esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The opto-electric servomechanism (OES) plays an important role in obtaining clear and stable images from airborne infrared detectors. However, the inherent torque disturbance and the noisy speed signal cause a significant decline in the inertial stability accuracy and low-speed performances of OESs. Traditional linear control schemes cannot deal with the nonlinear torque disturbance well, and the speed obtained by the finite difference (FD) method cannot effectively balance the tradeoff between the noise filtering and phase delay. Therefore, this paper proposes a strap-down stability control scheme, in the combination of a proportional-integral(PI) controller and a state-augmented Kalman filter (SAKF), where the PI is used to regulate the linear part of the servomechanism, and with the SAKF performing torque disturbance observation and speed estimation simultaneously. The principle and the implementation of the controller are introduced, and the tuning guidelines for the controller parameters are presented as well. Finally, the experimental verifications based on OESs with three transmission types (i.e., the direct-driving, the harmonic-driving, and the rotate vector-driving(RV-driving) OESs) are carried out respectively. The experimental results show that the proposed control scheme can perform better speed observation and torque disturbance compensation for various types of OESs, thus effectively improving the low-speed performance and stability accuracy of the mechanism.  
OES; inertial stability accuracy; low-speed performance; speed observation; disturbance observation; state-augmented Kalman filter; composed control scheme
# Validation and Application of Two New Core Stability Tests in Professional Football
###### Abstract  
The purpose of the first study was to validate two newly proposed core stability tests; Prone Plank test (PPT) and Closed Kinetic Chain test (CCT), for evaluating the strength of the body core. Subsequently, these tests were employed in a longitudinal prospective study implementing a core stability training program with a professional Spanish football team. For the validation study, 22 physically active men (_Tegner Scale 6_-7) performed three trials of the PPT and CCT tests in two different testing sessions separated by one week. In the longitudinal study, 13 male professional football players were equally evaluated (PPT and CCT) before and after the competitive session in which they completed a core training program. Intra-/intersession, and intertester, reliability was analyzed. PPT and CCT demonstrated excellent to good test-retest reliability and acceptable error measurement (ICCs for intratester and intrasession reliability ranged from 0.77 to 0.94 for the PPT, and 0.8-0.9 for the CCT) in all but one of the testing conditions (female tester for CCT test; ICC = 0.38). Significant improvements on core strength were found from pre to post evaluation in both the PPT (\(p<0.01\)) and CCT (\(p<0.01\)) after the implementation of a core training program in professional football players.  
core function; football; field test; reliability +
Footnote †: journal: Applied Sciences
## 8 Experimental Setup
### Feature Engineering  
Text data are inherently highly dimensional in nature. The curse of the multidimensionality issue arises when ML techniques are implemented on high-dimensional data, thus resulting in sparsity, which impacts the classifier's predictive accuracy. Identifying and categorizing learning patterns from voluminous data is extremely challenging and computationally expensive since repetitive and inappropriate features impede performance in text classification problems. Moreover, In the Roman Urdu language, due to huge lexical variations (refer to Section 3), a single word gives rise to many dimensions making it more problematic. Extracting significant and relevant features is mandatory to cope with this curse of dimensionality.  
The text vectorization was carried out using Count vectorizer and Tfidf techniques after the removal of domain-specific stop words. Count Vectorizer transforms the text document collection into a matrix of integers. It yields a sparse representation using the scipy.sparse.csr_matrix in python. TF-IDF (Term frequency-Inverse document frequency) is an algorithm based on word statistics for text feature extraction. It is intended to reflect the significance of a term in the corpus or the collection. Mathematically, it is described by Equation (1).  
\[w(d,t)=TF(d,t)-log\frac{N}{df(t)} \tag{1}\]  
where w(d, t) is the TF-IDF of the weight of a term t in a document N represents the number of documents and df(t) highlights the number of documents in the corpus containing the term t. In the above-given equation, the first term enhances the recall, whereas the second term enhances the word embedding accuracy [45].  
Apart from statistical features, the word-level n-gram features are used in this study, i.e., unigrams, bigrams, trigrams, and hybrid features, i.e., uni-bigram (unigram + bigram), uni + trigrams (unigrams + trigrams) and bi-trigrams (bigrams + trigrams). They are used in combination with countvectorizer and tfidf. Consider a Roman Urdu text: \"Wo tha bhi itna bonga aadmi\". This will generate the sequence of unigrams + bigrams as: ('wo'), ('tha'), ('bhi'), ('itna'), ('bonga'), ('aadmi'), ('wo', 'tha'), ('tha', 'bhi'), ('bhi', 'itna'), ('itna', 'bonga'), ('bonga', 'aadmi'). The drawn features were also used in the form of feature sets.
## 2 Preliminaries and Network Model
### Motivation  
In this subsection, we investigate the impact of packet loss on the CS recovery performance relying on the fixed routing. Figure 1 presents the performance of the CDG [9] algorithm with cluster topology in unreliable links. In this scheme, there are 100 nodes and the member nodes forward the packets to the cluster head via a one-hop route. When the packet loss rate is 10%, the recovery accuracy is worse than the accuracy in the ideal link. Furthermore, increasing the measurements cannot improve the algorithm's performance. For \(\mathbf{M=50}\) measurements, Figure 2 indicates that the accuracy declines with the increase of packet loss rate.  
Figure 1: Performance of CDG with ideal link and lossy link.  
Figure 2: The relationship between the packet loss rate and the NMAE.  
We consider one of the clusters containing \(\mathbf{N_{1}}\) nodes. For the CDG algorithm with fixed routing, the cluster head receives the data vector \(\mathbf{X_{N_{1}\times 1}}=\left(\mathbf{x_{1}},\cdots,\mathbf{x_{i}},\cdots,\mathbf{x_{N_{1}}} \right)^{T}\) in reliable links. The measurements \(\mathbf{Y}\) can be represented as  
\[\mathbf{Y}=\left(\begin{array}{c}\mathbf{y_{1}}\\ \mathbf{y_{2}}\\ \vdots\\ \mathbf{y_{M}}\end{array}\right)=\left(\begin{array}{ccc}\mathbf{\phi_{11}}&\cdots &\mathbf{\phi_{1N_{1}}}\\ \vdots&&\vdots\\ \mathbf{\phi_{M1}}&\cdots&\mathbf{\phi_{MN_{1}}}\end{array}\right)\left(\begin{array} []{c}\mathbf{x_{1}}\\ \vdots\\ \mathbf{x_{i}}\\ \vdots\\ \mathbf{x_{N_{1}}}\end{array}\right). \tag{7}\]  
If the packet of node \(i\) is missing due to unreliable links, then its cluster head will receive \(\mathbf{X^{\prime}_{N_{1}\times 1}}=\left(\mathbf{x_{1}},\cdots,\mathbf{x^{\prime}}_{i}, \cdots,\mathbf{x_{N_{1}}}\right)^{T}\) and the measurement \(\mathbf{Y}\) can be represented as  
\[\mathbf{Y^{\prime}}=\left(\begin{array}{c}\mathbf{y^{\prime}}_{1}\\ \mathbf{y^{\prime}}_{2}\\ \vdots\\ \mathbf{y^{\prime}}_{M}\end{array}\right)=\left(\begin{array}{ccc}\mathbf{\phi_{11} }&\cdots&\mathbf{\phi_{1N_{1}}}\\ \vdots&&\vdots\\ \mathbf{\phi_{M1}}&\cdots&\mathbf{\phi_{MN_{1}}}\end{array}\right)\left(\begin{array} []{c}\mathbf{x_{1}}\\ \vdots\\ \mathbf{x^{\prime}}_{i}\\ \vdots\\ \mathbf{x_{N_{1}}}\end{array}\right). \tag{8}\]  
According to Equations (7) and (8), one missing packet affects every element \(\mathbf{y_{i}}\) of the measurement vector. Thus, the sink recovers all the data \(\mathbf{X}\) using \(\mathbf{Y^{\prime}}\) and \(\mathbf{\Phi}\), which leads to an imprecise or invalid reconstruction. Furthermore, the accuracy is even worse under tree-based routing. This deficiency occurs because if one packet of a parent node is missing, then all the information from its child nodes is lost too. Additionally, simply increasing the number of measurements or the number of retransmissions does not help much in improving the recovery accuracy. Therefore, the CS-based algorithm is sensitive to packet loss. In the next section, we investigate how to resist unreliable links, while using fewer transmissions and receptions by utilizing broadcasting properties.
## 3 Results
### Directional Forest Canopy Reflectance
#### 3.3.2 Comparisons with DART Simulations  
Through the comparison of the forest canopy reflectance values obtained from ALS- and DART-based methods, similar trends were observed in solar principal and perpendicular planes at the fixed sun position whose zenith and azimuthal angles were 37\({}^{\circ}\) and 150\({}^{\circ}\), respectively. In the solar principal plane, the ALS-based forest canopy reflectance of 0.047 in the red band was lower than the DART-based one at 0.064 in the hotspot area, shown in Figure 10a. This was explained by the different reflectance values of forest background obtained from hyperspectral images caused by mixed reflectance of bare earth and understory, and used in the DART software including bare soil reflectance. Similar variation patterns of forest canopy reflectance in the red band was found in the solar perpendicular plane, and the maximum values were observed in the nadir direction for both ALS- and DART-based results, shown in Figure 10b. In the near-infrared band, the ALS-based forest canopy reflectance generally underestimated by 13.8% and 15.9% than the ones obtained from the DART-based method in solar principal and perpendicular planes, shown in Figure 10c,d. With the increase of the VZA to 60\({}^{\circ}\), the DART-based forest canopy reflectance showed an increasing trend instead of a continuous decreasing trend of ALS-based results. This discrepancy might be because the multiple scattering effects were not considered in the ALS-based method.  
Figure 10: Comparisons between ALS- and discrete anisotropic radiative transfer model based forest canopy reflectance values in red (insets **a,b**) and near-infrared (insets **c,d**) bands with the view zenith angles ranging from -60\({}^{\circ}\) to 60\({}^{\circ}\) in solar principal and perpendicular planes for forest plot WPA-P2. The solar zenith and azimuth angles are 37\({}^{\circ}\) and 150\({}^{\circ}\), respectively.
# Experimental Investigation and Optimization of a Glazed Transpired Solar Collector
## 2 Materials and Methods
### Optimization of the GTC Design
#### 2.1.2 Results and Discussion
Based on Equation (1), the variation in the heat exchange effectiveness with the airflow rate for the two GTC configurations is shown in Figure 4. It should be noted that the uncertainty in the derived results of GTC heat transfer effectiveness, based on measurement uncertainties (according to the values in Table 1), is around \(\pm\)0.02. The uncertainty propagation calculation was performed using a deterministic method (differential error analysis) [55]. It can be seen that the heat transfer efficiency within the GTC was superior in the case of the 30 mm distance between the absorber plate and the glass layer. Furthermore, for this configuration, the heat transfer effectiveness was constant, around 50-55%, regardless of the airflow rate through the solar collector. Figure 4: GTC heat transfer effectiveness. The GTC performance can be also assessed by another criterium, related to the global efficiency of solar collectors [18]:  
\[\eta=\frac{V\rho c_{p}(T_{outlet}-T_{ambient})}{I_{T}A_{S}} \tag{2}\]  
where \(V\)--GTC airflow rate (m\({}^{3}\)/s); \(\rho\)--air density (kg/m\({}^{3}\)); \(c_{p}\)--air specific heat capacity (J/kg/\({}^{\circ}\)C); \(T_{outlet}\)--GTC outlet air temperature (\({}^{\circ}\)C); \(T_{ambient}\)--ambient air temperature (\({}^{\circ}\)C); \(I_{T}\)--total incident solar radiation on the GTC absorber (W/m\({}^{2}\)); and \(A_{S}\)--GTC absorber area (m\({}^{2}\)). Figure 5 shows the variation in the GTC global efficiency with the airflow rate for the two considered configurations, according to Equation (2). The uncertainty in the final values of the GTC global efficiency resulting from the measurements' uncertainties was under \(\pm\)0.5, based on the same method (differential error analysis). It can be observed that the GTC configuration based on reduced thickness of the air gap confined between the absorber and cover glazing was much more efficient, especially for higher airflow rates. According to the above results, the GTC design with a distance of 30 mm between the absorber and the glass layer was more efficient than the GTC with a distance of 50 mm. Consequently, this GTC configuration with the air layer of 30 mm between the perforated plate and the glazing will be further studied by integration in the facade of a building. This study is presented further down.
## 4 Results and Discussions
### Comparison of Time Delay and Convection Time  
Experimental tests yielded the phase-lag, from which the time delay (\(\tau_{d}\)) of HRR and velocity fluctuations can be calculated in combination with the excitation frequency (\(\tau_{d}=\Delta\varphi/(2\pi f)\)). It is commonly believed that the disturbance induced by the acoustic excitation propagates along the flame at convective velocity [27]. Based on the jet velocity of air at the nozzle (\(u_{cv}=u_{0}=3.52\) m/s) and \(P_{fly}\), the convective time (\(\tau_{cv}\)) can be calculated as (\(\tau_{cv}=P_{fly}/u_{cv}\)). Figure 7 shows the comparison between the time delay and convective time under different excitation frequencies and amplitudes. Anomalous values can be observed in the time delay under low-amplitude excitation at 20 Hz, and significant delay is observed in these conditions. Under other conditions, it can be observed that the time delay rapidly decreases with increasing frequency in the low-frequency range, and after  
Figure 5: The time-averaged flame length (\(L_{f}\)) and height of the COM (\(P_{fly}\)) with different excitation frequencies and amplitudes.  
Figure 6: The calculated \(St_{f}\) and the generated FDF: (**a**) The evolution of the \(st_{f}\) with the excitation frequency; (**b**) The generated gain of the FDF with \(St_{f}\).  
the excitation frequency exceeds 20 Hz, it remains relatively constant at around 0.01 s. Moreover, after the frequency exceeds 20 Hz, under different amplitude excitations, the delay time also remains around 0.01 s and is not sensitive to changes in the excitation amplitude. The results of convective time (\(\tau_{cv}\)) and time delay (\(\tau_{d}\)) are consistent under high frequency and high amplitude excitation, whereas under other conditions there is deviation between the two. The distribution range of convective time in the 0.01-0.02 s interval is generally consistent with that of time delay. Deviation mainly occurs under low frequency and low amplitude excitation conditions. In this operating range, the relatively long flame length may lead to inconsistencies between the height of the flame COM and the actual main oscillation zone of the flame. Additionally, the \(u_{cv}\) may vary under different flame lengths. These two factors could potentially result in the deviation. Under high amplitude and high frequency, the flame is relatively short, and the deviation between the flame COM and the actual main oscillation zone of the flame is small, resulting in smaller deviations between the two. This problem deserves further research.
## 4 Application Example
### Influence of the Length of the Air Intake Wells  
The influence of the length of the air intake wells is studied in four cases, in which the length is 0.5 times, 1.0 times, 2.0 times and 5.0 times the original length. The air velocity and air pressure in the different cases are shown in Figure 13. The corresponding total ventilation rates of the air supply system are 4555, 4537, 4502 and 4405 m\({}^{3}\)/s, respectively.  
The ventilation rate of the air supply system decreases slightly, and the air velocity in the air intake well and spillway tunnel decrease as well. Furthermore, with the increase in the air intake well length, the sub-pressure in the air intake well and spillway tunnel increase slightly. This result is because the influence of the air intake well length on the ventilation characteristics is mainly reflected by the frictional resistance. In hydraulic engineering, the ratio of length to area of the air intake well is  
Figure 12: Influence of the cross-sectional area of the air intake wells on the ventilation characteristics.  
very large, causing a small ratio of the frictional resistance to the sum of frictional resistance and local resistance in the air intake well.  
The analysis indicates that the air intake well length has a slight effect on the ventilation rate, air velocity and pressure in the air intake well and spillway tunnel. If possible, the selection of a straight air intake well will decrease the local resistance of the airflow, making air flow smoothly and decreasing the air velocity and sub-pressure.
## 5 Intervalley Scattering and Franck-Condon Transition
### Analogy to the Dynamical Jahn-Teller Effect  
The mechanism of the elastic e-ph scattering is basically the same as the dynamical Jahn-Teller effect in optical absorption [46]. To see this, let us consider the three-level localized electronic system, coupled with the \(xy\) phonon. The ground state is the \(\mid s\rangle\), and the excited states are doubly degenerated \(\mid p_{x}\rangle\) and \(\mid p_{y}\rangle\). When there exists the dynamical lattice distortion \(Q_{xy}\) due to thermal randomness, the \(\mid p_{x}\rangle\) and \(\mid p_{y}\rangle\) states are hybridized. Here, \(Q_{xy}\) is the lattice distortion measured from the equilibrium position. The Hamiltonian of this system is written as:  
\[H=\begin{bmatrix}0&0&0\\ 0&E_{0}&SQ_{xy}\\ 0&SQ_{xy}&E_{0}\end{bmatrix}+\frac{1}{2}\omega_{xy}Q_{xy}^{2}, \tag{22}\]  
where the energy of the \(\mid s\rangle\) level is set to zero and those for \(\mid p_{x}\rangle\) and \(\mid p_{y}\rangle\) are \(E_{0}\). \(S\) is the e-ph coupling constant, and \(\omega_{xy}\) is the phonon frequency. Equation (22) is easily diagonalized within the excited state, and the resultant adiabatic energy as a function of \(Q_{xy}\) is shown in Figure 11. In the excited state, \(\mid p_{x}\rangle\) and \(\mid p_{y}\rangle\) are no longer the eigenstates of the system; instead, the hybridized state \((\mid p_{x}\rangle\pm\mid p_{y}\rangle)/\sqrt{2}\) becomes the eigenstate of the system. When the completely \(x\)-polarized white pulsed light is shone onto this system at time \(t=0\), the photo-excited electrons are injected only into the \(\mid p_{x}\rangle\) state at first. Afterwards, the electrons in the \(\mid p_{x}\rangle\) state are transferred into the \(\mid p_{y}\rangle\) one. In the short time limit, this transition rate is easily obtained as:  
\[\langle p_{y}\mid e^{-it(H-E_{0})}\mid p_{x}\rangle\propto\mid SQ_{xy}\mid t. \tag{23}\]  
Figure 10: The time constant by the elastic e-ph scattering is given. The energy is referenced from that of the L valley bottom. \(T\) represents the phonon temperature, and thus, \(T=T_{\rm p}\). Adapted with the permission from [12], copyright Journal of the Physical Society of Japan, 2016.  
Thus, we can see that the increasing speed of the \(\mid p_{y}\rangle\) component does not directly depend on \(\omega_{xy}\). It does not depend on the time evolution of the \(xy\) phonon, but depends on the thermal excitation randomness in the starting ground state as:  
\[\frac{1}{2}\omega_{xy}Q_{xy}^{2}\sim k_{\rm B}T. \tag{24}\]  
Strictly speaking, this is not a transition, but a diffusion of carriers inside of one eigenstate \((\mid p_{x}\rangle\pm\mid p_{y}\rangle)/\sqrt{2}\).  
The practical situation in the present intervalley transition is a little different from the above simple case, but the basic physical concept is entirely the same. Due to the thermal randomness in the ground state, the eigenstates of \(\Gamma\) valley and the L one are mixed up, and the ultrafast intervalley transition becomes possible.
## 4 Discussion
### Uncertainties and Further Studies  
Previous studies have reported that MODIS produces a systematic error for GPP and ET and WUE is compared to other satellite-based products (e.g., JUNG flux products, see [62]) due to the algorithms in tropical regions [10]. With this point, only summer active ecosystems in extratropical areas were taken into account in this study, which is helpful in reducing the uncertainty around the findings. In further studies, multi-source data, as well as model simulation, are helpful to improve on our present analysis to characterize robust relationships between WUE and phenology. Additionally, more ecosystems should be comprehensively considered across climatic gradients, especially those where greening is closely associated with water supplement availability [63].  
Mismatch between ground- and satellite-observed phenology is always a difficult question in phenology research due to temporal and spatial scales, mixed pixel, etc. For the validation of satellite-based phenology, it is more appropriate that the comparison is made using interannual fluctuation changes or long-term trends rather than actual dates [28]. Moreover, an ensemble of diverse algorithms is helpful to reduce uncertainties in phenology modeling [42].  
Ecosystem-scale WUE response to nitrogen availability is not considered in our present work. It is reported that photosynthetic rates and stomatal conductance to water vapor may be stimulated by increased deposition of reactive nitrogen (e.g., resulting from human activities) in nitrogen-limited ecosystems [64; 65; 66]. Although little contribution of nitrogen deposition to the variability of seasonal WUE was reported [4], it is helpful to take effects of nitrogen deposition into account in further studies to improve on the present analysis.  
Agricultural ecosystem is included in our analysis. However, it is worth noting that although there are not clear differences in WUE response to phenology between agricultural and other ecosystems, some uncertainties in the results may be caused by data quality [67] and human management (e.g., anomaly in phenology due to planting and harvesting, as well as that in carbon and water flux due to fertilization and irrigation). Hence, more detailed investigations into agricultural WUE response to phenology will be needed.
## 4 Result and Discussion
### Performance Validation of the Model
The experimental results demonstrate that this model, through combined feature selection, effectively reduces the computational complexity of energy consumption data after signal decomposition, achieving precise predictions for the energy consumption of large public buildings while improving model computation speed. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R2** & **RPD** & **MAPE** \\ \hline CEEMDAN-MIC-FCBF-BiLSTMAM & 8.41 & 141.21 & 11.92 & 0.996 & 15.54 & 0.011 \\ CEEMDAN-FCBF-BiLSTMAM & 19.62 & 771.92 & 27.83 & 0.975 & 6.33 & 0.027 \\ CEEMDAN-BiLSTM & 27.24 & 1313.12 & 36.25 & 0.957 & 5.85 & 0.034 \\ CEEMDAN-CNN-BiLSTM & 22.55 & 1196.53 & 34.67 & 0.961 & 5.12 & 0.032 \\ CNN-BiGRU & 28.37 & 1310.38 & 36.25 & 0.957 & 5.21 & 0.038 \\ BiLSTM & 50.84 & 4604.14 & 67.94 & 0.851 & 2.67 & 0.066 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction accuracy evaluation metrics comparison table. Figure 14: Radar map of prediction accuracy evaluation metrics.
# Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
## 6 Demonstration  
We are currently in the Noisy Intermediate-Scale Quantum (NISQ) era, which means that the quantum computers available today have relatively few qubits, and require complex error mitigation protocols far beyond the scope of this paper. Therefore, we opt to demonstrate our algorithm via a classical simulation of very small, six-pixel signals.