esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Phase Transfer Entropy and Directed Phase-Amplitude Interactions
#### 3.1.2 Experimental Setup  
We simulate 100 pairs (trials) of 2 s long signals, sampled at 1000 Hz, with directed phase-amplitude interactions from the \(\theta\) band (\(f_{l}=6\) Hz) to the \(\beta\) band (\(f_{h}=24\) Hz), with a timeshift of 20 ms. We detrend and normalized the simulated signals, before contaminating them with normalized white (\(\eta\)) and pink (\(\eta_{p}\)) noise, as follows:  
\[\mathbf{x}_{\eta}=\mathbf{x}+10^{-\frac{\text{SNR}}{24}}\left(0.6\eta^{ \mathbf{x}}+0.4\eta_{p}^{\mathbf{x}}\right),\]  
\[\mathbf{y}_{\eta}=\mathbf{y}+10^{-\frac{\text{SNR}}{24}}\left(0.6\eta^{ \mathbf{y}}+0.4\eta_{p}^{\mathbf{y}}\right),\]  
where the parameter SNR controls the signal to noise ratio. We vary this to simulate low (\(\text{SNR}=6\)), moderate (\(\text{SNR}=3\)), and high (\(\text{SNR}=1\)) noise conditions. For each scenario, we also mix the noisy signals, \(\mathbf{x}_{\eta}\) and \(\mathbf{y}_{\eta}\), aiming to reproduce the effects of volume conduction, by defining \(\mathbf{x}_{\eta}^{w}=\left(1-\frac{w}{2}\right)\mathbf{x}_{\eta}+\left( \frac{w}{2}\right)\mathbf{y}_{\eta}\) and \(\mathbf{y}_{\eta}^{w}=\left(1-\frac{w}{2}\right)\mathbf{y}_{\eta}+\left( \frac{w}{2}\right)\mathbf{x}_{\eta}\), with \(w=0.25\) the mixing strength. Then, we downsample \(\mathbf{x}_{\eta}^{w}\) and \(\mathbf{y}_{\eta}^{w}\) by a factor of 4 (we keep only every fourth sample) to 250 Hz, and estimate the directed phase-amplitude interactions present in the data for a square frequency grid ranging from 3 Hz to 45 Hz, in 3 Hz steps, using the three approaches described in Section 2. Finally, to determine whether the estimated interactions are statistically significant, we perform permutation tests based on randomized surrogate trials [19; 36] for each evaluated condition and frequency pair. The Bonferroni-corrected significance level for the tests is set at \(4.4\times 10^{-5}\).
# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
## 2 Related Works
### Instance-Level 6D Pose Estimation  
Instance-level 6D pose estimation aims to predict the translation and rotation of an object based on its precise 3D CAD mode 1 [14,15]. Methods such as [16, 17, 18] compare feature points extracted from all pixels within the region of interest with those on the CAD model to obtain 2D-3D correspondences, after which the 6D pose of the target object is calculated using the Perspective-n-Point (PnP) algorithm [19]. Other approaches like [9,20,21] render a series of images from different angles using the 3D CAD model, and then apply a sliding window algorithm or voting mechanism to match the RGB image with the generated renderings, selecting the highest similarity match as the object pose. Additionally, methods like [22,23] directly regress the 6D pose of the object through neural networks.
# Integration of Production Planning and Scheduling Based on RTN Representation under Uncertainties
## 2 Mathematical Model
### Scheduling Layer Model  
The continuous-time modeling method was adopted to establish the scheduling layer model. The optimization objective of the scheduling layer is to complete the production tasks assigned from the planning layer as much as possible. Therefore, the minimization of the value of the difference between the task amount assigned by the planning layer and the task amount completed by the scheduling layer was taken as the objective function. Resource balance constraint, capacity constraint, demand constraint, time constraint, sequence constraint, utility supply and utility time constraint were also considered. In scheduling layer, fuzzy theory is utilized to describe the uncertainty of the utility. Finally, the uncertainty model of utility is transformed into a deterministic model with membership degree by corresponding mathematical method.
## 3 Experiment Results and Analyses
### Impact Velocity  
In this experiment, if different air pressures are used to drive the impact bar, the impact bar will move at different velocities and then strike the incident bar. Figure 7 illustrates the variation in loading velocity with impact air pressure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Impact** \\ **Pressure (MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Specimen** \\ **Number** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Dynamic** \\ **Compressive** \\ **Strength (MPa)** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Dynamic Strain** \\ **(\(\times\)10\({}^{-3}\))** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Dynamic Elastic** \\ **Modulus (GPa)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Average Strain** \\ **Rate (s\({}^{-1}\))** \\ \end{tabular} } \\ \hline
0.300 & PJ31-07 & 71.47 & 3.49 & 22.94 & 51.4 \\  & PJ31-08 & 76.57 & 3.88 & 23.35 & 55.5 \\ \hline
0.325 & PJ31-10 & 80.71 & 3.97 & 24.59 & 63.5 \\  & PJ31-12 & 83.94 & 4.19 & 24.05 & 67.0 \\ \hline
0.350 & PJ31-13 & 94.67 & 4.39 & 25.92 & 78.2 \\  & PJ31-14 & 92.93 & 4.48 & 25.34 & 80.3 \\ \hline
0.375 & PJ31-17 & 91.20 & 4.62 & 27.24 & 92.1 \\  & PJ31-18 & 97.12 & 4.90 & 26.19 & 95.8 \\ \hline
0.400 & PJ31-19 & 99.22 & 5.39 & 28.50 & 100.0 \\  & PJ31-20 & 99.29 & 5.03 & 27.09 & 103.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results for prefabricated fractured rock samples.  
Figure 6: Dynamic stress–strain curve of sandstone samples with prefabricated cracks under axial compression.  
Figure 7 indicates that the loading rate rises with increasing shock air pressure, which increases in an approximately linear manner. If the pressure of the impact air is the same, then the speed of the load will basically be the same.
## 2 Materials and Methods
### Data Source and Preprocessing  
NPPs that were derived from the MODIS17A3H dataset were available at [[https://ladsweb.nascom.nasa.gov/](https://ladsweb.nascom.nasa.gov/)]([https://ladsweb.nascom.nasa.gov/](https://ladsweb.nascom.nasa.gov/)) (accessed on 21 January 2023), with temporal and spatial resolution resolutions of 1 year and 500 m. The MRT (MODIS Reprojection Tool) tool was utilized to reproject and format the vegetation NPP data. The climate data came from the China Meteorological Data Network ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 24 January 2023)), which included daily precipitation, daily average temperature, accumulated temperature (sum of temperatures greater than 10 \({}^{\circ}\)C), and sunshine data. Because kriging interpolation may cause interpolation errors compared with traditional methods, the difference result is more accurate. These datasets were interpolated into 1 km grids using ArcGIS 10.7 (Environmental Systems Research Institute, Inc., Redlands, CA, USA). Land use, socioeconomic (populationdensity and GDP), and geographic partition data were retrieved from [[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)) (accessed on 21 January 2023). The elevation and slope data were extracted from SRTM 90 m, which was downloaded at [[http://www.Gscloud.cn](http://www.Gscloud.cn)]([http://www.Gscloud.cn](http://www.Gscloud.cn)) (accessed on 23 January 2023). Based on the Arcgis10.7 Clip tool for cutting, the abnormal values for the vegetation NPP data were removed, resampled to 1 km, and projected to a Krasovskii projection. The related driving factors and references are shown in Table 1. The soil distribution map, temperature and precipitation distribution map selected by driving factors are shown in Figures 2 and 3.
## 3 Test Results
### Vibration Response around the Site  
During construction, vibration acceleration is split into vertical and horizontal directions. Upward motion is considered positive on the z-axis, and horizontal vibration's maximum is grad (\(x,y\)). By subtracting initial bias, \(\Delta\)\(z\) and \(\Delta\) grad (\(x,y\)) are derived for change values, as depicted in Figure 9.  
Figure 8: Radial variation of excess pore pressure ratio of PGP pile.  
It displays the vibration magnitude caused by two types of pile during construction. The driven pile produces a maximum vertical acceleration of 0.008 g at the pile driver, which can be uncomfortable. At 20 m, amplitude is 0.005 g, which is still perceptible. At 50 m, pipeline fluctuation is 0.001 g, which is affecting it. For driven piles, max horizontal acceleration at the driver surpasses 0.24 g, causing severe discomfort. At 20 m, horizontal acceleration is around 0.002 g. Horizontal vibration attenuates more quickly than vertical. Vertical vibration is significant at a factory wall 20 m away. At 50 m, 0.001 g fluctuation impacts transport pipelines.  
The max vertical acceleration during PGP pile construction on the drill rig is 0.035 g, which is considered tolerable. Initial drilling causes a 0.0025 g vertical vibration on the pile driver situated 10 m from the pile driver. After 310 min, the drilling stops, and the pile driver starts, maintaining 0.0025 g of vertical vibration at 10 m. The highest horizontal acceleration of the PGP pile, measuring 0.15 g, is observed on the drill rig, below the maximum of 0.14 g from the pile driver. No vibration changes are observed at a 20-m distance. At the start of construction, the drill rig induces 0.05 g of horizontal vibration on the pile driver, 10 m away from the pile driver. Later, after 310 min, with the drilling ceased and pile driving initiated, the drill rig experiences 0.05 g of horizontal vibration at 10 m from the pile driver.  
Figure 9: Vibration acceleration change curve during construction. (**a**) Vertical direction of driven pile; (**b**) Horizontal direction of driven pile; (**c**) Vertical direction of PGP pile; and (**d**) Horizontal direction of PGP pile.  
Figure 10 plots peak ground acceleration (PGA) against distance to demonstrate attenuation. The human body can feel the vibration at 0.003 g, feel uncomfortable at 0.05 g, and cannot tolerate it at 0.5 g. Considering that the horizontal vibration is greater than the vertical vibration, the driven pile construction should be 25 m away before the human body can tolerate it. If there is no perception, it should be 40 m away. However, the PGP pile only needs to endure at 15 m away. The vibration cannot be felt at 25 m away.
## 2 Materials and Methods
### Dataset Construction
#### 2.4.1 Data Labeling  
The sample images were analyzed by visual interpretation using true-color synthesis (4-3-2), and then manually annotated. Visual interpretation has very high accuracy [33; 34]. In Figure 3, the areas with smoke are marked with red boxes.  
Figure 3: The 4-3-2 band composite image.  
Figure 2: Sample of (**a**) positive and (**b**) negative images.  
In Figure 3, we can see the thin smoke produced by burning straw. During sampling, it was found that the 4-3-2 band composite images do not show the thin smoke clearly enough, so false color synthesis is necessary [34]. In this paper, for the synthesis, we considered one fire index (the modified normalized difference fire index (MNDFI)), two combustion indices (the burned area index (BAI) and the normalized burn ratio (NBR)), the normalized difference tillage index (NDTI), the modified crop residue cover (MCRC), and the normalized difference vegetation index (NDVI), as listed in Table 3.  
B12 is the reflection value of the Band 12, B8 is the reflection value of the Band 8, B4 is the reflection value of the Band 4, B11 is the reflection value of the Band 11, and B3 is the reflection value of the Band 3.  
Combining any three of the indices in Table 3, finally, the six best combinations based on the visual interpretation were selected and presented in Figure 4. The resulting synthetic images corresponding to Figure 3 are shown in Figure 4. The combination BAI-MCRC-MNDFI shows the thin smoke over land more clearly than the other combinations. Therefore, the analysis of the image samples in this paper was mainly based on the 4-3-2 band composite images, supplemented by images synthesized with the BAI-MCRC-MNDFI combination. Then, the images were labeled with boxes using the LabellImg tool. Since the base model uses YOLOv5s, all images were saved in YOLO format after the labeling was completed. For each image, the text file contained details such as the category of the sample and the label box dimensions (width and height of the label box, and center point coordinates).
# High-Luminance Mid-Air Image Display for Outdoor Viewing by Focusing Sunlight
## 1 Introduction  
Augmented reality (AR) technology is currently used in the entertainment industry to make fictional characters appear as if they exist in real space. One means of achieving AR is by superimposing images on an HMD of a device worn by the user, or on a tablet or other device. However, methods are also available that do not burden users, such as displaying virtual images of computer graphics (CG) characters by using a half-mirror or superimposing CG images on images captured by a camera to display the characters on the screen of a tablet device. The CG characters displayed in the former are virtual images, whereas the characters are displayed only on the screen in the latter; this makes it difficult for observers to interact with the characters in the real world.  
Mid-air imaging technology, in which real images are formed in the air by reflecting and refracting the light emitted from a light source, is one of the most useful methods for creating CG and real space. However, it is difficult to observe a mid-air image in bright locations owing to its low luminance. Therefore, it is challenging to use mid-air images outdoors or in bright rooms; thus, the environments in which mid-air images can be used are limited. To address this problem, we propose increasing the luminance of the light source to display a high-luminance mid-air image.  
In this study, an optical system for displaying mid-air images with high luminosity is proposed to make such images visible in outdoor environments. As the brightness of a mid-air image is proportional to the brightness of the light source, using a high-luminance display as the light source can produce a high-luminance mid-air image. However, a high-luminance display suffers from the problem of high the power consumption of the backlight, which is not suitable for the design of a high-luminancedisplay considering the sustainable development goals (SGDs). A high-luminance display with low power consumption is realized using concentrated sunlight as backlight for a transparent LCD, and a high-luminance mid-air image is displayed. The design of the light concentrator is optimized using lighting simulation software. Moreover, two experiments are conducted to select a diffuser for the display and to measure the luminance of the mid-air image displayed by collecting sunlight.
## 2 Methods
### Pre-Process Analysis
#### 2.2.1 Raw Data Sanity Checks  
Before the actual data analysis begins, the LPP performs automated sanity checks to ensure the integrity of the measurements. These checks help to detect hardware failures, misconfigurations, and transmission errors that could compromise the data. By implementing these safeguards, the system can identify issues early, ensuring early warnings to users about possible issues and that only high-quality data are processed.  
An initial routine verifies that the LIDAR system has detected a sufficient number of LUTR readout channels. The absence of active channels may indicate hardware disconnection or miscommunication. If no channels are detected, the system immediately stops the analysis and logs an error, prompting the user to inspect the hardware and connections. In addition, the LPP inspects each channel for zero-valued data arrays, indicating issues such as a disconnected signal cable or an inactive or too low PMT high-voltage setting. If all channels produce zero data, the analysis is stopped and the user is asked to review the system's connections and data acquisition processes. If only certain channels are affected, they are flagged and excluded from further analysis to prevent biased results.  
The efficiency of the PC channels is evaluated by analyzing the percentage of data with non-zero counts. Channels where fewer than a configurable percentage (default: 20%) of data register photon counts are flagged as unreliable.  
Oscillations in detected signals can introduce noise and distort results. To investigate this effect, the LPP performs spectral analysis of the amplitude channels to detect oscillations that exceed predefined thresholds. The LPP generates Power Spectral Density (PSD) plots to highlight irregular oscillations. If oscillations are found, the system logs an error and suggests checking the signal stability or hardware.  
Figure 6 shows the PSD as a function of frequency for a readout channel affected by an oscillation of the ground line with a frequency near 0.24 MHz. The supply line of that channel was fixed later with appropriate low-pass filters.
## 5 Low-Power Cellular Technologies for IoT
### NB-IoT
#### 5.2.3 NB-IoT Positioning Accuracy  
Positioning accuracy is an enhancement of the NB-IoT network that could increase the application of NB-IoT across different sectors. Positioning accuracy is a vital aspect of IoT and applicable to most use cases such as asset tracking, environmental monitoring, wearable, and smart agriculture. The NB-IoT release 13 on which the test was based can only associate the UE to the serving cell. Release 14 and other higher release standards provide enhanced serving cell identity (SCID) measurement and observed time difference of arrival (OTDOA), which are more reliable options of obtaining the UE position more accurately. The time advance (TA) eCID is a round-trip measurement (initial round trip time (iRTT)) between the eNodeB and the UE, measured as 48.5 \(\upmu\)s, for sending 44 bytes of data through a 5744-window size. The value was used to compare the UE position to the serving cell. The OTDOA uses the measurement of the time-of-arrival (ToA) on the set of a DL narrowband positioning reference signals (NPRSs) from a set of time-synchronised eNodeBs serving the UE. In LoRaWAN, the time-of-difference arrival (TDoA) and received signal strength indicator (RSSI) are two methods of determining IoT module position. It is important to state that the methods discussed above do not require the use of Global Positioning System (GPS) or Assisted Global Positioning System (AGPS) which would make IoT devices more complex, expensive, and power hungry [67].
## 3 DMD Algorithm
\[\frac{d\mathbf{X}}{dt}=\mathbf{A}x\quad\rightarrow\quad x_{(i,t)}=\sum_{k=1} ^{n}\Phi_{k}e^{\omega_{k}t}b_{k} \tag{6}\]  
where \(\Phi_{k}\) and \(b_{k}\) represent the complex-valued spatial distributions that correspond to a particular dynamic mode and coefficients of the initial conditions, respectively. The term \(x_{(i,t)}\) denotes the state of the flow scalar \(x\) corresponding to the \(i\)th grid element at time \(t\). The real part of \(\Phi_{k}\) represents the physical displacement or shape of the mode at different points in space while the imaginary part corresponds to the phase shifts, indicating oscillatory motion over time if there is an oscillatory component in the eigenvalue. Matrix **A** is an \(m\times m\) matrix that can become very large. For example, in a 3D flow field with millions of grid points and thousands of time snapshots, matrix **A** could contain on the order of \(10^{12}\) elements. Storing such a matrix requires terabytes of memory. Additionally, the eigendecomposition of this large, sparse matrix using classical methods may become unstable and costly, and there is no guarantee that useful dominant dynamic modes can be extracted. Therefore, solutions are needed to address these issues. First, we decompose matrix **X** using the singular value decomposition (SVD). In linear algebra, SVD is a factorization method that decomposes a real or complex matrix into an orthogonal eigenbasis. The orthogonal basis functions from SVD provide more stable solutions (see Equation (7)). \[\textbf{X}^{\textbf{k}}\approx\textbf{U}\Sigma\textbf{V}^{*} \tag{7}\]  
where **U**, \(\Sigma\), and **V** are the left unitary matrix (rotation), singular values (rescaler), and right unitary matrix (rotation), respectively. This guarantees the existence of eigenvalues and eigenvectors. To avoid the \(m\times m\) matrix, we use reduced SVD, also known as economy SVD. This approach provides a unitary matrix **U** of size \(m\times(n-1)\), singular values of size \((n-1)\times(n-1)\), and a unitary matrix **V** of size \((n-1)\times(n-1)\). By substituting these decomposed matrices into Equation (1), we can obtain matrix **A** as follows:  
\[\textbf{A}=\textbf{X}^{(k+1)}\textbf{V}\Sigma^{-1}\textbf{U}^{*} \tag{8}\]  
The size of matrix **A** remains \(m\times m\). For the final rank reduction, we can add another step by choosing a reduction rank manually, which can vary depending on the application. We can select this rank \(r\) to be less than the total number of snapshots, or it may be equal to the total number of snapshots. \[\tilde{\textbf{A}}=\textbf{U}^{*}\textbf{X}^{(k+1)}\textbf{V}\Sigma^{-1} \tag{9}\]  
The eigenvalues and eigenvectors of matrix \(\tilde{\textbf{A}}\) provide useful information about spatial phases and their unique temporal characteristics.
## 5 Structural Vulnerability
In the case of the ductile mechanisms such as the flexural behavior of the RC cores, the design spectra were obtained by dividing the elastic spectrum by the behavior factor \(q=3.00\). Conversely, in the case of brittle mechanisms such as the shear failure of RC cores, the seismic actions were defined by reducing the elastic spectrum by a behavior factor \(q=1.50\). Moreover, evaluation of the maximum inter-story drift was carried out using \(q=1.00\) for the relevant spectrum. Reference is made to Section 7.3 of NTC2018. For the structural typology under investigation, the failure of steel members can be considered a brittle mechanism. Therefore, for such members, a behavior factor \(q=1.50\) was used. However, all the safety checks concerning the columns were satisfied, and the demand-to-capacity ratios were less severe than those occurring under the gravitational load combinations. For these reasons, the results are not reported in the manuscript. Figure 10 shows two significant views of the obtained results. In particular, Figure 10a reports the demand-to-capacity ratios of the RC cores under bending and axial actions, while Figure 10b refers to shear actions. The seismic performance indices (\(\zeta_{E}\)) were defined according to the following relationship:  
\[\zeta_{E}=\frac{\text{PGA}_{\text{CLS}}}{\text{PGA}_{\text{DLS}}} \tag{6}\]  
where PGA\({}_{\text{CLS}}\) is the value of the peak ground acceleration corresponding to the achievement of a specific limit state (capacity), while PGA\({}_{\text{DLS}}\) is the value of the peak ground acceleration for the specific limit state (demand). Their numerical values are shown in Table 10 for each considered limit state. For the SLO limit state, the performance indices were evaluated based on the maximum inter-story drift (D). Conversely, in the case of the SLV and SLD limit states, the performance indices were computed for combined bending and compression actions of the RC cores (FF) and the shear failure of each RC wall (SF_w). When reviewing the numerical results, it can be noted that the main failure mechanisms are related to the resistance of the RC cores, in particular, the shear behavior of the concrete walls dominates. When comparing these results with those provided in [17], some differences can be noted. In particular, the numerical values provided in Table 10 are lower than those presented in Table 1. The reasons are mainly related to the different seismic analysis procedures employed to evaluate the structural vulnerability of the building.