esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
\[P_{IL_{In}}=\frac{1}{2}m\big{|}\ddot{x}\big{|}\big{|}\dot{y}\big{|}\times\sin( \varnothing_{x}) \tag{14}\]  
where \(\varnothing_{x}\) is the phase difference between \(x\) and \(y\). In the case of prescribed displacement, there is no phase difference and \(x=y\). We use Equation (1) together with Equation (13) to define the input power under prescribed displacement, resulting in Equation (15). \[P_{PD_{In}}=F\times\dot{y}=\big{(}m\ddot{x}+C_{M}\dot{x}+K_{M}x+f\big{)}\dot{x} \tag{15}\]  
In both cases, this leads to an efficiency according to Equation (16) (see derivation in Supplementary Materials). \[\Gamma=\frac{P}{P_{In}}=\frac{\alpha\beta\xi_{C}}{1+\big{(}\beta\gamma\big{)} ^{2}+\alpha} \tag{16}\]  
In summary, we derive (i) the dimensionless average power generated over the load resistance, Equation (11), based on a lumped second-order mass-spring-damper system with inertial load, Equation (1), and the lumped element model, Equation (4), for both the PEH and EMEH; (ii) the dimensionless average power assuming a prescribed proof mass displacement, Equation (12), and (iii) an expression for efficiency, Equation (16), relating load power to mechanical input power, valid for both the PEH and EMEH under both inertial load and prescribed displacement. Next, we analyze these general expressions to determine the common and dual characteristics of piezoelectric and electromagnetic energy harvesters.
## 4 Discussion
### The Potential Effect of NEP Temporal Change  
Precipitation is the main factor affecting NEP changes in alpine grassland ecosystems [62; 64]. By analyzing the relationship between precipitation and NEP, it was found that changes in the two were not synchronized, and there was a lag effect. The peaks and valleys of NEP were always one year behind the time when the peaks and valleys of precipitation appeared. For example, the peak of precipitation appeared in 2010, while the peak of NEP appeared in 2011; the valley of precipitation appeared in 2006, and the valley of NEP appeared in 2007. By analyzing the partial correlation between NEP and the precipitation of the current year (Figure 7d-f) and the precipitation of the previous year (Figure 9a-c), it was found that the correlation between NEP and the precipitation of the previous year was strong and positive. The positive correlation covered 74% of the total area, which was 10.8% more than the positive correlation between NEP and the precipitation of the current year. This result also showed that if the precipitation in the previous year was sufficient, grassland growth would increase in the current year, and the grassland would have a strong carbon sequestration ability. The correlation between NEP and the temperature of the previous year was also stronger than that of the current year, with 44.1% of the area showing positive correlation, which was 14.9% more than the correlation between NEP and the temperature of the current year. Therefore, good hydrothermal conditions in the previous year will promote growth of NEP in the current year. The correlation between NEP and the solar radiation of the previous year showed an insignificant correlation (92.3%) in most areas, which was basically consistent with the correlation between NEP and the solar radiation of the current year (94.3%).  
Figure 8: Different elevation classifications of (**a**) C source or C sink area, (**b**) mean precipitation, (**c**) mean air temperature, and (**d**) mean solar radiation.  
Figure 9: Spatial patterns of the partial correlations between (**a**) NEP and precipitation, (**b**) NEP and temperature, (**c**) NEP and solar radiation of the previous year.
## 6 Experimental Results
### Calibration of Sensors
#### 6.1.1 Ultrasonic Sensor HC-SR04  
The results of the Ultrasonic Sensor HC-SR04 include the calculation used to measure the distance and the calibration involved to measure the distance accurately. The validation of the sensor is done, and error was calculated.  
(i) Calibration  
The Ultrasonic sensor senses the distance of a minimum of 3 cm and a maximum of 400 cm. A distance less than 3 cm will not be sensed by the sensor. The sensor sends a trigger pulse of 10 us and the echo signal length is calculated. The duration of the Echo signal is converted to distance.  
(ii) Calculation  
It includes the formula for calculating the distance from the duration of echo and echo signal length. The speed of sound is 340 m/s, i.e., 0.034 cm/us. Equation (1) determines  
Figure 10: Setup of node 1 and node 2.  
the formula for calculating the distance and Equation (2) determines the calculation of the distance by ultrasonic sensor.  
\[Distance=Speed*Time \tag{1}\]  
\[Distance\ from\ sensor=duration\ of\ pulse*\ \frac{0.034}{2} \tag{2}\]  
(iii) Validation  
The validation has been done by a measuring scale and comparing the result of the measuring scale and the sensor. Finally, the error of the Ultrasonic Sensor was calculated.  
Figure 11 shows the validation of the Ultrasonic Sensor using a measuring scale to compare the reading and get the error between the readings. Different readings from the sensor and the scale were taken and the error is calculated with Equation (3).  
\[Ultrasonic\ reading=23(Height\ of\ tank)-Sensor\ Reading \tag{3}\]  
Table 2 shows a comparison of the reading of the measuring scale and the ultrasonic sensor. It has been seen that an error of 1 cm is present, i.e., the ultrasonic sensor shows 1 cm less reading than the measuring scale.
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 4 Covalent Organic Frameworks (COFs)
### COF Features for SPME Extraction  
Among the interesting features of COFs, their low density, large surface area, and adjustable pore size and structure, as well as customizable properties, are the most attractive features that make them exceptional SPME materials. As previously reported, the thermal and chemical stability of COFs depend on their crystalline structure, particularly on both the bond strength and the electrostatic forces between the interlayers. Different methodologies have been proposed to enhance the stability of COFs, including the application of highly planar building blocks able to increase the interlayer force, the linkage conversion, and the conversion from reversible (e.g., imide-linked) to irreversible (e.g., amide-linked) COFs [14; 30; 95].  
The most common typologies of COFs are 2D and 3D COFs. The 2D COFs exhibit intralayer covalent bonds and interlayer non-covalent interactions, such as \(\pi\)-\(\pi\) stacking. Adsorption usually occurs via hydrophobic interaction with planar aromatic analytes between the layers. In contrast, 3D COFs present a framework linked with strong covalent bonds and accessible open binding sites with a high surface area characterized by a hierarchical intrinsic nanoporosity. This feature increases the extraction selectivity and enrichment performance of the SPME coating compared to 2D frameworks due to the requirements in terms of steric complementarity [98; 99]. Recent studies have introduced heteroporous COFs as an alternative solution for the simultaneous extraction of molecules with different sizes, thus enhancing both the selectivity and the extraction performance of the coating material.  
Pore dimensions can be tuned by changing the reaction conditions or the utilized building blocks; however, it has to be considered that micropores and small mesopores generally limit mass transfer and accessibility to the inner surface of the framework. Post-functionalization of the binding sites can also be performed to induce the formation of specific analyte-COFs interactions, improving both the extraction performance and selectivity [95]. This strategy can involve either the modification of the organic groups already present in the original framework or the incorporation inside the COF of predesigned structures able to host target analytes [20; 95]. Finally, the use of a protective, thin layer mostly made by a polyimide polymer proved to be useful in improving both the mechanical strength and life span of COF-based coatings [100].
###### Abstract
The traditional dynamic pricing of mobile data plans focuses on finding the optimal pricing parameters by assuming that network capacity is unlimited. However, with the rapid growth of data traffic, network capacity has become a bottleneck that affects how service providers can address customers' demand. This research attempts to offer new insights into managing demand when network capacity is limited. Compared with the all plans always open method, which is currently implemented by most mobile service providers, our dynamic plan control method can dynamically open a subset of data plans for new customers at the beginning of each period. This dynamic control allows the service provider to adjust data network utilization and achieve high customer satisfaction and a low churn rate, which reflect high service supply chain performance. Second, we provide a framework to model the behaviors of service providers and customers under connection-speed-restriction pricing. Despite the growing popularity of connection-speed-restriction pricing, little research has been devoted to demand management under this pricing scheme. Our study addresses this issue and models the behaviors of both service providers and customers under connection-speed-restriction pricing. Third, the service provider's optimization problem is a dynamic programming problem. Due to the high-dimensional property of the problem, it is difficult to implement backward induction. To solve the problem efficiently, we propose an equivalent mixed integer linear programming (MILP) formulation. Through numerical evaluation, the efficiency of the solution method is further validated. The remainder of this paper is organized as follows. We conduct a brief review of the relevant literature in Section 2. In Section 3, we describe the models of the service provider and customers. Section 4 provides the solution approach, and Section 5 examines the effect of our model with numerical experiments. Finally, Section 6 offers some concluding remarks and future research directions.
# Digitalization of the Ex-Core Neutron Flux Monitoring System for APR1400 Nuclear Power Plant
## 2 Design of the Digital ENFMS for APR1400
### Logarithmic Power Signal Generation
#### 2.1.1 Data Acquisition  
The function of the front-end electronics of the start-up and wide-range monitoring instrumentations is to acquire the electric charge pulses generated by a neutron flux detector, and to condition them to be suitable for pulse counting and MSV calculation. Nowadays, the availability ofvery fast and high precision A/D converters, permits the design of data acquisition systems in which the A/D conversion occurs at the detector preamplifier outputs without any analog signal shaping. The data acquisition system captures the detailed shape of the preamplifier signals using fast A/D converters without loss of information, provided that the Nyquist criteria is satisfied, but is affected by errors due to the quantization noise and to other sources of electronic noise. To process detector signals with the rise time of 0.05 micro-seconds and to satisfy requirements on the measurement range and system accuracy, the A/D converters require the sampling rate of 500M samples/second and a resolution of 10 to 14 bits. A combination of currently available A/D converters and FPGAs with processing speeds up to 500M samples/second and 16-bit resolution makes possible the digitizing of pulse trains at speeds of 1 or 2 nanoseconds [11].  
The proposed digital implementation of the logarithmic power signal generation for the start-up and wide-range monitoring instrumentations can be configured as shown in Figure 2. The preamplifier acquires the electric pulse signals generated by the detector and converts to voltage signals with a range fit to the input span of the A/D converter. The preamplifier remains in analog form because it needs to process very low signals with high speed and short response time. However, the reactor power calculation function and other connected signal conditioning and processing functions from pulse shaping to output data and signal generation will be digitalized.
## 5 Experiment
### Experiment Preparation  
In our experiments, we select 60% of the data as the training set, 20% as the validation set, and the remaining as the test set. This proportion was chosen to construct the relevant dataset because we made multiple attempts from multiple proportion components and ultimately found that the dataset with this proportion performed the best, including the dataset allocation ratios of 70%:20%:10% and 80%:10%:10% that we tried. Additionally, we normalize the dataset using standard normalization methods to stabilize the training process. For these three available parking space datasets, the observation step size and prediction step size are both set to 12, and 1 h of historical data is used to predict the status of vacant parking spaces in the next 60 min. All experiments in this section were conducted on an Intel Core i5-12600 [EMAIL_ADDRESS]-GHz computer with a GeForce RTX 3060 Ti GPU card. Our experiments are based on the PyTorch framework, with the batch size set to 64 and the number of training rounds set to 50. We use the ADAM optimizer for training with a learning rate of 0.001. To prevent overfitting, we also set up an early stopping strategy with a patience value of 10. In the experiments, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percent error (MAPE) are used to evaluate the prediction performance of our proposed model.  
\[\text{MAE}(x,y)=\frac{1}{|\Omega|}\sum_{i\in\Omega}|x_{i}-y_{i}| \tag{11}\]  
\[\text{RMSE}(x,y)=\sqrt{\frac{1}{|\Omega|}\sum_{i\in\Omega}\left(x_{i}-y_{i} \right)^{2}} \tag{12}\]  
\[\text{MAPE}(x,y)=\frac{1}{|\Omega|}\sum_{i\in\Omega}\left|\frac{x_{i}-y_{i}}{ x_{i}}\right| \tag{13}\]  
where \(x_{1},\dots,x_{n}\) represents the true value and \(y_{1},\dots y_{n}\) represents the predicted value. \(\Omega\) represents the number of observation samples. In our experiment, the value of \(|\Omega|\) is 12.
## 3 Structural Monitoring via Global Navigation Satellite System: The Case of High Wind Turbines
On both sides of the base, one cylindrical spring (1417 kN/m stiff) and one SA MR damper are installed (Figure 4b, where \(l_{s}=13.04\) m and \(l_{d}=9\) m) in parallel. Each damper may have two operating modes, ON and OFF, that correspond to an equivalent damping constant equal to \(c_{d,ON}=6^{\prime}173\) MNs/m and \(c_{d,OFF}=1.235\) MNs/m, respectively. A finite element model [17] has been implemented in a MATLAB environment in order to simulate the dynamic behavior of the case study structure. It consists of 37 elements; 36 elements simulating the tower, while the last element (37 th) is more rigid and represents the connection of the top of the tower to the barycenter of the nacelle. The nacelle and its internal components are represented by a concentrated mass at the top of the structure, with no dynamic interaction considered. The base support has been modeled by a rotational spring and a Maxwell element (representing the MR dampers) working in parallel. The Maxwell element, as is known, consists of a spring and a linear viscous damper: the controllable part of this device is represented by the damping constant, while the stiffness of the spring has been simply assumed to be high enough so as to behave like a rigid link. Figure 4: (**a**) “Fixed base” configuration of the case-study wind turbine (dimensions in mm). (**b**) Base restraint for the SA controlled configuration. An extreme operating gust (EOG) loading has been considered in the following as reference wind action: a sharp increase, then a decrease, in wind speed within a short period of time. [PERSON] and [PERSON] [58] defined an equivalent base acceleration time history (Figure 5), that is, the base input that would provide the same top mass response of the real fixed base structure subjected to the wind action. The values adopted to calibrate the SA control algorithm are \(x_{\text{lim}}\)= 900 mm and \(\sigma_{\text{lim}}\)=10 MPa. The control algorithm establishes in which instants it is more appropriate to command the OFF state for the MR dampers (i.e., \(i=0\)) and in which it is more convenient that they are set to ON (\(i=i_{\text{max}}\)) according to Equation (1). To do this, it is necessary to know the demand for bending stress at the base of the tower, as well as the displacement and the horizontal velocity at the top of the tower, in the same instant of time. The stress monitoring at the base is carried out indirectly, starting from the measurement of the deformation, through the use of traditional strain gauges and knowing the modulus of elasticity of the material. Here, a focus is made on how to measure the other two quantities, namely top displacement (direct measurement with GNNS) and top velocity (indirect evaluation, as an online derivative of displacement). The following sections carry out a sensitivity analysis for the GNSS system by analyzing the role of frequency, latency and accuracy in measurements influencing the effectiveness of the proposed \"smart\" strategy for structural control.
## 2 Results and Discussion
### Influence of TBAF Concentration on Pullulan Calibration Curves  
Firstly, the influence of the TBAF concentration was studied on the pullulan standards. Three calibration curves with various TBAF concentrations 0, 5 and 10 mg/mL (named respectively CC DT 0, CC DT 0.5 and CC DT 1.0) were obtained (Figure 1) by polynomial regression, with satisfying correlation coefficients (R\({}^{2}\) > 0.99%).  
However, increasing TBAF concentration involved significant modifications of the elution peaks shape and position (Figure 2). In the case of the low molecular weight samples (Mn lower than 100 kDa), for each standard a new elution peak appeared in addition to the one observed without TBAF. This new peak had a lower elution volume, suggesting the apparition of a second population with a higher hydrodynamic volume in the sample (Figure 2B). The increase of the hydrodynamic volume with TBAF concentration in solution can be explained by the formation of TBAF aggregates on pullulan chains in the presence of TBAF/DMSO due to the formation of the complex [DMSO-TBAF-pullulan].  
For high molecular weight pullulan samples (higher than 100 kDa), this aggregation phenomenon had a slightly different consequence. The elution peaks were flattened, widened and displaced to lower elution volume, and an exclusion peak (elution volume between 5 mL and 6 mL) appeared  
Figure 1: SEC calibration curves in DMSO (60 \({}^{\circ}\)C and 0.5 mL/min) of pullulan standards with different concentrations of TBAF: (**A**) 0 mg/mL; (**B**) 5 mg/mL; (**C**) 10 mg/mL.  
and increased in intensity with the increase of pullulan molecular weight as indicated by the red arrows (Figure 2A).  
The exclusion rate of higher molar mass pullulans (i.e., 970 kDa & 1720 kDa) increased with TBAF concentration, but without affecting the major elution volume. The same phenomenon, i.e., the non-uniform effect on the higher- and lower-molecular weight macromolecules has been previously observed with pullulan standard solutions prepared in LiCl/DMAc, with an increased amount of LiCl in the standards solutions [33; 34].  
Despite these observations, the calibration curves obtained in these different conditions could be satisfactorily superimposed. As the one obtained in DMSO alone gave the most defined peaks and the higher correlation coefficient, it will be used as calibration curve for all the rest of the study.
# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 5 Discussion  
In this study, the periodic map spectrum was used as a feature map and combined with GoogLeNet in transformer fault diagnosis, which addressed the influence of traditional diagnostic methods that ignore the additional harmonic and non-harmonic components of transformer vibration signals on fault diagnosis based on vibration signals. The aim of this study was to combine SCA-VMD with improved GoogLeNet to build a diagnostic model for transformer fault diagnosis. The feasibility of the proposed method and the accuracy of fault diagnosis were found through experimental validation. The results show that compared with the traditional diagnostic model, the method had significantly fewer iterations in data processing and fault type identification than the comparative algorithms, and operated more efficiently and with higher identification accuracy.
## 5 Discussion  
Many of the works presented in the previous sections that deal with DAS+PRS techniques employed a reduced set of fiber optic events for classification (mostly three-class classification problems), and the experimental setup is far from being a realistic scenario in terms of the length of the fiber cables, signal recording durations, and rigorous experimental design. Most of the reported works claim an accuracy above 90%, mainly due to the low number of classes involved in the experiments and the favorable experimental conditions. The most relevant exceptions are the works presented in [22; 23; 24; 25]. These works provided a robust experimental setup in terms of signal recording durations, recording scenario, and classification experiments. The rates presented in those works are worse than the rest, mainly due to the more difficult and realistic experimental setup (e.g., different weight excavators are aimed to be correctly identified). Nevertheless, these results have been generated by the worst possible case in what respect to the sensing mechanism: a DAS unit based on amplitude measurements [44; 71]. Recent advances in the design of DAS interrogators with a linear behavior (either based on chirped pulses [72] or phase detection [73; 74]) will undoubtedly generate improved and more consistent signals, so that the pattern classification processes will be greatly benefited. With these improvements, we can expect that the classification results will be better than the ones reported here.  
Deciding which is the best feature extraction method and pattern classification algorithm based on the results obtained by these different works is a hard task. The different experimental setups of these works both in terms of the number of events to classify and the recording scenarios make impossible a relevant and rigorous comparison. To make this possible, the same experimental setup should be used in the works to compare. With respect to this problem, we would suggest future proposals in the literature to provide both the used data and the applied algorithms (provided there are no intellectual property issues) to the scientific community, in line with the _reproducible research_ movement [75]. This would ease the comparison, foster competition, and very probably lead to significant improvements, as more research teams would be able to work on a common database.  
What is very clear from the review is that real field deployment of a pattern recognition system based on DAS technology is still a challenging area of research, far from being fully solved. Better performance rates and realistic solutions in field deployment should be developed to allow an industry wide adoption of the DAS+PRS strategy for pipeline surveillance.