esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The application of Google Earth Engine is not restricted to crop phenology but is used in other dimensions of our environment. Measuring the effect of climate change on rangelands and broad-leaved forests in the Aosta Valley of northwest Italy was attempted for the first time using phonological metrics. The study incorporated the time series MODIS dataset with the CHIRPS collection in the Google Earth Engine platform. The study depicted that between 2000 and 2019, there was an indication of advancement at the start of seasons and a delay at the end of season in terms of vegetation growth. The study also concluded that phonological metrics, evapotranspiration from the vegetation, anomaly in the air temperature, and melting of snow cover experienced considerable changes over time [30]. The application of satellite-based remote sensing for the detection of Urban Heat Islands (UHI) was adopted in the metropolitan city of Turing in a segment of Italy. Landsat 8 data set was used for the time span of 2013 to 2018. In the study, Land Surface Temperature (LST) was measured using Radiative Transfer Equation (RTE), and Land Surface Emissivity was estimated through the NDVI threshold technique. The study concluded that over time, UHIs were more prominent in areas where vegetation cover and availability of water were lower and were higher wherever asphalts and concrete buildings were more prominent [31]. Application of NDVI entropy for modeling of Canine Distemper Virus (CDV) in Wildlife in Northwestern Italy was among few studies where Google Earth Engine was used. In the study, it was found that remote sensing data and its application in Google Earth Engine is valuable in monitoring CDV. The significant relationship between altitude and CDV, along with changes in the NDVI entropy over the time period, was also reported in the study [32]. An innovative approach was adopted for the appraisal of land management practices in a segment of Africa. A geospatial-driven model was proposed to depict a subsistence-based agricultural system as well as business-oriented agricultural system. Sentinel-2 satellite images were used for the classification of land cover. Further, for the depiction of a production system, entropy threshold value-based binary classification was attempted. The study showed that the proposed model was effective and robust in portraying the prevailing land distribution under a varied production system [33]. In the present work, an attempt has been made to depict cropping patterns and cropping intensity in the segment of an arid western plain zone of Rajasthan, India. Google Earth Engine (GEE) was taken as a cloud computing platform for crucial data analysis. Further, it was fused with the GIS platform and supported by ground verification and accuracy assessment.
## 2 Results and Discussion
### Method Validation  
In order to investigate the analytical performances of the developed DI- and EE-DI-SPME-GC-FID procedures, their linear dynamic ranges (LDRs), limits of detection (LODs) and relative standard deviations (RSDs) were obtained under the optimum conditions. LDR, LOD and RSD of the DI-SPME-GC-FID method were found to be 0.1-10 \(\mu\)g mL\({}^{-1}\) (\(n\) = 7, R\({}^{2}\) = 0.995), 10 ng mL\({}^{-1}\) and 6.1%, while for EE-DI-SPME-GC-FID, 0.001-8 \(\mu\)g mL\({}^{-1}\) (\(n\) = 7, R\({}^{2}\) = 0.998), 0.3 ng mL\({}^{-1}\) and 4.6%, respectively. The relative recovery values for the analysis of 1 \(\mu\)g mL\({}^{-1}\) nicotine were obtained as 75-105% for DI- and 91-110% for EE-DI-SPME-GC-FID. The absolute recovery for the extraction of a solution containing 1 \(\mu\)g mL\({}^{-1}\) nicotine was found as 9.73% for DI-SPME and 12.71% for EE-DI-SPME.  
Figure 7: Optimization of the desorption conditions: (**a**) desorption temperature and (**b**) desorption time.  
The volume of sorbent (Vfibre coating = Vfibre - Vfiber core) was estimated as 1.256 \(\mu\)L. By considering the sample volume (5 mL), the sorbent volume (1.256 \(\mu\)L), absolute recovery percent (R %) and length of the coating (\(\approx\)2 cm), the enrichment factors (EFs) were calculated as 38,734 and 50,597 for DI-SPME and EE-DI-SPME, respectively.  
For greater elucidation of the reliability and applicability of the DI- and EE-DI-SPME-GC-FID methods, their analytical figures of merit were compared with some similar published reports [30; 31; 32; 33; 34; 35]. The method applied, type of sample, LODs, LDRs and RSDs of the mentioned analytical strategies are compared in Table 1. LOD and LOQ of the developed EE-DI-SPME-GC-FID method are lower than all reported methods, except that using LC-MS (liquid chromatography mass spectrometry). Its LDR and RSD are also more acceptable than most other strategies. In addition, the analytical performances of DI-SPME-GC-FID are almost better than the presented procedures. Consequently, the priority of the proposed EE-DI-SPME-GC-FID can be given. In terms of LOD and LDR, the proposed method is not comparable with the LC-MS instrument. LC-MS is a very expensive and sensitive instrument and usually cannot be found in many research laboratories [35].
## 2 Materials and Methods
### Models for Estimating Reference Crop Evapotranspiration  
#### 2.3.1 FAO Penman-Monteith Model  
As a widely accepted model for estimating ET\({}_{0}\), the FAO Penman-Monteith equation was employed in this study. It is also considered a standard method to compare the accuracy of other models. The P-M model is described as follows:  
\[\text{ET}_{0}=\frac{0.408\Delta(\text{R}_{\text{n}}-\text{G})+\gamma_{\text{T}} \frac{900}{+273}\text{U}_{2}(\text{e}_{\text{s}}-\text{e}_{\text{a}})}{\Delta+ \gamma(1+0.34\text{U}_{2})} \tag{1}\]  
where \(\Delta\) is the slope of vapor pressure curve (kPa \({}^{\circ}\)C\({}^{-1}\)), R\({}_{\text{n}}\) is surface net solar radiation (MJ m\({}^{-2}\) d\({}^{-1}\)), G is soil heat flux density (MJ m\({}^{-2}\) d\({}^{-1}\)) and \(\gamma\) is the psychrometric constant (kPa \({}^{\circ}\)C\({}^{-1}\)). T is mean air temperature (\({}^{\circ}\)C), a mean value of T\({}_{\text{max}}\) and T\({}_{\text{min}}\), U\({}_{2}\) is wind speed at 2 m height (m s\({}^{-1}\)), e\({}_{\text{s}}\) is saturation vapor pressure (kPa) and e\({}_{\text{a}}\) is actual vapor pressure (kPa).
# Web-Based Toolkit for Performance Simulation and Analysis of Power Line Communication Networks
## 2 PLC Technologies
### Iee 1901
Analogous to cellular communication systems, frequency bands can be reused at a greater distance if an adequate guard distance is provided over MV cables to avoid interference or if smart notching techniques are applied to mitigate their effects, but at the expense of data rates [29].
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 2 Cybersecurity Vulnerabilities in Smart Grids and Mitigation Requirements
### Security Standards of Control Systems Mitigation
* SA-99 contains advice and guidance on many security technology products for industrial automation and control systems. It deals with risk analysis, countermeasures, and cybersecurity management systems [48]. * NIST 800-82 provides a direct security checklist and provides security requirements and solutions for risk assessment studies. The standard examines the hardware and software components used in the cybersecurity infrastructure, makes recommendations for more secure network and application services, and provides examples [62]. \begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Paradigm Change**} & **Digitalization** & **Standardization** \\ \hline \multirow{4}{*}{**Impact On**} & & Easy maintenance & Interoperability and Interchangeability \\  & Operation & Serves Scalability & Addition of new equipment is easy \\  & & More and High-Quality & Paves the way for Plug and Play (PnP) \\  & & Data Collection & \\ \hline \multirow{4}{*}{Cyber Security} & Physical Security is compromised & Security by obscurity is lost \\  & & Easier Access to Networks & Hackers can use legitimate models to identify \\ \cline{1-1}  & & Connectivity is disadvantageous & All the data objects are known \\ \hline \end{tabular}
\end{table} 
Table 2: Paradigm changes in power systems. NIST 800-82 control systems security guideline is listed under the following four sub-headings [59]:  
1. An overview of the reasons for security needs as well as physical measures take in control systems;
2. Differences between control and communication systems within the scope of openness, threats, and events;
3. Suggestions for assembling security solutions into typical grid structures found in control systems, with decompression point on network distinction implementations;
4. Summary of managerial, operational, and technical controls. * NERC 1300 standards are developed for the identification and certification of procedures. The standards can be applied to entities performing the specified activities such as control regions and generation company owners [43]. It contains comprehensive information on critical issues under the following headings [69]:  
1. 1301 Security Management Issues;
2. 1302 Critical Cyber Assets;
3. 1303 Personnel Subjects and Training;
4. 1304 Electronic Security;
5. 1305 Physical Security;
6. 1306 System Security Management;
7. 1307 Incident Response Plans;
8. 1308 Recovery Plans. Different species of control systems (CS) holding imitative behaviours and many of the suggestions from [53] are practicable and could be used as a sample to protect systems in the face of cyber-secure assaults. Even though numerous different systems such as construction, medical, transportation, defence, and logistics use different procedures and standards, they all run in similar modes and have similar characteristics to conventional CS [53].
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 3 Proposed Augmented Lagrangian-Based Reinforcement Learning
### Definition of State, Action and Reward in RL
#### 3.2.1 State Space  
In reinforcement learning, the state space represents the environment observed by an agent. Hence, in our scenario, the wireless environment, i.e., the channel condition between BSs and devices, is defined as the state. Since in the factory, the location of devices are fixed, the channel condition only related to the channel fading gain, that is, the state space, can be expressed as  
\[\mathbf{s}^{(t)}=\{h^{(t)}_{nm},\forall m,\forall n\}. \tag{16}\]  
Based on the state, we can calculate the SINR by (1), and then the transmission rate and other parameters involved in the optimization problems.
While recent studies assessed the potential of several substrates for biogas production in Malaysia [6; 7], there is only little knowledge available about the utilization of residues from cassava processing and its environmental consequences. In Malaysia, cassava is mainly cultivated for starch production [8]. Different technologies exist for processing of cassava roots to cassava starch [9]. In addition, alternative food products can be produced that are rich in carbohydrates, like cassava crisps, which are deep-fried cassava slices. [PERSON] et al. have shown that there can be trade-offs between economic and ecological benefits in agricultural production systems [10]. However, despite the economic and nutritional importance of cassava, there is only limited knowledge available regarding the overall environmental impacts of cassava starch production or the production of alternative food products like cassava crisps. In several studies, the carbon footprint was evaluated, while full life cycle assessment (LCA) studies for cassava starch production are not available yet to our knowledge, especially not for Malaysia. For Thailand, [PERSON] and [PERSON] [11] found that producing 1 of cassava starch causes about 600 kg of CO\({}_{2}\)-equivalents (CO\({}_{2}\)-eq) where the agricultural production dominated the impact (40-59%). [PERSON] et al. [12] reported higher emissions with 609-966 kg CO\({}_{2}\)-eq. For starch production in Thailand, Vietnam, Colombia, [PERSON] et al. [9] reported 93-539 kg CO\({}_{2}\)-eq without taking into consideration the production of cassava roots. [PERSON] et al. assessed the environmental impacts of processing cassava roots to flour [13] and reported 11 kg CO\({}_{2}\) eq per kg of flour. For the production of cassava crisps, however, there is no information available yet. The lack of knowledge about the environmental impacts of cassava products becomes relevant in the context of the international goals for sustainable development [14]. [PERSON] and [PERSON] propose a method based on life cycle assessment (LCA) as an indicator and basis for monitoring the sustainable development goal No. 12 \"sustainable production and consumption\" [15]. This study presents an environmental assessment of different scenarios of cassava production and processing by a life cycle assessment (LCA) approach. In addition to estimating the absolute level of environmental impacts, the question is answered to what extent the production of biogas from residual materials can contribute to a reduction in the environmental impacts of cassava starch production. As previous studies indicate a major influence of primary agricultural production on the greenhouse gas emissions of cassava starch production, it is also hypothesized that other environmental impacts are significantly influenced by this production step. Therefore, a specific focus is set on primary production.
## 5 Discussion  
Changes in the subpolar gyre appear to have a tenuous link to the subtropical gyre of the North Atlantic during the past [50; 51]. Depending on the thermal conditions of the mixed-layer and thermocline, this apparent link might have varied between weak and strong states. Data from core Hu90-08 illuminate on this link, which is broadly divided into four periods: Penultimate deglaciation including the termination II, MIS5, MIS3, and the last deglacial period including the Holocene. Five published records (Table 1), namely CH69-K09 [23], EW9303-37 JPC (hereafter 37 JPC; [24]), MD03-2664 [25; 26], SU90-24 [19], and the Ocean Drilling Program (ODP) site 984 [27] in the subpolar gyre are plotted with Hu90-08 in Figures 6 and 7 as well, allowing us to provide an integrated assessment of broader changes in the sea-surface characteristics during the last glacial cycle. It should be stated a priori that some of the published records used in the study uses dated chronology due either to unavailability of the initial depth-scale data or \({}^{14}\)C-dates/tie points or the unwillingness of the authors to share those data for unknown reasons. A two-pronged approach was employed to use those published data: publications in which depth-scale data are unavailable, for example, published \(\xi^{18}\)O from core 37 JPC and benthic foraminifera \(\xi^{18}\)O for SU90-24, we used the dated age models. In contrast to cores 37 JPC and SU90-24, data from cores CH69-K09, 984, and MD03-2664 are updated following our age-model construction protocol (see Section 3.2.5).  
In terms of choosing proxies used to correlate among the records illustrated in Figures 6 and 7, two approaches were employed. The most commonly used proxies that reflect changes in the sea-surface, namely the IRD/g, %_N. pachyderma_, and %_T. quinqueloba_ is used for the MIS5-4 period (Figure 6). Unavailability of _N. incompta_, _G. bulloides_, and _G. inflata_ census data from cores 37 JPC, MD03-2664, and 984 prevented us from making a similar correlation for MIS5-4 as was carried out for MIS3-2 (Figure 7). It should be stated that the IRD/g and %_N. pachyderma_ are used as a common guide for both warm (MIS5 and I) and cold (MIS4-2) in assessing cold and freshwater input into the subpolar gyre of the North Atlantic [27; 52].  
Figure 6: Paleo-proxy records of changes (**a**-**i**) in the sea-surface characteristics of the subpolar North Atlantic between 60 and 150 ka. Downward simple-headed triangles exhibit the tie points used to construct the age model of core Hu90-08 (this study). The vertical discontinuous line represents termination II (TII). Hx = Heinrich events; Cx = Cooling events.
## 5 Discussion
### Cybersecurity Challenges in Technical Subsystem
#### 5.2.1 Technology-Related Cybersecurity Challenges  
The technology-related cybersecurity challenges subtheme describes all cybersecurity challenges that appeared due to technical issues exclusively amidst the major crisis in the HERS with real examples from the case participants. Technology-related cybersecurity challenges are defined as all the cybersecurity issues related to the technical usage of tools and resources in an organization that are used to carry out various tasks.
## 4 Lifetime Improvement Mechanisms in Routing
### Mobile Sink Mechanism  
Recently another solution for network partitioning caused by energy depletion around the sink has been by applying an efficient mobile sink strategy into the multi-hop routing protocols. Since the sinks' neighbors should forward their sensed data accompanied with data packets from nodes that are far away, they lose their energy faster than the other parts of the network. Instead of replacing these nodes, the key idea is that of maintaining the sensors stationary while moving the sink periodically to the parts of the network with sufficient energy. Since the sink's neighbors are permanently changed in time, the energy consumption and the traffic load could be balanced all over the network. On the other hand, employing only one mobile sink can avoid disconnection between the sensor nodes and the sink that caused by sink neighborhood problem. It consequently increases the network lifetime [8].  
Three basic types of sink mobility patterns are _Random/stochastic mobility_, _controlled mobility_, and _fixed path/predictable mobility_. In stochastic mobility, a random path is followed by the sink node in the network. Meanwhile, the data collection from the sensor nodes will be implemented based on a pull strategy where the sink requests the data from either one or \(k\) hop neighbors. According to the controlled mobility pattern, the sink can move through the network autonomously and change its position based on the energy factors in the sensor field. In the predictable mobility method, on the other hand, the sink node moves along a fixed path [39].
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 3 Proposed Method
### Efficient Competition Learning Particle Update Strategy
#### 3.1.3 Binary Decision Variable Update Strategy  
Like the particle update strategies in PSO and LMOCSO, the particle update strategy in ECLUS is only applicable to particles in real continuous space. However, for the imaging satellite task planning problem in this paper, in addition to the satellite swing angle as a real decision variable, there is also a binary decision variable of whether each strip participates in imaging. To update the binary variables in the imaging satellite task planning problem, we propose a BCSO by combining binary PSO (BPSO) and CSO.  
The strategy for updating the velocity variables for the loser in the BCSO is the same as in ECLUS for the real variables (see Equation (14)). Moreover, the position updating adopts the particle position update strategy in the BPSO.  
Firstly, the velocity vector of the loser is mapped to the range of [0, 1] through the following sigmoid function:  
\[s(v_{l}(t))=\frac{1}{1+\exp(-v_{l}(t))} \tag{22}\]  
Then, the position vector of the binary variable of the loser is updated according to the following equation:  
\[x_{l}(t)=\left\{\begin{array}{l}1\;if\;rand()\leq s(v_{l}(t))\\ 0\;otherwise\end{array}\right. \tag{23}\]  
where \(rand()\) represents a random number within the range of [0, 1] and \(s(v_{l}(t))\) represents the probability that the loser binary vector takes a value of 1.  
Finally, Equations (14), (22), and (23) together constitute the updating strategy for the loser binary variables in ECLUS.
# Characterization of Congolese Woody Biomass and Its Potential as a Bioenergy Source
## 3 Results
### Proximate Analysis, Ultimate Analysis, and HHV
Fixed carbon, representing the residual fraction after volatile release, is moderate, with a standard deviation (1.09) indicating some variability. Carbon content significantly contributes to heat energy, and a low standard deviation shows good homogeneity. Hydrogen content, which improves combustion quality, shows low variability. A low nitrogen percentage is favorable for limiting NOx emissions, though the variability is high. High oxygen content, typical for biomass, decreases energy density but shows good sample homogeneity, corroborating results reported by [26]. The HHV using the [PERSON] method shows a mean value of MJ/kg \(\pm\) 0.35, based on elemental composition, with low variability, indicating reliable results. The HHV using the [PERSON] method (17.76 MJ/kg \(\pm\) 0.34) offers slightly higher values due to an empirical approach specific to biomass, showing good consistency between samples. The HHV from proximate analysis shows a mean value of 16.83 MJ/kg \(\pm\) 1.43, with the greatest variability, indicating that the heating value strongly depends on the proportions of fixed carbon and volatile matter, corroborating results reported by [26]. Parameters such as volatile matter, carbon (C), hydrogen (H), and calculated HHV show low variability, suggesting the biomass is relatively homogeneous. Moisture and nitrogen show significant standard deviations, indicating variability in storage conditions or sample nature. These biomasses, with average HHVs ranging from 14.58 to 17.76 MJ/kg, are well-suited for thermochemical applications (combustion, gasification, and pyrolysis), although moisture management remains a key issue.
## 3 Simulations and Results
### Retrieval of Specific Attenuation
(**d**) The newly divided nine areas and the retrieved specific attenuations for both horizontal and vertical polarizations. used. For each area, \(\hat{a}_{H}(40)\) is then calculated by averaging the previously retrieved \(\hat{a}_{H}(40)\) over all of the voxels within the area. For example, \(\hat{a}_{H}(40)\) for area \(I\) is the average of eight voxels in the top left in Figure 2(a) and for area \(V\) it is the average of 16 voxels in the center. The calculated \(\hat{a}_{H}(40)\) for each of the areas is also shown in Figure 2(d). Note that the relative errors in \(\hat{a}_{H}(40)\) for the center 16 voxels are very low so \(\hat{a}_{H}(40)\) for area \(V\) is expected to be very accurate. Hence, using \(\hat{a}_{V}(40)/\hat{a}_{H}(40)\) of area \(V\) is the best choice for estimating the slope parameter \(\Lambda_{2}\). In order to achieve the best result of solving \(\hat{a}_{V}(40)\), we need to use only SNR measurements close to a 40\({}^{\circ}\) elevation angle but also to ensure there are enough data points for the least-squares algorithm to work. Nine groups of simulations were carried out to find the optimal balance between the two requirements. In each group, a certain number of SNR measurements from 40\({}^{\circ}\) onwards are used to retrieve \(\hat{a}_{V}(40)\), i.e., two measurements (\(\hat{\rho}_{V}(40)\) and \(\hat{\rho}_{V}(41.4)\)) are used for the first group and sequentially ten measurements (\(\hat{\rho}_{V}(40)\) to \(\hat{\rho}_{V}(54.3)\)) for the ninth group. Each group contains 100 independent simulations. Figure 4 shows the mean and standard deviation of \(\hat{a}_{V}(40)\) for area \(V\) in the 100 simulations for each group. It can be seen that the standard deviation is very large when less than five SNR measurements are used, indicating that the retrieved \(\hat{a}_{V}(40)\) could be erroneous. These simulations suggest that using 7 to 10 SNR measurements will lead to the best overall results for retrieved \(\hat{a}_{V}(40)\).