esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 State Assessment Model  
Based on the trained model, abnormal input signals usually lead to abnormal outputs, which further leads to large prediction errors. Therefore, the change in the operation state of an electronic motor will lead to an increase in the prediction error of the normalbehavior model. It is of great significance to identify this mode in advance to prevent failures. In the literature on fault detection, the EWMA (exponentially weighted moving average) statistic [20] can be used to continuously monitor prediction errors and identify abnormal changes. Based on the weighted average of recent and historical observations, the EWMA statistic smooths uncontrollable noise. EWMA is very sensitive to the abnormal state transition, and it can detect abnormalities effectively. To facilitate later discussions, we will present how to construct the EWMA statistic as follows.  
First, the MAPE (mean absolute percentage error) and APE (or SDAPE) of the prediction model can be calculated to measure the prediction performance of the model, i.e.,  
\[APE=\frac{|y-\hat{y}|}{y}\times 100\%, \tag{9}\]  
\[MAPE=\frac{1}{n_{t}}\sum_{i=1}^{n_{t}}APE_{i},\quad SDAPE=\sqrt{\frac{1}{n_{t} -1}\sum_{i=1}^{n_{t}}\left(APE_{i}-MAPE\right)^{2}}, \tag{10}\]  
where \(y\) and \(\hat{y}\) denotes the ground truth and predicted values, respectively. And in Equation (10), \(n_{t}\) indicates the test set size.  
In order to find the upper and lower bounds of the EWMA statistic, we first define a statistic \(s_{t}\) as  
\[s_{t}=\varphi APE_{t}+(1-\varphi)s_{t-1}, \tag{11}\]  
in which \(t\) is the time index, \(\varphi\in(0,1)\) is a weight to reflect the contribution of historical APEs to \(s_{t}\). In general, \(s_{0}\) can be taken as the mean of all past APEs.  
Based on Equation (11), the mean and variance of \(s_{t}\) need to be computed, i.e.,  
\[\mu_{s_{t}}=\mu(APE),\sigma_{s_{t}}^{2}=\sigma^{2}(APE)\bigg{(}\frac{\varphi} {2-\varphi}\bigg{)}[1-(1-\varphi)^{2t}], \tag{12}\]  
where \(\mu(APE)\) and \(\sigma^{2}(APE)\) represent the mean and variance of APEs when the predictor variable of the wind turbine is in normal operation.  
Then, the upper and lower bounds of EWMA can be constructed as  
\[LCL(t)=\mu(APE)-L\sigma(APE)\sqrt{\frac{\varphi[1-(1-\varphi)^{2t}]}{2-\varphi }}, \tag{13}\]  
\[UCL(t)=\mu(APE)+L\sigma(APE)\sqrt{\frac{\varphi[1-(1-\varphi)^{2t}]}{2-\varphi }}, \tag{14}\]  
In which the default value of \(L\) is 3 [10]. For specific problems, the optimal \(L\) and \(\varphi\) can also be determined by grid search.  
Based on the regression prediction model, the real-time monitoring parameters can be predicted. Subsequently, the upper and lower bounds of the monitoring data for each component of the pitch motor can be calculated by the above-mentioned EWMA method. If the prediction error exceeds any one of the two boundaries, the early warning of faults will occur in status monitoring, so as to achieve the purpose of fault early warning.
## 1 Introduction
The scheduling of MG energy resources is optimized for minimizing the MG operation cost while guaranteeing a very low probability of critical load interruption during an event. We developed a nested chance-constrained programming model for dealing with load interruption risks from the uncertainty in electricity loads and power generation from solar PV and wind at pre-event times. The MG operator increases the possibility of an uninterrupted power supply to critical loads because the suggested operating strategy integrates the MG operational cost and conditions for various times when an outage can occur in the problem formulation. * Modeling the expected load shedding: The expected load shedding (ELS) is evaluated analytically. The ELS is discovered to be a nonlinear nonconvex function. However, we find that the expected cost of load shedding (ECLS) included in the cost function is a convex function if the forecasting errors of load and renewable generation follow a Gaussian distribution. We find that ECLS, which is included in the cost function, is a nonlinear convex function if the forecasting errors of load and renewable generation follow a Gaussian distribution. The ECLS is calculated by the multiplication of ELS and the probability of load shedding. The ECLS does not compromise the solvability of the optimization problem. However, we approximate ECLS to be a piecewise linear model for representing the problems as a mixed integer linear program (MILP). * Demonstrating the effectiveness of the proposed proactive microgrid management strategy: We conducted simulations using two cases--when the loads are (1) low and (2) high during event periods--to verify the advantages of using the proposed method to improve MG resilience. The proposed method can increase load survivability and reduce ECLS during the event periods by the proactive decision of the battery and generator operations during the normal mode compared to the operation without preparation. The remainder of this paper is organized as follows. Section 2 describes the MG system configuration and operations considered in this study. Sections 3 and 4 introduce the optimization problem formulation for realizing the proposed energy scheduling method and the numerical examples, respectively, verifying the effectiveness of the proposed method. Finally, Section 5 concludes the paper.
## 4 Grating Coupler
### Polarization-Dependent Loss of the Grating Coupler  
Polarization-dependent loss of the grating coupler (\(\eta_{\text{p}}\)\({}^{\prime}\)) can be calculated as follow:  
\[\eta_{\text{p}}\)\({}^{\prime}=\left|\eta_{\text{TE}}\)\({}^{\prime}-\eta_{\text{TM}}\)\({}^{\prime}\right| \tag{8}\]  
where \(\eta_{\text{TE}}\)\({}^{\prime}\) denotes the coupling efficiency between the SMF and grating coupler when TE mode is in the SMF, \(\eta_{\text{TM}}\)\({}^{\prime}\) denotes the coupling efficiency when TM mode is in the SMF. Coupling efficiency \(\eta_{\text{TE}}\)\({}^{\prime}\) and \(\eta_{\text{TM}}\)\({}^{\prime}\) are 0.35 and 0.05 respectively when parameters in the grating part are final values in Table 1, width \(b\) is 6 \(\upmu\)m, and length \(L_{\text{t}}\) is 300 \(\upmu\)m. Therefore, polarization-dependent loss of the grating coupler (\(\eta_{\text{p}}\)\({}^{\prime}\)) is up to 0.30.
## 5 Discussion
For example, in this research, we observed that the CNN-RF needs approximately 695.9 s to validate a prediction model by classification using a relatively small dataset (1920 samples), compared to the SVC-RBF, the best time execution model obtained, whose run time is only 1.7 s. With this evidence, an RBF approach allows for faster training even though these models do not always achieve good generalization and robustness in solving specific problems that could be scaled in magnitude and complexity. However, the scores obtained by the SVC-RBF model were compared to those of the CNN-RF model. The two models obtained comparable values for SPE (0.9713-0.9867) and SEN (0.9139-0.9603), but the training times were significantly different: 694.2 s for the CNN-RF model versus 1.7 s for the SVC-RBF model. Finally, it should be noted that the MLP model also obtained good efficiency performances, with an accuracy of 0.9156. Its execution time is quite fast, taking only 10.7 s for its evaluation. These experiments suggest that the SVC-RBF model can be used to evaluate and predict traffic accident risk levels quickly and effectively.
# A Unified Nonlinear Elastic Model for Rock Material
## 2 Common Nonlinear Elastic Models
### Nonlinear Elastic Models of Joints  
The typical stress-strain curve of the rock specimen under uniaxial stress is shown in Figure 1, which can be divided into four stages: (1) the compression of pores and fractures (section OA); (2) elastic deformation and the stable development of microfractures (section AC), where point B is the elastic limit; (3) the unstable developmental stage of the fractures (section CD); and (4) the stage after rock rupture (after point D). As shown in Figure 1, the deformation rate slows down with increasing stress in the compression stage (section OA), which is because the elastic modulus increases with decreasing rock specimen length. In order to distinguish between the nonlinear elastic and linear elastic deformation of the rock in Figure 1, a straight line (OB' parallel to AB) through the coordinate origin was drawn, and the difference between the curve OAB and the straight line OB' was the curve OA', as shown in Figure 2. The straight line OB' and the curve OA' represent the linear elastic and the nonlinear elastic deformation of the rock, respectively.  
As shown in Figure 2, both linear elastic deformation and nonlinear elastic deformation will occur when the strain is below point A. Nonlinear elastic deformation will terminate after reaching its maximum allowable deformation when the strain reaches point A, and only linear elastic deformation will occur when the strain exceeds it. The stress corresponding to point A is called the closing stress of the initial microcrack, which is also one of the important parameters of the rock.  
Figure 1: The stress-strain curve of a uniaxial compression test adapted from Corkum. Adapted from [21].  
Since a rock is not an ideal linear elastic material and is a heterogeneous material composed of solid skeletons with pores and fractures filling the space between them, [PERSON] [29; 30] divided the rock into hard and soft parts, which have linear elastic and nonlinear elastic deformation characteristics, respectively. The two-part [PERSON]'s model [29; 30] formula is as follows:  
\[\varepsilon=\gamma_{e}\frac{\sigma}{E_{e}}+\gamma_{t}\bigg{[}1-\exp\bigg{(} \frac{-\sigma}{E_{t}}\bigg{)}\bigg{]} \tag{7}\]  
where \(E_{e}\) and \(E_{t}\) are the elastic modulus of the hard and soft parts, respectively, and \(\gamma_{e}\) and \(\gamma_{t}\) are the proportions of the hard and soft parts of the rock, respectively. This model can be used for composite materials [36], such as material combined with coal and rock [37].  
If the deformation of the soft part to its initial length is \(\varepsilon_{t}\), the nonlinear elastic deformation of the soft part in the two-part [PERSON]'s model can be expressed as  
\[\sigma=E_{t}\ln\bigg{(}\frac{1}{1-\varepsilon_{t}}\bigg{)} \tag{8}\]
## 2 Result  
### Phylogenetic Analysis of _RrCaCAs_  
Utilizing Hmmsearch combined with BLAST search based on _Arabidopsis CaCA_ sequences, 22 _CaCA_ genes were identified in _R. roxburghii_. Phylogenetic analysis revealed that these genes can be classified into four families: nine belong to the CAX family (_Rrox6G00416820_, _Rrox6G00416880_, _Rrox2G00097160_, _Rrox1G00011530_, _Rrox6G00416850_, _Rrox6G00416870_, _Rrox2G00097180_, _Rrox2G00093860_, _Rrox1G00006540_), six to the NCL family (_Rrox1G00048700_, _Rrox1G00048690_, _Rrox1G00048730_, _Rrox2G00115170_, _Rrox1G00048660_, _Rrox1G00066160_), five to the CCX family (_Rrox6G00389590_, _Rrox6G00389620_, _Rrox2G00146460_, _Rrox6G00425230_, _Rrox165G00437120_), and two to the MHX family (_Rrox7G00173680_, _Rrox7G00173670_). Based on their homologous genes in _Arabidopsis_, we further renamed these genes (Figure 1, Table S1).  
The lengths of the KrCaA protein range from 143 (RxCAX3c) to 655 (RxCCX4) amino acid residues. The molecular weight (MW) of these proteins ranges from 15,626.26 Da (RxCAX3c) to 71,192.28 Da (RxCCX4). The predicted isoelectric points (pJ) of these proteins vary from 4.72 (RxCAX1b) to 9.43 (RxCCX11a). All proteins were acidic except RxCCX11a, and the instability index ranged from 26.00 (RxCAX3c) to 61.55 (RxCCX11a), with five proteins classified as unstable (instability index > 40) and the remainder considered stable. According to the grand average of hydropathy (GRAVY), RxCCX11a and RrCAX1b were hydrophobic, while the others were hydrophilic. RxCCX11b had the highest hydrophilicity index (0.81), and RxCCX11a had the lowest (\(-\)0.699). Subcellular localization predictions indicated that, except for RrCAX1b, which is predicted to be extracellular, and RxCCX11a, predicted to be nuclear, the remaining proteins are localized to the plasma membrane (Table S1).  
Figure 1: Phylogenetic tree of _CaCA_ gene family. Constructed using the maximum likelihood (ML) method with 1000 bootstrap replications, the tree classifies 22 _CaCA_ genes from _R. rxburghii_, 12 from _A. thaliana_, and 19 from _V. duclouxiii_ into 4 subfamilies.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 2 Background
### Brands and Market in China
#### 2.2.1 HZBike  
To deal with the severe air pollution problem, Chinese government has introduced into the public bicycle programs in several large cities, for example, Beijing, Wuhan and Hangzhou. Among these programs, Hangzhou public bicycle (HZBike) is most successful, which won the 2017 Ashden Awards because of creating a sustainable transport system [40]. HZBike is a bicycle-sharing program by Hangzhou government, which uses smart-card and RFID for check-in and check-out. HZBike is still improving their products to meet the users' requirements. There are some highlights of HZBike model, which are summarized as followings in [40].  
* Bicycle docks distribute scientifically.
* The smart card can be used across all public transport systems, including taxis and public bicycle.
* There is an integrated and efficient transfer system in Hangzhou city.
* The cost is very low in using.
* It is a government-led business model.
* A complete real-time monitoring and arrangement system is used.
# Multiband and Lossless Compression of Hyperspectral Images
###### Abstract  
Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (_i.e._, hyperspectral sensors, _etc._). We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images.  
hyperspectral images; lossless compression; low complexity; 3-D data +
Footnote †: journal:
# Improving VulRepair's Perfect Prediction by Leveraging the LION Optimizer
## 3 Methodology  
The purpose of this study was to examine VulRepair and improve the model. We gained insights by reviewing AlBugHunter [1], LineVul [4], and VulRepair [7]. In the VulRepair project, there are threats to internal validity. VulRepair outperforms other models such as VRepair and CodeBERT. Although it outperforms the leading models, the goal was not to ensure the use of optimal hyperparameters. In fact, [PERSON] et al. provided hyperparameter settings for replication, which can be used to extend their study. Inaddition, [PERSON] et al. pointed out a limitation of the T5 architecture, where the vulnerable function is limited to 512 tokens. So, VulRepair is more accurate when vulnerable functions have fewer than 500 tokens. The performance also decreases with repairs that require more than 10 tokens. More research is needed to improve these limitations [7].  
After understanding the areas of VulRepair that needed improvement, we downloaded VulRepair from GitHub [22]. This provided the code to replicate [PERSON]'s experiment [7]. We downloaded the code to Google Colab. We upgraded to Colab Pro+, which facilitates long training and testing sessions. Google Drive is attached to Colab. We increased the memory to 100 GB to ensure that we had ample memory to hold the VulRepair files. In addition, we used the Google Chrome browser. When using Colab, we used Google Colab V100 GPU, a high-performance graphics processing unit renowned for its suitability for deep learning and its ability to handle tasks that require substantial memory and processing power [28].  
When running the models, we looked for deprecated warnings and fixed them. For example, AdamW is deprecated. We looked up fixes from Hugging Face and Stack Overflow. They essentially recommended replacing AdamW with PyTorch AdamW, as explained later. We replicated the experiments by [PERSON] et al. and gained insight into their models [7]. Next, we looked at the optimizer. Although AdamW is a popular optimizer, we searched for recently developed optimizers suitable for the VulRepair platform. After researching the LION optimizer, we determined that it would be a good fit for the T5 models. We then fine-tuned the hyperparameters by selecting values around their default settings. Due to time and cost constraints, we did not exhaustively test all hyperparameter values. We tested configurations that best minimized the loss function, thus optimizing the model's performance. Our experiments allowed us to improve VulRepair.
## 2 Materials and Methods  
### Field Physicochemical Measurement  
Well-inventories were carried out on a total number of 230 shallow hand-dug wells, shallow tube wells, and boreholes from the eastern Dahomey Basin (EDB), 96 in the wet season, and 134 in the dry season between May 2017 and April 2018. The physicochemical parameters were measured in the field using a Teckoplus 6-in-1 pen-type water quality tester Model 99720 pH/conductivity meter capable of measuring total dissolved solids (TDS), salinity, temperature, and redox potential (Eh/ORP). The depth of the wells and static water level was measured with the aid of a water depth meter while the coordinates of each sampled well were recorded using Global Positioning System (GPS). The 230 groundwater samples were collected in three separate sets of 50 ml polypropylene tubes labelled A, B, and IS. Samples labelled A were acidified to a pH < 2 after collection with 0.4 ml of concentrated nitric acid (HNO\({}_{3}\)). Samples B and IS were filtered with a 0.45 \(\upmu\)m filter and preserved in an ice-packed cooler to keep the samples' temperature below 4 \({}^{\circ}\)C before being transported to the laboratory for further analysis.
# 3D Printed High Gain Complementary Dipole/Slot Antenna Array
## 3 Measured and Simulated Results  
To measure both the 3D printed and aluminum version of the 16 \(\times\) 16 complementary dipole antenna arrays, the rectangular waveguide input port at the bottom layer was connected to a Millitech Inc. WCA-28-QSF00A waveguide-to-SubMiniature version A (SMA) adaptor. The reflection coefficient was measured using an Agilent Technologies E8363C network analyzer. Figure 4a shows the measured  
Figure 3: Prototype of the 3D view of 16 \(\times\) 16 complementary dipole antenna array made of pure aluminum.  
Figure 2: Prototype of the 3D printed 16 \(\times\) 16 complementary dipole antenna array: (**a**) 3D view; (**b**) top view of first layer; (**c**) bottom view of first layer; (**d**) top view of second layer; (**e**) bottom view of second layer; (**f**) top view of third layer; (**g**) bottom view of third layer.  
and simulated reflection coefficients of the 3D printed and aluminum arrays. For reflection coefficient \(\leq\)\(-\)15 dB, the measured impedance bandwidths of the 3D printed and aluminum arrays are 14.8% from 25.74 GHz to 29.85 GHz and 14.7% from 25.74 GHz to 29.81 GHz, respectively. In contrast, the simulated impedance bandwidth is 14.6% from 25.74 GHz to 29.80 GHz. The lower operating frequency in all three cases is 25.74 GHz and the maximum error in the upper operating frequency is only 0.17%.  
The gain and radiation patterns are measured with a near-field antenna measurement system by Nearfield Systems Inc., (NSI). The measured gains for the 3D printed and aluminum arrays and the simulated gains are illustrated in Figure 4b. A high and stable gain of more than 31 dBi across the operating band is obtained for both the measurements and the simulation. The measured gain of the 3D printed version is only 0.4 dB lower than that of the aluminum version.  
Figure 4c-h shows the measured radiation patterns of the 3D printed and aluminum arrays, as well as the simulated patterns at 25.74, 27.80, and 29.85 GHz, where the complementary dipole antenna array operates in a broadside symmetrical radiation in the phi = 0\({}^{\circ}\) and 90\({}^{\circ}\) planes. The measured first sidelobe levels of the 3D printed and aluminum arrays at 27.80 GHz are 13 dB at 0\({}^{\circ}\) and 90\({}^{\circ}\) planes. They are similar to the simulated levels at 27.80 GHz of 13.8 dB at 0\({}^{\circ}\) and 90\({}^{\circ}\) planes. The measured 3 dB beamwidths of the 3D printed and aluminum arrays in the 0\({}^{\circ}\) and 90\({}^{\circ}\) planes are 4.2\({}^{\circ}\) and 4\({}^{\circ}\) at 27.80 GHz, respectively. This is because the position of the dipole and the slot are placed at an orthogonal orientation and the electric dipole and magnetic dipoles are produced. From the above performances of the proposed antenna array, it demonstrates excellent radiation characteristics, including wideband, high gain, low back radiation, low cross-polarization levels, high front-to-back ratio, and symmetrical radiation patterns in the phi = 0\({}^{\circ}\) and 90\({}^{\circ}\) planes.  
Figure 4: _Cont._