esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Temporal Variability  
As for temporal differences in VOCs composition during the flowering phase, the comparison between floral volatiles emitted at the beginning and at the end of the flowering exhibited the presence of a similar number of compounds (min 15; max 22; mean 18.3). The mean value is in agreement with findings from the sampling performed to assess the spatial variability, where a mean value of 19.4 compounds was found. The VOC compositions for each sample are reported in Table 2. In particular, comparing the two flowering phases, we found the same number of compounds in sample 2 and a slight decrease in sample 1 at the end of the flowering. Additionally, in this case, despite the fact they come from the same population (S. Arcangelo), composition quantitative analysis confirmed the presence of a strong association between individuals' variability. In sample 1, the two most abundant compounds were cyanophyllene and 2,3-dihydrofarnesol, whereas in sample 2, they were citronellol and \(\beta\)-sesquiphellandrene. Caryophyllene remained the dominant compound in the sample 1 even at the end of flowering, whereas the abundance of 2,3-dihydrofarnesol strongly decreased; then, the second most abundant compound became 4-methyltetradecane. In sample 2, instead, the dominance between the first two compounds was reversed at the end of the flowering, with \(\beta\)-sesquiphellandrene becoming the most abundant compound followed by citronellol. The decrease in the number of compounds in sample 1 (from 22 to 15) is linked to the non-detection at the end of flowering of various compounds which, previously, had low abundance (area% < 2), whereas only nonadecane, with an area value of 0.37%, was found in addition at the end of flowering. On the other hand, in sample 2, five compounds were no longer identified at the end of flowering, replaced by an equal number of compounds not present in the first flowering phase. Among these, \(\alpha\)-terpineol acetate stands out for its abundance, characterized with a value of 13.83% in sample 2 at the end of flowering. Figure 4: Mass spectra of **(a)**\(\beta\)-bisabolene; **(b)** Caryophyllene **(c)**\(p\)-menth-8-en-1-ol; **(d)** citronellol; **(e)**\(\beta\)-sesquiphellandrene; **(f)**\(cis\)-\(\alpha\)-bergamottene. Figure 5: Multivariate analysis of VOCs emitted by flowers of _Barlia robertiana_ based on the betweenness Bray–Curtis similarity: (**a**) non-metric dimensional scaling; (**b**) UPGMA hierarchical clustering. Labels of samples are accordance with Figure 1.  
As for inter-annual variations in VOC compositions, unexpectedly, a huge difference was found in the pairwise comparison of the three plants consecutively analyzed in 2018 and 2019 (Table 3). These plants came from the populations of Calciano, Potenza and S. Arcangelo 2; the 2018 samples were also used for spatial variability analyses. The plant from Calciano, as described above, had pristane and ethyl tetradecanoate as main
## 4 Results and Discussion
### Discussion  
The proposed method was implemented in Matlab 2017b (i7-4770, 8G RAM, Windows 7 system). The estimating time of defect depth and width was less than 0.01s. This speed can be improved by implementing the method in C or C++. Compared with the measurement time of the laser confocal  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Depth**} & \multicolumn{3}{c}{**Width**} \\ \cline{2-10}  & **PCA +** & **Iso. +** & **LLE +** & **E +** & **SVR** & **PCA +** & **Iso. +** & **LLE +** & **LE +** & **SVR** \\  & **SVR** & **SVR** & **SVR** & **SVR** & **SVR** & **SVR** & **SVR** & **SVR** & **SVR** \\ \hline MRE (\%) & 6.54 & 3.64 & 5.36 & 4.23 & 7.85 & 3.31 & 1.96 & 2.43 & 2.95 & 3.81 \\ SRE & 0.0231 & 0.0173 & 0.0204 & 0.0201 & 0.0212 & 0.0016 & 0.0008 & 0.0008 & 0.0012 & 0.0009 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The MREs (mean relative errors) of defect depth and width estimation. PCA stands for principal component analysis, LLE represents local linear embedding and LE is Laplacian eigenmaps.  
microscope of approximately 60s, the proposed method has much higher efficiency, while retaining accuracy that meets the manufacturer requirements for identifying defects. Therefore, the proposed method successfully trades little performance for a substantial increase in efficiency as well as savings in hardware and labor cost.  
However, there is still much room for improving the performance. The most significant impact on this result is due to the small set of defective samples. To mitigate the impact, we will refer to the data augmentation techniques commonly used for deep neural networks, such as adding noise and affine transformation. Generative adversarial network (GAN) is also a promising tool for generating meaningful samples [24].  
About the defect imaging using saturated imaging method, the contrast is very sensitive to the saturation degree, which requires careful manual selection. It is an important future work to develop an easily deployed method to automatically get the optimal saturation degree.  
For the purpose of industrial application, we will encounter some problems that need to be further explored. For example, our training samples are within the \(48\times 58\) range centered on defect position. The process of defect detection should thus be performed before the 3D shape estimation. On the other hand, defect detection is seriously affected by the contrast of defect areas. According to our previous work [15], however, saturation level needs to be carefully selected to get the optimal contrast. Therefore, we should also clarify the latent relation between the saturation level and the contrast.
## 2 Materials and Methods
### Fuzzy Logic Model (FLM)
#### 2.3.1 Inputs  
As discussed earlier, the quality of crop soil plays a significant role in determining yield production, including the concentrations of stevioside and rebaudioside in stevia [3; 11]. For this study, seven parameters were selected: soil pH, temperature, humidity, electrical conductivity, nitrogen, phosphorus, and potassium. These parameters represent the physical, chemical, and nutrient composition of the soil, which, according to [12], can be used to determine soil quality. Other variables, including biological processes, could also contribute to assessing soil quality, but measuring each variable, as shown in the literature, can be expensive and not easily automated using a WSN due to the need for human intervention. Therefore, the aforementioned seven parameters were chosen as they represent different aspects of the soil and encompass the most important nutrients [3; 11]. Additionally, this choice aligns with the number of parameters that the JXBS-3001 soil sensor can measure, making it a practical selection for the monitoring system. Importantly, input values were not normalized as they did not display significant dispersion among them. These parameters play a crucial role in determining SQ and have a direct impact on crop health and productivity. For a more comprehensive understanding of the fuzzy inputs of the model for each parameter, refer to Table 1. This table provides a detailed description of the linguistic variables used in the FLM and their corresponding membership functions (MF). It is worth noting that all the membership functions (MFs) in the model inputs are represented using trapezoidal functions. This decision was based on the work of [43], which highlights how trapezoidal functions are convenient for representing fuzzy ranges. Figure 7: FLM composed of three interconnected fuzzy models that determine physicochemical quality macronutrient concentration, and soil quality. The MFs of each input in the fuzzy model can be visually represented by plotting the functions using their respective parameters. In Figure 8, two input plots are displayed. In (a), the pH input is shown with seven MFs, each representing a different linguistic term. It can be observed that there is an overlap of 0.1 units before and after each limit of the MFs. For instance, if the pH value is considered extra acidic until a limit of 5, the transition from extra acidic to very acidic begins at a pH value of 4.9 and continues until reaching 5.1. In (b), the temperature input is depicted with three MFs. The transitions between linguistic terms are noticeably smaller, with only a 2.5 \({}^{\circ}\)C overlap before and after each crisp limit.
# Resonant Fatigue Tests on Drill Pipe Connections with Different Geometries and Sizes
## 1 Introduction
Moreover, the concept of exploiting the resonance to obtain fatigue failure was employed to test various other mechanical components. In ref. [44], a resonant bending fatigue test for large-scale structural components, in particular a railway, was presented, showing a lower duration and a lower energy cost than a quasi-static hydraulic test. An interesting point of this proposed test rig was that the authors made use of springs to also apply a mean stress to the tested samples. Another resonant bending test was applied to marine risers connected by a flange joint in ref. [45] and by welding in ref. [46]. In addition to this, in ref. [47], a biaxial resonant test method was exploited to simultaneously perform fatigue tests on a wind turbine blade in the flap and edge direction, and in ref. [48] a novel rotor blade fatigue test set-up (with elliptical biaxial resonant excitation) was proposed. Nevertheless, not only large-scale components can be tested in this way; in fact, the concept of a resonant test rig can be applied to assess the fatigue strength of small-scale specimens too. For example, in ref. [49], a vibration-based test was proposed on small specimens. The sample was jointed to the shaker arm through a specially designed support, and a successful validation of the equipment was conducted on specimens of 6061-T6 under fully reverse bending. In ref. [50], a computerized electrodynamic resonant fatigue test machine was employed to assess the fatigue strength of automotive components, such as crankshafts and in ref. [51] a resonant plate test bench with I-beam topology was used to reduce the time related to shaft testing. This kind of test can even be applied for structures that are outside of the field of mechanical engineering, such as in ref. [52], where resonant fatigue tests were conducted on samples of concrete. Other examples can be found in ref. [53] to obtain the bending fatigue properties of Polycarbonate and in ref. [54] to characterize the fatigue behavior of a medical rehabilitation exoskeleton device. In this study, the resonant bending fatigue test rig designed by the University of Pisa in collaboration with ACTA SrI is initially presented, including a global overview of all the principal components of the machine, such as the control system, the employed electronic devices, and the strain gauges calibrations. After that, four types of drill pipe geometries tested on this test rig are introduced, and the experimental fatigue strength results of their corresponding samples are shown. Finally, a discussion to interpret the main causes behind the locations of the fatigue crack initiation is provided with some suggestions for further research.
## 2 Related Work
### Sales Time-Series Forecasting  
Sales forecasting is a crucial aspect of business operations, providing valuable insights for decision-making and strategic planning. To improve forecasting accuracy and efficiency, various methods and techniques have been explored across different domains, including e-commerce, livestream sales, and B2B environments. In the e-commerce industry, ref. [9] conducted research on sales forecasting for imbalanced classification data, offering practical solutions to enhance market competitiveness and economic benefits for enterprises. With the advent of the big-data era, e-commerce sales forecasting has emerged as a key factor in maintaining market competitiveness. For instance, ref. [10] leveraged machine learning models to predict sales-pattern changes and surges triggered by local events in specific geographical locations. By capturing the complex relationships between events and their impact on sales, this approach significantly improved inventory management and sales surge prediction. In the context of livestream sales prediction, ref. [11] introduced SaleNet, an interpretable deep learning model designed to account for various factors influencing sales volumes. This model provided a powerful tool for businesses to achieve accurate sales forecasts in livestream environments, where dynamic and rapidly changing factors play a significant role. In multi-channel retail settings, ref. [12] proposed OCCPH-MHA, a two-stage deep learning approach for sales forecasting. The model first identifies consumer group preferences based on individual purchasing behavior and then integrates these preferences with time-series demand data using a global-local attention mechanism. This method significantly improved multi-step forecasting accuracy by effectively combining behavioral insights with temporal data. For B2B sales forecasting, ref. [13] explored time-series forecasting techniques, utilizing Recurrent Neural Networks (RNNs) and Echo State Networks (ESNs) enhanced with a modified metaheuristic optimizer for parameter selection. This approach incorporated decomposition techniques to improve prediction accuracy, resulting in a notable performance increase from an initial score of 0.082790 to 0.646967. These results demonstrate the potential of advanced neural network architectures in addressing complex forecasting challenges in the B2B domain. While these studies have significantly advanced sales forecasting in various domains, existing methods still struggle to simultaneously capture short-term fluctuations and long-term trends in dynamic environments like the FMCG sector. To address these challenges, and building on refs. [14; 15; 16], this research proposes the STL-DCSInformer-ETS hybrid model, which can enhance forecasting accuracy and scalability for FMCG sales. To more clearly illustrate the distinctive aspects of this work and its contributions to the existing knowledge base. Table 1 presents a comparison between previous related work and the significant contributions of this study.
## 5 Conclusions  
Peripheral US neuromodulation is capable of both enhancing and suppressing neural activities which are likely dependent upon the range of US intensity and strength. Unlike the neurons in the brain, low-intensity US (\(<\)1 W/cm\({}^{2}\)) is unable to evoke action potentials in the peripheral nerve endings or axons. US of intermediate intensity (1 to 200 W/cm\({}^{2}\)) exerts mainly acoustic radiation force on tissues, with no apparent thermal or inertia cavitation effects. US of intermediate intensity activates low-threshold mechanosensitive nerve endings likely through the regular mechanotransduction process, by opening mechanosensitive ion channels to evoke action potentials. US of intermediate intensity also enhances the neural activity of peripheral nerve axons, leading to increased nerve conduction velocities in both A- and C-type fibers, which is likely caused by mechanical gating of other ion channels like the NaV, K2P, ASIC, TRP, and Piezo channels. In addition, enhanced neural activity could be attributed to a direct effect of acoustic radiation force on the lipid-bilayer neural membrane. Plausible mechanisms include a transient capacitive current from rapid changes of local membrane capacitance (the NICE model) and transmembrane pore formation to allow sodium and potassium ions to pass through. High-intensity US (\(>\)1000 W/cm\({}^{2}\)) consistently inhibits the action potential transmission in peripheral nerves (i.e., nerve block) likely through a thermal effect. In addition, inertia cavitation from high-intensity US could lead to irreversible damage of peripheral nerve axons. In conclusion, US neuromodulation of the PNS has profound therapeutic potential especially through the non-thermal non-cavitation bio-effect in the intermediate intensity range, which is able to non-invasively and reversibly enhance peripheral neural activities.  
Conceptualization, B.F., L.C., and S.J.I.; investigation, B.F., L.C., and S.J.I.; writing--original draft preparation, B.F.; writing--review and editing, B.F., L.C., and S.J.I.; visualization, B.F. and S.J.I; supervision, B.F.; funding acquisition, B.F.  
**Funding:** This research was funded by the U.S. National Institutes of Health, grant number K01 DK100460 and R03 DK114546; U.S. National Science Foundation, grant number 1727185.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Model Building
### Data Processing
#### 4.1.2 Dataset Segmentation  
Training a predictive function's parameters and then testing it on the same data is conducive to a methodological error: a model that simply memorizes the labels of the samples it is trained on will achieve a perfect score, yet it will not offer meaningful predictions for unseen data. To circumvent this issue, the standard practice is to split the data into two groups for supervised machine learning experiments [26]. In this study, 50 sets of test data were generated from the DRS-1 tests, totaling nearly 6060 data points. Consequently, approximately 90% of the data forms the first group used for model training, while the remaining 10% (not utilized during training) serves as the validation set to assess the model's predictive ability. Once the dataset is partitioned, the test set comprises five data groups, as outlined in Table 4. The distribution of prediction parameters is illustrated in Figure 5.
## 2 Methodology  
### Satellite-Based Augmentation System (SBAS) Orbit and Clock Correction Evaluation Method  
IGS final products exhibit an orbit and clock accuracy at 2.5 centimeters and 75 picoseconds level ([[http://www.igs.org/products](http://www.igs.org/products)]([http://www.igs.org/products](http://www.igs.org/products))). Therefore, the precise products can be used as the reference to evaluate SBAS orbit and clock corrections. In this section, the methods to calculate the orbit and clock with SBAS corrections are presented first, then the method for assessing the accuracy of SBAS-derived orbits and clocks with respect to IGS final products is discussed.
## 5 Open Problem and Research Challenges
### Open Challenges
#### 5.1.8 Limited Bandwidth  
Noise, multi-paths, path loss, and the Doppler effect affect the communication UWSN greatly which leads to a limitation of channel bandwidth [90]. Many researchers have put forward their solution, but there lie many more challenges such as medium access protocol (MAC) protocols in the data layer. The main objective of MAC protocols is to harmonize all the nodes in the UWSN network and their shared channels. MAC protocols should check the validity of the message before sending the message to the sink node.
## 4 Test Results and Discussion
### Distribution of Acceleration Magnification Factors
#### 4.2.1 Distribution of AMFs of the Free-Field System Model Test
As for the Kobe wave, it is evident that AMFs at the ground surface demonstrate a positive correlation with the increase in the table input acceleration. This is due to the higher dominant frequency of the table input acceleration for the Kobe wave, about 23.1 Hz, leading to small soil displacement, as shown in Table 4. As a result, the soil behaves elastically, with the characteristic frequency of the surface response exceeding the fundamental frequency in the white noise case. Therefore, there is minimal change in the energy dissipation of the soil, but the input energy of the system increases, leading to an increase in AMFs at the surface.
# A Fiber-Optic Six-Axis Force Sensor Based on a 3-UPU-Compliant Parallel Mechanism
## 2 Bending-Sensitive Optical Fiber  
### Characteristics of Bending-Sensitive Optical Fiber  
As is well known, the optical loss of an untreated plastic multimode fiber caused by bending is minimal, which is insufficient to detect the bending deformation [17]. To enhance the sensitivity of the fiber to bending curvature, a special sensitive region is introduced on the fiber, which increases optical leakage during fiber bending. The sensitive region is produced by removing a part of the fiber core, as shown in Figure 1. Compared to untreated optical fiber, this kind of bending-sensitive optical fiber has significantly increased sensitivity to bending.  
The static characteristic analysis has been conducted on bending-sensitive optical fiber. It exhibits excellent linear output during curvature measurement when the bending radius is greater than 60 mm [18]. The relationship between the electrical signal output of the fiber and the bending curvature of sensitive region can be expressed as:  
\[U=aC+b \tag{1}\]  
where \(U\) is the output signal of the fiber (the optical signal is converted into a voltage signal by a phototransistor), \(C\) is the curvature of the sensitive region, \(a\) and \(b\) are constantsrelated to the sensor. The optical fiber used in this study is a plastic multimode fiber with a diameter of 0.5 mm, which is produced by Mitsubishi Company (Tokyo, Japan). LED IF-E96-R made by Industrial Fiber Optics Company (Tempe, AZ, USA, the wavelength is 660 nm) is used as a light source and the corresponding receiver is phototransistor IF-D92 (made by Industrial Fiber Optics Company).
## 3 Results
### Quaternary Clathrate Cs\({}_{8}\)Na\({}_{16}\)Al\({}_{24}\)Si\({}_{112}\)
In other words, predicting the Cs rattling behavior from the harmonic oscillator model in Cs\({}_{8}\)Na\({}_{16}\)Al\({}_{24}\)Si\({}_{112}\) needs to be reconsidered unless the self-consistent phonon (SCP) model [39] is applied to derive the temperature-dependent frequency of guest vibration. Moreover, the given nominal composition with respect to the Al occupancy on each framework site is anticipated to influence the \(U_{\text{iso}}\) in a slight fashion. The Cs vibration mode in Cs\({}_{8}\)Na\({}_{16}\)Ge\({}_{136}\) was also employed to compute the isotropic atomic displacement parameters at a specified temperature in the previous work [22], leading to a good quantitative agreement with the calculated \(U_{\text{iso}}\) regarding Cs motion in Cs\({}_{8}\)Na\({}_{16}\)Al\({}_{24}\)Si\({}_{112}\) configurations. Such similar trends are interpreted as meaning that the \"tuned\" parent framework switched from \"Al\({}_{24}\)Si\({}_{112}\)\" to \"Ge\({}_{136}\)\" does not effectively impact the rattling frequency. It is notable that the predicted \(\kappa_{\text{L}}\) is not sensitive to a different Al occupancy on the host sites (96\(g\)) in Figure 6. The dotted line acts as a guile for the eye, showing the Al3/Si3 composition independence with respect to \(\kappa_{\text{L}}\). This yields approximately 1 W m\({}^{-1}\) K\({}^{-1}\) for the predicted lattice thermal conductivity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**8\(a\) (Al1/Si1)** & **32\(e\) (Al2/Si2)** & **96\(g\) (Al3/Si3)** & \(a\) (Å)** & \(K\) (GPa) & \(\Delta E\) (meV) \\ \hline
0/1 & 0.125/0.875 & 0.208/0.792 & 14.9147 & & \\ (0.2/0.8) * & (0.1/0.9) * & (0.2/0.8) * & (14.9153 *) & 70.41 & 0 \\
0/1 & 0.25/0.75 & 0.167/0.833 & 14.9103 & 70.92 & 157 \\
0.5/0.5 & 0.25/0.75 & 0.125/0.875 & 14.8947 & 71.00 & 520 \\
1/0 & 0.5/0.5 & 0/1 & 14.8861 & 71.35 & 914 \\ \hline \hline \end{tabular}
* Reference [38].  
\end{table} 
Table 3: The occupancy of framework atoms (Al, Si) on different Wyckoff sites, including 8\(a\), 32\(e\), 96\(g\) and the EOS parameters for the energetically stable configuration (the second row) as well as other configurations. In contrast to the measured \(\kappa_{\mathrm{L}}\) (\(\sim\)4.5 W m\({}^{-1}\) K\({}^{-1}\)) [38], the 4-fold reduction of lattice thermal conductivity still questions the validity of the resonant \"scattering centers\" model relating to acoustic phonons. Instead, the cubic anharmonicity effect needs to be reviewed in order to achieve a quantitative agreement between the first-principles calculation and experimentally observed \(\kappa_{\mathrm{L}}\) in quaternary system Cs\({}_{8}\)Na\({}_{16}\)Al\({}_{24}\)Si\({}_{112}\).