esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 3 Input Data
### Land Cover Map  
Global land cover (LC) map in 2013 at 500 m resolution from the Global Land Cover by National Mapping Organizations (GLCNMO) ([[https://globalmaps.github.io/glcnm-o.html](https://globalmaps.github.io/glcnm-o.html)]([https://globalmaps.github.io/glcnm-o.html](https://globalmaps.github.io/glcnm-o.html)), accessed on 3 July 2021) is used in this study. There are 19 LC types based on the Land Cover Classification System (LCCS) [48] (Figure 2). For global ET simulation, the detailed LC types were lumped into nine PFTs. Specifically, EBF, DBF, ENF, DNF, MF, and shrub are kept as they are. Herbaceous is labeled as grass. Cropland, paddy field, and Cropland/Other Vegetation Mosaic were labeled as crop. Other LC types in Figure 1 were  
Figure 1: Global distribution of meteorological variables in 2015: (**a**) mean hourly air temperature in \({}^{\circ}\)C; (**b**) mean hourly downward solar radiation in W m\({}^{-2}\); (**c**) annual total precipitation in mm yr\({}^{-1}\); (**d**) mean hourly relative humidity. Global means of air temperature, downward solar radiation, precipitation, and relative humidity for 2015 were 4.1 \({}^{\circ}\)C, 76.3 W m\({}^{-2}\), 303 mm yr\({}^{-1}\), and 25.8%, respectively.  
labeled as others. For each 1\({}^{\circ}\) modeling pixel, the areal fraction of each PFT was calculated to upscale from 500 m to 1\({}^{\circ}\) resolution [49].  
In this study, the BEPS model was run at 1\({}^{\circ}\) resolution for ET simulation. Thus, the GLCNMO global land cover map at 500 m resolution is good enough for this study. The latest GLCNMO land cover map available online is the Version 3 data which was produced from the MODIS data in 2013. This is the reason why we used the GLCNMO2013 land cover map. There is only two years' difference between 2015 and 2013 and the changes of land cover types should be minor within two years. Thus, we assumed that this global land cover map in 2013 could still be used for our ET simulation in 2015.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 4 Mission Process Control System Modeling
### System Equations
#### 4.3.3 Dynamics Equation
At this time, we have \(t\)\(=T_{PS}^{m_{i}}(t)\) and \(m_{i}\in\mathcal{M}_{1}(t)\). According to (21), when \(s_{m_{i}}(t-\Delta t)=2\) and \(s_{m_{i}}(t)=1\), there is \(T_{AS}^{m_{i}}(t)=t\). This moment is recorded as \(t_{AS}^{m_{i}}\), and we have \(T_{AS}^{m_{i}}(t_{AS}^{m_{i}})=T_{PS}^{m_{i}}(t_{AS}^{m_{i}})=t_{AS}^{m_{i}}\). Thus, we can obtain \(T_{AS}^{m_{i}}(t)=T_{PS}^{m_{i}}(t)\). When \(m_{i}\in\mathcal{M}_{0}(t)\), according to (21), we can obtain \(T_{AS}^{m_{i}}(t)=T_{AS}^{m_{i}}(t-\Delta t)=T_{AS}^{m_{i}}(t_{AS}^{m_{i}})=t_ {AS}^{m_{i}}\) when we have \(t>T_{AS}^{m_{i}}(t)\). According to (38) in Theorem 3, when \(t>T_{AS}^{m_{i}}(t)\), it can be obtained that \(T_{PS}^{m_{i}}(t)=T_{PS}^{m_{i}}(t-\Delta t)=T_{PS}^{m_{i}}(t_{AS}^{m_{i}})=t_ {AS}^{m_{i}}\) Therefore, it can be obtained that \(T_{AS}^{m_{i}}(t)=T_{PS}^{m_{i}}(t)\). In summary, (40) is proven. Second, prove (41). When \(m_{i}\in\mathcal{M}_{2}(t)\), the mission \(m_{i}\) has ended, then there must be \(t\geq T_{AF}^{m_{i}}(t)\). When \(t=T_{AF}^{m_{i}}(t)\), let \(t=T_{AF}^{m_{i}}(t)=T\), then there is \(s_{m_{i}}(T-\Delta t)=1\) and \(s_{m_{i}}(T)=0\). According to the system dynamics equations shown in (36) and (37), there is \(\omega_{m_{i}}(T)=0\), so when \(t\geq T\), we have \(T_{PF}^{m_{i}}(t+\Delta t)=T_{PF}^{m_{i}}(t)=T\). According to (22), when \(t\geq T\), we have \(T_{AF}^{m_{i}}(t+\Delta t)=T_{AF}^{m_{i}}(t)=T\). Therefore, it can be obtained that when \(t\geq T\), there is always \(T_{AF}^{m_{i}}(t)=T_{PF}^{m_{i}}(t)=T\), and (41) is proven. This completes the Proof of Theorem 4. Theorem 4 shows that the process parameters of \(m_{i}\) and the plan parameters of \(m_{i}\) remain the same when observing that any mission \(m_{i}\) ends. Therefore, the plan parameters can be used to represent the process parameters. Since the unstarted mission is concerned with the schedule parameters, and the finished mission is concerned with the process parameters, as a result, the computation of the two types of time parameters can be unified in the system dynamics equations shown in (36) and (37). Furthermore, according to the conclusion of Theorem 4, the analytical expression of the system output (31) can be expressed as  
\[ \begin{split} y(t)&=\sum_{m_{i}\in\mathcal{K}(t),T_{ PS}^{m_{i}}(t)<T_{PS}^{m_{i}}(t)}x_{m_{i}}(t)\\ &=\sum_{m_{i}\in\mathcal{K}(t),T_{PS}^{m_{i}}(t)<T_{PS}^{m_{i}}( t)}T_{PF}^{m_{i}}(t)-T_{PS}^{m_{i}}(t)\end{split}  \tag{42}\]  
where, \(m_{i}\) is the mission that belongs to the critical path and has a plan start time before \(m^{*}\). This way, the two types of event parameters, plan parameters and progress parameters, are unified.
# Collision Avoidance Resource Allocation for LoRaWAN
## 3 Technical Overview of LoRa/LoRaWAN
#### Limitations of the LoRa MAC  
This section presents some studied limitations of this protocol that significantly influence on the network capacity.  
As mentioned, LoRaWAN employs a light MAC protocol based on P-ALOHA for scheduling uplink transmissions from end-devices. It is a simple multiple access protocol in which, every time the end-device has data to transmit, a packet is sent without any link coordination. Thus, if two or more nodes transmit data simultaneously, a collision will occur. The higher the number of end-devices accessing the network, the higher the number of collisions. Hence, this situation increases the packet error rate (PER), resulting in the degradation of the performance and the reduction of the network capacity.  
[PERSON] et al. [15] analyzes several research problems in LoRa networks such as energy consumption, communication range, multiple access, security, and so forth, and identifies many current works which try to provide appropriate solutions to mitigate these problems. One of the problems which significantly affects the performance of a LoRaWAN network is the medium access, which is related to the link coordination and the resource allocation in order to handle collisions and allocate reasonable resources to end-devices based on the deployed environment.  
[PERSON] et al. [4] highlights the importance of the coordination in a single shared infrastructure running many applications with different requirements (in terms of reliability, maximum latency etc.) using an ALOHA-based access.  
These LoRaWAN MAC challenges are being investigated and there are some proposed solutions adapted to different scenarios which will be described below.  
Figure 2: LoRaWAN network architecture.
## 5 Results and Discussion
### Faulty Routes Analysis  
Table 8 shows the comparative analysis of the faulty pathways of the four existing methods with the proposed approach.  
Figure 9 represents faulty pathways as a function of the number of nodes. The proposed approach shows a minimal faulty pathway of 6.51%, while the existing approaches Sectrust-RPL, HBEER, SEHR, and SEAMHR show 17.07%, 13.31%, 10.46%, and 7.83%, respectively. This is a result of the improvement of the decision to route the heuristic function pathway in the proposed protocol. Furthermore, the heuristic function generates smart conclusions that take into account a variety of factors, such as connection integrity, that promote the consistency of the data.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**No. of Nodes** & **Sectrust-RPL** & **HBEER** & **SEHR** & **SEAMHR** & **Proposed** \\ \hline
50 & 13.57 & 10.78 & 7.81 & 5.56 & 4.45 \\ \hline
100 & 14.68 & 11.23 & 8.43 & 6.13 & 5.50 \\ \hline
150 & 16.25 & 12.45 & 9.98 & 7.24 & 5.95 \\ \hline
200 & 17.07 & 13.31 & 10.46 & 7.83 & 6.54 \\ \hline
250 & 22.34 & 15.11 & 12.12 & 9.35 & 8.45 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative analysis of proposed faulty routes (%) with state-of-art approaches.  
Figure 8: Energy consumption.  
Figure 9: Faulty routes.
## 7 Performance Evaluation
### Evaluation Metrics  
The evaluation metrics adopted for this work are: (1) efficiency of the energy consumption metrics; (2) and efficiency of the predictor metrics. Efficiency of energy consumption metrics are defined as--the total average of energy consumption in the network in Joule from the transmission of application packets (E\({}_{\text{trans}}\)); the total average of energy consumption in the network in Joule from the reception of application packets by broadcast of the neighbor sensor nodes--gossiped (E\({}_{\text{recp}}\)); the number of times that the multivariate spatial correlation was detected by sensor nodes (C\({}_{\text{spatial}}\)); and the percentage of saved energy in the versions with linear regression (versions 2 to 4) in face of the original version (E\({}_{\text{saved}}\)). Predictor efficiency metrics are defined as--the prediction error rate (SSerr); and the predictor improvement based on the coefficient of determination (R\({}^{2}\)).  
Energy waste in data communication is addressed by the energy consumption metric. According to each application version, the packet length is smaller in initial versions and is bigger in final versions. Thus, the energy consumption tends to be higher in the final version.  
The spatial correlation is measured by the amount of times it is detected, showing how an application version saved energy by not sending a large data packet. Perhaps there are no significant differences between the applications versions, since this mechanism has not been modified, but only adapted for each other.  
SSerr shows how many errors each application version has over the other. Probably, the initial versions has a higher prediction error than the last versions, because the use of correlated variables in prediction ensures fewer errors.  
Coefficient of determination measures the improvement of predictor in relation to its error. Unlike SSerr, the improvement tends to be better in final versions.  
Our work aims to improve prediction accuracy and is not more focused on saving energy than current solutions, but nevertheless we have checked the impact of our solution in face of current solutions to measure how feasible it is in a WSN.
## 2 ELM-AE and Shrinkage-Thresholding Algorithms
### The Shrinkage-Thresholding Class of Optimization Algorithms
#### 2.2.3 Linearly Convergent FISTA  
LC-FISTA is an accelerated version of FISTA derived from the research in [45; 46]. Under specific error conditions, LC-FISTA works with global linear convergence for a large group of real applications, which minimizes the formulation of the convex problem of the equation in (4). The iterative steps of the algorithm are as follows:  
1. Set both \(\mathbf{\tau}_{0}=\mathbf{z}_{0}=\mathbf{0}\), and \(\alpha\), \(\theta\), \(\mu\)y \(L_{f}\).
2. Update: \(\mathbf{y}_{k}=\frac{1}{1+\theta}\mathbf{\tau}_{k}+\frac{\theta}{1+\theta}\mathbf{z}_{k}\).
3. Update: \(\mathbf{\tau}_{k+1}=\text{prox}_{\rho\phi}(\mathbf{y}_{k}-\rho\
abla f(\mathbf{y}_{k}))\).
4. Update: \(\mathbf{z}_{k+1}=(1-\theta)\mathbf{z}_{k}+\theta\mathbf{y}_{k}+\alpha(\mathbf{\tau}_{k+1}-\bm {y}_{k})\).  
With a mathematical analysis analogous to that presented in [45; 46], the sequence \(\{\mathbf{\tau}_{k}\}_{k\in\mathbb{N}}\) generated by the LC-FISTA scheme exhibits a linear convergence rate, for fixed values of \(\alpha=(\frac{L_{f}}{\mu})^{1/2}\) and \(\theta=(\frac{L_{f}}{L_{f}})^{1/2}\). The practical use of LC-FISTA requires tuning parameters such as the strong convexity parameter \(\mu\) for the differentiable function \(f\). In addition, we need to set \(\mathbf{\tau}_{0}=\mathbf{z}_{0}\) to obtain convergence rates similar to the accelerated variant of FISTA presented in [50]. It is common to set \(\mathbf{x}_{0}=0\), since the location of the global minimum of the objective function \(h\) is not known.  
**Remark 1**.: _The proximal operator \(\text{prox}_{\rho\phi}(\mathbf{u}_{k})\) is the step that is common to FISTA, G-FISTA, and LCFISTA. This value \(\mathbf{\tau}_{k+1}=\text{prox}_{\rho\phi}(\mathbf{u}_{k})\) is closer to the global minimum of \(h\) compared with the maximum descent step \(\mathbf{u}_{k}:=\mathbf{z}_{k}-\rho\
abla f(\mathbf{z}_{k})\). Since \(t_{k}\) causes oscillations in the FISTA scheme when it has a value of one, G-FISTA shortens the interval between two restarts by setting \(t_{k}=1\), which is an essential condition for G-FISTA to converge with fewer iterations. In LC-FISTA, the \(\theta\) and \(\alpha\) momentum terms incorporated in the two additional equations (see (1) and (3) for LC-FISTA) improve both the accuracy of the gradient and the speed of convergence of LC-FISTA via the proximal operator._
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis  
There are various applications of an HRV analysis. It has emerged as an important diagnostic tool in fields as diverse as cardiology, endocinology, psychiatry, fetal monitoring, neonatal care, and many others. An HRV analysis can also help in understanding the effects of various external consumption factors, like alcohol, drugs, smoking, etc., on the pursuit of the autonomic nervous system. Some of the prominent applications are described in this section.
# In-Band Pumped Thulium-Doped Tellurite Glass Microsphere Laser
## 2 Theoretical Model
(b) Normalized gain function proportional to (\(\sigma_{em}n_{2}-\sigma_{abs}n_{1}\)). \[g_{p}=-\frac{c\Gamma N_{Tm}}{\sqrt{\varepsilon}}\sigma_{abs}(\lambda_{p})n_{1} \tag{7}\]  
Figure 1:0  
where \(\Delta\omega\) is detuning of the pump with amplitude \(E_{pump}\) from the nearest WGM (further we take \(\Delta\omega=0\)); \(T_{eff}\) is the effective lifetime \(1/T_{eff}=1/T_{0}+1/T_{ext}\); \(T_{0}\) is the intrinsic lifetime (without allowance for ground state absorption); \(T_{ext}\) is the coupling lifetime; \(T_{RT}\) is the round trip time (circulating time inside the microsphere); \(\varepsilon\) is the permittivity (the square of the refractive index). We neglect the difference between these lifetimes for different wavelengths and WGMs inside the gain band. The intracavity laser power is \(P_{s,l}=1A_{s,l}\)\(|\)\({}^{2}\) and the output laser power is  
Figure 1:1  
where the coupling coefficient \(\kappa\) is  
Figure 1:2  
For a steady-state solution, the time derivatives in Equations (2), (5) and (6) are equal to zeros  
Figure 1:3  
Figure 1:4  
Figure 1:5  
Therefore, the system of Equations (2), (5) and (6) with (1), (3), (4), (7) and (8) taken into account becomes algebraic, and the number of equations originating from (6) is determined by the number of the considered WGMs in the gain band. However, the solution of a nonlinear system containing a lot of algebraic equations is not a trivial task. Therefore, we use the following semi-analytical approach. First of all, we assume that lasing is single-mode (which is certainly valid near the threshold). We use an exhaustive search for each \(l\) in Equation (6) and after that choose only stable physically meaningful solutions (with \(0<\)\(n_{1}<\)1 and \(P_{s,l}>0\)). Stability of the solutions is analyzed as follows. If a lasing WGM has the order \(l^{*}\), then for all other WGMs with orders \(l\
eq l^{*}\) the zero solution \(A_{s,l}=0\) must be stable, that is the increment in Equation (6) must be \(<\)0:  
Figure 1:6  
Therefore, for the WGM with number \(l\), Equation (6) with allowance for Equation (8) takes the form  
Figure 1:7  
Using Equation (1), we find \(n_{1}\) and \(n_{2}\) from Equation (15):  
Figure 1:8  
Figure 1:9\[n_{1}+n_{2}=1. \tag{1}\]0  
After that, from Equation (2) with Equation (1) and the expressions (3), (4) taken into account, we determine the intracavity power at the laser wavelength  
\[n_{1}+n_{2}=1. \tag{1}\]1  
Therefore, we have obtained the analytical expression (19) for \(\left|A_{s,l}\right|^{2}\) for each WGM with number \(l\) from the gain band, where \(n_{1}\), \(n_{2}\), and \(\left|A_{p}\right|^{2}\) are given by the expressions (16), (17) and (18), respectively. Further, we execute exhaustive search for all \(l\), select only physically meaningful solutions and for them examine the stability using the expression (14). We verified that for any set of parameters, there is only one stable solution resulting in laser generation at a fixed wavelength (or no lasing under the threshold).
# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
### Related Work  
The contributions of this paper along with related work and a literature review are summarized as follows. In [14], the authors introduce the Listen Interval Adaptive Adjustment (LIAA) scheme to enhance the energy efficiency and reduce the end-to-end delays in event-sparse IoT systems, focusing on wireless sensor networks. LIAA includes three strategies (BALI, CL, and RALI) leveraging energy imbalances to optimize listen intervals, resulting in a 24.03%, 23.45%, and 39.41% reductions in delays for each strategy. The paper also addresses challenges in energy consumption and security. The paper in [15] addresses challenges in TSCH networks in industrial environments, proposing a distributed PHY and parent selection heuristic for slot bonding multi-PHY TSCH sensor networks. The paper in [16] introduces g6 TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e). g6 TiSCH enables nodes with multiple radios to dynamically switch between them based on link quality, creating a trade-off between latency and power consumption. An experimental evaluation on a 36-node testbed demonstrates that g6 TiSCH outperforms traditional 6 TiSCH stacks on individual physical layers, achieving a lower latency and network formation time while maintaining comparable battery lifetime. The paper in [17] explores how the Industrial Internet of Things (IIoT), which uses wireless connectivity and IP networking, can revolutionize industries such as Cyber-Physical Systems (CPS). Examples of CPS include industrial control systems, water systems, robotics systems, and smart grids, etc. To meet industrial demands, the paper focuses on the TSCH MAC and surveys 76 different schedulers, organizing them into categories such as Centralized, Collaborative, Autonomous, Hybrid, and Static. The analysis shows a trend of increased complexity alongside the adoption of centralized principles. The paper also outlines prospective research directions, including fault tolerance concerning latency and reliability, as well as scalability. The paper in [18] addresses challenges in beacon scheduling and Guaranteed Time Slot (GTS) allocation within IEEE 802.15.4 Deterministic and Synchronous Multichannel Extension (DSME) networks, commonly used for IoT. It proposes a non-conflicting beacon scheduling mechanism using association order to tackle slot collisions and introduces a distributed multi-channel DSME-GTS schedule for optimal allocation across channels. The goal is to minimize time slot usage while maximizing channel utilization. The simulation results in the paper show that the proposed mechanisms outperform existing schemes in energy efficiency, transmission overhead, scheduling efficiency, throughput, and latency.
## 4 Results
### Naive Bayes (NB)  
In this section you will find short descriptions that will tell you what the Naive Bayes (NB) algorithm's results are, so as to underline its value in the context of federated study.  
1. Global vs. Local Model Training: In many aspects, the Naive Bayes classifier performs admirably according to the assessment criteria shown in Figure 10. The model suggests a considerable degree of general correctness in its predictions, with an accuracy of 87%, or the percentage of properly identified instances. For global and local cases, respectively, the model shows that it can provide accurate affirmative identifications with remarkable precision scores of 98% and 97%. Furthermore, the model demonstrates competence in detecting pertinent cases of selective forwarding attacks, even though it has much lower recall scores of 76% and 75% for the global and local examples, respectively. Computed as the mean of accuracy and recall, the F1 scores always reach 86%. This suggests a performance in controlling the trade-off between false negative and false positive that is balanced. The results show that the Naive Bayes classifier is quite good at spotting trends that point to selective forwarding attacks in RPL-controlled IoT networks. This highlights the promise of the classifier as a trustworthy detection technique in the particular research field.
2. Confusion Matrix of NB: We use the confusion matrix presented in Figure 11 to extensively assess the efficacy of a Naive Bayes classifier in identifying selective forwarding attacks in RPL-enabled IoT networks. Although the model misclassifies 14,382 examples as such, it is remarkably accurate at correctly detecting events without sending out assaults only in certain cases. Furthermore, the detection of selective forwarding attacks is quite sensitive; 11,728 examples of this behavior have been found. It is shown that, save from a few instances of false positives (214) and false negatives  
Figure 9: SVM Confusion Matrix.  
(3675) results, the model can usually differentiate between beneficial and detrimental network activity. The results indicate that the Naive Bayes classifier proves to be a trustworthy detection technique to enhance the security of IoT systems with RPL capability. Its ability to thwart selective forwarding attacks has to be enhanced, and misclassifications have to be reduced.
## 4 Analysis on the Impact of Confined Water Approaching Construction on the Existing Station
### Effect of Depressurization Dewatering on Station Deformation  
According to the actual situation, the drainage is conducted in the pit, given the influence of confined water (VI) and confined water (VII), it is calculated into three conditions. Condition I: confined water (VI) pumping water of 180 m\({}^{3}\)/d 5 days; Condition II: confined water (VI) pumping water of 180 m\({}^{3}\)/d for 5 days; confined water (VII) pumping water of 210 m\({}^{3}\)/d for 5 days; Condition III: confined water (VI) pumping water of 180 m\({}^{3}\)/d for 5 days; confined water (VII) pumping water of 420 m\({}^{3}\)/d for 5 days.  
The confined water (VII) adopts the dewatering outside the pit, and the dewatering well is 5 m away from the edge of the pit, which is arranged every 20 m. The calculation results are shown in Figure 7.  
Figure 6: Cumulative deformation of earthwork excavation station.  
Figure 7: Deformation curve of existing station.  
In Figure 7, the depressurization dewatering of confined water (VI) and (VII) can effectively control the floating of existing stations. The maximum floating of the existing structure under the three working conditions is 7.78 mm, 5.11 mm, 4.17 mm, respectively. After decompression and dewatering, the confined water support force of the upper stratum decreases, and the confined water level also causes the decline of the upper groundwater level, which consolidate the soil particles under the effective stress, thus causing the formation deformation and reducing the floating of the station. Dewatering not only reduces the deflection in the middle of the station, but also the settlement at both ends, lessening the deformation difference between the middle of the station and both ends of the station, and protecting the station structure. At the same time, it can be seen that there is a hydraulic connection between the pressure aquifer, and convection exists in the two aquifers during dewatering.