esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, tower-based measurements were used to validate and improve the MODIS-based GPP products in an alpine swamp on the central Tibetan Plateau. Our results indicated that the MOD17 GPP strongly underestimated GPP_EC with a daily mean less than 40% of EC measurements. The large inaccuracies of MODIS GPP were mainly caused by the underestimation of \(\epsilon_{\text{max}}\) and followed by that of FPAR. The interaction of FPAR:\(\epsilon_{\text{max}}\) also affected the GPP estimation. We suggested that the suitable \(\epsilon_{\text{max}}\) for this alpine swamp was about 1.61 g C MJ\({}^{-1}\) which was considerably larger than the default 0.68 g C MJ\({}^{-1}\) for grassland. The FPAR\({}_{\text{M}}\) underestimated 22.2% of the FPAR\({}_{\text{G}}\) during the whole study period. However, the DAO meteorology data in the MOD17 algorithm did not exert a significant affection in the MODIS GPP underestimations. Therefore, site-specific optimized parameters inputs, especially \(\epsilon_{\text{max}}\) and FPAR\({}_{\text{G}}\), are necessary to improve the performance of MOD17 algorithm in GPP estimation, in which the calibrated MOD17A2 algorithm (GPP_MODR3) could explain 91.6% of GPP_EC variance for the alpine swamp meadow. Our results not only improved the accuracy of the site-specific MODIS GPP estimation greatly, but provide a simple approach to quantify GPP more accurately in other similar alpine swamp meadow on the Tibetan Plateau whose field observation was still scanty despite of their extensive lands area and significant position in the winter rangeland for the local livestock.  
We thank the editors and reviewers for their insightful and valuable comments. This work was supported by the National Natural Science Foundation of China (No. 41571042. Name: Quantitative identification on the influence of climate change and human activities in the alpine meadow ecosystem on the Northern Tibet) and the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDB03030401. Name: Effect of climate warming on alpine shrub and meadow ecosystem on the Tibetan plateau).  
[PERSON], [PERSON] and [PERSON] conceived and designed the study. [PERSON], [PERSON] and [PERSON] collected and analyzed the data; [PERSON], [PERSON], [PERSON] and [PERSON] contributed observation apparatus; [PERSON] wrote the paper and all other co-authors provided valuable suggestions for the revision.  
The authors declare no conflict of interest.
# First-Principles Study for Gas Sensing of Defective SnSe\({}_{2}\) Monolayers
## 4 Discussion
### Gas Molecules on the Se-Vacancy SnSe\({}_{2}\) Monolayer  
When NO\({}_{2}\) adsorbed on the Se-vacancy SnSe\({}_{2}\) monolayer, the values of \(\rm E_{ad}=-1.84\) eV and \(\rm\Delta Q_{b}=-0.926\)e were the maximum among that of the SnSe\({}_{2}\) monolayers. This reveals strong interaction between NO\({}_{2}\) molecule and the Se-vacancy SnSe\({}_{2}\) monolayer. For the Se-vacancy SnSe\({}_{2}\) monolayer, NO\({}_{2}\) adsorption induces wide-ranging trap states crossover \(\rm E_{F}\) to decrease the carrier mobility as represented by the pattern of DOS in Figure 4b.  
However, the NH\({}_{3}\) molecule is adsorbed on the Se-vacancy SnSe\({}_{2}\) monolayer with higher \(\rm E_{ad}=-0.82\) eV and lower \(\rm\Delta Q_{b}=0.016\)e compared to the pristine SnSe\({}_{2}\) monolayer (\(\rm E_{ad}=-0.18\) eV, \(\rm\Delta Q_{b}=0.028\)e [8]). NH\({}_{3}\) adsorption on the Se-vacancy SnSe\({}_{2}\) monolayer contributes DOS below \(\rm E_{F}\), as shown in Figure 6b. However, the charge transfer amount of NH\({}_{3}\) on the Se-vacancy SnSe\({}_{2}\) monolayer is smaller than that of NH\({}_{3}\) on the pristine SnSe\({}_{2}\) monolayer. Therefore, we cannot be sure of the conductivity difference of the Se-vacancy SnSe\({}_{2}\) monolayer with/without NH\({}_{3}\) adsorption.  
The Se-vacancy SnSe\({}_{2}\) monolayer shows excellent sensitivity for NO\({}_{2}\) molecules, whereas the Se-vacancy SnSe\({}_{2}\) monolayer cannot show obvious conductivity difference for NH\({}_{3}\) adsorption. In brief, the Se-vacancy SnSe\({}_{2}\) monolayer shows enhancement only for NO\({}_{2}\).
## 3 Results and Discussion
The band gap of the BF-5 photocatalyst is 1.9797 eV, which allows it to absorb visible light in the solar spectrum region and makes it suitable for solar photocatalysis applications. It should be noted that the synthesis conditions applied in this study do not affect the catalyst band gap in the BiOI/magnetite system since the values of \(Eg\) are similar in the materials obtained. The composition and chemical bonding of the synthesized structures were studied using FT-IR. As shown in Figure 7, the FT-IR spectra of pure BiOI show specific IR bands assigned to the BiOI structure between 510 cm\({}^{-1}\) and 1300 cm\({}^{-1}\). This is how the characteristic peaks of the tetragonal crystalline bonds of BiOI are observed at 510 cm\({}^{-1}\) and at 766 cm\({}^{-1}\), which demonstrate the asymmetric stretching vibration of the Bi-O bond [59]. Similarly, the bands observed around 3700 cm\({}^{-1}\) at 1600 cm\({}^{-1}\) belong to the stretching and bending vibrations of the hydrogen-bonding -OH group, which are also visualized in the spectrum of BF-5 [60]. The IR of Fe\({}_{5}\)O\({}_{4}\) shows two typical bands, one at 430 cm\({}^{-1}\) and another one at 563 cm\({}^{-1}\), which are attributed to the stretching vibration of the Fe-O bond in octahedral sites (Fe\({}^{2+}\)-O) and octahedral and tetrahedral sites (Fe\({}^{3+}\)-O) [61], respectively. These bands were shifted to higher wavenumbers, corresponding to 476 cm\({}^{-1}\) and 572 cm\({}^{-1}\), after the magnetite was coated with BiOI [62]. The vibration at 1395 cm\({}^{-1}\) corresponds to hydroxyl groups, which is also observed in the spectrum of BF-5 [63]. The infrared spectrum of BF-5 compared to the IR of the pure BiOI shows a difference in the region of 450 cm\({}^{-1}\) to 580 cm\({}^{-1}\), indicating the stretching vibration of the bonding bands due to the interaction that occurs by the adhesion of BiOI on the Fe\({}_{5}\)O\({}_{4}\) surface [22]. In addition,  
Figure 6: Tauc Plot of the synthesized nanomaterials. specific absorbance bands assigned between 870 cm\({}^{-1}\) and 1290 cm\({}^{-1}\) are observed in both IR spectra (curves a and c), which demonstrate the asymmetric stretching vibration of the Bi-O [60].
## 2 Methodology
### Process Description
#### 2.1.2 Learning Process  
* _Convolutional Neural Network (CNN) architecture definition_: The architecture of the CNN is defined based on experimentation and trials. The number, type and order of layers is the important aspect that can lead to effective network architecture.
* _Functions definition_: Various functions that are used throughout the network are being defined through bibliographic research and trials. We carefully choose the activation function, which is the function that defines the output of the layer, and the loss function, which is the function that is used for the optimization of the network's weights.
* _Training_: This is the process where the algorithm tries to create a function that describes the desired relation, based on the training data. It then makes predictions based on this function and moves to the next step.
* _Validation_: Following the previous step, the function that the algorithm created is being evaluated against the validation dataset. The error in the predictions is being calculated, and the algorithm tries to find a way to minimize this error. This part defines the \"learning\" of the process.
* _Finalizing the model_: The training-validation process repeats itself until the algorithm cannot improve itself anymore. When the training loss and the validation loss have become almost the same, lowest-possible value, and before the validation loss starts to increase, we stop the procedure and finalize our model.
## 2 Theoretical Formulation
### Effective Material Properties  
The material properties are here defined according to a modified Halpin-Tsai model, such that the effective Young's modulus of the GPL/polymer composite \(\overline{E}\) reads as follows [34]:  
\[\overline{E}=\frac{3}{8}\frac{(1+\xi_{L}\eta_{L}V_{GPL})}{(1-\eta_{L}V_{GPL})}E_{ M}+\frac{5}{8}\frac{(1+\xi_{W}\eta_{W}V_{GPL})}{(1-\eta_{W}V_{GPL})}E_{M} \tag{1}\]  
where  
\[\xi_{L}=2\frac{L_{GPL}}{t_{GPL}},\xi_{W}=2\frac{W_{GPL}}{t_{GPL}},\eta_{W}=- \frac{1-\left(\frac{E_{GPL}}{E_{M}}\right)}{\xi_{W}+\left(\frac{E_{GPL}}{E_{M}} \right)},\eta_{L}=\frac{\left(\frac{E_{GPL}}{E_{M}}\right)-1}{\xi_{L}+\left( \frac{E_{GPL}}{E_{M}}\right)} \tag{2}\]  
with \(E_{M}\) and \(E_{GPL}\) are the Young's moduli of the polymer matrix and GPLs, respectively; \(V_{GPL}\) is the GPL volume fraction, \(\xi_{L}\) and \(\xi_{W}\) are the parameters characterizing both the geometry and size of GPL nanofillers; \(L_{GPL}\), \(W_{GPL}\) and \(t_{GPL}\) are the average length, width, and thickness of GPLs, respectively.  
In line with findings by [PERSON] et al. [35], the effective [PERSON]'s modulus of GPL/polymer nanocomposites is well-approximated by the modified Halpin-Tsai model. The result determined by Equation (1), indeed, is only 2.7% higher than the experimental predictions. Based on the same rule of mixtures, the effective Poisson's ratio and mass density read as follows:  
\[\overline{\rho}=\rho_{GPL}V_{GPL}+\rho_{M}(1-V_{GPL}),\quad\overline{v}=v_{GPL }V_{GPL}+v_{M}(1-V_{GPL}) \tag{3}\]  
while, the effective shear modulus is defined as:  
\[\overline{G}=\frac{\overline{E}}{2(1+\overline{v})} \tag{4}\]  
As also depicted in Figure 2, we select three different distribution patterns of GPLs along the thickness direction of the structure, whose analytical expressions take the following form [36]:  
Figure 1: Rectangular plate embedded on and elastic foundation.  
\[V_{GPL}(z_{j})=\left\{\begin{array}{ll}\left\{\begin{array}{ll}2\,V_{GPL}^{*} \big{(}1-2\frac{Z}{h}\big{)}&0\leq Z\leq\frac{h}{2}\\ 2\,V_{GPL}^{*}\big{(}1-2\frac{(h-Z)}{h}\big{)}&\frac{h}{2}\leq Z\leq h\\ 2\,V_{GPL}^{*}\big{(}1-2\frac{(\frac{h}{2}-Z)}{h}\big{)}&0\leq Z\leq\frac{h}{2} \\ 2\,V_{GPL}^{*}\big{(}1+2\frac{(\frac{h}{2}-Z)}{h}\big{)}&\frac{h}{2}\leq Z\leq h \\ \end{array}\right.\begin{array}{ll}GPL-X\\ 2\,V_{GPL}^{*}\big{(}1-2\frac{(\frac{h}{2}-Z)}{h}\big{)}&0\leq Z\leq\frac{h}{2 }\\ 2\,V_{GPL}^{*}\big{(}1+2\frac{(\frac{h}{2}-Z)}{h}\big{)}&\frac{h}{2}\leq Z\leq h \\ \end{array}\right.\begin{array}{ll}GPL-O\\ \big{(}V_{GPL}^{*}\big{(}0\leq Z\leq\frac{h}{2}\\ V_{GPL}^{*}\big{(}\frac{h}{2}\leq Z\leq h\\ \end{array}\right.\begin{array}{ll}UD\\ \end{array} \tag{5}\]  
being \(V_{GPL}^{*}=\frac{\Lambda_{GPL}}{\big{(}\frac{G_{GPL}}{PM}\big{)}(1-\Lambda_{ GPL})+\Lambda_{GPL}}\) and \(z_{j}=\big{(}\frac{1}{2}+\frac{1}{2n}-\frac{j}{N_{L}}\big{)}h\), \(j=1,2,3,\ldots,N_{L}\).
# Dynamic Response of Dual-Disk Rotor System with Uncertainties Based on Chebyshev Convex Method
## 2 Dynamic Equation of Motion of the Rotor System
### Mathematical Model of Flexible Rotor System  
A rotor system structure diagram is shown in Figure 2. It consists of a shaft, two elastic bearings, and several disks. According to the FEM, the high-speed flexible rotor system is divided along the axial direction into \(n-1\) shaft elements, \(k\) disks, and two bearings.  
Figure 1: Elastic shaft element.  
The rotor system with \(n\) nodes connected by \(n-1\) finite elements. The displacement vector of the system is  
\[\left\{U_{1}\right\}=\left[x_{1},-\theta_{x1},x_{2},-\theta_{x2}, \cdots,x_{N},-\theta_{xN}\right]^{\mathrm{T}}\] \[\left\{U_{2}\right\}=\left[y_{1},\theta_{y1},y_{2},\theta_{y2}, \cdots,y_{N},\theta_{yN}\right]^{\mathrm{T}} \tag{4}\]  
Establish the differential equation motion of rotor system by Equations (1) and (3), and shown as follows:  
\[\left[M_{1}\right]\left\{\ddot{U}_{1}\right\}+\left(\left[C_{1}\right]+\Omega \left[I_{1}\right]\right)\left\{U_{2}\right\}+\left[K_{1}\right]\left\{U_{1} \right\}=\left\{F_{1}\right\}\] \[\left[M_{2}\right]\left\{\ddot{U}_{2}\right\}-\left(\left[C_{2} \right]+\Omega\left[J_{2}\right]\right)\left\{\dot{U}_{1}\right\}+\left[K_{2} \right]\left\{U_{2}\right\}=\left\{F_{2}\right\} \tag{5}\]  
When,  
\[\left\{U\right\}=\left\{\begin{array}{c}U_{1}\\ U_{2}\end{array}\right\} \tag{6}\]  
The differential equation of motion of the rotor system changes to  
\[\left[M\right]\left\{\ddot{U}\right\}+\left(\left[C\right]+\Omega\left[J\right] \right)\left\{\dot{U}\right\}+\left[K\right]\left\{U\right\}=\left\{F\right\} \tag{7}\]  
where, \(\left[M\right]\) is the global mass matrix, \(\left[C\right]\) is the damping matrix, \(\Omega\left[J\right]\) is the rotary matrix, and \(\left[K\right]\) is the rigidity matrix. They are symmetric banded sparse matrix with order of \(4N\times N\). \(\left[F\right]\) is the generalized external force matrix, and is the column matrix with order of \(4N\times 1\).
## 2 Methods and Models
### The Fuzzy Logic Controller
#### 2.3.2.2 Neural Network  
The load-following control strategy requires some knowledge of the load to follow in order to customize the set of rules to adopt within the fuzzy inference system (e.g., residential load, school load, etc.). This information is provided by an artificial neural network (ANN), whose task is to discriminate (runtime) the load type connected to the plant and switch to the most suitable set of rules. Hence, by using an ANN to classify the load connected to the plant, it is possible to employ the same load-following control strategy to different users, each one characterized by a custom set of fuzzy rules. The adopted neural network is of perceptron type and requires as input the day, hour, \(u(t)\) (defined in Section 2.3.2.1), its mean value \(\overline{u}(t)\), its variance \(\sigma_{u}^{2}(t)\), and the root mean square (RMS) value. The output of the neural network is a discrete variable that represents the load type. A graphical representation of the network is detailed in Figure 6.  
The network can be easily trained with time series obtained by using the simulator built upon the model in Section 2.2, considering different months and labeling each time series with the relevant load profile.
# Nitrite: From Application to Detection and Development
## 6 Intelligent Detection Methods  
With the development of IoT and AI technologies, intelligent detection methods have been gradually emerging in nitrite detection. The application of smart sensors and wireless transmission technology makes online monitoring and the real-time reporting of nitrite possible. In addition, data processing methods based on machine learning and deep learning have provided new ideas and solutions for nitrite detection, and there are some gas sensors that can be used as a method of detection. The designed handheld detector transmits the excitation signal indirectly via Bluetooth to transmit the response data to a smartphone [121]. Similarly, a new sensor for aquaculture was proposed with a wireless portable intelligent sensing system that was used to monitor water quality in real time and receive feedback from experts. Furthermore, machine learning algorithms were used to train the microcontroller-based system, determine the temperature of the water samples, and read the concentration of the substances. The performance of the sensors and sensing system is highly reproducible, reliable, and stable and is part of a smart sensing network for continuous water quality monitoring [122]. In addition, automated analyzers based on pulsed-flow analysis systems using solenoidal micropumps have emerged. They perform analysis cycles every six hours, making them suitable as versatile analytical platforms for environmental research [123]. In addition, a convenient portable gas manometer method was developed for the detection of nitrite (NO2-). NO2- and sweeteners reacted specifically under acidic conditions to produce cyclohexene and nitrogen, and the pressure of the reaction bottle was measured with a portable gas manometer. A gradual increase in pressure inside the bottle is proportional to the increase in NO2- concentration, resulting in the detection of NO2-[124]. In addition, compared to the traditional method, the stacked limit learning machine model based on information entropy weights will have a higher measurement accuracy than the traditional model, and the test will be faster than the traditional method [125]. Future directions of detection may include the following: (1) the application of new nanomaterials, e.g., gold nanoparticles and carbon nanotubes, which can improve the sensitivity and selectivity of detection methods; (2) the combination of multimodal detec
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 2 Related Work
### Encapsulation Key Recovery Systems  
A key recovery system is an important part of an encryption system. If a private key or session key used for a ciphertext is lost or corrupted, or a Law Enforcement Agency (LEA) wishes to intercept suspicious ciphertexts lawfully, it must be possible to recover the key. There have been several proposals related to such key recovery systems. [PERSON] et al. (2018) proposed a highly available distributed session key recovery system. It provides high availability and attack detection for secure session key management and group authentication while using Multi-Key Recovery Agents (M-KRA) to solve the single point of failure problem encountered in the traditional KRA approach. However, many problems remain, such as the risks of forgery, counterfeiting, and collusion attacks for user-generated KRFs, which can cause problems for the key recovery service.  
[PERSON] et al. (2018) proposed an encapsulation-based M-KRA key recovery system. They attempted to solve the problem that the M-KRA must communicate directly with one or more KRAs in existing M-KRA scheme, and the user must directly perform a complex key recovery process. Their scheme provides secure session key management and recovery using a new type of M-KRA to solve this problem. However, problems may arise in the key recovery service the forgery or modification of KRFs and non-repudiation problems related to user-generated KRFs.  
[PERSON] et al. (2018) proposed an identity-based key escrow scheme to prevent malicious key use by LEAs. If an LEA maliciously obtains the key, it can read the encrypted data to the desired user. In other words, an LEA can intercept and obtain the users' keys to read all encrypted data. To solve this problem, the scheme prevents LEAs from obtaining a key by themselves after generating a user's key pair with the KGC generated master key and the user's ID. However, since it is a single KRA, it is vulnerable to problems such as a single point of failure weakness and group authentication attacks, causing problems with the key recovery service.  
[PERSON] (2018) proposed the Security Key Recovery System with Channel Quality Awareness (SKRS-CQA) for smart grid applications. If a Smart Meter Unit (SMU) loses the keys used for correcting to the smart grid, it needs to be recovered. To solve this problem, key recovery proposed, providing improved reliability, system availability, and data confidentiality. In addition, system reliability was improved by using amplification and forwarding relay protocols and a cooperative communication network with optimal power allocation.
# Experimental and Numerical Study of Orifice Coefficient of Cargo Tank Design of LNG Vessels
## 4 Numerical Simulation
### Simulation Model Evaluation Comparing with Experimental Results  
The flow rate and the flow velocity distribution were calculated for each pressure condition and each crack specimen from the simulation analysis. For the results of this analysis, the material properties of the fluid applied for water are incompressible fluid, so density of water is 997 kg/m\({}^{3}\) and dynamic viscosity coefficient is \(8.899\times 10^{-4}\) kg/m-s [16]. The pressure distribution was calculated. To represent the CFD analysis results, plot the analysis results for Specimen No. 1 to confirm that the pressure drops rapidly as the fluid is released into the air while the pressure is distributed as shown in Figure 12. Additionally, the velocity distribution of fluids can be observed to see a significant increase in velocity at the area of leakage, as shown in Figure 13. Compare the results with the CFD results  
Figure 12: Pressure distribution for water flow of \(0.5\times 7\) mm\({}^{2}\) crack. (**a**) LC 1 (0.7 bar). (**b**) LC 2 (2 bar). (**c**) LC 3 (3 bar).  
Figure 14: Comparisons of leakage rate between experiment and CFD result. (**a**) LC 1 (0.7 bar). (**b**) LC 2 (2 bar). (**c**) LC 3 (3 bar).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen No.**} & \multirow{2}{*}{**Dimension [mm]**} & \multirow{2}{*}{**Load Case**} & \multicolumn{2}{c}{**Water Leakage Rate [kg/min]**} \\ \cline{3-5}  & & & **Experiment** & **CFD** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{\(0.5\times 7\)} & LC1 & 0.86 & \(1.15\) \\  & & LC2 & 1.62 & 1.94 \\  & & LC3 & 2.40 & 2.37 \\  & & LC1 & 3.51 & 3.50 \\
2 & \(1.0\times 7\) & LC2 & 5.87 & 5.93 \\  & & LC3 & 7.02 & 8.33 \\  & & LC1 & 7.58 & 8.08 \\
3 & \(1.0\times 14\) & LC2 & 12.49 & 13.70 \\  & & LC3 & 14.98 & 16.90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between experimental and CFD results with water.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing
### Signal Multi-Feature Extraction and Combination
#### 2.5.2 Lyapunov Exponent  
The Lyapunov exponent can measure the chaotic state of a system. A higher Lyapunov exponent suggests the system may exhibit chaotic behavior, indicating poorer local stability [41]. Since the ECG signal is stochastic, this paper employs the trajectory-based Wolf algorithm to solve for the Lyapunov exponent, with seven chosen as the embedding dimension and three as the time delay. The following formula defines the maximum Lyapunov exponent:  
\[\lambda_{max}=\frac{1}{t_{m}-t_{0}}\sum_{k=1}^{M}\ln\frac{L(t_{k})}{L(t_{k-1})} \tag{2}\]  
where \(L(t_{k})\) represents the distance between the two points closest to each other at the time \(t_{k}\); \(M\) is the total number of steps calculated.