esa-sceva/satcom-chunk-collection huggingface dataset:
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 2 Methods
### ILD Estimation  
The ILD of an HRIR is estimated in this paper as follows. The HRIR is passed through a 128 th order linear-phase high-pass filter at a cutoff frequency \(f_{c}=1.2\) kHz and a \(-60\) dB stop band frequency \(f_{stop}=500\) Hz, followed by a fast Fourier transform (FFT) of window size two times the number of samples in the impulse response. A single value of ILD for the HRIR in dB is then estimated, as in [41], as the mean magnitude difference between the left and right frequency bins across 30 equivalent rectangular bandwidth (ERB) frequency bands between 20 Hz-20 kHz (equating to roughly 1/3 octave intervals) such that  
\[\text{ILD}(H)=20\log_{10}\frac{|H_{left}|}{|H_{right}|} \tag{8}\]  
where \(H_{left}\) and \(H_{right}\) are the left and right signals of the HRIR, respectively.  
Figure 2 illustrates the estimated ILD of binaural Ambisonic rendering and HRIRs on the horizontal plane, for \(\{M=1,M=2,...,M=5\}\), and \(M=36\). Binaural Ambisonic renders were made using the method detailed in Section 2.1 and setting \(S=1\) in (2). The \(M=36\) renders used a 2702 pt. Lebedev loudspeaker configuration. This figure illustrates the reduced ILD reproduction of low-order Ambisonics and how this issue becomes less pronounced at higher orders of Ambisonics.
## 4 Bayesian Learning-Based Clustered-Sparse Channel Estimation
### Partition-Based Clustered-Sparse Bayesian Learning Algorithm
13:\(\mathbf{B}_{d}\leftarrow\frac{1}{L_{d}^{\prime}}\sum\limits_{i=1}^{L_{d}^{ \prime}}\frac{\mathbf{\Sigma}_{\mathbf{h}}^{d,j}+\boldsymbol{\mu}_{\mathbf{h} }^{d,j}\left(\boldsymbol{\mu}_{\mathbf{h}}^{d,j}\right)^{H}}{\gamma_{d,j}}\) using the \(L_{d}^{\prime}\) most significant continuous paths. 14:\(\beta_{d}\leftarrow\frac{\alpha_{1}}{\alpha_{0}}\), where \(\alpha_{1}\) and \(\alpha_{0}\) can be obtained through \(\mathbf{B}_{d}\). 15:endfor
16:Cluster Partition:
17:\(\mathbf{p}^{(0)}\leftarrow\text{FindIndex}(0<\boldsymbol{\gamma}_{list}< \boldsymbol{\gamma}_{th})\). 18:if\(\mathbf{p}^{(0)}\) is not empty then
19:\(\boldsymbol{\gamma}_{list}^{(old)}\leftarrow\boldsymbol{\gamma}_{list}\) and \(\boldsymbol{\gamma}_{list}[\mathbf{p}^{(0)}]\leftarrow\mathbf{0}\). 20:\(\mathbf{CLS}\leftarrow\text{Split(CLS, }\mathbf{p}^{(0)})\), where \(\mathbf{CLS}\) is splitted into \(C\) clusters according to \(\mathbf{p}^{(0)}\). 21:\(\mathbf{p}^{(1)}\leftarrow\text{FindIndex}(\boldsymbol{\gamma}_{list}\geq \boldsymbol{\gamma}_{th})\) and \(\mathbf{U}_{\mathbf{P}}\leftarrow\mathbf{U}_{\mathbf{P}}[:,\mathbf{p}^{(1)} \otimes(1:M)]\). 22:endif
23:for\(d=1,2,\ldots,C\)do
24: Update \(R_{d}\), \(\mathbf{\Gamma}_{d}\) and \(\mathbf{B}_{d}\) in \(\mathbf{CLS}\) according to \(\mathbf{p}^{(0)}\), \(\boldsymbol{\gamma}_{list}\) and \(\beta_{d}\), respectively. 25:endfor
26: Check stopping conditions:
27:\(r\gets r+1\). 28:return the sub-part of Channel Estimation until \(r\geq r_{max}\) or \(\|\gamma_{list}-\gamma_{list}^{(old)}\|_{2}^{2}<\epsilon\). 29:the pruned channel \(\boldsymbol{\mu}_{\mathbf{h}}\) with the covariance matrix \(\mathbf{\Sigma}_{\mathbf{h}}\) and the cluster list \(\mathbf{CLS}\). ```  
**Algorithm 1**Partition-Based Clustered-Sparse Bayesian Learning Algorithm
## 1 Introduction
However, in the given paper on modelling the LFC process, we are going to study the effects of energy release in a transparent material for a moving beam with a high pulse repetition rate (PRR) of up to several megahertz, for a set of long filaments, but not for almost point focusing, and also taking into account the mutual influence of filaments on each other, as well as at a step of their following, comparable or many times exceeding the spot diameter. Such a study has not been performed before. In practice, the LFC can be completely set for a particular material by four following parameters--the pulse energy inside the material \(E_{1}\), the pulse duration \(\tau\), the distance (step of following, or pitch) \(s_{1}\) between neighbour filaments, and the PRR \(f\). The optimal values of these parameters, resulting in high-energy efficiency, and/or highly smooth sidewalls of the cut, and/or high-throughput cutting results should be determined in the model depending on the optical, thermophysical and mechanical strength properties of the material, as well as the irradiation parameters. Two of the mentioned parameters, \(E_{1}\) and \(\tau\), have been considered as the first stage of modelling in our previous work [39]. In the given paper as the second stage of analytical modelling, two other parameters from above mentioned are modelled and quantitatively calculated--the step of following between neighbour filaments \(s_{1}\) and the PRR \(f\). The focus of the research is on the achievement of an important feature for practical applications--a so-called \"smooth\" cutting processing mode--where the surface of the sidewalls of the cut has a better smoothness and does not contain small cracks and roughness of micron size, which may arise when the cut propagates through the material. Obtainingsuch a result is of great practical importance since cut sidewalls with better smoothness are highly resistant to damage and cracking due to mechanical impacts during further use of the workpiece, for example, as displays and protective plates in mobile phones, and also car windshields, etc. A strategy for obtaining such a better smoothness cutting is formulated. It consists of fulfilling several necessary conditions. Their implementation, in turn, requires, as the model shows, the use of certain irradiation parameters --the energy and duration of the laser pulse, the step of the filaments following in the material, the pulse repetition rate--as well as of certain material parameters and their matching with each other. Along with this, LFC modes are predicted that are characterized by high throughput, provide very high cutting speeds of the order of 1-25 m/s or more, depending on the material, which are tens or hundreds of times higher than traditional mechanical cutting speeds or allow cutting with an extremely low average laser power, but with an acceptable speed for applications.
# Low-Photon Counts Coherent Modulation Imaging via Generalized Alternating Projection Algorithm
## 5 Conclusions  
We proposed a reconstruction algorithm for low-photon count coherent modulation imaging. The algorithm uses the total variation constraint under the GAP framework. We compared the newly proposed CMI-GAP algorithm with the current algorithm for CMI using extensive simulations and experiments of various levels of illumination photons. The CMI-GAP algorithm still preserves the fast convergence speed and the high robustness against noise of CMI. Our results show the method outperforms the current CMI algorithm in convergence and image quality when the flux of illumination is lower than \(10^{7}\). When the number of photons was reduced below \(10^{6}\), CMI failed completely, while some sample features could still be retrieved with the CMI-GAP algorithm. CMI-GAP is robust to noise, with enhancement as much as 20 dB on PSNR and three times on RMS.  
The CMI-GAP method could be further extended. First, the algorithmic parameters can be adaptively adjusted to the photon flux level, for which the Adam optimizer could be adopted. Second, the dataset statistics could be incorporated into the algorithm. Third, the continuity of diffraction patterns may be used as prior. It is necessary to mention that the convergence rate will decrease substantially when the computation array gets larger. The possibility of using the ADMM algorithm to accelerate the convergence is worth investigating.  
Figure 9: Reconstruction of the far-field CMI experimental data. Top-left: Reconstructed amplitude and phase with original algorithm. Top-right: Reconstructions with proposed algorithm. Bottom-left: Reconstructions of original algorithm with 2 \(\times\) 2 subdivision. Bottom-right: Reconstructions of proposed algorithm with 2 \(\times\) 2 subdivision.  
**Author Contributions:** Methodology, M.S., T.L. and F.Z.; Resources, G.B. and Y.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Natural National Science Foundation of China (NSFC) (12074167, 117/5105); Shenzhen Key Laboratory of Robotics Perception and Intelligence (ZDSYS20200810171800001), Southern University of Science and Technology; Shenzhen Science and Technology Program (Grant No. KQTD20170810110313773). Centers for Mechanical Engineering Research and Education at MIT and SUSTech (MechERE Centers at MIT and SUSTech) (No. 6941806); S&T Program of Hebei (No. SZX2020034).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Acknowledgments:** F.Z. acknowledges [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for the acquisition of the X-ray data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks
## 3 Experimental Results
### Surface Inspection for the Backplate of a GPU Card  
In addition to a gold finger connector, the experiments for surface inspection for the backplate of a display card are also considered. The training set for the experiments contains 100 images (i.e., \(K=100\)) augmented from the 16 normal samples of the backplate of the display card. The dimension of images \(S_{k}\), \(X_{k}\), and \(Y_{k}\) for the experiments is \(M=512\times 512\). Figure 13 shows examples of normal and defective samples of the backplate and their corresponding templates produced by the AE. We can observe from Figure 13 that accurate reconstruction is possible for normal samples. Furthermore, the AE is able to remove most of the defective regions for the flawed samples. Therefore, similar to the results shown in Figure 9 for the gold finger area, images produced by the AE can also be effectively used as templates for the backplates for surface inspection.  
Due to the high complexities of the surface of a backplate, it may be necessary to separate the surface of the backplate into more than two regions. An example is to separate the surface into five regions. An individual region can then be inspected independently with its own threshold value. The segmentation results produced by the FCN for various test images are shown in Figure 14, where each region is associated with a different color.  
Figure 13: Examples of the input test images and their reconstructions by the AE for the backplate surface of a display card: (**a**) normal test sample; (**b**) reconstructed normal test sample by the AE; (**c**) defective test sample; (**d**) reconstructed defective test sample by the AE.  
From Figure 14, we see that the fan of the backplate is separated into blue and yellow areas, where the blue regions indicate the fan blades and fan center. The remaining part of the fan is colored by yellow. The area outside the fan of the shell is segmented into three regions, labeled by green, red, and black colors, respectively. The green region has higher brightness than that of the other two areas. On the contrary, the black region has lower brightness. We can observe from Figure 14 that the FCN is able to effectively separate each test image into these different regions for subsequent inspections.  
Figure 15 shows some visualization results for the inspection of the backplate surface. We can observe from Figure 15 that the proposed algorithm is able to identify the defective areas effectively, even though some areas are actually small. In addition to the effectiveness of the AE for producing the templates, the high accuracy for the segmentation of templates by the FCN plays a key role in the surface inspection. The segmentation process allows the inspection for each region to be carried out independently by selecting the threshold best-matched to that region.
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
In addition, a reluctance branch describes the leakage flux of how the core window inner corner is modeled, which could contribute to improving the accuracy of the inductance especially when the core is saturated. By the series and parallel combination of the reluctance branches, the flux density distribution of the corner is fully described and accurately calculated, and the inductance can also be accurately calculated. The rest of this paper organized as follows. In Section 2, the local MEC model of the corner is established and the corresponding reluctance calculation approach is demonstrated. Section 3 models the square core-type inductor MEC and gives the methods to calculate the inductance. Section 4 describes the experiments and analysis, where the core branch flux density \(\mathbf{B}\) distribution of the local corner MEC model and FEM simulation are compared. Then, the inductance with the current of the local corner MEC model and IEC model are compared to physical prototype measured results, whereas the relative errors are analyzed. The number of corner branches is also discussed. Section 4 concludes the study and gives further research points.
## 7 Validation of Quantitative HydroSAR Information Layers
### Validating HYDRO30
Figure 13b shows a similar comparison for the Brahmaputra River in Bangladesh, with SAR data from 13 September 2022 and forecasting information from 12 September 2022. Good correspondence for the SAR-observed and forecasted water extents can be seen, with both data sources indicating similar areas becoming covered by water. Note that the forecast products only captured riverine flooding and did not include the standing water bodies seen in HYDRO30 at locations away from the river. Figure 11: Validation area used to compare HYDRO30 water extent products to water extent maps derived from Landsat-8 and Sentinel-2. The site included the Missouri River at the corner of Nebraska, Kansas, and Missouri in the USA. These comparisons to multispectral and forecasting data, together with positive feedback from end users in the region, give us confidence in the performance and validity of the HYDRO30 product. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline Skill Score & S1: 04/04 & S1: 06/27 & S1: 08/02 & S1: 08/26 & S1: 11/13 \\  & S2: 03/13 & S2: 06/24 & S2: 07/19 & S2: 08/28 & S2: 11/12 \\ \hline Accuracy & 0.98 & 0.99 & 0.99 & 0.99 & 0.99 \\ Precision & 0.79 & 0.85 & 0.71 & 0.64 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example performance metrics for five pairs of SAR (S1) and optically derived (S2) surface water extent estimates over an area of the Missouri River. SAR water extents were derived using the HyDRO30 algorithm. Optical water detections were extracted using the mNDWI. Acquisition dates are shown for each SAR/optical pair. Data were selected to be within a 5 day window of each other to ensure they measured similar water extents. Accuracy and precision are reported as skill scores. These were calculated by using the optical water detections as a reference. Figure 12: Comparison of HYDRO30 surface water detections of information derived from Sentinel-2 data using the mNDWI. Data acquired over the Missouri River in Nebraska (Sentinel-2 on 4 April 2019; Sentinel-1 on 31 March 2019). Figure 13: Examples of qualitative comparisons of HYDRO30 water extent products (blue) with Google-provided flood forecasts (hash pattern): **(a)** Kameng River in India, with SAR and forecast data from 26 June 2021, and **(b)** Brahnaputra River in Bangladesh, with SAR data from 13 September 2022 and forecast data from 12 September 2022.
## 5 Experimental Results
### PMSM Motion Control Experiment
Moreover, compared with the PMSM-DC, the rate of reduction of the AVG, RMS, and MAX errors are 59.72%, 57.04%, and 46.64%, respectively. Therefore, the PMSM motion control experiment can validate that, for the helical path, the PCB-ILCC can effectively reduce contour errors and exhibit performance with robust control and disturbance suppression. Figure 8: Variations in RMS of contour errors (PMSM motion control experiment). Figure 9: Contour error variations in PMSM motion control experiment.
# Nanotechnology, Nanomedicine, and the Kidney
## 4 Nanotechnologies for Renal Imaging
### Nuclear  
Nuclear imaging, including positron emission tomography (PET) and single-photon emission computed tomography (SPECT), is used to monitor biological processes in real time using radioisotope labeling; thus, removing the depth limitation observed in optical microscopy. However, radiations, however small, present a risk for the patient's health. Therefore, radiolabeled nanoparticles have been developed to reduce radiation doses and improve the imaging sensitivity of renal nuclear imaging. [PERSON] et al., applied ultra-small glutathione-coated gold nanoparticles (Au-GSH) to measure organ kinetics [43]. NPs were labeled with radioactive copper-64 (\({}^{64}\)Cu), which has a half-life of 12.7 h, to form \({}^{64}\)Cu-NOTA-Au-GSH, where NOTA was the chelator. Using dynamic PET imaging, the authors could monitor the biodistribution and the elimination of \({}^{64}\)Cu-NOTA-Au-GSH from the kidney [43]. More recently, [PERSON] et al., synthesized renal clearable nanometer-sized polyoxometalate (POM) clusters (\(\sim\)1 nm in diameter) radiolabeled with Zirconium (\({}^{89}\)Zr). The formed nanoparticle \({}^{89}\)Zr-POM presented various advantages. First, using PET imaging, the authors demonstrated the possibility of distinguishing the early vs. end-stage of kidney dysfunction in a UUO model [44]. In addition, POM NPs possess antioxidative properties that are useful in scavenging ROS in AKI mice, a key component of the AKI pathophysiology and a target for novel therapies [45]. Moreover, the POM cluster can self-assemble to larger nanostructures when sensing an acidic tumor microenvironment. Consequently, compared to free \({}^{89}\)Zr, larger radiolabeled nanostructures were retained in tumors for up to 24 h. Finally, POM clusters have a strong NIR adsorption and are, thus, helpful for photothermal imaging and therapy in tumors [44].
## 3 Results
### Article Screening Process  
To select the scientific publications to be analyzed in this systematic review, the articles identified in the initial search were passed through a series of exclusion criteria (EC) [49]. Figure 5 shows the procedure and its results at each stage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Approach** & **Field** & **Question** & **Value** \\ \hline DEC9 & Content-based perspective & Measurement Timescale & RQ3: What timescale is used to record measurements with sensor technologies? & e.g., manual measurements (not in real-time), and/or automatic measurements (in real time) \\ \hline DEC10 & Content-based perspective & Measurement Spatial Scale & RQ4: Are the data measurements conducted remotely or on-site with sensor technologies? & e.g., on-site measurements (in place), and/or remote measurements (using remote sensors) \\ \hline DEC11 & Content-based perspective & Sensor Technology & RQ5: What types of sensor technology are applied? & e.g., piezometer sensor, deformation sensor, air quality sensor, among others \\ \hline DEC12 & Content-based perspective & Sensor Connectivity & RQ6: What connectivity technology is applied in sensors? & e.g., using cables, fiber optics, or wireless methods such as WiFi, Zigbee, Bluetooth, Xbee, or LoRa, among others \\ \hline DEC13 & Content-based perspective & TSF Area Application & RQ7: Where are sensors applied within a mine TSF? & e.g., tailings transport area, TSF dam area, and TSF reservoir area, among others \\ \hline DEC14 & Content-based perspective & Technologies in Industry 4.0 & RQ8: What technologies related to Industry 4.0 are currently being linked to sensors? & e.g., data analytics, machine learning (ML), Internet of Things (IoT), and artificial intelligence (AI), among others \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 5: Scientific publication selection flowchart.  
Figure 5 shows the extraction of publications from the WoS and Scopus databases with the combinations of keywords described above, which resulted in 194 articles. A first filter or exclusion criterion 1 (EC1) was used on these publications to eliminate duplicate documents, leaving 95 articles. Then, a second filter or exclusion criterion 2 (EC2) was used, which consisted of analyzing the publications' titles to determine which were related to sensors used to monitor mine TSFs, resulting in 76 articles. Finally, the last filter or exclusion criterion 3 (EC3) was applied, in which the full text of the abstracts was assessed, leaving 52 articles for analysis. These 52 selected publications deal specifically with sensors used for health, structural, and safety management monitoring in mine TSFs.
## 3 Methodology Description  
### Exponential Moving Average  
An exponential moving average (EMA) is a type of moving average (MA) that emphasizes the most recent data points (i.e., it is a type of weighted MA where more weight is given to the latest data points as the weight exponentially decreases for older data points) [45]. The EMA is also called the exponential weighted moving average (EWMA) or the exponential smoothing method [7].  
The EMA model can be described using the following equation [45]:  
\[\hat{E}_{t+1}\ =\ \ E_{t}\cdot K+(1-K)\cdot\hat{E}_{t} \tag{1}\]  
where the weighted multiplier \(K\) is given by \(K\) = 2/(\(N+1\)), with \(N\) as the period of the EMA calculation. Moreover, \(E_{t}\) is the SPVG at the current time, \(\hat{E}_{t}\) is the estimated EMA of the SPVG at a previous time, and \(\hat{E}_{t+1}\) is the future value of the estimated SPVG.  
The EWMA [46] is a concept that expands the formula of EMA when more weighting factors exist through the repeated application of the formula using different times. It is expressed using the following equation [46]:  
\[\begin{array}{rcl}\hat{E}_{t}&=&K\cdot\left[PV_{t}\ +(1-K)\cdot PV_{t-1}+(1-K)^{2 }\ \cdot PV_{t-2}\ +\ \cdots\ +\ (1-K)^{\varphi}\cdot PV_{t-\varphi}\right]+\\ &&(1-K)^{\varphi+1}\cdot\hat{E}_{t-(\varphi+1)}\end{array} \tag{2}\]  
for any suitable \(\varphi\in(0,\ 1,\ 2,\ldots,\ n)\).