esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Acoustic Data Processing Method  
In this work, data from an in-house experiment and a number of highly cited publications were utilized to validate the further developed PVPM model. In these experiments, microphones were employed to record acoustic pressure fluctuations. The pressure data were further processed, and the acoustic impedance and the magnitude of any acoustic attenuation were derived. Three different acoustic data collection and processing methods exist: the two-microphone method [32], the four-microphone method [21] and an in situ impedance method [48]. The data processing method used in each experiment will be clarified in the Results section for each experiment. Static pressure monitors were defined in the CFD simulations at positions directly corresponding to where microphones were placed in the experiments to extract the acoustic wave information. The same data processing method as employed in the experimental work was then employed to process the pressure data to obtain the acoustic impedance or acoustic damping information. Acoustic impedance or acoustic damping information acquired by the numerical method and the experimental tests are presented in the following section in order to validate the proposed PVPM model.
## 4 Mathematical Modeling and Analysis
### Simulation Results
#### 5.2.2 Scenario 2: Different Transmission Power Levels for CUE and DUE  
Figures 10 and 11 show the SINR and throughput at DUE-Rx when aggregate interference from all interfering nodes are present in the network. In this scenario, all DUEs use the same transmission power levels (100 mW or 20 dBm), even those that are located at different distances; the CUEs also exhibited the same transmission power levels (250 mW or 24 dBm). Figures 10 and 11 show that the received SINR and throughput of the target DUE-Rx are higher in the proposed FFR-ABS compared to those of the conventional FFR at the cell edge area.  
Figure 8: Combined graphs of signal to interference and noise ratio (SINR)-FFR and SINR-ABS received at the target DUE-Rx.  
Figure 9: Combined graphs of FFR and ABS throughput received at the target DUE-Rx.  
Figure 12 shows the throughput received at the target DUE-Rx with respect to the variable DUE transmission power. It is obvious from Figure 12 that the proposed scheme achieves higher throughput for the target DUE-Rx in the cell edge area compared to the conventional scheme. As shown in Figure 12, CUEs experience much higher interference levels compared to DUE pairs. Even at the same transmission power levels, CUEs outperform the DUE pairs. Thus, the use of the proposed hybrid FFR-ABS can reduce aggregate interference that could reach the target DUE-Rx. Once aggregate interference to the target DUE-Rx is significantly reduced, SINR and throughput can be significantly increased for any DUE transmission power level, as shown in Figure 12.  
Figure 11: Channel capacity received at the target DUE-Rx with constant CUE-Tx and variable DUE-Tx power levels.  
Figure 10: SINR received at the target DUE-Rx with constant CUE-Tx and variable DUE-Tx power levels.  
We also assume equal transmission power levels for the target transmitter (DUE-Tx) and all interferers (DUEs and CUEs). As shown in Figure 12, when the same transmission power levels are applied, aggregate interference increases due to a high level of interference from the CUEs. Therefore, for the same transmission power levels, the interference of target DUE-Rx would increase depending on the location of the interferers. The closer the interferer, the higher the aggregate interference, resulting in a reduction to the SINR and throughput. By applying the ABS, interference from the CUEs can be suppressed during ABS time. Due to non-CUE transmission, aggregate interference is reduced, but SINR and throughput are increased as shown in Figures 13 and 14.  
Figure 12: Interference levels from both interferers for variable Tx power.  
Figure 13: SINR received at the target DUE-Rx under the same CUE-Tx and DUE-Tx power levels.
## 2 Materials and Methods
### Inputs for Estimating Feedstock Availability  
A meta-analysis of three recent studies for biomass availability was performed to understand the range of sustainable domestic (EU27 and UK) biomass feedstock that could be available to contribute towards the 2030 policy targets. The three studies are:  
* Directorate General for Research and Innovation (DG RTD). 2017. Research and Innovation perspective of the mid- and long-term Potential for Advanced Biofuels in Europe [20].  
* European Commission Joint Research Centre (JRC). 2015 (2019). ENSPRESO--an open data, EU-28 wide, transparent, and coherent database of wind, solar and biomass energy potentials [21].
* Concawe Sustainable Biomass Availability (2021) [22]--focus on selected feedstocks from Annex IX of RED II [5] (Part A and B). This study does not include food and feed crops, and other sustainable feedstocks accepted by RED but not included in Annex IX.
* Other literature sources, i.e., [23].  
All three studies were commissioned to inform on biomass availability in the EU, but their aims, assumptions and modelling scenarios differed. The JRC EU TIMES study was commissioned to deliver consistent datasets for biomass potentials that would complement the datasets that the model had for the other RES technologies, e.g., wind, solar, etc. The DG RTD study was commissioned with the aim to illustrate how various R&I practices would broaden the biomass supply base and to estimate what this would mean in terms of additional biomass, cost reductions, and respectively higher shares for advanced biofuels transport in the future. The Concawe study aimed to provide an estimation of the sustainable biomass potential availability for domestic (EU27 and UK) feedstocks of agricultural, forest, and waste origin included by 2030 and 2050 and to provide an evaluation of the advanced biofuel potential.  
It is relevant to highlight that all the studies followed consistent and harmonised methodological approaches for the same types of agriculture, forest, and biowaste feedstocks and for all EU member states and the United Kingdom.
# Quantum Stream Cipher Based on Holevo-Yuen Theory
## 5 Concrete Applications of Quantum Stream Cipher  
As mentioned above, the Y-00 quantum stream cipher has not yet reached its ideal performance, but in practical use, it has achieved a high level of security that cannot be achieved with conventional techniques, and it can be said that the ciphers are now at a level where they can be introduced to the market. To date, the development of transceiver for the Y-00 quantum stream cipher has been funded by the university president's discretionary fund, as well as external funds from the Ministry of Education, Science and Technology (MEXT), and the Defense Acquisition Agency (DEA). Here, we introduce examples of the use case of the Y-00 quantum stream cipher.
# The Ongoing Greening in Southwest China despite Severe Droughts and Drying Trends
## 2 Materials and Methods
### Vegetation Data  
Previous studies have proposed various indicators to characterize vegetation based on remote sensing data; these indicators have different ecological significance [21; 22]. In this study, LAI, EVI, and GPP are used to characterize vegetation coverage, vegetation greenness, and vegetation productivity; detailed information of the original data is shown in Table 1. The LAI data came from the Global Agricultural Monitoring System (GIMMS) LAI3g, which was based on GIMMS NDVI3g and artificial neural network model inversion [23]. Due to its high accuracy and long coverage time, it is widely used in various research works. EVI was obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD13C2 product; the use of this data is mainly based on the following two considerations: (1) Compared with NDVI, EVI can solve the problem that NDVI may be saturated in densely-vegetated areas to a certain extent, the vegetation coverage in the study area is high, and saturation of NDVI may occur; (2) one of the purposes of this research is to explore the impact of drought events on long-term vegetation trends. A major drought in SWC occurred from 2009 to 2012. The vegetation change trend after drought events is particularly important, and EVI products continue to be updated, so that we can explore the changes in vegetation after 2012. The GPP came from the GPP product developed by [PERSON] et al. (2020) [24]; the dataset was based on the revised EC-LUE model, and took into account some environmental factors, such as the CO2 fertilization effect, the difference between sunlit leaves and shaded leaves.  
The time ranges of vegetation datasets used in this study were LAI (1982-2016), EVI (2000-2019), and GPP (1982-2016). All vegetation data were resampled to 0.5 degrees and the time resolution was unified to a monthly scale to meet time and space consistency.
###### Abstract  
Despite several reports on the endocrine-disrupting ability of atrazine in amphibian models, few studies have investigated atrazine toxicity in the heart and cerebellum. This study investigated the effect of atrazine on the unique Ca\({}^{2+}\) channel-dependent receptor (Inositol 1,4,5-trispphosphate; IP\({}_{3}\)R) in the heart and the cerebellum of adult male _Xenopus laevis_ and documented the associated histomorphology changes implicated in cardiac and cerebellar function. Sixty adult male African calaved frogs (_Xenopus laevis_) were exposed to atrazine (0 \(\mathrm{\SIUnitSymbolMicro}\)/L (control), 0.01 \(\mathrm{\SIUnitSymbolMicro}\)/L, 200 \(\mathrm{\SIUnitSymbolMicro}\)/L, and 500 \(\mathrm{\SIUnitSymbolMicro}\)/L) for 90 days. Thereafter, heart and cerebellar sections were processed with routine histological stains (heart) or Cresyl violet (brain), and IP\({}_{3}\)R histochemical localization was carried out on both organs. The histomorphology measurements revealed a significant decrease in the mean percentage area fraction of atrial (0.01 \(\mathrm{\SIUnitSymbolMicro}\)/L and 200 \(\mathrm{\SIUnitSymbolMicro}\)/L) and ventricular myocytes (200 \(\mathrm{\SIUnitSymbolMicro}\)/L) with an increased area fraction of interstitial space, while a significant decrease in Purkinje cells was observed in all atrazine groups (_p_ < 0.008, 0.001, and 0.0001). Cardiac IP\({}_{3}\)R was successfully localized, and its mean expression was significantly increased (atrium) or decreased (cerebellum) in all atrazine-exposed groups, suggesting that atrazine may adversely impair cerebellar plasticity and optimal functioning of the heart due to possible disturbances of calcium release, and may also induce several associated cardiac and neural pathophysiologies in all atrazine concentrations, especially at 500 \(\mathrm{\SIUnitSymbolMicro}\)/L.  
Keywords:atrazine; Purkinje; cerebellum; myocytes; toxicology; IP\({}_{3}\)Rs +
Footnote †: journal: Applied Sciences
# Phase Portraits of Families VII and VIII of the Quadratic Systems
## 2 Preliminary Definitions
### Poincare Compactification  
Let \(X=(P,Q)\) be the vector field defined by the polynomial differential system (1). Roughly speaking, the Poincare compactification consists of creating a vector field \(p(X)\) in a 2-dimensional sphere, \(\mathbb{S}^{2}\), such that its phase portraits (in the open northern and southern hemispheres) is a copy of the phase portrait of the vector field \(X\), and the equator of the sphere plays the role of the infinity of the phase portrait of \(X\); for details, see [10], or Section 5 of [8]. In this way, we can study the orbits of the vector field \(X\), which go to or come from infinity.  
Let \(\mathbb{S}^{2}=\{x=(x_{1},x_{2},x_{3})\in\mathbb{R}^{3}:x_{1}^{2}+x_{2}^{2}+x_{ 3}^{2}=1\}\) be the _Poincare sphere_. We denote by \(T_{x}\mathbb{S}^{2}\) the tangent plane to \(\mathbb{S}^{2}\) at a point \(x\in\mathbb{S}^{2}\). We consider the vector field \(X\) defined on the plane \(T_{(0,0,1)}\mathbb{S}^{2}\). Then the central projection \(f:T_{(0,0,1)}\mathbb{S}^{2}\to\mathbb{S}^{2}\) defines two copies of \(X\) in \(\mathbb{S}^{2}\), one in the northern hemisphere and the other in the southern hemisphere. Obviously the equator \(\mathbb{S}^{1}=\{y\in\mathbb{S}^{2}:y_{3}=0\}\) represents the _infinity_ of \(\mathbb{R}^{2}\). The projection of the closed northern hemisphere of \(\mathbb{S}^{2}\) on \(x_{3}=0\) under \((x_{1},x_{2},x_{3})\longmapsto(x_{1},x_{2})\) is called the _Poincare disc_, and it is denoted by \(\mathbb{D}^{2}\). As \(\mathbb{S}^{2}\) is a differentiable manifold, we define six local charts, \(U_{i}=\{x\in\mathbb{S}^{2}:x_{i}>0\}\), and \(V_{i}=\{x\in\mathbb{S}^{2}:x_{i}<0\}\) for \(i=1,2,3\); with the corresponding diffeomorphisms, \(F_{i}:U_{i}\to\mathbb{R}^{2}\) and \(G_{i}:V_{i}\to\mathbb{R}^{2}\) for \(i=1,2,3\) which are the inverses of the central projections from the tangent planes at points \((1,0,0)\), \((-1,0,0)\), \((0,1,0)\), \((0,-1,0)\), \((0,0,1)\) and \((0,0,-1)\), respectively.  
We denote by \((u,v)\) the value of \(F_{i}(x)\) or \(G_{i}(x)\) for any \(i=1,2,3\), so a few simple calculations (for \(p(X)\)) lead to the following formulae in the corresponding local charts (see Section 5 of [8]):  
\[v^{d}\bigg{(}Q\bigg{(}\frac{1}{v},\frac{u}{v}\bigg{)}-uP\bigg{(} \frac{1}{v},\frac{u}{v}\bigg{)},-vP\bigg{(}\frac{1}{v},\frac{u}{v}\bigg{)} \bigg{)}\] in \[U_{1},\] \[v^{d}\bigg{(}P\bigg{(}\frac{u}{v},\frac{1}{v}\bigg{)}-uQ\bigg{(} \frac{u}{v},\frac{1}{v}\bigg{)},-vQ\bigg{(}\frac{u}{v},\frac{1}{v}\bigg{)} \bigg{)}\] in \[U_{2},\] \[(P(u,v),Q(u,v))\] in \[U_{3},\]  
where \(d\) is the degree of the polynomial differential system (1). The formulae for \(V_{i}\) are similar to the formulae for \(U_{i}\) with a multiplicative factor of \((-1)^{d-1}\). In the coordinates for \(i=1,2\), points \((u,v)\) of the infinity \(\mathbb{S}^{1}\) satisfy \(v=0\).
## 4 Experimental Results and Analysis
### Discriminability  
Discriminability is a crucial aspect of a zero-watermarking algorithm, ensuring that different images generate distinct zero-watermarks, especially when copyright identifiers are the same. In ZWNet's training, we assessed the similarity between images of Koala and Panda in the evaluation mode and observed the changes in the NC values, as depicted in Figure 12.  
From Figure 12, it is evident that the NC value decreases from around 0.70 as the training epoch increases. After the 14 th epoch, the NC value drops to approximately 0.54. Considering the NC threshold is set at 0.8, a value of 0.54 is relatively low, indicating that the zero-watermarks are dissimilar and the copyright will not be confused.  
To conduct a more precise assessment of ZWNet's discriminability, we used the Hamming distance to compare zero-watermarks generated by different images with the same copyright. The Hamming distance is calculated as follows:  
\[dist(A,B)=\sum_{i=0}^{n}A[i]\oplus B[i]\]  
Figure 12: Similarity changes of test images in the training stage.  
Here, A and B represent two zero-watermark sequences, n is the sequence length (which is 256 for ZWNet's watermark block) and \(\oplus\) represents the exclusive OR operation. Hamming distances among the zero-watermarks of the four test images are detailed in Table 3.  
Table 3 displays the Hamming distances among the zero-watermarks of the four test images. The high Hamming distances indicate that the zero-watermarks differ significantly from one another, with more than 80 different bits. Given that the total length of the zero-watermark is 256, this observation suggests that the zero-watermarks of each image are substantially distinct from the others. Hence, the discriminability of ZWNet is substantiated.  
Two factors contribute to this discriminability. Firstly, using LK-PAN within ZWNet helps extract local features and fuse them with the global context during feature map generation, as described in Section 2.2. Secondly, incorporating unique identifiers in the watermark block plays a crucial role. During the training phase, ZWNet is trained to extract robust features, assigning the same identifier to identical images and different identifiers to distinct images. As a result, ZWNet strives to produce dissimilar feature maps for different inputs, resulting in its discriminative capability. Importantly, this discriminative feature is not a result of overfitting since, as previously mentioned, ZWNet has not seen the test images during training.
###### Abstract  
Quickly detecting and accurately diagnosing early bearing faults is the key to ensuring the stable operation of high-precision equipment. In actual industrial applications, it is common to face the issues of big data and poor fault identification accuracy. To accurately and automatically realize the diagnostics of rolling bearings, a convolutional neural network algorithm and fault feature enhancement method is proposed. A two-dimensional space feature extraction method based on the Cyclostationary theory and wavelet transform shows good results in noise suppression. Firstly, the cyclic demodulation of wavelet transform coefficients is performed on bearing vibration signals to convert one-dimensional vibration data into a two-dimensional spectrogram for enhancing the weak fault feature. Secondly, the image segmentation theory is introduced, which can obtain more data and improve the calculation accuracy and efficiency on the basis of data dimension reduction. Finally, the augmented 2D spectrograms are inputted into a convolutional neural network. Through the analysis of the actual planetary gearbox bearing data, and compared with other mainstream intelligence algorithms, the effectiveness and superiority of this method are verified.  
planetary bearing; weak fault identification; feature enhancement mechanism; image segmentation; convolutional neural network +
Footnote †: journal: Applied  
Article  
Data Augmentation in 2D Feature Space for Intelligent Weak Fault Diagnosis of Planetary Gearbox Bearing. _Appl. Sci._**2022**, 12, 8414. [[https://doi.org/10.3390/app1278414](https://doi.org/10.3390/app1278414)]([https://doi.org/10.3390/app1278414](https://doi.org/10.3390/app1278414))
## 4 Materials and Methods
### Data Pre-Processing and Statistical Analyses  
GC-MS chromatograms were pre-processed in MZmine-2.52 [47] after conversion to netCDF. The same pre-processing step were applied for VOCs and VCCs data, including crop filtering, peak detection, chromatogram builder, deconvolution and alignment with slightly differences in parameters as listed in Table S6. After pre-processing, artefact peaks from the fiber and chromatographic column (e.g., siloxanes and cyclosiloxanes) were manually removed from the matrices. A whole matrix was constructed by concatenating VOCs and VCCs matrices (133 observations \(\times\) 10,709 variables), followed by total area normalization. Before multivariate analysis, a variable selection method was applied to the concatenated matrix based on the unpaired Mann-Whitney U test, performed in Metabaanaly 4.0 [17]. Therefore, a final matrix containing only the variables with _p_-value < 0.05 was scaled to pareto and used for multivariate analysis. PCA, PLS-DA, seven-fold cross-validation and permutation test were performed using the final matrix for BC patients vs. cancer-free controls (109 observations \(\times\) 4184 variables), stage T1 vs. Ta/Tis (43 observations \(\times\) 495 variables), stage \(\geq\) T2 vs. Ta/Tis (36 observations \(\times\) 486 variables) and stage \(\geq\) T2 vs. T1 (27 observations \(\times\) 856 variables) in SIMCA-P 15 (Umetrics, Sweden). The metabolites considered important for group discrimination presented a VIP \(>\) 1 and _p_-value < 0.05. The choice of a univariate test for variable selection with respect to other more conventional approaches, such as PLS-VIPs or PLS-weights, relied on the degree of group separation and the predictive ability of the PLS-DA models generated, which were better when the univariate test was used. One limitation of this approach is the fact that it does not account for intercorrelations between metabolites that may reflect proximity in metabolic pathways. Ideally, the PLS-DA models would be validated independently on a test set inside the cross-validation procedure, but it was not possible due to the small sample cohort.  
Receiver operating characteristic (ROC) curves were computed for each PLS-DA model after variable selection and the set of discriminant compounds formally (L1) and putatively (L2) identified, along with the AUC and confusion matrix (Metabaanalyst 4.0 [17]). Correction for multiple comparisons was performed using the false discovery rate (FDR) method [19] in R 3.5.3 software. Effect size [18], percentage of variation and AUC were also computed for each statistically significant compound. Finally, the correlation coefficients between age and the set of statistically significant compounds found alteredbetween BC and the control group were computed in R 3.5.3 software, to investigate a potential influence of this confounding factor.
## 2 Materials and Methods
### Feature Importance  
The RF-based Boruta algorithm [25] was used to determine which components of the modified ECG signals were most important in detecting differences between the modified and baseline ECG traces. The Boruta algorithm generates randomly permuted copies of all variables as shadow attributes and feeds them into RFs to generate importance (Z-scores) for all attributes. The algorithm accepts variables with an importance significantly higher than the maximal importance of random attributes. Thus, it is possible to determine which time points are the most important for the algorithm to classify the ECG signal.
## 3 Time-Frequency Representation
### Cohen Classes  
The Cohen classes time-frequency analysis is a modification of the WVD, which can be expressed in a unified form:  
\[\mathrm{CTFD}(t,f)=\iint\phi(\tau,\ \theta)\mathrm{WVD}(t-\tau,f-\theta)\ d\tau d \tag{7}\]  
In the formula, WVD is the Winger-Ville distribution, and \(\phi\) (\(\tau,\theta\)) is called the kernel function [31].  
Commonly used Cohen Classes distributions include pseudo-Wigner-Ville distribution (PWD), smoothed Wigner-Ville distribution (SWD), Born-Jordan distribution (BJD), Generalized rectangular distribution (GRD), Choi-Williams distribution (CWD), Zhao-Atlas-Marks distribution (ZAMD), etc.