esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Confounding Effect Model for Software Defect Prediction
## 3 Confounding Effect Model for Software Defect Prediction
### Predictive Model with Controlling Confounder  
We use \(Y\) to represent the software defect, and \(X\) represents the existing software metrics. Y has two values which are 0 and 1. \(Y=1\) means the relevant software model has one defect or more than one defect, and \(Y=0\) stands for the relevant software model with non-defect. When we use \(X\) to train the model for predicting \(Y\). The prediction model parameters are distorted due to the confounding effect. In order to revise the model parameters, we train the prediction model under controlling the confounder. This way, the model parameters can more accurately and appropriately reflect the relationship between software metrics and software defects. As the formula shows,  
\[E(Y|X,C^{\prime})=\frac{1}{1+e^{-(b+a_{1}X_{1}+\cdots+a_{n}X_{n}+a_{n+1}C^{ \prime})}} \tag{9}\]  
where \(C^{\prime}\) is the transformed value of the confounding noise C. For instances that contain at least one defect, we convert \(Q_{1}\) to \(dQ_{1}\), and for instances that do not contain defects, we convert it to \(nQ_{1}\). Parameters \(d\), \(n\) are constants. The grid search method is used to determine the values of \(d\) and \(n\). Under the grid search, suitable values of \(d\), \(n\) are determined, which helps the prediction model achieve high performance. Based on the above, we propose the Algorithm 1 presents the pseudocode.  
```
0: Training data (\(X\) represents software metrics; \(Y\) represents defect-proneness; \(C\) and \(C^{\prime}\) present confounding noise)
0: Prediction model
1: Use logistic regression to fit \(Y\) by \(X\), obtain \(Y:=f(X)\)
2: Predict Y by \(X\), obtain \(E[Y|X]:=f(X)\)
3: Calculate C based on half-sibling regression, obtain \(C:=Y-E[Y|X]\)
4: Transform C to \(C^{\prime}\). If \(Y\)==1, \(C^{\prime}\) equals to \(d^{\bullet}\)C; if \(Y\)==0, \(C^{\prime}\) equals to \(n^{\bullet}\)C
5: Determine values of d and n using the grid search method, which could get \(f(X,C^{\prime})\) fitting \(Y\) well.
6: Establish the prediction model by logistic regression, obtain \(Y:=f(X,C^{\prime})\)
```  
**Algorithm 1** Predictive model.
###### Abstract  
Recently, as renewable and distributed power sources boost, many such resources are integrated into the smart grid as a clean energy input. However, since the generation of renewable energy is intermittent and unstable, the smart grid needs to regulate the load to maintain stability after integrating the renewable energy source. At the same time, with the development of cloud computing, large-scale datacenters are becoming potentially controllable loads for the smart grid due to their high energy consumption. In this paper, we propose an appropriate approach to dynamically adjust the datacenter load to balance the unstable renewable energy input into the grid. This could meet the demand response requirements by taking advantage of the variable power consumption of datacenters. We have examined the scenarios of one or more datacenters being integrated into the grid and adopted a stochastic algorithm to solve the problem we established. The experimental results illustrated that the dynamic load management of multiple datacenters could help the smart grid to reduce losses and thus save operational costs. Besides, we also analyzed the impact of the flexibility and the delay of datacenter actions, which could be applied to more general scenarios in realistic environments. Furthermore, considering the impact of the action delay, we employed a forecasting method to predict renewable energy generation in advance to eliminate the extra losses brought by the delay as much as possible. By predicting solar power generation, the improved results showed that the proposed method was effective and feasible under both sunny and cloudy/rainy/snowy weather conditions.  
**Keywords:** renewable energy; load regulation; datacenter; smart grid; demand response +
Footnote †: journal: Applied Sciences
# Railway Track Stress-Strain Analysis Using High-Precision Accelerometers
## 1 Introduction  
One of the challenging problems of railway infrastructure monitoring is unmanned track defects diagnostics [1]. At present, the monitoring and control of deformations of the railway track is carried out mainly with the help of a track-testing car, a mobile complex for monitoring the states of technical objects of the railway infrastructure [2, 3]. The modern laboratory track-testing car is a fully automated diagnostic complex that can be equipped with a variety of systems in accordance with the customer's requirements.  
Despite the many advantages of their use in practice, the implementation of constant monitoring of critical important sections of the railway track remains limited due to increased train traffic. Hence, additional removable measuring instruments are used, equipped with specialized sensor equipment [4, 5]. Promising research in this area is related to the implementation of the concept of the Internet of Things [6, 7, 8].  
A straightforward form of nondestructive railway track testing is carried out using strain gage transducers [9, 10, 11]. When applied in practice, it is efficient to use high-precision accelerometers for this purpose. However, this approach requires calculation ofthe stress-strain state based on linear displacements. In order to aid the development of this methodology, we propose a new solution based on railway track stress-strain analysis in this paper.  
We developed a 3D model of a railway track section, which enabled determination of the impacts of rolling stock on a section of a railway track. Digital modeling of the deformation of the railway track makes it possible to estimate the final mechanical stresses arising in the process of interaction with the rolling stock with a high degree of reliability. Using the input data from digital accelerometers makes it possible to determine the expected movement of the rail and the structural stress in the elements of the railway track. More details are given below.
## 4 Performance Evaluation  
In this section, we describe the performance evaluation results of the transmission success ratio and resource occupancy ratio. We compare scenarios where the mobile device moves between networks using the graph construction and resource allocation schemes from [15, 21], and the proposed schemes. First, in Section 4.1, we describe the performance evaluation environment and factors. In Section 4.2, we evaluate the classification performance of the decision tree for linear, non-linear, and random patterns. We present the performance evaluation results for the average transmission success ratio based on the device's movement speed in Section 4.3. Section 4.4 presents the results of the performance evaluation and analysis of the average transmission success ratio based on the number of mobile devices. Finally, in Section 4.5, we show the resource occupancy ratio results for each scheme.
# Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
## 4 Results
### Variation of the CO\({}_{2}\) Fertilization Effect (CFE) with Stand Age  
Both remote sensing data types showed that in coniferous forests and broadleaf forests of 100 years of age, \(\beta_{GPP}\) initially exhibited a trend of rapid increase with age, whereas after reaching a maximum, \(\beta_{GPP}\) declined. During the declining period, the rate of decline gradually decreased until \(\beta_{GPP}\) ultimately stabilized. (Figure 7). In both forest types, the maximum values of \(\beta_{GPP}\) occurred in younger stands for the broad-leaved forests at around 10 years of age (\(\beta_{EC-LUE-max}^{BF}\) = 179.44, age = 9; \(\beta_{NIR-max}^{BF}\) = 188.52, age = 12) and for coniferous forests at around 20 years of age (\(\beta_{EC-LUE-max}^{CF}\) = 203.46, age = 19; \(\beta_{NIR-max}^{CF}\) = 209.75, age = 18), and the minimum values occurred in older stands (\(\beta_{EC-LUE-min}^{BF}\) = 59.10, age = 99; \(\beta_{NIR-min}^{BF}\) = 15.79, age = 100; \(\beta_{EC-LUE-min}^{CF}\) = 32.97, age = 97; \(\beta_{NIR-min}^{CF}\) = 47.09, age = 86). The regression coefficients for the different remote sensing products and stand types are presented in Table 2. The R\({}^{2}\) and the RMSE indicate that the nonlinear regression model we used could fit well the relationship between \(\beta_{GPP}\) and stand age.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stand Type—Remote Sensing Product** & \(a\) & \(b\) & \(c\) & \(d\) \\ \hline BF-EC-LUE & 108.52 & 2.67 & 6.93 & 0.76 \\ BF-NIRv & 63.66 & 5.66 & 14.52 & 0.85 \\ CF-EC-LUE & 75.30 & 5.19 & 14.58 & 0.79 \\ CF-NIRv & 91.59 & 3.44 & 7.51 & 1.36 \\ BF & 89.49 & 3.47 & 11.28 & 0.71 \\ CF & 81.25 & 4.53 & 12.87 & 0.91 \\ \hline \hline \end{tabular} \(a\), \(b\), \(c\), and \(d\) are nonlinear regression model coefficients in Equation (8).  
\end{table}
Table 2: The four regression coefficients across different datasets.  
Figure 7: Estimating the CO\({}_{2}\) fertilization effect (CFE) trend with age in broad-leaved forests (BF) using EC-LUE GPP (**a**) and NIRv GPP (**b**). Estimating the CFE trend with age in coniferous forests (CF) using EC-LUE GPP (**c**) and NIRv GPP (**d**).
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 3 Rosenbrock-Algorithm-Based Beam Training
### Rosenbrock Search
#### 3.2.1 Probe Moving  
In order to discover a new starting point and search direction, probe moving is performed along the \(n\) orthogonal directions. In the analysis of this paper, \(n=2\) for the 1-D antenna array. Corresponding to two search directions, the initial move steps are denoted by \(\xi_{1}\) and \(\xi_{2}\), respectively. The initial solution during each probe round is given by \(x^{(1)}\) and the solution along the \(ith\) (i.e., \(i=1,\ 2\)) direction by \(x^{(i+1)}\). Given the initial solution \(s^{(1)}=\left(p^{(1)},\ q^{(1)}\right)\), the initial directions \(d^{(1)}\) and \(d^{(2)}\), the magnification factor \(\alpha\geq 1\), the shrinkage factor \(\beta\in[-1,\ 0)\), the probe moving can be elaborated as follows.  
Starting from \(x^{(1)}\), we first probe along \(d^{(1)}\). In addition, it is noteworthy that the beam indexes (i.e., \(p\) and \(q\)) are integers. Accordingly, the output beam index used in the next probe moving should be rounded to the nearest integer. If we have \(sn\!\left(x^{(1)}+round\!\left(\xi_{1}d^{(1)}\right)\right)\geq sn\!\left(x^{ (1)}\right)\), this probe operation is considered successful, and let  
\[x^{(2)}=x^{(1)}+round\!\left(\xi_{1}d^{(1)}\right) \tag{6}\]  
where \(round\!\left(x\right)\) denotes rounding the element \(x\) to the nearest integer. The probe step is updated by \(\xi_{1}=\alpha\xi_{1}\), which leads to a larger movement in the next probe moves.  
Otherwise, if \(sn\!\left(x^{(1)}+round\!\left(\xi_{1}d^{(1)}\right)\right)<sn\!\left(x^{(1)}\right)\), this probe operation is considered a failure, we set  
\[x^{(2)}=x^{(1)} \tag{7}\]  
In this case, the probe step is updated by \(\xi_{1}=\beta\xi_{1}\). Since \(\beta\) is negative, a \(back\_off\) search is employed in the next probe round. After probing along \(d^{(1)}\), the similar operations will be implemented on \(d^{(2)}\). Finally, the probed solution \(x^{(n+1)}\) can be acquired and one round of probes is completed. The initial solution for the next round of probe iterations is set to \(x^{(1)}=x^{(n+1)}\).  
When all the direction moves fail, this probe movement iteration is terminated. Then, after the \(kth\) iteration, the new solution can be expressed as  
\[s^{(k+1)}=x^{(n+1)} \tag{8}\]
## 1 Introduction
### VIEs (Virtual Immersive Environments)  
Two different kinds of VIEs can be identified: (1) augmented reality (AR); and (2) VR. AR uses an existing scene and imposes descriptive detailed information over it [14]. VR, however, gives the perception of being physically present in a non-physical world [15].  
Research shows the roles of simulation in creativity education and how highly interactive VIEs may develop positive learning behavior to enhance the learning experience [16]. Several characteristics of VIEs, such as visualization of 3D data and the opportunity to simulate a realistic and safe environment, encourage student engagement. VIEs provide a hands-on and immersive experience, which is a novel way of learning for students who may not have experienced this before. Engagement is the primary objective in gamified VIEs, and research indicates the importance of this factor [17]. For example, a virtual field trip to Mars or the bottom of the sea may spark new interest in subject matter, provide a shared experience for class discussion, and enhance overall engagement [18].  
Moreover, VIE characteristics are built upon situated and constructive learning theory [19]. One of the salient features of this theory and its implementation is the issue of transfer [20]. In near transfer, the goal is to help learners to apply steps learned in training to a similar real-world situation. The far transfer, however, is the ability that enables learners to adapt strategies learned in training to different work problems [21]. The advantage of using an immersive interface is that by simulation of the real world, students can attain near transfer, skills, and knowledge which are used the same way every time, to prepare for future learning [22]. Other researchers also have described student involvement in immersive learning environments that offer authentic learning experiences [23; 24].
###### Abstract
A slight variation in design criteria, such as a longer operating duration, a different data sampling rate, or a different set of IAQ parameters, would require a completely new design. Furthermore, the database systems adopted by such studies, while capable, are evidently not optimal for time-series data. One common observation from recent studies that utilized IoT technology is the difficulty in replicating them. From the perspective of one who seeks to implement an IAQ monitoring study in their own workplace, sufficient guidelines or instructions have not been established in detail. Furthermore, an IAQ monitoring framework to meet the requirements of a specific workspace have not been generalized yet. The complexity of the system integration task increases tremendously when incorporating wireless sensors with different communication protocols, power requirements, measuring parameters, and other factors. All these barriers have prevented small and medium sized enterprises from adopting sensor-based IAQ systems to monitor and improve the air quality and safety of their workspaces. Motivated by the surging need for COVID-safe protocols and mitigation strategies to eliminate hidden chains of infection in the workspace, this paper aims to formulate a framework and implementation strategy of a generic IoT system architecture for the real-time monitoring and processing of ambient parameters. As such, this paper seeks to demonstrate a generic methodology for implementing an IAQ monitoring framework using commercially available off-the-shelf (COTS) sensors and address a broad spectrum of attributes relevant to the sensing framework that an end-user may face. This will be addressed through the following research questions:  
* RQ1: What are the essential elements, design criteria and IoT architecture framework to implement an IAQ monitoring system? * RQ2: What are the strategies that integrate the essential elements and IoT architecture for continuous data capture and storage? In order to address the research questions, an indoor environment monitoring scenario was conducted in an open plan workspace. Environmental and climate data were collected, stored, and visualized on a dashboard for management to view. Furthermore, the data collected within the workspace will be used to determine COVID-19 transmissibility. The collected data will form inputs and boundary conditions to determine aerosol transmission and COVID-19 risk investigations, which are not within the scope of this paper.
## 1 Introduction
The RIS codewords may change at any time, and as such, the dataset containing RSSI measurements used for offline training is unlikely to contain fingerprints corresponding to all possible codewords. Then, during online inference, the localization system will possibly make inferences using fingerprints of unknown codewords, leading to large estimation errors, because the location estimator only fits the codeword distribution of the training dataset. In the field of statistical learning, such performance degradation caused by differently distributed training and test data is known as a _domain generalization_ (DG) problem [36], where the domain here contains all possible RIS codewords. Fortunately, as codewords directly control signal propagation in RIS-enhanced environments, the impact of codewords on fingerprints is not irregular. This observation inspired us to learn from this correlation and try to decouple the dependency of fingerprints on codewords to obtain codeword-independent representations of fingerprints for localization in RIS-enhanced environments. Such a system will only be able to generate fingerprint representations highly related to locations. By doing so, we can (i) solve the RIS codeword domain generalization problem and avoid large estimation errors for fingerprints of unknown codewords, and (ii) predict the locations without the corresponding codewords during online inference. We realized this idea using adversarial learning applied on the codeword domain using a domain-adversarial neural network (DANN) [37] framework consisting of three parts, i.e., a fingerprint feature extractor, a location estimator, and a codeword discriminator. Specifically, during the offline training stage, the feature extractor and the location estimator together behave like conventional fingerprint-based localization systems, i.e., minimizing the location-estimation error. Simultaneously, the codeword discriminator performs adversarial learning on codewords by reversal gradients during backpropagation to guide the feature extractor to generate codeword-independent representations. Our main contributions are as follows:We analyze the localization problem in RIS-enhanced networks in depth and propose a novel paradigm without additional assumptions on the RIS codewords, which also supports online inference without codewords. 2. We propose a localization solution based on codeword-independent representation learning using the domain-adversarial neural network framework to solve the DG problem. 3. Our proposed solution is extensively evaluated using the DeepMIMO dataset [38]. We designed oracle and baseline cases for comparison, which convincingly demonstrate that our solution achieves accurate localization even for unknown RIS codewords. Additional experiments on the system parameters further demonstrate the rationality and robustness of the proposed solution.
# Rain Removal of Single Image Based on Directional Gradient Priors
## 4 Experiments
### Experiments on Real Rainy Images  
To verify the effectiveness of the proposed method in removing rain layers from real rain images, we tested various comparison methods for real rain images, and randomly selected two images to display the experimental results, as shown in Figures 8 and 9. To better observe the results of the rain removal results, we select a small area in the image to enlarge to compare the details of the image. It can be seen from Figures 8 and 9 that the traditional methods, such as the proposed method and the JCAS method, obtain better results for real rain images. Because of the neglect of the difference between the synthetic training set and the real rain images, the algorithms based on deep learning, such as DetailsNet and LPNet, cannot remove the rain streaks thoroughly. For example, in the enlarged areas of Figures 8e,f and 9e,f, the residual rain streaks can be clearly observed. To solve the difference between the synthetic training set and the real rain images, real-time training of the proposed multi-scale dictionaries is employed to detect the information of  
Figure 6: The subjective visual effects of the synthetic image (“ship”) are compared in total. The first image is the synthetic rain image, the second image is the ground truth, and the following images are the results of GF, GMM, JCAS, DetailsNet, LPNet, and our method.  
Figure 7: The subjective visual effects of the synthetic image (“bamboo”) are compared in total. The first image is the synthetic rain image, the second image is the ground truth, and the following images are the results of GF, GMM, JCAS, DetailsNet, LPNet, and our method.  
research should focus on real scene images of rainy days, particularly when considering the influence of rainstorms, which will be helpful for the recognition and detection of outdoor computer vision systems used on rainy days.  
Conceptualization, S.H., Y.X. and Y.Y.; methodology, S.H.; software, Y.X. and M.R.; validation, Y.X., M.R. and W.W.; formal analysis, W.W. and M.R.; investigation, S.H. and Y.Y.; resources, Y.X.; data curation, Y.X.; writing--original draft preparation, S.H. and Y.X.; writing--review and editing, S.H., Y.X. and Y.Y.; visualization, Y.X. and W.W.; supervision, S.H. and Y.Y.; project administration, S.H. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 61862030 and No. 62261025), by the Natural Science Foundation of Jiangxi Province (No. 20192 ACB20002 and No. 20192 ACBL21008), and by the Talent project of Jiangxi Thousand Talents Program (No. jssq2019201056).  
The authors declare no conflict of interest.
## 3 Results
### Systematic Evaluation of the Identified Literature
#### 3.4.6 Machinery Prognostics and Maintenance Management  
A total of 10.71% of the identified papers were assigned to the cluster \"machinery prognostics and maintenance management\". The identified papers in this cluster are more focused on the machine health management and investigate measures to enable predictive maintenance and forecast the remaining lifespan of machine parts. To increase the utilization rate of key machines, a DT for a manufacturing company in the electronics industry is developed by [PERSON] et al. The DT incorporates various data in real-time, such as the number of finished goods, down- and working time, vibration, temperature, information of suction nozzles and feeders, and force measurement data of surface mounting machines. It is used for predicting machine failures and, thus, for predictive maintenance as well as for changing parameter settings of the machines. Due to the application focus, it is difficult to adopt this approach to other products and production systems [39]. [PERSON] et al. establish a DT for a welding production line. To enable synchronous simulation, an OPC UA server for communication via the internet, and a socket server for communication via local area networks are used. The physical entity could mirror the system with a maximum delay of 1 s. Real-time visual monitoring of the production process is realized to ensure the operational efficiency of the equipment and provide information about the welding quality [22]. A case study of wind turbines also shows the potential of DT for PHM processes. The traditional fault cause prediction based on vibration signals (P-Method) is compared with the DT approach and it is shown that the prediction of tooth wear could be improved by 17%, tooth fatigue by 30%, and tooth breakage by 20% [27].