esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations
## 2 Methodology
### Empirical Formula  
As mentioned, various PPV prediction techniques are available but only the empirical formula of Equation (6) has been used to predict PPVs for blasting designs in South Korea [18]. Therefore, in this study, the empirical formula developed by USBM was selected to assess ground vibration and identify the optimal prediction method. In Equation (6), the values of K and m are obtained through linear regression of the blasting datasets consisting of PPV and the scaled distance (SD) expressed in Equation (7) [19]. Here, W is a charge weight per delay, and D is the distance between blasting and monitoring points.  
\[\mathrm{PPV}=\mathrm{K}(\mathrm{SD})^{\mathrm{m}} \tag{6}\]  
\[\mathrm{SD}=\mathrm{D}/\sqrt{\mathrm{W}} \tag{7}\]
## 5 Experiments and Results
### Metrics  
Reconstruction accuracy was evaluated using the normalized mean absolute error (NMAE). The NMAE for each of the three features considered in the dataset was determined by comparing the lateral slices of the input data with the reconstructed low-rank tensor.  
\[NMAE=\frac{\sum_{i}^{n_{2}}\lvert\mathcal{L}(:,i,:)-\hat{\mathcal{L}}(:,i,:) \rvert}{\sum_{i}^{n_{2}}\lvert\mathcal{L}(:,i,:)\rvert} \tag{24}\]  
The original tensor is represented by \(\mathcal{L}\), and the reconstructed low-rank tensor is denoted by \(\hat{\mathcal{L}}\). A comparison between the two tensors served as the foundation for the obtained results.
# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 4 Methodology
### CSI Pre-Processing  
The CSI received signal is a combination of useful information as well as undesirable noises embedded in the signal. It is essential to filter and calibrate CSI amplitude and phase measurements, respectively. The human gestures have a relatively low frequency as compared to noise frequency. To remove high frequency noise, we apply a second order low pass Butterworth filter. We adjusted packets sampling rate (\(F_{s}\)) at 80 packets/s, the same as the normalized cutoff frequency \(w_{n}=2\pi f/F_{s}=0.025\pi\) rad/s. CSI raw phase measurements behave extremely randomly because of the unsynchronized time clock of transmitter and receiver. In order to extract the actual phase and eliminate channel frequency offset; phase calibration and linear transformation are performed by following [46].
## 1 Introduction
The RF method has also been used in crop yield estimation, especially for large areas of maize [7; 41; 42], soybean [43] and wheat [44]. Therefore, most available studies were on yield estimations of maize and wheat, but few studied yield estimations of sunflower, an important economic crop in arid regions of Northwest China. The key factors of crop yield estimation using machine learning methods are the determination of predictors and model calibration and validation. The periodic variation of vegetation index is closely related to crop growth period, and is therefore usually used as predictors [45; 46]. Since most crops have growth periods of several months, remote sensing data with fine temporal resolution are usually needed, such as moderate resolution imaging spectroradiometer (MODIS) [7] or advanced very high-resolution radiometer (AVHRR) [6]. However, the finest spatial resolution of these remote sensing data is 250 m, which will affect the feature extraction of crops from possible mixed pixels, especially in small-sized cropland blocks that is common in China [47]. The inaccuracy of model predictors would inevitably lead to uncertainty in crop yield estimation. Therefore, it is necessary to make use remote sensing data with both high temporal and spatial resolutions to improve the yield estimation accuracy. In the processes of model calibration and validation, the precision of crop distribution maps has a great influence on yields extraction of different crops [7]. Therefore, a high precision crop distribution map is the basis for the calibration and validation of crop yield estimation models. China launched the HJ-1A/1B CCD satellite constellation with 30 m spatial resolution and two days revisit period on 6 September 2008 [48]. Previous studies have indicated that NDVI derived from HJ-1A/1B CCD images could be applied to regional crop identification [49; 50] and phenological characteristics estimation [51]. However, few studies had applied HJ-1A/1B CCD data to crop yield estimation in arid and semi-arid areas [52]. [PERSON] and [PERSON] [49] mapped multi-year maize and sunflower in Hetao Irrigation District from 2009 to 2015 with the mean relative statistical error of 10.82% for maize and 4.38% for sunflower, which provided the basis for further yield estimation in this region. The main objective of this study is to develop practicable RF models for yield estimation of major crops in Hetao Irrigation District of China based on the NDVI series derived from HJ-1A/1B CCD images and high precision crop distribution maps.
# A Critical Review of the Status of Current Greenhouse Technology in China and Development Prospects
## 2 The Current Situation of Greenhouse Development in China
### Development Status of Greenhouse Industry in China  
In recent decades, China's greenhouses have been developing steadily, and the greenhouse area in China now ranks first in the world, accounting for a large proportion of the area used for fruit and vegetable cultivation. Greenhouses play an important role in improving the utilization rate of agricultural resources and the yield and quality of agricultural products. In 2023, the cumulative total area of new greenhouses in the country was 1835.87 ha, of which the area of new plastic film greenhouses was 1094.1 ha, accounting for about 60% of the total, the area of glass greenhouses was 234.32 ha, the area of polycarbonate greenhouses was 12.48 ha, the area of solar greenhouses was144.17 ha, and other types of greenhouses (landscape greenhouses, greenhouse restaurants, etc.) make up 350.8 ha (Figure 2) [4].  
As can be seen in Figure 3 [5], there has been a gradual change in the total area of greenhouses in China, showing a year-to-year decline, from 2168 thousand ha in 2015 to 1864 thousand ha in 2021, a 14% decrease compared to 2015, with a slight increase in 2022. There are three main reasons for this trend: first, as crop yields rise, more greenhouses are no longer needed. The second is that modern transport is becoming more convenient and efficient, making it cheaper to transport vegetables from the south (warmer climate), which reduces the demand for greenhouses in the north (colder climate) of the country. Third, with improved environmental protection requirements, plastic is now considered to have a greater impact on the environment, resulting in a decrease in the construction of plastic greenhouses.  
Figure 2: Types and share of new greenhouses in China.
# Heuristics for a Two-Stage Assembly-Type Flow Shop with Limited Waiting Time Constraints
## 4 Computational Experiments
The last analysis shows the effect of the waiting time constraints on solutions. For this analysis, we generated 10 instances of (\(m\), \(n\)) = (5, 300) in Set A. The waiting times were generated randomly with a range of [1, 100], and these values were treated by multiplying the ratios (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0). We analyzed the results obtained by solving each instance independently five times using SA\({}_{90}\) which showed the best performance. As shown in Figure 15, the makespan decreased as the waiting time ratio increased. This is because tight limited waiting times caused delayed starts; hence, the makespan increased accordingly. The decline of the makespan slowed when the ratio exceeded 2. That is, if the limited waiting times are twice the processing times, the effect of the waiting time limit on the solution is reduced. These results suggest the need for manufacturing technologies and systems that can achieve product quality while setting enough limited waiting times. It may also provide a guideline for considering the trade-off between production efficiency  
Figure 12: RDIs of the heuristic algorithms on large instances. Figure 13: Number of instances that the heuristic found the best solution. and product quality. Meanwhile, the range of the objective values increased as the ratio increased. This may be due to the loose waiting time limits that increased the time range in which jobs could start. In other words, tight waiting time limits reduced the solution space for start times in a problem. Figure 14: 95% CIs for the mean RDI of algorithms. Figure 15: Effect of limited waiting time on solutions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Set**} & **Sample 1** & \multirow{2}{*}{**Test Statistic**} & **Std. Error** & **Std. Test Statistic** & **Sig.** & **Adj. Sig. 1** \\  & **-Sample 2** & & & **Error** & **Statistic** & **Sig.** & **Adj. Sig. 1** \\ \hline \multirow{4}{*}{A} & SA\({}_{90}\)-IG\({}_{90}\) & 53.800 & 10.398 & 5.174 & 0.000 & 0.000 \\  & SA\({}_{90}\)-GA\({}_{90}\) & 103.840 & 10.398 & 9.987 & 0.000 & 0.000 \\  & IG\({}_{90}\)-GA\({}_{90}\) & 50.040 & 10.398 & 4.813 & 0.000 & 0.000 \\ \hline \multirow{4}{*}{B} & SA\({}_{90}\)-IG\({}_{90}\) & 13.480 & 6.937 & 1.943 & 0.052 & 0.156 \\  & SA\({}_{90}\)-GA\({}_{90}\) & 35.480 & 6.937 & 5.114 & 0.000 & 0.000 \\  & IG\({}_{90}\)-GA\({}_{90}\) & 22.000 & 6.937 & 3.171 & 0.002 & 0.005 \\ \hline \multirow{4}{*}{C} & SA\({}_{90}\)-IG\({}_{90}\) & 13.987 & 6.937 & 2.016 & 0.044 & 0.131 \\  & SA\({}_{90}\)-GA\({}_{90}\) & 34.853 & 6.937 & 5.024 & 0.000 & 0.000 \\ \cline{1-1}  & IG\({}_{90}\)-GA\({}_{90}\) & 20.867 & 6.937 & 3.008 & 0.003 & 0.008 \\ \hline \hline \end{tabular}  
* Significance values adjusted by the Bonferroni correction for multiple tests.  
\end{table} 
Table 9: Pairwise comparisons from Kruskal–Wallis tests.
## 2 Literature Review and Cost Estimation Methods
### Cost Estimation Techniques
#### 2.1.1 Qualitative Approaches  
We can say that qualitative approaches are based on the knowledge of the estimator about the requested project, the field of activity, and the basis of the influencing factors,  
Figure 1: Representative diagram of modeling techniques dedicated to cost estimation.  
these being divided into two classes: expert judgment and heuristic rules. When applying expert judgment, everything depends on the good or poor results of previous knowledge-based judgments. According to an article [25], the expert judgment technique relies on the advice of experts and experienced peers to validate the estimation result. Therefore, in the case of the Coestim solution, we consider the implementation of distinct packages between suppliers, sellers, and manufacturers, and these packages will generate a response comparable to the service estimates designed by specialists, which are basic models in the estimate trial. This technique relies more on intuition, along with extrapolation techniques, and problems are solved later with the help of experts who ensure the reliability of the whole process. However, these heuristic rules in the cost estimation process are due to intuitive judgments, and the estimation process is optimized based on data extracted from similar projects that have identical characteristics in some places. We can say that quantitative approaches are defined as methods that are based on the process of collecting and analyzing historical data. Data analysis and the application of quantitative models, techniques, or estimation tools lead to a design cost estimate. Thus, the approaches dedicated to cost estimates and known to date are classified into three broad categories: statistical, analog, and analytical.
# Biosynthesis Progress of High-Energy-Density Liquid Fuels Derived from Terpenes
## 4 Strategies for Constructing Efficient Microbial Cell Factories for Terepenes
### Cell-Level Optimization
#### 4.3.3 Cell Mutagenesis and Adaptive Evolution  
Adaptive evolution is a powerful tool for improving the industrial production properties of some strains or unraveling the complex physiological mechanisms of certain strains (Figure 2E). _E. coli_ was mutated with atmospheric and room-temperature plasma (ARTP); then, the resulting cells were evolved in media containing 0.5%, 1.0%, 1.5% and 2.0% pinene to select evolved strains based on the IPP/FPP sensor, finally resulting in a 31% increase in pinene production [47]. Additionally, by gradually increasing the concentration of sabinene during _E. coli_ cultivation, a strain with significantly improved tolerance to sabinene was obtained, and the sabinene production was increased by 8.43 times compared to the original strain [163]. It was reported that cell growth is severely inhibited when the concentration of limonene exceeds 500 mg/L for _Y. lipolytica_[164]. [PERSON] et al. identified an unknown protein, YALI0F19492p, which resulted in an 8-fold increase in the production of limonene by comparative transcriptome analysis. Through further adaptive evolution, the strain achieved a 52% increase in limonene production [165]. [PERSON] et al. selected strains with high \(\beta\)-caryophyllene production through several generations of strain evolution using oxidative stress as the selective pressure. The optimal strain achieved a \(\beta\)-caryophyllene titer of 104.7 mg/L, which was 3 times higher than the initial strain. A genome sequencing analysis revealed that a-factor exporter _SET6_ mutation was a key factor in the product improvement [166]. [PERSON] et al. obtained a Crabtree-negative strain of _S. cerevisiae_ through adaptive evolution, resulting in a 20.5% increase in farnesene production compared to the wild-type strain [167]. In addition, it is possible to enable the host to utilize alternative carbon sources such as xylose and biomass hydrolysate for growth and product synthesis with adaptive evolution [168].
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 4 _Ulva_ Compounds of Interest
### Fatty Accids  
Fatty acids constitute vital components of lipids and hold significant roles in human health and metabolism. The composition and concentrations of fatty acids can vary depending on factors such as species, geographic location, and environmental conditions [128].  
[PERSON] et al., 2017 [87] obtained for wild stalks lipids 5.4 \(\pm\) 0.4%, while for sterile stalks, lipid content was 13.8%. The lipid content for sterile thalli was higher compared to other _Ulva_ species: 2.5-3.5% in _U. clathrata_ and 7.9% in _U. lactuca_. There was also a high proportion of monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), omega-3 fatty acids (n3-FA), and Omega-6 fatty acids (n6-FA) such as linoleic acid (LA) and gamma-linolenic acid (GLA) [87; 128].  
Omega-6 fatty acids are crucial for preserving healthy cell membranes, moderating inflammation, and promoting overall well-being. In a study by [PERSON] et al. (2023) [154], a thorough examination of the fatty acid composition in _U. lactuca_ unveiled notable quantities of various fatty acids. Palmitic acid (16:0) emerged as the primary constituent, accounting for 21.51%, while oleic acid (18:1n9c) contributed 10.60% as a moonusaturated fatty acid. Some literature also shows that green algae also contain large amounts of palmitolinenic (16:3o3) and palmitidonic (16:4u-3) acid [118; 128; 155; 156].  
Also, notable components are linoleicaidic acid (LA; C18:2u-6), which is found in most of the species, and \(\alpha\)-linoleic acid (ALA, C18:3o-3), which is characteristic of Ulvales. Furthermore, the omega-3 fatty acid eicosapentaenoic acid is 1.67%. The presence of these fatty acids in _U. lactuca_ suggests its potential as a dietary source of essential fatty acids [128].
## 3 System Model
### Summarizing the System Model  
The system model being proposed combines a hybrid MAC protocol with adaptive power control and RL-based optimization to tackle the obstacles in underwater communication. The mathematical model captures the changing conditions of the environment and offers a strong solution for enhancing throughput, minimizing collisions, and improving energy efficiency in UWSNs. Table 3 presents the key input parameters used in the AC-RL-based optimization component of the proposed MAC framework.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Transportation Mode
#### 4.2.1 Marine Transportation  
Maritime intelligent transportation systems (MITS), or e-navigation systems, apply real-time data collection, communication, and automation to enhance vessel traffic management, port efficiency, and environmental sustainability [66]. These systems improve navigation safety through Vessel Traffic Services (VTSs), collision avoidance, and route optimization while optimizing port logistics with IoT-enabled smart ports and automated terminal systems. For example, in [67], the authors developed Marine Sensor Networks (MSNs) for monitoring ocean conditions. In marine transportation, using middleware is essential for enhancing operational efficiency and safety, which evolves to meet modern maritime challenges and improve navigational precision, thereby becoming more reliable for global marine operations.  
A notable contribution in this area is the work of [PERSON] et al. [68]. They proposed a supervisory control framework and modular software architecture built around the Lightweight Communication and Marshaling (LCM) publish/subscribe message passing system for marine robotic applications. They used LCM to develop an unmanned port security vehicle from a commercial off-the-shelf autonomous underwater vehicle for visual feature-based navigation. The system is designed for easy adaptation to various marine robotic applications, enhancing its usability across different scenarios and ensuring low-latency, real-time performance, which is crucial for the effective operation of marine robots.  
However, the system has some weaknesses, such as complex implementation and limited environmental testing.
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 3 Empirical Formulae for Damping Ratio of RC Buildings for Wind Design
### Frequency- and Amplitude-Dependent Damping Ratio
#### 3.2.3 Engineering Science Data Unit (ESDU) 83009  
The damping ratio provided in Engineering Science Data Unit (ESDU) 83009 [15] is presented as the sum of the frequency-dependent damping ratio and the amplitude-dependent damping ratio, as follows:  
\[\zeta=\zeta_{so}+\zeta_{s}^{\prime}\Big{(}\frac{x}{H}\Big{)} \tag{14}\]  
where \(\zeta_{so}\) is the frequency-dependent damping ratio, and \(\zeta_{s}^{\prime}\) is the increase in damping due to increase in amplitude. The values of \(\zeta_{so}\) and \(\zeta_{s}^{\prime}\) are listed in Table 5, where \(D\) denotes building dimensions.  
[PERSON] [16] provided a simpler solution with which engineers can use Equation (14) more easily based on an analysis of the data given in ESDU 83009, as follows:  
\[\zeta=0.0076f_{1}+150\Big{(}\frac{X}{H}\Big{)}+0.003 \tag{15}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Building Height (H)** & **Steel Structures** & **RC Structures** \\ \hline H = 40 m & 1.8\% & 2.0\% \\ H = 50 m & 1.5\% & 2.0\% \\ H = 60 m & 1.5\% & 1.5\% \\ H = 70 m & 1.5\% & 1.5\% \\ H \textgreater{} 80 m & 1.0\% & 1.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of structural damping ratio, \(\zeta_{str}\), in ISO 4354.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean** & **Lower Limit** \\ \hline \(\zeta_{so}\) & \(f_{1}/100\) & \(f_{1}/250\) \\ \(\zeta_{s}^{\prime}\) & \(10^{\sqrt{D}/2}\) & \(10^{\sqrt{D}/25}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Values of \(\zeta_{so}\) and \(\zeta_{s}^{\prime}\) in Engineering Science Data Unit (ESDU) 83009.
# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
## 4 Analytical Review
### Category p2: Features, Signal Processing
in [131] use short-time Fourier transform (STFT) for features extraction, whereas health status classification is performed by a convolutional neural network (CNN) and verifiedthat the proposed method achieves very high accuracy and robustness for bearing fault diagnosis even under noisy environments. In [135], [PERSON] and [PERSON] present a method based on frequency patterns obtained using the decomposition of wavelet packets as features, applied to induction motor bearings diagnostics. In the context of methods based on spectral images used as features, [PERSON] et al., in [138], propose the combination of a quaternion invariant moment feature extraction method and a gray level-gradient co-occurrence matrix feature extraction method, through a probabilistic neural network algorithm and a geometric learning algorithm. The features were obtained through the processing of vibration signals with the pseudo Wigner-Ville distribution, and the approach was applied to rolling bearing faults recognition, obtaining good results. [PERSON] et al. in [146] discuss a new method for feature extraction based on the higher order wavelet spectral cross-correlation (WSC), and provide the results of the comparison vs. HOS technologies (wavelet bicoherence and wavelet ticoherence) when applied to a gearbox fault detection. The experimental comparison revealed the clear superiority of the WSC technologies. [PERSON] et al. in [11] present the results of a characterization of the vibrations of a wind turbine by spectrogram, scalogram, and bi-spectrum analyses. Non-stationary and non-Gaussian stochastic properties, mode-coupling instability of the wind turbine tower were found. A significant number of articles address hybrid solutions applied to bearings CM. Among them, [PERSON] et al. in [175], consider, for a convolutional neural network classifier, spectral images features obtained through a normalized amplitude of the spectral content, extracted from segmented temporal vibratory signals using a time-moving segmentation window. [PERSON] et al., in [186], compare cyclostationary analysis, empirical mode decomposition (EMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) applied to rolling bearing and gear defects diagnostics. For a laboratory test-rig all three methods proved to be efficient, but the authors conclude that for the industrial field, further improvements are needed for a successful application.