esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 5 Discussion
### Understanding UAV Images  
It is asserted in this paper that UAVs are becoming a personal RT platform and a standard tool in professional disaster reconnaissance activities in recent years. In practice, UAVs-based RS images possess special characteristics due to their much flexible imaging geometrics. In general, UAV images according to their coverage and imaging heights can be categorized into three types: (T1) elevation view of buildings if the imaging UAV captures at an AGL height comparable to building heights; (T2) overhead view of one or several buildings when the AGL heights are higher than buildings; (T3), aerial view of a large number of buildings when the AGL is much high (e.g., hundreds of feet above ground). In general, the authors state that the developed models in this work should work well for UAV images from the first category (T1) as they are similar to any ground-level mobile images. For images in T2, the images may still be similar to the mobile imagesin this paper as one may capture images at a higher ground than buildings. For T3, they should be processed first using photogrammetric methods used for traditional RS images.  
In this effort, the models \(M_{HT}(FRC,RN)\) and \(M_{DL}(FRC,RN)\) developed previously are applied to some sample UAV images, which were captured from a recent tornado disaster reconnaissance (Jefferson City, Missouri). The images were shot at a low altitude over an apartment complex. As shown in Figure 11, two of three damaged buildings are classified correctly as the tornado hazard-type. The lower left one is classified as the tsunami type because its roof is disappeared, which is a scene not appeared in the tornado-scene images in this paper (that were captured mostly with the front view). For damage-level classification, three buildings are detected in Figure 11; the predicted damage-level conform to the hazard-agnostic damage classification. This extrapolation effort demonstrates that UAV images may deserve some specific processing to improve accuracy. One possible solution is to exploit the 3-dimensional (3D) embeddings hidden in many overlapped UAV images hence a 3D reconstruction of the scene can be obtained [8, 48]. With 3D image-based learning, more granular and representative features may positively augment the model accuracy.
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 3 Presented Method: ESC-MASD-Net
### Multi-View Attention Block
At last, we express the final result of the local attention path as: \[x_{l}^{\prime}=x_{l}\times\alpha_{l}.\] (9)
* Path Aggregation: Following the three attention paths, each output is concatenated and then passed through a convolution layer. A residual gate with sigmoid activation, hyperbolic tangent activation, and ReLU is used to process the resulting output further. Finally, a residual connection is made.
## 4 Enabling Technologies for FSO Communication
### Robust Beam and Multi-Modulation Compatible System Design
#### 4.1.1 Robust Beam Design
Numerical simulations demonstrate that IPBs have a significantly lower scintillation index than other beam classes such as PBs, BBs, collimated Gaussian beams (CGBs), and focused Gaussian beams (FGBs) in atmospheric turbulence regimes [120; 121]. Also, IPBs carrying OAM can further mitigate intensity scintillations under moderate to strong irradiance fluctuation conditions [122]. As a member of the pseudo-non-diffracting OAM-carrying beam family, HyGG vortex beams exhibit self-reconstruction and abrupt autofocusing properties. Notably, HyGG vortex beams can be converted into Gaussian mode, modified BG (MBG) mode, modified exponential Gauss (MEG) mode, and modified LG (MLG) mode by setting specific parameters of the beam source. Due to these characteristics, HyGG beams hold significant potential for use in OWC [103; 104; 105; 106].
## 2 High-Voltage Direct Current (HVdc) Grid Protection
### Malfunctioning of Direct Current (dc) Breaker  
The operation time of the existing dc breakers affects the protection time frame. Backup protection strategy in event of dc breaker malfunctioning is explained in Figure 2. During the malfunctioning of the dc breaker \(B_{1}\), backup protection will trip all the adjacent breakers (\(B_{2},B_{3}\) and \(B_{4}\)) as shown in Figure 2. Hence, the operation time of the backup dc breakers have to be added within the protection time frame.  
Moreover, operation of backup protection can also be explained with the help of voltage current (V-I) plane as shown in Figure 3[2; 3].  
Figure 1: Timing diagram for a direct current (dc) grid protection.  
Figure 2: Backup protection strategy in event of direct current (dc) breaker malfunctioning.  
Points 1 and 2 represent events of fault inception and tripping of dc breaker. Voltage and current variations between fault detection by primary protection (PP) and trip action by breaker \(B_{1}\) is represented by a solid line from points 1 to 2. Faulty line is successfully isolated because of opening of breaker \(B_{1}\) under the tripping command of primary protection relay. Under this scenario, loci of voltages and currents are denoted via dashed line from point 2 to 5. Voltage increases to \(V_{f}^{0}\) and fault current decreases from \(I_{f}^{2}\) to 0 after clearance of fault.  
However, in the case of primary protection failure, loci of voltages and currents are represented by a solid line between points 2 and 4. Both voltage and current attain steady state values of \(U_{f}^{s}\) and \(I_{f^{\prime}}^{s}\), respectively. If the backup protection relay clears the fault, current and voltage variations follow the path shown by a combination of solid and dashed lines between points 2 to 3 and then to 6.  
Further, in the event of dc breaker failure, fault is cleared by backup breakers as tripping command is sent to them from backup protection relay as shown in Figure 2. In this event, not only current is reduced to zero, but voltage is also reduced to zero. The loci of dc voltage and current follow the path represented by the line from points 2 to 3 and then 7. This highlights the effect on protection time frame during malfunctioning of dc breaker [2; 3].
## 4 Experimental Results
### Discussion
#### 4.7.1 Explanation of Results  
Empirical experiments described in Section 4.6 well validate the effectiveness of the proposed encoder-decoder and attention modules. Each of these modules in AED-CNN can bring a significant improvement in the detection accuracy. In Sections 4.3 and 4.4, we compare our method with several published state-of-the-art methods, on two challenging datasets. From the experimental results, we found our proposed CNN-based architecture significantly outperforms hand-crafted methods. The proposed method outperforms the hand-crafted method by 5.1% and 23.78% in AP on the KMU and CVC-09 pedestrian dataset, respectively. This result inspires us to explore in depth how to develop more effective CNN architectures to further improve the performance of IR pedestrian detection. As far as I know, there are only few research works which apply CNN to IR pedestrian detection.  
Another research direction we can further explore is how to improve the confidence score of detected pedestrians in adverse environments. One can compare the result visualization of Figures 6 and 8, to easily find that the confidence score in Figure 8 is lower. The confidence score of some detected pedestrians in Figure 8b,d are about 0.7. The reason is that the difference in brightness between pedestrians and the background in the CVC-09 data is lower than the KMU data. It is necessary to explore how to further emphasize pedestrian regions. Some recent works apply CNN for saliency detection in images [45], which shows promising results. Saliency detection aims to learn the significant difference between different groups. Saliency detection helps to reduce the complicity of the background and detect the foreground objects. For the task of IR pedestrian detection, the pedestrian and background are two groups, we can apply saliency detection algorithms to learn specific features to find pixels that belong to saliency regions (pedestrian regions) [45]. Therefore, how to incorporate saliency detection into IR pedestrian detection can be a future research problem.
## 4 Results and Discussion
### Influence Analysis of Pipeline Parameters
#### 4.3.3 Pipeline Section Shape  
Figure 15a,b show the vertical deformation, horizontal deformation, stress and axial force of the pipeline with the same cross-sectional area in square and circular, respectively. The maximum vertical deformation of pipeline under two conditions were 15.8 and 17.3 mm, the maximum horizontal deformation of pipeline were 39.2 and 41.4 mm, the maximum pipeline stress were 1.34 and 1.11 MPa, and the maximum pipeline axial force was 67.0 and 54.8 kN, respectively. When other factors were the same, the vertical and horizontal deformation of the circular pipeline caused by tunnel excavation was larger than that of the square pipeline. However, the stress and axial force of the square pipeline were greater than those of the circular pipeline. The reason was that the square pipeline had sharp corners, which was easy to cause stress concentration. The distribution of pipeline deformation, stress and axial force basically remained unchanged.  
Figure 15: Vertical deformation, horizontal deformation, stress and axial force of pipeline with different section shapes: (**a**) Square; (**b**) Circular.  
Take the settlement of the top of pipeline 1 above the central axis of the tunnel to analyze the influence of different section shapes on the settlement of the pipeline, as shown in Figure 16. When other influencing factors were the same, the vertical deformation of the circular pipeline was increased by 1.00 mm at most than the square pipeline in tunnel excavation. Therefore, in the actual construction, more attention should be paid to the deformation of the circular pipeline.
# Editorial on Wireless Power Transfer (WPT): Present Advancements, Applications, and Future Outlooks
###### Abstract  
We present a novel approach to wireless power transfer (WPT) in wireless power transfer (WPT). We propose a novel approach to wireless power transfer (WPT) in wireless power transfer (WPT).  
wearable health monitoring systems that provide continuous data on patients' vital signs, enabling real-time diagnostics and telemedicine.  
**WPT in the Internet of Things (IoT):** With the rapid proliferation of the Internet of Things (IoT), which envisions a world where billions of devices are interconnected, WPT is emerging as a critical enabler of continuous power for sensors, actuators, and other low-power devices [6, 7, 8]. WPT systems, particularly in smart cities and industrial automation, are expected to provide a solution to the limitations of battery-powered devices by enabling contactless and uninterrupted energy delivery. The integration of WPT into IoT technologies would enable the realization of \"smart environments\" where energy is wirelessly distributed to connected devices, creating more efficient and sustainable infrastructures.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 4 Target Distortion Optimization
### Problem Solution
#### 4.3.2 The Bandwidth Distortion Optimization  
We exhaustively search the values of \(K\) in ascending order for the minimal bandwidth use \(K\). The subproblem is solved for each \(K\) as follows:  
\[MSE(\rho)=\arg\min_{\rho}\frac{1}{N}\Big{(}\sum_{i=1}^{K}\frac{\lambda_{i}}{ \rho_{i}+1}+\sum_{i=K+1}^{N}\lambda_{i}\Big{)} \tag{27}\]  
\[\text{s.t.}\frac{1}{N}\sum_{i=1}^{K}\rho_{i}\leq SNR_{clk} \tag{28}\]Using the Lagrange multiplier technique, the optimal power \(\rho_{i}^{*}\) is derived as  
\[\rho_{i}^{*}\approx\frac{N\cdot\sqrt{\lambda_{i}}}{\sum_{j=1}^{K}\sqrt{\lambda_{ j}}}SNR_{clk} \tag{29}\]  
In Algorithm 3, under a given distortion \(\overline{MSE}\) and power budget \(SNR_{clk}\), the corresponding \(K\) is infeasible if the objective value (27) is greater than the expected distortion. The search continues by increasing \(r\) by \(1\) and solving the problem (27) until the search reaches a feasible \(K\). It is also possible that an exhaustive search for a particular GOP does not reach a feasible \(K\), which means the expected distortion requirement is too high for a specific \(SNR_{clk}\) or the power budget \(SNR_{clk}\) is insufficient to improve transmission up to a particular quality. In both cases, all \(N\) channels are used. Hence, more resources must be allocated. Here, it is suggested that the power budget is increased with a 0.01 step size until a value is reached that will lead to a feasible \(K\).  
```
1:\(N\), \(SNR_{clk}^{\dagger}\), \(SNR_{clk}^{\ddagger}\), \(\overline{MSE},c=\)[texture,depth]
2:\(K^{c},\rho_{i}^{c}\)
3:initialization:\(\lambda_{i}=0,r=0,MSE_{r}=\infty\)
4:for\(i=1\)to\(N\)
5: Compute chunk variance \(\lambda_{i}^{t}\) and \(\lambda_{i}^{d}\)
6:end
7:for\(c=1\)to\(2\)
8:while\(MSE_{r}^{c}>\overline{MSE}\)do
9:\(r=r+1\)
10: Calculate optimal power allocation \(\rho_{i,r}^{c}\) via (29)
11: Calculate \(MSE_{r}^{c}\) via (27)
12:end
13:\(K^{c}\gets r\), \(\rho_{i}^{c^{*}}\leftarrow\rho_{i,r}\), for all \(i\)
14:end
```  
**Algorithm 3**Bandwidth Distortion Optimization
## 3 Results and Discussion
### Morphology  
Figure 6 shows the scanning electron micrographs (SEM) of the selected mix design inputs of MK/HAp after sintering at 900 \({}^{\circ}\)C. The results indicated that the MK/HAp specimens with the highest added amount of metakaolin as shown in Figure 6c,d exhibited grain growth with a visible thick HAp neck formation. At this stage, pores were reduced and resulted in lower porosity compared to specimens in Figure 6a,b. The micrographs of the HM30 specimen clearly show the necking formation that occurred during sintering. On the other hand, the specimens with the lower added amount of metakaolin, which are HM10 (Figure 6a) and HM20 (Figure 6b), showed the development of grain boundaries and grain contact growth morphology with the presence of loosely packed particles. This suggests an initial stage of the sintering mechanism that was contributed by the incomplete densification of the specimens [48]. Thus, the micrograph proved that the sintering mechanism of the activated MK/HAp specimens can be achieved at a lower sintering temperature of 900 \({}^{\circ}\)C and this is in agreement with [PERSON] et al. in their study [31]. This was attributed to the chemical composition of the metakaolin as well as the alkali activator consisting of sodium and potassium which acted as a self-fluxing agent that lowered the sintering temperature, as reported by several previous studies [31; 49; 50]. This is a significant finding because much research on the sintering of the HAp observed that the optimized sintering temperature of the HAp ceramic can only be obtained within the range from 1150 \({}^{\circ}\)C to 1250 \({}^{\circ}\)C [24; 51; 52].
## 4 Calibration and Application Experiments
### Calibration Results  
The proposed precision level was calibrated by a commercial autocollimator (AutoMAT Co. model 5000U; resolution: 0.01 arc-sec; accuracy: \(\pm 0.1\) arc-sec; repeatability: 0.05 arc-sec; uncertainty: 0.119 arc-sec). It is known that the output signal of QPD will show obvious nonlinearity in a large range [22]. In order to obtain the most suitable linear sensitivity coefficient on different measurement  
Figure 4: The leakage prevention design: liquid in the container in different tilted states.  
Figure 5: The prototype of the dual-axis precision level: 3D design and photo.  
ranges, the two inclined directions of the dual-axis precision level were calibrated for the different measurement ranges of \(\pm\)100 arc-sec, \(\pm\)400 arc-sec, and \(\pm\)800 arc-sec, separately. The results of the averaged linearity curve and residual curve of five times calibration are shown in Figure 6. It can be seen that the residual error increases with the increase in the measurement range, but the peak-to-valley value of residual errors never exceeds 1% of the measurement range. For the measurement range of \(\pm\)100 arc-sec, the peak-to-valley value of the residual was less than 0.5% of the measurement range. The calibration results show that an accuracy of \(\pm\)0.6 are-sec in the range of \(\pm\)100 arc-sec was obtained, and in the full measurement range of \(\pm\)800 arc-sec, the accuracy was \(\pm\)4 are-sec. The resolution was 0.1 arc-sec. The overall performance is very satisfying.
## 3 Results and Discussion
### Medical Aspects
#### 3.3.1 Clinical Outcomes and Challenges
[21] presented a TTC arthrodeis performed for Charcot neuropathy, completed with a 3D-printed sphere, nail, allografts, and additional screw fixation for the midfoot. The reported results at 1-year follow-up were good in terms of correcting foot deformity and achieving TTC and peritalar arthrodeis, as well as in the functional outcome. In [PERSON] et al. 's [38] retrospective study, the authors reported the 32-month follow-up results for 21 cases with severe bone destruction. TTC was performed using 3D-printed custom-made implants and retro nails and grafts. The cages had a rectangular shape, with a central hole for the nail and a truss feature to enhance the strength to weight. The reported results were good in terms of achieving fusion, functional scores, and implant survival. At the 32-month follow-up, only one below-knee amputation was necessary for pain persistence, and, in another, fusion was not achieved. In the remaining 19 cases, fusion was achieved and patients reported improved functional scores. Critical size bone defects may be a major challenge in foot and ankle surgery, especially in Charcot, diabetic, and rheumatoid foot, as well as in revision surgery. These cases may need an ankle fusion as a surgical solution. A 3D-printed implant is a valid choice in such cases, based on accurate defect restoration and appropriate fitting inside the defect, with no need for further modeling during surgery and good anchorage options. The reported results in such cases can be found in Table 1. [PERSON] et al. [16], in their study from 2022, reported a need for additional surgery in 33% of cases, while, in 26% of cases, the 3D implants needed to be removed, as the goal of achieving ankle fusion was not fulfilled. Moreover, the authors highlighted neuropathy as being one of the risk factors for the failure of arthrodeis and the need for implant removal, associated to a high degree--in 60% of cases--with infection. A different study, by [PERSON] et al. [29], on a smaller series, reported a 71% union rate when using 3D-printed cages combined with intramedullary nail fixation. This combined technique added supplementary ankle stabilization and seemed to contribute to improving the fusion rate.
## 2 Materials and Methods  
### Patient Cohort  
The results shown here are in whole or part based upon data generated by The Cancer Genome Atlas (TCGA) Research Network ([[http://cancergenome.nih.gov/](http://cancergenome.nih.gov/)]([http://cancergenome.nih.gov/](http://cancergenome.nih.gov/))) and The Cancer Imaging Archive (TCIA) (accessed on 18 May 2022) [29, 30]. No institutional review board approval was required to use information from TCIA and TCGA databases in the present study since these databases contain publicly available datasets in which all data are anonymized. MRI data were retrospectively obtained from TCIA, which contains MRimages for a subset of the LGG patients from TCGA. Corresponding molecular genetics and clinical information were obtained from TCGA. There were no missing data.  
We used a candidate prognostic three-gene signature biomarker developed in a recent publication for LGG as the genomic classifier [12]. Genomic data came from TCGA-LGG collection and were based on RNA-seq data. Based on the proposed gene signature model, the risk score [(0.4470 \(\times\) expression level of WEE1) + (\(-\)0.1530 \(\times\) expression level of CRTAC1) + (\(-\)0.3723 \(\times\) expression level of SEMA4G)] of each patient was calculated. Patients were divided into low- and high-risk groups, with the median risk score as the cut-off value. In the original publication, the high-risk group had significantly poorer survival results than the low-risk group (hazard ratio (low-risk vs. high-risk) = 0.198, 95% CI (0.120-0.325)) with this classification [12].  
All patients identified met the following criteria: (i) pathologically confirmed WHO grade II and III gliomas; (ii) available preoperative MR imaging consisting of pre-contrast axial T1-weighted (T1), post-contrast axial T1-weighted (T1C), axial T2-weighted (T2), and T2-weighted fluid attenuation inversion recovery (FLAIR) images; (iii) available genomic data for calculation of the adopted gene signature; (iv) available clinical data including age, sex, and Karnofsky performance status (KPS); (v) available ROI segmentations supplied in TCIA.
## 4 Parametric Study
### Effects of Changing of the Unit Cell Size  
To optimise the size of the proposed metamaterial, the unit cell with different substrate sizes were simulated. Based on Figure 4, the substrate of sizes 11 mm and 13 mm with a thickness of 1.6 mm exhibited three resonances with low bandwidth. Contrarily, the 9 mm substrate with the same thickness demonstrated three major resonances at 2.89 GHz, 9.42 GHz, and 15.16 GHz. Hence, the \(9\times 9\times 1.6\) mm\({}^{3}\) dimension was fixed as the proposed unit cell size.
# Single-Class Data Descriptors for Mapping
###### Abstract  
Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive samples only. _Panax notoginseng_ is a rare medicinal plant, which also has been a highly regarded traditional Chinese medicine resource in China for hundreds of years. Until now, _Panax notoginseng_ has scarcely been observed and monitored from space. Remote sensing of natural resources provides us new insights into the resource inventory of Chinese material media resources, particularly of _Panax notoginseng_. Generally, land-cover mapping involves focusing on a number of landscape classes. However, sometimes a subset or one of the classes will be the only part of interest. In term of this study, the _Panax notoginseng_ field is the right unit class. Such a situation makes single-class data descriptors (SCDDs) especially significant for specific land-cover interpretation. In this paper, we delineated the application such that a stack of SCDDs were trained for remote-sensing mapping of _Panax notoginseng_ fields through P-learning. We employed and compared SCDDs, i.e., the simple Gaussian target distribution, the robust Gaussian target distribution, the minimum covariance determinant Gaussian, the mixture of Gaussian, the auto-encoder neural network, the k-means clustering, the self-organizing map, the minimum spanning tree, the k-nearest neighbor, the incremental support vector data description, the Parzen density estimator, and the principal component analysis; as well as three ensemble classifiers, i.e., the mean, median, and voting combiners. Experiments demonstrate that most SCDDs could achieve promising classification performance. Furthermore, this work utilized a set of the elaborate samples manually collected at a pixel-level by experts, which was intended to be a benchmark dataset for the future work. The measuring performance of SCDDs gives us challenging insights to define the selection criteria and scoring proof for choosing a fine SCDD in mapping a specific landscape class. With the increment of remotely sensed satellite data of the study area, the spatial distribution of _Panax notoginseng_ could be continuously derived in the local area on the basis of SCDDs.  
m 2018 8 1448 10.3390/app8091448