esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
For the subsets of the KADID-10k database, the data for 5 and 10 metrics have been considered separately. This database contains the following groups of distortions [73]: blurs (##1-3), color distortions (##4-8), compression (##9-10), noise (##11-15), brightness change (##16-18), spatial distortions (##19-23), over-sharpening (#24), and contrast change (#25). For 5 metrics, the results for the NN-based metrics are close to the best elementary metric whereas different elementary metrics are the best for different subsets. It confirms the observation of the benefits achieved for the combination of the elementary metrics that are not the best themselves but somehow complementary, utilizing various \"nature\" of image data (different features, data representation, transforms, etc.) and based on different assumptions. To illustrate the idea of this \"compensation\", the scatter plots obtained for elementary metrics and their combination for the KADID-10k database are presented in Figure 4. Different outliers may be easily observed for elementary metrics and the NN-based metric provides the smallest number of obvious outliers and practically linear dependence between MOS and the metric. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**\#** & **NN Neurons** & \multicolumn{2}{c}{**5 Metrics**} & \multicolumn{2}{c}{**10 Metrics**} & \multicolumn{2}{c}{**15 Metrics**} \\  & **SROCC** & **PLCC** & **SROCC** & **PLCC** & **SROCC** & **PLCC** \\ \hline
1 & 1 L & 0.8173 & 0.8483 & 0.8768 & 0.8889 & 0.8991 & 0.9106 \\
2 & 2 L, equal & 0.8114 & 0.8409 & **0.8813** & 0.8906 & 0.8996 & 0.9122 \\
3 & 3 L, equal & 0.8171 & 0.8475 & 0.8811 & 0.8932 & **0.9190** & **0.9268** \\
4 & 4 L, equal & 0.8199 & 0.8512 & 0.8730 & 0.8856 & 0.9118 & 0.9221 \\
5 & 2 L, half & **0.8208** & **0.8527** & 0.8787 & 0.8932 & 0.9060 & 0.9146 \\
6 & 3 L, half & 0.8155 & 0.8411 & 0.8797 & **0.8946** & 0.9057 & 0.9168 \\
7 & 4 L, half & 0.8107 & 0.8362 & 0.8739 & 0.8914 & 0.9051 & 0.9155 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the cascade NN obtained for the NA subset using the 5, 10, and 15 input metrics (1 L-4 L denote the number of hidden layers, “equal” and “half” are the number of neurons; the best SROCC and PLCC correlations are marked with bold fonts). Figure 3: The performance of the NN-based metrics for various numbers of input metrics. Figure 2: Scatter plots illustrating the correlation between the subjective (MOS values) and objective metrics for the NA subset: (**a**) the “best” elementary metric (IL-NIQE); (**b**) the “best” combined weighted sum of 5 metrics (\(CM_{5}^{+}\) metric); (**c**) the “best” combined NN-based metrics using 5 inputs; (**d**) the “best” combined NN-based metrics using 15 inputs.
## 1 Introduction
### Modeling of Landing Gear Selector Valve  
Figure 3a illustrates the physical essence of the landing gear selector valve, which is a Y-type, three-position, four-way solenoid directional valve. When the P-port is closed and the A, B, and T ports are connected, the floating piston, the ability to move in response to external forces, and hydraulic pump not unloading are the characteristics that define its median function. Figure 3b-d depict the landing gear selector valve's neutral, left, and right operational states. Figure 4 depicts the bond graph model of the landing gear selector valve.  
Figure 3: Landing gear selector valve and its working state: (**a**) landing gear selector valve; (**b**) neutral function; (**c**) left position status; (**d**) right position status.  
Figure 2: Basic structure of landing gear R/E hydraulic system.  
Port P is the supply port, Port T is the return port, and Ports A and B are connected to the subsequent pipes. Four paths (PA, PB, AT, BT) are formed by the valve body and spool's relative positions, and the corresponding fluid resistance are \(R_{PA}\), \(R_{PB}\), \(R_{AT}\), and \(R_{BT}\).  
Neutral, left, and right working states have the following meanings [35]:  
1. Neutral: Now that the high-pressure source P port is disconnected from the system, the A and B ports are attached to the return port T. Hydraulic oil returns to the tank through the hydraulic resistance \(R_{AT}\) of the AT path and the hydraulic resistance \(R_{BT}\) of the BT path, respectively. At this time, liquid resistance \(R_{PA}\) and \(R_{PB}\) are not considered.
2. Left: Port A is connected to the return port T, whereas port B is connected to the high-pressure source port P. Through \(R_{PB}\) of the PB path, the high-pressure hydraulic oil exits from port B. Through \(R_{AT}\) of the path AT, hydraulic oil from the return line flows back to the tank. Liquid resistance \(R_{PA}\) and \(R_{BT}\) are not taken into consideration at this time.
3. Right: Port B is connected to the return port T, whereas port A is connected to the high-pressure source port P. Through \(R_{PA}\) of the PA path, high-pressure hydraulic oil exits from port A. Through \(R_{BT}\) of the path BT, hydraulic oil from the return line flows back to the tank. Liquid resistance \(R_{PB}\) and \(R_{AT}\) are not taken into consideration at this time.
## 2 Materials and Methods
### Riemann Approximation EM  
#### 2.2.1 Context and Motivation  
In this section, we introduce one specific case of Approximate EM useful in practice: approximating the conditional probability density function \(p_{\theta}(z)\) at the E step by a Riemann sum, in the scenario where the latent variable \(z\) is continuous and bounded. We call this procedure the \"Riemann approximation EM\". After motivating this approach, we prove that it is an instance of the Approximate EM algorithm and verifies the hypotheses of Theorem 1, therefore benefits from the convergence guarantees.  
Consider the case where \(z\) is a continuous variable and its conditional probability \(p_{\theta}(z)\) is a continuous function. Even when \(h(z;\theta)\) can be computed point by point, a closed form may not exist for the renormalisation term \(g(\theta)=\int_{z}h(z;\theta)dz\). In that case, this integral is usually approximated stochastically with a Monte Carlo estimation, see for instance [5; 7; 10]. When the dimension is reasonably small, a deterministic approximation through Riemann sums can also be performed. For the user this can be a welcome simplification, since MCMC methods have a high hyper-parameter complexity (burn-in duration, gain decrease sequence, Gibbs sampler, etc.), whereas the Riemann approximation involves only the position of the Riemann intervals. The choice of which is very guided by the well known theories of integration (Lagrange, Legendre, etc.), and demonstrated in our experiments to have little impact.  
We introduce the Riemann approximation as a member of the Approximate EM class. Since \(z\) is supposed bounded in this section, without loss of generality, we will assume that \(z\) is a real variable and \(z\in[0,1]\). We recall that \(p_{\theta}(z)=h(z;\theta)/g(\theta)=h(z;\theta)/\int_{z}h(z;\theta)dz\). Instead of using the exact joint likelihood \(h(z;\theta)\), we define a sequence of step functions \(\left\{\tilde{h}_{n}\right\}_{n\in\mathbb{N}^{*}}\) as: \(\tilde{h}_{n}(z;\theta):=h(\left\lfloor\varphi(n)z\right\rfloor/\varphi(n);\theta)\). Where \(\varphi\) is an increasing function from \(\mathbb{N}^{*}\to\mathbb{N}^{*}\) such that \(\varphi(n)\underset{n\to\infty}{\longrightarrow}\infty\). For the sake of simplicity, we will take \(\varphi=Id\), hence \(\tilde{h}_{n}(z;\theta)=h(\left\lfloor nz\right\rfloor/n;\theta)\). The following result, however, can be applied to any increasing function \(\varphi\) with \(\varphi(n)\underset{n\to\infty}{\longrightarrow}\infty\).  
With these steps functions, the renormalising factor \(\tilde{g}_{n}(\theta):=\int_{z}\tilde{h}_{n}(z;\theta)dz\) is now a finite sum. That is: \(\tilde{g}_{n}(\theta)=\frac{1}{n}\sum_{k=0}^{n-1}h(\left\lfloor kz\right\rfloor/ n;\theta)\). The approximate conditional probability \(\tilde{p}_{n}(\theta)\) is then naturally defined as: \(\tilde{p}_{n}(\theta):=\tilde{h}_{n}(z;\theta)/\tilde{g}_{n}(\theta)\). Thanks to the replacement of the integral by the finite sum, this deterministic approximation is much easier to compute than the real conditional probability.
# Clock Ensemble Algorithm Test in the Establishment of Space-Based Time Reference
## 3 Experimental Analysis
### Frequency Stability of the Timescales Generated by the Three Algorithms
For all three systems, Galileo's satellite clock is more stable in the short term, and the resulting timescale is more stable. For the RKT algorithm, as mentioned above, since this experiment is primarily a hydrogen clock, there is no clock with better short-term stability to produce better short-term stability for the generated timescale. The result is nearly identical to that of the NKT algorithm. For the TKT algorithm, because the first-stage filtering filters out high-frequency noise, the weight of each clock is determined by its long-term stability. Considering that the stability of hydrogen clocks is higher than rubidium clocks. Therefore, for the three systems, the satellite clocks of the BDS and Galileo systems are mainly composed of hydrogen masers, the stability of the generated timescales is similar, and they are all better than the GPS, mainly composed of rubidium clocks.
## 5 Discussion
### Advantages of the Hybrid Model  
Aiming at the unreasonable weight distribution of the traditional bivariate statistical models, in this study, GPR in machine learning was used to optimize the factor weights. More reasonable weight values were obtained, which finally improved the performance of the landslide susceptibility model. Using GPR, the trend of factor values changing with weights can be intuitively displayed, which helps to better grasp the relationship between factors and landslides. This process is primarily derived from interpolation, which indicates that adjacent regions should have the same characteristics.  
Improving the accuracy of LSM by combining different models and forming a hybrid model is a common method. At present, many scholars have combined traditional statistical models and opinion-driven models with machine learning-based algorithms, and the performance of the resulting hybrid models is better than that of the original models [9; 25; 26; 74]. These studies show that hybrid models have good application potential, but the key is how to combine models effectively. Machine learning-based models can mine useful information from a large volume of data, while statistical models have clear mathematical meanings and are conducive to the analysis of the relationships between factors and landslides. Hybrids of both models have been used. The RF-CF (random forest-certainty factor) model proposed by [PERSON] et al. [25], the FT-IV-RF (fractal theory-information value-random forest model) model proposed by [PERSON] et al. [86], and the EBF-KLR (evidential belief function- kernel logistic regression) model proposed by [PERSON] et al. [74] are innovative combinations of statistical models and machine learning-based models that have been proven to outperform the single models. In this study, a machine learning-based model was used to obtain the distribution pattern of factor weights based on statistical models. The hybrid model combines the advantages of both models, is straightforward to interpret, and can mine the potential information of factor weights. Therefore, by integrating models, the advantages of different models can be effectively combined, which provides a promising method for landslide susceptibility assessment.
## 2 Materials and Methods  
Similar to [PERSON] et al. [30], the search strategy consisted of three steps (Figure 2):  
Identification: The key word searches were conducted in the Web of Science(tm) database to find articles about EO in the EMMENA region for the past five years. As search terms, we selected the \"Topic\" option which searched journals' title, abstract, and keywords for the following keywords: \"Remote sensing OR Satellite OR Earth observations OR GIS OR geographical information systems\". With the \"AND Topic\" option, we constrained the research to the EMMENA region by inserting the following keywords: \"Algeria OR Bahrain OR Cyprus OR Egypt OR Iran OR Iraq OR Israel OR Jordan OR Kuwait OR Lebanon OR Libya OR Malta OR Morocco OR Oman OR Palestine OR Qatar OR Saudi Arabia OR Syria OR Tunisia OR Turkey OR United Arab Emirates OR Yemen\". Additionally, with the \"AND Year published\" option we further constrained the search to the past five years by inserting the following: \"2018-2022\". Screening: Duplicates and articles not written in the English language and not having a digital object identifier (DOI) were excluded from the analysis. The relevance of the papers was further examined by consulting the keywords, titles, and abstracts. Furthermore, in case of uncertainty the full texts were evaluated. Articles of which their study area covered more than one country were named as \"regional\". Author names and affiliations, the journal title, keywords, and abstracts, the number of citations, and the DOI were exported and analyzed in Excel(r) (Microsoft, Redmond, Washington, DC, USA) and in ArcGIS Pro (ESRI, Redlands, CA, USA). Eligibility: The papers were categorized into the following thematic areas based on the relevant EO applications (Table 1): Atmosphere (ATM), water (WAT), agriculture (AGR), land (LAN), disaster risk reduction (DRR), cultural heritage (CH), energy (EN), marine safety and security (MSS), and big Earth data (BED). The latter thematic area, which includes data mining and information extraction, machine learning and artificial intelligence, visual exploration and semantic enrichment, and geoinformation, can also be considered as a horizontal thematic area as its applications are often supplementary to the rest of the thematic areas. Articles which were not classified into any of the above thematic areas were named as \"other\" and articles which were classified into more than one thematic area were named as \"interdisciplinary\". Thus, some articles were categorized into more than one thematic area. Assuming that citations are a relevant measure of the impact of an article, the top 20 highly cited articles (up to 13 March 2023) from each thematic area were included in the review. The categorization was carried out by two people, the person responsible for the overall review and an independent reviewer for each country. Figure 2: Flow diagram of the scoping review methodology.
# Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images
## 2 Materials and Methods
### Methodology
#### 2.2.2 Malignancy Classification  
Given the end-to-end characteristic of this approach and the backpropagation based learning, a Multi-layer Perceptron (MLP) was used as a classifier to perform the intended predictions. An MLP is an artificial neural network composed of an input layer where all the input values are received, an output layer with a number of neurons depending on the classification task in hands, and in between a variable number of hidden layers. Since it is a fully-connected neural network, each neuron is connected to all neurons of the following layer. When approaching a classification task with TL techniques, the developed model will consist of the feature extraction pre-trained layers and a classifier stacked on top to be completely trained with the new target data (Figure 2). Each feature extracted by the convolutional block is used to feed the input fully-connected layer of the classifier. For regularization, a dropout layer was employed before this input layer to reduce the number of features taken into account at each training iteration, forcing the classifier to learn in a more robust manner. Additionally, the output of each neuron in the input and hidden layers passed through a ReLU activation, and a sigmoid activation was employed for the output neuron. The binary cross-entropy was used as the cost function to be minimized. The MLP is a backpropagation neural network, which works by approximating the non-linear relationship between the input and the output by adjusting the weight values internally. The ability of this neural network-based classifier to propagate the prediction error to the feature extractor layers allowed fine-tuning the higher-level layers of the convolutional encoder, where the most useful patterns are detected. The feature extractor is an encoder network pre-trained to reconstruct images in the same domain, not optimised to detect representative patterns related to nodule malignancy. To capture the most useful features for this classification task, the feature extractor needed to be fine-tuned, and this process is possible due to the backpropagation-based learning of MLP.  
In this binary malignancy classification task, a more extensive search was employed to find the set of hyper-parameters that achieved the desired performance, with considered values depicted in Table 2.
## 2 Materials
### Finite Element Modelling
[17] started by modeling the C4-C5 FSU and validating it before creating a new more completed (C2-C7) model in [11]; furthermore, this model was calibrated by subdividing it into single FSUs (C2-C3, C4-C5, and C6-C7). Later, [PERSON] et al. [12] created and validated a C0-T1 cervical FEM by simulating segments of the model, one of the upper cervical spine (UCS) and six of the LCS. Thus, it was decided that the model would be validated by initially simulating and validating an FSU consisting of two vertebrae, and the complete model would be validated in a future work. For this study, the C6-C7 segment was selected, resulting in a smaller model consisting of 239,381 nodes, 718,143 DOF. Figure 1: Methodology used to model the cervical spine: (**a**) Geometrical acquisition from CT scans; (**b**) Segmentation of the desired vertebrae; (**c**) Conversion of the model into an STL file; (**d**) Creation of the intervertebral disc geometry and smoothing; (**e**) Mesh generation; (**f**) Separation of the different materials on the components created. Considering that the selected FSU is located in the LCS, only five ligaments were considered. These were the anterior longitudinal ligament (ALL), posterior longitudinal ligament (PLL), capsular ligament (CL), ligamentum flavour (LF), and interspinous ligament (ISL). The intertransverse ligament was not modeled, as it often blends with the intertransverse muscles [13]. Furthermore, the supraspinous ligament (SSL) was not explicitly modeled, because in many anatomical studies it is difficult to distinguish it from the ISL. Therefore, it was considered to be aggregated with the ISL in the model [17]. The number of the elements for the ALL, PLL, ISL, LF, and CL were 5, 5, 5, 6, and 16, respectively [16; 27]. These were modelled using truss elements (T3D2) working in tension only. It is worth nothing that, apart from the CL surrounding the facet cartilages, the placement of the ligaments was based on anatomical descriptions [28]. Furthermore, each ligament element was assigned a cross-sectional area, with that the total ligament area representing values from the literature [29]. Lastly, the model was imported to Abaqus (Simula, USA) to perform the simulations. In the end, the FSU model (as seen in Figure 2) had a total of 216,540 elements and 243,756 nodes.
# Computer-Generated Holography Methods for Data Page Reconstruction Using Phase-Only Medium
## 3 Methods for Synthesizing Phase Fourier CGH of Data Page
### Double-Phase Coding Method  
Double-phase coding method implements complex values of the object beam model. It is based on the vector addition of two elements with a pure-phase modulation characteristic. An arbitrary complex number \(z[k,l]\) can be described as a sum of two phasors:  
\[z[k,l]=|z[k,l]|e^{i\psi[k,l]}\propto\frac{1}{2}\left(e^{i\eta_{1}[k,l]}+e^{i \eta_{2}[k,l]}\right) \tag{9}\]  
where \([k,l]\) are coordinates (pixel positions) of the concrete value.  
Two neighboring pixels of phase-only SLM can act as two phasors [11], representing a single value of the object beam complex field. This is an example of an element-wise representation of the hologram fringe pattern.  
Any complex number can be presented using \(\eta_{1}[k,l]\) and \(\eta_{2}[k,l]\) according to Equation (9). Phasor values can be found in the following form [28]:  
\[\eta_{1}[k,l]=\psi[k,l]+\Delta\psi \tag{10}\]  
\[\eta_{2}[k,l]=\psi[k,l]-\Delta\psi \tag{11}\]  
where angle difference can be determined from the modulus of the complex number:  
\[\Delta\psi=\arccos\left(\frac{|z|}{2}\right) \tag{12}\]  
The hologram is displayed onto phase SLM. Therefore, the hologram pixels are equal to:  
\[\Gamma\left[\hat{k},l\right]=\left\{\begin{array}{c}\eta_{1}[k,l],i\hat{f}k= 2k;\\ \eta_{2}[k,l],i\hat{f}k=2k+1.\end{array}\right. \tag{13}\]  
where \(\hat{k}\) is a new coordinates count.  
There are ways to improve the accuracy of image reconstruction by using 4 pixels. Two pixels correspond to the phasor \(\eta_{1}[k,l]\), and two others correspond to the phasor \(\eta_{2}[k,l]\). Pixels corresponding to one phasor must be arranged in a checkerboard pattern [34]. An example of the CGH fragment synthesized by the double-phase coding method is shown in Figure 2b. The fragment size is \(128\times 128\) pixels. The number of phase levels is \(256\). The black pixels correspond to the 0 phase shift. White pixels provide a \(2n\) phase shift. The  
Figure 2: Fragments of holograms generated by bipolar intensity (**a**) and double-phase coding (**b**) methods.  
method provides sharper phase value changes between pixels than the bipolar intensity method (see Figure 2a).  
In the case of a double-phase hologram, the reconstructed light field contains only the object image and its spatially separated second-order non-inverted copy. If the phase modulation characteristic of the holographic carrier is strictly linear within the modulation depth of 2\(\pi\) radians, there is no central zero order presented at the output field, and an undistorted on-axis object image can be obtained.
## 3 Results
### Outlier Elimination  
In spectral data analysis, the presence of abnormal samples may cause deviations in the model, resulting in reduced reliability of the prediction results. Therefore, before establishing a general model, this study uses the Mahalanobis distance anomaly detection algorithm to identify and eliminate potential abnormal samples in spectral data. For the spectral datasets of 'L25', 'Bluecrop' and 'Lexi', the Mahalanobis distances between each sample and the mean of the corresponding variety dataset were calculated, respectively, so as to realize the identification of outliers. Figure 5 illustrates the abnormal value calculation  
Figure 4: Distribution of the first 3 principal component scores for the three varieties. (**a**) Principal component scores for raw spectra. (**b**) Principal component scores of preprocessed spectra.  
Figure 3: Loadings distribution of different chemical components in the first three principal components: (**a**) load value of L25; (**b**) load value of Bluecrop; (**c**) load value of Lexi.  
results of the three varieties. Among them, 'L25' removed two abnormal values, 'Bluecorp' removed three abnormal values, and 'Lexi' removed eight abnormal values.