esa-sceva/satcom-chunk-collection huggingface dataset:
# Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response
## 4 Simulation Results
### Conventional Kalman Filter versus KF-TOT  
In the second set of experiments, we compare the performance of the conventional KF with the proposed KF-TOT (using \(P=2\)). For a fair comparison, both algorithms evaluate the uncertainty parameters in the same manner, as explained before. The input signal is an AR(1) process, the echo path changes after 3.75 s from the beginning of simulation, and different SNRs are used. First, in Figure 3, we set \(\text{SNR}=20\) dB. As we can notice, the KF-TOT outperforms the conventional KF in terms of both performance criteria, i.e., convergence/tracking and misalignment. This is due to the fact that the conventional KF involves a single (long-length) filter, with \(L=512\), while the proposed KF-TOT combines the estimates provided by three (much) shorter filters, with \(PL_{12}L_{2}=64\), \(PL_{11}L_{2}=64\), and \(L_{2}^{2}=4\) coefficients, respectively.  
Figure 3: Normalized misalignment of the conventional KF and KF-TOT (with \(P=2\)). The input signal is an AR(1) process, \(\text{SNR}=20\) dB, and the echo path changes after 3.75 s.  
Figure 2: Normalized misalignment of the KF-TOT using \(P=2\) and different constant values of the uncertainty parameters. The input signal is an AR(1) process, \(\text{SNR}=20\) dB, and the echo path changes after 15 s.  
These performance gains are also valid in low SNR environments, as we can notice in Figure 4a,b, where the SNR is set to 10 dB and 0 dB, respectively. Even if there is a slightly slower tracking reaction of the KF-TOT in heavy noise conditions, like in Figure 4b, it still outperforms by far its conventional KF counterpart.
## 3 Results and Discussion
### Evaluation of SFE-CO\({}_{2}\) of Hops at 10-15 MPa Pressure
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**SFE-CO\({}_{2}\) Parameters**} \\ \cline{2-4}
**Samples** & **SFE-CO\({}_{2}\) I** & **SFE-CO\({}_{2}\) II** & **SFE-CO\({}_{2}\) III** & **SFE-CO\({}_{2}\) IV** \\  & **10 MPa, 40 \({}^{\circ}\)C,** & **12.5 MPa, 40 \({}^{\circ}\)C,** & **15 MPa, 40 \({}^{\circ}\)C,** & **37 MPa, 43 \({}^{\circ}\)C,** \\  & **300 min** & **300 min** & **300 min** & **80 min** \\ \hline Extract yield, g/100 g HP & 9.33 \(\pm\) 0.46 \({}^{\text{a}}\) & 19.11 \(\pm\) 0.88 \({}^{\text{b}}\) & 22.09 \(\pm\) 0.76 \({}^{\text{c}}\) & 26.32 \(\pm\) 0.46 \({}^{\text{d}}\) \\ TEACORAC & & & & \\ mg TE/g E & 1251.79 \(\pm\) 6.30 \({}^{\text{a}}\) & 1281.94 \(\pm\) 41.22 \({}^{\text{a}}\) & 1515.16 \(\pm\) 26.30 \({}^{\text{b}}\) & 1481.17 \(\pm\) 50.87 \({}^{\text{b}}\) \\ mg TE/g HP & 116.79 \(\pm\) 0.59 \({}^{\text{a}}\) & 244.98 \(\pm\) 7.88 \({}^{\text{b}}\) & 334.70 \(\pm\) 5.81 \({}^{\text{c}}\) & 389.84 \(\pm\) 13.39 \({}^{\text{d}}\) \\ \hline \hline \end{tabular}  
* E: extract; HP: hop pellets; SFE-CO\({}_{2}\): supercritical carbon dioxide extraction; TEAC: Trolox equivalent antioxidant capacity; TE: Trolox equivalents; ORAC: oxygen radical scavenging capacity. Different superscript letters in the same row indicate significant differences (one-way ANOVA and Tukey’s test \(p<0.05\)).  
\end{table} 
Table 1: Yields and TEACORAC of _Ella_ hop SFE-CO\({}_{2}\) extracts obtained under the different experimental conditions. Figure 1: The influence of the extraction time on the _Ella_ hop SFE-CO\({}_{2}\) extract yield (g/100 g pellets) at 10–15 MPa pressure at 40 \({}^{\circ}\)C. SFE-CO\({}_{2}\): supercritical carbon dioxide extraction. Different superscript letters indicate significant differences for each graph individually (one-way ANOVA and [PERSON]’s test \(p<0.05\)). considered a more efficient approach for such purposes, preferably within shorter times to reduce the operational costs of the process.
# How Successful Is Transfer Learning for Detecting Anorexia on Social Media?
## 4 Methodology
### Deep Neural Networks
#### 4.1.2 Bidirectional Long Short-Term Memory  
Bidirectional Long Short-Term Memory (BiLSTM) is an extension of traditional LSTM that can improve model performance on sequence classification problems [35]. BiLSTM trains two instead of one LSTM on the input sequence, the first one using the normal input sequence, and the second one on a reversed copy of the input sequence. This can provide additional context to the network and make learning faster and more complete about the problem.
# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 2 Materials and Methods
### Adaptive Channel Weighting--ACW  
In the deep neural network, due to the deepening of the network, the feature information will inevitably become more abstract. Therefore, in the multi-channel network design,  
Figure 4: Visualization with AFF module added.  
Figure 5: AuxBranch overall structure.  
Figure 3: Visualization without AFF module added.  
this paper selects a 1 \(\times\) 1 convolution kernel to construct a complete feature extraction network to maximize the possibility of retaining certain input information. In addition, the channel weights are added by design to improve the feature extraction in AuxBranch, as shown in Figure 6.  
First, take the output \(X_{i}\)\(\in\)\(R\)\({}^{C\times h\times w}\) of the \(i\)-th node in the network, where \(C\) represents the number of channels of the node, \(h\) represents the height, \(w\) represents the width, and \(X_{i}\) is composed of \(C\) feature maps:  
\[X_{i}=[F_{1},F_{2},F_{3}\dots F_{C}] \tag{3}\]  
Then perform _Softmax_ on \(X_{i}\) to get \(\widetilde{X_{i}}\), which is:  
\[\widetilde{X_{i}}=Softmax(X_{i})=\frac{e^{X_{i}}}{\sum_{C=1}^{C}e^{X_{C}}} \tag{4}\]  
Finally, after obtaining the weight of the channel dimension, use the Hadamard product and the same-dimension feature map for weighting processing:  
\[AX_{i}=X_{i}\,^{\circ}\,\widetilde{X_{i}} \tag{5}\]  
After adaptive channel weighting, the auxiliary recognition network can help the main network to obtain the key information of facial expressions, reduce the weight of irrelevant information, improve the recognition accuracy, and reduce the calculation amount of redundant information. The specific data will be described in the experimental section.
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
###### Abstract  
In real-world scenarios, images may be affected by additional noise during compression and transmission, which interferes with postprocessing such as image segmentation and feature extraction. Image noise can also be induced by environmental variables and imperfections in the imaging equipment. Robust principal component analysis (RPCA), one of the traditional approaches for denoising images, suffers from a failure to efficiently use the background's low-rank prior information, which lowers its effectiveness under complex noise backgrounds. In this paper, we propose a robust PCA method based on a nonconvex low-rank approximation and total variational regularization (TV) to model the image denoising problem in order to improve the denoising performance. Firstly, we use a nonconvex \(\gamma\)-norm to address the issue that the traditional nuclear norm penalizes large singular values excessively. The rank approximation is more accurate than the nuclear norm thanks to the elimination of matrix elements with substantial approximation errors to reduce the sparsity error. The method's robustness is improved by utilizing the low sensitivity of the \(\gamma\)-norm to outliers. Secondly, we use the \(l_{1}\)-norm to increase the sparsity of the foreground noise. The TV norm is used to improve the smoothness of the graph structure in accordance with the sparsity of the image in the gradient domain. The denoising effectiveness of the model is increased by employing the alternating direction multiplier strategy to locate the global optimal solution. It is important to note that our method does not require any labeled images, and its unsupervised denoising principle enables the generalization of the method to different scenarios for application. Our method can perform denoising experiments on images with different types of noise. Extensive experiments show that our method can fully preserve the edge structure information of the image, preserve important features of the image, and maintain excellent visual effects in terms of brightness smoothing.  
image denoising; nonconvex low-rank approximation; total variational regularization; robust principal component analysis 2023
## 2 Modeling and Simulation of an Aircraft Electrical Power System (EPS)  
In a typical twin-engine aircraft like a Boeing 737, the EPS is powered by two engine-driven generators. The EPS network generates 3-phase, 115/200 V, 400 Hz AC power using either the Constant Speed Drive unit, the Variable Speed Constant Frequency unit, or the Integrated Drive Generator to link with the engines. The EPS network is connected to the engine-driven generators through a series of bus arrangements. The two gen buses are powered by either generator, and they supply power to main loads like hydraulic pumps and windows heating. Electricity is then passed on to the main buses, which supply power to the loads like the fuel boost pumps [17]. The alternating current generated is also stepped down to 28 V AC power for the instrument lighting and converted to 28 V DC power to provide power to components of other aircraft systems like the engine bleed valves or the fuel system shut-off valves. The standby buses for AC and DC are powered either using the AC transfer bus and the DC bus or with batteries via inverters and battery buses, respectively. The AC power sources from the two engine-driven generators do not function in parallel; when an AC power source of a gen bus takes priority, the other existing source is disconnected. The APU driven generator operates identically to the engine-driven generators. It powers the aircraft on ground and serves as a backup for either engine during flight [17; 18].  
As discussed in the previous paragraph, since only one AC source is active at a given time, this paper considers only the section of the EPS that is powered by one AC source for modeling and the subsequent analysis. To emulate the behavior of an aircraft electrical power system, the EPS digital twin is developed in MATLAB-Simulink R2019 asoftware, taking references from some of the existing works [19; 20; 21]. More details on the modules used to build the EPS digital twin and the mathematics behind them can be found in these references [22; 23; 24].
# A Novel Liquid-Solid Fluidized Bed of Large-Scale Phase-Changing Sphere for Thermal Energy Storage
## 5 Conclusions  
A liquid-solid fluidized bed is built in this study as a thermal storage device. Large-scale spheres with paraffin filled in make up the solid phase and water is the liquid phase as well as the heat-transfer fluid. Its fluidization status and heat-transfer characteristic are obtained by the visual measurement technique. The main conclusions are as follows:  
(1) The lighter solid PCM spheres can be fluidized by the water flowing from upside down. Slug fluidization modes are found to be stable with the expansion ratio increasing with the flow rate. The pressure drop due to the flow resistance of the fluidized bed increases with the flow rate but is always lower than that of the fixed bed. The expansion ratio as well as the flow resistance does not change along the melting or solidifying process in the PCM sphere, indicating the small-scale flow status internally merely impacts all the flow characteristics.  
(2) In comparison to the fixed bed, the heat-storage rate can be greatly accelerated. The melting duration of the PCM sphere can be reduced by 22.6%, while the visual solidifying duration can be reduced by 50%. However, the influence of the increasing flow rate of external water on the phase changing is not obvious since the main thermal resistance lies inside the PCM spheres.  
(3) Shifting, rotating and colliding movements of the PCM spheres caused by the fluidization, which stimulate internal relative motions of solid-liquid interface and liquid-gas interface, are found to be responsible for the enhanced heat-transfer rate during heat storage of the fluidized bed.  
Conceptualization, X.Q. ([PERSON]); methodology, X.Q. ([PERSON]); software, X.Q. ([PERSON]), X.Q. ([PERSON]) and D.F.; validation, X.Q. ([PERSON]); formal analysis, D.F.; investigation, X.Q. ([PERSON]) and D.F.; writing--original draft preparation, X.Q. ([PERSON]); writing--review and editing, X.Q. ([PERSON]); visualization, X.Q. ([PERSON]) and X.Q. ([PERSON]); supervision, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This study is financially supported by National Natural Science Foundation of China (52276157). Natural Science Fund for Outstanding Young Scholars of Shandong Province (ZR2023 YQ048), and Fund for Taishan Youth Scholars of Shandong Province (tsqn202408179).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Calculation Method of Earth Pressure Considering Wall Displacement and Axial Stress Variations
## 2 Theoretical Analysis
### Calculation Method for Earth Pressure Considering Material Non-Linear  
The modulus of the soil in practical engineering tends to increase with increasing confining pressure, and the tangent modulus \(E_{t}\) in the Duncan-Chang non-linear elastic constitutive model has exactly this property.  
\[\begin{split} E_{t}&=\left[1-\frac{R_{f}(1-\sin\varphi )(\sigma_{1}-\sigma_{3})}{2\cos\varphi+2\sigma_{3}\sin\varphi}\right]^{2}KP_{a} (\frac{\sigma_{3}}{P_{a}})^{n}\\ &=\left(1-R_{f}S\right)^{2}E_{i}\end{split} \tag{7}\]  
In the formula, \(S\) is the stress level; \(E_{i}\) is the Janbu empirical formula; \(R_{f}\) is the failure ratio, the value range is 0.75\(\sim\)1.0; \(K\) is the modulus parameter, the range of soft clay is 50-200, the range of hard clay is 200-500, the range of sand is 200-3000; \(n\) is a dimensionless index, which is about 0.3\(\sim\)0.8; \(P_{a}\) is the atmospheric pressure, the value is 101.325 kPa.  
The \(E_{t}\) is used to replace the modulus \(E\) in the previous section, and the improved calculation equations for \(\sigma_{x}\) and \(F_{\Sigma x}\) are shown below:  
\[\sigma_{x}=K_{0}\sigma_{z}+\frac{\left(1-R_{f}S\right)^{2}}{1-\mu^{2}}E_{i} \varepsilon_{x
## 1 Introduction
One study used an MLP-MC predictive model to analyze dynamic rural landscapes with complex environmental gradients across the Eastern Mediterranean region in Turkey, and the results showed human-induced land conversion as well as positive trends of afforestation [27]. Other studies using predictive models show rapid urban expansion of Jodhpur and Patna in India, respectively [28; 29]. Neural networks have been used to show land cover transitions caused by deforestation [30], as well as to model intense urbanization around major historical sites such as the Great Pyramids in Cairo, Egypt to assist decision-makers in sustainable development planning [16]. In Malawi, past studies used LULCC scenario-based analyses to assess the impacts of human activities including management strategies and practices, on forest cover [31; 32; 33]. Other studies examined spatiotemporal land-cover change in Malawi's river catchment areas [34; 35] or forest-cover change and modeling of carbon emissions crucial for developing national forest monitoring systems and improving REDD+ project design and implementation [36]. Two studies modeled Malawi's streamflow and sediment transfer in water catchment areas using MLP neural networks [37; 38], and recently, [39] developed a CAM-Markov Chain model for land-cover changes and predictions for 2025 and 2035 to show an increase in agricultural lands while reporting decreases in other land covers. Our study attempts to quantify, simulate, and predict spatiotemporal dynamics across two heterogenous landscapes to understand anthropogenic and environmental processes embedding short- and long-term drivers that affect future land management and policy planning in Malawi. We focus on two rural districts in Central Malawi that are similar in many social-environmental aspects but present quite differently. The objectives are: (1) to determine land use and land cover (LULC) change for two districts in central Malawiusing three-time epochs (2001, 2009, and 2019), (2) to determine the main drivers of LULC changes in both districts between 2001 and 2019 and (3) to use the observed drivers to simulate and predict future land use and cover scenario for 2050. The outcome from this study gives quantifiable spatial-temporal information that can inform short-medium term policies at the district level to promote sustainable landscape management aligned with Malawi's Vision 2063.
# Habitat Loss and Other Threats to the Survival of _Parnasius apollo_ ([PERSON], 1758) in Serbia
## 2 Materials and Methods
### Climate and Thermal Data Analysis
Hydrological dynamics were evaluated using MOD16A2 Version 6 [84], which provides evapotranspiration (ET), potential evapotranspiration (PET), and latent energy (LE). These variables, expressed in mm/month (ET, PET) and MJ/m\({}^{2}\)/month (LE), integrate meteorological, vegetation, and land cover data to reflect energy-water fluxes. All variables were standardized to a monthly time step, quality-filtered using internal MODIS flags, and interpolated where necessary. The resulting harmonized dataset supports high-resolution analysis of temporal trends, interannual variability, and vegetation-climate interactions relevant to montane ecosystems. To quantitatively evaluate differences in vegetation structure between sites with and without the presence of _Parnassius apollo_, we applied the Mann-Whitney U test, a non-parametric method chosen due to the asymmetrical distribution of variables and unequal sample sizes across groups. This decision was based on visual inspection and exploratory data analysis. The test was conducted for three key vegetation parameters: percent cover of exposed soil, grass, and shrubs/trees. Comparisons were made between two main groups (butterfly-present vs. butterfly-absent sites), as well as between the two butterfly-inhabited locations (LOC1 and LOC3). All statistical tests were performed in Python (version 3.10.12) using the scipy.stats module, with the significance level set at \(\alpha\) = 0.05. To examine the relationship between daytime land surface temperature (LST) and gross primary productivity (GPP), we fitted a second-order polynomial regression model to capture the expected non-linear response of photosynthetic activity to temperature. The modeling was based on monthly MODIS data from 2010 to 2024, using numpy.polyfit and scipy.stats in Python. Additionally, descriptive statistics (mean, standard deviation, minimum, maximum) were calculated for all key variables, and pairwise correlations among LST, GPP, and related climatic and vegetation indices (e.g., LAI, FPAR, PET, TCI)were assessed using Pearson correlation coefficients. Although no formal interaction terms were included in the models, ecological interactions were interpreted based on spatial and temporal overlap between environmental variables and vegetation productivity patterns.
## 2 Datasets  
### SAR Data  
A number of 123 C-band Sentinel-1 (S-1) images, acquired by both A and B satellites, VV-polarised, both ascending and descending, Level 1 products, i.e., Interferometric Wide Swath Ground Range Multi-Look Detected High-Resolution images (afterwards, IW), are downloaded from the Copernicus Open Access Hub.  
The S-1 dataset spans from January 2015 to November 2020 and covers the Atlantic European northwest shelf area. Figure 1 shows the full-frame coverage of all selected images, which are five descending (i.e., SIA_6234_37_14, SIA_20205\(8\)16, SIB_5838_37_13, SIB_10709\(8\)13, SIB_11482_81_13) and eight ascending (i.e., SIB_3687_161_11, SIA_4010_88_16, SIA_16931_59_11, SIA_17558_161_15, SIA_20562_15, SIA_21933_161_1, SIB_2360 _59_10, SIB_8616_15_22). The study area is chosen among different benchmark sites of interest for the funding project E-SHAPE--myEcosystem showcase, which will serve focal user groups such as research, environmental assessment, reporting and management by offering seamless access to consistently scaled environmental information from various sources (e.g., SAR-derived products, like SSW maps). Nevertheless, the developed technique is applicable independently from the selected site.  
Those S-1 images characterised by a clear visibility (i.e., by human eye) of wind induced linear patterns, such as those from WSs [32], BLRs [33; 44] or any other wind aligned patterns, hereafter \"wind rows\", are chosen aiming at extracting the local SSW direction exploiting such SAR signatures. In particular, the ultimate multi-scale version of the LG-Mod algorithm [40] is applied to serve this purpose.  
The exploitation of C-band S-1 IW product type is to achieve the best MS LG-Mod performance in SAR derived SSW direction and uncertainty estimation (as experimented in [40]). Moreover, the choice of VV-polarised images is motivated to obtain the higher wind rows visibility, according to actual meteorological conditions, since SAR VV polarizations are slightly more sensitive than HH to wind pattern signatures [44].  
Figure 1: Atlantic European northwest shelf. Full-frame coverage is in orange and cyan for descending and ascending Sentinel-1 (S-1) Interferometric Wide Swath Ground Range Multi-Look Detected High-Resolution (IW) images, respectively. Background image (Data SIO, NOAA, US Navy, NGA, GEBCO; Image Landsat/Copernicus) is from Google Earth.