esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Approach
### Rolling-Shutter Effect  
Most cameras, e.g., smartphones and webcams as well as most DSLR (Digital Single-lens Reflex) cameras, are using a rolling shutter. This means the picture is not captured as a whole, but line by line from top to bottom. When taking a photo with a DSLR camera, the shutter opens from top to bottom, then the image sensor is exposed to light, then the shutter closes again from top to bottom. This procedure for a rather slow shutter speed or long exposure time, respectively, of 1/30 of a second is depicted in the images in Figure 1 recorded with a high-speed camera. Figure 2 shows the same recorded operation with the exposure time set to 1/8000 of a second. One can see that immediately after the shutter starts to open, it already starts closing. Only a narrow gap is opened to pass through light to the image sensor and this gap moves from the top of the image to the bottom. This also means that the top of the image is captured earlier than the bottom part of the image.  
If the scene changes while the image is captured, the rolling-shutter effect gets visible in the image. As shown in Figure 3, the propeller of the plane moved while the image was captured and appears skewed in the image; however, when taking the image with a camera using a global shutter, no distortion happens. The same effect appears when taking an image of a fast-flickering light source. The light turns on and off while the image is captured, and a stripe pattern is visible in the image. The number and the width of those stripes hldepend on the frequency of the flickering of the light source, as shown in Figure 4.  
As [PERSON] et al. [1] showed, the stripe pattern in the resulting image is only visible in those parts of the image that are occupied by the modulated light source. Hence, if the modulated light source only takes up a small part of the image, only a small part of the stripe pattern can be captured by the camera. As shown in Figure 5, if the taillights of a car  
Figure 1: Rolling shutter at 1/30 s exposure time [19].  
Figure 2: Rolling shutter at 1/8000 s exposure time [19].  
are modulated and the camera is far away from the car, only one stripe or state, respectively, is visible in the image. However, the captured state of the taillight, depends on the position of the taillight within the frame, which is also visible in Figure 5. In the top image sequence, the car's vertical position is slightly lower and hence, in the rightmost image, captured from big distance, a bright part of the stripe pattern is shown in the area of the taillights. In the bottom image sequence, the vertical position is slightly higher and hence, a different part of the stripe pattern is visible, which is dark. Thus, from one single image, just the current states of the taillights in the image can be detected; however, the receiver is not able to get any information regarding encoded data from it.
## 5 Conclusions  
The COSMIC profiles of temperature, pressure, vapor pressure, and refractivity from 0 to 40 km are compared with radiosonde soundings in China between 2014 and 2015. Results from this study show that the average temperature difference between COSMIC and radiosonde data is \(-0.10\) K, and the temperature difference is positively correlated with the station altitude. The temperature difference increases in the QTP region, mainly because of its complex topography and the limited ability of the model used for COSMIC profile retrieval. An average bias of 0.69 hPa is found between COSMIC and radiosonde pressure profiles. Although the pressure difference is negatively correlated with station latitude, it is generally larger in the QTP region than in other regions at the same latitude because of model uncertainty and the complex terrain in the QTP area. A small difference in vapor pressure is found between COSMIC and radiosonde data, with a bias of \(-0.01\) hPa. As with the pressure, the vapor pressure difference is negatively correlated with station latitude.  
Atmospheric water vapor influences the measurement accuracy of COSMIC refractivity. The average refractivity bias between COSMIC and radiosonde data is 0.17 N, and the refractivity difference is negatively correlated with station latitude. Moreover, the negative refractivity bias between COSMIC and radiosonde data occurs below 5 km, and the refractivity difference is large in wet southern China, with a value of less than 1%. This result may be related to more humid conditions and super-refraction.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 41375057, 41375041, 41620104009, and 91637211) and the open project of the Institute of Plateau Meteorology, CMA, Chengdu, through Grant LPM2014009. The authors are grateful to the UCAR and the TACC for providing the COSMIC werff products. Special thanks to all reviewers for providing a lot of useful suggestions.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed to data processing; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Recording Conditions  
The most common recording parameters were (1) sampling rate of 44.1 kHz and (2) 16-bit depth [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52]. However, other sampling frequencies (e.g., 96 kHz and 16 kHz) and bit depths (e.g., 32 bits and 24 bits) were selected [10; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70]. In the case of speech sounds, the highest frequencies of the human voice are in the limit of 3 kHz; therefore, having a sampling frequency of 8 kHz should be enough to adequately capture all the information of the mouth according to the Nyquist theorem [71].  
According to the reviewed literature, specialized devices with external microphones have been reported, and microphone placement ranged between 25 and 50 cm [33; 39; 40; 42; 44; 45; 52; 60; 61; 72; 73; 74; 75; 76]. Those distances seemed to achieve a good representation of the information obtained. Some studies included filters, such as low-pass, band-pass, and high-pass filters, to reduce unwanted noises [37; 39; 44; 51; 68; 77; 78; 79; 80; 81; 82].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**What Is the** & **What Was Done to** & **How Was the** & **What Was Used to** & **Which Are the Fields** \\
**Main Objective?** & **the Signal?** & **Signal Acquired?** & **Process the Signal?** & **of Study?** \\ \hline Sound & Processing & Recording & Algorithm & Application \\ Mouth & Analysis & Acquisition & Code & COVID-19 \\ Audio & Extraction & Conditions & Devices & Coronavirus \\ Features & Classification & & Instruments & SARS-CoV-2 \\ Characteristics & & & & \\ \hline \end{tabular} COVID-19: Coronavirus disease 2019.  
\end{table}
Table 1: List of words used for this research. These keywords were used in the different databases to limit the research of material to only the topics of interest.  
Figure 4: Most common trends across studies, from mouth sounds to applications. Here, important aspects regarding the processing and recording of mouth sounds, as well as the application of their analysis, are described.  
In many studies, the most used software packages for signal analysis were PRAAT [41; 51; 53; 60; 69; 73; 83; 84; 85; 86; 87; 88; 89] and MATLAB [39; 44; 59; 74; 57; 77; 81; 90; 91]. Moreover, Audacity was commonly used for audio recording [38; 41; 43; 50; 61; 64; 84; 92; 93; 94].  
Recording hardware portability was found to play an important role in data acquisition. The most common devices were smartphones [41; 44; 52; 61; 63; 64; 68; 69; 93; 95; 96] and small recorders, such as H4 Zoom [46; 54], H5 Zoom [42; 97], H6 Zoom [57], and Olympus [51; 98]. On the other hand, the most used microphones were headsets and head-mounted microphones from many different manufacturers [39; 45; 47; 48; 53; 66; 70; 74; 75; 89; 99; 100; 101]. From this, we can find that there is no specialized and standardized hardware for signal acquisition. Hence, there is a need to establish it.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 4 Experiment
### Evaluation Metrics  
To evaluate the performance of our proposed method, we employed both full-reference and no-reference quality assessment metrics. For paired datasets, we used the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) [64] to assess distortion, as well as the Learned Perceptual Image Patch Similarity (LPIPS) [65] to evaluate the perceptual quality based on human visual perception. For unpaired datasets, we used no reference perceptual quality metrics, including the Natural Image Quality Evaluator (NIQE) [66], to assess image quality in scenarios where reference images are unavailable, ensuring a comprehensive evaluation of the effectiveness of the method.
## 3 Results
### Validation of ET  
ET is the amount of water that land surface and plants release to the atmosphere [9]. The ET outputs from the DA and OL runs are evaluated against GLEAM ET according to the NIC of NCRMSE (Figure 3). GLASS LAI DA shows an overall improvement except for some areas in the western region of CONUS. SMAP DA shows limited improvement and some degradation in the random error over the southwest part of CONUS. This region often experiences severe droughts, is located nearby the coast, and characterized by complex terrain. Therefore, the soil moisture observed by the SMAP satellite may not be accurate enough to improve the ET model simulation [64]. Such degradation due to the SMAP DA is also translated into the joint DA run, which nonetheless presents the same improvement provided by the GLASS LAI DA in the remainder of the domain.  
Next, the performance of the three DA techniques proposed in this work is evaluated based on the difference in ACC (Figure 4). Similar to results obtained from the analysis of the random error, GLASS LAI shows an overall improvement in ACC compared to the original model simulation. The SMAP DA shows a degradation in the southwestern region but slightly higher correlations (compared to the ones obtained by the OL) in the rest of the study area. The joint DA map again reflects the ACC from both GLASS LAI DA and SMAP DA.  
Figure 2: Percentage difference in the estimation of LAI, ET, NEE, SSM, and RZSM between DA and OL: GLASS LAI DA (left panels), SMAP SM DA (central panels), and joint DA (right panels). Red color highlights a decrease in the variable estimation after the application of DA, whereas blue color shows an increase after DA.  
Figure 4: Difference of ACC between DA and OL ET with respect to GLEAM ET from 2016 to 2018. Blue (red) color indicates improvement (degradation) after DA.  
Figure 3: NIC of NCRMSE between DA and OL ET with respect to GLEAM ET from 2016 to 2018. Blue (red) color indicates improvement (degradation) after the application of DA.
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 3 Numerical Experiments
### Data Generation  
Modal analysis is conducted using the finite element model above to generate the training dataset for both the input and output. As mentioned before, 7 frequencies and their corresponding mode shape parameters at 14 beam-column joints are measured and defined as the input. Seventy elemental stiffness parameters are generated as the output. The output is normalized to the range between 0 and 1, where 0 represents the totally damaged state whereas 1 represents the intact state of the structure element. For an instance, if a particular stiffness parameter is equivalent to 0.9, it indicates there is 10% stiffness reduction at this element.  
Both single damage and multiple damage cases are considered in the 70 elements model. For single damage cases, there are total 2100 data sets generated from the baseline model based on the varying stiffness parameter of each element from 1, 0.99, 0.98... to 0.7 with leaving the other elements undamaged. Hence, for single element damage cases, 30 data sets are generated for each element with introducing a local damage. While 10,300 data sets are generated for multiple damage cases, two or more elements are considered as damage statuses with the stiffness parameter changing from 1, 0.99, 0.98... to 0.7. Meanwhile, the other elements are considered as intact statuses. Overall, 12,400 baseline data sets are generated based on the finite element model for training and validation.  
Apart from the clean baseline datasets, noise datasets with measurement noise and uncertainty are also included in our study to further investigate the effectiveness and robustness of our proposed parallel structured model. For the noise datasets, 1% and 5% Gaussian noises are added to first seven frequencies and the associated mode shapes respectively with consideration of structural frequencies measured more accurately as reported in [21]. Besides, 1% uncertainty is considered in the stiffness parameters to simulate the finite element modelling errors. We expect to make the proposed model trained by these noise data more robust for predicting unknown data with measurement noises and uncertainties. Both baseline datasets and noise datasets will be used in comparison with the state-of-the-art models and the proposed model in the following experiments.
## 4 Analysis and Discussion
### Characteristics of the Reflected Pulse Wave in Monitoring Interval II  
This study attempts to locate the flaw in the non-detection zone using the reflected pulse wave in monitoring Interval I and monitoring Interval II based on a simulation and experimental results. The primary reasons are listed below. Firstly, the amplitude of the reflected pulse in the monitoring Interval II exceeds that encountered in the following monitoring intervals. Secondly, because the existence of the flaw, an increasing number of derivative pulse waves will occur as the propagation time increases; the condition of the pulse wave superposition is simpler in monitoring Interval II than in the following monitoring Intervals. Therefore, we used the square steel bar with a length of 6000 mm as an example and analyzed the variation of the reflected pulse waveform in monitoring Interval II.  
Due to the variation of the pulse width of the terminal-reflected pulse wave and the waveform superposition in the actual experiments, the non-detection zone is observed in advance when \(L=5300\) mm. Therefore, this study focused on the simulation range of [5300 mm, 5900 mm] with \(D0=10\) mm and the simulation interval of 100 mm. The analysis indicates that the amplitude changed with the changes in \(D0\), leading to new changes in the envelope superposition of the incident pulse in Interval II and especially in the superposition of the amplitudes. We used \(D0=10\) mm as an example of the representative and significant waveform change; other values of \(D0\) can also be used for the same method.  
Figure 8 shows the simulation results. It can be observed that, as the flaw location (\(L\)) changes, the amplitude of the superposed pulse wave exhibits complex changes in the time domain, especially in Interval II. Moreover, as the flaw location (\(L\)) changes, the superposition envelope of multiple reflected pulses changes significantly in monitoring Interval II, i.e., both of these factors showed a one-to-one correspondence. Within the monitoring Interval II, there exists multiple reflected wave energy; at first, at around \(L=5300\)-5500 mm, the highest reflected pulse gradually moves backward; at around \(L=5600\)-5700 mm, the two highest reflected pulses appear in adjacent time-domains; at around \(L=5800\)-5900 mm, the flaw position is close to the terminal, the energy gradually becomes concentrated, and the amplitudes of these flaw-reflected pulse waves are significantly higher than the amplitude of the first terminal reflected pulse wave for the two conditions. Therefore, we attempted to use the above-mentioned correspondence and estimated the location of the flaw through reverse deduction based on envelope comparison.  
Figure 7: Curves of the non-detection zone ranges for different materials.  
Figure 8: Simulated waveforms of the square steel bar with a length of 6000 mm at \(L\in\) [5300 mm, 5900 mm].
## 3 Proposed Energy Management Strategy
### Fuzzy Logic Control  
The deterministic rule-based methods are not adaptive. But fuzzy logic control can deal with multi-variable systems with uncertainties. The advantages of the proposed fuzzy logic in this paper are listed as follows: the FLC is used to reduce fluctuations of FC and battery output power; presented FLC reduces operational modes of the proposed EMS, and FLC Keeps battery SOC and UC VDR in the determined regions. The proposed FLC has defined with two outputs {\(P_{FC}\), \(P_{Bat}\)} and four inputs {demand power, SOC, VDR, VSM}. The presented concepts for states are used to define SOC, VDR, and VSM trapezoidal membership functions. The complete data set of proposed fuzzy logic control can be found in [28]. The fuzzy subset of demand power is divided into {Low, Negative, Max, Medium, Zero}; the fuzzy subset of SOC is divided into {BHER, BLCR, BLER, BMER}; and the fuzzy subset of VDR is divided into {UHC, UHER, UMER}. Instead of classifying driving cycles,  
Figure 4: State division of Lead-acid battery based on BCE.  
Figure 5: Typical MAXWELL ultracapacitor combined efficiency.  
the movement conditions of the vehicle are described using five linguistic trapezoidal terms, including {Acc, Brk, Id, Reg, Tra}.  
In this paper, the Mamdani inference procedure is used to design fuzzy supervisor. Furthermore, FC output power subsets are described using {OFF, FCHER, FCP3, FCMER, FCP5, FCLER}; and the battery output power subset is defined using {Ch, Zero, LD, MD, HD}. In this paper, the FLC rules are formulated as \"If inputs are \(A_{i}\), Then outputs are \(B_{i}\),\" and number of fuzzy rules are equal to \(3\times 4\times 5\times 5=300\). This FLC generates the initial reference value for the FC and battery output power, and the ECMS will be distributed the HESS power in the next step of proposed multi-level EMS.
# Object Detection Using Multi-Scale Balanced Sampling
## Experiments  
### Benchmarks  
The proposed approach are evaluated on two datasets: Object Detection in Aerial Images(DOTA) and e Unmanned Aerial Vehicle Benchmark(UAVB) [15; 16], the detail of them are as the follow:  
1. DOTA contains over 2000 remote images. All of the images are large size about over \(4000\times 4000\) pixels. Images are annotated by experts in aerial and remote image interpretation using 15 common object categories, such as plane, ship, harbor, etc. The objects' distribution of each category are shown in Figure 5a, the abbreviation of each category will be shown in Table 2.
2. UAVB contains a unmanned aerial vehicle dataset, each frame is of the size about \(560\times 1000\) pixels and contains high density small objects. Vehicle category include car, truck and bus. The objects' distribution of each category are shown in Figure 5b.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **SSD** & **RetinaNet** & **SRetiinaNet** & **FPN** & **GA-RPN** & **Libra RCNN** & **MB-RPN** \\ \hline plane (pl) & 81.3 & 88.7 & 86.2 & 88.9 & 88.4 & 89.7 & 90.2 \\ baseball-diamond (bd) & 50.6 & 64.5 & 57.8 & 75.8 & 75.2 & 73.6 & 77.0 \\ bridge (bg) & 39.1 & 47.5 & 21.4 & 48.6 & 43.1 & 49.7 & 53.0 \\ ground-track-field (gtf) & 44.2 & 49.0 & 49.5 & 53.9 & 53.5 & 54.8 & 53.3 \\ small-vehicle (sv) & 56.0 & 58.1 & 61.1 & 63.8 & 67.7 & 70.3 & 70.4 \\ large-vehicle (lv) & 54.2 & 57.8 & 59.0 & 63.6 & 66.3 & 65.0 & 66.0 \\ ship (sp) & 61.1 & 69.2 & 74.4 & 76.7 & 77.0 & 77.1 & 76.8 \\ tennis-court (tc) & 84.3 & 88.2 & 70.3 & 90.7 & 90.1 & 90.8 & 90.8 \\ basketball-court (bc) & 68.6 & 73.5 & 58.1 & 78.2 & 78.4 & 75.0 & 78.9 \\ storage-tank (st) & 61.5 & 75.6 & 80.5 & 81.0 & 84.1 & 83.0 & 83.7 \\ soccer-ball-field (gbf) & 21.3 & 28.4 & 16.7 & 36.5 & 36.8 & 37.2 & 41.2 \\ roundabout (ra) & 43.6 & 49.1 & 55.2 & 56.8 & 58.9 & 60.0 & 59.4 \\ harbor (hb) & 51.6 & 56.7 & 35.9 & 67.3 & 66.7 & 67.6 & 68.3 \\ swimming-pool (st) & 47.4 & 61.1 & 64.3 & 71.1 & 72.0 & 72.6 & 70.9 \\ helicopter (hl) & 28.0 & 38.6 & 57.1 & 44.7 & 50.3 & 62.6 & 56.5 \\ mAP & 56.9 & 60.6 & 58.1 & 65.5 & 66.8 & 67.6 & 68.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative performance(AP%) of our model on DOTA benchmark datasets compared with comparison approaches. The best performance on each category is colored in red.  
Both the DOTA and UAVB dataset contain all kinds scale and small objects account a large proportion. It means that both the ability of detecting small objects and all the scale of objects are important.
## 5 Experiments
### SPORTS Classification Experiment with DCNN  
Data set with images of different sports; from here, 30,000 pairs have been taken for training and 5000 for testing. The neural network is DCNN, the same as the previous experiment, except for the dimension of the input layer: \(21\times 28\). The entries are grayscale images to classify into ten different classes. This is the experiment in that the low spatial resolution of the images, as well as the fact that they capture a moment of sports practice, makes the task of recognition more difficult.  
Ten training processes of three epochs are executed with each of the algorithms. Batch-size 256, except for FDEKF. The loss function is MSE. For REKF: \(\mathbf{P}_{0}=l,\mathbf{Q}_{0}=l\cdot 10^{-5}\), \(\mathbf{R}=1\). For FDEKF: \(\mathbf{P}_{0}=l\cdot 5\times 10^{3}\), \(\mathbf{Q}_{0}=l\cdot 10^{-8}\), \(\mathbf{R}_{0}=10^{3}\), \(\mathbf{R}\) is adjusted every 256 samples based on the average of values in \(\mathbf{P}\) of the output node. Hyper-parameter settings for Adam and sKAdam: \(\alpha=0.001\), \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-8}\).  
In this experiment, as in the previous one, with FDEKF, the value of the loss function decays much faster in the training process, and the highest precision is obtained for the test data set, as observed in Figure 5 and Table 2, respectively. Here, the advantage of FDEKF over other algorithms in precision is greatest. This result supports one of our hypotheses, that with FDEKF, high precision can be achieved in image recognition tasks in which first-order algorithms do not.