esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### Implementation Framework for Multi-Regional Load Forecasting Integration
#### 2.5.2 Multi-Source Data Preprocessing  
Effective data preprocessing is critical for multi-regional integration due to the inherent heterogeneity across provincial data sources. Our preprocessing pipeline addresses four key challenges: data quality assessment, anomaly detection, temporal alignment, and standardization.  
Beyond standard electricity consumption data, our framework incorporates the following:  
* Sectoral electricity usage data (industrial, commercial, residential).
* Real-time electricity price information.
* Operational data from distributed generation sources.
* High-resolution meteorological data (temperature, humidity, precipitation).
* Socioeconomic indicators (holidays, population density, economic indices).  
We implement a dual-stage anomaly detection approach with an isolation forest algorithm for outlier detection and Least Squares Support Vector Regression (LS-SVR) for missing value imputation and smoothing. Feature selection is performed using a two-stage approach with Pearson correlation coefficient for initial screening and Elastic Net regression for refined selection. Z-score standardization is used for feature scaling.
# A Potential \"Vitaminic Strategy\" against Caries and Halitosis
## 2 Materials and Methods
### Potential Ability of Vea(r) Oris Treatments to Modulate \(H_{2}\)S Production  
Since _Fusobacterium_ has been largely demonstrated as the most relevant genus associated with halitosis [19; 20; 21], _F. nucleatum_ subsp. vincentii DSM 19507 was also investigated for its ability to form a malodorous H\({}_{2}\)S product under Vea(r) Oris treatments. Our experimental setup is described in detail and also schematically shown in Figure 3.  
_F. nucleatum_ was cultivated in Columbia Broth at 37 \({}^{\circ}\)C for 48 h. The culture was centrifuged at 5000 rcf for 3 min, and the cells were collected at the bottom of the culturing tube. The bacterial pellet was resuspended in 40 mL of sulfide indole motility (SIM) medium (Thermo Scientific(tm)). SIM medium allows for the detection of H\({}_{2}\)S produced due to its specific formulation. It contains ferrous ammonium sulfate that, by reacting with the volatile compound, forms ferrous sulfide, a black precipitate that ultimately acts as a detector of H\({}_{2}\)S produced [58; 59].  
Three-milliliter aliquots of SIM cultures were distributed in 15 mL falcon tubes containing 150 (5% \(v/v\)), 300 (10% \(v/v\)), and 600 (20% \(v/v\)) \(\upmu\)L of Vea(r) Oris, respectively. Each Vea(r) Oris treatment was assessed in triplicates. Three-milliliter triplicates of controls and spectrophotometric blanks were also produced with _F. nucleatum_ cultures without Vea(r) Oris and with SIM broth alone, respectively. One milliliter of liquid sterile paraffin was added to the top of all tubes to provide anaerbiosis conditions. The tubes were incubated  
Figure 3: Experimental procedure to detect H\({}_{2}\)S produced by _F. nucleatum_ in SIM broth under the shown treatments.  
at 37 \({}^{\circ}\)C for 48 h. After incubation, the formation of the black compound was spectrophotometrically estimated. The absorbance of all inocula was measured first within the wide range of 400-700 nm at 1 nm steps with the Lambda 25 spectrophotometer (PerkinElmer). The final values of absorbance in this range for different treatments were calculated as the average of values measured for each set of triplicates. All values were previously normalized for blanks. The resulting absorbance spectra of Vea(r) Oris treatments were compared to the control (no Vea(r) Oris addition). The normality of data was checked using the Shapiro-Wilk test (\(\alpha\) = 0.05), and the significance of comparisons was assessed using the Student's _t_-test with \(p\leq 0.05\) applied to triplicates of the normalized absorbance values from all treatments.  
The wavelength at which the darkened control of _F. nucleation_ produced the maximal absorbance was considered the detection wavelength of the black compound and consequently of H\({}_{2}\)S. This control absorbance was considered to be 100% of H\({}_{2}\)S production. The variation of H\({}_{2}\)S induced by Vea(r) Oris treatments was calculated as a percentage variation with respect to the control value.
## 4 Numerical Simulation
### GMM-FTC Simulation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Units** \\ \hline Initial relative position, \(r(0)\) & \(\left[\begin{array}{ccc}-12&-3&1\end{array}\right]^{T}\) & m \\ Initial relative velocity, \(\dot{r}(0)\) & \(\left[\begin{array}{ccc}0&0&0\end{array}\right]^{T}\) & m/s \\ Desired relative position, \(r_{f}\) & \(\left[\begin{array}{ccc}-2&1&0\end{array}\right]^{T}\) & m \\ Desired relative velocity, \(\dot{r}_{f}\) & \(\left[\begin{array}{ccc}0&0&0\end{array}\right]^{T}\) & m/s \\ Angular velocity of target spacecraft, \(\omega_{b}\) & \(\left[\begin{array}{ccc}0&0&0.05\end{array}\right]^{T}\) & rad/s \\ Angular acceleration of target spacecraft, \(\dot{\omega}_{b}\) & \(\left[\begin{array}{ccc}0&0&0\end{array}\right]^{T}\) & rad/s\({}^{2}\) \\ Initial attitude of target spacecraft, \(\sigma(0)\) & \(\left[\begin{array}{ccc}0&0&0\end{array}\right]^{T}\) & rad \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Initial relative motion parameters expressed in frame \(\mathcal{F}_{b}\). Shown Figures 6-9 are the trajectory and state parameters in frame \(\mathcal{F}_{b}\). As shown below, Figures 10 and 11 are the position of servicer and control force expressed in LVLH frame \(\mathcal{F}_{l}\), respectively. It is obvious that the servicer maintains flight around with the target after reaching the desired position in the \(x\)-\(O\)-\(y\) plane and the control force keeps oscillating to keep the servicer in the desired position. Figure 12 illustrates the trajectory of servicer and the attitude change of target in the frame \(\mathcal{F}_{l}\). The figures are projections in \(x\)-\(O\)-\(y\) plane. The red curve is the trajectory of the servicer, the blue and green points are the initial and time-variant desired positions, respectively. Note that one antenna is depicted by red straight line in order to better show the attitude changes of the spacecraft, i.e., counterclockwise rotation. From Figure 12, it can be seen that servicer can approach to the desired position without any collision. After 150 s, the servicer has reached the desired position and moved to the following phase. It can be seen that the servicer can follow the rotating target and keep it in the desired position well. Figure 10: Time response of relative position in local vertical local horizontal (LVLH) frame. Figure 9: Time response of control force \(\mathbf{u}\). Figure 11: Time response of control force \(u^{l}\). Figure 12: The servicer’s trajectory and the target’s attitude change.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 3 PIDL for Deterministic Tse
### Numerical Data Validation for Three-Parameter-Based LWR
Specifically, for five loop detectors, an estimation error of \(3.186\times 10^{-2}\) is obtained, and the model parameters converge to \(\delta^{*}=4.86236\), \(p^{*}=0.19193\), \(\sigma^{*}=0.10697\), \(\rho^{*}_{max}=1.00295\), and \(\epsilon^{*}=0.00515\), which are decently close to the ground-truth. The observations demonstrate that PIDL can handle both TSE and system identification with five loop detectors for the traffic dynamics governed by the three-parameter-based LWR. We conduct sensitivity analysis on different numbers of collocation points and how they are identified. The details are presented in Table A2: A larger collocation rate (i.e., the ratio of the number of collocation points to the number of grid points) is beneficial for both TSE and system identification, because it could make the estimation on the collocation points physically consistent by imposing more constraints on the learning process. Empirically, more collocation points can cause a longer training time and the performance does not improve too much when a certain collocation rate is reached.
## 3 Results
### Embryonic Heart Rate of Zebrafish and African Clawed Frog  
The results of the embryonic heart rate as another sublethal endpoint are highlighted in Table 5. The most significant changes were observed in the embryonic toxicity test on the zebrafish, especially in the tests with fluoxetine hydrochloride and a combination of both antidepressants.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Group** & **Concentration** & **24 hpf** & **48 hpf** & **72 hpf** & **96 hpf** \\ \hline control & - & 0 & 5.6 & 11.1 & 11.8 \\ \hline  & 0.1 ug/L & 0 & 0 & 11.1 & 33.3 \\  & 1 ug/L & 0 & 22.2 & 22.2 & 23.5 \\ F & 10 ug/L & 0 & 16.7 & 17.6 & 23.5 \\  & 100 ug/L & 0 & 11.1 & 11.1 & 11.8 \\  & 1000 ug/L & 0 & 16.7 & 29.4 & **41.2*** \\  & 10,000 ug/L & 0 & 5.9 & - & - \\ \hline  & 0.01 ug/L & 0 & 0 & 27.8 & 27.8 \\  & 0.1 ug/L & 0 & 16.7 & 22.2 & 23.5 \\  & 1 ug/L & 0 & 11.1 & 11.1 & 5.6 \\ C & 10 ug/L & 0 & 11.1 & 16.7 & 16.7 \\  & 100 ug/L & 0 & 22.2 & 32.3 & **52.3*** \\  & 1000 ug/L & 0 & 16.7 & 17.6 & 23.5 \\  & 10,000 ug/L & 0 & 16.7 & 16.7 & 17.6 \\  & 100,000 ug/L & 0 & 22.2 & - & - \\ \hline  & low & 0 & 0 & **41.2*** & **50.0*** \\ F + C & medium & 0 & 5.9 & 29.4 & 31.3 \\  & high & 0 & 5.6 & 16.7 & 17.6 \\ \hline \end{tabular} Abbreviations: F—fluoxetine hydrochloride; C—citalopram hydrochloride; hpf—hours post-fertilization; low—fluoxetine hydrochloride 0.1 ug/L, citalopram hydrochloride 0.01 ug/L; medium—fluoxetine hydrochloride 100 ug/L, citalopram hydrochloride 100 ug/L, citalopram hydrochloride 100 ug/L, citalopram hydrochloride 100 ug/L, citalopram hydrochloride 100 ug/L.  
\end{table}
Table 4: Malformations (%) in African clawed frog embryos exposed to antidepressants as single substances and as a mixture. The results are related only to living individuals. Bold font and asterisks indicate a significant difference (* \(p<0.05\)) between the control and the experimental groups at the same time of exposure.  
Almost all the tested concentrations of fluoxetine hydrochloride resulted in a highly significant increase in the heart rate of more than 10% compared to the control group (\(p<0.01\)). Surprisingly, this significant increase was also observed in the experimental group exposed to an environmentally relevant concentration of fluoxetine hydrochloride. The exception was only the experimental group exposed to the highest concentration; the heart rate decreased in this group, but non-significantly. A significant increase in heart rate was found also in the experimental groups exposed to citalopram hydrochloride, but only at concentrations of 1000 \(\mu\)g/L and 10,000 \(\mu\)g/L (\(p<0.01\)). All the experimental groups exposed to a mixture of both antidepressants reported highly significant (\(p<0.01\)) increases in heart rate, of more than 15%.  
Rare changes in heart rate were recorded in the toxicity test on the African clawed frog embryos. A significant increase in heart rate (\(p<0.05\)) compared to the control group was found in only the experimental group exposed to citalopram hydrochloride at 1 \(\mu\)g/L.
## 5 Conclusions  
In this paper, we have presented a temporal graph refinement method via multi-stage key frame selection to recognize skeleton-based human actions. Our method is capable of reducing the scale of the temporal graph and improving the efficiency of the information propagation between graph vertices. We have introduced a multi-stage key frame selection (MSKFS) method to select representative frames and discard redundant and misleading frames. The proposed MSKFS includes two stages: salient posture frame selection, key frame refinement and alignment. The MSKFS learns the salient features and subtle dynamic features that are consistent within inter-class for better spatial-temporal representation of skeleton-based human action sequences. Guided by the action recognition task, an end-to-end variational inference approach is employed to estimate the parameters of the posterior distribution for key frame selection. Experiments on several challenging datasets on skeleton-based action prediction demonstrate the superior performances of the proposed method.  
Conceptualization, L.S. and J.H.; methodology, J.H., L.S. and Y.Z.; software, L.S. and J.H.; validation, J.H., L.S. and Y.Z.; formal analysis, [PERSON] and L.S.; investigation, L.S. and J.H.; resources, [PERSON], L.S. and Y.Z.; data curation, L.S. and J.H.; writing--original draft preparation, [PERSON] and L.S.; writing--review and editing, [PERSON] and Y.Z.; visualization, [PERSON] and L.S.; supervision, [PERSON] and Y.Z.; project administration, J.H. and Y.Z.; funding acquisition, J.H. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of China, grant numbers 62106021 and U20A20225.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. The NTU RGB+D 60 dataset [16] and the NTU RGB+D 120 dataset [31] were used. This data can be found here: [[https://rosel.ntu.edu.sg/dataset/actionRecognition](https://rosel.ntu.edu.sg/dataset/actionRecognition)]([https://rosel.ntu.edu.sg/dataset/actionRecognition](https://rosel.ntu.edu.sg/dataset/actionRecognition)) (accessed on 5 September 2024).  
The authors declare no conflicts of interest.  
Figure 5: Visualization of (**a**) the key frame selection and alignment procedure and (**b**) the attention maps of the selected key frames. A–H are selected key frames.
# Design and Accuracy Analysis of a Micromachine Tool with a Co-Planar Driving Mechanism
## 3 Error Model and Analysis
### Error Model
\[\left(\Delta\text{E}_{\text{Py}}\right)_{\delta c^{\prime}\delta\theta_{4}}= \delta d^{\prime}\times\tan(\theta+\delta\theta_{4}) \tag{18}\]  
\[d\left(\Delta\text{E}_{\text{Py}}\right)_{\delta c^{\prime}\delta\theta_{4}}= \,\,\tan(\theta+\delta\theta_{4})d\delta d^{\prime}-\delta d^{\prime}\,\,\sec ^{2}(\theta+\delta\theta_{4})d\delta\theta_{4} \tag{19}\]  
The first sensitivity parameter of \(\frac{\partial\delta d^{\prime}\times\tan(\theta+\delta\theta_{4})}{\partial \delta d^{\prime}}\) is \"\(\tan(\theta+\delta\theta_{4})\)\". This parameter will not change when the platform position increases, but it will change when the tilt-drive angle changes (as shown in Figure 10). Furthermore, the sensitivity parameter will decrease as the tilt-drive angle increases. That is, the larger the error of the tilt-drive angle, the greater the effect of this parameter. Another sensitivity parameter of \(\frac{\partial\delta d^{\prime}\times\tan(\theta+\delta\theta_{4})}{d\delta \theta_{4}}\) is \"\(\delta d^{\prime}\,\,\sec^{2}(\theta+\delta\theta_{4})\)\", in which \(\delta d^{\prime}\) will change when the ballscrew position changes. This parameter will change when either the platform position or the tilt-drive angle increases (as shown in Figure 11). By substituting the conditions of the prototype platform--in which the tilt-drive angle \(\theta\) is 25.84, the assembly error \(\delta\theta_{4}\) is 0.05\({}^{\circ}\), and the total ballscrew motion error is 0.5 \(\upmu\)m--into Equation (20), it is noted that the machine error can be controlled within a range of 1 \(\upmu\)m, which meets the design target of the prototype. Additionally, it  
Figure 9: Relationship between the tilt-drive angle assembly error \(\delta\theta_{3}\) and parameter sensitivity. can also be concluded that the ballscrew motion error source has a greater impact (almost linearly proportional) on the overall Y-axis error. \[\frac{|\tan(\theta+\delta\theta_{4})|}{\left|\delta\mathrm{d}^{\prime}\ \sec^{2}(\theta+\delta\theta_{4})\right|}>1 \tag{20}\]
# Adaptive, Observer-Based Synchronization of Different Chaotic Systems
## 3 Master Chaotic Systems Transformable into ONP Form  
General, necessary, and sufficient conditions for the existence of local change in coordinates \(\mathbf{z}=\mathbf{\Phi}(\mathbf{x})\) transforming a nonlinear system  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x})+\mathbf{f}_{\mathbf{0}}(\mathbf{x},\mathbf{u})+\mathbf{\theta}^{T}\mathbf{ f}(\mathbf{x},\mathbf{u}) \tag{14}\]  
with parameters \(\mathbf{\theta}\) into the ONP form (1), (2) are given in [33; 43]. In this contribution, we concentrated on third-order chaotic systems transformable into the ONP form. For example, consider shifted Arneodo system  
\[\begin{split}\dot{x}_{1}&=x_{2},\\ \dot{x}_{2}&=x_{3},\\ \dot{x}_{3}&=a(x_{1}+r)-bx_{2}-x_{3}-(x_{1}+r)^{2}, \end{split} \tag{15}\]  
which is a classical Arneodo system for \(r=0\), chaotic for a certain subset of parameters \((a,b)\)[11]. The system (15) is transformed by a linear transformation  
\[\mathbf{z}=\mathbf{\Phi}(a,b)\mathbf{x},\;\;\mathbf{\Phi}(a,b)=\left[\begin{array}{ccc}1&0&0 \\ 1&1&0\\ b&1&1\end{array}\right] \tag{16}\]  
into the ONP form  
\[\begin{split}\dot{z}_{1}&=-z_{1}+z_{2},\\ \dot{z}_{2}&=-bz_{1}+z_{3},\\ \dot{z}_{3}&=a(z_{1}+r)-(z_{1}+r)^{2}\end{split} \tag{17}\](see Appendix A for more details). Assuming that the output is \(y=z_{1}=x_{1}\), and comparing with (1), (2), we obtain:  
\[\mathbf{A}=\left[\begin{array}{cc}0&1&0\\ 0&0&1\\ 0&0&0\end{array}\right]; \mathbf{\varphi}(y,r)=\left[\begin{array}{c}-y\\ 0\\ -(y+r)^{2}\end{array}\right]; \mathbf{F}(y,\mathbf{r})^{T}=\left[\begin{array}{cc}0&0\\ 0&-y\\ y+r&0\end{array}\right]; \tag{18}\] \[\mathbf{\theta}=\left[\begin{array}{c}a\\ b\end{array}\right]; \mathbf{\epsilon}^{T}=[1\;0\;0].\]  
The inverse transformation is given by:  
\[\mathbf{x}=\mathbf{\Phi}^{-1}(\mathbf{\theta})\mathbf{z},\;\;\mathbf{\Phi}^{-1}(\mathbf{\theta})= \left[\begin{array}{ccc}1&0&0\\ -1&1&0\\ 1-b&-1&1\end{array}\right]. \tag{19}\]  
Motivated by this example, we consider as a master system any third-order nonlinear system with state variables \(\mathbf{x}\), the output \(y=x_{1}\), and unknown constant parameters \(\mathbf{\theta}\), given by  
\[\begin{array}{c}\dot{x}_{1}=x_{2},\\ \dot{x}_{2}=x_{3},\\ \dot{x}_{3}=f_{0}(\mathbf{x},r)+\mathbf{\theta}^{T}\mathbf{f}(\mathbf{x},r),\end{array} \tag{20}\]  
and transformable into the ONP form (1), (2), assuming that the inverse transformation is given by:  
\[\begin{array}{c}x_{1}=z_{1},\\ x_{2}=\mathbf{\alpha}^{T}\mathbf{z},\\ x_{3}=\mathbf{\beta}_{0}^{T}\mathbf{z}+\sum\limits_{i=1}^{p}\theta_{i}\mathbf{\beta}_{i}^{T }\mathbf{z},\end{array} \tag{21}\]  
where vectors \(\mathbf{\alpha}^{T}\), \(\mathbf{\beta}_{i}^{T}\) are constant and \(\theta_{i}\) are unknown, constant parameters.  
As the output \(y=z_{1}=x_{1}\) is available, we construct an observer (4) for the ONP system (1), (2).
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 8 Preliminary Analysis B: Factorial Design using the Strict Frequency Reuse Algorithm  
The second step towards the proposed solution consists of selecting one of the ICIC algorithms to evaluate the impact of its most relevant parameters on system performance. This analysis has two further steps: a 2k factorial design and a full factorial design.  
These studies will be conducted in the Strict FR algorithm for its good compromise between complexity and performance.
## 3 Results
### Data Analysis
To quantify the error between theoretical and microwave capacitances, we present in Figure 10 the relative error between the two types of data after removing the reference capacitors used for calibration and erroneous microwave data (probe tip non contacting the DUT). From Figure 10, we demonstrate that the smallest MOS capacitance values present errors reaching 100%. The main reason is that the measurement accuracy depends on the reference devices used for the calibration. The smallest capacitances have not been considered in this study to focus mainly on capacitors whose measurements present a good signal-to-noise (SNR) ratio. Indeed, in contrast with capacitances values greater than 300 aF, those capacitors exhibit large relative fluctuations in the observed microwave signals. Further complicating this, the drift of the microwave signal observed in the \(Y\) scanning direction (see Figure 5d) has more impact on the determination of the smallest capacitances as they are physically positioned at the beginning and end of the scanning area. Further discussion on this point is proposed in the following section. For all other capacitors (>300 aF), the relative error reaches a maximum value of 20%, with a median value of 8.7%. In the following section, we analyze the capacitance fluctuation considering a straight 1D profile as presented in Figure 11a (non calibrated topography). In particular, we focus on the smallest capacitors related to the probe tip directly in contact with the silicon oxide layer. Figure 11b shows a zoomed-in version of Figure 11a in the \(x\)-range of 17-25 \(\upmu\)m. Two steps of silicon oxide with absolute thicknesses of 198.3 and 137.1 nm are identified. The related extracted microwave capacitance profile demonstrates that the corresponding capacitances are about 40 and 22 aF, respectively, with fluctuations of about \(\pm\)5 aF. Figure 10: Relative error (in log scale) in the determination of the microwave capacitance at 30 GHz. Figure 9: Three-dimensional (3D) capacitance image of the reference kit measured at 30 GHz.
# Enhanced Phosphorus Removal from Wastewater Using RSPRC and a Novel Reactor
## 3 Results and Discussion
### Effect of RSPRC Distribution in Reactor on Phosphorus Removal  
As mentioned in Table 6, phosphorus removal performance reached a low point under experimental condition a. The concentration of effluent P amounted to 0.34 mg/L and the phosphorus removal rate was merely 32%. Based on further analysis, the adsorbent was concentratedly distributed between adjacent diversion walls. Although the adsorbent can absorb phosphorus in the nearby wastewater with a higher efficiency, overall, P concentration of the wastewater is lower, and the solute concentration gradient affecting the diffusion rate is smaller. Therefore, the phosphorus in the wastewater is diffused with difficulty and it is unable to sufficiently contact the adsorbent. Ultimately, the adsorption capacity of RSPRC is not fully utilized [31; 32]. Under the experimental condition d, the equal spacing of the adsorbent is evenly distributed between the adjacent diversion walls, which increases the contact area with the sewage. As a result, the phosphorus adsorbate in wastewater can be efficiently absorbed [33] and the role of the adsorbent can be completely utilized to increase the unit adsorption amount of the adsorbent. The effluent P concentration in experimental condition d was the lowest at only 0.08 mg/L and the phosphorus removal rate reached up to 84%. [PERSON] observed that the removal of P increased with the increase in rpm to some extent and attributed it to the dispersal of the adsorbent particles in the aqueous solution, which leads to a reduced boundary mass transfer [34]. The dispersive distribution of the adsorbent increases the removal rate, whose mechanism is similar to that of the increase in rpm. In an attempt to achieve the best static adsorption phosphorus removal result in the concentric circular diversion wall adsorption reactor, the experimental condition d was selected as the adsorbent distribution in the subsequent experiments.