esa-sceva/satcom-chunk-collection huggingface dataset:
# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 2 Materials and Methods
### Modeling Method
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Vegetation Index**} & **Formula** & **Reference** \\ \hline Enhanced difference water index (NDWI) & \(\mathrm{NDWI}=(\mathrm{r}_{\mathrm{h}3}-\mathrm{r}_{\mathrm{h}8})/(\mathrm{r}_{ \mathrm{h}3}+\mathrm{r}_{\mathrm{h}8})\) & [11] \\ \hline Enhanced difference vegetation index (NDVI) & \(\mathrm{NDVI}=(\mathrm{r}_{\mathrm{h}8}-\mathrm{r}_{\mathrm{h}4})/(\mathrm{r}_{ \mathrm{h}8}+\mathrm{r}_{\mathrm{h}4})\) & [37] \\ \hline \multirow{3}{*}{Enhanced multi-band drought index (NMDI)} & \(\mathrm{NMDI1}=(\mathrm{r}_{\mathrm{h}4}-(\mathrm{r}_{\mathrm{h}10}-\mathrm{r}_{ \mathrm{h}11}))/(\mathrm{r}_{\mathrm{h}4}+(\mathrm{r}_{\mathrm{h}10}+\mathrm{r} _{\mathrm{h}12}))\) & \\  & \(\mathrm{NMDI2}=(\mathrm{r}_{\mathrm{h}4}-(\mathrm{r}_{\mathrm{h}10}-\mathrm{r}_{ \mathrm{h}12}))/(\mathrm{r}_{\mathrm{h}4}+(\mathrm{r}_{\mathrm{h}10}+\mathrm{r }_{\mathrm{h}12}))\) & [38] \\  & \(\mathrm{NMDI3}=(\mathrm{r}_{\mathrm{h}4}-(\mathrm{r}_{\mathrm{h}11}-\mathrm{r}_{ \mathrm{h}12}))/(\mathrm{r}_{\mathrm{h}4}+(\mathrm{r}_{\mathrm{h}11}+\mathrm{r }_{\mathrm{h}12}))\) & \\ \hline Enhanced vegetation index (EVI) & \(\mathrm{EVI}=2.5(\mathrm{r}_{\mathrm{h}8}-\mathrm{r}_{\mathrm{h}4})/(\mathrm{r}_{ \mathrm{h}8}+\mathrm{r}_{\mathrm{h}4}-7.5\mathrm{r}_{\mathrm{h}2}+1)\) & [39] \\ \hline Simple ratio water index (SRWI) & SRWI = \(\mathrm{r}_{\mathrm{h}8}/\mathrm{r}_{\mathrm{h}4}\) & [40] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of selected optical vegetation indices used in this study based on Sentinel-2. The research schema (Figure 2) is as follows:  
Figure 2: Research schema.
###### Abstract  
This study focuses on the recently emerged Internet of Vehicles (IoV) concept to provide an integrated agricultural vehicle/machinery tracking system through two leading low power wide area network (LPWAN) technologies, namely LoRa and NB-IoT. The main aim is to investigate the theoretical coverage limits by considering the urban, suburban, and rural environments. Two vehicle tracking units (VTUs) have been designed for LoRa and NB-IoT connectivity technologies that can be used as reference hardware in coverage analysis. On this basis, the closed-form explicit analytical expressions of the maximum transmission range have been derived using the Hata path loss model. Besides, the computer simulation results have been validated via the maps from XIRIO online radio planning tool. In light of the obtained findings, several evaluations have been made to enhance the LPWAN-based agricultural vehicle tracking feasibility in smart farms.  
LoRa; NB-IoT; LPWAN; coverage; vehicle tracking; smart farming +
Footnote †: journal: Sensors
# A Review of Cell Operation Algorithm for 3D NAND Flash Memory
## 3 Read Algorithm
### Read Failure Improvement
#### 3.3.1 The WL Pitch Scaling  
Because cell-to-cell interference between WLs is a major source of increase of the read failure rate, several approaches have been derived. In NAND flash memory, since cells are arranged side by side, amount of Vt shift of a selected WL varies depending on the programming level of the adjacent WL [74]. In addition, after 1 block programming operation is completed, a charge shift occurs due to lateral migration toward the adjacent WL around the selected WL, aggravating the Vt shift [75].  
To improve the above problem, [PERSON] et al. proposed a read level adjustment method according to WLn + 1 Pattern [58], as shown in Figure 11. First, to broadly classify WLn + 1 pattern into two types, a pre-sensing operation is performed on WLn + 1. Next, since the amount of Vt shift of WLn varies according to the program level of WLn + 1, the read level of WLn is carefully adjusted. This method increases the read time overhead and the additional latch that stores the WLn + 1 pre-sensing result must be inserted [32].  
[PERSON] et al. demonstrated that when reading the WLn, cell-to-cell interference can be greatly improved by adding an offset read bias of 1.5 V or more to adjacent WLs compared to unselected WL [76]. In general, cell-to-cell interference is worsen when the channel potential of WLn changes rapidly right after WLn + 1 is programmed. The proposed method keeps the channel potential fluctuation of WLn to a minimum even when WLn + 1 is programmed. However, this method causes soft programming read disturbs due to increases in read bias of adjacent WLs. To improve the charge loss caused by the reduced the pitch between WLs, D.-h. [PERSON] et al. proposed an adaptive-read scheme [36]. First, they measure the number of cells in the highest state that are most likely to cause retention loss. When the page read command is invoked, the chip starts reading in the highest states and count the retention loss of the highest verify level cell in advance and  
Figure 11: Read level adjustment scheme according to N + 1 WL pattern. Reprinted/adapted with permission from Ref. [58], 2022, IEEE.  
then correct the bias of other lower read levels thereafter. This method improves the overall read margin.
# A Multidimensional Data Collection and Edge Computing Analysis Method
## 2 System Hardware Design
### Sensor Acquisition Module  
Figure 1b illustrates the hardware structure of the LoRa based wireless communication module with the inclusion of GPS sensors and temperature sensors. The hardware structure is a signal acquisition side and a data preprocessing side. Likewise, considering the complexity of the deployment environment, the hardware structure is designed to reflect portability and low power consumption, which makes the acquisition and research of spatial data more accessible. Data acquisition by GPS sensor and temperature sensor is used to effectively cooperate with image acquisition and improve target detection accuracy. Note that the video collection is at 10-min intervals, while other sensors only collect every 5 min. The sensor data specifications and sampling rates are shown in Table 1. Among them, there is an age factor, which is extracted from our target detection. We split the residents into children and adults. And we use \"1\" for children and \"2\" for adults. The different age identification of adults is not researched in this system.  
Figure 1: Hardware deployment and hardware structure diagram: **(a)** Hardware deployment floor plan; **(b)** Sensor acquisition modules; **(c)** Edge computing module.
## 5 Parameter Optimization
### Plackett-Burman Test  
#### 5.2.1 PB Test Design and Results  
The purpose of the Plackett-Burman (PB) test is to screen parameters and identify those that significantly affect the volume ratio. As previously mentioned, the main parameters influencing the adjustment range include mechanism parameters (\(m\), \(n\), \(M\), and \(N\)); the geometric relationships between the axis of driving pairs (\(D=\varphi\), \(E=\theta\), and \(G=\psi\)); and the drive trajectory of the driving pair of the limb (\(l_{\rm 1 min}\), \(l_{\rm 1 max}\), \(l_{\rm 2 min}\), and \(l_{\rm 2 max}\)). In this paper, Design-Expert software is used to design the PB test, and there are 11, 19, etc., parameter tables to choose from. Each parameter takes two levels of high and low, and the values of the two levels should be reasonable: the difference may be too large and may cover the role of other factors or the difference may be too small to reflect the role of the factors; therefore, the ratio of the high level to the low level is set to be between 1.2 and 2 in this paper. In  
Figure 13: Distribution of design points of BBD test.  
addition, it is necessary to set some dummy variables when designing the PB test and try to distribute the dummy variables as evenly as possible. The purpose of this is to estimate the error and identify the effect of different parameters at the same time.  
The PB test was designed using the 19-factor table of Design-Expert 12 software in this paper. Eleven of the main influencing parameters were used as real variables and the other eight variables were used as dummy variables. It is particularly important to determine reasonable parameter levels using single-factor analysis before using response surface optimization. Based on the results of the single-factor analysis, a reasonable range of values for each parameter was selected, as shown in Table 1.  
After determining the high and low levels of the parameters, a PB test was conducted to calculate the volume ratio of the posture adjustment mechanism under different parameter combinations. The design and results of the test are shown in Appendix A.
## 3 Explicit MPC Controller Design
### Construction of Explicit MPC Model
\begin{split} J(x(k))=(\Delta R-S_{x}\Delta x(k)-S_{u}\Delta U)^ {T}W_{Q}(\Delta R-S_{x}\Delta x(k)-S_{u}\Delta U)+\Delta U^{T}W_{R}\Delta U\\ =\frac{1}{2}\Delta U^{T}H\Delta U+\Delta U^{T}\cdot\left(-2S_{u}^ {T}W_{Q}S\right)+S^{T}W_{Q}S\end{split}  \tag{22}\]  
where \(H=2\big{(}S_{u}^{T}W_{Q}S_{u}+W_{R}\big{)}\), and \(S=\Delta R-S_{x}\Delta x(k)\). \(\Delta U\) is the optimization variable, while the term \(S^{T}W_{Q}S\) has nothing to do with the optimization variable. So the term \(S^{T}W_{Q}S\) is removed. As \(\Delta x(k)=x(k)-x_{0}\) and \(x_{0}\) are constant, \(\Delta x(k)\) is replaced with \(x(k)\). The updated cost is as follows:  
\[J(x(k))=\frac{1}{2}\Delta U^{T}H\Delta U+\Delta U^{T}Lx(k) \tag{23}\]where \(L=-2S_{u}^{T}W_{Q}\Big{(}\Delta Rx(k)^{T}/|x(k)|-S_{x}\Big{)}\). There are many constraints on the control sequence and the state sequence. For the sake of description, the constraints can be written as the following uniform form:  
\[G_{x}\Delta U\leq W_{x}+E_{x}x(k) \tag{24}\]  
where \(G_{x},W_{x}\), \(E_{x}\) are determined according to the constraint conditions, then the problem is transformed into solving the following multi-parameter quadratic programming (MPQP) problem:  
\[ \begin{split}\underset{\Delta U}{\min}J(x(k))&= \frac{1}{2}\Delta U^{T}H\Delta U+\Delta U^{T}Lx(k)\\ s.t.& G_{x}\Delta U\leq W_{x}+E_{x}x(k)\end{split}  \tag{25}\]  
To simplify the MPQP problem, a new optimization variable \(Z=\Delta U+H^{-1}Nx(k)\) is defined. The final MPQP optimization problem is as follows:  
\[J^{*}(x(k))=\underset{Z}{\min}J_{Z}(x(k))=\frac{1}{2}Z^{T}HZs.t.~{}~{}G\Delta U \leq W+Ex(k) \tag{26}\]  
where \(G=G_{x}\), \(W=W_{x}\), \(E=E_{x}+G_{x}H^{-1}L\), \(J_{Z}(x(k))=J(x(k))+\frac{1}{2}x(k)^{T}N^{T}L^{-1}Nx(k)\). The next goals are: within the parameter limit, find the expressions of the value function \(J^{*}(x(k))\) and the optimal value function \(Z(x(k))\) corresponding to the parameter \(x(k)\), and prove that the optimal value function \(Z(x(k))\) is a continuous PWA function on \(x(k)\).
# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 2 Materials and Methods
### Decision Trees  
Decision trees are a type of predictive modeling algorithm used in statistics, data mining, and machine learning. Decision trees serve as a versatile tool in data analysis, capable of tackling both classification and regression challenges. They operate by constructing a hierarchy of \"if, then\" statements that logically lead to a decisive classification or value estimation. In the realm of data mining, they are recognized for their predictive capabilities and ease of interpretability. They segment the dataset into branches to form a tree structure, where classification trees assign categorical labels, and regression trees predict continuous outcomes. The iterative process of building a decision tree involves examining each variable and its possible splits to optimize the selection at every branch [33; 34].  
Creators of the method suggest using the Gini coefficient, often referred to as a metric of dispersion or heterogeneity within nodes. They advocate for dividing the entire space spanned by \(k\) dimensions, \(R^{k}\), into q separate and distinct sectors, ensuring that \(R_{1}\) joined with \(R_{2}\) and so on through \(R_{q}\) completely encompasses \(R^{k}\). For a specific node m, where m ranges between 1 and \(q\), aligning with the sector \(R_{m}\), the Gini coefficient is computed as follows (3):  
\[Q_{G}(m)=\sum_{j=1}^{s}p_{mi}(1-p_{mi})=1-\sum_{j=1}^{s}p_{mi}^{2} \tag{2}\]  
where \(p_{mi}\) denotes the conditional probability of the \(j\)-th class within a node, and \(s\) represents the total number of classes. For node \(m\), which contains \(n_{m}\) observations, the conditional probability for the \(j\)-th class is given by (3):  
\[p_{mi}=\frac{\#\{y=c_{i}:x\in R_{m}\}}{n_{m}} \tag{3}\]  
The development process of Decision Tree (DT) models was meticulously structured, centering on the mean-squared error as the optimization criterion. This specific criterion was chosen to refine node splits, aiming to minimize MSE for enhanced predictions in relation to the training data. Key parameters, such as the tree's maximum depth, the minimum number of samples required for a node split, and the minimum number of samples for a leaf node, were systematically fine-tuned. This fine-tuning was pivotal to balance the model's complexity against overfitting risks. Additionally, the model's structure underwent evaluation by varying the count of trees from 50 to 200, in increments of five, which was instrumental in identifying the most precise model configuration.
## 4 Channel Models
### Example  
We provide a specific example here to demonstrate how the channel models work. Note that we will also justify the channel models for a wider range of parameters by conducting simulations in Section 5. In the following example, the SNR, BER, and successful delivery ratio are computed.  
First, we set the transmitter power, transmission range/distance, and frequency as 2 W, 300 m, and 10 kHz, respectively. When the transmitter power is 2 W, the source level \(S_{level}\) can be computed by Equation (6):  
\[S_{level}=10\Big{[}\log(2)-\log\Big{(}4\pi\times 300^{2}\Big{)}-\log\Big{(}0.6 7\times 10^{-18}\Big{)}\Big{]}\approx 124\text{ dB}\]  
Second, given the transmission distance (300 m) and frequency (10 kHz), the transmission loss \(T_{loss}\) can be computed by Equation (7):  
\[T_{loss}=20\log(300)+\alpha(10)\times 300\times 10^{-3}\approx 50\text{ dB}\]  
Third, by substituting the frequency (10 kHz) into Equation (10), the noise level \(N_{level}\) is roughly:  
\[N_{level}=50-18\log(10)=32\text{ dB}\]  
Combining all of the above results and Equation (2), i.e., the combination is equivalent to Equation (13), we can compute the SNR as follows:  
\[\gamma=124-50-32+0=42\text{ dB}\]  
Then, substituting the SNR into Equation (14), we can compute the bit error rate \(BER(\gamma)\):  
\[BER(\gamma)=\frac{1}{2}\Bigg{(}1-\sqrt{\frac{10^{42/10}}{1+10^{42/10}}}\Bigg{)} \approx 1.58\times 10^{-5}\]  
Finally, for a packet with size 3 KB, the successful delivery ratio can be computed by using Equation (16):  
\[P^{m}_{successful}(\gamma)=\Big{[}1-\Big{(}1.58\times 10^{-5}\Big{)}\Big{]}^{3 \times 1024\times 8}\approx 0.6782\]  
The successful delivery ratio is about 67.82%. The numerical example examines the equations of the channel models step-by-step for a specific case. For more cases (and more discussion), we conducted the simulations in Section 5.
Unlike the traditional one-factor-at-a-time (OFAT) method, which isolates a single variable while holding others constant, an FFD enables a comprehensive assessment of the main effects and interactions between variables, yielding statistically robust conclusions [10; 12]. Previous research has primarily focused on geometric optimizations [9; 23], nanofluid integration [10; 21], and flow rate variations [11], but has lacked a structured factorial approach to analyze thermal performance under diverse operational conditions. These studies collectively elucidate key mechanisms of heat dissipation, providing clear pathways for developing more efficient and reliable cooling systems tailored for high-power-density electronic devices, which inform the contributions of this proposal. Therefore, this study explores the application of a full factorial design (FFD) to investigate the effects of three variables--power supply, ambient temperature, and volumetric flow--on the total thermal resistance and heat transfer coefficient of a mini heat exchanger. This heat exchanger is specifically designed to dissipate heat from electronic components, such as computer processors. The primary contributions of this study involve the design of a mini heat exchanger tailored to dissipate heat from electronic components, such as processors, thereby optimizing heat transfer efficiency. Furthermore, the application of the full factorial design (FFD) method enables a systematic examination of how key operational variables--specifically, the power source, ambient temperature, and volumetric flow rate--affect the determination of global thermal resistance (\(G_{TR}\)) and the heat transfer coefficient (h). This research also offers valuable insights into enhancing cooling systems for high-thermal-density electronic components, highlighting their potential application in miniaturized devices. Additionally, a significant contribution is the establishment of a robust experimental procedure that includes apparatus assembly, data collection, and statistical analysis. This comprehensive methodology serves as a reference for future heat transfer studies, ensuring reliability and reproducibility in similar research contexts.
## 4 Conclusions  
Using the QMD simulation results for dense beryllium (Be), we conducted a comparative analysis to evaluate the accuracy and applicability of the Yukawa one-component plasma model in describing the ion structure and transport properties. Our analysis considered the radial distribution function, the velocity autocorrelation function, mean-squared displacement, and the diffusion coefficient of ions. Additionally, we examined the applicability of the Chapman-Enskog method for calculating the diffusion coefficient at tempera  
Figure 9: Diffusion coefficient \(D\) as a function of temperature \(T\), computed from the MSD and VACF of ions. The results are from the QMD, the Yukawa–MD, the Thomas–Fermi model, and the Chapman–Enskog approach using the Coulomb logarithm (see Figure 1).  
tures up to 32 000 K. The results clearly indicate that the Yukawa model-based molecular dynamics simulations can accurately capture the dynamics of ions, as demonstrated by the VACF and MSD, provided that the Yukawa potential parameters are appropriately selected. Our comparative analysis of the QMD, Yukawa-MD, and Chapman-Enskog methods allowed us to quantify the values of the effective coupling parameter at which the pair-collision model used in the Chapman-Enskog method remains accurate. These conclusions are also expected to apply to other transport properties due to the same underlying mechanism of particle dynamics. Generally, it is not guaranteed that an effective potential model reproducing the correct RDF of ions will also provide adequate transport properties. Thus, validating and determining the applicability range of plasma models requires benchmarking them against results from QMD simulations of real materials.  
Conceptualization, M.I.; methodology, M.I., N.D., T.R. and S.K.; software, M.I., N.D. and S.K.; validation, M.I., N.D., T.R. and S.K.; formal analysis, M.I., N.D., G.O. and A.K.; investigation, M.I., N.D., G.O. and A.K.; resources, M.I.; data curation, M.I., N.D., G.O. and A.K.; writing--original draft preparation, M.I. and N.D.; writing--review and editing, M.I.; visualization, M.I., N.D., G.O. and A.K.; supervision, M.I.; project administration, M.I.; funding acquisition, M.I. All authors have read and agreed to the published version of the manuscript.  
This study is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan Grant AP19579511 \"Investigation of the properties of warm dense matter based on the density functional theory\".  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Weather Forecasting Models
It complemented the precipitation radar by providing information on total hydrometeor content within precipitation systems (see Figure 5). The combination of active (PR) and passive (TMI) instruments, along with the inclined orbit of the TRMM satellite sampling the atmosphere throughout its orbit, enabled the generation of high-quality precipitation data relative to the operational capabilities available during TRMM's mission. This integration led to significant scientific advances by supplying additional input parameters to hydrological and land-surface models, enhancing our understanding of land-atmosphere interactions over timescales ranging from days to years [24]. Figure 4: U.S Weather Forecasting Satellite (Wikipedia)—GOES 16 (formerly known as GOES-R). Building on recent technological advances and leveraging the success of the TRMM mission, a next-generation satellite program was launched on 27 February 2014, as part of a continued partnership between NASA and JAXA. The Global Precipitation Measurement (GPM) core observatory aims to extend precipitation measurements to lower intensities (on the order of millimeters per hour) while expanding coverage beyond tropical and subtropical regions. By integrating a constellation of satellites operating in LEO, the GPM program achieves near-global precipitation observations approximately every three hours. Compared to the instruments aboard TRMM, the GPM payload offers significantly enhanced precipitation measurement capabilities, providing detailed insights into the internal structure of cloud volumes, including hydrometeors and other atmospheric features. This is accomplished through the GPM's Dual-Frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI). The DPR's Ku-band radar, operating at 13.6 GHz, is comparable to the TRMM's Precipitation Radar (PR), but it introduces a second operating frequency at 35.5 GHz in the Ka-band. This higher frequency enables the detection of frozen precipitation and light rain, facilitating the generation of more detailed drop size distributions and improving the accuracy of rainfall and snowfall prediction models. The GMI, a 13-channel scanning microwave imager, is designed to detect the backscattered power of electromagnetic waves (as described in earlier sections) and generate a layered representation of a target atmospheric volume. This capability enables the estimation of light rain and snowfall intensity by operating across a frequency range of 10 to 89 GHz, which is also used for the detection of moderate and heavy rainfall, similar to the functionality of the TRMM's Precipitation Radar (PR). The added value of the GMI lies in its use of four additional high-frequency channels, ranging from 166 to 183 GHz, specifically intended for the measurement of moderate and light precipitation. This enhancement significantly improves the accuracy and resolution of precipitation forecasting models [25].