esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, it does not address the topography or canopy-clumping effects that STEPS do [32]. TSEB, in comparison, is a simple dual-source energy balance model that estimates two-layer evaporation separately for soil and canopy from the satellite-based LST in which wind speed and canopy height are used to compute aerodynamic and surface resistances. It simulates turbulent fluxes in the loop of given boundary layer conditions and uses empirical and semi-empirical relationships to parameterize the effects of surface temperature, vegetation cover, and aerodynamic resistance on the energy fluxes. In TSEB, the choice of soil resistance formulation can affect the accuracy of ET estimation. Different studies proposed and evaluated different soil resistance formulations [33]. Apart from others, it has the flexibility of choosing different ET methods, i.e., the Priestley-Taylor equation, dual-time difference approach, or component soil and canopy temperatures approach; all these methods require different climatic inputs, such as the maximum and the minimum air temperature, solar radiation, relative humidity, wind speed, etc. All these models are unique in their ET determination methods, input data, output frequency, assumptions, and sources of uncertainty. This study evaluates the performances of these ET models (STEPS, SCOPE, and TSEB) across the Mediterranean region and compares them with those of existing global ET products (MOD16A2, GLEAM, and TerraClimate) and ground-based measurements, to identify their strengths, weaknesses, and potential for ET estimation in this complex environment. In the literature, mostly a comparison of annual and monthly estimates has been provided. This study provides a deeper insight into the modeled ET at a sub-regionalscale to see if the modeled ET is reliable for all regions. This is a concern because low accuracy can change the water budget estimate if ET is not modeled accurately, and if some environmental constraints are ignored especially in arid and humid climates. Thus, our research contributes to a better understanding of ET dynamics in the Mediterranean region to support informed water resource management.
## 6 Conclusions  
This study presents a novel approach that integrates a search and rescue team optimization algorithm into adaptive CMSR systems, extending its application to the field of valve-leakage signal enhancement. The cross-correlation gain is used as the adaptive optimization index, and the preprocessed signal is input into the system after optimization to obtain the system output. Experimental validation using check valve-leakage signals demonstrated the efficacy of the proposed method in achieving simultaneous noise reduction and signal enhancement. The results indicate significant improvement in fault frequency peak detection, with enhanced capability for leakage-signal characterization in noisy operational environments. This advancement establishes a robust foundation for subsequent leakage pattern recognition and quantitative diagnostic analyses in hydraulic valve systems, potentially contributing to more accurate and reliable condition monitoring in industrial applications.  
Through comprehensive analysis of simulation signal parameters, this study concluded that effective optimization of the CMSR system requires not only the adjustment of control system parameters but also the optimization of controlled system parameters. This dual-parameter optimization strategy was empirically validated through measured signal processing, demonstrating its critical role in enhancing system performance. The experimental results confirmed that the simultaneous optimization of both parameter sets significantly improves the system's processing capabilities.  
Figure 13: Signal-processing diagram of eccentric spool defect check valve when \(a=b=1\).  
This study successfully accomplished noise suppression and signal enhancement in the detection of one-way valve leakage. Considering the universal characteristics of hydraulic valve-leakage mechanisms, the proposed algorithm has the potential for generalization to internal leakage signal enhancement across different valve types. Future research directions could involve integrating this methodology with deep learning techniques to advance valve-leakage fault diagnosis capabilities.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, C.T. and X.X.; formal analysis, C.T.; investigation, X.X.; resources, X.X.; data curation, C.T.; writing--original draft preparation, Y.Z.; writing--review and editing, C.T. and X.X.; project administration, C.T.; funding acquisition, C.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hunan Province under Grant 2024J5209 and the Scientific Research Foundation of Hunan Provincial Education Department under Grant 22B0186. This work was supported by the China Scholarship Council (CSC) under grant no. 202308430207.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request. The data are not publicly available due to privacy restrictions.  
The authors declare no conflict of interest.
## 1 Introduction
In this new model, we used the upper partition of detected faces, namely the eyes/brow area, and the full face as input images for our dual classifier. Because our goal was to produce an online model with a fast response time to generate alerts in a timely manner, we avoided an extensive computation time typically needed for deep learning system by using the optimal one of four pretrained networks (VGG16, InceptionV3, ResNet50, ResNeXt50) after freezing all the convolutional blocks and using the resulting weight in our shallow dual CNN classifier (e.g., transfer learning). Using a camera, the proposed FEAPAS monitors a patient in bed; the backend code reads the video frame by frame and sends each frame to the classifier after detecting the patient's face and extracting the upper face area. An alarm is activated if the classifier outputs a positive pain score. The specific frame is stored with the associated pain level, date, and time to include in the report for the medical team. To achieve a fast and robust FEAPAS, the following two challenges must be overcome. First, the system's performance heavily depends on the classifier's performance; a classifier with high accuracy and a fast prediction process increases the system's reliability. To obtain an efficient classifier and overcome the limited face shapes in the system training dataset, transfer-learning is used on four CNN models (VGG16, InceptionV3, ResNet50, ResNeXt50) via freezing convolutional layers and replacing the prediction layer with a shallow CNN. This is then tested with four different optimizers (SGD, ADAM, RMSprop, and RAdam). The following two critical measurements were considered when developing the optimal concurrent shallow CNN with VGG16/InceptionV3/ResNnet50/ResNeXt50 with frozen layers, i.e., the accuracy of 10-fold cross-validation and the accuracy of test data for an unseen subject. The resulting shallow model was embedded in our FEAPAS to generate timely and accurate alerts. The second challenge is to speed up the system's response time. To do so, we selectively sampled the frames being tested. Instead of testing every single frame in the input sequence, we tested two frame-selection approaches. The first approach tests two segments from each end of a video sequence (boundary test), while the second tests one segment in the middle of the video sequence frames (middle test). The rest of the paper is organized as follows: Section 2 reviews transfer learning models and optimizers; Section 3 includes previous research on automatic pain assessment in chronological order; Section 4 describes the dataset and the proposed models; Section 5 presents the experimental results; and Section 6 offers concluding remarks.
## 5 A New Prediction Model for Vertical DRFs  
To understand the general features of and to provide the best estimate of the DRFs for the vertical component of ground motions, the mean DRFs are computed and compared for different damping ratios regardless of the seismic parameters (see Figure 12). According to Figure 12, the mean DRFa and DRFd values are close to each other at low damping ratios (\(\xi<10\%\)), but significant differences can be observed for damping ratios greater than 10% for all the period ranges. Differences evidently appear around periods of 1 s, 0.6 s, 0.2 s, 0.1 s, and 0.005 s at damping ratios of 10%, 15%, 20%, 30%, and 40%, respectively. Although some differences can be noted at around 3.0 s at low damping ratios of 1% and 3%, these differences are minor and can be neglected. From very short periods to 0.02 s, the DRFv values become narrow and approach unity, and the trend in the DRFv is different from those of the DRFa and DRFd in this respect. Compared to the DRFv and DRFd values, the DRFa values increase dramatically with an increasing period at a damping ratio greater than 10%. Moreover, the DRFd and DRFv are less sensitive to the period and damping ratio than the DRFa. Based on the above results, it can be said that different mathematical equations should be proposed to obtain the best predictions for the DRFa, DRFv, and DRFd values.  
In the next step, new period-dependent equations are developed to characterize the mean damping reduction factors for the selected earthquakes. The matching of the predicted and computed DRF values is used as a criterion, and the correlation between the predicted and computed DRFs is evaluated. In addition, coefficient of determination (R\({}^{2}\)), Sum of Squares for Error (SSE), and Root Mean Square Error (RMSE) values are used to determine the efficiency of the proposed equations. Nonlinear regression is used to analyze the complex relations between the dependent and independent variables [51; 52], used here to obtain the best fit. This operation is performed using the Levenberg-Marquardt algorithm, used as a standard technique to solve nonlinear least-squares problems. After several trials, Equations (2)-(4) are proposed to approximate the DRFs (DRFa, DRFd and DRFv). It is noted that the obtained equations present a high correlation with real DRF values.  
Figure 11: Effect of site conditions on DRFs; (**a–d**) DRFa, (**e–h**) DRFd, and (**i–l**) DRFv.  
Figure 12: Comparison between mean DRFs obtained from acceleration spectra (DRFa), displacement spectra (DRFd), and velocity spectra (DRFv) at different damping ratios; (**a**) \(\xi=1\%\), (**b**) \(\xi=3\%\), (**c**) \(\xi=10\%\), and (**d**) \(\xi=30\%\).
## 3 Results and Discussion
### Inner Walls
#### 3.1.1 Sleeper
To further investigate the displacement patterns of Tunnel 2 sleepers, Figure 7 compares the \(D_{\rm V}\) and \(V_{\rm V}\) variations in A\({}_{1}\)-A\({}_{5}\) conditions. The calculation formulas are provided in Equations (9) and (10). \[D_{V} =D_{Vmax}-D_{Vmin} \tag{9}\] \[V_{V} =V_{Vmax}-V_{Vmax} \tag{10}\]  
where \(D_{\rm Vmax}\) is the maximum value of vertical displacement; \(D_{\rm Vmin}\) is the minimum value of vertical displacement; \(V_{\rm Vmax}\) is the maximum value of vertical velocity; and \(V_{\rm Vmin}\) is the minimum value of vertical velocity. The \(D_{\rm V}\) variations at monitoring points of Tunnel 2 sleepers are shown in Figure 7a. The maximum and minimum \(D_{\rm V}\) variations are 2.36 mm and 1.12 mm, respectively. Under the NW condition, \(D_{\rm V}\) variations (2.27-2.36 mm) significantly exceed those of the other four conditions (1.12-1.43 mm), further confirming the decisive role of wall2 in reducing \(D_{\rm V}\). Comparing W12 and W234, the differences between S\({}_{1}\)-S\({}_{5}\) monitoring points are 0.02, 0.02, 0.03, and 0.03 mm, respectively. The slightly higher \(D_{\rm V}\) variations in W234 indicate that horizontal wall1 contributes more to \(D_{\rm V}\) reduction than vertical wall3 and wall4. The \(V_{\rm V}\) variations at monitoring points are shown in Figure 7b. The \(V_{\rm V}\) curves nearly overlap (15.40-17.85 mm/s) under A\({}_{1}\)-A\({}_{4}\) conditions, while NW exhibits significantly higher values (41.61-42.99 mm/s). Compared to T2, NW shows \(V_{\rm V}\) variation increases of 152.97% (S\({}_{1}\)), 155.48% (S\({}_{2}\)), 161.10% (S\({}_{3}\)), 166.75% (S\({}_{4}\)), and 170.13% (S\({}_{5}\)). This underscores wall2's dominant role in suppressing \(V_{\rm V}\), while wall1, wall3, and wall4 have negligible effects. Compared to Figure 7a,b, point S1 exhibits the largest \(D_{\rm V}\) and \(V_{\rm V}\) values, while point S5 shows the smallest under A1-A5 conditions. This indicates that sleeper regions closer to T1 experience greater vertical vibration impacts. The effectiveness of internal walls in reducing \(D_{\rm V}\) and \(V_{\rm V}\) for Tunnel 2 sleepers is ranked as wall2 \(>\) wall1 \(>\) wall3 \(=\) wall4, with wall2 playing a decisive role. As shown in Figures 6 and 7, adjacent tunnel sleepers oscillate vertically with train loads under metro train-induced vibrations. The \(D_{\rm V}\) and \(V_{\rm V}\) of Tunnel 2 sleepers under NW condition are significantly higher than other conditions, confirming that internal walls markedly reduce both static and dynamic responses. The stabilizing effect of Tunnel 2 internal walls follows the hierarchy: wall2 \(>\) wall1 \(>\) wall3 \(+\) wall4. Wall2 is critical for suppressing the static-dynamic characteristics of Tunnel 2 sleepers.
# CTIMS: Automated Defect Detection Framework Using Computed Tomography
## 4 Data Pre-Processing
### Limitations  
The main limitations of the pre-processing module are presented as follows:  
* Resizing the image or volume is required to meet the computational resources: memory and GPU speed. However, some small defects will become undetectable after scaling down the original volume;
* Registration will further affect the defects' detection by smoothing the image/volume. This will introduce fake surface defects' detection, of which we need to be aware.
## 2 Materials and Methods
### Identification and Quantification of Phenols by HPLC-DAD/ESI-MS  
The main phenolic compounds of the SLE and SLPF samples were accurately identified using High-Performance Liquid Chromatography coupled with Diode Array Detection and Electrospray Ionization Mass Spectrometry (HPLC-DAD/ESI-MS) in an HPLC Agilent 1100 Series equipped with an Agilent Technologies 6120 Quadrupole LC/MS (Agilent Technologies, Inc., Santa Clara, CA, USA). The samples were injected into an ACE Excel 3 SuperC18, \(150\times 3\) mm column (Avantor, Inc., Radnor, PA, USA) at a flow rate of 0.5 mL/min. The injection sample was 10 \(\mu\)L (5 mg/mL) and the analyses were performed in ESI negative ion acquisition mode and spectra were recorded in the 50-1000 \(m/z\) range. The interface conditions were as follows: fragmentation 300 V, drying gas flow 10 L/min, nebulizer  
Figure 1: A schematic overview of the complete extraction and purification process, from ground saffron leaves to the final saffron leaf phenolic fraction (SLPF).  
pressure 40 psig, drying gas temperature 340 \({}^{\circ}\)C, vaporizer temperature 150 \({}^{\circ}\)C, capillary voltage: 4000 V, charging voltage 2000 V. Mobile phase A (0.05% formic acid in water) and mobile phase B (acetonitrile) were used in gradient for the separation of polyphenols [11]: initial 95% A, 5% B; 10 min 85% A, 15% B; 30 min 75% A, 25% B; 35 min 70% A, 30% B; 50 min 45% A, 55% B; 55 min 10% A, 90% B; 57 min 0% A, 100% B; 58 min 95% A, 5% B; 75 min 95% A, 5% B. Phenolic compounds were tentatively identified by comparing their UV-Vis and mass spectral data, as well as chromatographic retention times, against those of commercial standards and reported compounds in the literature, with additional support from ChatGpt-assisted interpretation [12]. Likewise, phenolics quantification was performed using the calibration curves of commercial glycones in the range of 0.02-5 \(\upmu\)g and expressed as mg/g of dry extract: apigenin (y = 2,074,864.16x \(-\) 28,892.21; R\({}^{2}\) = 1.00), kaempfer (y = 1,781,684.79x + 1801.24; R\({}^{2}\) = 1.00), luteolin (y = 1,430,139.3483x \(-\) 10,870.64; R\({}^{2}\) = 1.00) and quercetin (y = 1,442,409.29x \(-\) 24,915.92; R\({}^{2}\) = 1.00).
## 4 Discussion
### Variability of CO\({}_{2}\) Fluxes from EC
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Country, Site** & **Genus** & **Period** & **NEE** & _GPP_ & **RECO** & **Reference** \\ \hline \multicolumn{8}{c}{**gC m\({}^{-2}\) year\({}^{-1}\)**} \\ \hline Denmark, Sore & _Fagus_ & 1996–2009 & \(-156\pm 103\) & \(1727\pm 136\) & \(1570\pm 97\) & [72] \\ France, Hesse & _Fagus_ & 1995–2005 & \(-386\pm 171\) & \(1397\pm 192\) & \(1011\pm 138\) & [6] \\ Germany, Hainich & _Fagus, Travrains_ & 2003–2012 & \(-483\pm 70\) & \(1498\pm 83\) & \(1015\pm 51\) & [73] \\ UK, Straits Inclosure & _Quercus_ & 1999–2010 & \(-486\pm 115\) & \(2034\pm 228\) & \(1548\pm 192\) & [40] \\ US, Duke Forest & _Quercus, Carya_ & 2001–2008 & \(-402\pm 96\) & \(1982\pm 300\) & \(1580\pm 237\) & [74] \\ US, Harvard Forest & _Acer, Quercus_ & 1992–2004 & \(-242\pm 100\) & \(1400\pm 164\) & \(1153\pm 105\) & [26] \\ Croatia, Jastrebarsko & _Quercus_ & 2008–2017 & \(-319\pm 94\) & \(1594\pm 109\) & \(1275\pm 94\) & this study \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Carbon fluxes in temperate deciduous broadleaved forests (average \(\pm\) standard deviation). The highest _NEE_ values that were recorded in 2009 and 2015 were due to favourable meteorological conditions with sufficient water and optimal light during the growing season. The relationship between _NEE_, \(\Delta\)_SWC_,_May-Sep_, and light is given in a 3D scatter plot (Figure A3). The poor correlation that was observed between _NEE_ and mean annual air temperature and precipitation (Figure 6B,C) is perhaps most noticeable if we look at the years 2008 and 2017. The year 2017 was, on average, 0.14 \({}^{\circ}\)C warmer with only 12 mm less precipitation compared to 2008. However, the difference in annual _NEE_ was 205 gC m\({}^{-2}\) year\({}^{-1}\). Although 2008 and 2017 did not differ much with respect to mean annual air temperature and precipitation, the difference in the pattern of precipitation is striking. There is almost a complete lack of precipitation during the spring and summer of 2017 followed by a sudden cooling and excess precipitation in September 2017 (Figure 3). Similarly to findings of [PERSON] et al. [81], our results also indicate that the intrayear weather patterns and short-term favourable weather conditions are more important than the annual means in control of interannual variability of carbon fluxes.
## 4 Simulation Results and Discussion
### Comparisons with the Existing Algorithms Based on the Shape of the Area of Interest
From the results, it can be observed that when the cell size is small and sufficient attention has not been paid to sensor/camera footprints size in area decomposition, the BCDH-CPP [53] and CA-CPP [54] algorithms (Figure 10b) need more sweeps compared to the proposed algorithm which can increase the path length in complex scenarios. In contrast, the proposed algorithm (Figure 10c) considers the sensor footprint as a coverage unit in the CPP and uses less sweeps to guarantee the perfect coverage.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 3 Simulation and Experimental Results
### Performance of the Thrust Distribution Agent  
The conditions are as follows: the pressure is set to 1. The divert control thrust commands (\(F_{ycmd}\), \(F_{zcmd}\)) undergo random step changes with an upper limit of 0.8, and the attitude control thrust commands (\(F_{pitchcmd}\), \(F_{yunccmd}\), \(F_{rollcmd}\)) undergo random step changes with an upper limit of 0.1. The results are shown in Figure 10.  
Figure 10 shows that the error for orbital control thrust (\(F_{y}\), \(F_{z}\)) ranges from 2% to 5%, while the error for attitude control thrust (\(F_{pitch}\), \(F_{yunc}\), \(F_{roll}\)) ranges from 5% to 10%. Pressure fluctuations due to thrust changes are within \(\pm\)2%. Excluding initial ignition and propellant depletion phases, the cumulative and step deviations between thrust command and actual thrust are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Divert Control**} & \multicolumn{2}{c}{**Attitude Control**} & \multirow{2}{*}{**Impact on Pressure**} \\  & & **Thrust** & & **Thrust** & \\ \hline Cumulative & & & & & \\ Deviation & & & & & \\ Step Average & & & & & \\ Deviation & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical results of thrust distribution performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**RL-PPO**} & \multicolumn{2}{c}{**PID**} \\ \hline \multirow{2}{*}{Item} & Cumulative & Average & Average & & Average \\  & Deviation & Deviation & Regulation & Deviation & Average & Regulation \\  & & & & & \\ Time, ms & & & & & \\ \hline
1 & 304.75 & 0.043 & 244 & 352.50 & 0.051 & 827 \\ \hline
2 & 300.12 & 0.042 & 235 & 356.8 & 0.052 & 839 \\ \hline
3 & 302.52 & 0.043 & 240 & 355.6 & 0.051 & 830 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical results of pressure control performance.  
Figure 9: Control performance by pressure control agent. (**a**) RL control; (**b**) PID control.
## 2 An Overview of the Cellular Networks
### General Features of Cellular Networks  
The key notion in cellular networks is that of the cell--that is, the smallest division of the area served by a radio base station (coverage). By means of proper transmitting/receiving units, each base station generates one or more radio cell that allows the exchange of information among devices and their users.
## 4 The Proposed MRMR-SATIDS System  
The main objective of any IDS system is to improve the accuracy of attack detection and discovering the attack type. This section provides a detailed description of the proposed SATIDS system which depends on both the MRMR FS technique and a LSTM network for the classification purpose. Then, SATIDS is applied to a WSN after its training and testing to study its performance when dealing with large number of mobile sensor nodes and its effect on the energy consumption.  
Figure 5 presents the inner structure of the proposed SATIDS system. Rectified linear unit (Relu layer) activation function performs a threshold operation to an input element concerning zero as some input data contain negative values. Relu layer passes the positive input and gives zero value to the negative values. Then, a number of LSTM layers are used; each layer is followed by a 0.2 probability dropout layer. The dropout layer probability value is chosen to be 0.2 using a trial and error method to give the highest overall performance metrics as presented in Figure 6.  
The dropout layer sets the input to avoid overfitting, which decreases the network performance parameters. The initial learning rate was set to 0.0001, and LSTM batch size of 2000 to provide the highest performance parameters. A fully connected layer flattens the output of the previous layers and converts it to a single vector. Finally, the Softmax layer eventually enters the classification layer classifying the data as benign or any form of attacks.