esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
## 3 Results and Discussion
### Radiometric Capability Evaluation of Huan Jing 1/Charge-Coupled Device (H)-1/CCD
For HJ-1B/CCD1 (Table 10), the TOA reflectance in 2009 is the most stable due to the slopes in the four bands being smaller than the other three years. A gradual decreasing tendency is observed in 2012 for all bands, while the increasing tendency is observed in 2009 for all bands. The TOA reflectance tendency in 2010 in the blue band and 2011 in all the four bands do not agree with linear rule; consequently, their slopes are not calculated. The highest variation of TOA reflectance occurs in 2011 in the blue band, up to 26.94%, while the lowest occurs in 2012 in the green band: 9.76%. Blue bands of HJ-1B/CCD1 have the largest variation over the other three bands. The mean TOA reflectances after spectral matching are higher than those of MODIS, except the NIR bands in 2009 in the NIR band: 6.35%. Blue bands of HJ-1B/CCD1 have the largest variation of the four bands except in 2011. The mean TOA reflectances after spectral matching are higher  
Figure 8: Time series of TOA reflectance of HJ-1/CCDs from 2008 to 2012 in reflective bands: (a) HJ-1A/CCD1; (b) HJ-1A/CCD2; (c) HJ-1B/CCD1; (d) HJ-1B/CCD2. than those of MODIS except the NIR bands in 2009, 2010, and 2011, and red band in 2010. All the standard deviations of the TOA reflectances are within 0.02, and the lowest standard deviations in blue, green, red and NIR bands occur in 2011 (0.0044), 2012 (0.0061), 2010 (0.0056) and 2009 (0.0055), respectively. 5. Both the TOA reflectances in blue and green bands of the four CCDs are higher than those of the MODIS, while the TOA reflectance in the red band is much closer to that of the MODIS. However, almost all the TOA reflectances in NIR bands are lower than those of the MODIS. 6. Compared to the HJ-1A/CCDs, the HJ-1B/CCDs are more stable because almost all the slopes of the TOA reflectance tendency of HJ-1B/CCDs are lower than those of the HJ-1A/CCDs in the corresponding year and band. 7. At the beginning of the operation of an instrument, the vicarious calibration once a year is maybe enough to ensure the radiometric capability; however, it cannot guarantee the radiometric capability after the beginning stage of the operation. Subsequently, some new and highly frequent calibration methods need to be proposed and executed by the instruments' surveillance department.
# Settlement Prediction and Differential Settlement Criterion for Heightening and Thickening Leveee
## 2 Methodology
### Finite Element Analysis
#### 2.4.2 Determination of Calculation Parameters  
ABAQUS is one of the most advanced finite element software packages in the world at present, but it does not directly provide the merchant model based on one-dimensional tension-compression viscoelastic parameters. ABAQUS adopts an integral constitutive equation of shear relaxation modulus and bulk relaxation modulus based on Prony series. To utilize the powerful numerical calculation ability of ABAQUS, it is necessary to establish the relationship between the viscoelastic parameters of a one-dimensional merchant model and the integral constitutive model parameters adopted by ABAQUS.  
A previous study [36] has provided the determination method for parameters. The method is as follows:  
\[G(0)=G_{H} \tag{8}\]  
\[\tau_{1}^{G}=\frac{\eta_{K}^{G}}{G_{H}+G_{K}} \tag{9}\]  
\[g_{1}=\frac{G_{H}}{G_{H}+G_{K}} \tag{10}\]  
\[G_{H}=\frac{E_{H}}{2(1+v_{H})} \tag{11}\]  
\[G_{K}=\frac{E_{K}}{2(1+v_{K})} \tag{12}\]  
Figure 5: One-dimensional merchant model [36].  
\[\eta_{K}^{G}=\frac{\eta_{K}}{2(1+v_{K})} \tag{13}\]  
Here, \(G(0)\) is the instantaneous shear modulus. \(G_{H}\), \(G_{K}\), and \(\eta_{K}^{G}\) are parameters of the shear viscoelastic parameters of the Merchant model. \(v_{H}\) and \(v_{K}\) are the Poisson's ratio corresponding to the Hook body and the Kelvin body of the merchant model. Usually, \(v_{H}=v_{K}=v\).  
The material parameters used for calculation are listed in Table 2. The determination of material parameters is mainly based on the following considerations: 1. The long-term deformation modulus of a levee is 30 MPa, based on Table 4; 2. It is assumed that both instantaneous deformation and viscoelastic deformation are 50% of total deformation [36]. Based on this assumption, \(E_{H}\) and \(E_{K}\) can be determined. 3. It is assumed that viscoelastic deformation can reach 90% in 1 year [36]. Based on this assumption, \(\eta_{K}\) can be determined.
## 3 Filters for Feature Extraction: LBP, HOG, and Saliency
### Local Binary Pattern (LBP)
#### 3.1.2 Applications  
* Texture Classification: The LBP descriptor is widely used for texture classification, as it efficiently captures the local features of the image. LBP is particularly effective in identifying texture patterns, making it a popular choice in texture classification tasks [44; 45].
* Face Recognition: LBP is used to extract features from facial textures, enabling facial recognition in images. It has shown great success in real-time face recognition applications due to its efficiency in capturing the texture information of the face [46].
* Image Segmentation: LBP is used in image segmentation to distinguish between different texture regions in the image. By analyzing local texture information, LBP provides a robust approach to segmenting images based on texture differences [44; 45].
## 3 Error Model and Cross-Identification Algorithm
### Geometric Parameter Error Model
#### 3.1.3. Differential Transformation Matrix \(\delta_{m}^{t}T\)  
For an n-DOF industrial robot, when there are deviations in the link parameters and transformation errors between the BCF and the MCF, as well as between the TCF and the TCF, the pose of the TCF in the MCF can be expressed as follows:  
\[\begin{array}{ll}{}_{m}^{t}T+d_{m}^{t}T&=\big{(}{}_{m}^{0}T+d_{m}^{0}T\big{)} \big{(}{}_{0}^{n}T+d_{0}^{n}T\big{)}\big{(}{}_{n}^{t}T+d_{n}^{t}T\big{)}\\ &\approx{}_{m}^{t}T+d_{m}^{0}T\cdot{}_{0}^{t}T+{}_{m}^{0}T\cdot{}_{0}^{t}T\cdot {}_{m}^{t}T+{}_{m}^{n}T\cdot d_{n}^{t}T\\ &={}_{m}^{t}T+\left(\delta_{m}^{0}T+{}_{m}^{0}T\cdot\delta_{0}^{n}T\cdot{}_{m}^ {0}T^{-1}+{}_{m}^{n}T\cdot\delta_{n}^{t}T\cdot{}_{m}^{n}T^{-1}\right)\cdot{}_{m }^{t}T\\ &={}_{m}^{t}T+\delta_{m}^{t}T\cdot{}_{m}^{t}T\end{array} \tag{18}\]  
According to Equations (11), (16) and (17), the differential transformation matrix of the TCF relative to the MCF is as follows:  
\[\begin{array}{ll}\delta_{m}^{t}T&=\delta_{m}^{0}T+{}_{m}^{n}T\cdot\delta_{ n}^{t}T\cdot{}_{m}^{n}T^{-1}+{}_{m}^{0}T\cdot\delta_{0}^{n}T\cdot{}_{m}^{0}T ^{-1}\\ &=\sum\limits_{u}C_{u}\Delta u+\sum\limits_{v}^{n}TC\,C_{m}^{u}T^{-1}\Delta v +\sum\limits_{i=1}^{n}\sum\limits_{v_{i}}^{0}TD_{w_{i}}^{u}\Delta v_{i}\\ &=\sum\limits_{u}C_{u}\Delta u+\sum\limits_{v}^{0}C_{v}^{\prime}\Delta v+\sum \limits_{i=1}^{n}\sum\limits_{w_{i}}D_{w_{i}}^{\prime}\Delta w_{i}\end{array} \tag{19}\]  
where \(u\in\{x,y,z,\alpha,\beta,\gamma\}\), \(v\in\{x^{\prime},y^{\prime},z^{\prime},\alpha^{\prime},\beta^{\prime},\gamma^ {\prime}\}\), and \(w_{i}\in\{\theta_{i},d_{i},a_{i},\alpha_{i},\beta_{i}\}\), which are the parameters to be identified in the calibration process. \(C_{v}^{\prime}\) and \(D_{w_{i}}^{\prime}\) are as follows:  
\[C_{v}^{\prime}={}_{m}^{n}T\cdot C_{v}{}_{m}^{n}T^{-1} \tag{20}\]  
\[D_{w_{i}}^{\prime}={}_{m}^{0}T\cdot D_{w_{i}}\cdot{}_{m}^{0}T^{-1} \tag{21}\]
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 2 Literature Review
### Advances in Computed Torque Control  
References [1; 2; 3] provide foundational literature on robotic systems. The first two works comprehensively cover theoretical and practical aspects of manipulators, emphasizing kinematics, dynamics, trajectories, and both linear and nonlinear control, along with programming languages. The third reference specifically addresses advanced robotic manipulator control, beginning with the fundamentals and development of dynamic equations for a 2-DoF robot. It then introduces Computed Torque Control as a fundamental control method, followed by simulation examples, advanced control definitions, and applications.  
In [14], researchers detail the design and implementation of a self-developed 5-DoF. They implement a Computed Torque Control in joint space for trajectory following. The study employs direct and inverse kinematics for robot modeling and utilizes dynamic equations to verify trajectory generation experimentally. The authors suggest exploring alternative nonlinear controllers in future work.  
Research presented in [15] explores redundant robots, which possess more DoF than required for specific motion tasks. This redundancy enables additional capabilities, such as joint limit and singularity avoidance. The authors propose and validate a two-loop motion control scheme through experiments on a laboratory-developed planar redundant arm.  
The foundational literature establishes the theoretical framework necessary for analyzing robot manipulator operations across medical, industrial, and scientific applications. The specialized research on modern controllers provides comprehensive insights into robot manipulator control, complemented by simulation methodologies. Further studies demonstrate the effectiveness of Computed Torque Control in nonlinear systems, particularly when integrated with dynamic modeling and kinematic redundancy, enabling additional capabilities such as obstacle avoidance. The present research investigates a 3-DoF robot system, incorporating dynamic modeling and disturbance considerations to achieve precise geometric trajectory execution through advanced Computed Torque Control, emphasizing smooth motion characteristics.
# Towards a Fast, Open-Path Laser Hygrometer for Airborne Eddy Covariance Measurements
## 2 Materials and Methods
### dTDLAS Methodology
In the case of challenging environmental conditions, the stability of the fit could be further increased by pre-calculating the Lorentz FWHM. This method is described in detail in [9; 11; 25; 32]. Figure 2a shows typical raw photodiode signals for 1759 ppm and 7324 ppm water vapor amount fractions. Here, the laser is modulated at a frequency of 139.8 Hz, resulting in a duration of 3.6 ms per up ramp. The model given in Equation (2) is fitted to the raw signal, and the baseline polynomial and the fit to the raw signal are shown. The use of a complete physical model for the description of the absorption signal enables a good estimation of the baseline, even if the edges of the absorption line extend beyond the fit range, as is the case with the signal at 7324 ppm. Figure 2b shows the optical density data (OD, also known as absorbance; derived from the data in Figure 2a) fitted with a Voigt profile. The residual between the measured data and the fit is plotted underneath. The time axis of the raw data is converted into a wavenumber axis by applying the measured non-linear dynamic tuning behavior of the laser. The W-shape in the residuals can be traced to a known deficiency of the Voigt profile when fitting absorption signals at high signal-to-noise-ratios [10; 25]. This systematic effect does not influence the optical resolution of the instrument, as it only depends on the line shape and the used Voigt profile and is, therefore, neglected when evaluating the optical resolution of the spectrometer. This is achieved by introducing the local standard deviation (\(1\sigma_{\mathrm{local}}\)) inside the blue box in Figure 2b to capture the statistical optical noise of the instrument. By dividing the maximum peak optical  
density (OD\({}_{\text{max}}\)) by the local standard deviation 1\(\sigma_{\text{local}}\), a signal-to-noise-ratio S/N\({}_{\text{local}}\) of 6507 can be determined.
# Advances in the Neural Network Quantization: A Comprehensive Review
## 4 Experimental Results  
### Experimental Setting  
We perform two sets of experiments, one comparing PTQ methods and the other comparing QAT methods with mixed bit-widths. In order to obtain the experimental results conveniently, software based on fake quantization v1.0 [30] modules is used to simulate the accuracy on the neural network accelerator. Fake-quantization models quantization errors in the forward passes. The biggest reason to apply fake quantization is to quicklysimulate the effects of quantization using simulated quantization operations. The purpose of the experiments is to compare the state-of-the-art quantization methods. The CPU is an Intel(R) Core(TM) i7-8700K, 3.70 GHz, and the GPU is a NVIDIA GeForce GTX1070.  
In the PTQ experiment, we utilize classic image classification models GoogleNet and VGG16, the YOLOv1 model from the YOLO series for object detection applications, and the classic Unet model for image segmentation applications. GoogleNet and VGG16 are tested using the publicly authoritative ImageNet dataset to highlight the scientific rigor and fairness of our comparative method. The YOLOv1 model is applied to a custom ship detection dataset, while the Unet model is used with a custom remote sensing image segmentation dataset. The aim is to assess the precision loss of quantized models across targets of various sizes.  
In the QAT experiment, we utilize the classic image classification ResNet50, which is evaluated through comparative experiments using the publicly authoritative ImageNet dataset.  
The accuracy is verified using software. The evaluation metrics are the accuracy metrics of the model. For image classification applications, we use Top-1 Accuracy (the one with the highest probability must be exactly the expected answer). For small target detection applications, we use mAP (Mean Average Precision). The calculation of mAP is the same as in the internationally renowned target detection competition PASCAL VOC Challenge. For image segmentation applications, we use the mean intersection over union (mIoU) metric to evaluate accuracy.
## 2 Experimental Method for Determining the Heat Transfer Coefficient
### Sample Preparation  
In the present work, several different sample diameters and steel grades were investigated (see Table 1). The samples were cut from industrial quality wire, straightened, and shortened to a length of 200 mm. The surface of the samples was not altered and is, therefore, the same as in industrial production. The samples were provided by four different wire manufacturers from Germany (DWK Drahtwerke Koln GmbH, Koln, Germany; H. Kunne GmbH & Co. KG, Hemer, Germany; Stahl-u. Drahtwerk Roslau GmbH, Roslau, Germany and Westfalische Drahthdustrie GmbH, Hamm, Germany).  
The key to a precise determination of the heat transfer coefficient during the cooling of the samples in the bath is to accurately measure the sample temperature at a known position inside the samples. To achieve this, the thermocouples were mounted radially in the centre of the samples. The required hole was eroded into the wire samples. The hole was eroded at a defined angle (see Figure 3) to avoid creating a fin on the wire with the thermocouple that would conduct heat away from the centre of the sample. Additionally, a groove was milled into the surface of the sample to further guide the thermocouple. The hole and the groove both are carried out with a diameter of 0.5 mm guaranteeing a tight fit with minimal gaps between the thermocouple and the samples, which reduces the potential heat transfer resistance. The distance of the thermocouple from the front surface regarding the axial direction was set to 30 mm for all samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Steel Grades** & \multicolumn{2}{c}{**Sample Diameter**} \\ \hline \multirow{8}{*}{Carbon grades} & \multirow{8}{*}{C50 D2} & 1.8 mm \\  & & 3.0 mm \\  & & 5.5 mm \\ \cline{2-3}  & & 1.8 mm \\  & & 5.5 mm \\ \cline{2-3}  & & 1.8 mm \\  & & 5.5 mm \\ \cline{2-3}  & & 588 D2 \\  & & 5.5 mm \\ \cline{2-3}  & & 572 D2 \\ \hline \multirow{3}{*}{SiCr-alloyed grades} & \multirow{3}{*}{54 SiCr6} & 2.6 mm \\  & & 5.5 mm \\ \cline{2-3}  & & 65 SiCrV6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Steel grades and diameter of the samples investigated in the experimental trials.
## 3 Results and Discussion
### Ability to Retain Bacteria  
As can be seen from Figure 8, when the pore ratio was 0.9, the capacity of residual bacteria decreased by about 10% compared with that of the same type when the pore ratio was 0.75. When the pore ratio was 0.75, the retention capacity of bacteria was between 60% and 80%, which indicated that more than half of the bacteria remained in the silt during the first injection of bacterial liquid. The higher the retention capacity of bacteria, the more Bacillus pasteurii octahedrin used for catalyzing urea hydrolysis. In the process of grouting, the bacteria liquid is usually injected first and then the cement liquid is injected. For the controls, similar to the findings of [PERSON] et al. [19], more than 70% of bacterial activity could not be retained. The retention capacity of bacteria is related to the bacterial suspension OD\({}_{600}\) that flows out during the first round of grouting, and has nothing to do with the injection amount of cement liquid. Therefore, there is little difference between OD\({}_{600}\) when the injection amount of cement liquid is 150 mL and 300 mL. As can be seen from Figure 9, in the case of the same pore ratio, the capacity of silt retention bacteria increases with the increase in fine particle content, which is also related to the adsorption  
Figure 7: SEM images of different silts. (**a1,a2**) SEM image of silt A; (**b1,b2**) SEM image of silt B; (**c1,c2**) SEM image of silt C.  
of fine particles. It can be seen from the figure that silt A with pore ratio of 0.75 and fine particle content of 75% has the highest bacteria retention capacity, which is 77%.
# Searchable Data Vault: Encrypted Queries in Secure Distributed Cloud Storage
## 2 Related Work  
### Systems  
One of the earliest comprehensive systems created for privacy-preserving queries on encrypted data is CryptDB, proposed by [PERSON] et al. [7]. CryptDB is a system for SQL-based encrypted queries to the database. It utilizes order-preserving encryption, homomorphic encryption, and [PERSON] et al. [8]'s SSE scheme, and can be considered as one of the early systems for providing such services. CryptDB provides layer-based mechanisms (called onions of encryption), in which different encryption schemes (e.g., SSE) are selected for different types of queries e.g., SEARCH) to ensure the most secure approach is used to perform encrypted queries in encrypted records. [PERSON] et al. [9] then proposed a system for mobile platforms known as M-Aegis. The system puts a transparent layer on top of existing applications, such as GMail and WhatsApp, so that it can be integrated without changing the look-and-feel of these applications. The transparent layer encrypts a message before it is passed to the underlying application, and allows for querying and decrypting the encrypted message, through a searchable encryption scheme. Other systems include BlindSeer [10; 11] and Mylar [12]. Blindseer is a practical system that caters for private SQL-based queries to the database, as in CryptDB, but uses different techniques (i.e., garbled circuit and tree) and allows arbitrary Boolean queries. It deploys an index server to manage indexes and queries. Mylar, on the other hand, is a multi-user system that provides searchable encryption using a pairing-based construct. Commercial systems that also provide searchable encryption include Skyhigh Networks [13], CipherCloud [14] and Bitglass [15]. These systems use a simple SSE approach, such as attaching keyword tokens to an encrypted document, as stated in [16]. We remark that all these systems focus on outsourcing to a single storage provider.
## 4 Results
### Global Smoke Map  
Figure 11 shows the frequency of global smoke occurrences accumulated from daily SSDA smoke detections during August 2018. Smoke occurred most frequently in the western North America, southern Africa, and northeastern Pakistan, with smoke cover of as many as 20 days during August 2018. The frequency of smoke occurrence was between 5 days and 15 days over a large part of South Africa, India, Indonesia, China, and Siberia. It was very low in other regions where fires were not prevalent, or the fire season was not in August. The frequency of smoke occurrence matched well spatially with the VIIRS fire density in August 2018, particularly in the fire-prone regions of North America, northern South America, Africa, Indonesia, and Siberia, although spatial patterns of smoke were also influenced by wind directions, smoke aerosol emission coefficients [19; 20], and other parameters. In the regions where forest fires were dominated, such as Siberia, fuels were very large and fire smoke emissions were correspondingly large. This means each fire  
Figure 10: Comparisons of manually delineated smoke reference data (gray) with the SSDA smoke detection (red), the TROPOMI smoke (blue), and aerosol detection product (ADP) (orange). (**a**,**c**,**d**–**g**) represent North America, South America, Africa, Indonesia, Siberia, and Australia, respectively.  
hotspot could release a large amount of smoke, result in large spatial extent of smoke plumes, and cause high smoke frequency in surround areas. However, the frequency of smoke occurrence seemed to be underestimated in fire-prone regions of South America and Australia when compared to the fire detection density map and true-color imagery on NASA worldview ([[https://worldview.earthdata.nasa.gov/](https://worldview.earthdata.nasa.gov/)]([https://worldview.earthdata.nasa.gov/](https://worldview.earthdata.nasa.gov/))). This phenomenon was consistent with the results (Section 4.2.2) that omission errors were highest in these two regions (Figure 10). In eastern Amazon (in South America), fire smoke detections are strongly obstructed by cloud contaminations, which results in the discrepancy between smoke occurrence frequency and fire density. In contrast, frequency seemed overestimated in regions of Pakistan and northern India, as the spatial pattern of fire detection density did not show any signal for the source of smoke in these two regions. This discrepancy could be attributed to fires that were not fully detected, such as crop fires in India [61], or heavy particulate pollution over these regions [62]. For the smoke detections over oceans, the frequency was generally low because of the disperse characteristic of smoke after being transported far from their source fires and the high omission error of smoke over oceans caused by oceanic glint impacts on smoke detection from the SSDA. The discontinuousness of smoke from inland fire source regions to the deep ocean is mainly caused by cloud and sun glint interference in the smoky days and the occurrence of higher smoke frequency in the ocean is likely associated with the influence of wind conditions.