esa-sceva/satcom-chunk-collection huggingface dataset:
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 2 Data  
### Outline of the Mine and Microseismic Monitoring System  
The experimental data were obtained from Fankou Lead-Zinc Mine (see Figure 1) in Shaoguan, Guangdong Province, China, using the Institute of Mine Seismology (IMS) system, which was installed in 2016. Two mining methods, the large-diameter longhole (LDL) mining method and the upward horizontal cut and fill stoping method, are used to extract the ore underground. The deepest mining depth is close to 900 m underground. The IMS system contains 6 triaxial geophones and 26 uniaxial geophones (see Figure 2), which are distributed at five levels of \(-280\), \(-360\), \(-455\), \(-550\), and \(-650\) m. Both triaxial and uniaxial geophones hold a natural frequency of 14 Hz, and their frequency response is 9 to 2000 Hz.  
Figure 1: Fankou Lead-Zinc Mine. (**a**) Mine’s position in China; (**b**) a view of the mine; (**c**) mine’s position in Guangdong province.  
The continuous voltage signal is recorded by the geophone and then transmitted to the eight-channel network analogue-to-digital converter (NetADC) via the customized eight-core signal cable for analog-to-digital conversion--that is, the analog signal is sampled by NetADC at a 6000 Hz sampling frequency to be converted into a digital signal. Then, the network seismological processor (NetSP) performs noise reduction and amplification processing on digital signals. After that, the signal data from each NetSP are transmitted to the data exchange center through the network communication cable, and are modulated into an optical signal by the fiber converter. Finally, the signal data are transmitted to the microseismic monitoring server on the ground through the optical fiber, so as to realize event location, statistics, analysis of rock mass stability, etc.
###### Abstract  
In this work, we propose to use four-level pulse amplitude modulation (PAM4) and multi-core fibers (MCFs) to support very high capacity datacenter interconnect (DCI) links. The limitations imposed by inter-core crosstalk (ICXT) on the performance of \(112\) Gb/s up to \(80\) km-long optically amplified PAM4 inter-DCI links with intensity-modulation and direct-detection and full chromatic dispersion compensation in the optical domain are analyzed through numerical simulation for high and low skew-symbol rate product (SSRP). With only one interfering core, we show that those PAM4 inter-DCI links achieve an outage probability (OP) of \(10^{-4}\) with a maximum ICXT level of \(-13.9\) dB for high SSRP and require an ICXT level reduction of about \(8.1\) dB to achieve the same OP for low SSRP. Due to using full dispersion compensation, for an OP of \(10^{-4}\), the maximum acceptable ICXT level shows only a \(1.4\) dB variation with the MCF length increase from \(10\) km to \(80\) km. When considering the ICXT induced by several interfering cores, the maximum ICXT level per interfering core for an OP of \(10^{-4}\) decreases around \(3\) dB when doubling the number of interfering cores. This conclusion holds for high and low SSRP regimes. For two interfering cores, we show that a single interfering core with low SSRP is enough to induce a severe reduction of the maximum acceptable ICXT level.  
bit error rate; inter-core crosstalk; inter-datacenter connections; multi-core fiber; outage probability; four-level pulse amplitude modulation. +
Footnote †: journal: _Photonics_  
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Footnote 0: Copyright** © 2021 by the authors. Liconsre MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/40/](https://creativecommons.org/licenses/by/40/)]([https://creativecommons.org/licenses/by/40/](https://creativecommons.org/licenses/by/40/))).
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Security Challenges in UIoT
#### 4.2.3 Methods to Prevent Node Compromise Attacks  
To defend against node compromise attacks in UIoT networks, a mechanism such as a high-level hardware protection scheme, trustworthiness, data management and configuration management should be adapted for UIoT networks.
## 3 Ground Motion Propagation of Nonlinear Soil
### Fitting Parameters and Implementation of Davidenkov Model in ABAQUS
#### 3.1.2 Justification of the Davidenkov Model in Ground Motion Propagation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Record**} & \multicolumn{3}{c}{**Peak Ground Acceleration Response (g)**} & \multicolumn{3}{c}{**Peak Ground Relative Displacement Response (m)**} \\ \cline{2-7}  & **Davidenkov Model** & **Linear Method** & **Relative Difference** & **Davidenkov Model** & **Equivalent Linear Method** & **Relative Difference** \\ \hline El centro & 0.083 & 0.095 & 14.46\% & 0.039 & 0.044 & 12.82\% \\ \hline Michoacan, Mexico VILE & 0.217 & 0.263 & 21.20\% & 0.092 & 0.111 & 20.65\% \\ \hline Chi-Chi, Taiwan & 0.127 & 0.148 & 16.54\% & 0.059 & 0.067 & 13.56\% \\ \hline Kocaeli, Turkey & 0.168 & 0.216 & 28.57\% & 0.421 & 0.544 & 29.22\% \\ \hline Iwate, Japan & 0.042 & 0.045 & 7.14\% & 0.0041 & 0.0044 & 7.31\% \\ AKTH05 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the Davidenkov model and equivalent linear method results. Figure 8: TKCH 080 acceleration time history records: (**a**) bedrock; (**b**) ground surface.
# An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm
## 4 Simulation and Analysis of Results
### Optimization of the Mean and Standard Deviation Weighting Factors  
According to Equation (1), \(\lambda\) is set to 0.2, and according to Equation (12), the impact of the mean and standard deviation weighting factors is given by \(\alpha\) = 0.25, 0.5, 0.75; \(\beta\) = 0.25, 0.5, 0.75 respectively.  
Thus, results are shown in Figure 5, where the x-coordinate is the proportion of the anchor nodes, and the y-coordinate is the node positioning error. Here, the proportion of anchor nodes was generally between 20% and 30% in the actual network deployment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration Information** \\ \hline Location area & 100 m \(\times\) 100 m \\ Number of all nodes & 100 \\ Transmission model & DOI \\ Signaling irregularity & 0.005 \\ Send energy \(P_{T}\) & 0 dBm \\ Reference distance \(d_{0}\) & 1 m \\ Signal strength \(PL(d_{0})\) & 55 dBm \\ Scale factor \(\eta\) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Default wireless sensor network (WSN) parameters in the algorithm simulation.  
Figure 5: Positioning error curves of variable weighting factors.  
From Figure 5, it can be seen that the node positioning error decreased as the proportion of anchor nodes increased. The minimum positioning error was obtained when the weighting factors were \(\alpha=0.5\), \(\beta=0.5\). Therefore, these weighting factors were adopted in the following simulations.
## 5 Experimental Results and Discussions
### Evaluation Measures
#### 5.2.6 Energy Consumption  
The amount of energy consumed in each number of rounds by the sensor node is termed as energy consumption. Equation (33) represents the standard deviation of energy consumption:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Techniques** & **Parameters** & **Ranges** \\ \hline \multirow{6}{*}{Ebola Optimization Search Algorithm} & Population size & 100 \\ \cline{2-3}  & Total number of iterations & 50 \\ \cline{2-3}  & Contact rate of infectious individuals & 0.1 \\ \cline{2-3}  & Hospitalization rate & \\ \cline{2-3}  & Recovery rate of human individuals & \([0,1]\) \\ \hline \multirow{6}{*}{Criminal Search Optimization Algorithm} & Population size & 50 \\ \cline{2-3}  & Maximum number of sub investigators & 25 \\ \cline{2-3}  & No. of informers & 15 \\ \cline{2-3}  & Total no. of iteration & 100 \\ \cline{1-1} \cline{2-3}  & Maximum limit of random number & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter description for proposed method.  
\[\varphi_{cc}=\frac{1}{q}\sqrt{\sum_{z=1}^{q}{(\textit{Average }\lambda_{\varepsilon}-J_{ consumed}(z))^{2}}} \tag{33}\]  
From the Equation (33), the number of alive nodes is represented as \(q\), the average residual energy is _Average_\(\lambda_{\varepsilon}\), energy consumed by \(zth\) sensor is denoted as \(J_{consumed}\).
## 2 Experimental Section
### Characterization and Measurements  
The charge/discharge performance, C-rate discharge capability, and cycle life performance of the cells under study and the reference cell with Celgard separator were characterized. For this purpose, a battery testing system (WBCS 3000, WonATech, Seoul, Korea) was used. The charge/discharge test was conducted at cutoff voltages between 3.5 and 4.9 V, and the rate capability was assessed at rates which ranged from 0.1 to 2 C.  
The morphology of the obtained porous film was examined using field-emission and high-resolution scanning electron microscopy (FESEM, HRSEM) (JEDL, JSM-7000F, Akishima, Tokyo, Japan). The surface features were quantitatively determined by measuring the SEM images using the Image J program. SEM images obtained under magnification of 3000\(\times\) were selected because a sufficient number of pores for the characterization of the pore sizes and pore number densities are visible in the images. The wettability of the sample was characterized by measuring the static water contact angle using a drop shape analyzer (Kruss DSA 100, Hamburg, Germany). The crystallinity of the porous film was characterized by means of X-ray diffraction (XRD, Cu K- alpha, \(\lambda\) = 1.5406 A; Bruker AXS, Germany). The chemical and molecular structures of the composite films were characterized using Fourier transform infrared spectroscopy (FTIR, Thermo Scientific, Waltham, MA, USA).  
To measure the electrolyte uptake and electrolyte retention, a piece (2 \(\times\) 2 cm\({}^{2}\)) of the dry membrane was soaked in a lithium-ion electrolyte for 2 h at room temperature. Aftersoaking, the membrane was taken out of the liquid electrolyte and then was gently blotted with a paper tissue. The electrolyte uptake was calculated using the following equation:  
\[\theta_{u}\ =\ \ \frac{W_{1}-\ W_{0}}{W_{0}} \tag{1}\]  
The electrolyte retention was calculated as follows:  
\[\theta_{r}\ =\ \ \frac{W_{1}-\ W_{0}}{W_{1}} \tag{2}\]  
where \(W_{0}\) is the weight of the membrane before absorption of electrolyte and \(W_{1}\) is the weight of the membrane after absorption of electrolyte.  
The mechanical properties of the membranes were characterized using a universal testing machine (Instron, Norwood, MA, USA). The test-samples were prepared with a width of 20 mm and a length of 40 mm. The tensile rate was found to be 5 mm min\({}^{-1}\) and the gauge length was 20 mm. The tensile strength, \(T\), was calculated as follows,  
\[T\ =\ \ \frac{P}{S} \tag{3}\]  
where \(P\) is the tensile force applied to the sample at break and \(S\) is the cross-section area of the sample.
## 3 Datasets and Methodology
### Methods
#### 3.2.6 Geographic Detector  
The geographic detector is a group of statistical approaches. This detector is used for detecting the spatial differences and uncovering the driving factors and influencing mechanisms behind them [59; 60]. This study examined the explanatory power of individual extreme climate factors and combinations of two extreme climate factors on the EQI using factor detectors and interaction detectors.  
Factor detector: The factor detector primarily quantitatively measures the spatial differentiation of each natural factor's impact on the EQI of the Tarim Basin by evaluating the factor's level of explanatory power. For the explanatory power q value, the formula is expressed below:  
\[\text{q}=1-\frac{\sum_{\text{h}=1}^{\text{L}}\text{N}_{\text{h}}\text{o}_{ \text{h}}{}^{2}}{\text{N}\sigma^{2}} \tag{4}\]  
In Equation (4), h represents the layer of the influence factor. N\({}_{\text{h}}\) refers to the sample size of the factor in layer h. Additionally, N refers to the sample size of the factor in the entire study area. Moreover, h refers to the variance of the factor in layer h; \(\text{\delta}_{\text{h}}{}^{2}\) refers to the variance of the factor in the EQI of the research area. The q values span from 0 to 1. A higher q value signifies a greater explanatory capacity of the factor in relation to the EQI space distribution. q = 0 indicates that the factor is not correlated with the EQI. Conversely, when q = 1, this suggests that the factor is capable of completely explaining the spatial pattern of the vegetation ecological quality.  
Interaction detector: The interaction detector is a tool utilized for the sake of determining if the interaction between two influence factors will enhance or weaken their explanatory EQI capability. It evaluates whether the impacts of these elements on the EQI are independent of one another. In this study, interactive detection was used to reflect the explanatory capability of the interaction between various extreme climate indices to the EQI. The criteria for making these judgments are listed in Table 2.
# Shifting from Conventional to Organic Filter Media in Wastewater Biofiltration Treatment: A Review
## 4 Challenges of Using Organic Filter Media  
Based on our review, organic materials show promising characteristics as biofilter media in terms of treatment performance; however, not all organic materials are suitable for biofiltration. [PERSON] et al. [61] commented that plant-based wastes such as peanut shells contain smooth surfaces and smaller specific surface areas, which are relatively difficult for the attachment of biofilm, while organic materials such as rice straws tend to release carbon at a faster rate, causing the prolongation of the denitrification process.  
A study conducted by [PERSON] et al. [99] stated that although banana stem media can filter out the excess solid particles from domestic wastewater, the inherent organic properties cause the development of bacteria and the occurrence of natural decay with the increase of exposure time to the water. The degradation rate of the organic media limits the operational lifespan and the sustainability of the organic biofilter. [PERSON] et al. [30] discovered that the expected lifetimes of wood chips and wheat straws organic media are 1.2 and 0.5 years, respectively, which are much lower compared to the conventional filter media. Extra steps and additional costs are needed in replacing the filter media once they reach their operating lifespan.  
Other than that, the degradation of organic materials can cause the leaching of humic acids which increases the concentration of certain contaminants in the treated water such as color, phosphorus, and COD [93]. [PERSON] and [PERSON] [92] witnessed the clogging problem and the existence of flies surrounding the organic filters, which are unfavorable in the treatment process. Moreover, there are also relatively few organic filter materials that have been investigated and accessed, which restrict their full-scale applications in many areas and regions such as semi-arid and arid zones [29]. Hence, it can be concluded that the lifespan, decay, and degradation properties of organic materials may also influence the performance of biofiltration. Therefore, future studies should focus on investigating different types of organic filter media that show good and long-term operation performances.
# A Sensor Drift Compensation Method with a Masked Autoencoder Module
We call the prompt a calibration feature vector, which functions as a continuous prompt generated by a feature embedding module called a calibration feature encoder (CFE) and it is concatenated to input sensor data. In a drifted environment, transfer samples are obtained by collecting sensor signals and subsequently determining the concentration of gases in that environment. The CFE is a masked-autoencoder-based module which is trained with past transfer samples to produce calibration feature vectors for future transfer samples. In contrast to fine-tuning methods, which tune a neural network using new transfer samples from the drifted environment, our method trains a neural network only once using past sensor samples with prompts produced by the CFE. The neural network learns to estimate gas concentrations with sensor data and drift information, which makes it robust to future sensor drift without fine-tuning while simultaneously improving the performance. Our method is capable of learning nonlinear drift behavior, does not require neural network fine-tuning, and shows superior performance compared to other models, including a neural network with fine-tuning. The main contributions of this paper are as follows:  
* We propose a training method for deep learning models that estimate gas concentrations by concatenating a prompt which contains sensor drift information with input sensor data. By training the model with the prompt, the model is robust to sensor drift without fine-tuning. * We utilize a masked-autoencoder-based CFE for the effective feature extraction of sensor drift information. The experimental results demonstrate that the deep learn ing model using a CFE outperforms the control group, indicating that the CFE can effectively generate prompts containing sensor drift information. The remainder of this paper is as follows. Section 2 explains the experimental data, overall concept, and the proposed methodology. The experimental settings and results are discussed in Section 3. Finally, Section 4 provides the conclusions and describes future works.
# Current Status of HbA1c Biosensors
## 3 Analysis of Samples from Human Blood
Given the advantages of minimal sample volume, rapid analysis time and wide accessibility to diagnosis, the global POC diagnosis market is expected to reach US$36.93 billion by 2021 ([[http://www.marketsandmarkets.com/PressReleases/point-of-care-diagnostic.asp](http://www.marketsandmarkets.com/PressReleases/point-of-care-diagnostic.asp)]([http://www.marketsandmarkets.com/PressReleases/point-of-care-diagnostic.asp](http://www.marketsandmarkets.com/PressReleases/point-of-care-diagnostic.asp))). More than a dozen commercial HbA1c POC devices are currently used in clinics, of which the principal detection method is either based on boronate affinity separation or immunoassay. Among most cited devices, In2 itTM (Bio-Rad, Hercules, CA, USA), AlemenTM (Alere Technologies AS, Oslo, Norway), Nycocard (Alere Technologies AS) and Clover A1c (Inforpia, Kyunggi, Korea) are built based on the affinity method, while DCA Vantage (Siemens Medical Solutions Diagnostics, Tarrytown, NY, USA)and A1 CNow series (Bayer HealthCare, Sunnyvale, CA, USA) are based on the immunoassay method. In a very recent review, [PERSON] and coworker independently reviewed 1739 records published before June 2015 in several databases (Medline, Embase and Web of Science) and carried out a meta-analysis on sixty-one studies to compare the accuracy and precision of eleven HbA1c POC devices [75]. The analysis results show that the majority of devices (9 out of 11) have a negative mean bias compared to laboratory comparator methods, as well as a large variability in bias within devices. The implication of using HbA1c POC testing results on medical treatment decision-making and patient outcomes needs to be evaluated further.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 3 Method
### Data Augmentation
#### 3.2.2 Mimicking a Certain Action That Was Executed by People of Different Sizes  
The advantage of skeleton-based HAR is that it can focus on the performance of human actions and ignoring human physical characteristics and background information. Furthermore, regardless of whether a human skeleton is estimated by RGB images or captured from depth cameras and the age or gender of a person, its structure is similar. Accordingly, we can scale up or down the human skeleton to produce samples of people of different sizes performing the same action. In other words, we can imitate a certain action executed by people of different sizes, which is a simple but effective method to generate skeleton samples. This method can be easily implemented by multiplying the original skeleton sequence by a suitable value. With reference to actual experience, we set the interval of this value to [0.5, 1.5].
# Modeling the Directional Clumping Index of Crop and Forest
## 2 Modeling and Evaluation
### Simulation Method of Directional Clumping Index
#### 2.2.2 Forest Scenario Settings  
Our goal was to directly count the gap fraction by traversing, so we must first construct a forest scenario. The main problems involve how to depict each tree and how to define the distribution pattern for multiple trees. The scenes can be extended to be infinite through juxtaposing identical scenes.  
The basic tree crown shape is modelled as a generalized ellipsoid as in the analytical forest model (Section 2.1.2). Then the crown shape could be defined by the major and minor axis lengths. The trunk was treated as a cylinder defined by height and radius at breast height. We defined similar trees in each simulation for efficiency, but this could be easily extended to various tree shapes by introducing random offsets. Then, we define the tree positions according to the different distribution patterns. If the trees are distributed randomly, the distribution follows [PERSON]'s law [8; 44; 45].  
However, trees are likely to cluster in different sized groups rather than truly distribute randomly. [PERSON] and [PERSON] [8] assumed the distribution of the groups and the trees in each group both followed [PERSON]'s law. This dual Poisson or Neyman distribution fits the actual situation better than the simple [PERSON]'s law [45; 46; 47]. The Neyman distribution PDF can be expressed as  
\[P_{N}(x;m_{1};m_{2})=e^{-m_{1}}\;\frac{m_{2}^{x}}{x!}\sum_{j=1}^{\infty}\frac {[m_{1}\;e^{-m_{2}}]^{j}}{j!}\;j^{j} \tag{14}\]  
where \(m_{2}=1\) implies that the tree distribution is close to, but not the same as, the random case.  
Canopy closure is directly linked to the gap fraction. Thus, the simulation was conducted using increasing \(m\) (Poisson distribution) and \(m_{1}\) (Neyman distribution), corresponding to increasing stand density (trees/m\({}^{2}\)).