esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### InSAR Site-Specific Analysis
Figure 7 shows the Carbona landslide's evolution in October and November 2019, the two months preceding the reactivation of the landslide, in ascending geometry. During October (Figure 7a), the moving area is small and located between 480 m and 510 m a.s.l. The maximum displacement rates along the LOS reach about 250 mm/year, indicating an ongoing relevant acceleration when compared to typical velocities of dormant/inactive earth ow [31]. In November, the moving area is clearly larger and involves a large portion of the medium-upper earth ow body, at a short distance from few houses and a secondary road. LOS-displacement rates exceed locally 300 mm/year. The progressive acceleration that occurred in October and November 2019 preceded the reactivation of the earth ow that happened at the beginning of December. The corresponding monthly stacks capture the highest displacement rates within our analysis period. In the four years preceding the catastrophic failure, lower speeds were recorded, with minimum values during the summer and winter seasons and relatively higher values during spring and autumn. The average of all these speed values leads to the numerical values in Figure 6.  
In the case of Spareda, we report three pairs of wrapped and unwrapped interferograms (Figure 8). The images are obtained by processing SAR data acquired from the ascending track 117. The temporal baseline of each interferogram is six days. The rst pair (Figure 8a,b) precedes the failure, the second pair (Figure 8c,d) includes the reactivation date, and the last (Figure 8e,f) follows the failure. Cold colors and negative values indicate that displacements are towards the satellite, while warm colors and positive values describe displacements away from the satellite along the LOS. Figure 7: Monthly stacks were obtained for the period preceding the reactivation (15–21 November 2019). October ( a), November ( b).
## 3 Results  
### Laboratory Calibration Results  
The calibration curve of CO\({}_{2}\) and CH\({}_{4}\) was established in the laboratory after calculating the areas of the selected IR bands of CO\({}_{2}\) and CH\({}_{4}\), as presented in Figure 1. The calibration procedure consists of 11 measurement points for CO\({}_{2}\) (Figure 4a) and 13 measurement points for CH\({}_{4}\) (Figure 4b), on which the measurements were repeated three times with new references IR absorption spectrum for each point. The reference gas concentrations have also been cross-validated using the Picarro CRDS G2201-i analyzer and gas chromatography.  
The lowest concentration of the standard gas tested is 101 ppm with an absorbance area (Abs) of 0.22. In comparison, the highest concentration tested is around 59,806 ppm, with an average absorbance area (Abs) of 94, As shown in Figure 1. In Table 1, the CO\({}_{2}\) concentration and the unit area have a large goodness fit of linear correlation with R\({}^{2}\) of 1 and a Y-intercept of \(-\)21.7 ppm. It also has a significant deviation from zero and a \(p\)-value of less than 0.0001. The CH\({}_{4}\) concentrations (50 to 5000) and the associated absorbance area have a large goodness fit of linear correlation with adjusted R\({}^{2}\) of 0.9999 and residual standard error of 32.14 ppm. It also has a significant deviation from zero and a \(p\)-value of less than 0.0001.  
Figure 4: The calibration curve of CO\({}_{2}\) (**a**) shows a polynomial fitting of the CO\({}_{2}\) calibration curve on FTIR ranging from 101 ppm to 59,806 ppm. As for the CH\({}_{4}\) (**b**), the highest concentration that was applied was 5000 \(\pm\) 99 ppm, with an average absorbance area (Abs) of 3.97 \(\pm\) 0.012. The lowest concentration tested was 50 ppm, with an average absorbance area of 0.046 \(\pm\) 0.024 (resolution: 1 cm\({}^{-1}\)).  
Figure 3: A vertical section of the soil profile of Tithonian limestone surmounted by valanginian sediments. On the surface, borehole measurement and a lattice tower (FT) were constructed with the distance around 355 m.
# A Cloud-Based Distributed Architecture to Accelerate Video Encoders
## 4 Experimental Setup and Results
### Scalability and Resource Usage
#### 4.2.3 Resource Usage  
During the experiments, the workers were monitored to obtain CPU and memory usage. All the data collected from the workers is concatenated to obtain the sample Probability Density Function (PDF).  
Table 4 shows the CPU usage and memory consumption for the two codecs with 15-workers encoding the video at 1.5 Mb/s. As can be seen, x265 uses all the CPU available, being the encoder with the more intensive CPU use of the two encoders, while libvpx-vp9 uses around a 73% of the available CPU. Moreover, both encoders are similar in terms of memory consumption.
## 3 Results and Discussion
### Comparative Analysis of the Investigated Scenarios
#### 3.2.1 Air Quality Analysis
Fine particulate matter (PM\({}_{2.5}\)), being more persistent and harder to remove, is especially sensitive to changes in vehicular flows and benefits significantly from the combined effects of reduced emissions and improved ventilation resulting from lower traffic volumes. It is also worth noting that the effectiveness of the interventions depends not only on the magnitude of traffic reduction but also on local urban configuration, the presence of physical barriers to pollutant dispersion (e.g., closely spaced buildings), and the capacity of surfaces and vegetation to function as pollutant sinks. Scenario 1, although less ambitious in terms of vegetation enhancement, demonstrated higher overall efficiency because it directly targets the primary source of air pollution. In summary, the results are consistent with recent literature [86; 87; 88], which highlights the value of integrating traffic reduction policies with microscale environmental strategies as an effective approach to improving air quality in dense urban settings. However, to achieve significant and widespread benefits, structural measures must be coupled with a substantial reduction in direct emission sources, particularly internal vehicular traffic.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 3 Mechanism of Chromium and Arsenic Stabilization
### Adsorption Mechanism  
The adsorption performance of stabilization materials for chromium and arsenic depends on their surface functional groups and pore structure, mainly including physical adsorption and chemical adsorption. Physical adsorption refers to the adsorption of chromium or arsenic on material surfaces caused by non-specific van der Waals forces. Chemical adsorption is caused by chemical reactions between pollutants and materials, forming covalent or ionic bonds [77]. [PERSON] et al. [78] prepared Schwertmannite by oxidizing Fe\({}^{2+}\) solution with different H\({}_{2}\)O\({}_{2}\) supply rates. A low supply rate of H\({}_{2}\)O\({}_{2}\) results in higher crystallinity, larger specific surface area (SSA), and larger particle size of Schwertmannite. The adsorption capacity of Cr(VI) reaches 1.89 mmol/g. This study highlights the importance of controlling the oxidation rate to optimize the material's adsorption properties. LDH-Pb was prepared by [PERSON] et al. [79] via in situ doping of Pb\({}^{2+}\) onto LDHs. After doping with Pb\({}^{2+}\), the particles perpendicular to it are formed, and the surface of LDH-Pb is fluffier and porous. Due to the large amount of protonated hydroxyl groups generated, it is electropositive over a wide pH range. The addition of Pb\({}^{2+}\) reduces the influence of other negative ions and organic matter and improves the adsorption performance of LDH, with an adsorption capacity of 188.7 mg/g. This research underscores the role of surface modification in improving adsorption efficiency. Lignin and Fe(NO\({}_{3}\))\({}_{3}\) were added in the tetrahydrofuran/water solution by [PERSON] et al. [80] to prepare LC-FLG@Fe\({}^{0}\) particles with graphene shells. At a pH of 3, the maximum adsorption was reached for As(III). The adsorption kinetics indicate that it is a chemical adsorption, and the maximum adsorption capacity is 107.2 mg/g. This study demonstrates the potential of using organic-inorganic hybrid materials to enhance adsorption capacity. Montmorillonite modified with amorphous ferritic iron ore (amFe@Mont) was synthesized by [PERSON] et al. [81]. Its specific surface area was measured to be 176.82 m\({}^{2}\)/g, and it was indicated by the H4 hysteresis loop that it had a microporous and mesoporous structure, with a majority of micropores. This material's high surface area and porous structure contribute to its effective adsorption properties. A modified lime mortar binder, called C5 lime concentrate, was used by [PERSON] et al. [82] for mine tailings treatment. Abundant needle-shaped C-S-H structures were shown in the SEM image. This enabled As elements to form fixed and non-leachable compounds under different conditions. This study highlights the potential of using modified binders for the effective immobilization of heavy metals in tailings.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 3 Fault Diagnosis Method Based on ECA-MRANet
### Based on Improved Residual Block Feature Extraction
#### 3.1.2 Residual CBAM Block  
In recent years, wider and deeper network structures seem to be the mainstream approach to convolutional neural networks. However, studies have demonstrated that as the layers of the network deepen, the training accuracy does not improve and even declines. The residual structure can form a jump connection, and directly add the input and the output after convolution, which ensures that the training accuracy will not decrease as the network deepens. In this paper, we propose a feature extraction module that integrates the residual structure with the convolutional attention module (CBAM). The structure is illustrated in Figure 7 below.  
In Figure 7, the backbone network performs feature extraction through three convolutional layers, where conv1 and conv3 are convolutional layers with a convolutional kernel of \(1\times 1\). Conv1 is a dimensionality reduction of the input feature x, and conv3 is a dimensionality upgrading operation of the features after conv2  
Figure 7: Residual CBAM block.  
is the convolutional kernel with a convolution of \(3\times 3\) or \(3\times 1\). After the input feature x is convolved in three layers, the CBAM module is introduced to make the network focus on more important channels and spatial locations, thus improving the performance of the network. The Identity Shortcut Connection in Figure 7 is a shortcut connection that can directly skip one or more layers to connect the input to the output layer. Conv4 is also a convolutional layer with a convolutional kernel of \(1\times 1\), whose role is to dimensionally transform the input feature x to ensure that the inputs and outputs have the same dimension. The output y is obtained by adding the result \(f_{4}(x)\) of the input features after the operation of conv4 with the features \(F^{\prime\prime}\) extracted by the backbone network, which is calculated as follows:  
\[y=M_{\text{s}}\times M_{c}\times\sigma[\sigma[\sigma(w_{1}\times x)\times w_{2 }]\times w_{3}]+[\sigma(w_{4}\times x)] \tag{17}\]  
where \(w_{1}\), \(w_{2}\), \(w_{3}\), and \(w_{4}\) are the parameters to be trained for convolution 1, convolution 2, convolution 3, and convolution 4, respectively, \(M_{\text{s}}\) is the weight coefficient of the Spatial Attention Mechanism, \(M_{c}\) is the weight coefficient of the Channel Attention Mechanism, and \(\sigma\) is the ReLU function.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 3 The NOMA System-Level Simulator
### Features of the System-Level Simulator
#### 3.3.2 Nineteen-Cell Scenario  
This scenario is illustrated in Figure 5b, wherein there are 19 cells in total that share a boundary(s). Each cell comprised two paired users connected to a single BS. Each user in a particular cell experiences interference from all of its 18 neighboring cells, and the link measurement model calculates the BER accordingly.  
Figure 4: Structure for implementation of a link to system-level Simulator.  
Figure 5: Considered scenarios: (**a**) 7-cell scenario; and (**b**) 19-cell scenario.
# Assessment on the Properties of Biomass-Aggregate Geopolymer Concrete
## 3 Results  
### Compressive Strength and Density  
The compressive strength values of concrete with different coarse aggregates are shown in Figure 4. In 3 days, the compressive strength values of CCSAC and SBRAC reached 67.9% and 91.9% of the compressive strength of NAC, respectively; as the curing age reached 28 days, the concrete was 28 days old, and the compressive strength values of CCSAC and SBRAC reached 67.9% and 86.6% of the compressive strength of NAC, respectively. The transition area between the aggregate and the cement paste, that is, the so-called interface transition zone (ITZ), is one of the important indicators affecting the performance of concrete. The CCS has a high water absorption rate, and when it is directly added to the concrete, a micro \"puddle\" will be formed around the CCSA, making the thickness of the ITZ between the concrete cement material matrix and the crushed coconut shell more than 30 mm, as shown in Figure 5a. This phenomenon can be the main cause of deterioration inside the concrete, thereby greatly affecting the properties of the concrete [25; 26; 27]. The synthesized aggregate treated with cement slurry has no volatilization of moisture in the aggregate and good volume stability. After long-term curing, an \"internal curing effect\" [28; 29], which favors the performance of the aggregate, reduces the thickness of the ITZ between the coconut shell aggregate and the outer cement slurry, as shown in Figure 5b.  
Figure 4: Compressive strength of concrete.  
At the age of 3 days, the SBRAC only had a few aggregate failures on the compressive failure surface. The strength of the cement slurry matrix in the concrete was weak and failed first. At the age of 28 days, there were many aggregate failures and a situation where the SBRA broke due to shear failure. The reason for this phenomenon may be due to the increase in the strength of the cement paste matrix in the concrete [30].  
The density values of concrete are shown in Table 6. The results show that the use of CCSA and SBRA to replace NA can effectively reduce the density of concrete. The density values of CCSAC and SBRAC are 1820 kg/m\({}^{3}\) and 1945 kg/m\({}^{3}\), respectively, which are lower than the 1950 kg/m\({}^{3}\) specified for light aggregate concrete.
# Cover Time in Edge-Uniform Stochastically-Evolving Graphs +
## 5 Cover Time with One-Step History  
We now turn our attention to the case where the current state of an edge affects its next state. That is, we take into account a history of length one when computing the probability of existence for each edge independently. A Markovian model for this case was introduced in [5]; see Table 1. The left side of the table accounts for the current state of an edge, while the top for the next one. The respective table box provides us with the probability of transition from one state to the other. Intuitively, another way to refer to this model is as the birth-death model: a dead edge becomes alive with probability \(p\), while an alive edge dies with probability \(q\).  
Let us now consider an underlying graph \(G\) evolving under the EUE model where each possible edge independently follows the aforementioned stochastic rule of evolution.  
RWD for General \((p,q)\)-Graphs  
Let us hereby derive some first bounds for the cover time of RWD via a min-max approach. The idea here is to make use of the \"being alive\" probabilities to prove lower and upper bounds for the cover time parameterized by \(\xi_{min}=\min\{p,1-q\}\) and \(\xi_{max}=\max\{p,1-q\}\).  
Let us consider an RWD walk on a general connected graph \(G\) evolving under EUE with a zero-step history rule dictating \(\Pr(e_{t}=1)=\xi_{min}\) for any edge \(e\) and time step \(t\). We refer to this walk as the Upper Walk with a Delay (UWD). Respectively, we consider an RWD walk when the stochastic rule of evolution is given by \(\Pr(e_{t}=1)=\xi_{max}\). We refer to this specific walk as the Lower Walk with a Delay (LWD). Below, we make use of UWD and LWD in order to bound the cover time of RWD in general \((p,q)\)-graphs.  
For any connected underlying graph \(G\) and the birth-death rule, the cover time of RWD is at least \(C_{G}/\xi_{max}\) and at most \(C_{G}/\xi_{min}\).  
Proof.: Regarding UWD, one can design a corresponding electrical network where each edge has a resistance of \(1/\xi_{min}\) capturing the expected delay till any possible edge becomes alive. Applying Theorem 2 gives an upper bound for the UWD cover time.  
Let \(C^{\prime}\) stand for the UWD cover time and \(C\) stand for the cover time of RWD under the birth-death rule. It now suffices to show \(C\leq C^{\prime}\) to conclude.  
\begin{table}
\begin{tabular}{l c c} \hline  & **Dead** & **Alive** \\ \hline dead & \(1-p\) & \(p\) \\ \hline alive & \(q\) & \(1-q\) \\ \hline \end{tabular}
\end{table}
Table 1: Birth-death chain for a single edge [5].  
In birth-death, the expected delay before each edge traversal is either \(1/p\), in case the possible edge is dead, or \(1/(1-q)\), in case the possible edge is alive. In both cases, the expected delay is upper-bounded by the \(1/\xi_{min}\) delay of UWD, and therefore, \(C\leq C^{\prime}\) follows since any trajectory under RWD will take at most as much time as the same trajectory under UWD.  
In a similar manner, the cover time of LWD lower bounds the cover time of RWD, and by applying Theorem 2, we derive a lower bound of \(C_{G}/\xi_{max}\).
## 6 Discussion
### Temporal Analysis of GPP Datasets  
In the analysis of the temporal trends of GPP values, the study employed various trend analysis methods, all of which were non-parametric, thus avoiding the strict preconditions associated with parametric methods like linear regression. Among these, the MK method is one of the most commonly used trend analysis methods. Although not a parametric method, it is generally preferred for data without high autocorrelation [103]. To enhance the reliability of the results, a modified MK method (MMK) was used in this study. Despite efforts to mitigate false trends arising from autocorrelation through pre-whitening processes, some studies have reported that MMK can still be prone to errors and may produce misleading results [104]. Notably, it interpreted significant trends far less often than ITA and EMD for the study datasets, such that the interpretation of GPP trends will often differ if MK (or MMK) or the graphical trend analysis methods, ITA or EMD, were used. ITA is adept at clearly delineating trends at both low and high values, while EMD provides information on how trends in GPP have increased or decreased over specific years. When assessing all regions collectively, the trend analysis results from ITA and EMD were similar.  
Analysis with the MMK method using monthly GPP data across all regions revealed that the FLUXCOM dataset, consistent with the literature [70; 105], showed a trendless pattern. In contrast, the MODIS dataset indicated a decreasing GPP trend during February, March, and April (Table 2)--although this was not observed in other datasets--which would lead one to search for the mechanisms underlying GPP changes during late winter and early spring. The most pronounced increases in GPP values were calculated during the summer months (Table 2), which would lead one to study how changes in climate, leaf area index, and perhaps agricultural intensity have altered GPP since the 1980s.  
Izmir and Andalya displayed similar trends in GPP values (Figure 7). It is believed that this similarity is influenced by the fact that both regions share the same Koppen climate classification (Csa), indicating a similarity in their climatic conditions.  
In examining the results of the EMD method, the FLUXCOM dataset consistently showed no trend across all regions, as expected [70; 105]. A key distinction of the EMD method was its indication of initially decreasing trends in GPP values, followed by increasing trends (Figure 9), due, likely in part, to its search for the oscillating signals that underlie a dataset [89]. The GOSIF dataset showed a slight decreasing trend in recent years in all regions except Erzincan (Figure 9). Overall, these findings highlight the complexity of analyzing GPP trends and the importance of selecting appropriate trend analysis methods that can accurately reflect the temporal dynamics of GPP in various ecological settings. As mentioned by Du et al., a potential reason is that the GOSIF dataset was reconstructed using MODIS surface reflectance data, which may have led to some biases in its algorithms [106].
# Entropy and Statistical Complexity in Bioelectrical Signals: A Literature Review
## 3 Results
### Entropy
In this context, the quantification of information present in a bioelectrical signal is made possible through the use of a wide range of Information Quantifiers (ITQs), including entropies, which have the ability to reveal significant properties of the P associated with a bioelectrical signal. ITQs provide metrics that encompass the space of all P within the dataset. This, in turn, enables the comparison of different datasets and their classification according to the properties of the underlying processes, whether deterministic or stochastic [1]. Entropy measures are based on various mathematical concepts, including information theory, probability theory, and permutation theory. These approaches not only have different properties but also multiple applications. The choice of the functional form of the entropy measure depends on the specific characteristics of the system under study and the research question of interest. In some cases, combinations of different measures can be used to achieve a more comprehensive characterization of the system [5]. Despite its long history, the term entropy remains difficult for many to understand. Its history is intricate and has been subject to multiple interpretations, making it confusing and complex to grasp, apply, and interpret. Currently, there are various approaches to entropy with different purposes and areas of application, leading to a wide diversity of descriptions and meanings within the scientific community. Researchers in various fields consider entropy an indispensable tool for the analysis of time series and apply it across a wide range of disciplines. Exploring the relevance and applicability of each entropy measure in time series could guide researchers toward selecting the most appropriate measures for their studies.