esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Effect of Dataset Size  
To assess the impact of the number of samples on the performance, we trained the best performing approach (CNN architecture with FSL) on datasets containing only a part of the original dataset. We started with 30% of the original dataset, and then increased the size in increments of 10%, until reaching 100%, i.e., the original dataset. The smaller datasets were set up by random sampling from the original dataset. To limit the selection bias, for each percentage we ran 20 experiments, where for each experiment a new random sampling was performed, and the CNN was initialized with a new random seed. The accuracy and the standard deviation for all the considered experiments is displayed in Figure 4.  
As expected, the dataset size has an important impact on the accuracy. Encouragingly, a relatively linear increase in performance can be observed, indicating that with larger datasets, the performance should further increase. Moreover, the variation, i.e., standard deviation, decreases as the dataset size increases. This is motivated by two aspects. First, the smaller the percentage of data are, the larger is the variability of the actual dataset that is employed for the leave-one-out cross-validation. When 100% of the data are employed, the variability stems only from the random seed that is used for the initialization. Secondly, the larger the dataset, the more robust the prediction will be, i.e., with a smaller variability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Age Interval** & **Accuracy [\%]** & **Sensitivity [\%]** & **Specificity [\%]** \\ \hline \textless{}58 & 81.2 (64.6–91.1) & 75.0 (53.2–88.8) & 91.6 (64.6–98.5) \\ \hline \textless{}66 & 69.2 (50.9–79.3) & 60.0 (35.7–80.1) & 75.0 (50.8–85.0) \\ \hline \textgreater{}66 & 83.8 (67.3–92.9) & 84.6 (57.7–95.6) & 83.3 (60.7–94.1) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Diagnostic performance and 95% CI of the model as a function of age.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vessel Length [cm]** & **Accuracy [\%]** & **Sensitivity [\%]** & **Specificity [\%]** \\ \hline \textless{}4.74 & 77.1 (57.9–85.8) & 53.8 (29.1–76.7) & 90.9 (66.6–92.5) \\ \hline \textless{}4.74–5.74 & 75.0 (57.8–86.7) & 78.5 (52.4–92.4) & 72.2 (49.1–87.5) \\ \hline \textgreater{}5.74 & 79.4 (63.2–89.6) & 80.9 (59.9–92.3) & 76.9 (49.7–91.8) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Diagnostic performance and 95% CI of the model as a function of the OCT sequence length.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gender** & **Accuracy [\%]** & **Sensitivity [\%]** & **Specificity [\%]** \\ \hline Male & 78.8 (67.7–85.1) & 73.8 (58.9–84.6) & 83.7 (67.3–90.2) \\ \hline Female & 70.5 (46.8–86.7) & 66.6 (29.9–90.3) & 72.7 (43.4–90.2) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Diagnostic performance and 95% CI of the model as a function of patient sex.
# Solar Explosive Evaporation Growth of ZnO Nanostructures
###### Abstract  
For the first time, we present a novel method of explosive evaporation (MEE) for the deposition of ZnO nanostructures using concentrated solar radiation for precursor evaporation. Zinc acetylacetonate powder and a mixture of ZnO with graphite powders are used as precursors for the deposition of ZnO nanostructures. ZnO nanostructures are deposited on Au/Si, Ag/Si, and unpolished Si substrates by MEE. The scanning electron microscopy, energy dispersive X-ray analysis, X-ray diffraction, Raman scattering, photoluminescence, and Fourier transformed infrared spectroscopy are used for sample characterization. We demonstrate that the changing of precursors and the substrate types allows ZnO nanostructures to be grown with diverse morphologies: hexagons, spheres, and needles. The properties of ZnO nanostructures deposited on unpolished, coated by Ag and Au silicon substrates are discussed. MME using concentrated solar radiation is promising method for applications in the semiconductor industry as an economically efficient environmentally-friendly method for the growth of nanostructures.  
ZnO nanostructures; growth method; morphology; Raman scattering; photoluminescence 2017 2017 383 383 383 383 383 384 385 385 386 387 388 389 380 389 380 389 380 381 381 382 383 384 385 386 387 388 389 380 381 382 3  
to problems with the uniformity and stoichiometric ratio of ZnO nanostructures [5; 6; 7; 8]. Therefore, the development of growth methods with high-speed precursor evaporation is interesting for the deposition of uniformly high-quality ZnO nanostructures. On the other hand, the depletion of fossil fuels has led to a great interest in renewable energy resources. Taking all this into consideration, we propose a method of ZnO nanostructures growth that makes use of a solar concentrator. In a solar concentrator, the energy of the Sun is focused on the place where precursor boat is arranged. So, the use of concentrated solar radiation allows the implementation of the condition for high-speed evaporation of the precursor. Firstly, we called the proposed method of deposition the method of explosive evaporation (MEE) because a precursor evaporated very quickly (resembling an explosion) due to the application of high power concentrated solar radiation. Furthermore, compared to traditional methods, the proposed MEE has the advantage of additional activation of ad-atoms by the ultraviolet (UV) part of solar radiation that is important for the deposition of high crystal perfection ZnO nanostructures. In PLD method for ZnO growth, the effect of ad-atoms activation is realized by UV eximer laser.  
It is shown in [3; 6] that the application of substrates covered by catalysts (Au or Ag) influences the morphologies of ZnO nanostructures. Moreover, the application of the unpolished substrates may be interesting for the deposition of nanostructures because the surface has its own nucleation centers. Hence, we report the study of the properties for ZnO nanostructures deposited at high-speed growth conditions the by method of explosive evaporation on unpolished Si, Au/Si, and Ag/Si substrates.
## 3 Methods
### Results Evaluation Strategy  
#### 3.4.1 Evaluation of the Data Fusion Result  
The accuracy assessment of the synthetic images was undertaken using a set of images independent from those used as ESTARFM inputs, and the five statistical criteria shown in Table 4 were chosen as the evaluation indicators. Since there are many negative values in the images of NDVI and LSWI, only the \(R^{2}\), root mean square errors (RMSE), average absolute deviation (AAD) and average deviation (AD) were selected to assess the prediction accuracy of the two indices.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicators** & **Formula** \\ \hline Determination coefficient (\(R^{2}\)) & \(1-\sum\left(Y-Y_{f}\right)^{2}/\sum\left(Y-Y_{m}\right)^{2}\) \\ Root mean square errors (RMSE) & \(\sqrt{\sum\left(Y-Y_{p}\right)^{2}/n}\) \\ Relative RMSE (RRMSE) & \(\frac{1}{n}\times\sqrt{\sum\left(Y-Y_{p}\right)^{2}/n}\) \\ Average absolute deviation (AAD) & \(\frac{1}{n}\times\left(Y-Y_{p}\right)/n\) \\ Average deviation (AD) & \(\sum\left(Y-Y_{p}\right)/n\) \\ \hline \hline \end{tabular} Note: \(Y\) represents the pixel values of the referenced images, \(Y_{m}\) represents the mean values of the referenced images, \(Y_{p}\) represents the values of the predicted images, and \(Y_{f}\) represents the values of the linear fitting.  
\end{table}
Table 4: Statistical indices for the assessment of the quality of the synthetic images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Value** & **Unit** \\ \hline \(\varepsilon^{*}\) & the maximum light use efficiency & 2.54 & g-MJ\({}^{-1}\) PAR \\ R & proportion of aboveground productivity & 0.90 & dimensionless \\ HI & the harvest index & 0.45 & dimensionless \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters used to estimate wheat biomass and yield.
# A Review on Bioactive Compounds and Pharmacological Activities of _Citrus unshiu_
## 4 Pharmacological Activities of Satsuma Mandarin
### Nasal Airway-Protective Activity  
The activity of _C. unshiu_ and its extracts to protect the nasal airways has been demonstrated in a few experiments. The objective of an experiment was to investigate the anti-allergic activity of _C. unshiu_ methanolic extracts in basophils of patients with allergic rhinitis and rat basophilic leukemia RBL-2H3 cells. Basophils from leukocyte fractions arranged by dextran sedimentation were suspended in 100 \(\upmu\)L of Tyrode buffer solution. Cedar pollen allergen extract (0.1 \(\upmu\)g/mL) was then added to basophils, and the effects of adding _C. unshiu_ extracts (1.6 mg/mL) or wortmannin (25 \(\upmu\)M) were examined. RBL-2H3 cells were washed with modified Tyrode buffer, wortmannin (25 \(\upmu\)M), or C. unshiu extracts (1.6, 8, or 16 mg/mL). Citrus hesperetin and nobiletin reduced histamine release from basophils in patients and \(\upbeta\)-hexosaminidase release from RBL-2H3 cells through the inhibition of Akt-1 phosphorylation and the PI3K signaling pathway [101]. In an experiment using an animal model, rats were orally administered unripe mandarin extracts (hesperidin, narirutin) using subcritical water, leading to a significant improvement in the peripheral blood flow after allergic sensitization with hen egg white lysozyme. Oral administration of extracts also inhibited nasal airway resistance in ovalbumin-challenged guinea pigs [102].
## 3 Results
### Data Characteristics  
A collimator, blackbody, and circular aperture target are combined to form a simulated infinite-distance target source system, as show in Figure 5. The radiative flux of the target source is controlled by adjusting the blackbody temperature, while the projected cross-sectional size of the target source is simulated by replacing circular aperture targets of different sizes.  
A rotating reflection device reflects the radiation from the target source at a fixed angle and, through a rotating arm moving within the vertical plane, generates an optically moving target. The optical axis of the infrared compound eye camera is aligned with the center of the target's rotational axis to ensure that the optical motion target appears to move with a certain line-of-sight angular velocity and angular acceleration within the compound eye camera's field of view. By setting the angular velocity of the rotating optical target, the equivalent angular velocity of the target relative to the compound eye camera can be precisely adjusted.  
Figure 6a-c show different small target datasets, where the targets simulate objects at an infinite distance, and the backgrounds represent the laboratory environment.  
To analyze the performance of our small target detection method across various scenarios, we captured image sequences using an infrared bio-inspired compound eye  
Figure 5: Schematic diagram of the small target data acquisition setup. Components 1–5 correspond to the (1) reflective mirror, (2) rotating reflection mechanism, (3) collimator, (4) circular aperture target, and (5) blackbody source.  
Figure 6: Experimental infrared images. Panels (**a–c**) present small targets with varying intensity responses, where the backgrounds represent the laboratory environment. Panels 1–5 display different background scenarios, including (**1**) buildings and distant background, (**2**) distant background and sky, (**3**) distant building clusters with roads, (**4**) forested areas, and (**5**) sky scenes with clouds.  
camera. By moving the camera at non-uniform speeds, we captured several common ground-based infrared scenarios as data backgrounds. Panels (1)-(5) in Figure 6 depict scenes of buildings at different distances and densities, forests, and sky with clouds, respectively.  
We employed the method mentioned in IPI to add targets:  
\[f_{D}(x,y)=\begin{cases}\max(tf_{T}(x,y),f_{B}(x,y))\ x,y\in T\\ \qquad\qquad f_{B}(x,y)\ otherwise\end{cases} \tag{49}\]  
Here, \(f_{T}(x,y)\) and \(f_{B}(x,y)\) represent the normalized target image and background image, respectively, where \(t\in[\text{h},255]\) and \(t\) is the maximum grayscale value of the background scene containing the target ommatidium.  
The data characteristics are listed in Table 1.
## 3 Results
### Accuracy Analysis of Real-Time PPP-Estimated ZTD
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Bias (mm)**} & \multicolumn{2}{c}{**STDEV**} & \multicolumn{1}{c}{**RMS**} & \multirow{2}{*}{**Bias (mm)**} & \multicolumn{1}{c}{**STDEV**} & \multicolumn{1}{c}{**RMS**} \\  & & **(mm)** & **(mm)** & & **(mm)** & **(mm)** & **(mm)** \\ \cline{2-6}  & \multicolumn{4}{c}{**During Rainfall**} & \multicolumn{4}{c}{**During Nonrainfall**} \\ \hline HKCL & 12.02 & 18.24 & 15.47 & 8.84 & 14.57 & 11.42 \\ HKFN & 13.23 & 17.75 & 16.85 & 9.06 & 13.44 & 12.25 \\ HKKS & 12.24 & 20.65 & 15.86 & 9.32 & 14.35 & 12.07 \\ HKKT & 12.07 & 17.66 & 15.21 & 8.44 & 12.23 & 11.35 \\ HKLM & 10.74 & 18.64 & 14.15 & 9.08 & 15.91 & 11.36 \\ HKLT & 12.95 & 17.37 & 16.66 & 8.54 & 11.58 & 10.83 \\ HKMW & 15.66 & 18.65 & 19.14 & 14.29 & 11.05 & 17.24 \\ HKNP & 10.11 & 13.96 & 13.08 & 9.14 & 11.27 & 11.75 \\ HKOH & 15.25 & 19.37 & 18.67 & 9.26 & 11.16 & 11.56 \\ HKPC & 11.53 & 16.74 & 14.74 & 7.84 & 9.94 & 9.91 \\ HKQT & 14.17 & 22.36 & 17.95 & 8.36 & 12.83 & 10.82 \\ HKSC & 11.88 & 18.19 & 14.66 & 8.15 & 11.54 & 10.05 \\ HKSS & 13.02 & 18.74 & 16.74 & 8.55 & 13.01 & 11.35 \\ HKST & 12.64 & 16.55 & 15.55 & 8.64 & 10.83 & 10.92 \\ HKTK & 13.76 & 22.06 & 17.46 & 10.95 & 15.45 & 14.66 \\ T430 & 13.12 & 17.83 & 16.97 & 9.06 & 12.81 & 12.24 \\ \hline \end{tabular}
\end{table} 
Table 6: Bias, STDEV, and RMS between PPP-ZTD and NWP-ZTD. Figure 4: (**a**) Comparison of PPP-ZTD and IGS-ZTD at HKSL station during nonrainfall periods; (**b**) Comparison of PPP-ZTD and IGS-ZTD at HKSL station during rainfall; (**c**) Comparison of PPP-ZTD and IGS-ZTD at HKWS station during nonrainfall periods; (**d**) Comparison of PPP-ZTD and IGS-ZTD at HKWS station during rainfall.
## 4 Discussion
### Limitations  
There were some limitations to this study. Firstly, the employment of only a single acceleration sensor posed a challenge in adequately capturing the localized construction signals from a large-scale HBM. Furthermore, the sensor could not detect the states of other construction equipment such as tower cranes, placing booms, and elevators. These factors potentially limit the scope and accuracy of the data collected.  
Secondly, the study was limited to the identification of four types of work states using vibration signal data. This approach limits the granularity of state identification. For instance, the construction state could be further subdivided into processes such as tying rebar, and closing and opening formwork. However, these intricate processes cannot be identified solely through vibration signals. Employing different types of sensors to capture varied signal characteristics may be necessary for further refinement.  
Thirdly, the study used the kNN classifier, which is only a practical application case based on HBM working characteristics. Although satisfactory accuracy was achieved, alternative classifiers or deep learning algorithms may provide even higher accuracy rates.  
Lastly, the study only explored engineering applications for one type of HBM equipped with a hydraulic climbing system. It did not include HBMs with different power systems. For HBMs with varying power systems, the vibration signals during climbing might exhibit unique characteristics. Thus, the applicability of the proposed method requires further exploration in relation to these different systems.
## 2 Methodology  
The objective of this study is to develop a physically informed neural network model capable of accurately predicting cement bond quality at both the casing-cement sheath and cement sheath-formation interfaces. To achieve this, a multi-stage workflow was designed, as illustrated in Figure 1.  
Figure 1: Workflow of this study.  
First, raw field data were subjected to initial screening, followed by interpolation and preprocessing to improve data completeness and reliability. To ensure the relevance of input features, correlation analysis was performed using [PERSON]'s Rank Correlation Coefficient (for continuous variables) and [PERSON]'s \(V\) (for categorical variables). Variables with low correlation to cement bond quality were excluded to avoid noise and overfitting. The cleaned and filtered dataset was then split into training, validation, and testing subsets, ensuring no overlap to maintain model generalization.  
During the construction of the neural network model, the key influencing factors were categorized into cementing operation data and geological engineering data. According to relevant literature [19; 20; 21] and the experience of field engineers, cementing operational parameters (such as cement slurry density, viscosity, preflush density, and slurry flow rate) largely determine the cement bond quality, while formation and engineering characteristics (such as well depth, well inclination, wellbore temperature, and annular clearance) reflect the complexity of the cementing environment and indirectly affect the bond quality by influencing operational and environmental conditions during the cementing process. Based on these interrelationships, this study developed a Wide and Deep neural network model, integrating shallow and deep features to improve prediction accuracy.  
Considering the significant impact of cement slurry rheology on bonding performance--and its temperature sensitivity--the study incorporated a power-law rheological model. Rheological parameters were calculated using data from five-speed rotational viscometers and corrected for temperature variations through nonlinear fitting. The corrected rheological values were then embedded into the neural network input structure as domain-specific features, enhancing the physical interpretability and generalization capability of the model.  
Finally, the model was trained and tested on over 30,000 records collected from eight wells in an oilfield located in western China, followed by field testing. Through multiple iterations of training and evaluation, a high-performance cement bond quality prediction model was established, providing practical support for prejob cementing risk control and parameter optimization.
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design
## 2 Reduced-Order Controller Design Problems
### Reduced-Order \(H^{\infty}\) Controllers  
Let us consider the following generalized plant:  
\[G:\ \begin{bmatrix}\hat{x}(t)\\ z(t)\\ y(t)\end{bmatrix}=\begin{bmatrix}A&B_{1}&B_{2}\\ C_{1}&D_{11}&D_{12}\\ C_{2}&D_{21}&0\end{bmatrix}\begin{bmatrix}x(t)\\ w(t)\\ u(t)\end{bmatrix},\quad t\geq 0, \tag{10}\]  
where we assume that \((A,B_{2})\) is stabilizable and \((C_{2},A)\) is detectable. Let \(T_{zw}\) denote the feedback connection (or the linear fractional transformation) [29] of \(G\) and a controller \(K\) such that \(u=Ky\). Then, the problem is to seek a controller \(K\) of order \(n_{c}\leq n\) such that the \(H^{\infty}\) norm of \(T_{zw}\) satisfies \(\|T_{zw}\|_{\infty}<\gamma\) with a given \(\gamma>0\). This problem is described as LMIs with a rank constraint [10,30].  
**Problem 2** (\(H^{\infty}\) controller).: _Find \(X_{1},X_{2}\in\mathcal{S}_{n}\) such that the rank constraint (6), the LMI (7), and the following LMIs:_  
\[\begin{bmatrix}B_{2}\\ D_{12}\\ 0\end{bmatrix}^{\perp}\begin{bmatrix}AX_{1}+X_{1}A^{\top}&X_{1}C_{1}^{\top}&B_{ 1}\\ C_{1}X_{1}&-\gamma I&D_{11}\\ B_{1}^{\top}&D_{11}^{\top}&-\gamma I\end{bmatrix}\begin{bmatrix}B_{2}\\ D_{12}\\ 0\end{bmatrix}^{\perp\top}\preceq-\epsilon I, \tag{11}\] \[\begin{bmatrix}C_{2}^{\top}\\ D_{21}^{\top}\\ 0\end{bmatrix}^{\perp}\begin{bmatrix}X_{2}A+A^{\top}X_{2}&X_{2}B_{1}&C_{1}^{\top }\\ B_{1}^{\top}X_{2}&-\gamma I&D_{11}^{\top}\\ C_{1}&D_{11}&-\gamma I\end{bmatrix}\begin{bmatrix}C_{2}^{\top}\\ D_{21}^{\top}\\ 0\end{bmatrix}^{\perp\top}\preceq-\epsilon I \tag{12}\]  
_hold for some \(\epsilon>0\)._
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 3 Evacuation Wayfinding Algorithms
### Off-Line Evacuation Wayfinding Algorithms
#### 3.1.6 Computer Agent-Based Algorithms  
Algorithms based on pure mathematical models have difficulty in fully representing and capturing the dynamics of an evacuation process. Hence, the agent-based algorithms, which normally represent a hazardous environment with a number of autonomous decision-making virtual agents, have drawn considerable attentions in recent years. One major advantage of the agent-based algorithms is the ability to evolve and learn, which can lead to unanticipated behaviours during simulations. This characteristic makes the agent-based algorithms a canonical approach to mimic the counterintuitive emergent phenomena [57]. For instance, the research in [58] utilised a multi-agent framework to simulate a metro system in the case of a tunnel fire; the passengers and metro personnel, the technological system, as well as the fire and smoke were simulated by separate agents and co-evolved in an interactive manner; an effective evacuation plan was designed by varying environmental factors, such as the number of passengers on the train, the time cost for the train driver to open the doors, etc.; with the aid of the multi-agent computer simulations, which could test different scenarios, the emergency personnel could quickly customise a rescue plan when a disaster occurred. The study in [60] presented a prototype multi-agent simulation system that could build a virtual environment with autonomous agents for safe egress analysis; the proposed system consisted of a geometric engine that represented the physical environment with AutoCAD, a population generator that could produce evacuee agents with diverse age, mobility, etc., a global database, which maintained all the state information of agents,an events' recorder that captured the behaviours of evacuee agents and a visualiser, which displayed the movement of evacuees, a crowd simulation engine that was assigned to each evacuee agent to manage the individual behaviour in terms of the perception-action approach [76]; each evacuee agent was modelled to make decisions based on three basic conventions: instinct, experience and bounded rationality [77]; some emergent behaviours such as competitive, queueing and herding were observed in the simulation.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 3 Results  
### Dryland Areas in China  
#### 3.1.1 Detection and Attribution of Vegetation Growth  
We identify the dry land of China through Arid Index. The areas are mainly located in the west and north of China (Figure 4). On the whole, most areas tend to be green over the past four decades (green area). However, desertification occurs in Xinjiang, Qinghai and Inner Mongolia (yellow area). We attribute the vegetation growth to mainly three driving factors (CO\({}_{2}\) fertilization effects, Land use, Climate change, Climate variability) and quantify the contributions of each one. We find that CO\({}_{2}\) fertilization has positive effects on all areas, especially in northeast China. This result is in line with previous studies that CO\({}_{2}\) fertilization effects are responsible for global green based on many observational evidences. For land use effects, they have negative effects mainly in Xinjiang, Qinghai and west Inner Mongolia. Overgrazing and urbanization account for most of the negative effects [34]. CC has positive effects chiefly in the west and negative effects in the northeast. The current internationally scientific point now is \"dry areas get drier, wet areas get wetter\". However, [PERSON] et al. (2002) found that the climate of arid regions in Northwest China are undergoing the shift from warm-dry to warm-wet, which shows that Chinese drylands are not in line with that hypothesis [35].  
Figure 3: Flowchart of the methods.