esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Land Use and Land Cover Simulation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**LULC Types**} & \multicolumn{8}{c}{**Area of Simulated LULC in km2**} \\ \cline{2-11}  & **2020** & **2021** & **2022** & **2023** & **2024** & **2025** & **2026** & **2027** & **2028** & **2029** \\ \hline Urban and built-up area & 145.03 & 148.29 & 151.54 & 154.81 & 158.07 & 161.32 & 164.55 & 167.83 & 170.92 & 174.34 \\ Paddy field & 0.13 & 0.13 & 0.10 & 0.09 & 0.07 & 0.06 & 0.05 & 0.03 & 0.02 & 0.00 \\ Field crop and horticulture & 3.47 & 3.52 & 3.82 & 3.76 & 3.99 & 4.19 & 4.30 & 4.44 & 4.39 & 4.67 \\ Perennial trees and orchards & 182.16 & 180.01 & 177.87 & 175.74 & 173.55 & 171.46 & 169.35 & 167.20 & 165.21 & 162.90 \\ Aquaculture & 8.47 & 8.47 & 8.45 & 8.45 & 8.45 & 8.30 & 8.25 & 8.17 & 8.17 & 8.07 \\ Idle land & 40.03 & 40.29 & 40.54 & 40.82 & 41.01 & 41.32 & 41.59 & 41.85 & 42.09 & 42.35 \\ Evergreen forest & 72.96 & 71.79 & 70.62 & 69.47 & 68.27 & 67.15 & 65.98 & 64.83 & 63.71 & 62.49 \\ Mangrove forest & 24.68 & 24.66 & 24.44 & 24.44 & 24.19 & 24.10 & 24.01 & 24.01 & 23.82 \\ Scrub forest & 26.85 & 26.84 & 26.81 & 26.79 & 26.74 & 26.76 & 26.75 & 26.74 & 26.77 & 26.69 \\ Waterbody & 14.98 & 14.98 & 14.98 & 14.98 & 14.98 & 15.04 & 15.04 & 15.04 & 15.02 \\ Miscellaneous land & 3.30 & 3.08 & 2.88 & 2.70 & 2.48 & 2.32 & 2.08 & 1.91 & 1.72 & 1.50 \\ \hline Total & 522.05 & 522.05 & 522.05 & 522.05 & 522.05 & 522.05 & 522.05 & 522.05 & 522.05 & 522.05 & 522.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Multiple linear regression equation of each LULC type and area under the curve value from logistic regression analysis. According to the results, the LULC types that will increase in 2029 are urban and built-up areas, field crops and horticulture, idle land, scrub forest, and water bodies. On the contrary, the LULC types that will decrease in 2029 are paddy fields, perennial trees and orchards, aquaculture, evergreen forest, mangrove forest, and miscellaneous land. These findings are based on the driving factors of LULC change, conversion matrix, and elasticity of LULC change and their land requirements, which were applied to the CLUE-S model for LULC simulation between 2020 and 2029. In this study, a slight difference was found between the land demand (required land area) and the simulated area of each LULC type in 2029. The deviation between the land demand and the simulated area in 2029 is \(-0.01\) km\({}^{2}\) because the deviation value depends on the iteration driving factors of each LULC type, which indicates the different maximum allowances between the required and allocated areas of LULC types under the CLUE-S model [57; 58; 59]. Figure 7: Spatial distribution of the simulated LULC scenario between 2020 and 2029.
## 3 Results and Discussion
### Identification of Bioactive Compounds by SPME-GC-Ms  
Analysis of the compounds in the bioactive HPTLC zones was performed after their elution with ethanol. Only in the marked zones (H1, H2, H3, C1, and C2 in Figure 3) were the components detectable by SPME-GC-MS. A high amount of \(\beta\)-caryophyllene (about 55%) in the H1 zone was next to lower amounts of muurolene, eudesnene, and cadinene isomers. A comparison of the size of the H1 inhibition zone with the \(\beta\)-caryophyllene content of the oils suggested that the minor components could be responsible for the bioactivity of the zone. In the H2 zone of Saaz oil, \(\beta\)-farnesene and \(\alpha\)-humulene were identified at a ratio of 1:1, but only \(\alpha\)-humulene was detectable in the H2 zone of Azacca oil (\(\beta\)-farnesene was not detectable in any zone of Azacca oil). Therefore, it could be stated that the role of \(\beta\)-farnesene in the \(\alpha\)-glucosidase inhibition and anti-_A. fischeri_ activity in the H2 zone was more determinative than that of \(\alpha\)-humulene. The \(hR_{\mathrm{F}}\) value and the bio-effects of \(\beta\)-farnesene were also confirmed by the standard. Dimyrcene isomers, \(\beta\)-myrcene, and 2-methylbutyl isobutyrate dominated in the H3, C1, and C2 zones, respectively. Compounds separated with the use of chloroform at \(hR_{\mathrm{F}}\) 35 and 52 were investigated by SPME-GC-MS and HPTLC-ESI-MS, but they did not give mass signals with the used ionization techniques.  
The anti-obesity effect of a hop extract was previously established, demonstrating inhibition of the increase in body weight and liver lipids induced by a high-fat diet in mice and improvement of glucose tolerance [51]. Hop bitter compounds, such as prenylated flavonoids and phloroglucinols, were found to be \(\alpha\)-glucosidase inhibitors [52, 53] and effective in the prevention and treatment of hyperlipidemiaemia, diet-induced obesity, and type-2 diabetes [54, 55, 56]. However, naturally occurring mono- and sesquiterpenoids also showed promising anti-diabetic potential in vitro and in vivo [57, 58]. Our results confirmed that both \(\alpha\)-glucosidase and lipase inhibitors were present in the hop oils, which could contribute to their anti-obesity activity.
## 3 Studies of the Nonlinear Kinetic Equation
Taking (17a), (18b) into account, we confirm the expressions (20), (21) from [1,7]. When \(r=3\), according to \(\rho^{(3\omega)}(\xi,\tau)=\sum_{n=1}^{\infty}\sum_{k=3}^{\infty}\gamma^{k} \Re_{k}^{(3\omega)}(n,\tau)\cdot\cos\bigl{(}\frac{\pi na}{\mathrm{d}}\xi\bigr{)}\) and taking account of (18), we obtain  
\[ \begin{split}\rho^{(3\omega)}(\xi,\tau)=-\Bigl{(}\frac{4a}{ \mathrm{d}}\Bigr{)}^{3}n_{0}\gamma^{3}\times\sum_{n=1}^{\infty}\sum_{k=3}^{ \infty}\left(\frac{8n_{0}\varphi_{1}\gamma}{\pi^{2}}\right)^{k-3}\\ \Bigl{\{}\Lambda_{0}^{k-3}\times\frac{(k-1)(k-2)}{2}\times n^{2} \varphi_{3}(n)+\Lambda_{1}\sin^{2}\bigl{(}\frac{\pi n}{2}\bigr{)}\times\sum_{ 8-3}^{k-1}\frac{(g-1)(g-2)}{2}\Lambda_{0}^{g-3}\cdot\Lambda_{2}^{k-g-1} \Bigr{\}}\times\frac{\exp\bigl{(}3i\frac{\omega}{\mathrm{w}(0)}\tau\bigr{)}} {\frac{1}{\tau_{n}}+i\bigl{(}3\frac{\omega}{\mathrm{w}(0)}\bigr{)}}\times\cos \bigl{(}\frac{\pi na}{\mathrm{d}}\xi\bigr{)}.\end{split}  \tag{22}\]  
From where, after calculating the sums of the series, we get  
\[\rho^{(3\omega)}(\xi,\tau)=-\frac{64a^{3}n_{0}\gamma^{3}}{\mathrm{d}^{3} \bigl{(}1-\frac{8n_{0}\varphi_{1}\Lambda_{0}\gamma}{\pi^{2}}\bigr{)}^{3}} \times\sum_{n=1}^{\infty}\biggl{\{}n^{2}\varphi_{3}(n)+\frac{8n_{0}\varphi_{ 1}\Lambda_{1}\gamma}{\pi^{2}}\cdot\frac{\sin^{2}\bigl{(}\frac{\pi n}{2}\bigr{)} }{1-\frac{8n_{0}\varphi_{1}\Lambda_{1}\gamma}{\pi^{2}}}\biggr{\}}\times\frac{ \exp\bigl{(}3i\frac{\omega}{\mathrm{w}(0)}\tau\bigr{)}}{\frac{1}{\tau_{n}}+i \bigl{(}3\frac{\omega}{\mathrm{w}(0)}\bigr{)}}\times\cos\bigl{(}\frac{\pi na}{ \mathrm{d}}\xi\bigr{)}. \tag{23}\]  
Based on the expression [1]  
\[\mathrm{P}^{(3\omega)}(\tau)=\frac{\mathrm{q}}{\mathrm{d}}\int_{0}^{\mathrm{d} }\mathrm{x}\rho^{(3\omega)}(\xi,\tau)\mathrm{dx}, \tag{24}\]  
taking account of (23)  
\[\mathrm{P}^{(3\omega)}(\tau)=\frac{128a^{3}\mathrm{q}n_{0}\gamma^{3}}{\mathrm{d }^{2}\pi^{2}\bigl{(}1-\frac{8n_{0}\omega_{0}\gamma}{\pi^{2}\epsilon_{0}\epsilon _{w}\mathrm{E}_{0}}\bigr{)}^{3}}\times\sum_{n=1}^{\infty}\Biggl{\{}\wp_{3}(n)+ \frac{8 aqn_{0}\Lambda_{1}\gamma}{\mathrm{n}^{2}\pi^{2}\epsilon_{0}\epsilon_{ w}\mathrm{E}_{0}}\Bigl{(}1-\frac{8 aqn_{0}\Lambda_{2}\gamma}{\pi^{2} \epsilon_{0}\epsilon_{w}\mathrm{E}_{0}}\Bigr{)}\Biggr{\}}\times\sin^{2}\Bigl{(} \frac{\pi n}{2}\Bigr{)}\times\frac{\exp\Bigl{(}3i\frac{\omega}{\mathrm{w}(0)} \tau\Bigr{)}}{\frac{1}{\tau_{n}}+i\bigl{(}3\frac{\omega}{\mathrm{w}(0)}\bigr{)}}. \tag{25}\]  
Expression (25), in addition to (8), confirms that odd \(r\) relaxation modes \(\rho_{k}^{(3\omega)}(\xi,\tau)\), as well as \(\rho_{k}^{(\omega)}(\xi,\tau)\), give nonzero contribution to polarization. Manifestation of this pattern at higher frequencies \((5\omega)\),\((7\omega)\),..., \(((2\lambda+1)\omega)\) is obvious.
## 4 Computational Simulations and Experimental Results  
### Communication System Block Diagram  
The FPGA-based functional board, developed by the research team, incorporates the XLINX xc7k325 tfgg676-2 chip as the FPGA and utilizes the ADS5463 chip as the ADC. Additionally, the system integrates a Hamamatsu H10720-210 PMT for ultraviolet light detection. To enhance the functionality and performance of the system, the research team has meticulously designed driver circuits and detection amplification circuits.  
Figure 4 illustrates the ultraviolet weak signal communication system block diagram., while Figure 5 depicts its hardware prototype, showcasing the integrated nature of the UV communication system.  
At the transmitter end, data generated by PC-Tx are transmitted to the FPGA board, where they are organized into frames and synchronized with pseudo-random headers. These framed data are then conveyed through the FPGA's pins to the half-bridge resonant drive circuit, which energizes the UV lamp. At the receiver end, a PMT functions as the UV light detector, converting received light into electrical pulses. Prior to processing, a UV optical filter is employed to attenuate environmental noise. The resulting electrical signals are then amplified by an op-amp current and sampled by an ADC. Subsequently, the FPGA undertakes various tasks such as photon counting, frequency detection, and clock synchronization on these digital signals, before relaying the reconstructed data to the Rx-PC.
## 3 Methodology
### Autoregressive Integrated Moving Average Model (ARIMA Model)  
The autoregressive integrated moving average (ARIMA) model is a time series forecasting analysis method [38]. ARIMA (\(p\), \(d\), \(q\)), in which AR is \"autoregressive\", and \(p\) is the number of autoregressive terms. MA is the \"sliding average\", \(q\) is the number of sliding average terms, and \(d\) is the number of differences to make it a stationary series [10]. In practice, \(d\) is usually less than 2, and is the key step to make the series smooth.  
Autoregressive (AR) model  
Under the condition that the time series satisfies smoothness, the AR model describes the treatment of the relationship between current and historical values, that is, to use the historical time data of the variable itself to predict itself [39]. The formula defines the P-order autoregressive process:  
\[y_{t}=\mu+\Sigma_{i=1}^{p}\gamma_{i}y_{t-1}+\varepsilon_{t} \tag{1}\]  
where \(y_{t}\) is the simulation error at time \(t\); \(\mu\) is the constant term; \(P\) is the lag order of the autoregressive process; \(\gamma_{i}\) is the autocorrelation coefficient; \(\varepsilon_{t}\) is the random perturbation term.  
(2) Moving average (MA) model  
The MA model is concerned with the accumulation of the error term in the AR model, that is, the error term \(\varepsilon_{t}\) in the AR model is viewed as a moving average term of order \(q\)[40]. The autoregressive process of order \(q\) is defined by the formula:  
\[y_{t}=\mu+\varepsilon_{t}+\Sigma_{i=1}^{q}\theta_{i}\varepsilon_{t-1} \tag{2}\]  
where \(y_{t}\) is the simulation error at time \(t\); \(\mu\) is the constant term; \(q\) is the lag order of the moving average process; \(\theta_{i}\) is the autocorrelation coefficient; \(\varepsilon_{t}\) is the random perturbation term.  
(3) Autoregressive integrated moving average (ARIMA) model  
The autoregression is combined with the moving average to obtain the ARIMA (\(p\), \(d\), \(q\)) model defined by the formula:  
\[y_{t}=\mu+\Sigma_{i=1}^{p}\gamma_{i}y_{t-1}+\varepsilon_{t}+\Sigma_{i=1}^{q} \theta_{i}\varepsilon_{t-1} \tag{3}\]  
From the formula, we know that we only need to specify three parameters \((p,d,q)\) when getting the ARIMA model, where \(d\) is the order and \(d=1\) is the first-order difference. \(p,q\) can be determined according to the partial autocorrelation function (PACF) and autocorrelation function (ACF) of sequences [41].
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 4 Discussion
### Influence of Spatial Resolution  
In recent years, the usage of high-resolution satellite data for treeline ecotone mapping applications increased [55]. Especially in mountain regions with a complex treeline ecotone, a high spatial sensor resolution is needed to accurately map the different vegetation species [56; 57]. We assumed that due to the higher resolution of PlanetScope, the mountain pine stands could be better delineated from conifers, especially in areas with strong intertwining between mountain pines and forested areas. However, our results indicate that Sentinel-2 behaves similarly to PlanetScope and even outperforms it by solely using the multitemporal image stacks (scheme 1). With the successive inclusion of textural and topographical features (schemes 2-4), the classification accuracy of PlanetScope and Sentinel-2 converges with only minimal differences in OA and AUC values. The best classification was achieved by the PlanetScope feature space (scheme 4) having the highest OA and AUC values. Yet, despite the marginally worse validation statistics, S2 + GLCMMAX + Topo + CHM still achieves excellent results in the delineation of mountain pine areas. A possible explanation for the similar behavior of PlanetScope and Sentinel-2 is the size distribution of the mountain pine fields. In the Sarntal Alps, mountain pine grows over large areas in contiguous patches. These can be accurately detected even with the lower resolution of Sentinel-2. Therefore, the good performance of Sentinel-2 also depends on the local specific growing forms of the mountain pines in the Sarntal Alps and may lead to worse results if the size distribution of the mountain pines changes (e.g., smaller, scattered patches). However, a good performance of Sentinel-2 was previously reported in other studies that classified mountain pine stands (e.g., PA 91.98%, UA 97.39% [10]; PA 85.3%, UA 93.9% [23]). The addition of the SWIR bands could further improve the classification results [26; 48]. In contrast to other studies [56; 57], it can be concluded that the higher spatial resolution does not necessarily lead to an improved classification of treeline species such as mountain pines. Further mapping approaches could therefore make use of the freely available Sentinel-2 imagery, which thus allows investigating larger study areas.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 3 Results
### IFA-RF Algorithm Model Optimization and Learning  
In this study, the number of static compaction cycles, _P5_ content, moisture content, humidity, and curvature coefficient were chosen as input variables, with dry density as the output variable. The IFA-RF algorithm was employed to construct the prediction model. To evaluate the prediction stability and generalizability of the proposed algorithm, this study adopted a five-fold cross-validation method, dividing the dataset into five parts, with four parts as the training set and one part as the testing set. This study specifically employed an RF algorithm designed for small-sample datasets, and several scholars have demonstrated the feasibility of predictions using small sample sizes [24; 25]. These datasets were input into the IFA-RF algorithm, and the IFA was used to optimize the parameters of the RF model. The optimal values for the _Ntree_ and _Mtry_ parameters were found to be 132 and 2, respectively. Subsequently, the RF model was trained using these optimal parameters, achieving high-accuracy predictions of the dry density.
## 3 Manipulation of Swarms Positions and Space Solution Using Multi-Threaded Techniques
### Proposition 1  
In order to minimize the amount of computation time, we suggest using automatic parallelization of the algorithms by dividing the population into several groups, which threads are burdened.  
From the perspective of nature, individuals analyze the environment and choose the best of them all. In the neighborhood of the best solution, smaller populations called groups may be formed. Suppose that at the beginning of the algorithm, the number of cores \(pc\) is detected. In analogy to the original version of the algorithms, an initial population consisting of \(n\) individuals is created at random. From this population, \(pc\) fittest individuals are chosen. Each individual will be the best adapted solution in the smaller group that will be created under his leadership. The size of the group will be determined as follows  
\[n_{group}=\left\lfloor\frac{n}{pc}\right\rfloor. \tag{24}\]  
The above equation uses the floor to obtain groups with the same population size for each core. The use of the floor guarantees that, regardless of \(n\), each group will have the same number of individuals, and the sum of all \(n_{group}\) will not exceed \(n\).  
With the size of the group and their leadership, we can begin to create groups. For every alpha male, we create one thread on which the population consisting of \(n_{group}\) individuals is created. Each individual in the group is placed in a random way at a distance of no more than \(d_{max}\) from the leader. This distance can be calculated by  
\[\begin{cases}\frac{|a-b|}{n}&\text{if}\quad a\
eq b\\ \frac{a}{n}&\text{if}\quad a=b\end{cases}, \tag{25}\]  
where \(a\), \(b\) are the values of the variable's range for the test function.  
For each group on a separate thread, all steps from an original algorithms are performed. After completing these steps, \(pc\) the best adapted individuals are found as a solution for the optimization problem and selected the best of them. Complete operation of the proposed method is shown in Algorithm 7.  
```
1:Start,
2:Detect the number of cores \(pc\),
3:Create an initial population at random,
4:Select \(pc\) best individuals,
5:Calculate the number of individuals \(n_{group}\) in groups using Equation (24),
6:Create \(pc\) groups consisting \(n_{group}\) individuals based on Equation (25),
7:Put each group on a separate thread,
8:Run chosen metaheuristic with a customized group as a population on each thread,
9:Choose the best individuals from all threads,
10:Stop.
```  
**Algorithm 7**Metaheuristic with devoted local search
## 5 Description of the Vehicle-to-Vehicle Visible Light Communications Prototype
In order to mitigate the effect of artificial light sources, which mainly introduce a strong 100 Hz sinusoidal component, the band-pass filter has a high-pass cutoff frequency of  
Figure 6: Schematic representation of the VLC system. 1 kHz, whereas in order to reduce the effect of high frequency noise sources (i.e., shot noise and thermal noise), the low-pass filter has an adjustable cut-off frequency, which for these tests was settled at 200 kHz. It should be mentioned here that the cut-off frequency of the low-pass filter is established based on a Power Spectral Density (PSD) analysis that is determined for each coding technique and for each data rate [29; 65]. After the band-pass filter, the signal passes through an Automatic Gain Control (AGC) circuit, that requires input signals with a minimum amplitude of 200 mV, in order to provide an adequate amplitude signal for the Schmitt trigger circuit. From this point, the Schmitt trigger circuit generates a square shape signal having an amplitude of 3.3 V.  
This signal is then fed to the 180 MHz microcontroller board in the final stage, where it is processed in order to extract the information. This process is based on a rising and falling edge identification and pulse width measurement. Complementary to the VLC emitter, the VLC receiver is able to process messages transmitted using DSSS or OOK modulation techniques, SIK, Manchester and Miller encoded messages, having data rates between 3 and 200 kb/s. To fulfill this goal, the microcontroller extracts the required information from the physical header of the data frame. The microcontroller is able to perform the data processing in real-time. Once the information is decoded, the microcontroller determines the BER which is used for performance analysis. Figure 7 shows the hardware implementation of the VLC system, whereas Tables 1 and 2 present the summary of the VLC system's parameters.
# An Efficient Indoor Positioning Method with the External Distance Variation for Wireless Networks
## 1 Introduction
The small-scale propagation model analyzes the change of the signal in a short distance or a short period of time, and the change of the signal in a short distance and a short period of time may be affected by the movement of surrounding objects, such as the movement of a large vehicle or the movement of the mobile phone itself or caused by natural environments. Wireless telecommunication signals change very quickly in mobile communication, a very important factor affecting communication quality. This phenomenon is unavoidable and the radio wave part is still on the bottleneck of communication performance in the overall mobile communication system. The factors affected by the signal strength of radio waves can be divided into three categories; the first category being the path loss [21], which is the main reason for the attenuation of radio wave signal strength. When a signal is sent by an antenna, its energy distribution is determined by the radio wave radiation. As the distance increases, the radio wave power per unit area decreases. The second category is the slow fading or shadowing. The main reason is that large-scale natural buildings exist on the earth's surface. The former are hills or forests, and the latter are residential buildings. Districts or large factories in wireless communication systems will cause interference effects on radio waves. The radio wave signal attenuation of these large-scale buildings is relatively unchanged with time. The strength of the wireless telecommunication signal will be inversely attenuated with the fourth or fifth power of the actual distance. The third category is the fast fading referred to a situation where the signal presents a large change in a short period of time. It may be affected by the movement of the receiving point or surrounding objects. The latter is a situation caused by an instantaneous change in the natural environment. A modelizeddescription of radio wave transmission is helpful for system operators to plan and build base stations. Many papers have proposed quite a lot of channel models, which can be roughly divided into the following three parts: propagation path loss model, large scale propagation model, and small scale propagation model [22].
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 4 Comparison and Discussion
Out of all these schemes whose objective is energy efficiency, the most promising solution in our opinion, is [51] The technique improves the network performance by assigning the SFs to end nodes under the constraint for the average energy consumption of each end node. The study of ADR in LoRaWAN networks reveals that common objectives for the proposed algorithms are scalability, throughput, and energy efficiency. Testbeds, simulations, and mathematical models are employed to develop and evaluate ADR algorithms and schemes. The mathematical models use machine learning and mathematical optimization to optimize the ADR schemes. Various ADR algorithms draw in different metrics that influence the standard transmission parameters where others consider coverage, channel access/ contention, RSS, PDR and so on, as shown in Table 3. There are trade-offs between achieving high data rates or energy consumption and the performance metrics required. Many schemes use a single gateway in their proposed solutions as it is a simple and straight forward network. Most of the literature reviewed evaluated their ADR schemes using the simulation tools such as NS-3, MATLAB, OMNET++, FLoRa and LoRaSim. The algorithms are mostly simulation based as testbeds prove to be very expensive.