esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Network Architecture  
The architecture of an IoT network is usually composed of two layers: the Access Network (AN) and the Backbone Network (BN). The AN contains two kinds of components (Figure 2): the Internet gateways (access points, base stations, NB, eNB, gNB [21], etc.) and a variety of deployed devices. The Base Stations (BS) acts as a gateway that allows network devices to connect with the Internet. On the other hand, the BN is mainly composed of the Internet that streams the collected data to a particular distant service (for network monitoring or saving and processing). In the k-hop intra-clustering, nodes of the same cluster exchange their information using a short-range communication. The IEEE 802.15.4 standard is commonly used for this purpose [22] and is adopted by many low-cost wireless interfaces such as Bluetooth [2], Zigbee [23], WirelessHART [24], 6 LoWPAN [25], Z-Wave [26], etc. Where the energy consumption is reduced and devices can work for years without replacement. In contrast, to access the Internet gateway (typically the BS), network devices usually use long-range communication technologies [27], such as Wi-Fi (IEEE 802.11 [28]), LTE, LTE-M [29], etc. Long-range communication presents significantly high energy consumption and constraints devices lifetime to few days. The choice of the communication interface depends on the system application. Moreover, advanced communication technologies for Low Power Wide Area (LPWA) [30] were proposed in the literature to manage even wider communication areas, such as BLE (Bluetooth Low Energy) [31], WAVENIS [22], LoRaWAN (LoRa) [32] and Narrowband IoT (NB-IoT) [30]. Table 1 shows a brief comparison of some prevalent existing communication technologies with their associate application.  
Figure 2: IoT network architecture.
## 3 Experiments
### Remote Sensing Investigations of Large Rivers Deltas
#### 3.5.9 Mississippi Delta  
The Mississippi River and its delta is the most extensive river system in North America, draining 41% of the water of the continental United States [458]. For millennia, the river delta has been relatively stable, though subject to continuous accommodation processes resulting from SLR and ground subsidence [459]. From the mid-1950s to the mid-1970s, a progressive loss of wetlands, including the formation of holes in the delta plain and the transformation of wetlands to open water, has occurred [460; 461; 462; 463]. Since the 1990s, satellite measurements have been widespread to investigate the Mississippi River plume structure, sediment transport, and water circulation [464]. Multi-scale temporal analyses of remotely sensed imageries were demonstrated to be a useful tool capable of precise measurements of land changes in wetland environments [160; 465]. Furthermore, the possibility of using SAR interferometry to predict future flood events in the river floodplain has been demonstrated [e.g., [466]]. The Great Mississippi/Atchafalaya river flood of 2001 [467] demonstrated that massive floods significantly change the seasonality of sediment discharge, requiring an update to flood management plans, and changes are likely more significant due to the numerous systems of levees and dams established to constrain the discharge to the ocean. The results also show that InSAR methods can develop and test hydrodynamic models to prevent future flood events and efficient risk management in near-real-time profit from parallel computation architectures. Additional information and extensive studies related to the region of the Mississippi River and the northern coastal area of the Gulf of Mexico through satellite remote sensing techniques can be found in [464; 468] and references are contained therein.
## 3 Methodology
### Econometric Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Unstandardized Coefficients**} & \multicolumn{1}{c}{**Beta**} & \multicolumn{1}{c}{**T-Student**} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \cline{2-5}  & **B** & **Std. Error** & & & & \\ \hline
**gender** & 0.340 ** & 0.218 & 0.075 & 2.657 & 0.010 \\ \hline
**ranking of university entrance** & 0.003 ** & 0.000 & 0.520 & 7,138 & 0.000 \\ \hline
**student’s family members gone through higher education** & \(-\)0.007 & 0.006 & \(-\)0.018 & \(-\)1.077 & 0.285 \\ \hline
**external regulation (EME-E)** & 0.065 ** & 0.023 & 0.288 & 2.853 & 0.006 \\ \hline
**introjected regulation (EME-E)** & 0.044 ** & 0.029 & 0.215 & 2.643 & 0.011 \\ \hline
**identified regulation (EME-E)** & \(-\)0.038 ** & 0.025 & \(-\)0.138 & \(-\)2.512 & 0.012 \\ \hline
**motivation to knowledge (EME-E)** & 0.070** & 0.027 & 0.296 & 2.589 & 0.012 \\ \hline
**motivation to achieve (EME-E)** & \(-\)0.074 ** & 0.031 & \(-\)0.330 & \(-\)2.418 & 0.018 \\ \hline
**motivation (EME-E)** & 0.021 & 0.023 & 0.086 & 0.898 & 0.373 \\ \hline
**anxiety index (DASS-21)** & \(-\)0.005 & 0.016 & \(-\)0.010 & \(-\)0.338 & 0.736 \\ \hline \(p\)-value: * \(p<0.1\), ** * \(p<0.05\), ** \(p<0.01\); F-Statistic = 12.728, \(p\)-value = 0.000; [PERSON]: \(-\)6.665764; [PERSON]: \(-\)6.412801; Hannan–[PERSON]: \(-\)6.565058. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimation result academic performance using two-stage least square (2 SLS). compares the joint effect of all the variable model together. Furthermore, the model presents adequate information criteria ([PERSON], [PERSON], and Hannan-Quinn). About the hypotheses associated with the model, residuals do not present residual autocorrelation (Breusch-Godfrey F-Statistic test = 1.842; \(p\)-value = 0.165), multicollinearity (variance inflation factors (VIF) \(<\) 2), or heteroscedasticity problems (White F-Statistic test = 1.055; \(p\)-value = 0.405).
## 3 Results and Discussion
### Microstructural Analysis
#### 3.4.3 FTIR Analyses
Interestingly, the consistently higher intensity at \(\sim\)518 cm\({}^{-1}\) in 15% CS Mg, suggests that SCM incorporation may favor the presence of aluminum within the magnesium silicate phases forming magnesium (alumino) silicate hydrates (M-(A)-S-H) [102]. Although M-(A)-S-H phases are reported in the literature for systems containing reactive Al sources, our data did not provide direct spectral evidence of aluminum incorporation. Therefore, the presence of M-S-H is confirmed, but the formation of M-(A)-S-H remains speculative in this study. Overall, incorporating 15 vol% of CS partially enhances the sulfate resistance of cement pastes by reducing the formation of brucite and gypsum, preserving the C-S-H gel, and possibly promoting the development of M-S-H or M-A-S-H phases. Although these magnesium-based phases are not cementitious [94], they are considerably less expansive and deleterious than gypsum, thereby contributing to reduced deterioration [91]. Moreover, the presence of CS supports greater bound water retention and improved structural stability under aggressive MgSO\({}_{4}\) exposure.
## Materials and Methods  
### Satellite Data  
The MODIS Nadir bidirectional reflectance distribution function (BRDF)--Adjusted Reflectance (NBAR) product was used for calculating VIs [54]. The bands used for calculation of VIs include near infrared (NIR)--band 2 (841-876 nm), red--band 1 (620-670 nm), blue--band 3 (459-479 nm), and, additionally, a zenith angle at local solar noon from MCD43C4 is used for calculation of PPI. MODIS NBAR data is widely used for vegetation analysis and is particularly suitable for PPI, which is a non-ratio based spectral index that requires standardized reflectance corrected for sun-sensor geometry [36]. We used two products of different spatial resolution for the analysis. Due to the large area studied, it was chosen to use the MCD43C4 (collection 5) product that has a 0.05\({}^{\circ}\) spatial resolution projected to a latitude/longitude Climate Modeling Grid (CMG). For the evaluation of VIs against flux tower data, we used the MCD43A4 (collection 5) with a 500 m spatial resolution for a better match with the spatial footprint of the flux towers. The average of a window of 3 \(\times\) 3 pixels  
Figure 1: (**A**) boreal zone of the Northern hemisphere delineation based on the Terrestrial Ecoregions of the World (TEOW) dataset [51]; (**B**) land cover classes based on The International Geosphere–Biosphere Programme (IGBP), derived from MODIS Land Cover Type products (MCD12Q1) (Land Processes Distributed Active Archive Center (LP DAAC), lpdaac.usgs.gov).  
encompassing the flux site was used. Both products are characterized by a temporal resolution of eight days from continuous 16-day observations [54].  
In order to account for possible influence from forest loss due to forest fires/logging on phenology trend assessment, the high-resolution dataset of global forest cover change by [55] was used in this analysis to mask out the areas classified as disturbed.
###### Abstract  
This paper proposes an efficient approach for the computation of voltage stability margin (VSM) in a large-scale power grid. The objective is to accurately and rapidly determine the load power margin which corresponds to voltage collapse phenomena. The proposed approach is based on the impedance match-based technique and the model-based technique. It combines the Thevenin equivalent (TE) network method with cubic spline extrapolation technique and the continuation technique to achieve fast and accurate VSM computation for a bulk power grid. Moreover, the generator \(Q\) limits are taken into account for practical applications. Extensive case studies carried out on Institute of Electrical and Electronics Engineers (IEEE) benchmark systems and the Taiwan Power Company (Taipower, Taipei, Taiwan) system are used to demonstrate the effectiveness of the proposed approach.  
load power margin; voltage stability; voltage collapse point; voltage stability margin +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### The Proposed Model  
The generation of insect sounds represents a complex and multifaceted research field, with the characteristics of these signals closely associated with the morphology [3], types of sound-producing organs [21], and habits [3] of insects. Each insect's sound signals exhibit monotony and regularity, displaying species-specific traits. Moreover, early monitoring of insect sounds has enhanced the capabilities of researchers who frequently encounter resource constraints when monitoring the distribution of insect populations. Orthoptera insects [22] produce sound by rubbing their forewings, a mechanism characteristic of the suborder Ensifera. They possess a row of rigid microstructures on the inner surface of the forewings, acting as a file, and a hardened portion on the wing edge, acting as a scraper. Sound is generated through the relative motion of these two structures. The number and arrangement of protrusions on the file, as well as the thickness of the wings and the speed of vibration, vary between species, leading to differences in the rhythms and pitches of their calls. Cicada insects (Hemiptera: Cicadidae) [23] create sounds using sound-producing organs located on the sides of the first abdominal segment. These organs include the tymbal, the tymbal membrane, the tymbal muscle, and an air chamber. In the field of deep learning, the general principles for processing insect sound classification are shown in Figure 1.  
The proposed approach comprises three main components. The initial phase involves preprocessing insect sound data. The second phase employs the Mel-scale spectrogram method to convert raw audio data into an image format. The final phase encompasses feature extraction and classification using the dual-tower network. Insects' sound clarity may offer vital insights into their species. Hence, this paper employs a series of signal processing techniques and feature extraction methods to acquire sound data that is more distinct and recognizable.  
Figure 2 presents the process of the proposed deep-learning model for insect sound classification. Preprocessing, data augmentation, feature extraction, and classification all constitute integral elements of the proposed deep-learning model for insect sound classification. The proposed model consists of two primary steps: the first step entails feature extraction using EfficientNet [24], while the second step further enhances classification performance through the use of DFSM.  
Figure 1: General principle of deep learning classification of insect species from their sounds.
# Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
## 3 The Proposed Model  
### VRP-RIDFU Model  
This model employs a GA as the primary algorithm to find suitable solutions. Although several research studies have suggested that a GA may not be the best algorithm for vehicle routing problems, a GA has mechanisms to handle uncertainty or changing data. The mechanisms for generating and updating solutions within a GA can create diversity within the population in each generation. A GA selects individuals with good fitness values in changing situations to improve and adapt solutions efficiently until meeting the specified conditions. The operation of VRP-RIDFU is illustrated in Figure 4.  
The operation of VRP-RIDFU consists of five main steps. The first step involves creating the initial population of solutions. The solution represents the sequence of nodes for the delivery by three vehicles. Node 0 represents the depot, while nodes 1 to 6 represent the disaster sites. The initial population comprises individuals generated through randomization and enhanced individuals. Enhanced individuals represent a subset of the population that has been enhanced for efficiency in addressing uncertain problems. The proportions of these two groups within the population are defined, as depicted in Figure 5, which will be discussed in Sections 3.2 and 3.3. Step 2, evaluating the quality of each individual in the population, will include the fitness value for assessment in the form of the sum of expected values, as elaborated in Section 3.4. Step 3, the population selection, involves ordering and selecting individuals according to predefined percentages. The method selects the best-performing individuals from the current generation to form the next generation's population. If the quality of solutions does not improve after evaluation in step 3 according to the specified rounds, enhanced individuals are generated and substituted for the least fit individuals in sequence. These enhanced individuals are created in proportion to the predefined ratio. Steps 4 and 5 involve enhancing the quality of solutions through crossover and mutation. Crossover is performed using the CSOX operator, which will be discussed in Section 3.5. As for mutation, the swap method is selected. Finally, the termination criterion is defined by the specified number of generations.  
Figure 4: VRP-RIDFU flowchart.
## 2 Materials and Methods
### Numerical Models, Solution Methods and Boundary Conditions
#### 2.2.5 Validation of Numerical Model
The experimental results were used to assess the applicability and accuracy of the numerical simulation method. Figure 6: Variation in cavitation bubble volume fraction with time at different rotational speeds (\(Q=1\) m\({}^{3}\)/h). Figure 7: Cloud diagram of the distribution of cavitation bubbles at different rotational speeds (Q = 1 m\({}^{3}\)/h). The experimental device is shown in Figure 8. The device is an open loop, consisting of a water tank, a booster pump, a frequency converter, a flow meter and a shear hydrodynamic cavitation generator. At the water reservoir, the tank supplies the booster pump with enough water to ensure that both the booster pump casing and suction line are filled with water before the pump is started. The booster pump and flow meter are used to pressurize the wastewater and control the inlet velocity of the hydrodynamic cavitation generator. The frequency converter is used to change the speed of the rotated teeth. The hydrodynamic cavitation generator, consisting of rotated and fixed teeth, generates cavitation bubbles to degrade the COD content of the wastewater. As shown in Figure 9, wastewater is stored in a tank and pressurized from the bottom by a booster pump. It then flows through a flowmeter into a hydrodynamic cavitation generator for treatment. After treatment, the wastewater returns to the tank, forming an open loop. The rotational speed is controlled by the variable frequency drive set at 1500 rpm. Once the rotor stabilized, the samples were taken at the sampling port to measure the COD content in the wastewater after cavitation treatment, and the samples were taken uniformly at a treatment time of 10 min. Figure 8: Experimental setup for cavitation performance testing. Figure 9: Flow chart of the experiment: 1 water tank; 2 ball valve; 3 centrifugal pump; 4 rotor flow meter; 5 tank; 6 shear hydrodynamic cavitation generator; 7 ball valve; 8 electric motor. \[\eta=\frac{1-\delta_{COD}}{\delta_{COD}} \tag{13}\]  
As shown in Figure 10, with an increase in the treatment volume, the degradation rate of COD in the printing and dyeing wastewater decreases, indicating a deterioration in cavitation effectiveness. The increase in treatment volume actually accelerates the fluid's inlet velocity, especially the axial velocity of the fluid, leading to an increase in the inter-tooth radial velocity. Consequently, the residence time of the fluid within the cavity decreases, resulting in a decrease in cavitation effectiveness. During the collapse of cavitation bubbles, there is inevitably energy wastage that is not completely utilized for COD degradation. Additionally, the quality of wastewater, involving gas and impurity content, also affects the efficiency of COD degradation. This is why the experimental results exhibit a similar trend to the numerical simulation results, while with lower COD degradation efficiency compared to the volume fraction of cavitation bubbles. Thus, the accuracy of the numerical model and methodology is validated.
## 5 Discussion
### Evaluation of Hydrological Performance of the GPM-Era SPPs
Hence, employing different hydrological models and conducting multi-model simulations and comparisons are necessary. This study shows that the utility of SPPs for flood simulations still largely relies on model calibration, which limits the applicability of SPPs in ungauged basins with no streamflow data available for calibration. To employ SPPs in flood simulations in ungauged basins, it is advisable to calibrate the hydrological model over a nearby gauged basin that is hydrologically similar to the studied ungauged basin and transfer the calibrated parameter set from the gauged basin to the ungauged basin. Another alternative is to use a hydrological model whose parameters are able to be estimated a priori from topography, land cover, and soil texture information. Actually, most of the GXAJ model parameters have clear physical meanings. [PERSON] and [PERSON] (2018) associated the free water storage capacity (SM) with the maximum plant root depth and soil porosity and established the empirical relationship of the outflow coefficients of interflow and groundwater runoffs with soil texture information. [PERSON] et al. (2018) derived an empirical equation that computes the tension water storage capacity according to the topographic index. Obtaining detailed topography, land cover, and soil texture information in ungauged basins and hence deriving model parameters through these empirical relationships for hydrological simulations are desirable.
###### Abstract  
This study investigates the potential of wood ash (WA), a by-product of wood-based energy production, as an eco-friendly alternative for removing fluoride from water. Kinetic analysis revealed that WA enables rapid fluoride removal, reaching equilibrium within 1 h (112.09 \(\pm\) 3.9 mg/g). Equilibrium analysis demonstrated that WA exhibits a Langmuir maximum capacity of 157.34 mg/g, indicating a high adsorption capacity that ranks within the top 10% of reported adsorbents (34 th out of 328). According to thermodynamic analysis, the adsorption process appears to be both endothermic and spontaneous at elevated temperatures. pH dependence studies showed that while the fluoride adsorption capacity of WA peaked under acidic conditions, it remained relatively stable (116.01 \(\pm\) 0.8 mg/g) over a wide range of pH levels (5 to 11). An optimal dosage of 6.67 g/L achieved a greater than 98% fluoride removal rate. Coexisting anions affected the fluoride adsorption capacity of WA, with the order of influence being \(\text{PO}_{4}{}^{3-}>\text{CO}_{3}{}^{2-}>>\text{SO}_{4}{}^{2-}>\text{NO}_{3}{}^ {-}\approx\text{Cl}^{-}\). Mechanistic analyses confirmed the surface precipitation of \(\text{CaF}_{2}\) as the primary mechanism responsible for fluoride removal. With a Ca content of over 66 wt.%, WA facilitates enhanced fluoride removal. Overall, this study highlights the efficacy of WA as a sustainable adsorbent for the removal of fluoride from water, contributing to the vaporization of WA in wastewater treatment applications.  
energy byproducts; fluoride; wood ash; fluorite; green adsorbent +
Footnote †: journal: Applied Sciences  
1  
[PERSON]  
1 Institute of Agricultural Environmental Science, Hankyong National University,  
Ansoep 17579, Republic of Korea 1  
[PERSON]  
4 Department of Environmental and Safety Engineering & Energy Systems Research, Ajou University,  
Susron 16499, Republic of Korea 4  
[PERSON]  
5 Institute of Green Bio Science and Technology, Seoul National University,  
Pyeongchang 25354, Republic of Korea 5  
[PERSON]  
1 Institute of Agricultural Environmental Science, Hankyong National University,  
Ansoep 17579, Republic of Korea 1
## 5 Research Case Study and Simulation Parameters
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{3}{c}{**System Component and Economic Indicators Specifications**} \\ \hline \multicolumn{3}{c}{**Economics**} \\ Discount rate & 4\% & \\ Inflation rate & 3\% & \\ Lifetime of the project & 20 & years \\ \multicolumn{3}{c}{**Specification of the PV system**} \\ Capital costs & 1691.5 & US \$/kW \\ O \& M costs & 26 & US \$/kW/yr \\ PV reduction factor & 0.85 & \\ lifetime & 20 & years \\ \multicolumn{3}{c}{**Specification of the PHES**} \\ Round trip efficiency & 70\% & \\ Power converters (Expander/compressor system) & 400 & US \$/kW \\ Energy storage unit & 15.08 & US \$/kWh \\ O\&M (Power converters units) & 12.76 & US \$/kW/yr \\ O\&M (Thermal Energy unit) & 0.03 & US \$/kWh/yr \\ self-discharge rate (hourly) \(d_{r}\) & 0.04\% & \\ Lifetime & 20 & years \\ \multicolumn{3}{c}{**Specification of the WT**} \\ Capital cost & 2030 & US \$/kW \\ O\& M & 76 & US \$/kW/yr \\ lifetime & 20 & years \\ Wind speed (Cut-in): & 4 & m/s \\ Rated wind speed: & 14.5 & m/s \\ Cut-out wind speed: & 25 & m/s \\ Wind Shear Coefficient & 0.143 & \\ Hub height: & 50 & m \\ \multicolumn{3}{c}{**Specification of the BESS**} \\ Capital Cost & 300 & US \$/kWh \\ O \& M & 10 & US \$/kWh/yr \\ Round trip efficiency & 85\% & \\ lifetime & 5 & years \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical, Cost & lifetime parameters of the system components. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Off-Peak Period** & **Peak Period** \\ \hline Off-peak period & \(-\)0.1 & 0.016 \\ Peak period & 0.016 & \(-\)0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DRP self and cross-price elasticities of demand [49; 50; 52].