esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis of the PTSR in the Underdamped State
### The Output SNR Comparison  
SNR is an important standard with which to evaluate the performance of the system. In the article, an SNR invented in Ref. [29] is used as a measure to compare the PTSR method under the underdamped state with the previous studies:  
\[SNR=10\log_{10}\frac{A_{d}}{A_{n}} \tag{6}\]  
where \(A_{d}\) represents amplitude at \(f_{d}\) of Equation (2), \(A_{n}\) represents the sum of other amplitudes.  
We adjust the parameters of the STSR method [30], the PTSR and the PTSR in an underdamped state to be the same (\(m=4,\ \ p=0.5,q=1,\ \gamma=1\)), and input the same periodic signal with noise into all three systems (A = 0.1, \(f_{d}=0.1\) Hz, D goes from 0.1 to 0.8). Due to the randomness of noise, the experiment is repeated ten times and de-averaged, then the SNR curves of the three systems are plotted in Figure 2. It shows that all three curves are going up and then down, which proves that the phenomenon of SR is generated in all the three systems. At the same time, the SNR of the PTSR system under the underdamped condition is significantly higher than that under the traditional overdamped condition, indicating that the PTSR method under the underdamped condition can obtain better output characteristics.
# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 3 Experimental Set-Up  
This section presents the experimental methodology used in this work. The process is summarized in four stages: (a) Input video, from which the video sequences to be studied were selected. (b) Detection of movement. In this step, the proposed model (MDBS) for motion detection by background subtraction was applied. (c) Adjustment of the model (using ASM). Finally, (d) evaluation of the effectiveness of the proposal.
## 8 Conclusions  
This paper has presented a complete overview of the research line carried out by UPC in the field of ship scattering characterization and classification. There, the development of a complete and realistic SAR simulation tool of complex targets (GRECOSAR) has been essential. First of all, it has allowed to relate the polarimetric scattering maps with the geometry of ships with a flexibility and control not possible in real scenarios. The derived conclusions regarding ship scattering, which have introduced the concept of PePS, have leaded to the definition of the _feature sets_ useful for classification. In a second term, the availability of simulated data obtained within realistic scenarios has made possible to evaluate the performance of the VCA classification algorithm. There, high classification ratios are found even under the presence of strong sea clutter. In this sense, the gained experience has shown that VCA has a simpler decision rule (only the comparison of the similarity value) with respect to the other solutions that increases its robustness.  
Regarding practical issues, VCA would permit to take profit of the data from the new second generation of orbital SAR sensors, such as TerraSAR-X, RADARSAT-2 or Cosmo-Skymed. The increased resolution and revisiting time seems to be suitable for the application specifications for which VCA has been thought, except they are not able to provide single-pass interferometric data. The future TanDEM-X mission can be an excellent opportunity for validating the performance of VCA with single-pass POLInSAR real data. But this would be only fruitful if measurement campaigns involving different institutions are promoted. Exploitation of airborne sensors may be also useful for validation purposes in spite of the limitations induced by the longer integration time, which makes the image more sensitive to vessel motions.
## 5 Case Studies
### Results and Discussion
#### 5.2.3 Results for the Third Case Study and Comparison  
In the third case study, the effect of changing in size of training set on the recognition rate was observed. There were 10, 15, 20, 25, 30, 35 and 40 images of each state randomly selected from the RTS transformation set as the training sets. Accordingly, the rest of images transformed by RTS composed seven corresponding testing sets. Similarly, a comparison of the methods based on RCMIs, HMIs and ZMIs was also performed in the third case study. The result for the third case study can be observed in Figure 7.  
According to Figure 7, it can be found that all the recognition rates reach their respective lowest points at the beginning. This is explained by the fact that the size of training set is so small that the description of images is not accurate. However, as the size of training set increases, the rate of recognition puts forward a rising tendency. In the end, all the recognition rates get to their respective maximum. Furthermore, when the size of training set is 40, the recognition rates of the methods based on HMIs and ZMIs are only 70.18% and 75.71% respectively. However, the method based on RCMIs can still achieve a recognition rate of 84.46% at this point. Besides, in the process of increasing the size, the fluctuation in the recognition rate of the method using RCMIs is smallest among these methods, showing that its stability is the best.
## 1 Introduction
The effects on measurement results from temperature and humidity should be considered during the optical analyzers' use. According to the literature analysis, these methods have demonstrated high measurement accuracy. However, measurements were carried out for a maximum of three components, which is not sufficient within the framework of the requirements in the Republic of Kazakhstan regulatory acts. And the use of combined emission monitoring systems, which use several of the above methods, turn out to be too expensive to use and maintain. Optical and electrochemical methods are the simplest in execution and do not require complex and expensive equipment. Considering this fact, as well as the fact that stationary methods are less time-consuming, electrochemical and optical methods are currently the most popular. They also show good accuracy results. However, such accuracy in practice is not always achievable due to the presence of various external factors that are absent in laboratory studies, therefore, they must be carefully monitored and eliminated, or their impact on indicators must be taken into account in some way. Taking into account the practical experience of enterprises, as well as the presence of comments from the environmental inspectorate on the accuracy of emission readings, one of the factors that introduces significant errors may be the presence of moisture in the exhaust gas filters. For this reason, dehumidification units are often used. However, the operation of these modules is not controlled and is too primitive. Consequently, the accuracy of such sensors may be reduced due to the lack of control over the drying process of the sample. Moreover, in this case, the implementation of such systems or the replacement of existingmeasuring systems can be expensive. For this reason, an analysis of existing solutions is necessary. The purpose of this study is to determine not only the accuracy of measurement by optical and electrochemical methods, but also the possibility of introducing correction factors in the presence of humidity in the exhaust gas sample. This study is a continuation of the studies in [47,48].
## 3 Components of VPN Architecture
### Operative Systems and Hardware Platforms  
In the current panorama, many operating systems and HW/SW platforms are dedicated to networking and secure communications. We have: MikroTik develops MikroTik RouterOS, the operating system of RouterBOARD boards; OpenWrt, developed by OpenWrt Project, an embedded Linux operating system, used on embedded devices for routing network traffic; PfSense, based on FreeBSD, an open source router and firewall with features that allow to manage unified threat, multi WAN and load balancing; OPNsense, another FreeBSD-based open source firewall that guarantees high security, Intrusion Prevention, Traffic Shaping and Captive Portal services; IPFire, based on Linux, a distribution designed  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature** & **LibreSwan** & **Strongswan** \\ \hline Pre-shared key authentication & Yes & Yes \\ Public-key authentication & Yes & Yes \\ IKEv1 key exchange & Yes & Yes \\ IKEv2 key exchange & Yes & Yes \\ AH support & Yes & Yes \\ NSS cryptographic library & Yes & No \\ Xauth and DNSSec & Yes & Yes \\ Network Manager & Yes & Yes \\ Virtual IP Addresses & Yes & Yes \\ MOBIKE & Yes & Yes \\ NAT Traversal & Yes & Yes \\ Route-based VPN & Yes & Yes \\ Policy-based VPN & Yes & Yes \\ Policy-based VPN and & & \\ Route-based VPN & Yes & No \\ simultaneous & & \\ Enable weak ciphersuites for & No & Yes \\ backwards compatibility & & \\ High Availability & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Strongswan vs. Libreswan comparison Table.  
to use the machine as an internal or perimeter firewall; VyOS, based on open Linux and distributed by the Sentrium company. It is open source and designed to protect the network and corporate data with high performance; Gargoyle, a firmware designed to use the machine as an internal or perimeter firewall; LibreMesh, an Open Source Software for Geek-free Mesh Community Networks. In this article, we will focus on the OpenWRT firmware and the Mikrotik platform for comparison.
## 5 Experimental Verification
### Experimental Results and Analysis
Figure 12: (a) Single \(\upmu\)MPMU 1 min network communication data amount; (b) single PDC 1 min network communication data amount. Figure 13: Data frames and application extension frames at a 1 ms time scale level. According to GB/T 26865.2-2011, a dynamic data record file is generated every minute, including the CFG-1 frame and 100 data frames per second. The data unit configuration is the same as when the above traffic calculation. Consider two cases here: Case 3 shows the calculation results according to the standard protocol; Case 4 shows the calculation result using the method from this work. The analog and digital quantities are all placed in application extension frames, the period is set to 5 s, and an application extension file is generated every minute to store the application extension frames. The space occupied by the 14-day data storage in the PDC is then calculated. The result is shown in Figure 10.0 The abscissa is the data unit quantity, the ordinate is the 14-day storage occupation, and the unit is gigabyte (GB). It can be seen from the calculated results shown in Figure 15 that the data generated from Case 2 are always smaller than Case 1 among different data unit ranges. As the number of data units increased from 1 to 4, Case 2 decreased from 58.17% of Case 1 to 55.72%, indicating the effectiveness of the method in reducing the amount of data stored.
## 7 Conclusions  
This work demonstrated why VCSEL-based transmitter systems were not able to capitalize higher-order PAM modulation formats to advance the link data rate towards 100 Gbit/s with BER of better than \(10^{-5}\). The paper first explained the non-linear and biasing-dependent VCSEL behavior to prove why SOTA VCSEL transmitters including linear equalization were not able to reach these data rate targets. Consequently, a digitally enhanced non-linear transmit equalizer was proposed to overcome voltage-dependent relaxation frequency shifts as well as data-dependent VCSEL bandwidth variations. The proposed digital non-linear equalizer was compared with a standard linear FFE structure using PAM-4 transmission experiments of 100 Gbit/s and beyond. The proposed non-linear equalizer revealed a BER reduction of more than 30-fold compared to a linear FFE at a data rate of 80 Gbit/s. Using the proposed adaptive FFE with seven taps and additional five taps of RX equalization, data throughput of up to 112 Gbit/s was successfully demonstrated at a BER of approximately \(10^{-7}\). The suggested novel equalization scheme is fully implemented in the digital domain within integrated transmitter circuits taking advantage of energy-efficient lookup table realization with minor power and silicon area penalty.  
Conceptualization, U.H. and N.L.J.; methodology, P.K., H.O.; software, U.H. and P.K.; validation, U.H., N.L.J. and P.S.; formal analysis, F.G. and N.L.; investigation, U.H., N.L.J. and P.S.; resources, F.G. and N.L.; data curation, U.H. and N.L.J.; writing--original draft preparation, U.H.; writing--review and editing, N.L.J., H.O., P.K. and F.G.; supervision, F.G.; project administration, F.G.; funding acquisition, F.G. and N.L. All authors have read and agreed to the published version of the manuscript.  
Figure 12: 112 Gbit/s eye diagrams using (**a**) no RX EQ and (**b**) 5 RX taps, (**c**) BER using different lengths of fiber.  
**Funding:** The authors would like to thank the Federal Ministry of Education and Research (BMBF) and the Fast Zwanzig20-Initiative for funding this work in the frame of the fast bits project --BMBF 03 ZZJ0525A. Some of the equipment used in this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)--INST 131/787-1 FUGG.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Analysis of Performance of the Final ANN Model
#### 3.3.1 Regression Analysis  
Figure 10a,b show the regression graphs between actual and predicted LBC of CFSS members using the training and testing data, respectively. All values of error measurement criteria are indicated in Table 2. In Figure 10, the linear fit is also presented, corresponding to a slope indicated in Table 2. A slope of 0.980 and 0.975 for the training and testing data, respectively, was obtained, corresponding to an angle between the linear fit line and the horizontal line of 44.429\({}^{\circ}\) and 44.263\({}^{\circ}\), respectively. It is shown that for two data points, the linear fit is very close to the diagonal line (i.e., 45\({}^{\circ}\)), which confirmed that the coefficient of determination R\({}^{2}\) is very good (i.e., R\({}^{2}\) = 0.989 and 0.975, for training and testing data, respectively). In terms of RMSE and MAE, the ANN model exhibits a strong prediction performance. As indicated in Table 2, RMSE = 217.717 kN and 294.424 kN; MAE = 139.685 kN and 191.878 kN, using training and testing data, respectively. In addition, error analysis shows that the ErrorMean is close to zero, whereas an ErrorStD of 218.062 kN and 295.445 kN is explored. The values of ErrorMean close to zero indicates that there is no sign of over- or under-estimation of LBC, i.e., the data points are located uniformly around the diagonal line, as shown in Figure 10. Moreover, as observed in Figure 10, the values of the predicted LBC are not systematically too high or too low  
Figure 9: Final architecture of the Artificial Neural Network (ANN) model.  
anywhere in the observation space. Indeed, the values of MAE show that the average magnitude of the residuals between the predicted and target LBC is lower than 200 kN. On the other hand, the standard deviation of such residuals is characterized by RMSE and ErrorStD (which approximately represent the same value, even though there is a different in the formula). As indicated in Table 2, the values of RMSE and ErrorStD are higher than ones of MAE (about 100 kN), showing that there is about 100 kN of variance in the individual residuals. From overall error measurements, good agreement between the predicted and the actual values of LBC of compressive CFSS members is obtained.
###### Abstract  
Climate change has exacerbated the frequency and severity of droughts worldwide. Evaluating the response of gross primary productivity (GPP) to drought is thus beneficial to improving our understanding of the impact of drought on the carbon cycle balance. Although many studies have investigated the relationship between vegetation productivity and dry/wet conditions, the capability of different drought indices of assessing the influence of water deficit is not well understood. Moreover, few studies consider the effects of drought on vegetation with a focus on periods of drought. Here, we investigated the spatial-temporal patterns of GPP, the standardized precipitation evapotranspiration index (SPEI), and the vapor pressure deficit (VPD) in China from 2001 to 2020 and examined the relationship between GPP and water deficit/drought for different vegetation types. The results revealed that SPEI and GPP were positively correlated over approximately 70.7% of the total area, and VPD was negatively correlated with GPP over about 66.2% of the domain. Furthermore, vegetation productivity was more negatively affected by water deficit in summer and autumn. During periods of drought, the greatest negative impact was on deciduous forests and croplands, and woody savanns were the least impacted. This research provides a scientific reference for developing mitigation and adaptation measures to lessen the impact of drought disasters under a changing climate.  
China; drought; SPEI; VPD; vegetation; GPP 2022, 14, 4658. [[https://doi.org/10.3390/rs14184658](https://doi.org/10.3390/rs14184658)]([https://doi.org/10.3390/rs14184658](https://doi.org/10.3390/rs14184658))
## 2 Study Area and Method
### Site Verification and Carbon Cycle Simulation  
This study simulated the carbon cycle of forest ecosystems in Zhejiang Province, based on the verification of the InTEC model using three different forest type flux sites. We used observed data of an evergreen broadleaf deciduous forest flux site [55; 56; 57] and bamboo forest flux site [58] in Zhejiang Province, Qianyanzhou, and a coniferous forest flux site in Jiangxi Province, near Zhejiang Province, to undertake the verification. The details of the three-flux site are shown in Table 2. Zhejiang and Jiangxi provinces are in subtropical regions. Evergreen broadleaf forests, bamboo forests, and coniferous forests are typical forest types in subtropical regions. Therefore, using the observation data of these three sites to verify the model is adequate.  
Based on the site verification of the InTEC model, this study took meteorological data, soil data, LAI, forest age data, N decomposition data, reference NPP, and forest distribution data of Zhejiang Province as the input data, with 2004 as the reference year of the forest age data and NPP data (Table 3), and utilized the InTEC model after optimization. The simulation flow chart is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Country** & **Latitude** & **Longitude** & **Forest Type** \\ \hline Anji & China & 30.46 & 119.66 & Bamboo \\ Tianmushan & China & 30.35 & 119.43 & Evergreen broadleaf \\ Qianyanzhou & China & 26.74 & 115.06 & Artificial coniferous \\ \hline \hline \end{tabular}
\end{table}
Table 2: The location of flux towers for the three kinds of forest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Index** & **Time** &
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & **Spatial Resolution** \\ \hline Forest distribution & Three forest & 1984–2014 & Every four years & 1 km \\ data & distribution data & \(\mathrm{T_{max}}\) & 1985–2015 & Monthly & 1 km \\ Meteorological & \(\mathrm{T_{min}}\) & 1985–2015 & Monthly & 1 km \\ data & Precipitation & 1985–2015 & Monthly & 1 km \\  & Relative humidity & 1985–2015 & Monthly & 1 km \\  & Radiation & 1985–2015 & Monthly & 1 km \\  & \multicolumn{2}{c}{Silt and clay} & & 1 km \\  & fraction & & & 1 km \\ Soil data & Soil depth & & & 1 km \\  & Soil water holding & & & 1 km \\  & capacity & & & 1 km \\  & \multicolumn{2}{c}{Silt point} & & 1 km \\  & Soil bulk & & & 1 km \\ LAI & Leaf area index & 2004 & Yearly & 1 km \\ Age & Forest age & 2004 & Yearly & 1 km \\ Ndep & N decomposition & 1985–2015 & Yearly & 1 km \\ NPP & Reference NPP & 2004 & Yearly & 1 km \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of model input data in Zhejiang Province.
## 3 Proposed Method  
### Overall Structure of the Dueling-DQN-Based Routing Algorithm  
In this paper, the overall flow of the Dueling-DQN-based routing algorithm for LEO satellite networks is as follows. First, the grid environment generation, behavior, and reward are defined. Actions are defined as up, down, left, right, and stop from the current position of the agent. For rewards, \(-1\) point is given for each time step, regardless of behavior, and \(1\) point is given for reaching the destination. If the agent collides with an obstacle (satellite link disconnection) while moving along the path, it will not be able to perform the action in the next time step, making the penalty larger.  
With this predefinition, we proceed to the Dueling-DQN part. The method of Dueling-DQN used in this paper is as follows. First, 2D convolution and ReLU are performed four times through the main layer. This process takes up the most time in the algorithm and is the target of the parallelization described later. After this, the advantage layer and state-value layer perform linear operations. It determines the action that corresponds to the highest result, then performs the action and calculates the position and reward value after the action. The obtained position and reward values are then delivered to the Dueling-DQN as input states and iterated to find the optimal routing path. Figure 1 shows this process schematically.
## 4 Implementation and Analysis
### Analysis  
The accuracy of any ML algorithm is highly dependent on the problem at hand, as well as on the integrity and complexity of the training dataset. The prediction performance of all six models on the dataset with SMOTE applied are listed in Table 3, with graphical representations displayed in Figures 4-6.  
The highest mean accuracy rate was achieved by the XGBoost algorithm, followed by SVM and then AdaBoost. LR had a low accuracy rate compared to the others, although it had a high F1-score of 0.8196, as seen in Figure 6 and Table 3. SVM displayed an accuracy rate of 0.8888, second to XGBoost, and from Figures 4-6 it can be seen that it had good performance on the basis of Precision, Recall, and F1-score. DT, RF, and AdaBoost had average performance on all four metrics.  
It is evident from these results that XGBoost delivers consistent performance on all four metrics with the highest mean accuracy and F1-score on our SMOTE-applied MENA dataset. The dataset was split into training and testing sets with test_size = 0.3. SVM performed well, though not as well as the XGBoost algorithm. Accuracy and F1-score were obtained after k-fold cross-validation on the training dataset, with k set to 10.