esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 4 ITGA Design  
The GA is an intelligent search algorithm with excellent performance [17]. However, when dealing with large-scale problems, due to its inherent disadvantages, such as slow convergence speed and susceptibility to getting trapped in locally optimal solutions [18], the GA often struggles to ensure efficient solution finding. Therefore, we propose combining the TSA with the crossover and mutation operators in the GA to form improved crossover and mutation algorithms that can enhance the efficiency of the GA in finding solutions. The execution flow of the improved tabu genetic hybrid algorithm is illustrated in Figure 4.  
After initializing the data transmission tasks to form a task set, the algorithm employs a task set partitioning method to divide the set into multiple sub-task sets based on data transmission conflicts. Each sub-task set is then solved separately. Within the same sub-task set, data transmission tasks may have conflicts, while tasks in different sub-task sets do not conflict with each other. Utilizing the task set partitioning approach can reduce the traversal of non-conflicting data transmission tasks to some extent, thereby decreasing algorithm runtime and enhancing computational efficiency.  
Furthermore, during the process of solving the task set, it is necessary to prune the data transmission durations of tasks reasonably in response to conflicts within the set, aiming to allocate ground station hardware resources to each data transmission task as much as possible. The following heuristic rules are defined as principles for the conflict resolution algorithm:  
Figure 4: Flow chart of ITGA.  
Only prune data transmission tasks with a priority of 1, ensuring effective data transmission durations for tasks with priorities of 2 or 3.  
The remaining effective data transmission duration after task pruning should not be less than the user's satellite data transmission requirements. If the effective duration is insufficient, the task is abandoned.  
Select the solution with the shortest pruned effective data transmission time and the longest actual effective data transmission time.  
All data transmission tasks that are not covered by the effective data transmission times of other tasks should be ensured to allocate receiving resources for data transmission.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 3 Results
### Pre-Processing
#### 3.2.2 alphashape3d's Adjusted Alpha Value  
Figure 9 represents the r\({}^{2}\), and RMSE curves for the various alpha values tested. Values tested ranged from 0.001 to 0.050, in increments of 0.001. It is evident from this figure that the alpha value selected (alpha = 0.010) satisfies a high coefficient of determination (r\({}^{2}\) = 0.605) combined with the lowest RMSE (703.301 cm\({}^{3}\)). Figure 10 provides a visual interpretation of the results from Figure 9. A low alpha value, such as 0.005 (Figure 10b), produced an r\({}^{2}\) of 0.520 and an RMSE of 2052.751 cm\({}^{3}\) (sample dataset mean volume = 1961.875 cm\({}^{3}\)). Conversely, a high alpha value of 0.050 (Figure 10d) produced an r\({}^{2}\) of 0.506 and an RMSE of 13 936.31 cm\({}^{3}\). The selected alpha value of 0.010 (Figure 10c) was subsequently used for all further analyses.  
Figure 9: The relevant r\({}^{2}\) and RMSE values for each alpha value tested for the _alphashape3d_ package in the custom R script. Alpha values incremented in 0.001, ranging from 0.001–0.050.
## 2 Preliminaries and Problem Formulation  
An FJR can be described by the following dynamic model [35]:  
\[M(q)\ddot{q}+C(q,q)\dot{q}+D(\dot{q})+G(q)+\tau_{d} =K(\theta-q) \tag{1a}\] \[J\ddot{\theta}+K(\theta-q) =u, \tag{1b}\]  
where \(q(t)\in\mathbb{R}^{n}\), \(\dot{q}(t)\in\mathbb{R}^{n}\) are the angular position and velocity of the joints, and \(\dot{\theta}\in\mathbb{R}^{n}\) and \(\dot{\theta}\in\mathbb{R}^{n}\) represent the angular position and velocity of the motors. \(M(q)\in\mathbb{R}^{n\times n}\), \(C(q,\dot{q})\in\mathbb{R}^{n}\), \(D(q)\in\mathbb{R}^{n}\), \(G(q)\in\mathbb{R}^{n}\), \(u\in\mathbb{R}^{n}\) represents the inertia matrix, the centripetal/Coriolis matrix, the friction torque vector, the gravity torque vector, and the system input vector, respectively. \(\tau_{d}\in\mathbb{R}^{n}\) is a bounded aggregate disturbance term that encompasses various sources of uncertainty, including inaccuracies in the model, uncaptured dynamics, and external perturbations. \(K\in\mathbb{R}^{n\times n}\) is a diagonal matrix representing the stiffness of the spring between the motor and the link, and \(J\in\mathbb{R}^{n\times n}\) is a diagonal matrix representing the inertia of the motors. Denote \(H_{r}(q,\dot{q})=C(q,\dot{q})\dot{q}+D\dot{q}+G(q)\), \(\tau_{1}=K(\theta(t)-q(t))\). Considering the fact that the torque \(\tau_{1}\) generated by the spring changes much faster than the position of the link \(q\), then by defining \(K=K_{0}/\varepsilon^{2}\), where \(\varepsilon\) is a small parameter, and \(K_{0}\in\mathbb{R}^{n\times n}\) is a positive-definite diagonal matrix, we can use a singular perturbation model to describe the FJR as  
\[M(q)\ddot{q}+H_{r}(q,\dot{q})+\tau_{d}=\tau_{1} \tag{2a}\] \[\varepsilon^{2}J\tau_{1}+K_{0}\tau_{1}=K_{0}(u-J\ddot{q}). \tag{2b}\]  
In control systems theory, the singular perturbation model is extensively employed to describe dynamical systems, where certain state derivatives are scaled by a small positive parameter \(\varepsilon\). These states scaled by \(\varepsilon\) are known as fast states, while the remaining states are classified as slow. A decrease in \(\varepsilon\) typically correlates with accelerated changes in the fast states. For such systems, the singular perturbation technique serves as a valuable tool for order reduction and controller simplification [36]. Although there is no standardized approach for selecting \(\varepsilon\), for flexible joint robots, it is customary to choose \(\varepsilon\) inversely proportional to the square root of joint stiffness_[37]. _As joint stiffness \(K\) increases, \(\varepsilon\) decreases, resulting in faster torque \(\tau_{1}\) changes.
## 2 Laser Control of Ring Currents in a Molecule Having a Single Aromatic Ring
### Beyond the Frozen-Nuclei Approximation: Nonadiabatically Coupled Vibronic Dynamics
#### 2.3.2 Population Transfer Dynamics  
We employed the same laser pulses as used in Figure 3. The temporal change in \(P_{X}(t)\) and \(P_{Y}(t)\) for \(\mathbf{e}^{(+)}\), \(\mathbf{e}^{(-)}\), \(\mathbf{e}_{+1}\), and \(\mathbf{e}_{-1}\) are plotted in Figure 4. When the laser pulse fully decays at \(t=t_{f}=7.26\) fs, \(P_{X}(t_{f})+P_{Y}(t_{f})\) reaches 0.84, 0.93, 0.89, and 0.91 for \(\mathbf{e}^{(+)}\), \(\mathbf{e}^{(-)}\), \(\mathbf{e}_{+1}\), and \(\mathbf{e}_{-1}\), respectively. The polarization-dependent reductions in \(P_{X}(t_{f})+P_{Y}(t_{f})\) from those under the frozen-nuclei approximation (0.995) are attributed to the coordinate dependence of \(\upmu_{X}(\mathbf{Q})\) and \(\upmu_{Y}(\mathbf{Q})\) because the laser pulses used were designed with the transition moments at the optimized geometry of \(|G\rangle\).  
The subsequent behaviors of \(P_{X}(t)\) and \(P_{Y}(t)\) are totally different among the four types of excitations. For \(\mathbf{e}^{(+)}\) (Figure 4a), a small portion of the population shifts from \(|X\rangle\) to \(|Y\rangle\) by \(t\sim 10\) fs and then a downward population transfer takes place around \(t\sim 10\)-14 fs by nonadiabatic transition. In the case of \(\mathbf{e}^{(-)}\) (Figure 4b), a substantial amount of the population is transferred from \(|Y\rangle\) to \(|X\rangle\) and consequently \(P_{X}(t)\) is more than seven times as large as \(P_{Y}(t)\) at \(t\sim 10\) fs. Afterwards, the direction of population transfer flips periodically with the rather small fraction of the population transferred. In contrast, for \(\mathbf{e}_{+1}\) (Figure 4c), a part of the population is continuously exchanged between \(|X\rangle\) and \(|Y\rangle\) and, thus, electronic relaxation is completed at \(t>30\) fs. The behaviors of \(P_{X}(t)\) and \(P_{Y}(t)\) for \(\mathbf{e}_{-1}\) (Figure 4d) are intermediate between those for \(\mathbf{e}^{(+)}\) and \(\mathbf{e}^{(-)}\): A considerable population transfer from \(|Y\rangle\) to \(|X\rangle\) is observed around \(t\sim 8\)-13 fs. The very different evolutions of \(P_{X}(t)\) and \(P_{Y}(t)\) will be explained in Section 2.3.4. These results manifest the notable effects of laser polarization on the nonadiabatic transition between \(|X\rangle\) and \(|Y\rangle\), which develops mainly after irradiation.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Phenology Extraction  
Due to cloud, atmosphere and snow contamination, we used Savitzky-Golay filtering to smooth the time series of GOSIF and MODIS EVI. Then, a double logistic function was fitted based on the smoothed time series to generate continuous curves. The double logistic is a flexible model for monitoring seasonal and inter-annual land surface dynamics based on satellite data, which has been widely used for various vegetation types at global or regional scales [30; 31]. The double logistic function can be written as follows:\[V(t)=V_{\min}+(V_{\max}-V_{\min})\times(\frac{1}{1+e^{-mS\times(t-S)}}+\frac{1}{1+ e^{mA\times(t-A)}}-1) \tag{1}\]  
where \(V(t)\) is the value of vegetation proxies (i.e., GOSIF or MODIS EVI) at day of the year (DOY) \(t\), \(V_{\max}\) is the maximum vegetation proxies in the year, \(V_{\min}\) is the minimum vegetation proxies in the year, \(mS\) and \(mA\) are the maximum slope of the curve in green up and in senescence, respectively, \(S\) and \(A\) are their corresponding DOYs. Finally, the SOS and EOS were estimated as follows [32]:  
\[SOS=\frac{2\ln(\sqrt{3}-\sqrt{2})}{mS}+S \tag{2}\]  
\[EOS=\frac{2\ln(\sqrt{3}-\sqrt{2})}{mA}+A \tag{3}\]
# Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks
## 2 The Proposed Algorithm  
In this section, we first provide a brief introduction of CNN, AE, and FCN. An overview of the proposed algorithm then follows. We then discuss the operations for each neural network of the algorithm. The training procedures for the neural networks are then presented in detail. The online inspection system based on the proposed algorithm is also presented so that the results of this study can be effectively applied for a field test. To facilitate understanding of the discussions in this study, Table 1 includes a list of frequently used symbols.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(\mathcal{C}\) & The set of defective blocks identified by the proposed algorithm. \\ \(D\) & The L2 distance between two matrices with the same size. \\ \(F\) & The AE in the algorithm. \\ \(G\) & The FCN in the algorithm. \\ \(K\) & The number of training images. \\ \(M\) & The dimension of images \(S_{k}\), \(X_{k}\), \(Y_{k}\). \\ \(N\) & The number of coherent regions. \\ \(P^{j}(i)\) & The probability that pixel \(y^{j}\) belongs to the \(i\)-th region of \(Y\). \\ \(P^{j}_{k}(i)\) & The probability that pixel \(y^{j}_{k}\) belongs to the \(i\)-th region of \(Y_{k}\). \\ \(S_{k}\) & The ground truth of \(X_{k}\) for training. \\ \(T_{i}\) & The threshold for defect detection for the \(i\)-th region of \(X\). \\ \(X\) & The image of the test target. \\ \(X(i)\) & The \(i\)-the region of image \(X\). \\ \(X_{k}\) & The \(k\)-th training image for the AE. \\ \(x\) & A block of image \(X\). \\ \(x^{j}_{k}\) & The \(j\)-th pixel of image \(X_{k}\). \\ \(Y\) & The reproduced image by the AE. It serves as the template for \(X\). \\ \(Y(i)\) & The \(i\)-th region of image \(Y\). \\ \(Y_{k}\) & The \(k\)-th training image for FCN. \\ \(y\) & A block of image \(Y\). Both \(y\) and \(x\) have the same size. \\  & The location of \(y\) in \(Y\) is also the same as that of \(x\) in \(X\). \\ \(y^{j}\) & The \(j\)-th pixel of image \(Y\). \\ \(y^{j}_{k}\) & The \(j\)-th pixel of image \(Y_{k}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of frequently used symbols in this study.
## 4 ARIMA-BP-Based Selective Offloading Strategy
### Estimation for Computation Capacity of Edge Cloud by ARIMA-BP
#### 4.2.4 ARIMA-BP for Prediction of Computation Capacity in Edge Cloud  
In mobile edge computing, computation capacity of the edge cloud is usually represented by computation resources \(f^{c}\) that can be allocated to mobile devices. We use \(f^{c}(t)\) indicate computation resources that edge cloud have been allocated to mobile devices during the time slot \([t-\Delta t,t]\), then the remaining computation resources of the edge cloud is \(f^{c}(t)=c_{s}-f^{c}(t)\). Suppose that \(m\) tasks \(u_{n,k}\) are processed at the edge cloud during the time slot \([t-\Delta t,t]\).We have  
\[f^{c}(t)=\sum_{i=1}^{m}f^{c}_{n,k},\quad\forall n\in N,\forall k\in K \tag{12}\]  
We use the ARIMA-BP model for predicting the usage of computation resources of the edge cloud during a time slot. First, we build the ARIMA model. The usage of computation resources to be estimated during \([t,t+\Delta t]\), denoted by \(\hat{f}^{c}(t+\Delta t)\), is expressed below:  
\[\hat{f}^{c}(t+\Delta t)=a_{0}f^{c}(t)+a_{1}f^{c}(t-\Delta t)+\cdots+a_{p-1}f^{ c}(t-(p-1)\Delta t)+b_{0}\epsilon_{t}+\cdots+b_{q-1}\epsilon_{t-(q-1) \Delta t} \tag{13}\]  
Then, we apply fractional difference method for catching long-term dependencies in the data. Finally, we use BP neural network to correct the residual error.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 3 RF Breakdown
### Initial Processing  
After installing a new accelerating cavity in the test setup, it undergoes the so-called initial processing. Processing starts with a low rf power that is gradually increased, while the vacuum level and rf breakdown rate are observed and maintained at empirically determined levels. The duration of the processing, after which the breakdown rate is in a steady state, varies greatly depending on the specifics of the structure manufacturing and preparation process and the processing algorithm. This may take from a few hours and thousands of breakdowns to weeks or months and millions of breakdowns. For some structures, the breakdown rate never stabilizes during the period allocated for the test. Typically, longer structures take longer to process. Depending on the initial conditions, the breakdown rate either decreases or increases with time. The breakdown rate maps discussed below were taken after initial conditioning when the breakdown rate is at a steady state over millions of rf pulses.  
The physics of this processing is not clear. For example, the traditional explanation that the initial processing clears the dust particles or eliminates pre-existing field emitters cannot account for structures that exhibited _anomalous processing_ with lower breakdown rates at the beginning of the test, which I observed in tests of the CuAg cavities.  
Recent analyses of the conditioning process of CLIC TW prototypes suggests that the phenomena are a result of the exposure of the metal surface to the pulsed rf fields, not due to accumulated breakdown events [70]. However my experiments with single-cell SW structures do not support this hypotheses, so this phenomenon may be a property of TW structures or cavity-surface processing specific for TW structures. Another possible explanation for the difference is a crosstalk between neighboring cells, the physics of which is also not understood.
## Appendix B Jury's Stability Test for \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) Filter  
The necessary and sufficient conditions for the stability of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter are derived using the Jury's test [18]. The Jury's table of the characteristic polynomial \(f_{\alpha\beta\gamma\delta}(z)\) is shown in Table 11, where:  
\[c_{0} =-\frac{(3\gamma(\alpha-2)+\alpha(\delta+6\beta))(3(\alpha-2)( \gamma+4\alpha)+\alpha(\delta+6\beta))}{36}, \tag{11}\] \[c_{1} =-\frac{(3\alpha\gamma(\alpha-2)(\delta-3\beta-6\alpha+12)+\alpha ^{2}\delta^{2}+3\alpha^{2}\delta(\beta+\alpha-2)-18\alpha^{2}\beta^{2}-36 \alpha^{2}\beta(\alpha-2))}{9},\] (12) \[c_{2} =\frac{9\gamma(\alpha-2)^{2}(\gamma-4\alpha)-\alpha^{2}\delta( \delta+12(\beta+4\alpha-8))-36\alpha^{2}\beta(\beta+2(\alpha-2))}{36}. \tag{13}\]  
Using this table, the stability conditions are obtained for the following conditions:  
\[f_{\alpha\beta\gamma\delta}(1) =\delta>0, \tag{14}\] \[(-1)^{4}f_{\alpha\beta\gamma\delta}(-1) =\delta-12(\beta+2\alpha-4)>0,\] (15) \[|a_{0}| <a_{4},\] (16) \[|b_{0}| >|b_{3}|,\] (17) \[|c_{0}| >|c_{2}|. \tag{18}\]  
By solving and simplifying (14)-(18), we obtain the stability conditions (36)-(39).
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Grasping the Idea of the Impact of Alcohol on ANS Activity  
Consuming excessive alcohol is related to an augmented hazard of sickness and death [178]. It is a global health problem, which is required to be dealt with as soon as possible. A rise in blood pressure, arrhythmias (most notably atrial fibrillation), liver cirrhosis, a hemorrhagic blow, wounds, and malignancies of the liver, colorectalum, breast, and upper digestive tract have been linked to alcohol consumption [179; 180]. Alcohol use over a prolonged period has been shown to have adverse effects on numerous body parts and structures, most notably the neurological system. The strong connection that exists between the ANS and the cardiovascular system (CV) is one of the primary issues that donate to the elevated risk of a cardiovascular ailment (CVD) in alcoholics [181]. The study of an HRV analysis can provide a non-invasive estimate of the imbalance in ANS physiology and determine the level of cardiovascular danger posed with various clinical conditions. Hence, many HRV analysis experiments have been executed to understand the consequence of alcohol consumption on the ANS. In a study by [PERSON] et al. (2021), the authors examined the 5 min HRV features of volunteers in the supine position, and the time and frequency domain HRV attributes were extracted [182]. According to their findings, binge drinkers and casual drinkers exhibited a minor alteration in the frequency domain HRV features. On the other hand, heavy drinkers had considerably lower values of time-domain HRV features (SDNN and RMSSD) and HF components. Further, the heavy drinkers had a higher LF/HF value, suggesting parasympathetic inhibition [182]. The GBT regression model revealed that age and alcohol intake had the highest impact on the HRVfeatures. In the same year, [PERSON] et al. (2021) also analyzed the effect of alcohol intake on cardiac autonomic regulation [183]. Fifteen volunteers participated in the study, where an HRV signal analysis was executed before, during, and after alcohol consumption. It was found that the values of HRV features were decreased, and mean HR was increased upon alcohol consumption. On the basis of the outcomes, the authors suggested that alcohol consumption induced sympathetic dominance and a decrease in parasympathetic activity, causing autonomic imbalance.
## 2 Preprocessing
### Features Selection  
#### 2.2.1 Calculated Entropy of EEG Signal  
Entropy in EEG signals refers to the measurement of irregularity or complexity in the signal. EEG signals originate from the brain's electrical activity and are typically distinguished by characteristic patterns and fluctuations that provide valuable insights into brain activity and function. The entropy analysis of EEG signals involves quantifying the level of disorder and randomness within the signal. Higher entropy values indicate greater complexity and irregularity, while lower entropy values indicate more regular and predictable patterns in the signal. Different entropy measures, such as sample entropy or approximate entropy, can be used to analyze EEG signals and Offer insights into the dynamic processes of brain activity. In our implementation, entropy is calculated using the scipy.stats.entropy() function, which computes the Shannon entropy of a discrete probability distribution. To do this, we first create a histogram of the truncated signal using 50 bins:  
hist = np.histogram(truncated_signal, bins=50)[0] ent = entropy(hist)  
This histogram captures the frequency distribution of the signal's amplitude values. The entropy() function then normalizes the histogram to produce a probability distribution and applies the Shannon entropy formula:  
\[H=-\sum_{i=1}^{n}p_{i}\log_{2}(p_{i}) \tag{2}\]  
where \(p_{i}\) denotes the normalized probability associated with the \(i\)-th bin.  
This approach yields a global measure of the signal's complexity, reflecting the diversity in amplitude values throughout the signal.  
The entropy analysis of EEG signals can help in understanding the complexity of neural processes, identifying abnormalities or changes in brain function, and providing  
Figure 1: Original EEG signal plotted over time. The signal exhibits high variability and nonstationary characteristics, highlighting the need for advanced preprocessing techniques such as the discrete wavelet transform (DWT) for effective analysis and feature extraction.  
Figure 2: The reconstruction signal from DWT preprocessing and ready to extracted features.  
valuable information for diagnostic and therapeutic purposes in neurological disorders such as epilepsy and neurodegenerative diseases. Overall, the entropy analysis of EEG signals offers a quantitative and objective way to assess the complexity and dynamics of brain activity, allowing for a deeper understanding of brain function and providing valuable insights for clinical and research applications.
## 5 Characterization
### Experimental Setup  
Figure 8 shows the experimental setups for the three schemes under investigation when used as upstream transmitters in the WDM-PON networks shown in Figure 1. A DC bias current \(I_{bias}\) drives the RSOA at the ONU. The distribution fiber (DF) links the ONU to the OBPF. An OBPF (Koshin Kogaku, Kanagawa, Japan) mimics the WGR WDM filter at the RN. For the case of the RSOA-FCL we use a Faraday rotator mirror (FRM, OZ Optics, Ottawa, ON, Canada) at the RN and a Faraday rotator (FR) in the ONU. The laser cavity is then formed by FRM and the RSOA, as shown in Figure 8b. The FRM and FR are needed, since RSOAs often have a polarization-dependent gain (PDG). With this arrangement the forward and backward propagating signals maintain the polarization state at the RSOA [35]. A 90/10 power splitter sends 10% of the power through the feeder fiber (FF) to the CO. At the CO, the signal is pre-amplified and directly detected with a photodiode. An optical spectrum analyzer (OSA, Yokogawa, Musashino, Japan) is used to measure the power and the optical spectrum. For the externally seeded RSOA scheme shown in Figure 8c, the seeding laser is also positioned at the CO. A circulator (Laser2000, Wessling, Germany) is used to split the DS cw seed and the US signal. We use a variable optical attenuator (VOA) to control the seeding power.  
Figure 8: Experimental setups to characterize and compare the three schemes; (**a**) RSOA ASE spectrum-sliced scheme; (**b**) self-seeded RSOA-FCL; (**c**) externally seeded RSOA. In all setups, the transmitter is placed at the optical network unit (ONU) and the receiver is placed at the central office (CO). An optical bandpass filter (OBPF) mimics the WGR WDM filter at the remote node (RN). In (**b**), a Faraday rotator (FR) and FR-mirror (FRM) are placed at the ONU and the RN, respectively, for polarization control in the RSOA-FCL [35], shown by the yellow shading. The ONU is connected to the RN and the CO through the distribution fiber (DF) and the feeder fiber (FF), respectively.