esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 2 Materials and Methods
### Dataset  
The DEAP dataset [41], a very important dataset for analyzing human emotional states through physiological signals, is used in this paper to verify the effectiveness of the proposed emotion recognition method. The dataset records some physiological signals of 32 participants (16 males and 16 females) when watching 40 music videos and the subjective scores of the participants in four aspects: valence, arousal, dominion, and liking. According to the 10-20 international standard lead system, 32 electrodes are placed in the frontal lobe, parietal lobe, occipital lobe, and temporal lobe of the participants' brains and their EEG signals are collected at a sampling frequency of 512 Hz. In addition to 32 EEG channels, 16 other channels are used in the dataset, including common signals such as EOG and ECG. Forty trials are performed on each subject, and the subjects need to watch a 60 s music video each trial. After watching the video, subjects need to give a subjective score of valence, arousal, dominion, and liking in a range from 1 to 9.  
The official preprocessed EEG data and corresponding subjective scores are used in this paper. After the preliminary preprocessing, the original EEG signals are downsampled from 512 Hz to 128 Hz, and the noise, such as eye blinking artifacts, is removed. At the same time, band-pass filtering is used to limit the signals to the frequency range of 4-45 Hz. The processed dataset contains the data of 40 channels, including 32 EEG signals, 2 EOG signals (1 horizontal EOG signal, 1 vertical EOG signal), 2 EMG signals, 1 GSR signal, 1 respiratory band signal, 1 plethysmography, and 1 temperature recording signal. Only the first 32 EEG channels are studied in this paper; we are not currently considering other channel signals. In addition, valence and arousal are used as emotional evaluation criteria for the proposed model. Valence indicates the degree of pleasantness, with scores from 1 to 9 corresponding to the transition from negative to positive, and arousal indicates the degree of excitement of one's mood, with scores from 1 to 9 corresponding to the transition from calmness to excitement. In the experiments, the samples are divided into two classes by setting the threshold value to 5. When the score is less than 5, the sample is marked as low, and when the score is greater than or equal to 5, the sample is marked as high.
## 2 Materials and Methods
### Search Strategy and Study Selection  
A search from the earliest record up to and including March 2021 was carried out using the electronic databases Medline, Scopus, and Web of Science (core collection). The following combination of terms was adapted for each database and applied to the title, abstract, and keyword search: (\"velocity loss\" OR \"velocity decline\" OR \"level of effort\" OR \"effort index\") AND (\"resistance training\" OR \"resistance exercise\"\" OR \"strength training\" OR \"strength exercise\"\" OR \"weight training\" OR \"weight exercise\"\") AND (neuromuscular OR functional* OR strength\" OR adaptation* OR performance OR jump OR sprint OR \"change of direction\"). Moreover, the main search process was complemented with a screening of the references and citations of studies included, as well as by fixing alerts on the above-mentioned databases to monitor any eligible study published after the date of the last search (15 March 2021). Records retrieved from the search were imported to Mendeley (v1.19.6, Elsevier, London, UK) and processed in Microsoft Excel 2016 (Microsoft Corporation, Redmond, WA, USA) by A.H.B. After automatic and manual removal of duplicates, two researchers ([PERSON] and [PERSON]) independently screened the titles and abstracts considering eligibility criteria (first-stage screening). Full texts of the remaining studies were subjected to a second-stage screening. Any discrepancy between [PERSON] and [PERSON] during the study selection process was solved by discussion with a third investigator external to the current study ([PERSON]).
## 3 Results
### Example 3. Li-Haimes Nonseparable Control Problem  
The dynamical system is described by three difference equations:  
\[x(1)=x(0)^{u(0)};\ \ \ x(2)=(1+u(1))\cdot x(1);\ \ \ x(3)=x(2)+u(2),\]  
where \(x\in\mathbf{R}\), \(t=0,1,2\), and \(u\in\mathbf{R}\).  
It is required to find such a pair of trajectory and control \((x^{*}(\cdot),\ u^{*}(\cdot))\) that the value of the performance index \(I\) is minimal:  
\[I=[x^{2}(0)+x^{2}(1)+(2x^{2}(2)+x^{2}(3))\exp(x^{2}(1))]\times[50+u^{2}(0)+(u^ {2}(1)+u^{2}(2))\exp(u^{2}(0))]^{1/2}.\]  
In this problem, the initial condition is known: \(x(0)=15\), and the constraints on the control are also given: \(-1\leq u(t)\leq 1\), \(t=0,1,2\). For this task, the number of stages is set: \(N=3\). The best-known value of the performance index obtained for this nonseparable control problem was obtained in [1]: \(\min I=1596.4796778\).  
The approximate value of the performance index obtained by the TFO algorithm was \(I_{TFO}^{*}=1596.47967783381\) and the runtime was \(0.13\) s. The same problem was solved by Perch School Search algorithm [10]. The runtime was \(0.19\) s and the value of performance index was \(I_{PSS}^{*}=1596.4796778389\). The new TFO algorithm makes it possible to obtain almost the same solution much faster.  
To solve this problem, the TFO algorithm was used with the parameters shown in Table 4. Figure 8 shows the obtained pair \((x(\cdot),\ u(\cdot))\). The values of the obtained control \(u(\cdot)\) and trajectory \(x(\cdot)\) are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline _NP_ & \(\gamma\) & \(\eta\) & \(\rho\) & \(c_{1}\) & \(c_{2}\) & \(c_{3}\) & \(K\) & \(h\) & \(L\) & \(P\) & \(\mu\) & \(\epsilon\) & \(\lambda\) & \(\alpha\) \\ \hline
100 & 0.1 & 0.1 & 5 & 3 & 3 & 3 & 10 & 0.1 & 5 & 100 & 2 & \(1\times 10^{-9}\) & 1.5 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The set of parameters of the TFO algorithm in Example 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline _NP_ & \(\gamma\) & \(\eta\) & \(\rho\) & \(c_{1}\) & \(c_{2}\) & \(c_{3}\) & \(K\) & \(h\) & \(L\) & \(P\) & \(\mu\) & \(\epsilon\) & \(\lambda\) & \(\alpha\) \\ \hline
300 & 0.3 & 0.9 & 2 & 2 & 0.5 & 3 & 10 & 0.05 & 10 & 100 & 1 & \(1\times 10^{-9}\) & 1.1 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The set of parameters of the TFO algorithm in Example 2.  
Figure 6: Graphical illustration of obtained \(u(\cdot)\) in Example 2.  
Figure 7: Graphical illustration of obtained \(x(\cdot)\) in Example 2.  
Examples 2 and 3 illustrate the possibilities of solving complex optimal control problems with nonseparable performance indexes, for which obtaining solutions using known optimality conditions is extremely difficult.
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## 3 Offline Resource Allocation Scheme
### Power Resource Allocation
Subsequently, according to Karush-Kuhn-Tucker (KKT) conditions \(\partial L(\cdot)/p_{m,nk}^{j}=0\), we can obtain the optimal solution \(p_{m,nk}^{j,\star}\):  
\[p_{m,nk}^{j,\star}=\left[\frac{1}{\frac{-\mu_{j}\ln 2}{\left(\lambda_{j,m}B_{ sc}-B_{ sc}\right)}+\frac{a_{m,nk}^{j}H_{lm,k}^{j}}{\sum\limits_{(i_{d})\
eq(j)}a_{j,nk}^{ j}p_{m,nk}^{j}H_{jm,k}^{j}+N_{0}}}-\frac{N_{0}}{a_{m,nk}^{j}H_{jm,k}^{j}} \right]^{+}, \tag{17}\]  
Then, we study the dual problem of the original problem:  
\[\max_{\lambda,\mu}\inf\ L(\cdot) \tag{18}\] \[s.t.\lambda_{j,m}\geq 0,\forall j,\forall m,\] \[\mu_{j}\geq 0,\forall j,\]  
Finally, we use the gradient-descent method to solve dual variables:  
\[\lambda_{j,m}^{t+1} =\left[\lambda_{j,m}^{t}-\Delta_{\lambda_{j,m}}\left(\sum_{j,m, k,n}B_{ sc}\left[\widetilde{U}_{m,nk}^{j}(\mathbf{p}^{\star},\overline{\mathbf{p}})-V_{m,nk}^{j} (\mathbf{p}^{\star})\right]\right)\right]^{+} \tag{19}\] \[\mu_{j}^{t+1} =\left[\mu_{j}^{t}-\Delta_{\mu_{j}}\left(P_{max}-\sum_{m=1}^{M} \sum_{k=1}^{K}\sum_{n=1}^{N}p_{m,nk}^{j,\star}\right)\right]^{+}\]  
When the initial values of \(\lambda_{j,m}\) and \(\mu_{j}\) are given, they can be updated iteratively through the above formulas until they converge. Finally, the power allocation scheme based on SCA is shown in Algorithm 2. ```
Input:\(\mathbf{A}_{m}^{j}\), \(H_{jm,k}^{j}\), \(T_{m}^{j}\), \(\forall j\), \(\forall m\), \(\forall n\), \(\forall k\) Output:\(\mathbf{P}_{m}^{j}\), \(\forall j\), \(\forall m\) Initialize:\(\left(\mathbf{P}_{m}^{j}\right)^{0}\), \(\forall j\), \(\forall m\); for\(t=1\) to \(t_{max}\)do \(\mathbf{p}^{\star}=\operatorname*{argmin}\limits_{j,m,k,n}\left[\widetilde{U}_ {m,nk}^{j}\left(\mathbf{p},\mathbf{p}^{t-1}\right)-V_{m,nk}^{j}(\mathbf{p})\right]\); \(\mathbf{p}^{t+1}=\mathbf{p}^{t}+\eta\left(\mathbf{p}^{\star}-\mathbf{p}^{t}\right)\); \(\forall\)\(\eta\) is iteration step \(t=t+1\); endfor
```  
**Algorithm 2** Power Allocation through the SCA  
For this offline algorithm, the computational complexity is mainly focused on convex optimization in power allocation. We let \(f(J,M,K,N)\) denote the complexity of the convex optimization in (11), which depends on the values of variables \(J,M,N,K\). Finally, the complexity of the offline algorithm is \(\mathcal{O}(t_{max}f(J,M,K,N))\).
# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 4 Results and Analysis
### Performance Matrices  
The models were assessed using various criteria, such as recall, accuracy, **F1** score, and precision. The recall is determined by dividing the number of _TP_s by the true positive examples in the dataset (_TP_ + _FP_). The majority of the positive examples in the dataset, or all of the highly engaged students, are likely correctly identified by the model, according to a high recall score [55].  
\[\textbf{{Recall}}=\frac{\textbf{{TP}}}{\textbf{{TP}}+\textbf{{FP}}} \tag{2}\]  
The precision is determined by dividing the total number of positive forecasts (_TP_ + _FN_) by the number of actual positive forecasts. A model with a high precision rating will produce fewer false positive predictions [56].  
\[\textbf{{Precision}}=\frac{\textbf{{TP}}}{\textbf{{TP}}+\textbf{{FN}}} \tag{3}\]  
By calculating the harmonic mean of a classifier, the _F1_ score integrates precision and recall into one metric.  
\[\textbf{{F1}}=\frac{2*\textbf{{precision}}*\textbf{{recall}}}{\textbf{{precision}}+\textbf{{recall}}} \tag{4}\]  
A key model evaluation metric, the AUC, is also used to compare the performance of multiple models. The AUC score is determined by comparing the TPR and FPR at various classification levels using the ROC curve. A high AUC value indicates an exceptional ability to discriminate between positive and negative categories [57].
## 3 Result
### Impact of Activation Energy  
Figures 11-13 illustrate the relationship between activation energy and the amount of remaining PM. Specifically, Figure 11 corresponds to an exhaust gas temperature of 600 \({}^{\circ}\)C, Figure 12 represents 700 \({}^{\circ}\)C, and Figure 13 depicts 800 \({}^{\circ}\)C. At 600 \({}^{\circ}\)C, when the activation energy exceeds 120 kJ/mol, the remaining PM amount remains nearly constant. Similarly, at 700 \({}^{\circ}\)C, the PM residue shows little variation when the activation energy is 140 kJ/mol or higher. At 800 \({}^{\circ}\)C, the remaining PM amount stabilizes beyond an activation energy of 160 kJ/mol. For efficient PM combustion at 600 \({}^{\circ}\)C, catalysts with activation energies below 120 kJ/mol are beneficial. When the exhaust temperature is increased to 700 \({}^{\circ}\)C, catalysts with activation energies of 140 kJ/mol or lower are more effective. Likewise, at 800 \({}^{\circ}\)C, catalysts with activation energies of 160 kJ/mol or less demonstrate better performance. The findings indicate that as the exhaust temperature rises, the range of activation energies that enable effective catalytic performance expands. Additionally, PM combustion efficiency improves at lower activation energies when the exhaust temperature is high.  
Figure 11: The relationship between activation energy and the amount of remaining PM after 5400 s of regeneration at an exhaust gas temperature of 600 \({}^{\circ}\)C. The red line represents a PM deposition of 1 g, the blue line corresponds to 10 g, the green line indicates 20 g, and the yellow line denotes 30 g.
# Three-Dimensional Imaging via Time-Correlated Single-Photon Counting
## 1 Introduction  
In recent years, single-photon counting lidar has been widely used in three-dimensional (3D) imaging under weak light conditions because of its single-photon sensitivity and picosecond time resolution. This technology has many significant applications, such as long-distance imaging, underwater target detection [1]. The core idea of this technology is time-correlated single-photon counting (TCSPC). TCSPC is the most effective way to measure the temporal structure of weak optical signals, which is based on the accurate measurement of photon arrival times and time difference [2, 3]. TCSPC was mainly used in the detection of fluorescence lifetime in the early stage [4, 5, 6, 7, 8, 9, 10, 11, 12]. After that, TCSPC began to be used in the field of laser ranging [13, 14, 15, 16, 17, 18] and gradually developed to the direction of laser long-distance 3D imaging based on time-of-flight (TOF) algorithm [19, 20, 21, 22, 23, 24, 25, 26]. Recently, [PERSON] et al. achieved the imaging of targets 45 kilometers away [27]. [PERSON] et al. [28] and [PERSON] et al. [29] detected the first photon in TCSPC system to image the object.  
In the general TCSPC system, the image result is greatly affected by environmental noise because of the ultra-high sensitivity of the single-photon detectors (SPD). Up to now, most of the research teams used the range-gated technology to decrease the impact of noise [20; 24; 27]. Besides, [PERSON] et al. used two Geiger-mode avalanche photodiodes and an AND gate to reduce false alarms caused by environmental noise [30]. [PERSON] et al. come up with a new method to decrease environmental noise by using correlative photons and spatial correlations [31].  
In this paper, we combine SPD, high repetition rate laser pulses illumination and TCSPC technology to design a new imaging scheme. The new scheme has the capability of de-noise, high-resolution imaging, accurate analysis of the photon flight time and 3D imaging. Three experiments are completed to demonstrate the power of the new imaging scheme. The first is the de-noise experiment which proves that our scheme can image the object when the signal-to-noise ratio (SNR) is decreased to \(-13\) dB and improve the structural similarity index (SSIM) [32] of imaging results by 10 times. The second proves that the scheme has a lateral imaging resolution of \(512\times 512\) and an axial resolution of 4.2 mm. The third is 3D imaging by photometric stereo algorithm which uses multiple detectors to reconstruct the 3D image of the object. Besides, the new imaging scheme has the potential to realize turbulence-free imaging [33], non-of-sight imaging technology [34] and imaging in a complicated environment.
## 4 Discussion
### The Limitation and Implication of This Study  
In this study, we validated the reliability of remotely-sensed SOS/EOS with eddy-observed GPP data in typical steppe and desert steppe. The significant and positive correlations between remotely-sensed SOS/EOS and GPP-based SOS/EOS indicate the remotely-sensed SOS/EOS could reflect spatiotemporal dynamics of ground vegetation phenology to some extent. Nevertheless, there are still some gaps between remotely-sensed SOS/EOS and GPP-based SOS/EOS, which may result from the different spatial scales. Another limitation of the validation is the lack of sufficient ground measured data, especially in meadow steppe. Therefore, the results and conclusions of this study need to be further tested with more extensive ground data sources.  
This study revealed a diverse response of vegetation phenology to climate change in meadow steppe, typical steppe, and desert steppe, which may indicate different succession directions and spatial patterns of them under the more complex future climate changes. The overall stronger sensitivity of SOS/EOS to hydrothermal conditions identified in desert steppe means plant activity in desert steppe is more unstable than that in meadow steppe and typical steppe. As detected, the whole growing season in desert steppe has significantly advanced and prolonged due to significant climate changes in the past 17 years. This could lead to an increase of annual C uptake through the extended period of photosynthesis [7,8]. Additionally, the shifts of water and heat exchange accompanied with phenology movement could also alter local climate status [8]. In developing process-based phenological models for grassland vegetation, we strongly recommend considering different responding characteristics of different grassland types to climate change. In perspective of rangeland management and conservation, we highly suggest to reasonably control and guide grazing activity in desert steppe due to its large sensitivity to climate change.
## 3 Results
### Accuracy Analysis of the Integrated STP Pseudo-Observations
#### 3.1.3 Accuracy Analysis of Integrating the STPs of GOCE and GRACE  
We only use the EGM2008 model up to degree and order 90 to establish the STPs of GOCE and GRACE A/B satellites with a sampling interval of 30 s. The STPs computed from the RD POD solutions (left part of Equation (3)) are used as the \"true values\" to validate the integrated STPs (right part of Equation (3)) from the EGM2008 model and the RD POD solutions. The errors of the integrated STPs with a sampling interval of 30 s are shown in Figure 5. According to the figure, the errors of the three coordinate components (\(x\), \(y\), \(z\)) are far less than 0.01 m, and the corresponding RMS are 1.9 mm, 1.9 mm, and 2.3 mm for GOCE, respectively, and 0.8 mm, 0.8 mm, and 0.8 mm for GRACE A/B, respectively. The errors of the computed STPs of GOCE are larger than those of GRACE because the ignored gravitational accelerations (e.g., ocean tide, solid tide) of GOCE are larger than those of GRACE owing to the lower orbital altitude of GOCE. However, the STP accuracies of GRACE and GOCE are much better than 0.01 m, consistent with the previous conclusions in Section 3.1.2.  
Figure 6 shows the three-dimensional root mean square (3D RMS) of the GOCE's STP errors corresponding to EIGEN5S [44] and EGM2008 with different maximum degrees and orders, and different sampling intervals. According to Figure 6, the differences between the results that correspond to EIGEN5S and EGM2008 are negligible, even with a sampling interval of 300 s and a maximum degree and order up to 150. The RMS of the STP errors  
Figure 5: Errors of STPs (in X/Y/Z direction) integrated with EGM2008 up to degree and order 90 with a 30 s sampling interval for LEOs: GOCE and GRACE A/B, on 25 November 2009. The RD POD solutions are used as the true orbit.  
increases as the sampling interval increases. Further, although the RMS of the STP errors decreases as the degree increases, the reduction after 90 degrees is very limited, consistent with the results shown in Figure 4. Therefore, only EGM2008 up to degree and order 90 is set as the only a priori dynamic model in the real data processing.  
As stated above, the acceleration errors caused by only using the EGM2008 up to degree and order 90 are less than \(10^{-5}\) ms\({}^{-2}\). Therefore, we set \(\sigma_{\overset{\rightarrow}{r}_{M}}\ =\ 10^{-5}\) ms\({}^{-2}\). Based on Equation (3), the corresponding error of \(\Delta\Delta\overset{\rightarrow}{r}_{L}\) with a sampling interval of 30 s is 9 mm, which is much larger than the \"true\" errors shown in Figure 5. Thus, the assumption of a stochastic model for \(\Delta\Delta\overset{\rightarrow}{r}_{L}\) is moderate, which will reduce the influence of the a priori dynamic model on the RD_STP solution.
## 2 Background
### Aquifer and Well Dynamics  
The water table drawdown (s), which is a function of the distance between the pumped wells (\(r\)) and the discharging time (\(t\)) of a completely penetrative well in a confined aquifer, can be described using the Theis equation [29]:  
\[s(r,t)=\frac{q}{4\pi T}\cdot W\left(\frac{Sr^{2}}{4 Tt}\right)=\frac{q}{4\pi T} \cdot W(u) \tag{1}\]  
where \(q\) is the water extraction flow in m/s; \(T\) is the transmissivity, defined as the capacity of the aquifer to transmit water, in m\({}^{2}\)/s; and \(S\) is the specific storage, defined as the water volume per unit of aquifer volume release when the hydraulic head declines by one unit. \(W(u)\) represents the well function (or exponential-integral), and is defined as:  
\[W(u)=\int_{u}^{\infty}\frac{e^{-x}}{x}\,dx \tag{2}\]  
For intermittent or variable pumping, the principle of superposition in Equation (1) can be used to obtain the following drawdown \(s\) at distance \(r\) and at time \(t(n)\):  
\[s(r,t(n))=\sum_{k=0}^{n}\frac{\Delta q(k)}{4\pi T}\cdot W\left(\frac{Sr^{2}}{4T \left(t(n)-t(k)\right)}\right) \tag{3}\]  
where \(\Delta q(k)=q(k)-q(k-1)\) is the increase or decrease in the water flow pumped between two sampling instants, and \(t(n)-t(k)\) is the time after pumping \(q(k)\) began. To ensure zero or minimal interference between wells, the decreasing water level \(s(r,t)\) must be monitored in terms of its yield. To avoid interactions between wells, the hydraulic threshold \(\varepsilon\) at a distance \(r_{o}\) of less than half the length between two neighboring wells should satisfy \(s(r_{o},t)\leq\varepsilon\quad\forall t\).  
The hydraulic head drawdown is shown in Figure 1, where the dashed line represents the static hydraulic head and the dotted line indicates the hydraulic head under the pumping conditions calculated by the Theis equation. Following Equation (3), the distance \(r=r_{o}\) is the maximum distance with drawdown.  
Figure 1: Schematic of the hydrological system.  
The Chaturvedi formula is used to determine the availability of water that can be extracted from an aquifer. This expression estimates the recharge of an aquifer based on the perceived rainfall in a given area:  
\[R=\alpha(P-\beta)^{\gamma} \tag{4}\]  
where \(R\) is the recharge over a year in m, \(P\) is the rainfall over a year in m, and the coefficients \(\alpha\), \(\beta\), and \(\gamma\) depend on the geographic conditions. Typical values are \(\alpha=2\), \(\beta=15\), and \(\gamma=0.4\). As this formula is intended to be used over a period of one year, it is modified as follows to obtain the average daily recharge:  
\[R=\left[2\cdot\left(\frac{365}{T}\cdot\sum_{i=1}^{T}P(i)-15\right)^{0.4} \right]\cdot\frac{1}{365} \tag{5}\]  
where \(T\) is the time horizon in days and \(P(i)\) is the rainfall on day \(i\) in inches.