esa-sceva/satcom-chunk-collection huggingface dataset:
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 3 Theory
### Weighted Contribution  
The simplest weighting function is a constant value; this gives global ranking. A limited weighting function is often used having 1 as the weight for a small area around the selected pixel, and 0 elsewhere. This weighting function is called a 'window', and the algorithm is a local histogram equalization. These histogram calculations can be implemented very efficiently. However, the most general form of weighting means evaluation of every pixel pair. If the image has \(n\) times \(n\) pixels, then the asymptotic complexity of this calculation is \(\mathcal{O}(n^{4})\).  
There is a special case when the weighting only depends on the distance of the pixel pairs.  
\[\text{W}\left(x,x^{\prime},y,y^{\prime}\right)=f(d(x-x^{\prime},y-y^{\prime})), \text{ where} \tag{11}\]  
\[d(x-x^{\prime},y-y^{\prime})=\sqrt{(x-x^{\prime})^{2}+(y-y^{\prime})^{2}}. \tag{12}\]  
In this special case, every pixel gives \(f(r)\) contribution at distance \(r\).  
Only pixels with this \(z_{0}\) intensity value can contribute to a histogram bin \(z_{0}\). These pixels are recorded in the plane \(z_{0}\) in the indicator array. Due to the translation invariant weights, the weighted histograms can be calculated convolving the indicator array in the plane \(z\) with the weighting function:  
\[H(x,y,z)=ID(x,y,z)\underset{x,y}{\star}W(x,y). \tag{13}\]  
Convolutions can be efficiently calculated in the Fourier domain using fast Fourier transform (FFT), and its inverse (iFFT):  
\[\tilde{f}(s,t)=FFT_{x,y}\left\{f(x,y)\right\}, \tag{14}\]  
\[f(x,y)=iFFT_{s,t}\left\{\tilde{f}(s,t)\right\}, \tag{15}\]  
\[H(x,y,z)=iFFT_{u,v}\left\{FFT_{x,y}\left\{ID(x,y,z)\right\}\cdot FFT_{x,y} \left\{W(x,y)\right\}\right\}. \tag{16}\]  
The result is a 3D array which contains locally weighted histograms along the \(z\)-direction for every corresponding pixel (see Figure 2). For further details, we refer the interested reader to [11].  
Figure 2: Columns in the \(z\)-direction contain the weighted histograms for corresponding pixels. Every pixel has its own local weighted histogram.  
Due to the nature of FFT, it is important to note that we used the'mirrored image' boundary condition, which is a frequently used condition for image boundaries when Fourier methods are used.
# Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses
## 4 Experiments  
The Swarm POD contains two sections. One is computing orbits by the best orbit strategy, and the other is assessing the quality of Swarm orbits. Given that we have not assessed absolute orbit accuracy, we can take advantage of different assessment methods to evaluate the orbit quality (e.g., internal quality assessment and external equality assessment). Two of the widely used methods to evaluate orbit accuracy are GPS phase residuals and independent orbit comparison. Independent orbit comparison contains two sections: orbit comparison between two independent tracking systems and orbit comparison between two different institutions. The assessment methods are successfully applied to CHAMP, GRACE and GOCE. We use GPS phase observation residuals to evaluate internal quality assessment of Swarm orbits. In addition, we use comparison with ESA PSOs and Satellite Laser Ranging (SLR) validation to evaluate external equality assessment.  
We validate the effectiveness of the ZD reduced-dynamic method and Swarm orbit strategy (containing optimized pseudo-stochastic pulses) by carrying out related experiments under high ionospheric activity in the following section.  
First, we choose 1-25 October 2014 (day of year (DOY) 274 to 298, 2014) of the Swarm space-borne GPS data. Figure 6 shows F10.7 values from 1-25 October 2014. The Figure indicates that F10.7 values vary from 110 sfu to 224.8 sfu (1 sfu = \(10^{-22}\) Wm\({}^{-2}\cdot\)Hz\({}^{-1}\)), which demonstrates that the ionospheric activity is high during this period. The chosen Swarm space-borne GPS data are suitable when conducting research on Swarm ZD reduced-dynamic POD under high ionospheric activity.  
Second, we use chose Swarm GPS data to participate in the Swarm ZD reduced-dynamic OD based on Swarm orbit strategy in the article.  
Finally, we use GPS phase observation residuals to evaluate the internal quality assessment of Swarm orbits. The external equality assessment is then evaluated by comparison with Swarm PSOs released by ESA and SLR validation.
###### Abstract  
Light use efficiency (LUE), which characterizes the efficiency with which vegetation converts captured/absorbed radiation into organic dry matter through photosynthesis, is a key parameter for estimating vegetation gross primary productivity (GPP). Studies suggest that diffuse radiation induces a higher LUE than direct radiation in short-term and site-scale experiments. The clearness index (CI), described as the fraction of solar incident radiation on the surface of the earth to the extraterrestrial radiation at the top of the atmosphere, is added to the parameterization approach to explain the conditions of diffuse and direct radiation in this study. Machine learning methods--such as the Cubist regression tree approach--are also popular approaches for studying vegetation carbon uptake. This paper aims to compare and analyze the performances of three different approaches for estimating global LUE and GPP. The methods for collecting LUE were based on the following: (1) parameterization approach without CI; (2) parameterization approach with CI; and (3) Cubist regression tree approach. We collected GPP and meteorological data from 180 FLUXNET sites as calibration and validation data and the Global Land Surface Satellite (GLASS) products and ERA-interim data as input data to estimate the global LUE and GPP in 2014. Site-scale validation with FLUXNET measurements indicated that the Cubist regression approach performed better than the parameterization approaches. However, when applying the approaches to global LUE and GPP, the parameterization approach with the CI became the most reliable approach, then closely followed by the parameterization approach without the CI. Spatial analysis showed that the addition of the CI improved the LUE and GPP, especially in high-value zones. The results of the Cubist regression tree approach illustrate more fluctuations than the parameterization approaches. Although the distributions of LUE presented variations over different seasons, vegetation had the highest LUE, at approximately 1.5 gC/MJ, during the whole year in equatorial regions (e.g., South America, middle Africa and Southeast Asia). The three approaches produced roughly consistent global annual GPPs ranging from 109.23 to 120.65 Pg/yr. Our results suggest the parameterization approaches are robust when extrapolating to the global scale, of which the parameterization approach with CI performs slightly better than that without CI. By contrast, the Cubist regression tree produced LUE and GPP with lower accuracy even though it performed the best for model validation at the site scale.  
light use efficiency; gross primary productivity; clearness index; parameterization approach; Cubist regression tree approach
# Effect of Calined Marble Powder and Magnetized Water on the Performance of Cement-Based Composites
## 3 Results and Discussion  
### Setting Characteristics  
The results of the initial and final setting times of cement pastes, conducted in accordance with EN 196-3 [29], are presented in Figure 4. Three tests were performed for each mixture, and the average values were calculated.  
As shown in Figure 4, regardless of the type of mixing water, both the initial and final setting times increase with higher replacement ratios of RMP and CMP. The initial setting times of cement pastes coded as \"R_/TW\" range from 160 to 205 min, while the final setting times vary between 255 and 335 min. Figure 5 illustrates the percentage change in initial and final setting times of samples with RMP and CMP replacement ratios of 5%, 10%, 15%, 20%, and 25% compared to the reference samples (0% replacement). As seen in Figure 5, the highest percentage increase is observed in the cement paste labeled R25/TW. Compared to the reference cement paste (R0/TW), the initial and final setting times increased by 28.13% and 31.37%, respectively. The increase in setting times for cement pastes with higher RMP and CMP replacement ratios is consistent with findings in the literature [37; 38; 39; 40]. This can be attributed to the lower specific surface area of MP compared to PC. The finer particles of PC hydrate more quickly; therefore, as the MP replacement ratio increases, hydration is delayed, resulting in longer setting times.  
Figure 4: Initial (**a**) and final (**b**) setting time of cement pastes.  
Figure 5: Change in initial (**a**) and final (**b**) setting times of cement pastes.  
The prolongation of initial and final setting times in cement pastes coded as C_/TW is lower than that observed in R_/TW cement pastes. This can be explained as follows: when RMP is calcined at high temperatures, the dolomite and calcite phases decompose to form free calcium oxide (CaO) and magnesium oxide (MgO). Free CaO contributes to the hydration process of the cement paste. Furthermore, the finer particle size of CMP may have accelerated the hydration process, thereby shortening the setting time [5].  
When comparing the initial and final setting times of cement pastes coded as C_/TW and C_/MW, it has been observed that using MW as the mixing water results in extended setting times. For samples with a 0%, 5%, 10%, 15%, 20%, and 25% CMP replacement produced with MW, the initial setting times increased by 9.4%, 9.1%, 14.7%, 11.1%, 13.5%, and 10.3%, respectively, compared to those produced with TW, while the final setting times increased by 9.8%, 7.3%, 5.2%, 6.6%, 6.5%, and 7.8%, respectively. The prolongation of setting times with MW can be attributed to the increased activation of water passing through a magnetic field, allowing greater penetration into the cement particles and extending the hydration reaction [40].
# Effects of Caprolactam Wastewater on Algal Growth and Nutrients Removal by _Arthrospira platensis_
## 3 Results and Discussion
### Investigation of Factors Inhibiting the Growth of _A. platensis_
#### 3.2.2 Effects of Peroxides in WCP on the Growth of _A. platensis_  
Studies have shown that WCP contains peroxides [14]. If peroxides in the WCP were the main inhibiting factor, then reduced WCP would no longer inhibit the growth of _A. platensis_, or the inhibition would be significantly relieved. Thus, reduced WCP produced by chemical reduction using Na\({}_{2}\)SO\({}_{3}\) was tested.  
The growth curves of _A. platensis_ exposed to different reduced WCP concentrations are shown in Figure 5A. After 6 d of cultivation, the biomass concentrations of _A. platensis_ were 2.34, 2.13, and 1.72 g/L at 12.5%, 25.0%, and 50.0% reduced WCP, respectively (Figure 5B). Compared with that of the control, the biomass of _A. platensis_ was inhibited by 6.8%, 15.1%, and 31.3% at 12.5%, 25.0%, and 50.0% reduced WCP, respectively (Figure 5C). The inhibition effect was very similar between the WCP and the reduced WCP, as shown in Figures 2 and 5. Thus, the inhibition of algal growth by WCP could not be alleviated by chemical reduction. The results implied that peroxides in the wastewater were not the main factors affecting the growth of _A. platensis_.
# Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography
## 3 Materials and Methods
### Optimal Solid Ink Density Matching Model
This suggests that in practical production settings, ink density cannot be reliably adjusted based solely on visual judgment or intuition. Furthermore, comparison of the Pearson coefficients indicates that the strength of correlation between chromaticity coordinates and solid ink density varies among the components. This suggests that even when ink density increases uniformly, the changes in chromaticity coordinates across different directions are not proportionally linear. Based on this analysis, the present study moves beyond traditional approaches to determining optimal solid ink density. Instead, it introduces a nonlinear modeling approach to quantify the relationship between chromaticity and density, with the goal of identifying optimal solid ink density parameters for pre-press calibration and process control. A review of the existing literature indicates a nonlinear relationship between the colorimetric value of the print color block and its density value. To establish the relationship between colorimetric values and density values, in this paper, we model the relationship between the input (colorimetric value of the print color block) and the output (solid density of ink) using a Random Forest machine-learning model. Specifically, the sample data of solid ink color block density values and colorimetric values from multiple print proof are used to construct a matching model with multiple inputs and a single output. Figure 4 shows the structure of the model. Initially, cyan, magenta, and yellow inks were printed at 100% dot coverage on coated paper. The solid ink density of each color was then measured, and the corresponding CIELAB color coordinates were recorded. Seventy data sets were collected for each ink color. The dataset is subsequently divided into a training set (75%) and a testing set (25%). The model's hyperparameters are optimized using correlation coefficients and mean square error, and the optimized model is then evaluated for performance. To further justify the choice of the Random Forest method, the final step involves comparing the performance of the Random Forest model with that of other machine-learning models to ensure the selection of the most optimal model. \begin{table}
\begin{tabular}{c c c c} \hline
**Ink** & **L** & **a** & **b** \\ \hline C & 0.92 & \(-\)0.82 & 0.78 \\ M & 0.89 & \(-\)0.79 & 0.82 \\ Y & 0.88 & \(-\)0.86 & 0.85 \\ \hline \end{tabular}
\end{table} 
Table 3: Pearson correlation coefficients of different ink colors.
## 3 Model and Background
### Network Structure
#### 4.1.2 Selection of Monitoring Nodes  
In order to perform secure data aggregation, each aggregator is monitored by its \(T\) neighboring nodes out of a total of \(s\) neighboring nodes, for \(s\geq T\). The monitoring nodes are selected by the Monitoring Node Selection (MNS) algorithm [9]. The basic idea of this algorithm is to assign indices to the neighboring nodes in some order and then compute \(T\) indices by applying modulus operation to the sum of some random numbers generated by the neighboring nodes. Any neighboring node whose index is equal to one of these \(T\) indices becomes a monitoring node. The advantage of this algorithm is that, aggregator and all neighboring nodes are involved with the selection of monitoring nodes, part of nodes compromised cannot damage the selection result, which can minimize the adverse impact of a compromised node.
# Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
## 2 Analysis
### OTDR Dynamic Range  
As mentioned above, the dynamic range between the maximal backscattered power \(P_{\text{BS}}\left(0\right)\) at the input of the fiber, and the effective noise equivalent power \(NEP_{\text{eff}}\)--the noise floor where SNR = 1 [9], can be presented as:  
\[DR\ [\text{dB}]=[P_{\text{BS}}\left(0\right)-NEP_{\text{eff}}] \tag{10}\]  
The residual BER can be accurately estimated only by OTDR of sufficient \(DR\) with enough margin against attenuation of not only the actual fiber, but also of connectors, splices and splitters that the testing OTDR encompasses.  
The high-SNR condition fulfilled with large enough OTDR dynamic range, is basically achieved with long enough transmitted pulses, Figure 5, but these also worsen the event resolution and enlarge the attenuation dead zone, Figure 6.  
Moreover, an OTDR trace being effectively an attenuated two-way squared response of the fiber link to the transmitted laser pulse, can only be considered the power-delay profile for a short enough laser pulse which is realistically considerable as impulse.  
In addition to pulse width, receiver bandwidth is another crucial parameter, which determines the actual OTDR resolution, as for short transmitted pulses, the OTDR receiver (photo-detector) bandwidth should be large enough, at least 40 % wider than the reciprocal pulse width [12; 13].  
Figure 5: OTDR trace; (**a**) small dynamic range, (**b**) large dynamic range.  
Figure 6: OTDR trace; (**a**) with short pulse for better event resolution; (**b**) with longer pulse for larger dynamic range.  
On the other hand, from the bottom-up perspective, with already chosen pulse width, dynamic range can be increased by lowering its lower bound \(NEP_{\text{eff}}\), with visual outcome similar to the one illustrated in Figure 5.
# Identification of Impact Frequency for Down-the-Hole Drills Using Motor Current Signature Analysis
###### Abstract  
In rotary-percussion drilling, the impact frequency is a crucial variable that is closely linked to operational factors that determine the efficacy of the drilling process, such as the rate of penetration, bit wear, and rock mass characteristics. Typical identification methods rely on complex simulation models or the analysis of different sensor signals installed on specially adapted setups, which are difficult to be implemented in the field. This paper presents a novel study where the impact frequency is identified by motor current signature analysis (MCSA) applied to an induction motor driving a DTH drilling setup. The analysis of the case study begins with the definition of characteristic drilling stages where the pressure and sound signals allow the detection of an impact frequency of 14.10 Hz, which is then used as a reference to validate three MCSA identification approaches. As a result of the analysis, the envelope approach is the most robust for nearly real-time implementations considering its simplicity and range of coverage. Experimental results provide evidence about the feasibility of the proposed MCSA methods to be integrated into Measurement-While-Drilling (MWD) systems to improve drilling condition monitoring and rock mass characterization.  
impact frequency; motor current signature analysis; DTH drilling; hammer pressure; frequency side bands; signal envelope; measurement while drilling
# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 3 Research Design  
To conduct this study, we applied systematic mapping as the research methodology for reviewing the literature. Since this method requires an established search protocol and rigorous criteria for the screening and selection of the relevant publications, we utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, as indicated in [21]. The primary goal of a systematic mapping review (SMR) is to provide an overview of the body of knowledge and the research area and identify the amount of publications and the type of research and results available. Furthermore, an SMR aims to map the frequencies of publications over time to determine trends, forums or venues, and the relevant authors by which the research has been conducted and published. In contrast to the classical systematic literature review (SLR), which focuses on the identification of best practices based on empirical evidence, the focus of an SMR is on establishing the state of evidence. It is also worth mentioning that, from the methodology standpoint, SLR is characterized by narrow and specific research questions, and the studies are evaluated in detail regarding this quality. On the other hand, SMR deals with multiple broader research questions, and studies are not assessed based on details regarding the quality.  
To ensure that all relevant studies were located and reviewed, our search strategy involved a stepwise PRISMA approach, consisting of four stages. The overall process of the search strategy is shown in Figure 3. The first stage in the PRISMA entailed the development of a research protocol by determining research questions, defining the search keywords, and identifying the bibliographic databases for performing the search. The second stage involved applying inclusion criteria, which was followed by stage three, in which the exclusion criteria were applied. The last stage was data extraction and analysis.  
The research questions (RQs) devised for this study were as follows:  
* RQ1. What are the most investigated aspects in the education domain with respect to sentiment analysis?
* RQ2. Which approaches and models are widely studied for conducting sentiment analysis in the education domain?
* RQ3. What are the most widely used evaluation metrics to assess the performance of sentiment analysis systems?
* RQ4. In which bibliographical sources are these metrics published, and what are the research trends and patterns?
* RQ5. What are the most common sources used to collect students' feedback?
* RQ6. What are the solutions with respect to the packages, tools, frameworks, and libraries utilized for sentiment analysis?
* RQ7. What are the most common data representation techniques used for sentiment analysis?
## 3 Experiment
### Inspection Robot Construction  
The intelligent patrol robot designed for our system, as shown in Figure 14, can be mounted on guardrails and autonomously operates in designated areas. Its hardware configuration primarily includes a main control board, a camera, a positioning module, a servo driver, a servo motor, limit sensors, networking equipment, and storage devices. The overall structure of the robot is depicted in Figure 15.  
Figure 14: Intelligent patrol robot in the high-speed site image.  
Figure 13: The detection results of YOLOv5s and YOLOv5s-LOTA. Column (**a**) denotes the original image, column (**b**) denotes the YOLOv5s’ detection result, and column (**c**) is our model’s detection result.  
The control board, positioned at the heart of the system, serves as the central processing unit, orchestrating the operations of the entire setup. We have chosen Nvidia's Jetson TX2, an edge computing module, as the core component, renowned for its low power consumption and high performance in real-time highway anomaly monitoring. Equipped with an Nvidia Pascal GPU and an 8-core CPU cluster that combines Cortex-A57 and Cortex-A53 cores, it supports FP16 computation and hardware-accelerated deep learning inference, enabling efficient multi-threaded processing. Network devices connected to the control board facilitate communication protocols and connectivity, providing channels for remote monitoring and control.  
In terms of motion control, the system utilizes an AC permanent magnet synchronous servo driver (SD300 series) with a high-performance chipset, offering speed, position, and torque control. These drivers operate effectively under vibrations up to 0.5 G and temperatures ranging from \(-20\)\({}^{\circ}\)C to 80 \({}^{\circ}\)C. Their compact design includes an IPM module, enhancing overload capacity. The servo motor has a rated torque of 1.27 N.m and a peak torque of 3.9 N.m, with a torque coefficient of 0.45 N.m/A and a rated voltage of 220 V. Its IP64 protection level ensures reliable operation in various climatic conditions. Magnetic induction proximity switches navigate the patrol robot between two magnetic sensing points for continuous monitoring, suitable for harsh environments, changing magnetic fields without physical contact to trigger switches.  
To effectively capture images within a four-lane highway, our system employs a fixed-focus camera with a 4 mm focal length. With an aperture of F1.2 and AGC ON, the camera ensures full-color imaging under minimal illumination conditions as low as 0.002 Lux, thus maintaining the quality of the footage captured on highways.  
Positioning is handled by a BDS/GNSS navigation module based on ATGM336H-5N, supporting multiple satellite systems for precise location tracking. The high-capacity storage host efficiently processes and stores high-resolution videos from the camera, ensuring comprehensive data management.