esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### RQ5: Network Security Attack in IoT and WSN Layers
#### 4.5.6 Wormhole Attacks  
A wormhole attack is a network layer attack in which an invader attacks the WSN through two or more compromised nodes. The invaders forward the data from one malicious node to another node at the end of the network through the tunnel. The wormhole appears to other nodes as a fictitious neighbor. Wormhole nodes usually transmit data directly from one node to the destination without including other nodes in the path. Due to this nature, other nodes in WSN easily trust those closest to other nodes, which causes many routing problems. Moreover, they can build better communication channels for long-range communication [37]. Wormhole attacks affect the performance of many network services, such as time synchronization, localization, and data fusion.
## 5 Conclusions  
In this study, we modeled the effects on GPP of solar radiation variation caused by changes in diffuse radiation in China over the period 1981-2012 using a two-leaf light use efficiency model (TL-LUE), which was driven by national global radiation in conjunction with other meteorological data and remotely sensed data. The main findings are as follows:  
(i) The annual mean \(R_{\rm g}\) in China significantly decreased over the period 1981-2012 by 2.18 MJ m\({}^{-2}\) y\({}^{-1}\), whereas the diffuse radiation increased. A significant reduction in \(R_{\rm g}\) and increases in the diffuse radiation fraction were observed in the Beijing-Tianjin-Hebei region and on the Qinghai-Tibet Plateau. The annual mean \(R_{\rm d}\) predominantly showed an upward trend over the study period, especially on the southeastern coast and the Qinghai-Tibet Plateau.  
(ii) During the period 1981-2012, the average annual total GPP of China's terrestrial ecosystems was 7.64 Pg C y\({}^{-1}\), comprising a contribution from \(R_{\rm d}\) of 5.69 Pg C y\({}^{-1}\). The total GPP decreased by 0.09 Pg C y\({}^{-1}\), although this decrease was partially compensated for by about 50% because of the increase in \(R_{\rm d}\). Clearly, the increase in LUE of canopy light associated with \(R_{\rm d}\) partly compensated for the reduction in total GPP caused by the reduced \(R_{\rm g}\) in China.  
The following are available online at [[http://www.mdpi.com/2072-4292/12/20/3355/s1](http://www.mdpi.com/2072-4292/12/20/3355/s1)]([http://www.mdpi.com/2072-4292/12/20/3355/s1](http://www.mdpi.com/2072-4292/12/20/3355/s1)), Figure S1: Distribution of sunshine duration, diffuse and global radiation station in China. Figure S2: Observed and interpolated daily global radiation at 12 sites in 2014; Figure S3: Observed and interpolated daily \(R_{\rm d}\) at 12 sites in 2014; Figure S4: Comparisons of estimated \(R_{\rm g}\) and \(R_{\rm d}\) between this study and that of [PERSON] et al. [30]; Figure S5: Spatial distribution of mean annual LAI in China from 1981 to 2012; Table S1: Proportions of sites distributed in different aerosol optical depth (AOD) levels. Table S2: Proportions of sites distributed in different AOD levels. Table S3: Scenario simulations of the impact of radiation change on GPP.  
Conceptualization, WJ.; formal analysis and methodology, X.W.; resources and validation, L.Z., Z.C., Y.L. and Y.S.; writing--original draft preparation, Y.Z.; writing--review and editing, W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2016 YFA0600202) and by the National Natural Science Foundation of China (42077419).  
The authors declare no conflict of interest.
## 3 The UDVRP Model
### Customer Satisfaction Function  
Assume that the optimal service time window of the distribution point is \([L_{i},U_{i}]\). If the vehicle arrives within this specified time frame, the customer satisfaction rating will be 1, and there will be no additional charges. Assume that the error time period that customers can tolerate is \([L_{i,min},U_{i,max}]\) If the vehicle is delivered within the time period, customer satisfaction will gradually decrease as the deviation time increases; similarly, if the vehicle arrives within the time period \([U_{i},U_{i,max}]\), customer satisfaction will also decrease accordingly, which will result in A certain penalty cost. If the vehicle arrival time exceeds the customer's tolerance, the customer satisfaction level will be 0, and the customer will directly cancel the order or refuse the delivery. The customer satisfaction \(S(i)\) is calculated by Equation (2).  
\[S(i)=\begin{cases}\frac{t_{i}^{k}-L_{i,min}}{L_{i}-L_{i,min}},&\forall k,i\in C,t_{ i}^{k}\in[L_{i,min},L_{i}]\\ \\ 1,&\forall k,i\in C,t_{i}^{k}\in[L_{i},U_{i}]\\ \\ \frac{U_{i,max}-t_{i}^{k}}{U_{i,max}-U_{i}},&\forall k,i\in C,t_{i}^{k}\in[U_{i},U_{i,max}]\\ \\ 0,&\forall k,i\in C,t_{i}^{k}\
otin[L_{i,min},U_{i,max}]\end{cases} \tag{2}\]
## 3 Communication Technologies and Wireless Sensors Networks
### Wireless Sensors Networks Categorization
#### 3.1.1 Categorized by Physical Environment  
* Underground: WSNs deployed beneath the Earth's surface, often used in mining or geological monitoring.
* Terrestrial: these networks operate on land, making them suitable for a wide range of applications such as environmental sensing and smart agriculture.
* Underwater: submerged WSNs are essential for oceanographic research, aquatic habitat monitoring, and underwater exploration. Multimedia: these networks handle multimedia data and are valuable in applications like surveillance, video streaming, and multimedia content distribution.
* Mobile WSNs: mobile WSNs are dynamic and adaptable, making them ideal for scenarios like wildlife tracking, vehicular networks, or mobile healthcare solutions [23].
# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## Materials and Methods  
### Study Area  
The Tanjung Karang Rice Irrigation Scheme (TAKRIS) is located at about latitude 3\({}^{\circ}\)25' to 3\({}^{\circ}\)45' N and longitude 100\({}^{\circ}\)58' to 101\({}^{\circ}\)15' E in the State of Selangor of Malaysia (Figure 1). The scheme has a 19,030 ha command area [15]. The diversion of water for irrigation is from the Bernam River Basin at the Bernam River Headworks (BRH) situated about 130 km upstream from the estuary of the Bernam River, and reaches the scheme through a feeder canal [6,24]. The feeder canal conveys water over a distance of about 14.5 km into the improved Tengi River, which runs a further 24.5 km into the Tengi River Headworks (TRH). The main canal runs for a total of 37.9 km with control structures to irrigate eight irrigation compartments in the scheme. The average annual rainfall in the region is about 2000 mm, the mean monthly temperature is 28 \({}^{\circ}\)C, and mean monthly humidity is 77% [25,26]. Rice is grown twice per year: during the dry or off-season (January-June) and the wet or main season (July-December). To ensure adequate irrigation deliveries to fields and better water management practices, rice cultivation in the area has been staggered into four Irrigation Service Areas (ISAs I, II, III, and IV) from January (ISA I) to June (ISA IV) during the dry season (Table 1). The process is replicated for the wet season from July (ISA I) until October (ISA IV) before the heavy rains start.  
Figure 1: Map of the Tanjung Karang Rice Irrigation Scheme [26].
## 3 Results and Analysis
### Reservoir Physical Property Evolution
#### 3.2.3 Hydrate and Gas Saturation  
Figure 8 depicts the evolution of hydrate saturation (\(S_{h}\)) for both cases without and with heating within 1080 days. According to the hydrate saturation field diagram for the case without heating, high-saturated secondary hydrates were formed in the reservoir near the wellbore in the TPL after 60 days of production and secondary hydrate saturation peaked about 360 days later. The temperature of the reservoir near the wellbore rises as bottom warm water enters the wellbore, causing the high-saturation secondary hydrates in the TPL to dissociate faster, boosting the gas production rate \(Q_{g}\). Only a limited quantity of low-saturated secondary hydrates accumulated in the reservoir around the TPL's wellbore during the first production stage (60 days) and peaked at roughly 360 days, as seen in the heating case's hydrate saturation field diagram. However, in the later stage, all secondary hydrates in this region were decomposed. Under a 4 MPa pressure difference, 100 W/m of heating power is insufficient to avoid secondary hydrate formation around the wellbore. Meanwhile, it is noticed that the hydrate dissociation front advances quicker and deeper in the heating condition. After 1080 days of production, the heating case has a wider range of hydrate dissociation in the GHBL, around 2-5 m.  
Figure 7: Evolution diagram of reservoir temperature without and with heating within 1080 days.  
Figure 9 presents the evolution diagram of gas saturation (\(S_{g}\)) for the cases without and with heating within 1080 days. Comparing the gas saturation field diagrams, it can be found that because of heating, the reservoir around the wellbore maintains a smooth flow channel. At the same time, more gas accumulates around the wellbore of the heating case, and most of the high-saturation free gas near the wellbore is extracted around 360 days. In the field diagram of the case without heating, it can be observed that the high-saturation secondary hydrates generated in the reservoir around the TPL block the flow channel here and force the high-saturation free gas near the wellbore located in the TPL to migrate to the upper and lower reservoirs and wellbore, respectively.  
Figure 8: Evolution diagram of hydrate saturation without and with heating within 1080 days.
## 3 Procedure, Data and Methodology
### Automatic Recognition of Lexical Situation Types
#### 3.3.1 Prediction Experiments Based on Linguistic Feature Vectors  
As mentioned earlier, we extracted co-occurrence frequencies between verbs and aspectual markers from both the CCL and Sinica corpora to simulate the collocational capacity between verbs and aspectual markers. Using the aspectual marker features of verbs as independent variables and the situation type of verbs as the dependent variable, we constructed two distinct vector matrices. Based on these matrices, we will, respectively, employ three classifiers to perform tenfold cross-validation. Through comparative analysis of model reports, we aim to evaluate the predictive effectiveness achieved by feature vectors derived from different corpora while simultaneously assessing performance differences among the three classifiers.
## 4 Results
### Two-layered Semi-infinite Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Layer-1 Thickness \(\rightarrow\)**} & **8 mm** & **10 mm** & **12 mm** \\ \hline \multirow{3}{*}{\%\(\Delta\) HbO} & Homogenous & \(-31.5\pm 17.3\) & \(-23.8\pm 18.4\) & \(-17.8\pm 19.1\) \\ \cline{2-5}  & Layer-1 & \(10.0\pm 4.5\) & \(8.2\pm 2.6\) & \(7.1\pm 1.4\) \\ \cline{2-5}  & Layer-2 & \(-16.6\pm 7.2\) & \(-15.0\pm 6.1\) & \(-15.9\pm 4.9\) \\ \hline \multirow{3}{*}{\%\(\Delta\) Hb} & Homogenous & \(6.1\pm 23.2\) & \(16.3\pm 23.3\) & \(23.6\pm 22.7\) \\ \cline{2-5}  & Layer-1 & \(-15.9\pm 2.6\) & \(-13.8\pm 1.9\) & \(-13.0\pm 1.2\) \\ \cline{2-5}  & Layer-2 & \(-0.3\pm 9.7\) & \(0.8\pm 5.2\) & \(2.4\pm 3.4\) \\ \hline \multirow{3}{*}{\(\Delta\) TOI} & Homogenous & \(8.3\pm 8.3\) & \(9.5\pm 9.3\) & \(10.4\pm 9.9\) \\ \cline{2-5}  & Layer-1 & \(-4.4\pm 0.5\) & \(-3.6\pm 0.2\) & \(-3.3\pm 0.1\) \\ \cline{2-5}  & Layer-2 & \(3.2\pm 1.4\) & \(3.1\pm 0.9\) & \(3.4\pm 0.5\) \\ \hline \multirow{3}{*}{\%\(\Delta\) Sa} & Homogenous & \(39.4\pm 0.6\) & \(38.8\pm 0.3\) & \(37.8\pm 0.2\) \\ \cline{2-5}  & Layer-1 & \(-8.2\pm 2.9\) & \(-8.2\pm 1.8\) & \(-8.1\pm 0.9\) \\ \cline{2-5}  & Layer-2 & \(7.9\pm 4.8\) & \(7.6\pm 1.6\) & \(6.4\pm 1.1\) \\ \hline \multirow{3}{*}{\%\(\Delta\) Sp} & Homogenous & \(-13.7\pm 0.3\) & \(-15.7\pm 0.4\) & \(-17.5\pm 0.5\) \\ \cline{2-5}  & Layer-1 & \(25.8\pm 0.4\) & \(26.1\pm 0.2\) & \(26.3\pm 0.1\) \\ \cline{2-5}  & Layer-2 & \(-44.9\pm 9.8\) & \(-48.8\pm 9.9\) & \(-52.4\pm 8.7\) \\ \hline \%\(\Delta\) L & Layer-1 & \(3.4\pm 4.4\) & \(3.4\pm 4.1\) & \(4.0\pm 3.1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average percentage error (%) in homogenous and two-layered parameter recovery for different ground-truth scattering parameters and different TOI values of layer-2. Figure 6: Estimated Layer-1 thickness from the two-layered parameter recovery method using multi-distance hyper-spectral CW data. The shaded region shows the standard deviation of recovery for different scattering parameters of both layers and different TOI values of layer-2.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 6 Evaluation
### Evaluation Process
The evaluation included such aspects as the scalability of the CPS hub to support presence management of larger set of resources owned by different stakeholders, trust checker of CPS hub for taking care of the dynamic grid of agreements dynamically negotiated between the parties/resources, trust checker negotiation with policy based authorization, extensible authorization framework as a service (SAFAX), in a simple security policies case dealing with an alarm type of event, and trusted publish/subscribe type of information sharing between resources so that the delivery of subscribed content happens only if it is allowed.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 6 Proof of Concept  
In this section, the results of the optimization process are shown. The optimization process for the two selected types of services (real-time video streaming and file transfer) was carried out in a simulated cellular environment. On the one hand, real-time video streaming services use the RLC UM mode, since it presents strict requirements in terms of latency. On the other hand, file transfer services use the RLC AM mode, since it is characterized by strict requirements in terms of the packet loss rate. Furthermore, to test the viability of the proposed optimization method in a real network, the optimization of the QoE perceived by the user of a file transfer service was performed using an LTE testbed, UMAHetNet (see Appendix A for more details).
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.19. EECOR [42]  
Ref. [42] suggested an energy-efficient cooperative opportunistic routing (EECOR) protocol. Using fuzzy logic, the authors select the optimal relays set among the adjacent nodes after the source node selects the forwarding node. To prevent other nodes from overhearing, the authors introduce a holding time. The protocol improves energy usage, packet delivery ratio, and end-to-end latency performance. On the other hand, the relay selection method performs poorly in a sparse environment when the nodes are far apart. Furthermore, when the locations of the nodes vary due to ocean waves, forwarding packets to the designated relay nodes becomes difficult, causing an extra delay.
###### Abstract  
The conservation, restoration and sustainable use of wetlands is the target of several international agreements, among which are the Sustainable Development Goals (SDGs). Earth Observation (EO) technologies can assist national authorities in monitoring activities and the environmental status of wetlands to achieve these targets. In this study, we assess the capabilities of the Sentinel-2 instrument to model Gross Primary Productivity (GPP) as a proxy for the monitoring of ecosystem health. To estimate the spatial and temporal variation of GPP, we develop an empirical model correlating in situ measurements of GPP, eight Sentinel-2 derived vegetation indexes (VIs), and different environmental drivers of GPP. The model automatically performs an interdependency analysis and selects the model with the highest accuracy and statistical significance. Additionally, the model is upscaled across larger areas and monthly maps of GPP are produced. The study methodology is applied in a marsh ecosystem located in Donana National Park, Spain. In this application, a combination of the red-edge chlorophyll index (CLr) and rainfall data results in the highest correlation with in situ measurements of GPP and is used for the model formulation. This yields a coefficient of determination (R\({}^{2}\)) of 0.93, Mean Absolute Error (MAE) equal to 0.52 gC m\({}^{-2}\) day\({}^{-1}\), Root Mean Squared Error (RMSE) equal to 0.63 gC m\({}^{-2}\) day\({}^{-1}\), and significance level \(p<0.05\). The model outputs are compared with the MODIS GPP global product (MOD17) for reference; an enhancement of the estimation of GPP is found in the applied methodology.  
SDGs; EO; GPP; Sentinel-2; ecosystem health 2023  
1562[[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))
## 3 Results and Discussion
### Absorption Coefficient and Refractive Index of PGRs  
According to Equations (2) and (3), the absorption coefficient and refractive index were directly related to the thickness of the sample. Therefore, sample thickness was a key factor affecting the amplitude of the THz spectrum. In this experiment, the measurements of thickness were repeated three times to reduce the influences of measurement error and surface scattering. The average thicknesses of 2,4-D, CPPU and IAA samples were 1.38 mm, 1.53 mm and 1.46 mm, respectively. The window function of the Blackman-Harris 4 term was applied to process the THz spectra. The absorption coefficient and refractive index of the three PGRs in the ranges of 0.2-3 THz are shown in Figure 3. The experimental results indicated that the average refractive index of 2,4-D in 0.2-3 THz was 1.46, and the characteristic absorption peaks were at 1.35, 1.57 and 2.67 THz. The average refractive index of CPPU was 1.52 and there were two absorption peaks at 1.77 and 2.44 THz, respectively. The average refractive index of IAA was 1.56 and there was one characteristic absorption peak at 2.5 THz. For these three PGRs, there was a spectral variation of the refractive index at the location of each characteristic absorption peak. This phenomenon indicated that there was an abnormal dispersion near the absorption peak. As the frequency increased, the baseline of the absorption spectrum raised, which was caused by the scattering of THz radiation. Generally, high absorption corresponded to low refraction. For the high absorption peak of IAA at 2.5 THz, the inverse tendency was shown in the refractive index (Figure 3F). This might be caused by the spectral baseline drift due to noise, such as random error of the instrument and the ambient noise of the environment. Since the absorption characteristics of THz radiation were obviously different, these absorption peaks could be used as fingerprints to identify the chemical components of the PGR samples.  
Figure 2: THz spectral processing of PGRs by window functions. (**A**) 2,4-D; (**B**) CPPU; (**C**) IAA.
## 1 Introduction  
The Particle Swarm Optimization (PSO) approach, proposed by [PERSON] and [PERSON] [1], is a metaheuristic algorithm based on the concept of swarm intelligence [2], which is a powerful technique for solving complex mathematical engineering problems. The PSO solves problems by having multiple agents--called particles--investigate and compare the quality of a population of candidate solutions. A particle iteratively moves from one candidate solution to another based on a mathematical formula involving the particle's current state, its own local best state (_lbest_) as well as the influence from the particle with the best solution (_gbest_) in the search space. This mathematical formula is given by the following two equations:  
\[v_{i}=w\ v_{i}+c_{1}r_{1}(p_{lbest}-x_{i})+c_{2}r_{2}(p_{gbest}-x_{i}) \tag{1}\]  
\[x_{i}=x_{i}+v_{i} \tag{2}\]  
where  
\(x_{i}=\) State of the particle at iteration \(i\)  
\(v_{i}=\) Velocity of the particle at iteration \(i\)  
\(w=\) Inertia weight  
\(c_{1}=\) Cognitive parameter \(c_{2}=\) Social parameter  
\(r_{1}\), \(r_{2}=\) Random parameters  
\(p_{lbest}=\) Best local position  
\(p_{gbest}=\) Best global position  
In Equation (1), \(w\)\(v_{i}\) is the inertia term which makes the particle move in the same direction and with the same velocity; \(c_{1}r_{1}(p_{lbest}-x_{i})\) is the personal influence term which improves the individual by making the particle return to a previous position, better than the current; and \(c_{2}r_{2}\left(p_{gbest}-x_{i}\right)\) is the social influence term which makes the particle follow the best neighbors direction. This means that in the process of determining its movement towards a promising global optimum, each particle modifies its position according to its current position and velocity and the distance between its current position and _pbest_ and _gbest_, respectively. In applying the PSO algorithm to solve the inverse kinematics for a serial robotic manipulator, the desired end-effector pose is set to be the target position \(X_{T}\), and a fitness function \(f_{i}\) is defined to evaluate the local and global best positions among the particles in the swarm:  
\[f_{i}=||X_{T}-X_{i}|| \tag{3}\]  
where \(X_{i}\) is the manipulator end-effector pose associated with the particle state \(x_{i}\) at iteration \(i\). A complete table of definitions of all symbols is available in the Abbreviations at the end of this manuscript and before the References section.  
In this paper, an algorithm is developed to solve the inverse kinematics of a general serial robotic manipulator using PSO and demonstrate its effectiveness on the manipulators of the Baxter Research Robot.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 2 Related Work
A multi-objective optimization genetic algorithm is presented, aimed at minimizing delay, energy consumption, and link cost for satellite-to-satellite connections. However, the authors did not consider the effects of interference and weather on satellite-to-satellite links, focusing exclusively on laser and Ka-band mixed satellite navigation systems. [PERSON] et al. [18] concentrated on the optimization design of visibility and topology of optical satellite-to-satellite links in ultra-large constellations, proposing a genetic algorithm-based optimization strategy to enhance the visibility and reliability of these links. Emphasis has also been placed on multi-objective optimization in this field of research. [PERSON] et al. [19] successfully employed a multi-objective genetic algorithm to balance various objectives, such as coverage, coverage uniformity, and system cost. [PERSON] et al. [20] and [PERSON] et al. [21] introduced reliable approaches for satellite network routing and resource allocation, based on multi-objective optimization, which improved system performance and efficiency. [PERSON] et al. [22] suggested a dynamic resource scheduling scheme, founded on the principles of edge computing. [PERSON] et al. [23] put forth a degree-constrained topology generation algorithm aimed at increasing the flexibility and controllability of software-defined satellite networks. Meanwhile, [PERSON] et al. [24] recommended a constellation design method that prioritized user demand and service quality, optimizing broadband networks on high-altitude platforms. Considering the strengths and weaknesses of these methods, our focus is on the role of multi-constraint optimization in generating the topology of optical communication satellite constellations. We aim to evaluate the impact of multiple attributes on satellites when establishing inter-satellite links.