esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Data Sources
#### 2.2.3 Meteorological Data  
Both daily mean temperature and radiation are required as input for the light use efficiency model. Daily air temperature and sunshine duration data from the nearby climate station were obtained from the China Meteorological Data Sharing Service System (CMDSSS) [32]. The sunshine duration was used to calculate the photosynthetically active radiation (PAR) based on the FAO equations [33]. The highest daily mean temperature occurred in August and was below 30 \({}^{\circ}\)C. In addition, we did not take the influence of altitude changes on temperature into account because the study sites are flat.
###### Abstract  
This study addresses a significant problem: it is difficult to choose a suitable mobile learning platform effectively since many learning platforms are readily available for users. For this purpose, the study proposes an efficient way to rank and choose the most suitable mobile learning platform by integrating risk analysis and multi-criteria decision-making methods. The selection of a suitable mobile learning platform is challenging due to the vast collection of available platforms. Traditional decision-making approaches often struggle to manage the inherent uncertainty and subjectivity in platform evaluation. To address this, we propose an enhanced methodology that combines grey relational analysis (GRA) and fuzzy analytic hierarchy process (FAHP), leveraging their complementary strengths to provide a robust and adaptive solution. The study employs ISO/IEC 9126 software quality standards to locate the most suitable mobile learning platform. FMEA is based on three risk factors: occurrence, severity, and detection. The fuzzy analytical hierarchy process (FAHP) is used to determine the relative weight of each risk factor to identify the grey risk priority number that can be calculated for each criterion. Mobile learning platforms are then ranked based on their grey risk priority number. The method was applied to five widely used mobile learning platforms with three decision-makers. In addition, the multi-criteria decision-making software was developed to aid users, educators, and administrators in their decision-making processes. The integrated FMEA-GRA-FAHP technique, using ISO/IEC 9126 standards, provides an effective way of locating the most suitable mobile learning platform and ranking them according to their reliability. This research is believed to be the only study applying an integrated FMEA-GRA-FAHP approach to evaluate the risks and quality of mobile learning platforms. The unique approach overcomes certain limitations of the standalone methods such as FMEA and FAHP, making it a valuable tool for identifying the suitability of mobile learning platforms. In addition, the study underscores the importance of inclusivity and equity in ensuring high-quality education and creating an environment conducive to lifelong learning for all.  
extent analysis method; grey relational analysis; FAHP; FMEA; ISO/IEC 9126 standard; mobile learning; sustainable learning +
Footnote †: journal: Applied Sciences
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Renewable Energy Integration in Operation
#### 3.3.3 Advanced Control Systems for Managing Renewable Sources  
Study [185] emphasizes the necessity for dynamic, adaptive control systems for the renewable energy shift, presenting a complex dynamic optimization model as a multi-stage decision framework. This model integrates solar energy and storage capacities, fossil fuel use, and electricity consumption, aiming to maximize utility within carbon budget and technological constraints. It addresses solar variability with probabilistic functions and analyzes the impact of cloud coverage on solar generation, offering insights into optimizing solar energy systems amidst environmental fluctuations.
## 3 MG Overview and Experimental Test Bench
### Lppt Control Strategies
#### 3.3.2 P&O with Variable Step Size Based on [PERSON]'s Method  
The second method used in this work is [PERSON]'s method. It was developed to improve the efficiency and rapidity of the traditional P&O. This method allows for calculating the variable perturbation step size according to the operating point [8]. Indeed, if the operating point is far away from the LPP, the step size increases rapidly to approach the LPP. Conversely, if the operating point is close to the LPP, the step size decreases. In this method, the root value is calculated through the following equation [33]:  
\[X_{n+1}=X_{n}-\frac{F(X_{n})}{F^{\prime}(X_{n})}, \tag{1}\]  
where \(X_{n}\) is the initial value of \(X\), \(F(X_{n})\) represents the value of the at the point \(X_{n}\) and \(F^{\prime}(X_{n})\) is the derivative of function at \(X_{n}\). The [PERSON]'s method applied in this study depends on the evolution of \(p_{diff}\) as a function of \(v_{WT}\). The calculation of step size following this technique is based on the iterative method mentioned in Equations (2)-(7) for each \(k\) instant as shown below:  
Figure 6: Principle of perturb and observe (P&O) method for limited power point tracking (LPPT) mode.  
\[X_{n}=v_{WT}(k), \tag{2}\]  
\[X_{n+1}=v_{WT}(k+1)=v_{WT\_REF}(k), \tag{3}\]  
\[F(X_{n})=F(v_{WT}(k))=p_{diff}(k), \tag{4}\]  
\[F^{\prime}(X_{n})=F^{\prime}(v_{WT}(k))=\frac{\Delta p_{diff}(k)}{\Delta v_{WT}( k)}=\frac{p_{diff}(k)-p_{diff}(k-1)}{v_{WT}(k)-v_{WT}(k-1)}, \tag{5}\]  
\[v_{WT\_REF}(k)=v_{WT}(k)+\Delta v, \tag{6}\]  
\[\Delta v=\frac{p_{diff}(k)}{\Delta p_{diff}(k)/\Delta v_{WT}(k)}, \tag{7}\]  
The variable step sizes provided by this method were limited for protection and saturated to 2 V, 4 V and 6 V in order to compare their performances with the ones provided by the method with the fixed step sizes.
## 2 Method  
Measurement data were collected from 25 NR BSs of the Telstra commercial network in Australia. Telstra was among the first operators to launch 5G, and in January 2020, more than 100,000 5G-enabled mobile devices were connected to its network [12]. The massive MIMO BSs selected for this study were located in dense urban areas, typically characterized by higher traffic than rural areas. The BSs operated within the NR band 78 (3300-3800 MHz) with a channel bandwidth varying among the sites and ranging from 40 to 80 MHz. The time division duplexing (TDD) downlink duty cycle, which is defined as the fraction of the time available for downlink transmission to the total transmission time, was about 75%.  
Ericsson Network Manager (ENM) was used to access information on the 5G BSs operation for 24 h over a weekday at the end of January 2020. By means of gathering data directly through the ENM, it was possible to simultaneously collect information and statistics for many sites over a relatively long period. Moreover, it allowed us to analyze the spatial distribution of the BS transmit power in a three-dimensional space within the scan range of the antenna rather than having to conduct measurements at a fixed location.  
The transmit power per direction or more specifically the equivalent isotropic radiated power (EIRP) in azimuth and elevation (\(\varphi,\theta\)) was determined as a function of time by continuously logging two metrics:  
* The total (cell-wide) time-averaged transmit power, \(P_{\text{avg}}\);
* The time-averaged gain, \(G_{\text{avg}}(\varphi,\theta)\).  
While the recently updated exposure guidelines, ICNIRP [2], have extended the averaging time applicable to whole-body exposure to thirty minutes, six minutes was applied in this study according to the previous version of the ICNIRP guidelines [13]. This choice was made in order to allow a direct comparison with what was predicted by the statistical models, e.g., [8], published when [2] was not yet available. Moreover, it might take some time until the updated ICNIRP guidelines are adopted in national regulations. Since the time-averaged power over thirty minutes is equal or less than what is obtained by averaging over six minutes, the results presented in this paper are also conservative with respect to the latest ICNIRP guidelines.
# Overview on Different Types of Solar Cells: An Update
## 3 Perovskite Solar Cells
### The Difficulties and Strategies for Improving Solar Cell Stability  
The hydrophilic and volatile nature of organic molecules make hybrid perovskite vulnerable to degradation through humidity and heat, which limits the long-term stability of perovskite solar cells for industrial applications. Several fundamental strategies have been adopted to slow down the degradation of the absorber materials and increase device stability: (i) develop a more resilient perovskite absorber via cation substitution/doping; (ii) controlling perovskite crystallization by novel approaches such as solvent engineering and the use of additives or non-halide, (iii) using carbon-based functional materials such as carbon nanotubes (CNT), graphene, or fullerene (C60) between the perovskite absorber and the hole-transport materials, and (iv) developing a protective layer (encapsulation technique) around the absorber material. To date, most perovskite solar cells with high stability are inverted (p-i-n) type (maintaining >90% of the initial power conversion efficiency over 1000 h).
# Design and Testing of a New Bionic Corn-Ear-Picking Test Device
## 3 Design of Key Components of a New Bionic Test Device for Corn Ear Picking  
### Structural Composition  
The mechanical experiments were conducted to compare with the tensile picking principle used by most current harvesters to prove that the bionic reverse picking is an excellent method with low force requirements and low power consumption levels, thus supporting the proposed method. Moreover, a fresh corn picking test stand was designed based on this principle.  
As shown in Figure 6, the new bionic device for corn ear picking had a frame made of angle steel and a compact structure. The device was composed of a clamping conveyor, left and right snapping rolls, a tension sprocket, an adjusting frame, a frame, and an adjustable speed motor. The clamping conveyor consisted of a conveying belt, a driving wheel, and fixed and movable tension rollers. It was installed at a certain angle in relation to the ground surface. Additionally, there was a certain gap between the two snapping rollers, which were installed symmetrically under the clamping conveyor. The angle between the snapping rollers and the clamping conveyor can be adjusted using the adjusting frame. In addition, the speed of the whole device can be adjusted using three motors.
## 5 Simulation Results  
In order to verify the performance of the proposed constrained SAC algorithm, we test it in various scenarios with different algorithms, including a DDPG approach incorporating penalty as constraint in its reward function, unconstrained SAC, and conventional SCA-based algorithm.
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 4 Results
### Experimental Evaluation of Container Distortion  
We used MSE and PSNR to assess container distortion. The obtained experimental results indicate that using chip codes generated according to rules 1, 3, 4, and 5 leads to approximately equal distortions of containers. The corresponding dependences of \(MSE(k)\) and \(PSNR(k)\) for different values are shown in Figures 9 and 10.  
Somewhat less distortion of the container is provided by using the rule for generating chip codes \(N2\); the corresponding dependencies are shown in Figures 11 and 12.  
Figure 8: \(BER(P)\) dependencies for different rules for generating chip codes, \(k=1\).  
Figure 10: _PSNR(k)_ dependencies for different values of \(P\) rules for generating chip codes \(N1\), \(3\), \(4\), and \(5\).  
Figure 9: _MSE(k)_ dependencies for different values of \(P\) rules for generating chip codes \(N1\), \(3\), \(4\), and \(5\).  
Figure 11: _MSE(k)_ dependencies for different values of \(P\) rule for generating chip codes \(N2\).  
Figure 12: _PSNR(k)_ dependencies for different values of \(P\), rule for generating chip codes \(N2\).  
The analysis of Figures 9-12 enables us to make the following observations.  
First, an increase in the number of hidden information bits leads to an inevitable increase in container distortion. Usually, PSNR values of about 30-40 dB are considered acceptable quality. If you focus on these values, then you can effectively hide no more than 10-20 information bits.  
Secondly, an increase in the power gain \(P\) also leads to an increase in container distortion. If we focus on PSNR of about 30.40 dB, then the values of \(P<8\) are acceptable (with \(k<4\)).  
Thus, the obtained results of experimental studies show that by increasing the length \(k\) of the hidden message, we inevitably distort the container. Raising \(P\) in order to reduce BER also leads to an increase in container distortion. Thus, there are several conflicting factors that directly affect the efficiency of the steganosystem. In order to find a compromise, it is necessary to study the influence of these factors on each other.
## 3 Results and Discussion
### The Relationship Between Detection Efficiency and Meteorological Factors
As shown in Figure 9d, the detection range of the DBS mode remains stable when relative humidity is below 75%, with the average detection range under low-visibility conditions slightly higher than the overall average. This suggests that the DBS mode's detection range is not significantly influenced by humidity. The increased concentration of particulate matter, along with its upward diffusion in low-visibility conditions, likely contributes to the improvement in detection efficiency. It is observed that high-humidity environments significantly affect the detection range. Therefore, data with relative humidity above 80% were selected for further analysis (Figure 10). During non-precipitation periods, when relative humidity is in the range of 80-90%, the detection range distribution of the PPI mode (Figure 10a) is more dispersed, with the maximum detection range reaching its upper limit. As relative humidity increases further, the detection range slightly decreases, and the distribution of data points becomes more concentrated. The detection range performance of the DBS mode (Figure 10c) is similar to that of the PPI mode. During precipitation periods, the detection range of both modes exhibits a clear decrease (Figure 10b,d), suggesting that the presence of hydrometeors may be responsible for the reduced detection efficiency in high-humidity environments. Due to the hygroscopic growth effect, hydrometeors tend to form larger aerosol particles in high-humidity conditions, and these large particles significantly enhance the scattering and absorption of laser beams [34], leading to a substantial reduction in backscatter signal strength. Additionally, the aerosol wet deposition effect caused by precipitation [35] may result in a decrease in aerosol concentration within the detection range, further weakening the backscatter signal strength.
# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
###### Abstract  
Fingerprint classification is a stage of biometric identification systems that aims to group fingerprints and reduce search times and computational complexity in the databases of fingerprints. The most recent works on this problem propose methods based on deep convolutional neural networks (CNNs) by adopting fingerprint images as inputs. These networks have achieved high classification performances, but with a high computational cost in the network training process, even by using high-performance computing techniques. In this paper, we introduce a novel fingerprint classification approach based on feature extractor models, and basic and modified extreme learning machines (ELMs), being the first time that this approach is adopted. The weighted ELMs naturally address the problem of unbalanced data, such as fingerprint databases. Some of the best and most recent extractors (Capelli02, Hong08, and Liu10), which are based on the most relevant visual characteristics of the fingerprint image, are considered. Considering the unbalanced classes for fingerprint identification schemes, we optimize the ELMs (standard, original weighted, and decay weighted) in terms of the geometric mean by estimating their hyper-parameters (regularization parameter, number of hidden neurons, and decay parameter). At the same time, the classic accuracy and penetration-rate metrics are computed for comparison purposes with the superior CNN-based methods reported in the literature. The experimental results show that weighted ELM with the presence of the golden-ratio in the weighted matrix (W-ELM2) overall outperforms the rest of the ELMs. The combination of the Hong08 extractor and W-ELM2 competes with CNNs in terms of the fingerprint classification efficacy, but the ELMs-based methods have been demonstrated their extremely fast training speeds in any context.  
fingerprint classification; fingerprint features; extreme learning machine; unbalanced dataset +
Footnote †: journal: Applied Sciences
## 4 Results
### Preliminary Phase
#### 4.1.2 Comparing Different Types of In Situ Measurements  
We compared models for Chl-a and phycocyanin, the two most commonly modeled phytoplankton pigments. Based on the finding that Chl-a models improved when data were limited to points above the eutrophic threshold (8 \(\mathrm{\SIUnitSymbolMicro g}\)/L), we evaluated whether applying a similar detection threshold approach to the phycocyanin dataset was also beneficial to improve model fit. Empirical models improved using phycocyanin concentrations above 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L (R\({}^{2}\) of 0.76) compared to those including all phycocyanin observations (R\({}^{2}\) of 0.35). Because of this, and in the absence of a widely accepted limnology-based low-phycocyanin threshold, the 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L threshold was applied to phycocyanin.  
Figure 5 shows a scatterplot of the Sentinel-2 index and in situ phycocyanin concentrations above 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L. This scatterplot was based on the training data and shows the best model for phycocyanin in this preliminary phase. The highest R\({}^{2}\) value for phycocyanin concentrations (0.76) was achieved when using MPH values. As shown in Figure 3b, the R\({}^{2}\) value for Chl-a was distinctly lower, with the best model based on training data being the 2 BDA model (0.51). Despite the lower R\({}^{2}\) value for Chl-a, we continued to work with the data because of the importance of Chl-a estimation to lake managers.  
Figure 4: Scatterplot between in situ Chl-a above 8 \(\mathrm{\SIUnitSymbolMicro g}\)/L and Sentinel-2-based 2 BDA (RE1: band 5 and R: band 4) index with concurrent in situ and satellite data (blue points and line), one-day time window (orange points and line), and two-day time window (green points and line).
# Computational Thinking Measurement of CS University Students
## 3 Results
### Comparison between Courses
#### 3.2.1 General Results  
Figure 5 shows the box plots for the CTScore and CTProg variables differentiated between the first and fourth years. As can be seen in Table 4, the central values of both variables are maintained in both years, with a greater dispersion being perceived for both in the fourth year. Using the Mann-Whitney U test (Table 5), it can be seen that these minimal differences are, in effect, not significant, so it can be ensured that there are no significant differences between both courses.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Duration**} & \multicolumn{2}{c}{**CTProg**} & \multicolumn{2}{c}{**CTScore**} \\ \hline Duration & \multicolumn{2}{c}{1} & \multicolumn{2}{c}{0.179 *} & \multicolumn{2}{c}{} \\ CTProg & \multicolumn{2}{c}{**0.179 *} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**0.398 **} \\ \cline{2-6}  & CTScore & \multicolumn{2}{c}{0.082} & \multicolumn{2}{c}{**0.398 **} & \multicolumn{2}{c}{1} \\ \hline  & **PBDS** & **PCON** & **PLOREPEAT** & **PFUNC** & **PLOFOR** & **PARR** & **PMAT** \\ \hline PBDS & \multicolumn{2}{c}{—} & & & & & \\ PCON & \(-\)0.014 & \multicolumn{2}{c}{—} & & & & \\ PLOREPEAT & \(-\)0.057 & 0.188 & \multicolumn{2}{c}{— } & & & \\ PFUNC & 0.129 & 0.126 & \(-\)0.123 & \multicolumn{2}{c}{—} & & \\ PLOFOR & 0.156 & **0.236 * & **0.088 ** & 0.115 & \multicolumn{2}{c}{—} & \\ PARR & 0.193 & **0.219 ** & **0.094 ** & 0.200 & **0.366 ** & \multicolumn{2}{c}{— **} \\ PMAT & 0.102 & 0.072 & \(-\)0.013 & 0.155 & **0.139 * & **0.113 * & \\ \hline  & \multicolumn{2}{c}{**Problem Solving**} & \multicolumn{2}{c}{**Abstraction**} & \multicolumn{2}{c}{**Algorithmic**} & \multicolumn{2}{c}{**Decomposition**} & \multicolumn{2}{c}{**Critical**} & \multicolumn{2}{c}{**Pattern Recognition**} \\ \hline Problemsolving & \multicolumn{2}{c}{—} & & & & & \\ Abstraction & 0.099 & \multicolumn{2}{c}{—} & & & & \\ AlgorithmicThinking & **0.156 * & **0.266 ** & \multicolumn{2}{c}{—} & & & \\ Decomposition & 0.086 & **0.181 ** & **0.239 ** & & & \\ CriticalThinking & 0.037 & 0.067 & 0.100 & 0.046 & & \\ CreativeThinking & 0.110 & **0.152 * & 0.059 & 0.098 & 0.116 & \\ PatternRecognition & 0.112 & 0.076 & **0.132 * & 0.093 & 0.075 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tau-b coefficients for each pair of variables. Those which are significant are marked in bold (*: \(p<0.05\); **: \(p<0.01\)).  
Now, the focus is on observing the differences between each of the variables studied. The following graphs (Figure 6, top and bottom) show the median of each of them as a representative of the central tendency in the data. The median is used instead of the mean, since there is no symmetry in the distribution of the data, with the median being a more robust measure. Although they are not statistically significant, changes can be seen between the first and fourth years in the variable for the programming concepts of repeat loops (PLOREPEAT), functions (PFUNC), arrays (PARR), and matrices (PMAT).
## 3 OFDM Model  
### Signal Model  
Consider an OFDM system utilizing \(N\) sub-carriers. In order to closely follow the continuous amplitude envelope of the time-domain OFDM signal, an oversampling factor \(\eta\geq 4\) is customarily assumed. The time-domain OFDM signal can be written as  
\[x[n]=\frac{1}{\sqrt{\eta N}}\sum_{k=1}^{\eta N}X[k]e^{\frac{\omega_{k}n}{\eta N }}\ \forall\ n=1,2,\ldots,\eta N\,, \tag{1}\]  
where \(\omega_{k}=2\pi k\) is the \(k\)-th sub-carrier carrying the data-bearing modulated symbol \(X[k]\). Quadrature phase shift keying (QPSK) modulation and quadrature amplitude modulation (16-QAM) are both considered subsequently.