esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Datasets and Methods
### Retrieval of Land Surface Temperature Using a Single Channel Algorithm  
A single channel algorithm was employed to derive Land Surface Temperature (LST) from Landsat 8's thermal data (Band 10). Following steps in Bhatti and Tripathis [61], digital numbers of band were converted to radiances (\(L_{\lambda}\)) using Equation (1):  
\[L_{\lambda}=M_{L}Q_{cal}+A_{L} \tag{1}\]  
\(M_{L}\) and \(A_{L}\) are band-specific multiplicative and additive coefficients obtained from the metadata file downloaded together with imageries, while \(Q_{cal}\) is the thermal band digital numbers.  
Equation (2) is the [PERSON]'s equation for Landsat 8 used to convert thermal radiances of Band 10 to blackbody of brightness temperature (\(T_{b}\))  
\[T_{b}=\frac{K_{2}}{ln\Big{(}\frac{K_{1}}{L_{\lambda}}+1\Big{)}} \tag{2}\]  
In Equation (2), \(K_{1}\) and \(K_{2}\) are calibration constants taking values of 774.89 and 1321.08 for Band 10 of Landsat 8, respectively [40].  
Brightness temperatures do not account for differences in surface properties, as it assumes that all objects have a uniform emissivity of 1. This assumption is not ideal, especially in a city where thermal properties vary within short distances. In order to solve this challenge, brightness temperature is converted to land surface temperature (LST) per pixel, by way of emissivity correction using Equation (3):  
\[LST=\frac{T_{b}}{\Big{[}1+\Big{(}\frac{AT_{b}}{\sigma}\Big{)}ln\Big{]}} \tag{3}\]  
\(\sigma\) is equivalent to \(\frac{hc}{\lambda}\) taking a value of 1.438 \(\times\) 10\({}^{-2}\) mk, h is [PERSON]'s constant (6.63 \(\times\) 10\({}^{-34}\) Js), c is the speed of electromagnetic radiation (3 \(\times\) 10\({}^{8}\) ms\({}^{-1}\)), while \(\sigma\) is the Boltzmann constant (1.38 \(\times\) 10\({}^{-23}\) J/K)--[62]. \(\varepsilon\) is surface emissivity calculated per every 30 m resolution pixel using Equation (4):  
\[\varepsilon=0.004 PV+0.986 \tag{4}\]  
\(PV\) is the proportion of vegetation in a pixel obtained using Equation (5):  
\[PV=\left[\frac{NDVI-NDVI_{min}}{NDVI_{max}-NDVI_{min}}\right]^{2} \tag{5}\]  
\(NDVI_{max}\) and \(NDVI_{min}\) are global maximum and minimum values in the NDVI image, respectively [62]. The normalized difference vegetation index (NDVI) is retrieved from the reflectivity of red (\(\rho Red\)) and near-infrared (\(\rho NIR\)) using Equation (6):  
\[NDVI=\frac{\rho NIR-\rho Red}{\rho NIR+\rho Red} \tag{6}\]
# Psychaocoustic Models for Perceptual Audio Coding--A Tutorial Review
## 4 Models for Parametric Audio Coding
### Parametric Spatial Audio Coding
As a novel algorithmic ingredient, a _decorrelator filter_ was introduced to synthesize decorrelation between the output channels, as specified by the transmitted ICC value in each band [65]. The inter-channel cues can be determined using the following formulae, from the energies \(E_{1}\), \(E_{2}\) of the first and second audio channels and their complex-valued spectral coefficients \(x_{1,k}\), \(x_{2,k}\) for samples \(k\) within a given frequency band and time-slot:  
\[IID=10\log_{10}\frac{E_{1}}{E_{2}}\]  
\[ICC=\left|\frac{\sum_{k}\Bigl{(}x_{1,k}\cdot x_{2,k}^{*}\Bigr{)}}{\sqrt{E_{1} \cdot E_{2}}}\right|\]  
\[IPD=\,\measuredangle\sum_{k}\Bigl{(}x_{1,k}\cdot x_{2,k}^{*}\Bigr{)}\]  
Generalizations of the PS concept to multi-channel and surround sound can be found in the _Binaural Cue Coding (BCC)_ scheme [66; 67] (which was even published before PS) and MPEG Surround [68; 69; 70], both utilizing very similar spatial cues. In all cases, the perceptual model of the coding scheme is represented by the extraction of the inter-channel cues. A further extrapolation of these concepts for coding of several audio _channels_ to parametric coding of several _object signals_ can be found in [71] and MPEG-D Spatial Audio Object Coding (SAOC) [72]. Instead or inter-channel cues, inter-object cues (such as _Inter-Object Level Difference (IOLD)_ or _Inter-Object Correlation (IOC)_) are employed. Both joint parametric coding of multi-channel sound and multiple objects can be extended with so-called residual signals that offer an option for approaching waveform preservation in selected frequency regions [71; 72]. This allows the saturation of the perceptual quality obtainable (as it is typical for parametric coding schemes) at the expense of considerable additional bitrate to be overcome. Similar concepts of mixed parametric and waveform coding with a focus on lower computational complexity can be found in a previous study [73].
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
###### Abstract  
The amalgamation of Vehicular Ad hoc Network (VANET) with the Internet of Things (IoT) leads to the concept of the Internet of Vehicles (IoV). IoV forms a solid backbone for Intelligent Transportation Systems (ITS), which paves the way for technologies that better explain about traffic efficiency and their management applications. IoV architecture is seen as a big player in different areas such as the automobile industry, research organizations, smart cities and intelligent transportation for various commercial and scientific applications. However, as VANET is vulnerable to various types of security attacks, the IoV structure should ensure security and efficient performance for vehicular communications. To address these issues, in this article, an authentication-based protocol (A-MAC) for smart vehicular communication is proposed along with a novel framework towards an IoV architecture model. The scheme requires hash operations and uses cryptographic concepts to transfer messages between vehicles to maintain the required security. Performance evaluation helps analyzing its strength in withstanding various types of security attacks. Simulation results demonstrate that A-MAC outshines other protocols in terms of communication cost, execution time, storage cost, and overhead.  
authentication; internet of vehicles; intelligent transportation systems; security; vehicular Ad hoc networks +
Footnote †: journal:  
_Future Internet_
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Multicast Routing
#### 2.1.2 Routing Scheme
If SMF is combined with admission control and preemption, as suggested in [46], it may be considered QoS multicast routing. However, it would only be suited in context where most nodes are receivers.
# Herbal Support for the Nervous System: The Impact of Adaptogens in Humans and Dogs
## 1 Introduction
### Mechanism of Action of Adaptogens  
Adaptogens are a class of plant-derived substances that support the body in adapting to stress and restoring homeostasis. Their mechanism of action involves effects on the HPA axis, the sympathetic nervous system, and a range of biochemical processes that regulate stress responses. In the context of nervous system support, adaptogens demonstrate the ability to modulate neurotransmitter levels, reduce oxidative stress, and influence neuroplasticity in both humans and dogs [45; 46] (Figure 1).  
One of the key mechanisms by which adaptogens function is their role in cortisol regulation. Chronic stress leads to excessive activation of the hypothalamic-pituitary-adrenal (HPA) axis, resulting in elevated cortisol levels, which negatively affect the nervous system. Adaptogens have been shown to normalize cortisol levels, leading to a reduction in stress symptoms, improved mood, and enhanced cognitive function [47].  
Additionally, adaptogens influence neurotransmission, which is crucial for proper nervous system function. Studies have demonstrated that compounds found in adaptogens can increase serotonin and dopamine levels, contributing to mood enhancement and greater resilience to stress [48]. In humans, this effect manifests as reduced fatigue and improved concentration, while in dogs, adaptogens may support the treatment of separation anxiety and other behavioral disorders [49].  
Oxidative stress is another significant factor impacting nervous system health. Free radicals can damage neurons, leading to cognitive decline and an increased risk of neurodegenerative diseases [50]. Adaptogens possess strong antioxidant properties, protecting neurons from damage and supporting their regeneration. This mechanism is particularly relevant in aging organisms, both human and animal, where adaptogens may help slow down neurodegenerative processes [51; 52].  
Another crucial aspect of adaptogen activity is their impact on neuroplasticity--the brain's ability to adapt and regenerate. Research indicates that compounds in adaptogens can increase the expression of neurotrophic factors such as brain-derived neurotrophic factor (BDNF), which promotes neuronal regeneration and enhances cognitive function [53; 54].
## 4 Proposed ECC-GA-PASR Approach
### ECC-Based Authentication for the Trusted Authority (TA)
\[\text{Top }\sec\text{ret key of }SV_{1} =K_{1}=\delta_{1}\ *\ \delta_{2}\ *\ HF(TS_{1}) \tag{14}\] \[\text{Top }\sec\text{ret key of }SV_{2} =K_{2}=\delta_{2}\ *\ \delta_{1}\ *\ HF(TS_{2}) \tag{15}\]  
As a result, the intruder fails to identify the private and top-secret keys among the \(SVs\). Figure 8: Vehicular Communication with ECC-based Top Secret Key. This is the process of ECC-based Authentication that is given to the trusted authority (TA) in the network. Through this method, the vehicle identity is highly secured, and it gets free from all kinds of misbehavior activities. Pseudo code of the proposed ECC-GA-PASR  
START  
Initiate Network Model Construction:  
Static and Mobile Devices  
Vehicular Network  
Roadside units  
Trusted Authority  
Remote Cloud  
Initiate Thread Model Construction:  
Sybil Attack  
Blackhole Attack  
Initiate Security Model:  
Elliptic Curve Cryptography (ECC)  
Initiate Algorithm:  
Genetic Algorithm  
Proposed ECC-GA-PASR Approach:  
Genetic Algorithm (GA) based Optimized RSU Distribution:  
Best road segment selection  
Optimal RSU distribution  
\(P_{1}=\)\(RSU_{0D}=\sum_{r=1}^{n}RoS_{r}*\mu_{r}*B_{r}\)  
\(P_{2}=\)\(Fit_{function}=XC_{p}+\frac{r}{l_{n}}\)  
\(P_{3}=\)\(I_{cost}=\sum_{r=1}^{n}N_{r}*I_{r}\)  
\(M_{f}=\alpha P_{1}+\beta P_{2}+\gamma P_{3}\) with \(\alpha+\beta+\gamma=1\)  
ECC-based Authentication  
ECC Expression \(y^{2}=x^{3}+\alpha x+b\) (\(mod\)\(p\))  
Private Key Creation \(\mathcal{SY}_{1}=\delta_{1}.Pr_{3}\) and \(\mathcal{SY}_{2}=\delta_{2}.Pr_{5}\)  
Secret Key Creation \(T_{1}=\)\(t_{1}*t_{1}\) and \(TS_{2}=\)\(t_{2}*t_{2}\)  
Hash Key Creation \(M=\)\(P_{2}+HF(TS_{1})+HF(TS_{2})\)  
Top secret key of \(\mathcal{SY}_{1}\)= \(K_{1}=\delta_{1}*\delta_{2}*HF(TS_{1})\)  
Top secret key of \(\mathcal{SY}_{2}\)= \(K_{2}=\delta_{2}*\delta_{1}*HF(TS_{2})\)  
STOP
## 3 Results and Discussion
### Adsorption Performances of DE and DE-LDH  
#### 3.2.1 Effect of pH  
The dye removal efficiency could be influenced by the adjustment of the pH of the solution and it is important to investigate the CR removal efficiency of DE and DE-LDH at various pH levels.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multicolumn{4}{c}{**Before Dye Adsorption**} & \multicolumn{4}{c}{**After Dye Adsorption**} \\ \cline{2-9}  & **a\({}_{\text{s,BET}}\)**,** & **a\({}_{\text{p}}\) (nm)** & **V\({}_{\text{p}}\)**,** & **Type of** & **a\({}_{\text{s,BET}}\)**,** & **d\({}_{\text{p}}\) (nm)** & **V\({}_{\text{p}}\)**,** & **Type of** \\  & **(m\({}^{2}\)/g)** & **(\(\pm\)0.5)** & **(\(\pm\)0.5)** & **Isotherm** & **(\(\pm\)1.5)** & **(\(\pm\)1.5)** & **(\(\pm\)0.03)** & **Type of** \\ \hline DE & 28 & 2.44 & 0.0225 & II & 18 & 2.44 & 0.0183 & II \\ DE-LDH & 51 & 3.33 & 0.0697 & II & 44 & 3.33 & 0.0712 & II \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface area and porosity measurement of adsorbents.  
Figure 4: The zeta potential measurement of DE and DE-LDH.  
However, the CR is not stable below pH 5. Thus, current study was carried out by varying the pH from 5 to 10 to remove 25 mL of 50 mg/L CR using 20 mg of adsorbent (Figure 5a). DE shows that the dye removal efficiency was gradually decreased from 8.2% to 0.55% with increasing pH from 5 to 10 [29]. This decrease in the dye removal efficiency is evident as the surface of DE becomes highly negative (-2.9 to -32 mV when the pH varied from 2 to 10 as shown in Figure 4). This negative charge on the surface of DE electrostatically repels negatively charged CR. On the other hand, the zero-point charge of DE-LDH was found to be 10.9. Thus, the surface of DE-LDH is positively charged (OH\({}_{2}\)\({}^{+}\)) below pH 10.9 and it electrostatically attracts negatively charged -SO\({}_{3}\)\({}^{-}\) groups of anionic CR. Therefore, DE-LDH showed maximum dye removal efficiency (93.3%) at pH 5 where it had an efficiency of 19.1 mV. The dye removal efficiency is decreased after pH 10 as the surface of DE-LDH becomes negatively charged, which electrostatically repels the negatively charged dye. However, the dye removal efficiency of DE-LDH did not vary much between pH 5-9. Thus, pH 7 was selected and we carried out all further experiments at pH 7. The removal efficiency was higher for DE-LDH compared to DE due to the surface area and surface functional groups of LDH that were present on DE, which played a vital role in the effect of pH.
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 3 Time-Localization Verification and Smoothing Localization Method
### Smoothing Localization Method
After the constrained optimization of Equation (33), the coordinates of the mobile terminal \(\mathbf{\chi}^{\rm{GV}}=(\mathbf{\chi}_{1}^{\rm{GV}},\mathbf{\chi}_{2}^{\rm{GV}},\ldots, \mathbf{\chi}_{n}^{\rm{GV}})\) are finally obtained, which are the localization results of the GNSS/Visual SLAM system to the mobile terminal calculated by the smoothing localization method. A flow-chart of GNSS spoofing identification and smoothing localization method is shown in Figure 4.
# An Approach to Designing Critical Railway Voice Communication
## 4 FRMCS Common Functions as RESTful Service Blocks
### FRMCS Critical Support Application API
Section 3 presents some use cases that illustrate the use of defined services in critical voice communication applications.
# Early Age Assessment of a New Course of Irish Fly Ash as a Cement Replacement
## 2 Materials and Methods
### Concrete Compressive Strength Assessment  
Nine 100 \(\times\) 100 \(\times\) 100 mm concrete cubes were cast for each fly ash replacement level and subjected to compressive strength testing at 7, 28 and 56 days. All the mixes had a fixed w/c ratio of 0.5 and a total cement/binder content of 400 kg/m\({}^{3}\), with a design slump of between 100 and 150 mm. The CEM I cement used complied with BS EN 197-1 [3], and the fine and coarse aggregates were obtained from local sources in Ireland. The fine aggregate used was medium-grade sand, and the coarse aggregate was crushed limestone with a maximum size of 20 mm. A summary of the mixes is shown in Table 4.  
Following mixing in a pan mixer, the concrete was poured into the moulds in 50 mm thick layers and vibrated on a vibrating table until no more air bubbles were visible on the surface. The cubes were cured for 24 h under a polythene sheet to trap evaporating moisture, demoulded and then placed into a water-filled curing tank set to 20 \({}^{\circ}\)C until testing.
## 4 Results and Discussion  
### Regression Model  
SPSS is the software used for statistical analysis in the current study. It has been used to relate independent parameters with dependent parameters in a regression model. The primary energy is a dependent variable in this study that represents the energy intensity of selected samples, and the independent variables are electricity, gas and clean water consumption. Figure 3 illustrates the predicted value of DECC clusters using the regression model.  
Figure 3: Prediction results of regression model; (**a**) cluster 1; (**b**) cluster 2; (**c**) cluster 3; (**d**) cluster 4.  
Figure 2: Topology of a fully connected three-layered MLP network.  
In Figure 3a cluster 1 has low number of samples (29). The predicted value of primary energy has less values than real values, which means the performance of these samples is not efficient. In this case, more efforts have to be done to improve or modify the energy plan. The coefficient of variation (CV) value was an average of 0.1 for this cluster. Cluster 2 has 157 samples of DECC database as shown in Figure 3b, the coefficient of variation CV = 0.08 as average. The predicted value is less than the real one, which means the energy performance of these samples is still not sufficient but it is better than the previous case. In Figure 3c, cluster 3 has sufficient size of samples (97) and has less variance between predicted and real values CV = 0.11. Last cluster has (51) samples and shows good results compared to cluster 1 as a small size of samples, it is illustrated in Figure 3d and it has a good value of coefficient variation where CV = 0.1.  
The main aim of using the regression model in benchmark processing is to determine the regression impact of each variable on the primary energy. These values of impact will be used in the general equation of benchmarking (1). This equation introduces a reference for energy evaluation between similar buildings within same cluster. The regression model defines the benchmark equation for DECC data as the following:  
Cluster 1 : \(EUI=369.52+1.01X+0.83Y+(-0.18)Z+421.47\) Cluster 2 : \(EUI=358.21+1.08X+0.45Y+0.035Z+111.114\) Cluster 3 : \(EUI=56.52+0.99X+0.9Y+0.07Z+153.38\) Cluster 4 : \(EUI=620.67+1.3X+0.13Y+(-0.01)Z+271.95\)  
where \(X\) is the electricity consumption (kWh/m\({}^{2}\cdot\)year), \(Y\) is the city gas consumption (m\({}^{3}\times 10^{-3}\)/m\({}^{2}\cdot\)year), \(Z\) is the clean water consumption (m\({}^{3}\)/m\({}^{2}\cdot\)year), and \(EUI\) is the energy use intensity (MJ/m\({}^{2}\cdot\)year).
# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 3 Results
### Evaluation of the XGBoost Model
A notable decline was observed in  
Figure 10: Scatter plots of WJ zonal velocity from the OSCAR dataset versus XGBoost-estimated WJ zonal velocity in (**a**) February, (**b**) May, (**c**) August, and (**d**) November in 2022. Figure 9: Spatial discrepancies between the XGBoost-reconstructed Wyrtki Jet zonal velocity and the OSCAR-reconstructed zonal velocity in the equatorial Indian Ocean in (**a**) February, (**b**) May, (**c**) August, and (**d**) November in 2022. August (0.11 m/s), reaching the minimum of \(-\)0.20 m/s in February. These variations are closely associated with the semi-annual reversals of the Indian Ocean monsoon system. The reconstruction results of the XGBoost model demonstrated exceptional consistency with observational data, successfully capturing the seasonal extremes of zonal velocity in the WJ in the equatorial Indian Ocean: the minimum value (\(-\)0.21 m/s) in February and the maximum value (0.35 m/s) in November. The reconstructions exhibited near-perfect correlation with the OSCAR data (\(R^{2}\) = 0.99) and an ultra-low root mean square error (\(RMSE\) = 0.004 m/s), robustly validating the model's reliability in resolving the seasonal variability of the WJ. A marginal underestimation (0.01 m/s error) was observed in September but remained within acceptable thresholds.
# Mail Man Ferry: A Novel Routing Protocol in Intermittently Connected Networks
## 5 Performance Evaluation
### Simulation Environment  
In this research, we used the ONE simulator [27] to study the effect of the Mail Man Ferry technique in an ICN routing setting. To put it simply, ONE is software for simulating \"one-off\" situations. It incorporates tracking locations, planning routes, viewing data, and reports generation. How a node moves in a simulation is set by its mobility model. The random waypoint model (RWPM) is frequently used and is based on random headings and speeds. For people carrying mobile devices, however, this kind of random node mobility is impractical. It is more practical to suppose that nodes travel toward one destination, then another, and so forth. Malls, restaurants, schools, and other commonplace establishments are examples of places of attraction. The more realistic shortest path movement model (SPMM), which has nodes traveling toward specific destinations, was used here.  
The simulation settings that were employed for this study are based on the real environment that was discussed in [28], which is where the Helsinki City Scenario (HCS) model is used. The scenario involves nodes moving throughout part of the central business district of Helsinki. The mobility of HCS nodes is determined by modeling the movements of sixty mobile persons as they navigate the streets of downtown Helsinki by foot, vehicle, and tram. Each node illustrates a person moving at a speed that is consistent with real life along the routes that are the shortest distance between a variety of points of interest (POIs) and locations that were chosen at random. In Helsinki, the trans travel along routes that were originally intended for them. The simulation area was designed to be 4500 \(\times\) 3400 m\({}^{2}\) in size. The simulation environment parameters were broken down into their essential components and are shown in Table 1 below.
# The Impact of GoLo Technology on the Quality Properties of Dealcoholised Wines
## 2 Materials and Methods
### Sensorial Analysis
#### 2.8.1 Chromatic Characteristic  
Wine colour analysis was carried out using the chromatic difference (\(\Delta\)C) between wine before and after the dealcoholisation process using GoLo technology. Equation (1), based on the OIV method [33], was used to calculate \(\Delta\)C. The intensity (I) of colour is given by the sum of absorbances using a 1 cm optical path at wavelengths of 420, 520, and 620 nm (Equation (2)). The other chromatic characteristic is the shade (N) and is expressed as the ratio of absorbance at 420 and 520 nm (Equation (3)). Absorbance was determined using glass cuvettes with a path length of 0.2 cm.  
\[\Delta\mathrm{C}=\Delta\mathrm{I}+\Delta\mathrm{N} \tag{1}\]  
\[\mathrm{I}=\mathrm{A420}+\mathrm{A520}+\mathrm{A620} \tag{2}\]  
\[\mathrm{N}=\mathrm{A420}/\mathrm{A520} \tag{3}\]