esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Edge Computing and Related Paradigms
### Cloud Computing  
Cloud computing has gained significant traction in recent years. It provides users with access to a shared pool of configurable computing resources over the internet, enabling ubiquitous, convenient, and on-demand network access to a wide range of services [54]. This model integrates various computing, storage, and software resources through distributed, utility, and parallel computing. Cloud computing has become widely adopted in academia and industry due to its ability to reduce computing and storage costs for users while enhancing ease of use.  
One of the key features of cloud computing is its ability to provide remote computing resources to consumers and businesses, allowing them to leverage powerful computational capabilities without the need for extensive local infrastructure [55]. Cloud computing architectures typically involve centralized data centers that host and manage these resources, offering users scalability, flexibility, and cost-effectiveness [56]. By distributing cloud services over the internet, cloud computing enables users to access applications, store data, and perform computational tasks without being tied to specific physical locations.  
Cloud computing has also paved the way for innovations in various domains, such as mobile edge computing, where cloud resources are extended to the edge of the network to provide services closer to mobile devices [57]. Additionally, cloud computing has been instrumental in developing collaborative computing systems that leverage local computing, edge cloud, and central cloud resources to optimize task offloading and resource allocation [58].
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## 3 Enforcing Quantum Statistics via Generalized Bath
### The Quantum Thermal Bath  
The quantum thermal bath was proposed by [PERSON] and coworkers [12] to approximate NQEs in general molecular dynamics simulations via a generalized Langevin equation. They use a Markovian friction kernel and a colored random force \(R(t)\) with an autocorrelation spectrum which is tailored according to the energy distribution \(\theta(\omega,T)\) of the quantum harmonic oscillator. The equations of the resulting dynamics are identical to those derived from the quasiclassical approximation by [PERSON] [32]. For one-dimensional systems, Equations (13) and (14) hold; their generalization to multiple atomic degrees of freedom is straightforward. However, contrary to [PERSON], who aimed at modelling the dynamics of a system coupled to an actual physical bath, in the QTB, the Langevin equation is simply used as a thermostat in order to enforce the quantum statistical distribution of energy (including zero-point energy effects). The expression of the random force autocorrelation, Equation (14), is universal, i.e., system-independent, so that the method does not require a priori knowledge of the system or of its detailed spectral density. Heuristically, this form of GLE tends to thermalize the vibration modes of the system with the average energy distribution of quantum harmonic oscillators in equilibrium at temperature \(T\), hence the lexical origin of the quantum thermal bath.  
Plots of \(\theta(\omega,T)\) and its temperature derivative, which enters the definition of key quantities such as the heat capacity, are provided in Figure 2 for three typical frequencies \(\omega\). One can easily check that:  
\[\theta(\omega,T)\ =\ \frac{\hbar\omega}{2}\ \coth\biggl{(}\frac{\hbar\omega}{2k_{ B}T}\biggr{)}=\frac{\hbar\omega}{2}\biggl{[}\frac{2k_{B}T}{\hbar\omega}+ \frac{\hbar\omega}{6k_{B}T}-\varrho\biggl{(}\frac{\hbar\omega}{2k_{B}T}\biggr{)} ^{3}\biggr{]}\ \ \ \text{for}\ \ \ \frac{\hbar\omega}{2k_{B}T}\ll 1 \tag{15}\]  
Therefore in the classical high-temperature limit, i.e., when \(k_{B}T\gg\hbar\omega\) for all relevant frequencies, \(\theta(\omega,T)\) approaches its classical value \(k_{B}T\), and substituting in Equation (14) shows that the QTB reduces to a classical Langevin thermostat. However, the typical temperature where the classical limit is reached can be rather high (see Figure 2). Even more delicate are quantities such as the heat capacity, i.e., the temperature derivative of the average energy that for a harmonic oscillator is given by \(d\theta(\omega,T)/dT\) which shows a very slow convergence to the classical value.  
Apart from a slight dependence on the friction parameter \(\gamma\) that is further analyzed in Section 4.1, the probability distributions obtained in QTB simulations are exact for harmonic systems, by construction of the method. For more general cases, it can provide a good approximation to zero-point energy effects in realistic systems, under some conditions that are further developed in Sections 4, 5 and 7.
## 7 Results and Discussion
Figure 3: System frequency response. In Figure 7, the frequency response of the wind generation \(f_{w}(t)\) is depicted, where it starts from the initial value \(f_{w}(0)=3\) (Hz), slightly increases, and then converges to the origin without overshoot. The output response of the hydraulic pitch actuator \(\beta(t)\) is shown in Figure 8, where it starts from the initial value of \(\beta(0)=4\) (KW) and approaches the origin in a short time without overshoot. Figure 9 presents the trajectory of the active power of the battery \(P_{b}(t)\), which starts from \(P_{b}(0)=3\) (KW) and converges to the origin in a short time, without overshoot. Figure 10 shows the sampling-induced errors for all considered states, with a zoom-in on the first 10 s for readability. All sampling errors are reset to zero at each triggering instant \(t_{k},k\in\mathbb{N}\), as discussed in (9). Figure 5: Trajectory of the active power of wind generation. Figure 6: Speed response of the governor valve. Figure 11 presents the generated sampling times by the proposed ETC mechanism (37). We observe that the first series of sampling times are typically produced by the periodic sampling rule with \(T=1.51\) s while the remaining transmission instants are much larger than \(T\). This behavior reflects the fact that, at the first stage of response, i.e., the transient response, more information needs to be submitted to the controller to update the control signal, such that the closed-loop stability can be ensured. Then, during the second stage, i.e., the steady-state stage, fewer control updates are needed, as all the states have already stabilized at the origin, as required. We can observe that the last transmission interval number is about 15 s as opposed to the periodic sampling period, \(T=1.51\) s, i.e., the transmission intervals by the ETC mechanism are 10 times larger than the sampling period \(T\) during the steady-state stage, demonstrating the efficiency of the ETC control in reducing the communication load over the network. Figure 8: Output response of the hydraulic pitch actuator. Figure 7: Frequency response of wind generation. To further illustrate the advantage of the approach, the obtained results are compared with [43] for sampling periods \(T=2\) s and \(T=4\) s, as shown in Tables 1 and 2. From the obtained results, we see that the approach in [43] ensures a larger periodic sampling time than what we can produce. However, the number of transmission times with our proposed ETC is fewer than those generated by the ETC in [43]; this reduction in transmission times is the primary objective for integrating the ETC into the feedback loop. Figure 10: Sampling-induced errors for the first 10 s.  
Figure 9: Trajectory of the active power of the battery. Figure 14: State response with random measurement noise. Figure 4:0 State response with random disturbance on the system frequency. Figure 4:1 State response with random disturbance on the wind generation frequency.
# Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
## 5 Experimental Verification
### Baseline Perturbation Analysis  
According to the experimental analysis above, when the baseline satisfied uniform sampling within a certain baseline perturbation, the system could achieve optimal imaging performance along the height direction with the aforementioned baseline model. Considering the actual situation of the baseline deviation, \(0\leq\Delta b_{\perp}\leq B_{span}/(MN-1)\) was selected and \(\Delta b_{\perp}=1\) m is the iteration step size, then \(T=100\) and \(B_{span}=500,\ 1000,\ 2000\) m were set. The actual baseline was simulated to explore the effect of the maximum baseline deviation on the RMSE value of PSLR and ISLR. The results are shown in Figures 7-9.  
It can be seen from the experimental results that the height-dimensional imaging performance of the system was basically stable within a certain baseline perturbation, and as baseline deviation increased, the height-dimensional imaging performance was reduced, which can be seen from the mean and variance of PSLR and ISLR.  
Figure 6: The imaging performance along the deviation direction with \(q=0.86\sim 1.16\): PSLR, ISLR, broadening coefficient.  
Figure 7: The imaging performance with \(B_{span}=500\) m: (**a**) PSLR; (**b**) ISLR.  
In order to ensure adequate imaging performance within the baseline deviation, the experiment selected \(\sigma_{e\_PSLR}=0.004\) and \(\sigma_{e\_PSLR}=0.04\) to obtain the result of the maximum baseline deviation with \(B_{span}=500\) m, \(1000\) m, \(2000\) m, as shown in Table 3.  
It can be seen from the above results that as the baseline span increased, the maximum baseline perturbation of the imaging performance increased linearly.  
The results of generating 100 sets of baseline with \(B_{span}=500\) m and \(e_{\max}=10\%,\ 20\%,\ 40\%\), and subsequent height-dimensional imaging performances are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \(B_{span}\)** **= 500 m** & \(B_{span}\)** **= 1000 m** & \(B_{span}\)** **= 2000 m** \\ \hline \(NDFT:PSLR\_e_{\max}\) & 18.0\% & 17.0\% & 15.0\% \\ \(NDFT:ISLR\_e_{\max}\) & 14.0\% & 14.0\% & 12.5\% \\ \(NDFT:e_{\max}\) & 14.0\% & 14.0\% & 12.5\% \\ \(CS:PSLR\_e_{\max}\) & 20.0\% & 20.0\% & 21.5\% \\ \(CS:ISLR\_e_{\max}\) & 22.0\% & 23.0\% & 22.5\% \\ \(CS:e_{\max}\) & 20.0\% & 20.0\% & 21.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The relationship of baseline span and critical deviation about the performance stability.  
Figure 8: The imaging performance with \(B_{span}=1000\) m: (**a**) \(\text{PSLR}\); (**b**) \(\text{ISLR}\).  
Figure 9: The imaging performance with \(B_{span}=2000\) m: (**a**) \(\text{PSLR}\); (**b**) \(\text{ISLR}\).  
It can be seen from the above experimental results that the imaging performance was more unstable as the maximum baseline perturbation \(e_{\max}\) increased (i.e., the farther the baseline deviated from the predetermined uniform baseline).
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
###### Abstract  
This study aims to improve the prediction accuracy of reference evapotranspiration under limited meteorological factors. Based on the commonly recommended PSO-ELM model for ET\({}_{0}\) prediction and addressing its limitations, an improved QPSO algorithm and multiple kernel functions are introduced. Additionally, a novel evapotranspiration prediction model, Kmeans-QPSO-MKELM, is proposed, incorporating K-means clustering to estimate the daily evapotranspiration in Yancheng, Jiangsu Province, China. In the input selection process, based on the variance and correlation coefficients of various meteorological factors, eight input models are proposed, attempting to incorporate the sine and cosine values of the date. The new model is then subjected to ablation and comparison experiments. Ablation experiment results show that introducing K-means clustering improves the model's running speed, while the improved QPSO algorithm and the introduction of multiple kernel functions enhance the model's accuracy. The improvement brought by introducing multiple kernel functions was especially significant when wind speed was included. Comparison experiment results indicate that the new model's prediction accuracy is significantly higher than all other comparison models, especially after including date sine and cosine values in the input. The new model's running speed is only slower than the RF model. Therefore, the Kmeans-QPSO-MKELM model, using date sine and cosine values as inputs, provides a fast and accurate new approach for predicting evapotranspiration.  
evapotranspiration; machine learning; prediction models; Quantum Particle Swarm Optimization; multi-kernel extreme learning machine; K-means clustering Article
## 3 Methods
### Risk Factor Coupling Analysis
#### 3.4.2 Wilcoxon Signed-Rank Test  
The Wilcoxon signed-rank test, proposed by [PERSON], is a nonparametric statistical test method [59]. The Wilcoxon signed-rank test determines whether the differences between two paired samples are statistically significant by comparing the signs and ranks of the differences. This method was developed based on the sign test for paired observational data, offering greater efficiency compared to the traditional test that relies solely on positive and negative signs. In the previous section, we can calculate the risk coupling values for different coupling types in both PA and SA. Importantly, the coupling scenarios in PA and SA are identical, forming paired samples. For example, \(T_{2,1}(H,V)\) in both PA and SA represents human-vehicle coupling. Therefore, we applied the Wilcoxon signed-rank test to compare the 26 risk coupling values in PA and SA and tested two hypotheses:  
* H0: There is no significant difference in the risk coupling values between PA and SA;
* H1: There is a significant difference in the risk coupling values between PA and SA.  
If the \(p\)-value in the test is less than 0.05, H0 is rejected; otherwise, it is not.
# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 3 Experiments
### Localization of Audio Copy-Move Forgery
#### 3.3.1 Optimization of the Correlation Detection Module  
The predefined parameter \(k\) plays a crucial role in optimizing the Correlation Detection Module, determining the number of features retained during Percentile pooling. A large \(k\) value may lead to feature redundancy, while a small one may result in feature loss. To assess the influence of \(k\) on model performance, four values were selected for comparison: 32, 64, 96, and 128. (Since changing the value of \(k\) implies the need to retrain the model in order to save training costs, we chose the four values mentioned above. The four values of \(k\) represent discarding 75%, 50%, 25%, and 0% of the features, respectively.) The experiment utilized a dataset comprising 3000 original audio clips and 3000 manipulated audio clips without post-processing attacks. Table 3 presents the accuracy, precision, recall, and F1-score obtained with different \(k\) values. Notably, the model achieved optimal performance when \(k\) was set to 96. Henceforth, subsequent experiments will adopt \(k=96\) as the default setting.
# Life Cycle Assessment on a Biorefinery Approach to Pyrosis Oil for Wood Modification Treatment
## 3 Results
### Impact of the Use of Different Biomass Feedstocks on Pyrolysis Oil in a Cradle to Gate Analysis  
To understand the impact of replacing the biomass feedstock for pyrolysis from forestry residues with maize digestate, the endpoint scores were determined in a cradle to gate analysis of pyrolysis oil. The difference in impact of 1 GJ pyrolysis oil produced from maize digestate and forestry residues is presented in Figure 2.  
Figure 2: Relative environmental impact of 1 GJ pyrolysis oil from maize digestate compared to forestry residues. The impact of forestry residues was set at 100%. Error bars represent the 95% confidence interval from the Monte Carlo analysis of 10.000 calculations. The error bar for the impact on human health of pyrolysis oil from maize digestate was cut off at 1200%, the total value was 2239%.  
As shown in Figure 2, the impact on damage to ecosystems from pyrolysis oil did not change significantly when a different feedstock was used for the production. However, there was a significantly larger impact on human health and on resource scarcity when maize digestate was used over forestry residues. The total impact on human health was more than ten times higher for pyrolysis from maize digestate than for pyrolysis from forestry residues. Moreover, pyrolysis from maize digestate increased the fossil resource scarcity by a factor of 2.5. Since pyrolysis oil from forestry residues led to lower overall environmental impacts, this system was further developed to include the wood modification process.
## 3 Data and Methods
### Cross-Correlation Method Based on Reshuffling Tests  
In order to answer the question \"whether there is a clear correlation between seismic activity and artificial inducements\", [PERSON] et al. used the cross-correlation and reshuffling tests to determine the cross-correlation coefficient and time lag between the time series of earthquake numbers and artificial inducements [23; 24]. Compared with the simple Pearson correlation coefficient method, the cross-correlation and reshuffling tests determine the statistical confidence interval and can detect accurate time lag signals [25].  
The algorithm is based on two discrete time series data sets \(D_{1}\) and \(D_{2}\). In the first step, the input data set should undergo dimensionality reduction (\(d_{i}=D_{i}-mean(D_{i})\)) before the standardized cross-correlation calculation, where \(i\) represents the serial number of \(D_{1}\) and \(D_{2}\). Through the translation of the time axis, the cross-correlation coefficient \(CC\) under different time lag conditions \(D_{1}\) and \(D_{2}\) is calculated, and the result ranges from \(-1\) to \(1\).  
Figure 4: Reservoir water level, monthly seismic frequency, and the earthquakes above M4.0 during the impounding process of the TGR (January 2001–December 2009, M \(\geq\) 0).  
In the second step, the cross-correlation coefficient thresholds under different confidence levels are calculated. On the premise of keeping the spectrum value unchanged to ensure the autocorrelation of the same period, for the univariate linear process \(d_{n}\), the substitution variable \(\overline{d}_{n}\) is:  
\[\overline{d}_{n}=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}e^{iak}|D_{k}|e^{-i2\pi kn/N}. \tag{1}\]  
\[D_{k}=\frac{1}{\sqrt{N}}\sum_{n=0}^{N-1}d_{n}e^{i2\pi kn/N}. \tag{2}\]  
Here, \(0~{}\leq\alpha k<2\pi\) is an independent random number. The Monte Carlo method is used to generate the random number and obtain \(N\) replacement data sets \(D_{1}\). By calculating the cross-correlation coefficient between the replacement data set and \(D_{2}\), the cross-correlation function distribution of \"noise\" is simulated, and the threshold value of \(CC\) under different confidence conditions is obtained. In testing the hypothesis, according to previous studies, a confidence level of 95% and above is usually selected as the minimum value of statistical significance. The peak value of \(CC\) represents the best guess of the time lag, and the peak width above the confidence threshold (such as 95%) represents the error caused by discrete sampling and noise.  
Figure 5: Spatial distribution of seismicity in the study area during the impoundment of the Three Gorges Project (January 2001–December 2009, M \(\geq\) 0). (**a**) Before the water impoundment; (**b**) 135 m stage; (**c**) 156 m stage; (**d**) 175 m experimental stage.
## 4 Computation Unit Design for the Decoder
### Cnu  
The schematic diagram of the CNU is presented in Figure 7. In the gate-level netlist, the XOR gate is dual-ported, which allows for a five-stage XOR tree as shown in Figure 7a. An example of the module used to determine the min1 and min2 of eight bits based on (4) is provided in Figure 7b. To obtain the min1 and min2 of all 32 bits, the output of this module is cascaded and the eight resulting values are used as inputs. The use of dual-ported characteristics simplifies the netlist, resulting in a requirement of only 239 nets and \(175\) cells in Vivado implementation, which is a significant reduction in resources, amounting to two-fifths of the original requirement.
## 5 Validation and Analysis
### Decoupling Validation  
The position step signal is given as the reference signal into the six inputs of the control system, respectively. The decoupling can be validated by observing the influence of one input reference signal on the other five degrees of freedom. The simulation time is set to be 0.07 s. One millimeter or 1 mrad is given as the reference signal and they are provided at 0 s, 0.01 s, 0.02 s, 0.03 s, 0.04 s, and 0.05 s, respectively. The simulation result is shown in Figure 6.  
It can be seen from the simulation result that when the position of each axis changes, other sets of axes show little fluctuation. For instance, when the \(y\)-axis increases from 0 mm to 1 mm at 0.02 s, the position of \(\phi\)-axis fluctuates between \(10^{-6}\) mm intervals according to its angle curve with step response. When \(\phi\)-axis increases from 0 mrad to 1 mrad at 0.03 s, the position of \(\theta\)-axis fluctuates between \(10^{-6}\) mm intervals seen from its angle curve with step response. Thus, it can be seen that the output of each degree of freedom only responds to its respective input and, thus, the decoupling of the proposed developed ADRC is validated.
# The Role of Platelet-Rich Plasma in the Management of Ankle Osteoarthritis: A Systematic Review
## 3 Results
### PRP Procedures  
All of the included studies described the followed PRP procedures, which were found to show great variability. In one study, the authors centrifuged the whole blood thrice [20], whereas in another study, the authors performed two centrifugations [21], the first at 800 g for 5 min, and the second at 1500 g for 8 min. Another two studies employed just one cycle at 1500 rpm for 5 and 8 min, respectively [19; 22], whereas a fifth study used a range of 500-1200 rpm for 8 min [23]. After centrifugation, the final product was cryopreserved in two cases at different temperatures [20; 21], whereas the other three cases employed no frozen phases [19; 22; 23].  
The addition of an activator varied minimally among the studies. One study employed calcium chloride to activate PRP immediately before the injection [21], whereas the remaining four studies did not report the use of any activator substances.  
The number of injections ranged from a single administration to four times during the therapy course, with frequencies ranging from once a week to once every two weeks. The post-treatment recommendations regarding medication allowance also differed, but there was a general consensus on not permitting heavy physical work right after the injection. Tables 3 and 4 show further details about PRP preparation techniques and intervention procedures, respectively.  
Figure 1: PRISMA flow chart.