esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### CO\({}_{2}\) Adsorption Performance
This indicated that the O-C coordination could be an adsorption mechanism for CO\({}_{2}\) adsorption on ACFs, where the CO\({}_{2}\) molecules (as an acidic gas) prefer to bind to the surface O-functionalities of ACFs [36]. For the purpose of comparison, Table 3 shows the CO\({}_{2}\) adsorption capacities in this study and on various supporting materials, with or without modifications, from the literature. This comparison indicates that the CO\({}_{2}\) adsorption on ACFs in this research were comparable or superior to other porous carbons. The adsorption technique applied in gas separation required the cyclic regeneration of the adsorbents. Thus, multiple CO\({}_{2}\) adsorption-desorption cyclic tests were conducted on the adsorbents to evaluate further practical applications. Figure 8 displayed ten cycles of adsorption-desorption experiments for CO\({}_{2}\) adsorption at 25 \({}^{\circ}\)C. Within each cycle, the adsorbents were desorbed by subjecting them to the conditions of a dynamic vacuum. The adsorption capacities of CO\({}_{2}\) were decayed by about 1.8% (ACF), 5.0% (aACF) and 2.1% (nACF) after 10 cyclic runs, respectively. Therefore, the adsorbents were judged to exhibit an excellent cyclability. The results presented in Figure 8 seem to suggest that the decay in adsorption efficiency was mostly taking place after the first adsorption cycle. It was expected that CO\({}_{2}\) molecules could get into all pores in the first adsorption cycle, especially the ultramicropores. When the adsorbents were desorbed in a vacuum, some CO\({}_{2}\) molecules could not escape effectively, most probably in the ultramicropores, because of the confinement effect. Thus, aACF, having the largest V\({}_{\rm ultra}\), exhibited the highest decay of the adsorption capacity after the first adsorption cycle. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Temperature (\({}^{\circ}\)C)** & **K\({}_{\rm F}\) (nmole/g/kPa\({}^{\
icefrac{{1}}{{n}}}\))** & **n** & **R\({}^{2}\)** & **ARE (\%)** \\ \hline ACF & 25 & 0.134 & 1.61 & 0.9997 & 2.26 \\ \hline  & 40 & 0.084 & 1.52 & 0.9996 & 2.37 \\  & 55 & 0.052 & 1.43 & 0.9995 & 2.38 \\ \hline aACF & 25 & 0.164 & 1.64 & 0.9998 & 1.89 \\ \hline  & 40 & 0.102 & 1.54 & 0.9998 & 1.76 \\  & 55 & 0.063 & 1.50 & 0.9993 & 1.54 \\ \hline nACF & 25 & 0.089 & 1.59 & 0.9998 & 1.48 \\ \hline  & 40 & 0.052 & 1.47 & 0.9999 & 1.06 \\  & 55 & 0.031 & 1.36 & 0.9999 & 1.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Curve-fitting of the Frenudlich equation for the CO\({}_{2}\) adsorption data at different temperatures.
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 2 Literature Survey
For binary and multiple-class classifications of attack labels, two separate experiments are run on each dataset, one for all attacks and the other just for DDoS attacks in both datasets. In the trials carried out on the BOT-IOT dataset they used the SMOTE approach and variants for class balancing. In all this research it can observed that multiple datasets related to DDoS attacks are used, such as CICDDoS, BOT-IOT, UNWS-NB, etc., and most of the ML and DL algorithms are giving good results for the models. It is observed that random forest, logistic regression, and naive bayes algorithms give really good results for DDoS attacks in IoT. According to previous work, the existing models are complex and not as reliable in detecting DDoS attacks when compared to the proposed model. The research has performed data standardization along with feature selection to retrieve the 15 best features out of 45 features from both datasets over which training and testing were performed. Hence, this approach makes the model a lightweight model. The SMOTE technique was used to cross out overfitting and resample the imbalanced data. Five-fold cross-validation was performed for multiple class classification to improve accuracy. Five ML algorithms, namely logistic regression, random forest, ANN, naive bayes, and KNN, were used to train and test the model (Table 1). Considering this above-mentioned methodology, it can be stated that this research is very much justified and effective in detecting DDoS attacks efficiently in IoT devices, and it managed to achieve higher accuracy than all other existing models. One of the most important factors that is required while detecting attacks in IoT devices, which is present in this model and is not present in any other state of the model, is training and prediction time for all algorithms.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### Service Placement Metric
#### 2.3.3 Cost  
Cost efficiency of placement and migration try to reduce the CDCs' service operation cost. In previous approaches, SP methods have been studied to minimize cost while considering QoS. Table 5 shows the cost metrics in the cost-efficient SP and migration approaches. We categorized the SP metrics. The following three metrics were typically applied in recent studies: VM cost, PM cost, and the distance between VMs and service users. These metrics were optimized according to different behaviors to minimize metrics. The approaches for cost saving are explained as follows. The following metrics were also considered for different service types.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Metric** & **Explanation** & **Optimization Behavior** & **Previous Research Reference Number** \\ \hline \multirow{2}{*}{Resource utilization (RU)} & The amount of resource usage for service execution & Maximize RU & [9; 10; 19; 20] \\ \hline \multirow{2}{*}{Energy overhead (EO)} & The amount of extra resources usage to perform SP & Reduce EO & [33; 34; 35] \\ \hline Number of migration (NM) & The NM for SP & Minimize NM & [17] \\ \hline \multirow{2}{*}{Energy consumption (EC)} & Amount of energy to perform SP in CDC & Reduce EC & [10; 13; 30; 34] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy efficiency metric.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Metric** & **Explanation** & **Optimization Behavior** & **Previous Research Reference Number** \\ \hline \multirow{2}{*}{Execution time (ET)} & The amount of time from start to finish during SP & Minimize ET & [7; 8] \\ \hline \multirow{4}{*}{Waiting time (WT)} & The amount of time that a certain VM is occupied resource, while the other VM is waiting for using the resource. & Reduce WT & [14; 26] \\ \hline Resource utilization (RU) & The average percentage for RU & Maximize RU & [9; 15; 23; 24; 25] \\ \hline \multirow{2}{*}{Transferred data (TD)} & The amount of transfer data during the SP & Reduce TD & [25] \\ \hline Number of migrations (NM) & The NM for SP & Minimize NM & [30] \\ \hline \multirow{2}{*}{Throughput (TP)} & The number of tasks until completion of SP. & Maximize TP & [32] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metric.
# Achievable Rate Maximization for Multi-Relay AF Cooperative SWIPT Systems with a Nonlinear EH Model
## Appendix A  
Let:  
\[C_{4,l}=\min\Bigl{\{}\eta_{l}P_{\text{ER},}^{\text{TS}},M_{l}\Bigr{\}}\big{|} h_{R_{l},D}\big{|}^{2}\Big{/}N_{0} \tag{11}\]  
and:  
\[C_{5,l}=P_{s}\big{|}h_{S,R_{l}}\big{|}^{2}\Big{/}N_{0}. \tag{12}\]  
By denoting \(\gamma_{sr,l}^{\text{TS}}\) as \(f_{l}(\alpha)\) and substituting (12) and (13) into (11), \(f_{l}(\alpha)\) can be expressed as:  
\[f_{l}(\alpha)=\frac{2\alpha C_{4,l}C_{5,l}}{2\alpha C_{4,l}+(C_{5,l}+1)(1- \alpha)}. \tag{13}\]  
By substituting (13) into the objective function in (25), the second derivative of \(R^{\text{TS}}\) with respect to \(\alpha\) can be obtained as:  
\[\left(R^{\text{TS}}\right)^{\prime\prime}=\frac{\sum_{l=1}^{L}\left[(1-\alpha )f_{l}^{\prime\prime}(\alpha)-2f_{l}^{\prime}(\alpha)\right]}{2\log_{2}\! \left[1+\sum_{l=1}^{L}f_{l}(\alpha)\right]}-\frac{(1-\alpha)\!\left[\sum_{l=1 }^{L}f_{l}^{\prime}(\alpha)\right]^{2}}{2\log_{2}\left[1+\sum_{l=1}^{L}f_{l} (\alpha)\right]^{2}}, \tag{14}\]where \(f_{l}^{\prime}(\alpha)\) and \(f_{l}^{\prime\prime}(\alpha)\) are the first and second derivative of \(f_{l}(\alpha)\) with respect to \(\alpha\), respectively. Since \(\alpha\in(0,1)\), it is obviously that the second term in (A4) is negative. For the first term in (A4), it can be derived that:  
\[(1-\alpha)f_{l}^{\prime\prime}(\alpha)-2f_{l}^{\prime}(\alpha)=\frac{-8C_{5,l}( C_{5,l}+1)C_{4,l}^{2}}{\left[2\alpha C_{4,l}+(C_{5,l}+1)(1-\alpha)\right]^{3}}.\] (A5)  
Since \(C_{4,l}>0\), \(C_{5,l}>0\) and \(\alpha\in(0,1)\), it can be easily obtained from (A3) and (A5) that \(f_{l}(\alpha)>0\) and \((1-\alpha)f_{l}^{\prime\prime}(\alpha)-2f_{l}^{\prime}(\alpha)<0\), and hence, the first term in (A4) is negative. Therefore, the second derivative of \(R^{\mathrm{TS}}\) with respect to \(\alpha\) given by (A4) is negative in \(\alpha\in(0,1)\), which proves that \(R^{\mathrm{TS}}\) is the concave function with respect to \(\alpha\) in \(\alpha\in(0,1)\).
# Extending Battery Lifetime by Avoiding High SOC
## 2 Methods
### Measurement Results  
To be able to compare the tested cells, regardless of DOD used during the tests, all test cycles were normalised to FCEs. In Figure 2, the capacity retention with various SOC levels are given as function of FCE. The lifetime tests were performed over a three-year period. Tests in higher SOC levels and higher C-rates did not require as much test time as tests conducted in lower SOC levels and using lower C-rates.  
During this long test period, maintenance of the test equipment had to be performed at several occasions. An RPT test was performed before and after a planned maintenance stop, thus showing two results for the same cycle number where some of the results showed rather big differences. One of this events is marked with a read ellipse in Figure 2b where a vertical drop can be seen in the capacity curve. During these rest periods, the cells are in most cases losing capacity, though in the case for the aggressive test using a 4C rate, the cell instead recovered capacity. The cells cycled at low SOC levels had an average of three of these planned stops, while the shorter tests conducted at high SOC levels only had two in average. Furthermore, the data have higher fluctuations than expected, compared to cycle tests conducted using a large DOD (see [6]). Too few reports have been published on testing in small DODs to explain this. Similar fluctuations, though not as large, can be seen in the data presented by [PERSON] et al. [3], supporting that tests in small DODs introduce a behaviour not seen when cycled in a large DOD.  
The data show two distinct ageing patterns, one for the lower SOC levels and one for the higher SOC levels. This has also been observed when performing post mortem on the investigated cells where two cells in 60-70% SOC and 10-20% SOC were studied. The study showed that cycling in 60-70% SOC generated greater loss of cathode material, thicker SEI (Solid Electrolyte Interface) layer, and higher resistance on the anode compared to the cell cycled in 10-20% SOC [12]. Surprisingly, in the lower SOC levels, even the 4C test is performing well. All the 10% DOD data were used to develop the empirical ageing model. The 0-30% SOC data was used as a verification case for the model.  
Calendar ageing in 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C at two SOC levels, 15 and 90% SOC, were performed for almost 700 days. The results, shown in Figure 3, clearly shows the difference between the low and high SOC levels, as well as the expected behavior for increased temperature.  
Figure 2: The ageing data used to parameterise the empirical ageing model in (**a**) 25 \({}^{\circ}\)C and (**b**) 35 \({}^{\circ}\)C. In (**b**), the data is also used to verify the model included. The vertical changes in capacity indicates stops in the testing due to maintenance, i.e., a rest period.
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 1 Introduction: Random Number Generation for Security
We analyzed a wide family of digital TRNGs, which can be summarized in Table 1.  
The Fibonacci-Galois Ring Oscillator (FiGaRO) TRNG is assessed to be the most suitable for the FPGA implementation and is therefore further investigated in terms of performances and resource utilization. The rest of the work is organized as follows. In Section 2 we give some definitions about the figures of merit we will use in the rest of the article, then we present the set of digital ring oscillators we evaluated. In Section 3 we present a method to evaluate TRNG randomness on FPGA technology; such a method is then used to select the best architecture, as a trade-off between entropy and complexity. In Section 4 the selected entropy source is further characterized following the NIST guidelines and FPGA implementation results \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref.** & **Architecture** & \
\begin{tabular}{l} **Physical Phenomena** \\ **Generating Entropy** \\ \end{tabular} &
\begin{tabular}{l} **Preliminary** \\ **Observation** \\ \end{tabular} \\ \hline
[18] & Transition Effect Ring Oscillator (TERO) & Latches oscillatory metastability & Small bandwidth, large dependence on placing \\ \hline
[19] & Metastable Ring Oscillators (Meta-RO) & Analogue metastability of inverter gates & PLL required, dependence on placing \\ \hline
[20] & Fibonacci Ring Oscillator (FiRO) & Jitter and Metastability & Good independence from placing \\ \hline
[20] & Galois Ring Oscillator (GaRO) & Jitter and Metastability & Good independence from placing \\ \hline
[21] & Fibonacci-Galois Ring Oscillator (FiGaRO) & Jitter and Metastability &
\begin{tabular}{l} Independence from placing, higher entropy and robustness respect to single Fibonacci and \\ Galois Oscillator \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Digital True-Random Number Generators (TRNGs) in literature. are shown for the selected random number generator. Finally, in Section 5 we conclude this work.
## 7 Conclusions  
An elevation-based shadowing statistical model was developed for low-altitude UAV (unmanned aerial vehicle) channels in various environments, including suburban, urban, dense urban, and high-rise urban settings. This model, formulated as a function of elevation angle, covers a frequency range from 1.8 GHz to 6 GHz and altitudes up to 500 m, considering both vertical and horizontal polarization. The shadowing path loss for non-line-of-sight (NLoS) conditions is presented with elevation angle. It is a valuable  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model Parameter** & **Value** \\ \hline Frequency & 5.8 GHz \\ \hline hUAV & 200 m \\ \hline h\({}_{\text{r}}\) & 2 m \\ \hline G\({}_{\text{o}}\) & 2.15 \\ \hline \(\theta_{3}\) & 67 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model parameters.  
Figure 10: Proposed model vs other models.  
tool for radio network planning to support the expansion of 5G communication in Indian metropolitan cities. Based on extensive simulations, the model provides an efficient formula that minimizes computational time while maintaining a strong correlation with existing air-to-ground models. Additionally, it analyzes the impact of frequency and UAV height adjustments, offering a structured framework for determining optimal UAV altitudes, ultimately maximizing ground coverage and enhancing communication performance. Derived from extensive simulations, the model provides a simplified formula for implementation, effectively reducing computational time. Notably, it demonstrates a strong correlation with other air-to-ground models. Furthermore, the model analyzes the effects of frequency and UAV height adjustments, providing a straightforward framework for determining the optimal UAV altitude to maximize ground coverage.  
Conceptualization, A.K.P. and R.D.J.; methodology, A.K.P.; software, A.K.P.; validation, A.K.P.; formal analysis, A.K.P. and R.D.J.; investigation, A.K.P. and R.D.J.; resources, A.K.P.; data curation, A.K.P.; writing--original draft, A.K.P.; writing--review and editing, A.K.P. and R.D.J.; visualization, A.K.P. and R.D.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The contributions made in this study are documented within the article. For any additional inquiries, please direct your questions to the corresponding author.  
This work was supported by the College of Engineering, Pune.  
Authors declare that there are no known conflicts of interest related to this study.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Data Collection and Processing
#### 2.2.2. Remote Sensing Data  
Several RS data from various platforms with different spatial resolutions were considered (Table 1). The Global Satellite Mapping of Precipitation (GSMaP) by the Japan Aerospace Exploration Agency (JAXA) was used, which provides global precipitation data using a combination of sensors at ~11 km [25]. It was developed in Japan specifically for the GPM mission [26]. The Land Surface Temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS), operated by the National Aeronautics and Space Administration (NASA), was obtained at a resolution of 1 km [27]. It should be noted that the thermal sensor in the MODIS only offers 1 km resolution. Landsat 8, operated by NASA and the United States Geological Survey (USGS), offers multispectral data at a resolution of 30 m [28]. Land cover classification and analysis of vegetation are potential applications of these data. Sentinel-2 was developed by the European Space Agency (ESA) and provides  
Figure 1. Overview of the location of the almond orchards, for each almond grower (AG) (**a**), and (**b**) the yield values, from 2017 to 2021, presented in kg/ha, for each AG.  
multispectral imagery at a resolution of 10 m. This is the best resolution imagery available for free today [29]. PlanetScope, which is a proprietary data source, is formed of several small satellites (constellation), operated by Planet Labs Inc., designed for high-frequency global imaging of Earth. The satellites acquire imagery in the visible and near-infrared spectra and provide a spatial resolution of 3 m. The proprietary high-resolution data provided by PlanetScope enables detailed mapping and monitoring of various features, including urban areas, vegetation dynamics, and environmental changes [30].  
Figure 2: Data processing workflow stages: (**1**) data collection; (**2**) dataset creation; (**3**) application of machine learning (ML) models; and (**4**) model evaluation.  
Monthly composites from GSMaP, MODIS Terra LST, Landsat 8, and Sentinel-2 (from 2017 to 2021) were computed using the Google Earth Engine (GEE). The GEE is an online infrastructure that archives satellite imagery and geospatial data, offering powerful analytics tools, leveraging cloud-based infrastructure. These benefits make it an invaluable tool for exploring Earth's dynamics and supporting fact-based decision making [31]. On the other hand, PlanetScope's monthly composites were acquired using the Planet Explorer platform, which is a fully automated, cloud-based imaging and analysis platform that grants users access to comprehensive, daily data from the PlanetScope and SkySat constellations.
## 5 Energy Consumption and System Optimization in WBSNs
As described previously, and based on a convex optimization model [49], only \(bE_{circ}\) will be evaluated for the optimization as follows:  
\[bEcirc_{i}\ =\ (Ptx_{i}SF_{i}M_{i})/DR\ +\ (Ptx_{i}\cdot O_{v})/(DR\cdot T_{i}) \tag{3}\]  
where \(Ptx_{i}\) is the transmission power; \(SF_{i}\) represents the sampling frequency of sensor \(i\) (samples per second); \(M_{i}\) is the length of measurement sample in bits per sample, data rate is defined by \(DR\); \(O_{v}\) and \(T_{i}\) are used to describe Overhead and Data updating time interval of sensor \(i\), respectively. \begin{table}
\begin{tabular}{c c} \hline
**Operation** & **Energy Consumption** \\ \hline Data sensing & \(E_{ss_{i}}(b)=bV_{inp}I_{ss}T_{ss}\) \\ Data register & \(E_{reg_{i}}(b)=bV_{inp}(I_{wr}T_{wr}+I_{rd}T_{rd})\) \\ TX and RX & \(E_{tx_{i}}(b,d_{ij})=bE_{circ}+bd_{ij}^{n}E_{pamp}\) \\ State shifting & \(E_{sh_{i}}=T_{A}V_{inp}[C_{N}I_{A}+(1-C_{N})I_{S}]\) \\ \hline \end{tabular}
\end{table} 
Table 4: Energy consumption according to different subsystems of sensor node \(i\).
## 5 System Implementation and Configuration
### Technical and Economic Criteria of the SPV/WTG System
#### 5.4.3 WTG  
The FT-1000L WTG model is considered in this study; this model is affordable [33]. In addition, Figure 11 summarises the output power at different wind speed values. The technical specifications and costs used in the HOMER simulation are based on the proposed WTG model and are listed below:  
1. Economic issues: the initial installation costs of a WTG, the replacement costs, and the annual O&M costs per 1 kW are $600, $600, and $50, respectively.  
Figure 10: Seasonal load profiles: (**a**) DC-load profile; (**b**) AC-load profile.  
2. Technical issues: the lifetime of a WTG and the height of the hub are 15 years and 50 m, respectively.
3. WTG size: the sizes of the simulation values of the applied WTG are 1, 2, and 3 kW.
## 4 Results and Discussion
### Effects of Settlement Amplitude  
Figure 12 shows the deformation curve and deformation amplitude of the track when the settlement amplitude increases from 5 to 30 mm and the settlement wavelength remains unchanged at 15 m.  
Figure 12a indicates that the deformation amplitude of the slab track gradually increases with an increase in the settlement amplitude of the subgrade. However, the change in the deformation wavelength of the slab track is not obvious. As shown in Figure 12b, the deformation amplitude of the slab track increases nonlinearly with an increase in the settlement amplitude of the subgrade. The growth rate of the deformation amplitude of the slab track decreases when the settlement amplitude of the subgrade is greater than 10 mm. In this case, there is a gap between the concrete base and subgrade. The track structure is in a suspended state.  
Figure 13 shows the gravel contact forces in the surface layer of the subgrade under different settlement amplitudes, as well as the growth rate of the contact forces compared to the unsettled condition.  
Figure 13 indicates that the average contact force of the gravel grains in the subgrade increases linearly with an increase in the settlement amplitude. However, the growth rate of the average contact force is small (less than 4%) compared to the unsettled condition. The maximum contact force increases nonlinearly with an increase in the settlement amplitude. The growth rate of the maximum contact force decreases and the track is in a suspended state when the settlement amplitude of the subgrade is greater than 10 mm. When the settlement amplitude is 5 mm, the maximum contact force is 138.55% greater than that in the unsettled condition. It can be inferred that the maximum contact force of the gravel grains in the subgrade increases significantly even when the settlement amplitude of the subgrade is small.  
Figure 12: Deformation of the track at different settlement amplitudes.