esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Change Detection and Time Series Analysis with Applications
## 3 Decomposition Methods into Time-Frequency Domain
### Methods of Analyzing Multiple Time Series Together
#### 3.7.2 Least-Squares Cross Wavelet Analysis (LSCWA)  
The LSCWA is a robust method of analyzing two time series together [60]. Since the sampling rates of the time series may not be the same, one may choose a common set of times for both time series, e.g., the union of the times in both time series, and compute a normalized spectrogram for each time series, namely, \(s_{1}\) and \(s_{2}\). Then, the Least-Squares Cross-Wavelet Spectrogram (LSCWS) is defined by \(X_{s}=s_{1}s_{2}\), which shows the coherency of the components in percentage variance when multiplied by 100. Higher and lower values of \(X_{s}\) indicate that the components of both series are highly coherent and incoherent within the corresponding time-frequency neighborhoods, respectively.  
Like XWT, LSCWA also provides the phase differences between the components of interest in both time series, usually displayed by arrows in the cross-spectrogram. The arrows follow the trigonometric circle principle in the two-dimensional Cartesian coordinate system. If an arrow points to the right or toward the positive \(x\)-axis for a peak in the cross-spectrogram, then the corresponding components of both time series are in-phase, and they are out-of-phase if the arrow points to the left. If an arrow is in the first or second quadrant with an angle \(\psi_{1}\) (\(0<\psi_{1}<180\) degrees) counterclockwise from the positive \(x\)-axis, then the component of the second time series lags the one in the first time series by \(\psi_{1}\) degrees. Furthermore, if an arrow is in the third or fourth quadrant with an angle \(\psi_{2}\) (\(0<\psi_{2}<180\) degrees) clockwise from the positive \(x\)-axis, then the component of the second time series leads the one in the first time series by \(\psi_{2}\) degrees. Given the frequency, one may convert the angle to time.  
The Least-Squares Cross-Spectral Analysis (LSCSA) is a special case of LSCWA, which multiplies the Least-Squares Spectra of both time series, and no windowing method is applied. Thus, LSCSA is not suitable for estimating the phase differences of unstable components over time because it averages them out, similarly for the signal estimation of LSSA vs. LSWA. Finally, the statistical properties of LSCWA and LSCSA follow from the normality assumption of the time series such as in LSSA and LSWA [60]. LSCWA inherits the advantages of LSWA over CWT, but it can be computationally slower than XWT. The time series in LSCWA do not have to be equally spaced with the same sampling rate. The LSCWA considers the covariance matrices associated with the time series as well as the constituents of known forms. A MATLAB software package for LSWA and LSCWA including a graphical user interface is freely available [61; 62; 63].
###### Abstract  
The aim of the study was to determine the effect of gaseous ozone, hydrogen peroxide and both factors used alternately on the profile of phenolic compounds of tomato fruits grown under cover. Phenolic compounds are natural substances, and their biosynthesis in plant tissues is affected by stress factors such as gaseous ozone and hydrogen peroxide. The experiment showed that the use of gaseous ozone at a dose of 2 ppm for 1.5 and 3 min significantly increased the total amount of phenolic compounds in tomato fruits compared to the control. In turn, in fruits obtained from plants to which hydrogen peroxide was applied at a concentration of 1 and 3% during vegetation, a lower amount of phenolic compounds was found compared to the control. In addition, the combined use of ozone and hydrogen peroxide in the case of four variants--2 ppm for 1.5 min + 1% H\({}_{2}\)O\({}_{2}\); 2 ppm for 3 min + 1% H\({}_{2}\)O\({}_{2}\); 2 ppm 1 min + 3% H\({}_{2}\)O\({}_{2}\); 2 ppm 3 min + 3% H\({}_{2}\)O\({}_{2}\)--significantly increased the amount of phenolic compounds compared to the control.  
tomato fruit; ozone fumigation; hydrogen peroxide; polyphenol profile +
Footnote †: journal: Applied sciences
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## Appendix A Derivation of the Frequency Sensitivity from Equation (18)  
The relation shown in (18) can be retrieved by the frequency response similar to an MTI filter. The range dependence vanishes within the guard duration \(\alpha\) of a cyclic prefix OFDM signal according to the FT time shift theorem. The following approach considers a single Doppler displacement \(f_{\mathrm{D}}(f_{\mathrm{c}})\) by just the carrier frequency \(f_{\mathrm{c}}\) either after compensation of the occupied bandwidth dependency over \(B_{\mathrm{occ}}\) by e.g., the Keystone transform or because the time-bandwidth is sufficiently small (Section 2.5).  
Starting with a zero-padded look on the OFDM ambiguity function from [24] with \(T_{\mathrm{b}}=T_{\mathrm{u}}\) yields  
\[\left|\chi(\tau,f_{\mathrm{D}})\right|=\left|\sum_{i=1}^{n\infty}\sum_{k=1}^{ n\infty}d_{i}d_{k}^{*}\exp(\mathrm{j}\,2\pi f_{k}\,\tau)\int_{\tau}^{T_{ \mathrm{b}}}\exp(\mathrm{j}\,2\pi\left(f_{i}-f_{k}+f_{\mathrm{D}}\right)\zeta )\,\mathrm{d}\zeta\right| \tag{19}\]  
If orthogonality is perfectly fulfilled, values will just result in case of \(i=k\). Therefore,  
\[\left|E[\chi]\right|=\left|\sum_{i=1}^{n\infty}\exp(\mathrm{j}\,2\pi\tau f_{i }t)\int_{\tau}^{T_{\mathrm{b}}}\exp(\mathrm{j}\,2\pi\,f_{\mathrm{D}}\,\zeta )\,\mathrm{d}\zeta\right| \tag{20}\]  
which is [24]  
\[\left|E[\chi]\right|=(T_{\mathrm{b}}-\left|\tau\right|)\left|\sum_{i=1}^{n \infty}\exp(\mathrm{j}\,2\pi f_{i}t)\right|\text{sinc}(\pi f_{\mathrm{D}}(T_ {\mathrm{b}}-\left|\tau\right|)). \tag{21}\]  
The signal voltage loss as difference to the optimum has to be squared to get a power factor  
\[\frac{1}{\mathit{Loss}_{\mathrm{PC}}(\tau,f_{\mathrm{D}})}=\Big{(}\big{(}T_{ \mathrm{b}}-\left|\tau\right|\big{)}\,\text{sinc}\big{(}\pi f_{\mathrm{D}}(T_ {\mathrm{b}}-\left|\tau\right|)\big{)}\Big{)}^{2}. \tag{22}\]  
If (22) is expressed for an overlapped cyclic prefix OFDM symbol train, it is independent of range within the guard duration \(\alpha\). Therefore, using \(u=\max(0,\,\tau-\alpha)\) instead of \(\left|\tau\right|\) in (22), the frequency sensitivity loss depends on the Gross Doppler displacement \(f_{\mathrm{D}}\) as shown in (18)  
\[\mathit{Loss}_{\mathrm{PC}}=\left(\frac{1}{\left(1-\frac{u}{T_{\mathrm{b}}} \right)\,\text{sinc}(\pi\,f_{\mathrm{D}}\left(T_{\mathrm{b}}-u\right))}\right) ^{2}.\]
## 3 Results
### Point Clouds  
In total, we processed 2860 UAV images (2020 images for north delta lobe and 840 images for the southern delta lobe), which is equal to an average of 200 images per grid. SfM Photogrammetry provided point clouds, DSM, and orthomosaic for each delta lobe. The generated raw point clouds had 400.5 million and 136.6 million points, which correspond to average point densities of 951.25 m\({}^{-2}\) and 736 m\({}^{-2}\) for the northern and southern delta lobes, respectively. The derived DSM has a resolution of 5.33 cm/pixel and an orthomosaic with 2.66 cm/pixel resolution. The products in total covered an area of 0.44 km\({}^{2}\) and were corrected based on GCPs with a total error of 0.06 m. The discrepancy between the planned flight area and the product is generated from other areas captured during the UAV image acquisition.  
A small subset of the point clouds is shown in Figure 6. The set of figures illustrates the step-by-step point clouds processing. Figure 6a shows subtle noise yielded on the SfM-based point clouds. The raw point clouds were refined by classifying the high and low noise, resulting in cleaned points as depicted in Figure 6d. The ground-classified points with the CSF method were then evaluated, especially those situated below the dense vegetation since SfM-based point clouds provide no information below the canopy cover. Hence, an exhaustive manual revision was conducted by checking and correcting the classified ground points. The final point classification categories chosen for the analysis were non-ground (1), ground (2), high vegetation (5), and noise (7). The final result of the point cloud processing is a height-normalised point clouds that can be described with all elevations were normalised with respect to the ground, i.e., an elevation of 0 m. Figure 7 shows DTM and DSM derived from the final classification of the point clouds.
# Simulation of Electronic Quantum Devices: Failure of Semiclassical Models
## 3 Partially Quantum-Mechanical Device Modeling Based on the Wigner-Function Formalism
### Failure of Local Dissipation Models
Indeed, the steady-state solution of the bulk-like Boltzmann collision term in Equation (10) is just the Fermi-Dirac distribution. The latter is space-independent and has nothing to do with the thermal-equilibrium Wigner function \(f^{\circ}(\mathbf{r},\mathbf{k})\) of the quantum device, which is space-dependent and significantly different from zero within the QW region only. \[n(z)=\frac{1}{2\pi}\,\int dk\,f(z,k)\, \tag{14}\]2 Low-temperature and low-density dissipation dynamics in the \(12\,\mathrm{nm}\) thick GaAs/AlGaAs QW of Figure 3 resulting from the fully 3D local Boltzmann model in Equation (10) in the low-density limit. (**a**) Carrier density \(n(z)\) in Equation (19) as a function of position at different times: \(0\,\mathrm{ps}\) (thin solid curve), \(0.8\,\mathrm{ps}\) (dashed curve), and \(1.6\,\mathrm{ps}\) (dash-dotted curve); the equilibrium carrier density is also shown for comparison (solid curve). (**b**) Bound-state subband populations [see Equation (20)] as well as continuum (cont) contribution as a function of time. Reprinted from [61]. As a final remark, we notice that the unphysical features of the low-density Boltzmann-type simulations just reviewed come out to be further amplified in the presence of high carrier concentrations. As discussed in detail in [64], in high-density conditions, the Wigner function may still be negative, but also greater than unity. This implies that, for high carrier concentrations, the role played by the Pauli factor (\(1-f(\mathbf{r},\mathbf{k})\)) in Equation (11) may deviate significantly with respect to the conventional semiclassical scenario (where it is always bounded between 0 and 1), leading to highly counter-intuitive effects such as a sort of \"negative dissipation\".
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 4 Operational Semantics
### Service Request  
One way to represent the requirements of an IoT service is to represent them as sets of timed data feeds. For each of these feeds, the service needs to secure appropriate sensor data feeds and carry out the required aggregations and customizations for different clients. This representation of a service request gives a client the ability to represent their service requirements without being too rigid. If the client is too rigid in defining their request, the service request is likely to be rejected if the directory service is too busy at these points of time.  
A service request is represented by \(\rho\) and is defined as follows:  
**Definition 4** (Service Request).: _A service request \(\rho\) is a set of sets of timed service data feeds. Each set \(\rho_{i}\in\rho\) has a sufficient number of service feeds for serving that request._  
A service request is represented formally as follows:  
\[\rho=\left\{\rho_{1},\dots,\rho_{n}\right\}\,\ \ \rho_{i}=\left\langle\zeta_{1}, \dots,\zeta_{m}\right\rangle \tag{3}\]  
where \(n\geq 0\) is the number of sets in \(\rho\), \(m\geq 0\) is the number of service feeds in \(\rho_{i}\), and \(\zeta\) represents a single timed service feed.  
Only one set \(\rho_{selected}\) out of \(\rho\) needs to be served. The selection of this \(\rho_{selected}\) can be said to be carried out by a function \(f\) as follows:  
\[\rho_{selected}=f(\rho) \tag{4}\]  
The necessary and sufficient condition for accepting a service request is stated in Axiom 1, as follows:Axiom 1: Accepting a service request.A service request \(\rho\) can be accepted in the system if and only if at least one member of \(\rho\), \(\rho_{selected}\in\rho\), can be served by a set of contributing services, \(S_{\rho_{selected}}\subset S\).  
We consider a continuous-time model for representing services. This continuous-time modeling approach provides more flexibility in describing IoT services because of their real-time requirements, represented as sets of temporally defined feeds. These feeds are constructed by aggregating sensor events --such as user activities, change in a geographical location, etc., which occur at particular points in time and lead to a change in the state of interest of an IoT service.
## 2 Background
### Overview of Eco-Materials for Sustainable Building
#### 2.2.3. Ferrock  
Another relevant example of a sustainable building material is ferrox, which is a mixture of iron powder, lime, fly ash, oxalic acid, and metakaolin [70]. The ferrox technology was introduced at the University of Arizona, where, while researching an alternative material to cement that had similar strength and workability, they discovered that iron reacts with \(CO_{2}\) to create iron carbonate to form ferrox [71]. Ferrock is a carbon-negative material compared to Portland cement, which is the primary source of \(CO_{2}\) emissions and air pollution during its manufacturing process [72]. In this sense, ferrox is considered a partial replacement material for cement [71], and tests showed that after 28 days, the compressive, flexural, and tensile strengths of ferrox concrete (8% replacement ratio) were improved by 12% compared to normal mixes [70; 73]. Moreover, it has better crack resistance and increased fire and thermal resistance [74]. The slump value of ferrox as a replacement for concrete is in accordance with the mix design specifications [75]. Nevertheless, ferrox's iron powder contains microparticles that pose health risks during the manufacturing process [71], and although it is cheaper than concrete, its price could see a significant increase if the demand for ferrox increases [76].  
Ferrock is a relatively new material [77] and there are no published official codes for design and construction using it. In addition, it has only been tested for small projects, and its performance in large projects is still unclear [76]. Figure 3 shows an example of construction using ferrox (left panel) and an illustration of the construction process using this material (right panel).
# Model for Determining Parking Demand Using Simulation-Based Pricing
## 2 Methodology
The simulation model is implemented in a deterministic Microsoft Excel spreadsheet environment, where demand for each user category is calculated using analytical exponential functions without the incorporation of random variables or probabilistic elements. As such, the model operates without iterations or randomness handling; its behavior is fully determined by the input prices and the derived elasticity parameters. Each pricing scenario is evaluated once per time slot, with results assessed against predefined occupancy and revenue criteria. The demand functions used in the model are analytically derived from economic theory and calibrated using observed price-demand data. Because the model operates deterministically, rather than through statistical learning or stochastic simulation, traditional model fit metrics such as R\({}^{2}\) or RMSE are not applicable in this context. Instead, the model's reliability is demonstrated through its internal consistency and its ability to reproduce observed user behavior patterns following price adjustments.
## 5 Simulation Model and Results
### Performance at Varying Bit Error Rate (BER)
#### 5.5.2 C-HARQ, CARQ and S&W ARQ against BER of All Links  
Figure 9(a),b presents the throughput efficiency and energy efficiency comparison, respectively, against the BER variations of all the links at the same time.  
Figure 9(a) clearly shows that C-HARQ outperforms CARQ and S&W, over the entire range of BER. However Figure 9(b) reveals that C-HARQ provides the best energy efficiency performance only at high BER, but at lower BER, S&W ARQ proves to be more energy efficient than the cooperative protocols. This is quite understandable, as under good channel conditions the cooperation process is not required so much and the cooperative protocols basically starts operating in S&W mode, but with the additional energy expenditure from _CNodes_ due to overhearing of the original transmission. With five _CNodes_ and ten times larger optimum payload size (compared to that of SW ARQ), these energy consumption becomes non negligible for C-HARQ and hence contributes to the degradation of the energy efficiency level.
## 2 Data and Segmentation Methods
### Methodology
#### 2.2.3 Training Process  
For both datasets all the considered architectures were evaluated based on their computation time and accuracy (see Results in Tables 3-7). They were trained for 50 epochs with the same setting of hyper-parameters. Details regarding hyper-parameter settings can be found in the next Section 2.2.4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Downsampling Block**} \\ \hline
**Type** & **Kernel (Pool) Size** & **Strides** & **Activation** \\ \hline Convolution 3D & 3,3,3 & 0,0,0 & Relu \\ Convolution 3D & 3,3,3 & 0,0,0 & Relu \\ Maxpooling 3D & 2(1),2,2 & 2,2,2 & - \\ \hline \multicolumn{4}{c}{**Upsampling Block Block**} \\ \hline
**Type** & **Kernel (Pool) Size** & **Strides** & **Activation** \\ \hline Transpose Conv.3D & 2,2,2 & 2(1),2,2 & - \\ Convolution 3D & 3,3,3 & 0,0,0 & Relu \\ Convolution 3D & 3,3,3 & 0,0,0 & Relu \\ \hline \multicolumn{4}{c}{**Bridge Block**} \\ \hline
**Type** & **Kernel (Pool) Size** & **Strides** & **Activation** \\ \hline Convolution 3D & 3,3,3 & 0,0,0 & Relu \\ Convolution 3D & 3,3,3 & 0,0,0 & Relu \\ \hline \multicolumn{4}{c}{**Output Convolution**} \\ \hline
**Type** & **Kernel (Pool) Size** & **Strides** & **Activation** \\ \hline Convolution 3D & 1,1,1 & 0,0,0 & Sigmoid \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of network layers—their convolution kernel size, strides and activation function.  
The training of the networks was done in two phases--(1) benchmark phase and (2) fine-tuning phase. First we wanted to compare all the used networks for benchmark purposes to examine whether the Dense-U-Net can achieve the highest accuracy among all the tested architectures. Due to the computation time needed for training we decided to evaluate the tested architectures after 50 epochs of training. When we verified that the Dense-U-Net network performs the best, the limit of 50 epochs was removed and during the fine tuning phase we tried to achieve the best possible accuracy.  
Thus the fine-tuned Dense-U-Net network was then trained for 99 epochs using brain dataset. The hyper parameters used were exactly the same as used for the benchmarking phase. This was repeated also for spine dataset using 3D Dense-U-Net network with weights initialized from denoise pre-training. This network was trained as well for 99 epochs. The learning rate was set higher to \(10^{-4}\) and the decay to \(1.99\times 10^{-6}\). The reason of that was we wanted to give the network higher learning rate at the beginning, which helped to escape from pre-trained auto-encoder local minimum and to continue on with searching for global minimum.
# Simulation of Lid-Driven Cavity Flow with Internal Circular Obstacles
###### Abstract  
The Lattice Boltzmann method (LBM) has been applied for the simulation of lid-driven flows inside cavities with internal two-dimensional circular obstacles of various diameters under Reynolds numbers ranging from 100 to 5000. With the LBM, a simplified square cross-sectional cavity was used and a single relaxation time model was employed to simulate complex fluid flow around the obstacles inside the cavity. In order to made better convergence, well-posed boundary conditions should be defined in the domain, such as no-slip conditions on the side and bottom solid-wall surfaces as well as the surface of obstacles and uniform horizontal velocity at the top of the cavity. This study focused on the flow inside a square cavity with internal obstacles with the objective of observing the effect of the Reynolds number and size of the internal obstacles on the flow characteristics and primary/secondary vortex formation. The current LBM has been successfully used to precisely simulate and visualize the primary and secondary vortices inside the cavity. In order to validate the results of this study, the results were compared with existing data. In the case of a cavity without any obstacles, as the Reynolds number increases, the primary vortices move toward the center of the cavity, and the secondary vortices at the bottom corners increase in size. In the case of the cavity with internal obstacles, as the Reynolds number increases, the secondary vortices close to the internal obstacle become smaller owing to the strong primary vortices. In contrast, depending on the sizes of the obstacles (\(R/L=1/16\), \(1/6\), \(1/4\), and \(2/5\)), secondary vortices are induced at each corner of the cavity and remain stationary, but the secondary vortices close to the top of the obstacle become larger as the size of the obstacle increases.  
L pacs: 1
Footnote : _Article_  
Simulation of Lid-Driven Cavity Flow with Internal Circular Obstacles
## 3 Combinatorial Fusion Approach (CFA)
### Feature Selection Using Performance and Cognitive Diversity  
The features from the EMG and HRV are viewed as a scoring system. The performance and the diversity between the feature sets in MSS have been carried out to select the relevant and non-redundant features, which can yield a better result when the features are fused. Thus the combination of the MSS can improve the result when the features of each of the individual scoring systems have relatively high performance and high diversity [52]. Therefore the aim of our feature selection is to find the features with relatively good performance as well as relatively high diversity.  
In our experiment, there are 32 features in total and 59 possible values for each feature. The correct rate is calculated to measure the performance and to make comparisons between the machine learning classifiers (see Figure 6). The classifiers used in this work are Naive Bayes (NB), \(k\)-Nearest Neighbors (\(k\)-NN with \(k=1\), 3, 5), and Decision Tree (J48). As shown in Figure 6, the highest average correct rate is J48 classifier with 72.19% of accuracy.  
After that, the final performance of each feature from the J48 classifier is sorted in decreasing order as illustrated in Figure 7. It can be seen that the highest correct rate with a rate of 86.44% is generated from feature O (LF_power (ms\({}^{2}\))) and feature d (maximum amplitude of EMG (ms\({}^{2}\))) generates the lowest correct rate of with a rate of 66.1%. From previous work in [70], there are significant gender differences (\(p<0.05\)) in the LF in male subjects compared to females. This implies that the male subjects are more active and dominant than the female subjects. Therefore, this is particularly suitable to be used to isolate male and female subjects in developing the rehabilitation devices, control system, and rehabilitation engineering.  
Figure 6: Average Correct rate comparison among classifiers.