1% and 29.7%, respectively. Although the HV-LED combined with L-shaped cells exhibited a favorable current spreading effect, the electrode-shading loss effect degraded the light extraction. The triangular cell design can efficiently improve the performance of vertical HV-LEDs. Furthermore, the fabrication techniques of the vertical LED are highly useful for HV-LED applications.  
This work was financially supported by the Ministry of Science and Technology (Taiwan) under Grant numbers 104-2622-E-005-005-CC2 and 104-2622-E-005-003.  
The manuscript was completed through contributions of all authors. [PERSON] conceived the experiments and gave the suggestions for the results. [PERSON] and [PERSON] performed the experiments and wrote the paper. [PERSON] contributed to the data analysis.  
The authors declare no conflict of interest.
## 3 Classification
### Role of VRU Devices
#### 3.8.1 Active  
When a VRU device participates actively in V2P communication, by sending information about the VRU location, speed, etc. periodically, it may be called as active participation. This requires the VRU device to be equipped with multiple technologies, such as, GPS, communication technologies. This type of participation is more widely used as seen from Table 1. This type of participation may increase network congestion caused by VRU safety messages adversely impacting potentially more crucial V2V and V2I communication [14]. Hence this type of system may need to employ mechanisms to optimize VRU transmissions.
###### Abstract  
In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively.  
deep learning; LSTM-FCN; structural damage identification; optical flow; structural displacement measurement +
Footnote †: journal: Applied Sciences  
10 March 2021
## 3 Circuit Level Perspectives of JCRS RF Hardware
### Phased Arrays and Beam Steering Systems  
BF is intended for electronically steering the beam pattern of an antenna system. Conventional radars already use BF for spatial filtering, while conventional communication systems prefer a wide and stable antenna pattern for coverage. However, the recent exploitation of the mmWave spectrum has forced communication systems to adapt to high gain antenna systems for counterbalancing the path loss, resulting in narrow beam utilization supported by BF for coverage.  
The exploitation of the mmWave spectrum has reduced the difference between antenna systems for radars and communication systems because BF has become a must for both; thus, the chances of JCRS are enhanced. In a broad categorization, a beam steering system is comprised of a phased antenna array and a BF network. Both sub-systems can be further classified into several categories and are independent of each other to a large extent, with respect to their design procedure and goals.
# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 3 Methodology
### Performance Measures
#### 3.3.2 Confusion Matrix  
In the field of machine learning and the problem of statistical classification, the confusion matrix is commonly applied to evaluate the efficacy of an algorithm. Table 2 presents an example of a confusion matrix. From the table, the true positive (_tp_) value and true negative (_tn_) value represent accurate classifications. The false positive (_fp_) value or false negative (_fn_) value refers to erroneous classifications.  
The commonly used metrics of the effectiveness of classification are generated from four elements of the confusion matrix (accuracy, precision, sensitivity, specificity and area under the receiver operating characteristic curve (AUC)).  
The predictive accuracy of a classification algorithm is calculated as follows.  
\[Accuracy=\frac{tp+tn}{tp+fp+tn+fn} \tag{14}\]  
Two extended versions of accuracy are precision and sensitivity. Precision measures the reproducibility of a measurement, whereas sensitivity--also called recall--measures the completeness. Precision in Equation (15) is defined as the number of true positives as a proportion of the total number of true positives and false positives that are provided by the classifier.  
\[Precision=\frac{tp}{tp+fp} \tag{15}\]  
Sensitivity in Equation (16) is the number of correctly classified positive examples divided by the number of positive examples in the data. In identifying positive labels, sensitivity is useful for estimating the effectiveness of a classifier.  
\[Sensitivity=\frac{tp}{tp+fn} \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual Class**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{**Predicted class**} & **Positive** & True positive (_tp_) & False negative (_fn_) \\ \cline{2-4}  & **Negative** & False positive (_fp_) & True negative (_tn_) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
Another performance metric is specificity. The specificity of a test is the ability of the test to determine correctly those cases. This metric is estimated by calculating the number of true negatives as a proportion of the total number of true negatives and false positives in examples. Equation (17) is the formula for specificity.  
\[Specificity=\frac{tn}{tn+fp} \tag{17}\]  
A receiver operating characteristic (ROC) curve is the most commonly used tool for visualizing the performance of a classifier, and AUC is the best way to capture its performance as a single number. The ROC curve captures a single point, the area under the curve (AUC), in the analysis of model performance [72]. The AUC, sometimes referred to as the balanced accuracy [73] is easily obtained using Equation (18).  
\[AUC=\frac{1}{2}\bigg{[}\bigg{(}\frac{tp}{tp+fn}\bigg{)}+\bigg{(}\frac{tn}{tn+ fp}\bigg{)}\bigg{]} \tag{18}\]
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 2 System Framework and Operation Process
### Brief Introduction of Fault Tracking Quasi-Real-Time Processing System  
The quasi-real-time processing system mainly deals with warning-level information, including the online monitoring information of circuit breakers and warning information of relay protection devices. Warning-level information is sampled in the substation by polling and is not reported to the dispatching center. When maloperation and refuse-operation information of circuit breakers and protections is transmitted from dispatching center to substations, quasi-real-time processing systems at substations initiates. According to warning-level information of related circuit breakers and relay protection devices, the reasons for incorrect actions are output work on the premise of considering the loss of partial warning-level information.
# Hardware Design for Cascade-Structure, Dual-Stage, Current-Limiting, Solid-State DC Circuit Breaker
## 4 Hardware Design of the Circuit Breaker Prototype
### Experimental Platform Design  
Shown in Figure 10a is the small-scale experimental prototype platform established for this project, operating with a system voltage of 30 V. The system's stable state rated current is 0.6 A, and through the experiment, a fault clearance of a short-circuit current peak up to 3.5 A was successfully achieved. The prototype circuit breaker is illustrated in Figure 10b. The circuit breaker's system voltage employs a capacitor power supply mode, utilizing the energy storage characteristics of capacitors to provide the system voltage by pre-charging them. The capacitor power supply is connected to a capacitor power switch, which is activated using a controller. The current acquisition module uses both a Hall current sensor and a sampling resistor method for current measurement. Various components of the experimental platform and functional branches of the circuit breaker have been marked in the figures.
###### Abstract  
Recently, deep learning techniques have been used for low-dose CT (LDCT) reconstruction to reduce the radiation risk for patients. Despite the improvement in performance, the network models used for LDCT reconstruction are becoming increasingly complex and computationally expensive under the mantra of \"deeper is better\". However, in clinical settings, lightweight models with a low computational cost and short reconstruction times are more popular. For this reason, this paper proposes a computationally efficient CNN model with a simple structure for sparse-view LDCT reconstruction. Inspired by super-resolution networks for natural images, the proposed model interpolates projection data directly in the sinogram domain with a fully convolutional neural network that consists of only four convolution layers. The proposed model can be used directly for sparse-view CT reconstruction by concatenating the classic filtered back-projection (FBP) module, or it can be incorporated into existing dual-domain reconstruction frameworks as a generic sinogram domain module. The proposed model is validated on both the 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset and The Lung Image Database Consortium dataset. It is shown that despite the computational simplicity of the proposed model, its reconstruction performance at lower sparsity levels (1/2 and 1/4 radiation dose) is comparable to that of the sophisticated baseline models and shows some advantages at higher sparsity levels (1/8 and 1/15 radiation dose). Compared to existing sinogram domain baseline models, the proposed model is computationally efficient and easy to train on small training datasets, and is thus well suited for clinical real-time reconstruction tasks.  
CNN; low-dose CT; sparse view; super-resolution network; sinogram domain
## 4 Results and Discussions
### Effect of Surcharge Magnitudes
#### 4.2.3 Earth Pressure  
Figure 16 depicts the non-dimensional earth pressure profiles on the battered piles under different surcharge magnitudes. The earth pressure would increase first and then decrease, and all the maximum earth pressures appear approximately at \(z_{\rm{a}}=0.5\). In addition, as anticipated, the earth pressure increases with the surcharge load. While the surcharge load increases from 34.375 to 84.375, 134.375, and 184.375 kPa, the maximum non-dimensional earth pressure increases from 0.01 to 0.05, 0.11, and 0.17 in the case of \(\beta=-20^{\circ}\) and varies from 0.04 to 0.17, 0.33, and 0.45 in the case of \(\beta=-10^{\circ}\), respectively.
# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 4 Results and Discussion
### Joso Flood Case
Therefore, flood detection in parking lots could be challenging for both \(\sigma^{\circ}\) and \(\gamma\). In the dense built-up area of R#6, radar shadows hamper flood detection in the front of buildings and the evidence of flood from \(\sigma^{\circ}\) is unnoticed. As the scatters are probably dominated by the roofs, the co-event \(\gamma\) is as high as the pre-event \(\gamma\) (e.g., above 0.6). Apart from the reasons of underestimation we discussed for R#5 and R#6, the underestimation in Joso city is also probably due to a floodwater recession at the time of SAR acquisition, since the inundation extent varies rapidly in time according to the inundation maps provided by GSI [56] and a large inundation area in Joso city disappeared on 12 September 2015. A large white area between the red areas in Joso city can be found in Figure 5b, which indicates that there is no significant coherence decorrelation occurring in this area. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Data** & **Precision** & **Recall** & **F1** & **FPR** & **OA (\%)** & \(\kappa\) \\ \hline \multirow{2}{*}{Whole study area} & Intensity + Coherence & 0.91 & 0.70 & 0.79 & 0.03 & 89.6 & 0.72 \\ \cline{2-7}  & Intensity & 0.85 & 0.59 & 0.70 & 0.04 & 86.0 & 0.61 \\ \hline \multirow{2}{*}{Joso city} & Intensity + Coherence & 0.94 & 0.66 & 0.78 & 0.03 & 84.3 & 0.66 \\ \cline{2-7}  & Intensity & 0.85 & 0.45 & 0.59 & 0.06 & 74.0 & 0.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative evaluation of the Joso flood case (FPR: false-positive rate, OA: overall accuracy). Figure 7: \(Cont.\)
## 3 Results and Discussion
### Physicochemical Properties
#### 3.2.3 Swelling Capacity  
The swelling of starch granules has occurred during gelatinisation. Starch swelled because the hydroxyl groups within the starch granules formed new hydrogen bonds with water molecules due to the disruption of hydrogen bonds between the hydroxyl group in the double helices of starch molecules [65]. Based on Table 2, the swelling capacity range was between 20.31 g/g and 23.60 g/g. Generally, rice flour produced from Bario Adan Halus (23.60 g/g) exhibited the highest swelling capacity value, and the lowest was presented by Bario Celum (20.32 g/g). Based on the result, pigmented rice flour exhibited significantly (\(p<0.05\)) lower swelling capacities compared with white rice flour. These current findings agree with the research performed by [PERSON] et al. [66] and [PERSON] et al. [67]. The reason for this is that the outer bran of the pigmented rice grains delayed water penetration into the starch granules, consequently lowering the swelling capacity of the flour. On the other hand, the swelling of granules can also be hindered by lipids [68]. Likewise, in this study, pigmented rice flour generally contained higher lipid content compared with white rice flour, resulting in a lower swelling capacity than white rice flour (TQR and Bario Adan Halus). In bread development, lower swelling capacity flour is not recommended because it might inhibit the swelling process of the final baked products [69]. For that reason, the possibility for Bario rice flour to be used in gluten-free bread production is high.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 3 Polyphenols: Properties and Significance in Food
### Application of Polyphenols in Food Technology
#### 3.3.4 Colour Stabilization and Enhancement  
Polyphenols play a vital role in enhancing the visual appeal of food products by contributing to vibrant and stable colours. This is particularly important as consumers often associate colour with freshness, quality, and flavour [108; 109]. Flavonoids, especially anthocyanins, are among the most prominent polyphenols contributing to the pigmentation of fruits and vegetables. Anthocyanins impart red, purple, and blue hues to products such as berries, grapes, and cherries, significantly enhancing their marketability. Their natural colour intensity makes them ideal for use in processed foods and beverages, offering an attractive alternative to synthetic dyes [110]. Polyphenols are increasingly used as natural colourants in food formulations, meeting the demand for clean-label products. For example, extracts from red cabbage, blackcurrent, and hibiscus, which are rich in anthocyanins, are employed to provide vibrant colours in confectionery, yoghurts, and soft drinks [111; 112]. In addition to enhancing colour, polyphenols contribute to colour stability, which is particularly important in products susceptible to discolouration during storage. For instance, anthocyanins stabilise the colour of fruit-based beverages, preventing the browning caused by enzymatic oxidation. Similarly, in confectionery and jams, polyphenols help maintain a consistent appearance by interacting with other food components to form stable colour complexes. Recent advancements have focused on improving the stability of polyphenol-based colourants under varying pH, temperature, and light conditions. Encapsulation techniques, such as microencapsulation and nano-emulsions, have been utilised to protect polyphenols from degradation, ensuring their longevity in food products. As consumer demand for natural and sustainable additives continues to grow, the use of polyphenol-based colourants is expected to expand. Research into optimising their extraction and application, as well as exploring new sources of vibrant polyphenols, remains a focus of innovation in food technology [113; 114].
###### Abstract  
As transportation networks continue to expand into mountainous regions with high seismic activity, ensuring the seismic safety of tunnels crossing active faults has become increasingly crucial. This study aimed to enhance our understanding of the impact of fault zones on the seismic behavior of tunnels and to provide optimized seismic design recommendations through a comprehensive experimental and numerical investigation. The focus of this research is the Xiangyangshan Highway Tunnel in China, which intersects a significant longitudinal fault. Large-scale shake table tests were performed on 1:100 scale physical models of the tunnel to analyze the seismic responses under various ground motion excitations. Detailed three-dimensional finite difference models were developed in FLAC3D and calibrated based on the shake table results. The tests indicated that strains, earth pressures, and accelerations experience localized amplification within 10-20 m of the fault interface compared to undisturbed ground sections. Common seismic mitigation measures, such as rock growing, seismic joints, and shock absorption layers, were observed to effectively reduce the amplified seismic demands. Grouting, in particular, led to an average reduction of up to 56.3% in circumferential strain and 38.5% in earth pressure. It was concluded that 6 m thick grouted zones and 20 cm thick rubber interlayers between tunnel lining shells provide optimal structural reinforcement against the effects of fault zones. This study provides valuable insights for improving the seismic resilience of underground transportation corridors in seismically active regions.  
extra-large section tunnel; shaking table test; surrounding rock grouting; shock absorption layer; seismic joint; fault Article  
1
# Exploiting Data Similarity to Improve SSD Read Performance
## 2 Background and Research Motivation
### Page Allocation Scheme  
The page allocation scheme plays a crucial role in determining how free physical pages are allocated to accommodate the data of write requests, which can significantly impact the read performance of the SSD as it determines the degree of access parallelism [11].  
Many studies have been conducted on page allocation schemes, and two primary types are widely used: static allocation and dynamic allocation. Static allocation is based on fixed striping, where logical pages are initially allocated to predefined channels, chips, dies, and planes, followed by allocation to the first available page in the active block of the target plane [12]. The target channel, chip, die, and plane addresses for a page-sized flash transaction are determined in a specific order of priority for these four levels of parallelism, usually calculated using specific formulas.  
On the other hand, dynamic allocation allocates physical pages dynamically, allowing logical pages to be assigned at runtime to any available physical page across the SSD. When a write request arrives, the dynamic allocation scheme selects free physical pages based on several factors, such as the idle/busy status of the channel and chip, the erase counts of the block, and the priority of the parallel level. It also takes into consideration the utilization of multiple parallel levels [13].  
While dynamic allocation is more flexible and adaptive in exploiting the parallelism of SSDs and generally provides better performance in most cases, static allocation is simple to implement and can be highly effective in certain workloads. Static allocation performs best for read operations in all workloads, while dynamic allocation tends to exhibit superior overall performance and durability under most workloads in aging SSDs [13].