esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Influence of the Perforation Parameters
#### 2.2.3. Perforation Damage  
During the process of jet-assisted perforating, high-temperature and high-pressure shock waves are generated around the perforation hole, forming a compacted zone with a permeability significantly lower than that of the original formation. The compaction damage process of the formation by shock waves is highly complex. To simplify the calculations and analysis, this study focused solely on simulating the impact of the perforation compaction degree on the productivity ratio, rather than simulating the physical mechanics of the perforation process. In perforated completions, the degree of the perforation compaction damage is typically quantified by the permeability reduction factor, which is defined as the ratio of the permeability in the compacted zone to the original permeability.  
Figure 5: Influence of the phase angles on the productivity ratio in isotropic formations.  
Figure 6: Influence of the phase angles and formation anisotropy on the productivity ratio.  
The impact of compaction damage on the productivity ratio is illustrated in Figure 7. It can be observed that the compaction damage has a noticeable effect on the productivity ratio. When other parameters remain constant, the productivity ratio decreases as the compaction damage increases (i.e., the permeability reduction factor decreases). The influence of the compaction damage on the productivity ratio becomes even more significant after the perforation hole penetrates the drilling mud-contaminated zone.
## 5 Parametric Study
### Comprehensive Study of Vibration Isolation
However, as the transverse spring stiffness \(k_{h}\) and vertical distance \(d\) remain unchanged, a smaller stiffness ratio means a smaller vertical spring stiffness \(k_{v}\) and a larger vertical spring original length \(l\), implying that the vertical springs are very soft, which does not meet the requirements of practical installation conditions. Therefore, it is necessary to consider the structural design requirements of vertical spring while reducing the stiffness ratio \(\alpha\) to obtain better vibration isolation performance. Figure 6: The vibration transmissibility \(T\) with different \(\alpha\) (configurations of \(k_{v}\) and \(l\)) of the proposed design. Finally, the effect of vertical spring damping \(c_{v}\) on vibration isolation performance is discussed. Figure 7 shows the vibration transmissibility curve under a different \(c_{v}\), and the values of other parameters are the same as those shown in Table 1. The base excitation amplitude is \(A=0.05\) m/s\({}^{2}\). Results show that the damping coefficient \(c_{v}\) has little influence on vibration isolation performance. With the increase of damping \(c_{v}\), the peak frequency only decreases slightly, and the peak amplitude also decreases slightly. Compared with the influence of stiffness \(k_{v}\), the influence of damping \(c_{v}\) can be ignored.
# A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection
## 2 Overall Architecture  
The architecture of the proposed ANN-based ECG processor ASIC is shown in Figure 1. The ASIC consists of a pre-processing engine, a data handler, a classification engine, and data interfaces. The digitized ECG data are inputted to the ASIC through a Serial PeripheralInterface (SPI), and the parameters for classification computing are also transmitted by SPI. Once the pre-processing engine detects an R peak, its index address in the ECG data buffer will be calculated and temporarily saved in a register that holds R-peaks indexes. The earliest index will be discarded when the latest index is saved. To ensure that 100 data points containing the complete R-peak can be obtained for subsequent classification, when the R-peak index register stores three indexes, 50 ECG data points before and after the index address of the penultimate R-peak will be transferred from the ECG data buffer to the input feature buffer of the classification engine, which is completed by the data handler. In addition, the R-R interval (RRI) is calculated simultaneously, which is an important parameter for obtaining the heart rate (HR). In this paper, RRI is represented by the number of sampling points between two peaks, so the heart rate can be calculated as follows:  
\[\text{HR}=60\times\text{SR/RRI}, \tag{1}\]  
where SR is the sampling rate of ECG, generally not exceeding 1 kHz. The classification engine performs an inference of the input feature data, and a parameter buffer is included to store bias values and weights. The inference computation is completed by the PU under the control of a finite state machine (FSM). At the end of the classification, the RRI value and inference result are packaged into a frame and sent out by a Universal Asynchronous Receiver/Transmitter (UART).
## 3 Results  
### Measuring Results  
The results obtained are presented in Table 11; for a better understanding of the relationships between the process parameters and the relative density, see Figure 4.  
The 3D scatter plot (Figure 3) shows the correlations between the relative density and selected process parameters--laser power, scanning speed and layer thickness--from a data set of 42 points.  
The \(x\)-axis indicates the laser power, which ranges from 100 to 200 W; the \(y\)-axis indicates the scanning speed, which ranges from 500 to 3000 mm/s; and the \(z\)-axis indicates the relative density, which ranges from 88% to 100%, with higher values indicating better material consolidation. The layer thickness is colour coded: 0.025 mm (purple), 0.035 mm (turquoise) and 0.05 mm (yellow). This shows that the highest relative densities (up to 100%) are achieved with the thinnest layer (0.025 mm), especially at moderate laser power (100-150 W) and scanning speeds (1000-2000 mm/s).  
The graph shows an accumulation of points with high densities (95-100%) for 0.035 mm and 0.05 mm thin films in comparable parameter ranges, while lower densities (88-92%) occur at higher scanning speeds (e.g., 2500-3000 mm/s).  
Figure 4: Scatter plot of process parameters vs. the relative density.  
The distribution of standard deviations within a data set is represented by the histogram (Figure 5). The \(y\)-axis shows the frequency of occurrence, which ranges from 0 to 7, while the \(x\)-axis shows the standard deviation values, which range from 0 to 0.025. The data are categorised into bins, and the standard deviations between 0 and 0.005 have the highest frequencies (around 6-7). With values of around 3 for the 0.01 bin, 2 for the 0.015 bin and 1 for the 0.02 and 0.025 bins, the frequencies gradually decrease with larger standard deviations. This means that there are fewer examples of higher variability and that most of the data points have a low standard deviation, indicating relatively low variability.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 5 Results and Discussion
### Radiation Pattern  
With a view to demonstrating that the designed antenna radiated across a broad frequency range, Figure 14 illustrates the simulated and experimented radiation patterns in the H-plane (\(\varnothing=90^{\circ}\)) and E-plane (\(\varnothing=0^{\circ}\)) for the passband resonance frequency of 2.53 GHz, 8.43 GHz, 11.88 GHz, and 18.51 GHz. It can be seen that the proposed antenna presented reasonably good bidirectional monopole E-plane patterns and omni-directional H-plane patterns over the different passband frequencies. At the higher frequencies (greater than 8 GHz), the radiation patterns in both planes were distorted on account of the higher order modes' excitation and non-uniform phase division. As a consequence, radiated modes may have promoted an increased level of electromagnetic interference (EM). In order to suppress these unwanted radiated modes, a designer can employ techniques such as introducing periodic structures, parasitic elements, split ring resonators (SRRs), etc. However, these techniques increase the complexity of the design and cost of fabrication.  
Figure 14: _Cont._  
Figure 13: Simulated and measured peak gain of proposed antenna with notched bands.
# A Watermark-Based In-Situ Access Control Model for Image Big Data
## 4 Proposed Scheme  
### Basic Settings  
We first describe a concrete process to explain our scheme, which consists of three steps as follows:  
1. Accessors registration. Accessors register for data access on servers. They are assigned a role or multiple roles by servers.
2. Data publication. Servers who are data publishers or distributors embed access control policies via watermarks in data such as images. Data is published, in which certain areas or layers may be encrypted by secret keys related to control policies.
3. Client conformance. Accessors request images via particular client tools, such as image browsers. Client tools ask accessors to present their roles and secret keys. Client tools enforce control policies by parsing from watermarks that are embedded in images, and decrypt corresponding areas or layers in images by responding secret keys.  
Obviously, data publication and client conformance are critical in the design. Next, we propose a hierarchical encryption model as a concrete scheme.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 5 Evaluation
### Proposed Preprocessing Process  
For the dataset including 1020 individual data, each of them was based on the strength of GPS signals corresponding to a specific place. All possible GPS signals were collected, and, using the mobile application, the analog input was transformed to digital data for further analysis. Here, the data included information about the GPS signal (zero to 10 features). We denoted the \(i\)th data as X\({}_{i}\), and the GPS signal from satellite \(j\) in X\({}_{i}\) data as \(x_{ij}\). We regularized the input data as follows.  
Step 1 Reduce the number of GPS signals to nine.  
The tenth GPS appears very infrequently, and, as the GPS signal is very small, this information is not useful for analysis.  
Step 2 Fill the missing datum.  
As mentioned previously, low-strength GPS signals are removed by the mobile application. Thus, if the GPS signal is too weak (in a building, to be blocked by obstacle), nine GPS signals cannot be obtained. The datum will be filled by zeroes; thus, these data will not affect the strongest signals, which contain the most information about I/O detection.  
Step 3 Rearrange data for further analysis.  
The strong signals contain the most information about I/O detection; however, the order of these signals is chaotic and independent from each other. Thus, we cannot find any pattern because the strong signals mix with the weak signal, which makes the entire dataset chaotic and unpredictable. Therefore, we propose a simple solution for this problem, i.e., we rearrange the GPS signals from strongest to smallest. As a result, strong GPS signals will take priority during the training and testing phases. Even if this does not work effectively, we can add a heavyweight to strong GPS signals before applying provided schemes.  
The preprocessing sequence is shown in Figure 13.  
Figure 12: Accuracy of I/O detection with semi-indoor samples (classified as indoor samples).
## 3 Results and Discussion
### Physicochemical Characteristics Analysis

Table 1: Physical and chemical indexes related to rose fermented beverages by different bacteria. decrease in pH. The L* values (lightness) of the fermented rose beverages followed the order: SFR > LSFR = UFR > LFR. The a* values (redness) showed the order: LFR > LSFR > SFR > UFR, while the b* values (yellowness) were in the order: UFR > SFR > LFR > LSFR. These results indicate that fermentation induced a shift in the color of the rose beverages, moving towards bluer and redder hues. Previous research has demonstrated that beverage color is influenced not only by the raw materials but also by reactions such as the Strecker and Maillard reactions that occur during fermentation [30]. The observed color differences among beverages fermented with different strains are likely correlated with the extent and nature of these reactions. Total phenolic content (TPC) and total flavonoid content (TFC) are crucial secondary metabolites in food products. In this study, both TPC and TFC in the rose beverages significantly increased after fermentation across all treatment groups compared to the unfermented control. Specifically, TPC and TFC increased by 11.26% and 15.00% in SFR, by 18.92% and 25.00% in LFR, and by 23.87% and 25.00% in LSFR. These findings are consistent with those of [PERSON] et al. [37], who reported that fermentation by both L. plantarum and _S. cerevisiae_ significantly (\(p<0.05\)) increased TPC and TFC in apple by-products, with the most substantial increases observed in co-cultured samples (\(p<0.05\)). This further supports the notion of synergistic effects between _S. cerevisiae_ and _L. plantarum_ on metabolic activities. Furthermore, the increase in total phenols and flavonoids during fermentation is related to the hydrolase production capacity of the fermentation strains and the biotransformation of these compounds, which are influenced by the metabolism of sugars, organic acids, and amino acids [38]. A comparative study by [PERSON] et al. [39] on fermented mango juice highlighted that the transformation ability of L. plantarum regarding TPC was significantly stronger than that of _S. cerevisiae_.
## 2 Materials and Methods
### Drought Classification  
In this study, the annual standardized precipitation index (SPI) and SPEI were employed to assess the appropriateness of using the TVDI for analyzing drought in Guilin city. The standardized precipitation index (SPI) can be calculated based on daily rainfall data as follows [41]:  
\[P_{year}=\sum_{i=1}^{N}P_{i} \tag{1}\]  
\[f(x,\alpha,\beta)=\beta^{\alpha}x^{\alpha-1}e^{-x/\beta}/\Gamma(\alpha) \tag{2}\]  
\[SPI=\Phi^{-1}(G(P_{year})) \tag{3}\]  
First, we aggregated the daily precipitation \(P_{i}\) to obtain the total annual precipitation \(P_{year}\). Then, we fit a gamma distribution to the annual precipitation data. The probability density function of the gamma distribution is determined by the shape parameter \(\alpha\) and the scale parameter \(\beta\). Based on the fitted gamma distribution, we calculated the cumulative probability of the annual precipitation \(G(P_{year})\). Finally, we converted the cumulative probability into standard normal scores for use as SPI values with the inverse function of the standard normal distribution.  
The TVDI can be calculated as follows:  
\[TVDI=LST-LST_{\min}(NDVI)/LST_{\max}(NDVI)-LST_{\min}(NDVI) \tag{4}\]  
where \(LST\) stands for Land Surface Temperature. \(LST_{\min}(NDVI)\) represents the minimum land surface temperature at a given \(NDVI\) value, \(LST_{\max}(NDVI)\) is the maximum land surface temperature for the same \(NDVI\) value.  
We defined the following drought categories in terms of the TVDI value [42]: 0-0.2, wet conditions; 0.2-0.4, normal; 0.4-0.6, mild drought; 0.6-0.8, severe drought; and 0.8-1, extreme drought.  
As an increase in the TVDI and decreases in the SPI and SPEI indicate worsening drought conditions, we inverted the TVDI and compared the inverted value with the SPI and SPEI to better validate the rationality of its use in assessing drought categories.
## 5 Conclusions  
To address the optimization of the power sector's \"dual carbon\" pathway supported by key low-carbon technologies, this paper first establishes a quantitative support model for three critical low-carbon technologies: new energy storage, CCUS (carbon capture, utilization, and storage), and hydrogen energy, focusing on their role in the low-carbon transition of the power sector. Then, considering the impact of these key low-carbon technologies on traditional power planning objectives and constraints, an integrated optimization model of the power sector's \"dual carbon\" pathway and key low-carbon technologies was developed. Based on this model, a simulation of the low-carbon transition pathway for China's power industry under the dual carbon goals was conducted. The simulation results show that the collaborative development of new energy storage and pumped storage effectively supports the rapid growth of renewable energy capacity, enabling the safe and orderly replacement of both existing and new coal-fired power generation. CCUS technology effectively facilitates the functional transformation of traditional coal-fired units, shifting them toward net-zero decarbonization, flexible regulation, and emergency backup roles. In the long term, the \"CCUS + biomass power generation\" technology pathway supports the achievement of carbon neutrality within the power sector. Additionally, the application of hydrogen energy supports the integration of renewable energy across systems and provides the power system with multi-time-scale and multi-type flexible regulation capabilities.  
Conceptualization, P.X.; Methodology, P.X. and Y.G.; Software, B.Y. and H.C.; Formal analysis, P.X.; Writing--original draft, P.X.; Writing--review & editing, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (No. 2022 YFB2403100), China Energy Investment Corporation Science and Technology Project (Grant No. GJPT-23-21), and Youth Talent Project of State Grid Energy Research Institute, \"Decision Analysis System for Grid based New Energy System Development\".  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We would like to thank our colleagues [PERSON] and [PERSON] for their contributions to data collection, experimental procedures, and result analysis.  
All authors were employed by the company State Grid Energy Research Institute Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 9: Trends in power generation capacity under different hydrogen energy technology development scenarios.
## 2 Photonic Microwave Distance Interferometer
### Measurement Repeatability  
The measurement repeatability was evaluated at a fixed target distance of about 1 m. Figure 2a shows a typical time-trace of distance measurement with an update rate of 10 Hz (blue) and 0.1 Hz (yellow), respectively. Over a time period of 1000 s, the measured distance fluctuates within a few tens of micrometers as clearly seen in the measurement histogram, appearing to be a Gaussian shape. For quantitative evaluation of the measurement repeatability, the Allan deviation of the time-trace measurement was calculated as shown in Figure 2b, from which the measurement repeatability is estimated to be 9.5 and 2.5 \(\upmu\)m at an averaging of 0.1 s and 10 s, respectively. It is important to note that the best measurement repeatability achievable near 10 s averaging is practically limited by the detection resolution of the used phasemeter of lock-in amplifier type, not by the photonic microwave signal stability of the mode-locked laser source.
## 2 Materials and Methods
### Instrumental Section  
A Perkin Elmer L20 spectrophotometer UV-Vis (range 180 nm-1100 nm) was utilized for absorption spectra. Current-voltage curves were obtained with a digital Keithley 236 multimeter connected to a PC and controlled by a homemade program. A LOT-Oriel solar simulator (Model LS0100-1000, 300 W Xe-Arc lamp, powered by LSN251 power supply equipped with AM 1.5 filter, 100 mWcm\({}^{-2}\)) provided simulated sunlight irradiation. A Si-based pyranometer was used to calibrate incident irradiance. Incident photon-to-current conversion efficiency (IPCE or \(\upeta\)) and relative photo-action spectra of sealed DSCs were measured by an IPCE station consisting of a 150 xenon light source (model ASB-XE, Spectral Products, Putnam, USA), a monochromator (model CM110, Spectral Products, Putnam, CT, USA) equipped with a slit set, a Si-calibrated detector (model 818-UV, Newport, Irvine, CA, USA), a picoammeter (model 6487, Keithley, Farnell\({}^{\upotimes}\), Milan, Italy), and IPCE Solarena Software. TiO\({}_{2}\) layers' thickness were measured by using a DektakXT profilometer (Bruker, Arcore, Italy) equipped with a diamond-tipped stylus (radius of 2 \(\upmu\)m) and selecting a vertical scan range of 524 \(\upmu\)m with 8.0 nm resolution, a programmed scan length of 6000 \(\upmu\)m, and a stylus force of 1 mg.
# Deep Learning Logging Sedimentary Microfacies via Improved U-Net
## 2 Materials and Methods
### Overall Implementation Process
#### 2.3.2 Data Enhancement  
An important feature of using deep learning algorithms to train neural networks is the need for a large number of samples. However, logging data are difficult to obtain and the sample size is small. In the traditional image segmentation process, data enhancement methods such as rotation, translation, scaling, random occlusion, horizontal flip and noise perturbation are commonly utilized to increase the number of samples, so that the constructed network can learn features more fully [23]. Allowing for the timing and vector feature of well logging curves, rotating, translating, flipping and other transformations on the well logging curve images will destroy the meaning of the sedimentary relationship, so traditional data enhancement methods are not suitable for well logging data.  
At the same time, due to the complexity and particularity of the formation of geological bodies, the identification of sedimentary microfacies using the shape of well logging curves must satisfy scale invariance. If the length and amplitude of well logging curves are different, but the overall shape is similar, they still represent the same sedimentary microfacies. Therefore, a data enhancement method of curve stretching transformation is proposed in this paper to make full use of existing logging data and increase the size of training samples. This method is principally used to stretch the curve segment representing a certain depositional environment in the entire logging curve, reflecting the variation characteristics of sediments with different thicknesses formed in the same depositional environment. Stretch transformation inserts the average value of two adjacent points to stretch the length of the curve. Aiming at the existing well logging curves, one curve segment belonging to the same type is selected for stretching with the permutation and combination method under the constraints of sedimentary laws, so as to construct new samples. On analyzing the sample data, the maximum thickness of the three sedimentary microfacies of the submerged bifurcated channel, river channel margin and river channel bend in this area are 104.7 m, 108.2 m and 124.4 m, respectively. The thickness will not exceed the maximum value so as to avoid violating the sedimentary law. The well logging curve contains many classes, so the proposed method can significantly augment the set of samples, which can make up for the insufficient number of samples of the well logging data in deep learning.