esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
### Implementation Details  
We learned six different CNN models separately in singular and hierarchical manners for the facial age classification task. The deep learning framework PyTorch [72] from Facebook's artificial research lab was used for network training. For faster processing and to facilitate a parallel computing platform, the Nvidia GeForce GTX 1080 Ti graphics processing unit (GPU) with 3584 CUDA cores and 11 GB of video memory was utilized. The overall training time for the large IMDB-WIKI dataset took almost half a day to train a deep network, whereas a light network only took a couple of hours.  
Figure 6: Exemplar images from IMDB-WIKI dataset after face mask overlay.  
For the age classification, we computed the expected age value using the Softmax probabilities corresponding to output neurons, similar to [6]. We trained the models designed in a singular and hierarchical fashion with a relatively balanced, synthetically generated masked face IMDB-WIKI dataset. Every age was considered as an individual class that ranged from 0 to 100 in the singular approach. Likewise, one model for age groups and three specific models were trained for individual groups in the hierarchical approach. We finetuned the deployed models with our experimented dataset and reshaped the network with a new output layer. In finetuning, we started with a pre-trained model and updated all the model's parameters for our task. Although both the source and target network performed classification, we performed finetuning rather than feature extraction because real age classification is a bit different from other object classification techniques. Hence, the output layer was trained from scratch, while the parameters of all the other layers were finetuned based on the parameters of the source model.  
For all experiments regarding masked face real age estimation, the CNN was initialized with the weights trained on ImageNet [73]. This pre-trained model was then further finetuned on the IMDB-WIKI image dataset for real age prediction. Finally, the CNN was tested with the in-the-wild synthetically masked face image dataset IMDB-WIKI. Every training set in either age estimation approach utilized 80% of the images for training, and the rest, 20%, was used for the system evaluation as a validation set and test set. We used separate validation and test sets that equally comprised 20% of the total samples to validate the system. We maintained the de facto data split strategy to ensure that the validation and testing set minimally contained data from all the age classes. We used weighted cross-entropy to ensure the same weight for all classes during the course of learning. The training was optimized with Adam optimizer, keeping the momentum value of 0.9, and a standard weight decay rate was maintained to ensure the regularization. The learning rate was adjusted, while the loss values did not decrease for five consecutive epochs.
## 2 Materials and Methods
### Data Sources and Preprocessing
#### 2.2.2 Vegetation data  
The vegetation data include GPP, ET, NDVI, EVI, and LAI, all derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments developed by the United States National Aeronautics and Space Administration (NASA). The relevant product numbers are: MODIS MYD17A2H, MODIS MOD16A2, MODIS MOD13C1, MODIS MOD13A1, and MODIS MOD13A1. The temporal resolution of GPP and ET data is 1 month, while the temporal resolution of the remaining data is 1 year. These data were downloaded  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Data Classification** & **Data Name** & \
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & **Year** & **Data Source** \\ \hline Land-use type data & CLCD & 1 year & 30 m & 2002–2022 & China Land Cover Dataset \\  & GPP/gC\(\cdot\)m\({}^{-2}\) & 1 month & 500 m & 2002–2022 & MODIS MYD17A2H \\ Vegetation data & ET/mm & 1 month & 500 m & 2002–2022 & MODIS MOD16A2 \\  & EVI & 1 year & 1000 m & MODIS MOD13A1 \\  & LAI & 1 year & 500 m & 2002–2022 & MODIS MOD13A1 \\  & T/\({}^{\circ}\)C & 1 year & 1000 m & 2002–2022 & National Earth System \\ Meteorological data & & & & & & \\  & Pre/mm & 1 year & 1000 m & 2002–2022 & Science Data Center \\  & VPD/Kpa & 1 year & 1000 m & 2002–2022 & TerraClimate \\ \hline \end{tabular}
\end{table}
Table 1: Types and sources of data.  
Figure 1: **(a)** Geographic location of Xinjiang. **(b)** Topographic features of Xinjiang. **(c)** The Xinjiang cropland region and the areas in which it is located.  
from the Google Earth Engine (GEE) platform ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/)) accessed on 1 December 2024) with a time series spanning from 2002 to 2022. Vegetation data were extracted using a mask derived from the CLCD data for Xinjiang's cropland region, yielding vegetation data for this area from 2002 to 2022. These data were then resampled to 1000 m using the nearest-neighbor method (Table 1).  
The meteorological data include temperature (T), precipitation (Pre), and vapor pressure deficit (VPD). Temperature (T) and precipitation (Pre) data are sourced from the National Earth System Science Data Center ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)) accessed on 7 December 2024), with a spatial resolution of 1 km and a temporal resolution of 1 year. VPD data are from the TerraClimate reanalysis dataset ([[https://www.climatologylab.org/terraclimate.html/](https://www.climatologylab.org/terraclimate.html/)]([https://www.climatologylab.org/terraclimate.html/](https://www.climatologylab.org/terraclimate.html/)) accessed on 9 December 2024), with a spatial resolution of 1 km and a temporal resolution of 1 year, and were downloaded from the GEE platform. The meteorological data were extracted using a mask derived from the Xinjiang cropland region's CLCD data to obtain the meteorological data for the Xinjiang cropland region from 2002 to 2022 (Table 1).
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 3 Noise in Compressive Sensing Measurements
### Mutual Coherence of a Matrix  
From [1], we can incorporate the following equations for CS analysis. First, we define \(\mu(A)\) as given in [1]:  
\[\mu(A)=\underset{1\leq i<j}{max}\frac{|<a_{i},a_{j}>|}{||a_{i}||_{2}||a_{j}||_ {2}} \tag{17}\]  
where \(A\) is the measurement matrix, and \(a\) represents a column of \(A\).  
Given \(A\), the sparsity of the signal, or the number of non-zero elements, \(k\), in a signal is given by Equation (18) [1].  
\[k<\frac{1}{2}\bigg{(}1+\frac{1}{\mu(A)}\bigg{)} \tag{18}\]  
Hence, we want \(\mu(A)\) to be as low as possible, in order to increase the bound for \(k\). This will ensure a higher accuracy in CS reconstruction with a higher sparsity tolerance in signals.
## 2 Materials and Methods  
### System Design  
#### 2.1.1 Layout of the BL08U1A Beamline at the SSRF  
The BL08U1A beamline at the SSRF is a soft X-ray spectromroscopy beamline with a 250-2000 eV energy range [16; 17], as shown in Figure 1. An elliptically polarized undulator (EPU) was used to produce soft X-ray photons. Slit 1 was set at a distance of 20 m downstream from the EPU to ensure that the acceptance angle was within \(\pm\)0.04 mrad in both horizontal and vertical directions. Then, an elliptical cylindrical mirror (M1), set at a distance of 30 m downstream from the EPU, was used to collimate the photon beam and suppress high-order harmonics using a water-cooling scheme that absorbed the heat load. The plane grating monochromator (PGM) set at 32 m consists of two Au-coated gratings including an 800 line/mm for 250-750 eV and a 1200 line/mm for 275-2000 eV. The last optical element, an elliptical cylindrical mirror (M2), was used to focus the photon beam both in the horizontal and vertical directions at Slit 2. The BL08U1A beamline began operating in 2009 with a scanning transmission X-ray microscopy (STXM) end station (End station 1 in Figure 1). An independent in situ spectroscopic end station was first installed in 2014 and then updated in 2018 [18], and a state-of-the-art ptychography-STXM end station (End station 2 in Figure 1) was installed in 2019. These three end stations have proven to be extremely useful for studies in environmental science [19; 20], physics [21], chemical, materials, and energy sciences [22; 23].  
The BL08U1A beamline is capable of a variety of experimental methods, including imaging methods such as STXM [24; 25], nano-CT [26; 27; 28], and ptychography [29; 30], as well as XAS methods such as total electron yield (TEY), X-ray Magnetic Circular Dichroism (XMCD), X-ray Magnetic Linear Dichroism (XMLD), and X-ray-Excited Optical Luminescence (XEOL) [31; 32]. In order to obtain satisfactory experimental results, XAS methods require the incident X-ray flux to be measured for data normalization, and all XAS and imaging methods require a high and stable incident X-ray light flux to improve the signal-to-noise ratio. BL08U1A beamline operators typically need to manually adjust the M2 position before the experiment to achieve optimal flux. However, it is impossible to observe the flux variation in real time and make corrections during the experiment, which may lead to a decrease in the experimental data quality and even lead to experimental failure in long-time imaging methods. Therefore, it is imperative to develop an automatic feedback  
Figure 1: Layout of the BL08U1A beamline at the SSRF. The M1 elliptical cylindrical mirror collimates the X-ray and suppresses high-order harmonics. The M2 elliptical cylindrical mirror focuses the X-ray in both directions through Slit 2. The tunable X-ray energy is 200–2000 eV. A spectroscopic end station and two STXM end stations are located downstream of the beamline.  
system that aligns the beamline and maintains the optimal X-ray light flux in order to achieve enhanced experimental efficiency and results.
## 5 Inclusion Complex Formation
### Effects of pH on Complex Formation  
The effectiveness of the adsorbent, as well as the interactions and formation of a complex between CD and ionizable guest elements, is contingent upon the pH levels [18; 39]. Variations in pH can result in the presence of heavy metals in different states (e.g., free ions, hydroxides, complexes), with only specific forms being amenable to interaction with cyclodextrin molecules. The cyclodextrin polymer possesses deprotonated hydroxyl groups, which exhibit a strong affinity for metal ions under primary provisions. The solidity against environmental factors was enhanced by the polymerization of the poly-CD/metal ion complex in conjunction with polyacid. The pH is crucial in deciding how effective CDs are in the remediation of heavy metals by influencing the speciation and charge of the metal ions, which subsequently affects their capacity to engage with the hydrophobic cavity of cyclodextrin; at optimal pH levels, cyclodextrin can effectively encapsulate and eliminate heavy-metal ions from a solution, whereas extreme pH values may impede this process by modifying the availability of metal ions for complexation [18]. The acid-base equilibria constitute an essential consideration due to the ionizable nature of many guest molecules, whose charge is relative to the pH of the solution. A given molecule's neutral and ionized forms exhibit divergent affinities for cyclodextrin. Numerous guest components are classified as ionizable substances, and the pH of the solution dictates the distribution among the various species of the guest molecule, which may exist in multiple ionization states (e.g., M+N\({}^{-}\) and N\({}^{-}\)). The guest molecules of polyprotic acids, bases, or amphoteric compounds may exhibit more than two ionization states. The neutral species of a guest molecule typically exhibits the most robust interaction with cyclodextrin, exhibiting greater affinity due to its hydrophobic characteristics. When the guest element is ionizable, the stability constant is a function of pH. This property can be exploited to manipulate the stability coefficient of a solution by altering the pH [39].
# Pixel Circuit Designs for Active Matrix Displays
## 4 Micro-LED Circuit Design
Some researchers have used NMOS, PMOS, and CMOS to drive micro-LEDs and found that CMOS used to drive micro-LEDs performs the best in terms of switching characteristics and does not suffer from incomplete turn-off [108]. [PERSON], [PERSON] et al. proposed a MOSFET pixel circuit (Figure 9c) for micro-LED using PAM driving, which addresses the leakage current and IR drop problems [96]. For the switching branch, the circuit uses M1 and M2, and C\({}_{\text{G1}}\) and C\({}_{\text{G2}}\) to form a \"T-switch\" structure, where C\({}_{\text{G1}}\) and C\({}_{\text{G2}}\) create a \(V_{\text{SAMPLE}}\) node, which is normally \(V_{\text{SAMPLE}}=V_{\text{DATA}}\). Delaying the change in \(V_{\text{SAMPLE}}\) with C\({}_{\text{G1}}\) and C\({}_{\text{G2}}\) reduces the difference between \(V_{\text{GATE}}\) and \(V_{\text{SAMPLE}}\). As a result, the \(V_{\text{DS}}\) of M2 is close to zero, reducing the leakage current of M2. For the driver branch, the combination of the \(V_{\text{CS}}\) of M\({}_{\text{DRV}\_{\text{-N}}}\) and M\({}_{\text{DRV}\_{\text{-P}}}\) determined the bias current of the OLED. By adding MOSFET, the \(V_{\text{GS}}\) deviation of the driving circuit is made independent of any ground reference, thus effectively suppressing the IR drop problem. [PERSON], [PERSON][PERSON] et al. used CMOS technology for the design of micro-LED display drivers with 10-bit current-mode PWM. They improved the driving speed of current-mode PWM by using two switching transistors for PWM control and scanning control, respectively. In addition, the use of PMOS transistors in the pixel circuit effectively prevents current spikes [109]. [PERSON]-[PERSON] et al. proposed an A-PWM MOSFET pixel circuit aimed at reducing the nonlinearity of the emitted grey level of the micro-LED display, where two transistors and a CMOS component together form an intra-pixel SRAM for stabilising the node voltages and fast switching when needed, thus reducing the rise or fall delay of the micro-LED emission [110].
## 2 Bolometers' Design and Experimental Set-Ups  
The design of antenna-coupled Ti-mB and its cross section are presented in Figure 1. As it can be seen, the device is constructed using air-bridged approach. The dual dipole THz antenna is coupled to the bolometer and serves as a filter that aims to efficiently collect the incident THz radiation and convert its energy to temperature change. The Ti-mB and THz antenna were processed on a few microns thin SiN membrane to enhance the sensitivity and the response time via reduction of thermal losses of the instrument. The metalized bottom plate under the antenna-bolometer acts as reflecting mirror, additionally amplifying the received signal on the dipole. More detailed description on the design and the technology used to manufacture such devices can be found in Refs. [23; 24].  
Experimental set-ups for terahertz beam profiles imaging and their control are shown in Figure 2. All experiments were performed using raster scanning technique with one pixel detector from linear array of 4 or 32 pixels to obtain beam profile images in _xy_ plane. The measuring time per pixel in most experiments was in between 10 ms and 20 ms. The technical details on used equipment and measurement techniques are presented in Section 4.
## 5 Discussions  
The exploration of the rapidly evolving tidal flat in Shanghai serves as a poignant illustration of the multifaceted nature of coastal topography, bringing to light the intricate dynamics and the urgent need for innovative monitoring and assessment strategies. This case study underscores the significance of employing advanced technological solutions to understand and manage the complexities inherent in such environments effectively.  
The tidal flat's rapid formation and constant change challenge traditional monitoring methods, necessitating a shift towards more agile, precise, and comprehensive approaches. UAV technology, especially when augmented with RTK and LiDAR sensors, emerges as a pivotal tool in this context. It enables the detailed mapping of coastal areas with good accuracy, allowing for a nuanced understanding of the morphological changes occurring over short periods. Such detailed data collection is instrumental in developing models that can predict future changes and identify potential risk areas before they pose a significant threat to ecosystems and human activities. Comparing the findings with previous studies, it is evident that while UAVs have been increasingly applied in coastal studies, the combined use of RTK and LiDAR for enhanced accuracy and detailed 3D terrain mapping represents a notable advancement. Previous works have demonstrated the utility of drones for monitoring beaches or tidal flats, yet often focused either on the UAV technology alone or the application of digital twins without deeply integrating UAV-derived data with ocean models for a comprehensive risk assessment. This study's approach of leveraging a digital twin framework enriched with precise UAV data and well-performing ocean modeling techniques provides a more dynamic and interactive tool for understanding and managing coastal risks.  
Figure 12: The simulated progression depicts the tidal level rising while an individual stands in the tidal channel. (**A–C**) represent moments ranging from the low tidal level to the high tidal level.  
Moreover, the Shanghai case study highlights the broader implications for coastal management strategies worldwide. As coastal environments continue to face pressures from natural phenomena and human-induced changes, the demand for adaptive and forward-thinking management approaches becomes increasingly critical. The integration of UAV-derived data into digital twin frameworks exemplifies how technological innovation can enhance our capacity to visualize, understand, and interact with complex coastal systems. This integration not only supports the immediate needs of risk assessment and mitigation but also contributes to the long-term sustainability of coastal zones by providing a basis for informed decision-making and proactive management. In order to further illustrate the universality of the digital twin framework introduced in this paper, we will discuss the following aspects.
## 2 Review of Related Works
### Infrared Thermography  
A thermal image is a particular type of visual information that represents thermal radiation into the visible range perceived by the human eye [28]. Thermal cameras and other infrared measurement instruments capture infrared radiation emitted by an object and convert it into an electronic signal which indicates temperature difference on the output thermal image [29; 30]. Several research works that have been published on the uses of infrared thermography for the field of photovoltaic systems follow below. In previous research presented in [31], infrared thermography was utilized for the detection of solar panel defects in terms of panel assembly and arrangement; again, the scarcity of the dataset used was one of the drawbacks of this work because of the limited period in which the dataset was acquired--in this work, the number of samples in the dataset was too small, which affected the results reliability--and the authors did not disclose even the basic information of the measured solar cell modules.  
In [32] the authors used a machine learning technique based on the extracted features of thermal images to classify defective and non-defective solar panels. However, the main limitation in the proposed approach was the insufficient number of available datasets, which affected the model's reliability. A previous study [33] presented a scheme for the image processing technique of thermal images for the automatic identification of solar modules that occur in a hot spot, and the experimental results exhibited 97% accuracy in the visual evaluation. Having a finite quantity of datasets does not make a model accurate. The author should have used publicly available datasets that contained sufficient images to make their results more reliable. Moreover, the research only used a bounded dataset. The authors of [34] implemented a similar set-up for aerial thermal image acquisition. The reliability of the model was not entirely dependable with a confined set. It suffered when an open-source dataset was used for verification.  
In the proposed scheme, the dataset images were also taken over 2 years to provide multiple variants of the thermal images. Using thermal images to detect even small temperature changes can provide insight into the health of a solar panel. Moreover, utilizing thermal imaging to inspect solar panels does not require a shutdown of the system. Finally, in this paper, various image processing techniques are implemented to enhance the aforementioned thermal images to provide an easy-to-understand domain for fault assessment. The significance of the cumulative distributive function in image processing was utilized for analysis.
## 4 Experimental Results
### Testing Results  
We applied the trained ADN and NADN models to 50 test scenarios to evaluate the generalization ability of the decision networks. Table 6 shows the total profit and runtimes for NADN and ADN. The results of ADN_LS are presented to verify the performance of the proposed local search. It can be observed from the table that the total profit of the algorithms increases with an increasing number of tasks. Algorithms with attention layers outperformed the NADN. ADN_LS obtained the highest total profit in all scenarios and consumed the longest running time. Nevertheless, for scenarios with up to 1000 tasks, ADN_LS could complete scheduling within 18 s.  
In the scenario with 100 tasks, all three algorithms achieved the same total profits. Figure 8 shows the average profit acquisition rates of the three algorithms under different scenarios. In the 100-task scenarios, the profit acquisition rates of the three algorithms were all equal to 1, indicating that all tasks in the scenarios were successfully scheduled. Figure 8 shows that the profit acquisition rate decreases as the number of tasks increases. This is because the task load in the scenarios (excluding the 100-task scenarios) exceeds the resource capacity of the AEOS. Meanwhile, an increase in the total profit indicates that the decision network can make good selections of tasks to execute from excess tasks.  
Table 6 suggests that ADN_LS performed better than ADN, but the difference is not significant, as shown in Figure 8. To ascertain the significance of the results, we performed a two-tailed Wilcoxon signed-rank test [50] for the pairing of ADN_LS and ADN (neither the results of each algorithm nor the differences between the pairs are normally distributed; therefore, we did not apply Student's \(t\)-test or the pa  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{3}{c}{**TP**} & \multicolumn{3}{c}{**Runtime (s)**} \\ \cline{2-7}  & **NADN** & **ADN** & **ADN\_LS** & **NADN** & **ADN** & **ADN\_LS** \\ \hline
100 & 540.2 & 540.2 & 540.2 & 0.600 & 0.688 & 1.016 \\
200 & 996.93 & 1050.8 & 1051.8 & 0.987 & 1.010 &  
results suggest that ADN_LS significantly outperformed ADN (\(p\) = 0.000) at the 1% level of significance; therefore, the attention layers and local search can improve the performance of the proposed algorithms.