esa-sceva/satcom-chunk-collection huggingface dataset:
# Methods for Underwater Gravity Measurement Error Compensations Based on Correlation Analysis
## 2 Materials and Methods
Remove the intrinsic mode functions that have no correlation with the gravity-measurement results, the upward-specific force, the upward-specific force derivative, the upward velocity, the upward acceleration, or the pitch angle change and accumulate the remaining intrinsic mode functions and the residual signals to obtain the reconstructed gravity-measurement results. Curve fitting is performed on the reconstructed gravity-measurement result to obtain the fitted gravity-measurement curve. Taking the fitting curve as the standard value, the difference between the reconstructed gravity-measurement result and the fitting curve is obtained; this is the gravity-measurement error of the target survey line. 5. Establish the relationship between the gravity-measurement error and the upward-specific force, the upward-specific force derivative, the upward velocity, the upward acceleration, and the pitch angle change, as shown in Formula (2): \[ \begin{array}{c}\delta g_{fitting}-\delta g_{1}=k_{1}f+k_{2}\dot{v}+k_{3} df+k_{4}dp+k_{5}dh+k_{6}f^{2}+k_{7}\dot{v}^{2}\\ +k_{8}df^{2}+k_{8}dp^{2}+k_{10}dh^{2}+k_{11}f\dot{v}+k_{12}fdf+k_{13}fdp\\ +k_{14}fdh+k_{15}\dot{v}df+k_{16}\dot{v}dp+k_{17}\dot{v}dh+k_{18}dfdp+k_{19}dfdh +k_{20}dpdh,\end{array} \] (2) where \(\delta g_{fitting}\) is the gravity-measurement result after fitting; \(\delta g_{1}\) is the reconstructed gravity-measurement result; \(\dot{v}\) is the upward acceleration; \(dp\) is the change in pitch angle; \(dh\) is the upward velocity; \(k_{n}(n=1,2,\cdots,20)\) is the error model parameter. 6. Each model parameter in Formula (2) is estimated by the least-squares fitting method to obtain the specific form of the error equation. According to the error model, reconstructed gravity-measurement results are error-compensated to obtain compensated gravity-measurement results. The error compensation equation is shown in Formula (3): \[ \begin{array}{c}\delta g_{compensate}=\delta g_{1}+k_{1}f+k_{2}\dot{v}+k_{3} df+k_{4}dp+k_{5}dh+k_{6}f^{2}+k_{7}\dot{v}^{2}\\ +k_{8}df^{2}+k_{9}dp^{2}+k_{10}dh^{2}+k_{11}f\dot{v}+k_{12}fdf+k_{13}fdp\\ +k_{14}fdh+k_{15}\dot{v}df+k_{16}\dot{v}dp+k_{17}\dot{v}dh+k_{18}dfdp+k_{19}dfdh +k_{20}dpdh\end{array} \] (3) where \(\delta g_{compensate}\) is the gravity-measurement result after compensation. Other survey lines are selected in the survey area with the same type of gravity-measurement result fitting curve as the target survey line. For example, if the gravity-measurement-result fitting curve of the target survey line is a quadratic curve, the gravity-measurement result fitting curve of the other selected survey lines should also be a quadratic curve. The reconstructed gravity-measurement results are obtained from the gravity-measurement results of other measuring lines according to steps (3) and (4), and error compensation is performed according to the error model formula of step (6) to obtain the compensated gravity-measurement results. Figure 1: Flow chart of error compensation method based on correlation analysis.
## 2 Handcrafted Representation-Based Approach
### Space-Time-Based Approaches
#### 2.1.2 Space-Time Trajectory
For estimation of camera motion authors used Speeded-Up Robust Features (SURF) descriptor and dense optical flow. This significantly improved the performance of motion-based descriptors such as histogram of optical flow (HOF), and motion boundary histogram (MBH). However, when incorporating high density with trajectories within the video, it increases the computational cost. Many attempts have been made to reduce the computational cost of the dense trajectory-based methods. For this purpose, saliency-map to extract the salient regions within the image frame was used in [57]. Based on the saliency-map a significant number of dense trajectories can be discarded without compromising the performance of the trajectory-based methods. Recently, a human action recognition method from depth movies captured by the Kinect sensor was proposed in [62]. This method represents dynamic skeleton shapes of the human body as trajectories on Kendall's shape manifold. This method is invariant to execution rate of the activity and uses transported-square root vector fields (TSRVFs) of trajectories and standard Euclidean norm to achieve the computational efficiency. Another method for recognition of actions of construction workers using dense trajectories was proposed in [67]. In this method, different descriptors such as HOG, HOF, and motion boundary histogram (MBH) were used for the trajectories. Among these descriptors, authors reported the highest accuracy with codebook of size 500 using MBH descriptor. Human action recognition in unconstrained videos is a challenging problem and few methods have been proposed at this end. For this purpose, a human action recognition method was proposed using explicit motion modelling [68]. This method used visual code words generated from the dense trajectories for action representation without using the foreground-background separation method.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 2 Materials and Methods
### Channel Domain Transfer Learning  
The technical roadmap for achieving complex pattern defect reconstruction using the SA-Pix2 pix network and transfer learning is depicted in Figure 3. It primarily comprises two stages:  
Channel Weight Estimation: Artificial defect patterns, referred to as \"artificial defects\", are created to resemble real defects closely. These artificial defects are utilized to train the SA-Pix2 pix model in the source domain. The channel weights are estimated by calculating the variation in the image reconstruction loss function. This process helps identify the channels and features that have the most significant impact on defect image reconstruction accuracy.  
Construction of Metric Criteria: Real defect images are treated as target domain data and are input into both the source domain network (the pre-trained SA-Pix2 pix model) and the target domain network (the SA-Pix2 pix model under training). These networks map the source domain data to the same feature space and measure the distance between the corresponding features of the source and target domain networks. The feature distance is then multiplied by the channel weights. A smaller sum of the feature distance and channel weight product indicates greater consistency in feature distribution between the source and target domains, facilitating the transfer of similar features between the two domains. However, relying solely on \"consistency in feature distribution\" cannot completely address the issue of inadequate defect reconstruction accuracy in the target domain network. To improve the reconstruction accuracy of complex pattern defect images, both the feature distance and the channel weight product sum must be incorporated into the loss function of the target domain network along with the image reconstruction loss function.  
Figure 3: Technical roadmap.
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
## 2 Materials and Methods  
### Retrieval of Temperature Profile  
The temperature profile of the atmosphere can be retrieved from the geometry of spacecraft occultation as described in Figure 1. The geometric parameters are used to calculate the bending angle and refractive index. Furthermore, the refractive index is translated into the number density of the atmosphere using the refractive volume for the Martian CO\({}_{2}\)-N\({}_{2}\) mixture [25; 26].  
\[\log(\mu_{j}) = \frac{1}{\pi}\int_{a_{j}}^{\infty}\frac{\delta(a)}{\sqrt{a^{2}-a_ {j}^{2}}}\,da \tag{1}\] \[r_{j} = \frac{a_{j}}{\mu_{j}}\] (2) \[n_{j} = \frac{\mu_{j}-1}{k_{j}}, \tag{3}\]  
where \(\mu\) is refractive index, \(a\) is the distance of the closest approach, \(j\) represents a radio occultation measurement point in the atmosphere, \(\delta\) is the bending angle, \(k_{j}\) is mean refractive volume that can be expressed as the sum of products of the refractive volume and mixing ratio of constituent gases, \(r\) is the radial distance to the planet, and \(n\) is the neutral number density of the atmosphere. The temperature and pressure profiles are retrieved from the number density assuming hydro-static balance [23; 26].  
\[(\frac{\partial P}{\partial z})_{j} = -n_{j}mg \tag{4}\] \[P(z_{j}) = n_{0}k_{B}T_{0}+m\int_{z_{0}}^{z_{j}}n(z^{\prime})g(z^{\prime})\,dz ^{\prime}\] (5) \[T_{j} = \frac{P_{j}}{n_{j}k_{B}} \tag{6}\]  
where \(P\) is pressure, \(k_{B}\) is Boltzmann's constant, \(T\) is the temperature, \(m\) is the gas mean molecular mass, and \(g\) is the gravitational acceleration. Thus, \(P\) and \(T\) can be described as a function of the altitude z.
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Signal Processing
#### Spectral Analysis Response  
The principle of spectral analysis response for pipeline leak detection is as follows: when a mini-leak occurs along the pipeline, it will generate a leak signal such as an ultrasonic wave. By extracting the characteristic parameters of leak signals for further analysis, we could obtain the corresponding relationship between ultrasonic spectrum and leak parameters such as aperture of leak point and pipeline inner pressure to judge the state and tendency of the leak point, thereby significantly enhancing the accuracy of the leak detection. Generally, spectral analysis response methods are based on Fast Fourier Transform (FFT) [22,23]. The accuracy of spectral analysis mainly depends on the eigenvalue problem, which can be solved by a filter diagonalization method (FDM) [24,25].
## 4 Evaluation
### Stash Size
#### 4.3.1 PosMap ORAM  
First, the scheme \(ORAM_{\infty}^{P,L}\) with infinite bucket size is introduced. The original scheme with bucket size \(S\) is denoted as \(ORAM_{S}^{P,L}\). After running the same access sequence with the same randomness in the two schemes, \(ORAM_{\infty}^{P,L}\) after a post-process algorithm has exactly the same state with \(ORAM_{S}^{P,L}\). Specifically, the two schemes have the same distribution of blocks over all buckets and the stash. For any rooted subtree \(T\) in \(ORAM_{\infty}^{P,L}\), if the number of blocks in \(T\) (denoted as \(X(T)\)) is larger than the capacity of the same subtree in \(ORAM_{S}^{P,L}\), the extra blocks in \(T\) are the ones stored in \(P\)_Stash_. Using a union bound and Catalan sequence bound, the probability that the size of \(P\)_Stash_ is more than \(R\) is as follows, where \(n(T)\) is the number of nodes in subtree \(T\).  
\[\Pr[PS\textit{stash}\geq R]<\sum_{n\geq 1}4^{n}\cdot\max_{T\
i(T)=n}\Pr[X(T) \geq n(T)\cdot S+R] \tag{1}\]The bound of expected block number in any subtree \(T\) (\(E[X(T)]\)) is calculated firstly. Following the analysis of [PERSON] et al. ([26], Section 3.2), we have \(E[b_{\infty}]\leq 1\) for the buckets in leaf nodes and \(E[b_{\infty}]\leq A^{\prime}/2\) for other buckets.  
In the end, based on the capacity of each rooted subtree \(n(T)\)-\(S\) and the expected number of blocks \(E[X(T)]\) in \(\mathit{ORAM}_{\infty}^{P,L}\), a Chernoff-like bound is used to analysis the size of \(\mathit{PStash}\). If \(N\geq 4\) and \(A^{\prime}\)=1, it holds \(E[X(T)]\leq 0.8\cdot n(T)\), which brings a conclusion that as long as \(S\geq 3\), the \(\mathit{PStash}\) overflow probability decreases exponentially in its size \(R\). Table 2 shows the extrapolated the size of \(\mathit{PStash}\) for different (\(S\), \(A^{\prime}\)) with overflow probability of \(2^{-\lambda}\), where \(\lambda\) is a statistical security parameter.
## 5 Discussion
Currently, a major challenge is that no high-resolution clumping index products are available, especially those products that match the GLAS footprint level, although several moderate-resolution clumping index products have been generated and released to users [59; 60], based on the hotspot-corrected bidirectional reflectance distribution function (BRDF) model [61; 62] and a linear model [63]. Considering the potential problems of using coarse resolution data, we focus on the effective LAI at present. The problem of the clumpiness effect will be solved in the future, for example, to develop a method that can estimate the clumping index with GLAS data. One of the major limitations of our method was that assuming all the reflecting components within the GLAS footprint behaved as Lambertian scatterers because of the lack of knowledge of the directional reflectance properties of various components at the GLAS resolution. This assumption has been widely used in the calculation of surface albedo [64; 65]. Regardless of this, the assumption of Lambertian scattering is a practical approximation of the potentially complex character of land footprints. The advantage of our work is that we can obtain the vertical foliage profiles at the landscape scale, which allows us to better understand vertical structural parameters in the forest. For example, our method can describe the understory vegetation very well, as shown in Figure 12 (i.e., LAI integration from 0-4 m). Understory vegetation is usually a forest ecosystem driver, and its productivity is probably comparable to that of trees [66]. Previous studies have suggested that the lack of vertical foliage distribution information caused approximately 50% underestimation of GPP [67; 68]. Our method provides an opportunity to obtain foliage profile information on the global scale, which should be helpful for improving the accuracy of ecological process simulations. Recently two new lidar observation platforms GEDI and ICESat-2 were successfully launched and operated. They have improved instrument technologies and spatial coverage compared with ICESat GLAS. In the next step, our method will combine with these two new platforms, which will allow for enhanced coverage and improved accuracy by leveraging the advantages of the individual instrument technologies and spatial coverage.
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 3 Algorithms for Graph Partitioning Considered in Our Study
### Edge Cut Graph Partitioning
#### 3.1.2 KaHIP  
Several multilevel graph partitioning algorithms are combined in _KaHIP_[16]. _KaHIP_ works similarly to _METIS_. A given input graph is first contracted, partitions are computed on the coarsest contraction level, and the partitions obtained in this way are projected back to coarser levels, where refinement algorithms are used to enhance the solutions. _KaHIP_ offers max-flow/min-cut [17; 18] and Fiduccia-Mattheyses refinement [15] for local improvement of the solution. Additionally, F-cycles [17] can be employed as a global search technique.
## 2 Navigation Model Based on GNSS  
The models of the navigation system include the state model and the measurement model, which is the basis for analyzing the observability of the navigation system and designing the navigation filter algorithm. Under the high-orbit environment, the force on the satellite is very complex. In addition to the gravity, the satellite is also affected by other perturbation forces. For the measurement equation, we only give the corresponding measurement model by measuring the inter-satellite pseudo-range based on the GNSS.
## 3 Proposed Methods Based on Causal Inference
### Calculation of Causal Strength between Nodes  
The causal strength between nodes represents the measure of impact in a causal relationship. In this study, the absolute value of the Pearson correlation coefficient, Equation (14) [23], is employed to quantify the causal strength between two continuous variables.  
\[S(A,B)=|r(A,B)|=\frac{|Cov(A,B)|}{\sqrt{Var(A)\cdot Var(B)}} \tag{14}\]Variables \(A\) and \(B\) are denoted by their covariance, \(Cov(A,B)\), and their respective variances, \(Var(A)\) and \(Var(B)\).  
Alternatively, the information gain ratio can be utilized to assess the causal strength between two discrete variables [24]. The information gain ratio is commonly employed in feature selection [25], as illustrated in Equation (15). This approach mitigates the issue of overfitting caused by selecting variables with large values due to inherent deviations. In Equation (15), the mutual information, \(I(A,B)\), quantifies the level of interdependence between the two variables, as shown in Equation (16). The information entropy, \(H(A)\), calculated in Equation (17), measures the uncertainty of the variables, where \(p(x_{i})\) refers to the probability within set \(A\). The conditional entropy, \(H(B|A)\), defined in Equation (18) [26], quantifies the uncertainty in variable \(B\) given variable \(A\).  
\[IGB=\frac{I(A,B)}{H(A)} \tag{15}\]  
\[I(A,B)=H(A)-H(A|B)=H(B)-H(B|A) \tag{16}\]  
\[H(A)=-\sum p(x_{i})logp(x_{i}) \tag{17}\]  
\[H(B|A)=\sum p(x_{i})\cdot H(B|A=x_{i}) \tag{18}\]  
\[S=\frac{I(A,B)}{\sqrt{H(A)\cdot H(B)}} \tag{19}\]  
In order to solve the issue of asymmetric information gain ratio, where the information gain ratio from variable \(A\) to variable \(B\) differs from that from variable \(B\) to variable \(A\), in this study, a modified information gain ratio is employed as the variable representing the causal strength between two discrete variables. Equation (19) illustrates this modified information gain ratio, where a higher value indicates a stronger causal relationship between the considered variables.  
Following the construction of a cause-and-effect diagram between features and variables using the NOTEARS causal discovery algorithm, features that have a direct cause-and-effect relationship with the target variable are identified. Subsequently, the selected features are ranked based on their causal strength, resulting in the final feature subset.
## 4 Channel Estimation Based on Deep Learning for RIS-Aided Communication Systems
Therefore, it can only carry out low-dimensional signal sampling at the transmitter and receiver to estimate the high-dimensional CSI. When the number of RIS reflective elements is substantial, the channel matrix that needs to be estimated becomes larger in dimension, increasing the overhead and computational complexity required for channel estimation. Suppose a few passive reflection elements of RIS are replaced with active elements (i.e., semi-passive RIS architecture). In that case, the sensing devices can estimate the CSI of the separate channels. Still, installing the active elements will diminish the benefit of RIS as a low-cost passive device. In response to the above challenges, numerous researchers have started to work on utilizing deep learning's strong ability to estimate RIS-aided channels. Massive MIMO, millimeter wave (mmWave), and multi-user (MU) systems are all popular areas of research in 5G and 6G communications. Therefore, this section will concentrate on channel estimation for RIS-aided communication systems based on deep learning related to them.
## 2 Materials and Methods
### \(\mu\)Vemp Device and Filtering After VEMP Data Acquisition  
The \(\upmu\)VEMP [12] is a multi-channel device with programmable input amplifiers feeding simultaneous-sampling 24 bit analogue-to-digital converters sampling at 4 KHz. In this study, the maximum available gain of 24 was used, yielding an input voltage range of \(\pm\)187 mV. To date, the largest DBS artifact seen was in the range of 10-15 mV for cVEMP recordings, so we predicted that the recording system would have a factor of 10 'headroom' before signal distortion was introduced. In addition, the inputs were DC-coupled and 24-bit analogue-to-digital converters were used, allowing small signals to be detected within large offsets and other noise. Most commercial VEMP recording systems use 16-bit analogue-to-digital converters and a high-gain input amplifier to resolve low-amplitude VEMP potentials. The artifacts generated by DBS devices can drive these systems into saturation and a relatively long recovery time can obscure the input signal. The timing for the data acquisition is derived from a high-stability crystal oscillator resulting in low jitter for the sampling interval and a lower spread in spectral components of the data. Continuous raw data have been recorded by the \(\upmu\)VEMP device to allow for separation of individual VEMP traces and filtering of DBS noise.  
For the analysis, a number of time and frequency domain filters commonly used to reduce DBS artifacts in EEG and ECG signals were evaluated, with the frequency domain Hampel identifier [15] and the time domain forward-backward [16] filter using various-order Butterworth, Chebyshev or Bessel functions being the most common. Ultimately, a fifth order forward-backward Butterworth filter using Matlab achieved the best elimination of the DBS artifacts without distorting the signal profile or, more importantly, introducing delays in the signal. A Butterworth filter has the least ripple in the passband and the forward-backward method produces no phase shifts in the filtered result and doubles the filter order, yielding a 60 dB per octave attenuation in the stop band.  
The first part of the validation of the filtering process was performed by taking VEMP data of a healthy subject (female, 31 yo, no DBS) and then adding sampled DBS noise, which was finally filtered and averaged with the same filters. The second part of the validation was performed by testing one participant with PD and DBS, using the same electrodes and stimulation/recording parameters during the same testing session, with their DBS switched ON, then under a neurologist's supervision, with the DBS switched OFF. The presence of visible VEMP responses and VEMP trace recordings were compared.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Application of kNDVI  
kNDVI has proved to be a better alternative indicator. kNDVI and NDVI are closely related (Equation (1) for NDVI, and (2)-(5) for kNDVI). Equation (5) is used to process the NDVI data obtained from GEE to generate a 25-year kNDVI data set of the Huojitu mining area from 1999 to 2023. To prove that kNDVI was more suitable than NDVI in this study, we compared them year by year.  
\[NDVI=\frac{n-r}{n+r} \tag{1}\]  
\[kNDVI=\frac{k(n,n)-k(n,r)}{k(n,n)+k(n,r)} \tag{2}\]  
where \(n\) is near infrared band, and \(r\) is red light band; \(k\) stands for kernel function, which measures the similarity between the near infrared band (NIR) and the red light band. The kernel function is usually implemented by the radial basis function (RBF), which has the form:  
\[k(a,b)=exp\left(\frac{-(a-b)^{2}}{2\sigma^{2}}\right) \tag{3}\]  
where \(\sigma\) is a length scale parameter, which is linearly proportional to the average reflectance of near-infrared light and red light of the obtained remote sensing image. Then calculate \(k(n,n)\) and \(k(n,r)\) using Equation (3), substitute Equation (2), and perform a mathematical transformation to convert it into the form of a hyperbolic tangent function:  
\[kNDVI=tanh\left(\left(\frac{n-r}{2\sigma}\right)^{2}\right) \tag{4}\]  
When \(\sigma\) = 0.5 \((n+r)\), a good compromise is achieved between accuracy and simplicity in order to better compute each pixel. The value of \(\sigma\) significantly influences the smoothness  
Figure 6: The spatial distribution of land use types in the Huojitu mining area in 1999 and 2023: (**a**) land use types in 1999, and (**b**) land use types in 2023.  
of the kernel function. A larger \(\sigma\) results in a smoother response, which can effectively reduce noise but may also lead to a loss of fine details. Conversely, a smaller \(\sigma\) preserves more details but may be more sensitive to noise. By setting \(\sigma\) to half the average of \(n\) and \(r\), we achieve an appropriate balance between smoothness and detail retention. This simple linear combination not only simplifies the calculation but also enhances processing efficiency, particularly for large-scale remote sensing data. Furthermore, experimental and empirical studies have demonstrated that this specific \(\sigma\) value offers a good compromise between accuracy and simplicity across various scenarios, leading to its adoption as the default setting [47]. The Equation (4) can hence be simplified:  
\[kNDVI=tanh\Big{(}NDVI^{2}\Big{)} \tag{5}\]