esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 3 Methodology
### The Biome-BGC Model  
Biome-BGC is a process-based model that can depict forest processes (photosynthesis, respiration, carbon, nitrogen, and hydrological cycle). It is developed from the FOREST-BGC model and relies on the Farquhar photosynthesis routine to calculate GPP for the illuminated and shaded foliage [42; 43]. The autotrophic respiration was separated into the maintenance respiration and growth respiration. Specifically, the former is calculated as a function of the nitrogen content and temperature of the tissues, and the latter is computed as a function of the amount of carbon allocated to the different pools [44]. The leaf onset and offset day is also described in the phenological module of Biome-BGC.  
Biome-BGC firstly operates through a spin-up run to find a quasi-equilibrium condition with the local environment. It requires three types of driving data: (1) daily meteorological data (maximum and minimum temperature, precipitation, VPD, solar radiation); (2) site information (latitude, topography, and soil texture); (3) ecophysiological parameters, including the ratio of carbon to nitrogen of leaf, fine root and coarse root, fraction of leaf N in Rubisco, and maximum stomatal conductance.  
The most recent version (ver. 4.2) of Biome-BGC was used in this study, including the complete biome types (e.g., evergreen needle leaf forest, evergreen broadleaf forest, shrub, grass). However, the settings for different types must be modified to adapt to the local environment.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## Appendix A Literature Review
### Score-Based Anomaly Detection Models  
Additional anomaly detection challenges are of general concern. Most of anomaly detection algorithms are score-based in the sense that these approaches return an anomaly score reflecting the extent to which the observation is considered abnormal by the model. In order to decide whether an observation is abnormal or not, a cutoff value of the scorehas to be selected. Empirical approaches are often used, consisting of setting the cutoff value as the quantile or elbow point of the distribution of the anomaly score. However, the selected cutoff value according to such methods remains arbitrary. Ref. [21] proposed to rely on the cost of misclassification based on a weighted classification accuracy. Approaches that calculate the \"optimal\" weights are described in [22] but they involve a heuristic grid search technique. Another alternative is to determine the cutoff value by cross validation of the training data [23]. Finally, some methods do not select any cutoff value but are based instead on a contamination rate. However, fixing a cutoff value or deciding on a contamination rate is not so different.
## 3 Experimental Procedures  
### Materials  
Inconel 718 superalloy is a high-strength, corrosion-resistant, nickel-chromium material used from \(-\)250 to +700 \({}^{\circ}\)C. It can be fabricated even for complex parts, combining good performance in terms of tensile, fatigue, creep, and rupture strength, which has resulted in its use in a wide range of applications, for example, gas turbine engines, liquid-fueled rockets, and cryogenic tankage [33]. Chemical composition of Inconel 718 analyzed by energy dispersive X-ray spectroscopy (EDS) is listed in Table 2. Density of this batch of raw material was 8180.1 kg/m\({}^{3}\).  
For most applications, Inconel 718 is used under a precipitation-hardened (aged) condition. This alloy is hardened by the precipitation of secondary phases into the metal matrix [33]. The specific process of direct aging treatment is as follows: solution at 720 \({}^{\circ}\)C for 8 h, then furnace cooling at a rate of \(-\)50 \({}^{\circ}\)C/h for 2 h to 620 \({}^{\circ}\)C and aging for another 8 h. The total treatment lasts 18 h, followed by air cooling.  
Optical microscope (OM) and scanning electron microscope (SEM) micrographs of Inconel 718 metallographic sample after aged heat treatment are shown in Figure 4. Metallographic sample was polished with SiC paper and diamond paste. It was then etched with Kalling's II reagent. Twin boundaries could be seen in the micrograph, whose length was less than 20 \(\upmu\)m (Figure 4b). Non-strengthened orthorhombic phase \(\delta\) was incoherent with \(\gamma\) matrix [34]. Lenticular-shaped \(\delta\) was distributed in the face-centered cubic (FCC) matrix. Lamellar-shaped \(\delta\) was nucleated in the grain boundary (Figure 4b,d). It was used to control the grain size in wrought materials and also is important for the notch ductility [35]. The diameter of the grains was smaller than 50 \(\upmu\)m and the average was 24.6 \(\upmu\)m, which was consistent with ASTM 8 [36]. Yield strength \(\sigma_{\mathbf{0.2}}\) of aged Inconel 718 was 1249.3 MPa and ultimate tensile strength \(\sigma_{\mathbf{UITS}}\) was 1456.7 MPa. Directly aged high-quality (DAHQ) was a special edition developed for advanced turbine discs, and its grain diameter was in the range of 4\(\sim\)10 \(\upmu\)m, which is consistent with ASTM 10-12. Specific geometry of Inconel 718 specimen is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Element** & **Ni** & **Cr** & **Fe** & **Nb** & **Mo** & **Ti** & **Al** \\ \hline wt.\% & 51.10 & 19.62 & 20.28 & 4.78 & 2.52 & 1.14 & 0.56 \\ \hline \end{tabular}
\end{table}
Table 2: Chemical composition of raw Inconel 718.  
Figure 4: Optical microscope (OM) and scanning electron microscope (SEM) micrographs of aged Inconel 718: (**a,b**) optical micrographs; (**c,d**) scanning electron micrographs.
## 2 Experimental Data and Evaluation Index
### Data Description  
The experimental datasets were provided by FCLAB Research Federation (FR CNRS 3539, Belfort, France, [[http://www.fclab.fr/](http://www.fclab.fr/)]([http://www.fclab.fr/](http://www.fclab.fr/)), accessed on 30 December 2021) [29]. The PEMFC stacks consists of 5 separated cells, each of which has an area of 100 cm\({}^{2}\), and each cell is composed of diffusion layers, machined flow distribution plates and commercial membranes. The maximum current density and the nominal current density are 1 A/cm\({}^{2}\) and 0.7 A/cm\({}^{2}\), respectively. Experiments were carried out on test facilities that enable normal or accelerated ageing of FCS (fuel cell system) stacks under dynamic current solicitations to emulate the effect of connecting a power converter at the output of the FC stack: a ripple current of 70 A with 7 A oscillations at a frequency of 5 kHz was imposed to the FC. Meanwhile, the characterizations were performed once per week (around every 160 h). A total of 127,372 sets were recorded from 0 to 1020 h.  
The test bench (Figure 1) was adapted for fuel cells with a power up to 1 kW (electrical power). More than 20 parameters in the  
* Gas flows, air and hydrogen hygrometry rates and stack temperature;
* Voltages of single and monolithic cells, inlet and outlet pressures (of air and hydrogen), inlet and outlet temperatures (of air, cooling water and hydrogen), inlet and outlet flows (of air, cooling water and hydrogen), current and current density, and inlet hygrometry (of air, estimated).  
The gas humidification was realized by two separate boilers placed on the stack. Air and hydrogen pass through their respective boilers before reaching the stack. However, only the air boilers were heated to obtain the required relative humidity. Besides, considering the operation of the dry anode gas, the hydrogen boiler was kept at room temperature. Meanwhile, the current was controlled by an electronic load and the temperature of the stack was guaranteed by a cooling water system.
###### Abstract  
Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European Space Agency (ESA) Sentinel 3 (S3) satellite, accurate LST retrieval methodologies are being developed by exploiting the synergy between the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). In this paper we explain the implementation in the Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM) and the use of one LST algorithm developed in the framework of the Synergistic Use of The Sentinel Missions For Estimating And Monitoring Land Surface Temperature (SEN4 LST) project. The LST algorithm is based on the split-window technique with an explicit dependence on the surface emissivity. Performance of the methodology is assessed by using MEdium Resolution Imaging Spectrometer/Advanced Along-Track Scanning Radiometer (MERIS/AATSR) pairs, instruments with similar characteristics than OLCI/ SLSTR, respectively. The LST retrievals were properly validated against _in situ_ data measured along one year (2011) in three test sites, and inter-compared to the standard AATSR level-2 product with satisfactory results. The algorithm is implemented in BEAM using as a basis the MERIS/AATSR Synergy Toolbox. Specific details about the processor validation can be found in the validation report of the SEN4 LST project.  
view and 740 km for the double view observation. One of the main objectives of the S3 mission is to provide Europe with continuity of the ENVISAT type measurement capability to determine sea, ice and land surface temperature. In this context, ESA funded the project \"Synergistic Use of The Sentinel Missions For Estimating And Monitoring Land Surface Temperature (SEN4 LST)\" [6; 7], which had the main objective to fully utilize the synergy between SLSTR and OLCI instruments to improve atmospheric correction (including cloud screening) and land surface emissivity (LSE) characterization for a better estimation of the land surface temperature (LST). As a result of the SEN4 LST project, a new land surface temperature processor plug-in has been implemented in the Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM). The synergistic algorithm in which is based is designed and implemented for improving cloud screening, global aerosol and atmospheric correction using the combined multi-spectral and multi-angle information from instrument pair measurements [8]. The implemented processor, the functionality of which is the main target of this paper, supports both MERIS/AATSR and OLCI/SLSTR (Sentinel 3) data pairs. Because there are still not available pairs of the OLCI/SLSTR, simulated data is used instead [9].
## 2 Analytical Model of the Drive and Transmission Mechanism
### Mechanical Efficiency of the Gearbox  
In mechanical gear transmissions, several types of losses are present. They are mainly caused by friction in terms of the contact of the gear teeth; however, if it is necessary to perform a deep analysis of the efficiency, other causes also, for example, the friction in the bearing of the gears and shafts or by the gear oil vortexes, have to be considered.  
Due to the fact that in the planetary gear train (PGT) and Babineaux planetary gear train (RPGT) the power is transmitted not only by the relative motion (with losses in the contact of the gear teeth) but also by the drifting motion (without losses in the contact of the gear teeth), it is not possible to use the relation for the efficiency calculation of a gearbox with fixed shaft axes.  
In the calculations, it is necessary to consider which part of the power is transferred by the relative and which part by the drift motion. Therefore, the procedure for the determination of efficiency is based on the general relation (1) for the mechanical transmission efficiency \(\eta_{g}\):  
\[\eta_{g}=\frac{P_{2}}{P_{1}}=\frac{T_{2}\omega_{2}}{T_{1}\omega_{1}}=\frac{i _{F}}{i_{k}} \tag{1}\]  
where \(P_{2}\) is the power on the driven shaft, \(P_{1}\) is the power on the driving shaft, and the same indexes are applied for the driven and driving torques \(T_{2}\) and \(T_{1}\) and angular velocities \(\omega_{2}\) and \(\omega_{1}\), respectively. In Equation (1), \(i_{k}\) is the kinematic transmission ratio, and \(i_{F}\) is the force transmission ratio.  
The kinematic ratio \(i_{k}\) (2) can be defined as a function of the PGT parameters \(\alpha\) that are in operation at a given gear:  
\[i_{k}=\frac{\omega_{1}}{\omega_{2}}=\frac{n_{1}}{n_{2}}=f(\alpha_{1},\alpha_{2 },\ldots,\alpha_{n}) \tag{2}\]  
The force transmission ratio \(i_{F}\) (3) is a function of the kinematic ratios (PGT parameters) and of the efficiencies of these PGT parameters \(\eta\), as it includes losses:  
\[i_{F}=\frac{T_{2}}{T_{1}}=f\big{(}\alpha_{1}\eta^{x1},\alpha_{2}\eta^{x2}, \ldots,\alpha_{n}\eta^{xn}\big{)} \tag{3}\]  
where \(\eta\) is the PGT efficiency (which is usually approximated to the value of 0.96), and \(x\) is the exponent with a value of \(\pm 1\). To determine the \(+\) or \(-\) sign of the exponent, the following relation (4) was derived:  
\[sgnx_{i}=sgn\frac{\alpha_{i}}{i_{ck}}\frac{\partial i_{ck}}{\partial\alpha_{i}} \tag{4}\]  
where \(i_{ck}\) is the kinematic gear ratio for a given gear.  
After determination and application of the force transmission ratio \(i_{F}\) (3) and the kinematic gear ratio \(i_{k}\) (4) values, the efficiency value for a given gear can be determined. The resulting values of the mechanical efficiencies of the ZF 6 HP260 gearbox are summarized in Table 1.  
Based on this table, it can be concluded that the ZF 6 HP260 gearbox achieves the best mechanical efficiency in the most frequently used years, which means from the 3 rd to 6 th gears.
## 1 Introduction
In [21], an adaptive weighted kernel support vector machine-based intrusion detection method is utilized. This model used a Pareto-based ensemble technique to achieve optimal features. A set of extraction criteria is utilized to identify the typical and unusual patterns of network traffic. Further, enhanced pelican optimization-based SVM [22] and other machine learning models [23] are employed. While traditional machine learning has established promising outcomes in identifying abnormal traffic, it excessively relies on the manual extraction of traffic features. More significant human intervention decreases its robustness as well as the ability for generalization. In addition, classical machine learning models have restricted learning and classification ability owing to their shallow structure. Deep learning is proficient in handling complex encrypted traffic and automatically learning feature representations from complex data with no requirement for human interaction, compared to conventional machine learning. Real-time detection is challenging with deep learning despite its distinct advantages because of its internal structure, which is more complex, and its comparatively high training and complexity in prediction time. These models also undergo various issues, such as overfitting problems, less classification ability, and so on. In addition, the evolution of cyber threats makes it challenging to keep labeled datasets current and indicative of actual attack circumstances. The model can be trained after labeling the data. The learning ability of the model is better as the quality of the labeled data is higher. However, the labeled data are currently far behind application requirements, and labeling is expensive. Thereby, a novel semi-supervised deep RL model is focused on in this paper to classify the instances into normal attacks. The significant multi-fold contributions of the paper are listed below:  
* A novel semi-supervised enhanced deep RL (EDRL) framework is designed to promote security improvement in the network. * To incorporate a deep autoencoder (AE) and improved flamingo search algorithm (IFSA) in deep RL to improve the classification performance and minimize the manual labeling cost. * To adapt an optimization process in EDRL based on IFSA for optimal policy selection, making it flexible so it can employ several complex problems. * To employ a support vector machine (SVM) classifier to classify the attack and normal instances based on the traffic features. * Extensive evaluations are carried out using the UNSW-NB15 dataset under various scenarios to prove the performance efficacy of the proposed method compared to existing works. The paper is structured as follows: a literature review of the existing intrusion detection and response schemes is presented in Section 2. The proposed methodology with algorithms is described in Section 3, the comparative analysis and results of the proposed framework are presented in Section 4, and Section 5 concludes the paper with future scopes.
## 4 Experimentation and Results
### Statistical Analysis
#### 4.5.3 Confidence Intervals  
To measure the uncertainty surrounding each performance metric, we computed the 95% confidence intervals (CIs) for the three primary metrics: accuracy, precision, and recall. Table 11 displays the confidence intervals for accuracy.  
These intervals indicate that the model performs consistently across datasets and that applying it to unknown data does not significantly reduce accuracy.
## 3 Results
### Diffuse Reflectance Spectroscopy  
#### 3.2.1 Overview of Diffuse Reflectance Spectra  
As shown in Figure 9, significant noise was evident at both the beginnings and ends of the spectra. This noise was primarily attributable to the characteristics of the light source and detector. In the short- and long-wavelength regions, the intensity of the light source was often lower, and the detector sensitivity in these ranges was also reduced, resulting in a lower signal-to-noise ratio and increased noise. Additionally, the response of the spectrometer might be uneven across different wavelength ranges, particularly at the edges of the spectrum, where the instrument response is weaker and more susceptible to noise. To enhance the effectiveness of spectral modeling, it was recommended to exclude spectral data affected by noise during the data modeling process. For the reflectance spectra of kiwifruit, a significant absorption peak was observed at 680 nm, likely due to the strong absorption characteristics of chlorophyll at this wavelength. Additionally, a smaller absorption peak around 720 to 750 nm might be associated with the fourth overtone vibration of C-H bonds.
###### Abstract  
Measuring of fluid properties such as dynamic viscosity and density has tremendous potential for various applications from physical to biological to chemical sensing. However, it is almost impossible to affect only one of these properties, as dynamic viscosity and density are coupled. Hence, this paper proposes kinematic viscosity as a comprehensive parameter which can be used to study the effect of fluid properties applicable to various fluids from Newtonian fluids, such as water, to non-Newtonian fluids, such as blood. This paper also proposes an ideal microplatform, namely polymeric suspended microfluidics (SPMF3), with flow plane orthogonal to the bending plane of the structure, along with tested results of various fluids covering a wide range of engineering applications. Kinematic viscosity, also called momentum diffusivity, considers changes in both fluid intermolecular forces and molecular inertia that define dynamic viscosity and fluid density, respectively. In this study a 3D suspended polymeric microfluidic system (SPMF3) was employed to detect changes in fluid parameters such as dynamic viscosity and density during fluid processes. Using this innovative design along with theoretical and experimental results, it is shown that, in fluids, the variations of fluid density and dynamic viscosity are not easily comprehensible due to their interconnectivity. Since any change in a fluid will affect both density and dynamic viscosity, measuring both of them is necessary to identify the fluid or process status. Finally, changes in fluid properties were analyzed using simulation and experiments. The experimental results with salt-DI water solution and milk with different fat concentrations as a colloidal fluid show that kinematic viscosity is a comprehensive parameter that can identify the fluids in a unique way using the proposed microplatform.  
Keywords:suspended microfluidics; microcantilevers; biosensors; kinematic viscosity; biod diagnostics; viscosity measurement +
Footnote †: journal: Applied Mechanics and Physics Letters
## 3 O-MAC Protocol Design Goals and Architecture
Once the DON announces the schedule for the interior nodes, all of the nodes must follow the announced schedule. The working process for exterior communication is described in Algorithm 2. ```
1. Input (\(D_{s}\): Dynamic operational node indication signal, \(A_{ck}\): Acknowledgement & \(\hat{\Pi}\): Hello messages)
2. Output (\(EX_{t}\): Exterior traffic, & \(EN_{t}\): Exterior traffic)
3. Parameters (\(C_{t}\): Current time, \(CA_{t}\): Next collection time, & \(D_{t}\): Next delivery time)
4. Initialization of network (\(BS\): Base station, : Adjacent region, \(D_{on}\): Dynamic operational node, \(S_{ns}\): Sensor nodes of region \(T_{n}\): Total nodes, & \(R_{A}\): Region of Network)
5. if\(D_{on}\) forwards \(\hat{\Pi}\) into \(R_{A}\) then;
6. Set \(S_{ns}\) response with \(A_{ck}\)
7. end if
8. if\(D_{on}\) gets \(A_{ck}\) then;
9. Set \(D_{on}\) broadcasts \(D_{s}\)
10. Set \(D_{s}\) = \(C_{t}\) + \(CA_{t}\) + \(D_{t}\)
11. end if
12. if\(S_{ns}\) receive \(D_{s}\) then;
13. \(S_{ns}\) forwards \(EN_{t}\)to \(D_{on}\)
14. for(\(S_{ns}\) = 0; \(S_{ns}\)\(\leq\)\(T_{n}\); \(S_{ns}\) + 1)
15. Store\(EN_{t}\) = \(EN_{t}\) + \(S_{ns}\)
16. Set \(EX_{t}\) \(\approx\)\(EN_{t}\)
17. end for
18. Set \(D_{on}\) forwards \(EX_{t}\)to \(BS\)\(\parallel A_{R}\)
19. end if
```  
**Algorithm 3** Exterior Communication  
### Exterior Communication  
In this section, we describe the problem of finding the optimal solution of the problem of finding the optimal solution of the problem of finding the optimal solution of the problem of finding the optimal solution of the problem of finding the optimal solution of the problem of finding the optimal solution of the problem of finding the optimal solution of the problem ofIn line 5, the DON forwards 3 \"Hello\" messages to the region's nodes to ensure that they are ready to receive the DONIS. In line 6, the region's nodes respond to the DON with an acknowledgement. In lines 8-9, the DON receives the acknowledgements and forwards the DONIS to the region's nodes. Line 10 shows the contents of DONIS. In lines 12 and 13, once the sensor nodes receive the DONIS, they respond by forwarding the data of the sensed events to the DON for the end node. In lines 14 and 15, the data of the exterior's nodes are stored in the buffer of the DON during the allocated scheduled time. In lines 16 and 18, the DON converts the collected interior traffic into exterior traffic for forwarding to either an adjacent region or the base station.