esa-sceva/satcom-chunk-collection huggingface dataset:
# Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System
## 3 Bi-Level Capacity-Operation Collaborative Optimization Method
### Objective Function
#### 3.2.2 Upper-Level Objectives  
The top-tier optimization, focusing on capacity distribution, grapples with a multi-objective challenge that seeks a balance between the financial aspects of the WSH-MES system and CO\({}_{2}\) emissions. The target function encompasses both the LCOE and CO\({}_{2}\) emissions.  
1. LCOE  
LCOE serves as a unique measure for assessing the overall expense of generating electricity throughout the entire life cycle of a system.  
\[LCOE=\frac{IC+\sum\limits_{n=1}^{\text{N}}\frac{C_{\text{OM}}(n)}{(1+i)^{n}} }{\sum\limits_{n=1}^{N}\frac{P_{\text{WP}}(n)\cdot(1-d_{\text{WP}})^{n-1}+P_{ \text{VP}}(n)\cdot(1-d_{\text{VP}})^{n-1}+P_{\text{CSP}}(n)\cdot(1-d_{\text{ CSP}})^{n-1}}{(1+i)^{n}}} \tag{3}\]  
In the given expression for LCOE, the numerator primarily focuses on the computation of investment and operation and maintenance (O&M) costs. Here, \(IC\) symbolizes the initial investment costs for the system under study, \(C_{\text{OM}}\) stands for the system's yearly O&M costs,  
Figure 4: Internal unit structure of LSTM.  
and \(i\) signifies the discount rate, selected as 0.05 for this research. Additionally, \(n\) refers to the operating cycle, \(N\) represents the system's expected lifespan (set at 25 years in this study), and \(d\) indicates the annual degradation factor for each subsystem. The denominator mainly accounts for the system's power generation, where \(P_{\text{WP}}\), \(P_{\text{PV}}\), and \(P_{\text{CSP}}\) correspond to the generation power of WP, PV, and CSP (in MWh), respectively. Further details on the key economic parameters of the WSH-MES system can be found in reference [37].  
(2) CO2 emissions  
When the energy produced with the WSH-MES system falls short of meeting user demand, additional power will be sourced from the grid. Notably, coal-powered generation remains a dominant contributor to grid power. Given that this study incorporates the CO2 emissions from such sources, the optimization goal is to minimize the CO2 emissions associated with power purchases. The computation for CO2 emissions is detailed as follows, based on reference [33]:  
\[E_{\text{CO}_{2}}=B_{\text{s,sum}}\cdot c_{\text{ef}} \tag{4}\]  
\[B_{\text{s,sum}}=P_{\text{purchase}}\cdot b_{\text{s}} \tag{5}\]  
In the provided equation, \(E_{\text{CO}_{2}}\) stands for the amount of CO2 emissions in tons (t). \(B_{\text{s,sum}}\) represents the yearly consumption of standard coal (t). The term \(c_{\text{ef}}\) is used to denote the CO2 emission coefficient, with a value of 2.78 as referenced in study [33]. The variable \(B_{\text{s,sum}}\) is determined with the system's yearly power acquisition \(P_{\text{purchase}}\) (measured in MWh) and the coal consumption for each unit of \(b_{\text{s}}\), kWh (t). In the context of this research, it is assessed that generating 1 kWh of electricity consumes 302 g of standard coal.
## 2 Materials and Methods  
### Study Site  
Farm data on yield measurements were provided by the Rostenice Farm in the Czech Republic. Data on crops, agronomic practices, and yield measurements were provided for the purposes of this paper.  
The farm, Rostenice a.s. (N 49.105 E 16.882; see Figure 2), manages over 10,000 ha of arable land in the South Moravian Region of the Czech Republic. The average annual rainfall is 544 mm and the average annual temperature is 8.8 \({}^{\circ}\)C. Within the managed land, the most prevalent soil types are Chernozem; Cambisol; haptic Luvisol; Fluvisol near to water bodies; and, occasionally, also Calcic Leptosols. The main programme is plant production, where the main focus is on the cultivation of malting barley, maize for grain and biogas production, winter wheat, oilseed rape, and other crops and products such as soybean and lamb.  
Plots \"Pivov\(\check{\text{a}}\)rka\" and \"Predni prostredni\", with an average of 44.5 ha and 61.2 ha, respectively, were selected for the pilot study. These plots are located in sloping terrain; for this reason, conservation practices with respect to soil tillage have been implemented to reduce soil water erosion. The farm has applied long-term soilless cultivation (mostly choppers) on its land, leaving all straw after harvest on the land. The high spatial variability of soil conditions has led to the adoption of precision farming practices, such as the variable application of fertilizers (since 2006) and crop yield mapping by harvesters (since 2010).  
The farm machinery measurements are collected by field harvesters with time resolution of one second at the average harvesting speed of 1.55 ms\({}^{-1}\); the mean value is thus 370 points per hectare. The pilot study included tens of thousands of points per average plot. The spatial resolution is a result of a combination of harvesting speed and the width of a cutting bar (9.15 m). A visualization of  
Figure 2: Geographical location of the Rostenice Farm.  
this point Big Data is a challenge as it entails approximately a distance of 0.16 mm between points in a row and 0.46 mm between lines at the 1:10,000 scale, or approximately the distance equal to 0.31 mm between points in a row and 0.92 mm between lines at the scale of 1:5000.
# A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
## 3 Methods
### Improved Ant Colony Algorithm Model  
During the voyage, the route safety from the beginning to the end is the most crucial factor, and the cost function \(P_{safe}\) for safe obstacle avoidance is as follows.  
\[P_{safe}=\left\{\begin{array}{ll}1,&d_{1}>dsaf\\ \infty,&d_{1}<dsaf\end{array}\right. \tag{17}\]  
where \(d_{1}\) is the straight-line distance between the ship and the obstacle, and \(d_{safe}\) is the safe distance between the ship and the obstacle. The comprehensive cost function \((J)\) of the path is determined by the following equation.  
\[min(J)=P_{safe}\cdot E_{i,i+1},0<|v_{i,i+1}|\leq v_{max} \tag{18}\]  
where \(E_{i,i+1}\) is the energy consumption cost from \(i\) to \(i+1\). \(v_{i,i+1}\) is the speed of the ship in the track from node \(i\) to \(i+1\), and \(v_{max}\) is the maximum sailing speed of the ship.  
\[fit_{i}=\frac{E_{min}}{E_{i}} \tag{19}\]  
where \(fit_{i}\) is the fitness value of each ant. \(E_{min}\) is the minimum energy consumption value of the current iteration, and \(E_{i}\) is the corresponding energy consumption of each ant.  
To prevent the algorithm from falling into local optimum, the local pheromone update strategy is utilized, in which the integrated cost function is also considered. Ants can implement different pheromone update strategies by introducing different weight coefficients. The update improvement rules for pheromone are as follows.  
\[\tau_{ij}(t+1)=(1-\rho_{a})\tau_{ij}(t)+\rho_{a}\sum_{k=1}^{m}\tau_{ij}^{k} \tag{20}\]  
\[\tau_{ij}^{k}=\frac{C\cdot fit_{i}}{J_{t}} \tag{21}\]  
where \(C\) is the weight coefficient, and \(J_{t}\) is the comprehensive cost function of the optimal path of the ant \(k\) in the \(t\)th iteration. In order for the pheromone concentration to demonstrate a better path, we set the weight as \(C=6.4\).
###### Abstract  
The opening access of global navigation satellite system (GNSS) raw data in Android smart devices has led to numerous studies on precise point positioning on mobile phones, among which single-frequency precise point positioning (SF-PPP) has become popular because smartphone-based dual-frequency data still suffer from poor observational quality. As the ionospheric delay is a dominant factor in SF-PPP, we first evaluated two SF-PPP approaches with the MGEX (Multi-GNSS Experiment) stations, the Group and Phase Ionospheric Correction (GRAPHIC) approach and the uncombined approach, and then applied them to a Huawei P40 smartphone. For MGEX stations, both approaches achieved less than 0.1 m and 0.2 m accuracy in horizontal and vertical components, respectively. Uncombined SF-PPP manifested a significant decrease in the convergence time by 40.7%, 20.0%, and 13.8% in the east, north, and up components, respectively. For P40 data, the SF-PPP performance was analyzed using data collected with both a built-in antenna and an external geodetic antenna. The P40 data collected with the built-in antenna showed lower carrier-to-noise ratio (C/N0) values, and the pseudorange noise reached 0.67 m, which is about 67% larger than that with a geodetic antenna. Because the P40 pseudorange noise presented a strong correlation with C/N0, a C/N0-dependent weight model was constructed and used for the P40 data with the built-in antenna. The convergence of uncombined SF-PPP approach was faster than the GRAPHIC model for both the internal and external antenna datasets. The root mean square (RMS) errors for the uncombined SF-PPP solutions of P40 with an external antenna were 0.14 m, 0.15 m, and 0.33 m in the east, north, and up directions, respectively. In contrast, the P40 with an embedded antenna could only reach 0.72 m, 0.51 m, and 0.66 m, respectively, indicating severe positioning degradation due to antenna issues. The results indicate that the two SF-PPP models both can achieve sub-meter level positioning accuracy utilizing multi-GNSS single-frequency observations from mobile smartphones.  
single-frequency precise point positioning; GRAPHIC model; uncombined model; multi-GNSS system; Android smartphone +
Footnote †: journal: Remote Sensing
## 2 Materials and Methods
### Accuracy Assessment  
The most crucial final procedure in image classification is accuracy assessment [58; 59]. Accuracy evaluation assists in providing a quantitative analysis of how efficiently and accurately pixels are allocated to the suitable land cover classes. Besides additional aspects, the level of resemblance between the generated and reference maps is described as accuracy. In recent years, a variety of strategies and indices for accuracy evaluation have been developed, and many of these have been enhanced. The overall accuracy and the kappa coefficient are the two frequently preferred accuracy indicators. Overall accuracy represents the probability of correctly classifying a randomly chosen area located on the maps. The kappa coefficient ranges from \(-1\) to 1 and indicates that the classification is much accurate or worse [60; 61]. A negative kappa value implies that the classification is significantly different from random. When the value is close to 1, it shows that the classification is more accurate [62; 63]. The F1 measurement is currently widely used in most machine learning applications in binary situations and multiclass conditions. We also evaluated the suggested method's performance using accuracy, precision, misclassification rate, recall, and specificity. The following formula is used to determine accuracy, misclassification rate, precision, recall, and specificity.  
\[\mathrm{K}\;=\frac{\mathrm{OA}\;+\;\mathrm{CA}}{1-\mathrm{CA}} \tag{7}\]  
\[\mathrm{F1}=2*\frac{\mathrm{Precision}\;*\;\mathrm{recall}}{\mathrm{Precision} +\mathrm{recall}} \tag{8}\]  
\[\mathrm{Precision}\;=\frac{\mathrm{Tp}}{\mathrm{Tp}\;+\mathrm{Fn}} \tag{9}\]\[\text{Recall}\,=\frac{\text{Tp}}{\text{Tp}\,+\text{Fn}} \tag{10}\]  
\[\text{Specificity}=\frac{\text{Tn}}{\text{Tn}\,+\text{Fp}} \tag{11}\]  
where OA represents the observed accuracy, and CA indicates the chance agreement. The metric for each class was calculated first, followed by the average score and between all categories, where Fn, Fp, Tp, and Tn, are the number of false-negative, false-positive, true-positive, and true-negative observations, respectively. First, the metric for each category was calculated, and then the average score across all categories was computed.
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 3 Algorithm  
In this section, we design 6 LEO constellations to evaluate the performance of 6 common algorithms for this problem. Subsequently, according to the characteristic that the LEO satellites are usually uniformly distributed, we improve the RLF algorithm to make it more suitable for our question.
## 2 Materials and Methods
### Study Area
#### 2.1.2 Memory-Map-Based Waveform Generator  
The memory-map-based waveform generator is one of the digital methods to generate a waveform. This method has the advantage that the output signal is stable and easy to implement because the stored data is loaded in the memory (FPGA programmable read-only memory (PROM), or read-only memory (ROM)). However, due to the increasing demands of various data types for SAR, more memory capacity is needed. Furthermore, in the case of memory capacity limitations or damage due to external factors, SAR may not function properly and cannot acquire the images. To overcome these problems, the use of DDS has been increasing recently [17].
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 2 Data Collection and Preprocessing
### Audio Segmentation and Silence Removal  
Silence is an unvoiced part of a speech signal and it is useful in detecting pauses between speech but most of the time it is useless because it makes extraction of actual information difficult [36]. We adjusted each clip to have minimal silence because silence makes it difficult for the network to classify an alphabet accurately due to the presence of useless information. Recorded data files consist of 29 letters and each of them is separated through silence, which is useful in the segmentation of a large file. We implemented a speech detection algorithm over the audio dataset, the algorithm is based on [38]. The algorithm detects the boundaries of the speech and discards the silence at the end and beginning of the speech. It transforms the audio signal to time-frequency representation with specified 'Window' and 'OverlapLength' (Number of samples overlapping between adjacent windows). For each frame, it calculates short-term energy and spectral spread and then creates their histogram.  
The spectral spread and short-term energy are smoothed over time by passing through the successive moving median filter to alleviate spikes that are left after noise removal and compared with their respective threshold to create the mask. The masks are combined and the speech regions are merged with 'MergeDistance' (Number of samples over which merge positive speech is detected) to declare a frame with speech. Figure 3 shows the detected speech levels discarding the silence between them. Later, we save these speech segments in separate audio files, so we can use them in an audio classification task.  
Figure 3: Audio levels are marked according to speech detected in the recorded file before splitting.  
Figure 2: Noise and background speech removal from the recorded audio samples. (**a**) Time-waveform of the noisy audio samples. (**b**) Time-waveform of the clean audio samples.
## 4 Results Analysis
### Tupu Analysis of LUCs
#### 4.2.2 Tupu Analysis from 2010 to 2020  
From 2010 to 2020, the total area of LUCs in Pution City, as detailed in Table 6, was 183.21 km\({}^{2}\), signifying a reduction of 63.17 km\({}^{2}\) when compared to the preceding period from 2000 to 2010. Among the various transitioning Tupu units, CL continued to be the most extensively transformed land type, with a total of 146.38 km\({}^{2}\) being converted. More than half of this area, specifically 58.41% (85.50 km\({}^{2}\)), was transformed into BL, with a significant concentration along the coastal regions, especially in the Xiuyu District, as illustrated in Figure 3b. The area of CL that was converted into other types totaled only 23.80 km\({}^{2}\), mainly from transitions involving FL and GL. The proportions of increase and decrease in CL were markedly imbalanced. The area of FL that underwent transition was  
Figure 3: Tupu of LUCs in Pution City from 2000 to 2020. Sources: Drawings from this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Transition Tupu Unit**} & \multicolumn{2}{c}{**Number of Tupu**} & \multirow{2}{*}{**Transition Area/km\({}^{2}\)**} & \multirow{2}{*}{**Change Ratio/\%**} \\  & & **Unit/No.** & & \\ \hline
16 & Cultivated land (CL)\(\rightarrow\)Built-up land (BL) & 78184 & 70.37 & 28.56 \\
46 & Grassland (GL)\(\rightarrow\)Built-up land & 65136 & 58.62 & 23.79 \\
17 & Cultivated land\(\rightarrow\)Unused land (UL) & 49782 & 44.80 & 18.18 \\
12 & Cultivated land\(\rightarrow\)Forest land (FL) & 18352 & 16.52 & 6.70 \\
21 & Forest land\(\rightarrow\)Cultivated land & 16516 & 14.86 & 6.03 \\
15 & Cultivated land\(\rightarrow\)Water body (WB) & 11246 & 10.12 & 4.11 \\
14 & Cultivated land\(\rightarrow\)Grass land & 9143 & 8.23 & 3.34 \\
24 & Forest land\(\rightarrow\)Grass land & 7113 & 6.40 & 2.60 \\
26 & Forest land\(\rightarrow\)Built-up land & 6781 & 6.10 & 2.48 \\
27 & Forest land\(\rightarrow\)Unused land & 3945 & 3.55 & 1.44 \\ \hline \hline \end{tabular} Sources: Drawings from this study.  
\end{table}
Table 5: Tupu unit ranking of LUCs in Pution City from 2000 to 2010 (Top 10).  
22.26 km\({}^{2}\), with the majority, 74.10% (16.50 km\({}^{2}\)), being converted to CL. Other conversions included 17.21% (3.83 km\({}^{2}\)) to GL, 8.14% (1.81 km\({}^{2}\)) to OL, and a minimal 0.55% (0.12 km\({}^{2}\)) to water bodies (WB). The area of FL that was converted into other types was 14.10 km\({}^{2}\), primarily from CL and OL. The GL area that was converted out, which was 9.01 km\({}^{2}\), was less than the area that was converted in (7.73 km\({}^{2}\)), resulting in a net loss of 1.28 km\({}^{2}\). The primary conversion out of GL was to CL (5.35 km\({}^{2}\)), while the primary source for the increase in GL area was the conversion from CL (7.49 km\({}^{2}\)). The shift of CL into GL was largely due to the abandonment of CL, which then naturally transitioned into GL. Subsequently, a significant portion of the GL was eventually repurposed for BL.
# A Dual-Polarized Omnidirectional Rectenna Array for RF Energy Harvesting Harvesting
## 2 Rectenna Array Design
### Rectifier Design  
A rectifier operating at 2.45 GHz is designed and optimized using the Advanced Design System (ADS) software, which is printed on a 30-mil-thick Rogers RO4350B substrate with a dielectric constant of 3.66 and a loss tangent of 0.0037. Its circuit schematic is shown in Figure 6a. The key parameters of the Schottky diode HSMS-2850 are breakdown voltage (B\({}_{\mathrm{V}}\)), threshold voltage (V\({}_{\mathrm{T}}\)), and junction capacitance (C\({}_{\mathrm{J0}}\)), which are 3.8 V, 0.35 V, and 0.18 pF, respectively. Compared with other commonly used RF diodes, the [PERSON] diode HSMS-2850 has a relatively small threshold voltage. Thus, it is advantageous to rectify low RF power and is suitable for REH applications. Furthermore, its junction capacitance supports its operation at 2.45 GHz. Therefore, the [PERSON] diode HSMS-2850 is used in this design.  
After fabrication, the photograph of the rectifier is given in Figure 6b. The optimized dimensional parameters of the proposed rectifier are listed in Table 2. Figure 7 shows the simulated PCEs of the rectifier under different load resistances. It is obvious that the operating input power range for PCE larger than 50% becomes narrow when the load resistance goes beyond 1900 \(\Omega\). On the other hand, when the load resistance gets smaller than 1900 \(\Omega\), the maximum PCE decreases sharply. Therefore, considering the operating input power range and the maximum PCE, a load resistance of 1900 \(\Omega\) is chosen as the optimal value in this design.  
Figure 8 plots the simulated and measured PCEs and output voltages of the designed rectifier. It can be seen that the fabricated rectifier arrives at a maximum measured PCE of 70.5% when the input RF power is 2 dBm, and the corresponding output DC voltage  
Figure 5: Radiation patterns of the antenna array at 2.45 GHz in the vertical polarization: (**a**) xoy plane; (**b**) yoz plane.  
Figure 6: Schematic and photograph of the proposed rectifier: (**a**) Schematic; (**b**) Photograph.  
is 1.46 V. Meanwhile, the measured PCE is higher than 50% over the input power range of approximately \(-8\) dBm to 9 dBm. In the following dual-polarized rectenna array, two identical rectifiers designed here will be arranged in parallel as a rectifier array.
## 2 Materials and Methods
### Data Analyses  
Ankle motor performance data were processed using Spike2 software (Cambridge Electronic Design, Cambridge, UK) and MATLAB (Version R2022b; MathWorks, Natick, MA, USA). Two primary performance metrics were computed to evaluate tracking accuracy during the visuomotor task:  
Figure 1: Experimental setup for the visuomotor ankle-tracking task. The illustration shows a participant seated with their leg secured in a custom-built apparatus that allows for controlled dorsiflexion (DF) and plantar flexion (PF) of the ankle. The task required participants to track a sinusoidal target waveform (pink line) by moving their ankle to match the pattern in real time, as reflected by their ankle trajectory (black line) over a 60-s trial.  
Spatiotemporal Error (NRMSE): This metric quantified the overall deviation between the participant's ankle movement and the sinusoidal target waveform over time. It was calculated as the root mean square error (RMSE) between the ankle position and target trajectory, then normalized to each participant's ankle range of motion (ROM). The error was computed over 6000 time points per 60-s trial and averaged across five trials for each time point.
2. Spatial Error (SE): This measure assessed the positional alignment between the participant's movement and the target waveform independent of timing. It was derived from a cross-correlation analysis of the ankle position time series with the target waveform. The maximum correlation coefficient (R\({}^{2}\)) was identified, and spatial error was calculated as 1--R\({}^{2}\). This value was then normalized to ankle ROM to ensure comparability across participants [23].  
Both NRMSE and SE values ranged from 0 (perfect accuracy) to 1 (maximum error), with lower values indicating better performance. For each participant, data were averaged across five trials per time point. Percentage changes from baseline were also calculated to capture relative improvements or declines in performance. Outliers were defined as values exceeding three standard deviations from the group mean for each condition and time point. These outliers were excluded from both statistical analyses and data visualization.