esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation of Single Sensor
#### 3.3.1 Viewed Snow Coverage Percentage Analysis
The SAR sensors cover most areas of these classes when the integration period is increased to three days, which helps mitigate the PMW sensor's limitations in areas, such as maritime and alpine snow. The LiDAR sensors represent the smallest viewing percentage. Even over a 30-day period, the viewed percentage can not meet a near-global requirement, per se, but does view relatively large amounts of most snow classes. These results suggest a wider swath width is likely required for LiDAR sensors or that more than one LiDAR will be required in the assessment of northern hemisphere snow. Figure 6: Viewed snow coverage percentage as a function of orbit swath width for 1-day, 3-day, and 30-day integration periods during the month of February near peak accumulation of snow in the northern hemisphere. The dot-dashed line represents 80% viewed snow coverage. The assumed weight of sensor efficacy listed in Table 3 is to be improved. For example, defining the PMW sensor's efficacy as zero over taiga regions is arguable [12; 32; 56]. Alternatively, [PERSON] et al. [19] showed there could be a significant correlation between PMW-observed SPD and SWE in a typical boreal forest region. In order to account for a range of feasible efficacies, we apply values ranging from 0.1 to 0.5 with an increment of 0.1. The results shown in Table 5 highlight how the effective coverage of PMW radiometry within taiga snow regions increases with increasing efficacy. During a 1-day integration period, the percentage increases linearly from 9.8% to 49%. During a 3-day or 30-day integration period, the effective coverage is similar to the 1-day integration period because of the relatively large swath width of the PMW radiometer. However, the efficacy of PMW varies in time and space considerably due to the variation of snow properties, such as snow depth, snow wetness, snow density, and snow grain size. This paper only provides a first-order estimate of the approximated efficacy; a dynamic efficacy (in space and time) is likely required in a follow-up study in order to improve model performance.
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 3 5G New Radio Hybrid MIMO-Based Fibre-Wireless System
### CNN Based Digital Pre-Distortion
#### 3.2.1 Convolutional Neural Network (CNN)-Based DPD Method  
Convolutional neural networks (CNN) cascades the RoF connection that develops a RoF NN model and train it to imitate the original RoF link because the output of an RoF connection is known. After we have created the appropriate CNN DPD model, we can backpropagate through this RoF CNN and tweak the parameters. The first layer of a CNN is the convolutional layer. It extracts features from input data and generates a feature map with the help of a kernel (convoluted data).  
A convolutional operation consists of the kernel or filter (kernel matrix), input data (input matrix) and feature map. Assuming that the investigated RoF connection has the \(H(n)\) function and \(y(n)\) as an output signal and that a baseband signal \(x(n)\) must be sent over it, DPD attempts to compute the inverse transfer function of this RoF link, represented by \(\hat{I}^{-1}\), whose output is then denoted by \(\hat{x}(n)\).  
This becomes  
\[\hat{x}(n)=\hat{I}^{-1}(x(n)) \tag{1}\]  
while  
\[y(n)=Gx(n)=I(\hat{x}(n)) \tag{2}\]  
as \(G\) represents the gain. The CNN determines the \(\hat{I}^{-1}\) used for pre-distortion in this case. Because the ideal \(\hat{x}(n)\) is unknown, direct training for creating the CNN for DPD is not possible, as shown in Figure 2.  
The second CNN first replicates the RoF link. For a generic RoF connection, \(\hat{x}(n)\) is the input and \(\frac{y(n)}{\hat{c}}\) is the output. As a result of this, the CNN can now determine an estimated transfer function I. Once the RoF CNN model is created, the model weights are set, and it is then coupled to the CNN DPD model. We now utilise the original input, \(x(n)\), and output as training data to determine the error and then use a loss function. We then reverse promote it through \(\hat{I}\) to train \(\hat{I}^{-1}\).
## 2 Materials and Methods
### Tracking Method  
#### 2.5.1 Analysis of the Problem  
Faced with the challenge that panoramic images cannot be directly processed, this study adopted a strategy of unfolding the panoramic images into two-dimensional plane images for further processing. Although effective, this method introduces a new issue: some targets may disappear at one end of the scene and suddenly reappear at the other end, as demonstrated in Figure 4. This behavior of crossing scene boundaries can lead to tracking failures, assigning different IDs to the same target, and poses significant difficulties for existing algorithms in accurately tracking targets across large spatial intervals.  
Figure 4: Tracking results for the same target at different frames.
###### Abstract
However, this is only feasible in the matching interface meshes owing to the features of the node-to-node contact condition. [PERSON] et al. presented a new topology optimization technique based on a surface-to-surface contact condition to handle both the matching and non-matching interface meshes [19]. The surface-to-surface contact condition was treated by the condensed mortar method and a filtering scheme considering the size of the element was introduced to alleviate some discontinuities present on the non-matching interface. Recently, [PERSON] et al. proposed a topology optimization technique to handle multiple deformable bodies in static and large deformation contacts based on a mortar formulation approach [20]. This paper presents a method to determine an optimum topological layout for the linear dynamic impact and frictionless contact conditions to present a more generalized application of topology optimization. Firstly, the condensed mortar method is used to handle the non-matching interface, which is inevitably caused by the relative motion near the impact region. The condensed mortar method presents several advantages when applied in topology optimization because it can be applied in the same manner as that of conventional structural analysis, even in the presence of a non-matching interface [19]. Since previous studies only considered a linear static problem [19], the proposed approach presents a way of applying the condensed mortar method in a linear dynamic topology optimization. Secondly, the solid isotropic method with penalization (SIMP) method is used to determine the optimum topological layout [13]. The mean dynamic compliance and mean squared displacement of a target DOF are considered as an objective function in this study to obtain an optimum topological layout for a linear dynamic system [13]. Furthermore, a filtering scheme that considers the size of an element is applied to handle discontinuities along the non-matching interface [19]. As a result, the approach proposed in this paper can be easily used in various applications including analysis and design because the non-matching interface are simply handled by applying the condensed localized mortar method to the well-known linear dynamic analysis methods. The remainder of this paper is organized as follows. Section 2 briefly introduces the concept of the condensed mortar method, which is a scheme used to handle non-matching interfaces. The validity of the condensed mortar method is verified through a simple verification example for the linear dynamic analysis. Section 3 describes a method to determine an optimum topological layout in the linear dynamic problems. The condensed mortar method is applied to handle the non-matching interface on the impact regions, based on the conventional dynamic response topology optimization framework. Section 4 presents the verification of the validity of the proposed method using an impact problem in which a beam and a flying block are impacted by frictionless contact conditions. Lastly, Section 5 presents the concluding remarks.
# Research Progress on the Mechanisms and Control Methods of Rockbursts under Water-Rock Interactions
## 4 Consideration of Water's Impact on Rockburst Prediction and Prevention
### Subsection  
In certain situations, water may promote the occurrence of rock bursts. For example, in underground mining, an increase in groundwater levels can raise the pore water pressure within the mined-out area, potentially leading to collapses and rock burst incidents [74; 75]. Conversely, water may also reduce or delay the occurrence of rock bursts. For instance, during natural disasters such as earthquakes, the differing reflection and refraction patterns of fluids and solid media during seismic wave propagation may affect the stress distribution and crack propagation within the rock, thereby reducing the risk of rock bursts.  
Predicting rock bursts with accuracy remains a complex challenge, making the development of reliable and precise prediction models crucial. Several researchers have explored this issue. [PERSON] et al. [76] proposed a new hybrid model for rockburst strength prediction. Firstly, this study was conducted in a specific engineering environment using two parts of data constructed by TBM method for model training, validation, and improvement. In other application scenarios, such as rockburst warning for tunnels constructed by the drill-and-blast method or rockburst warning for tunnels under different stresses and rock conditions, further validation and improvement are needed. Secondly, due to the limitation of data completeness, this study only quantified information on previous rockburst events and excavation progress, excluding geological conditions other than microseismic parameters. Using more diverse and comprehensive information as inputs could improve the accuracy of the warning system. [PERSON] et al. [77] investigated an intelligent dynamic warning method for rockbursts. The prediction model combined rough set theory with a multidimensional cloud model to establish a hybrid prediction model for rockbursts. The accuracy of the model was validated using 18 rockburst cases in the test dataset, but since the dataset is limited, subsequent phases of the study will involve extending the dataset to cover a wider range of actual rockburst cases. To prevent and control rock bursts, key strategies include managing intrinsic factors and enhancing surrounding rock performance through high-pressure water spraying and microwave heating; addressing rock burst stress conditions with techniques like borehole pressure relief and pressure-relief blasting; and bolstering surrounding rock resistance using shotcrete, energy-absorbing bolts, and steel supports [78]. [PERSON] et al. [32] and [PERSON] et al. [29] confirmed that injecting water into surrounding rock effectively reduces rock burst intensity during tunnel excavation.
## 1 Introduction  
### Research Motivation  
As communication technology enters the 5G era, a large number of new mobile applications have appeared simultaneously. The traffic in mobile networks has also increased significantly. In the future, there will be more user devices and data from computation-intensive applications in the networks. Traffic will continue to increase as the networks develop [1]. Correspondingly, increased traffic and more complex apps mean that more time and energy will be required for processing. Meanwhile, the consumption of time and energy with applications running will significantly affect the performance of applications, and even entire networks.  
In most mobile networks, communication facilities are powered by the grid [2]. Compared with battery-powered user devices, these facilities are less sensitive to energy consumption during network working. However, for most user devices, the impact of energy consumption during the running of applications is critical. For example, if people had to carry a large-capacity power bank when using smart phones, the portability of mobile devices would be greatly reduced. Therefore, reducing the energy consumption of mobile devices when running computation-intensive apps is an important topic in current research [3]. In addition to the energy consumption of user devices, some 5G applications also have very strict requirements for delays in service delivery, for instance, telesurgery, automatic driving, VR [4], etc. Obviously, a common requirement for these applications is to reduce the time required for application processing as much as possible. Generally, such applications require a large amount of computing power; thus, users' mobile devices cannot independently complete the computation and analysis of massive data via local computing. Therefore, in order to solve a series of problems brought by the exponential growth in access and requirements of user devices [5], and to improve network performance in scenarios where cloud computing is not applicable, researchers proposed mobile edge computing (MEC) in 5G scenarios a few years ago. So-called MEC allocates computing power close to users; at this time, user devices are only responsible for data transmission and result visualization.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 1 Introduction
### Contributions  
The datasets were obtained from [1], which already had four induced faults, i.e., gain, offset, stuck-at, and out of bounds. These faults are injected at different fault rates (10%, 20%, 30%, 40%, and 50%). Each dataset consists of observation vectors \(Vt\), and each observation vector is composed of two humidity and two temperature measurements at three successive instances. The main contributions are summarized as follows:  
* In this paper, two more faults are induced in the datasets, which are listed below:
* spike fault, and
* data loss fault.
* Following that, six classifiers are applied on the datasets, and an extensive simulation study is conducted in our scenario to detect faults in WSN. The classifiers used are: SVM, RF, SGD, MLP, CNN, and PNN.
* The performance of classifiers is evaluated by four widely-used measures, which are stated below:
* Detection Accuracy (DA),
* True Positive Rate (TPR),
* Matthews Correlation Coefficient (MCC), and  
The rest of the paper is organized as follows: in Section 2, we discuss the related work on fault detection and anomaly diagnosis. Section 3 is based on an overview of faults in WSNs. Section 4 gives the basic explanation of the classifiers applied in our scenario, while Section 5 is a detailed profile of the system model. The results are included in Section 6, and the paper is concluded in Section 7.
## 2 Algorithm
### Double Differenced Positioning Algorithm in Pseudolite System
#### 2.1.2 Parameter Estimation  
Extended Kalman filter (EKF) is applied to estimate the parameters in high-precision positioning solution. EKF is mainly to solve the linearization problem in KF. It can be considered that the coordinates remain the same because the rover station is a state of low dynamic or static in indoor positioning. The transition matrix is \(I\) and the covariance matrix of the system process noise is \(O\). The measurement noise should be considered. The observation and the state model are as follows,  
\[\begin{array}{c}X_{k}=\Phi_{k-1}X_{k-1}+\omega_{k-1}\\ Z_{k}=H_{k}X_{k}+v_{k}\end{array} \tag{2}\]  
where \(k\) is the current epoch. \(X\) represents the state vector. \(\Phi\) is the transition matrix \(I\). \(\omega\) is the dynamic process noise of the system. \(Z\) is a measurement vector after linearization. H is a design matrix \([I_{r}\,\lambda]\)and \(v\) indicates the measurement noise. If the dynamic process noise and the measurement noise of the system are Gaussian white noise, then the parameters have the following statistical characteristics,  
\[\begin{array}{c}E(\omega_{k})=0,Cov(\omega_{k},\omega_{k})=Q_{k}\\ E(v_{k})=0,Cov(v_{k},v_{k})=R_{k}\\ Cov(\omega_{k},v_{k})=0\end{array} \tag{3}\]  
After the initial coordinate being obtained, the corresponding initial state parameter \(X_{0}\) and its variance-covariance matrix \(P_{0}\) can be given. The basic formula of EKF follows,  
\[\begin{array}{c}K_{k}=P_{k}^{(-)}H_{k}^{T}W_{k}\\ W_{k}=\left(H_{k}P_{k}^{(-)}H_{k}^{T}+R_{k}\right)^{-1}\\ P_{k}^{(-)}=\Phi_{k-1|k}P_{k+1}^{(+)}\Phi_{k-1|k}^{T}+Q_{k-1},P_{k}^{(+)}=(I-K_ {k}H_{k})P_{k}^{(-)}\\ X_{k}^{(-)}=\Phi_{k-1}X_{k-1}^{(+)},X_{k}^{(+)}=X_{k}^{(-)}+K_{k}V_{k}\\ V_{k}=Z_{k}-H_{k}X_{k}^{(-)}\end{array} \tag{4}\]  
where subscript \(k\) denotes the current epoch. \(X\) is state vector; \(P\) is covariance matrix. \(K\) is Kalman gain matrix and \(V\) is innovation vector. \(W\) is weight matrix. \(R\) is measurement noise matrix. \((+)\) and \((-)\) denote predicted and updated values. When positioning, the iterative EKF is usually used to weaken the linearization error. In order to avoid the problem of divergence caused by the imprecise of a given dynamic model during filtering, the weight can be adjusted according to the residual matrix to achieve the robust Kalman filter [36].
# Agreement Between Tele- and Face-to-Face Assessment of Neuromotor Development in High-Risk Children
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline  & **Face-to-Face** & **Tele-Assessment** & **ICC (95\%CI)** \\ \hline AIMS total score (mean \(\pm\) standard deviation) & 23.89 \(\pm\) 10.59 & 24.07 \(\pm\) 11.02 & 0.996 (0.994, 0.998) \\ \hline \multicolumn{4}{c}{AIMS subscales score (mean \(\pm\) standard deviation)} \\ Prone position & 8.87 \(\pm\) 4.79 & 8.89 \(\pm\) 4.89 & 0.994 (0.989, 0.997) \\ Supine position & 6.71 \(\pm\) 1.96 & 6.49 \(\pm\) 1.99 & 0.965 (0.934, 0.981) \\ Sitting position & 5.38 \(\pm\) 3.16 & 5.78 \(\pm\) 3.40 & 0.977 (0.951, 0.988) \\ Standing position & 2.98 \(\pm\) 1.81 & 2.91 \(\pm\) 1.94 & 0.989 (0.980, 0.994) \\ \hline \end{tabular}
\end{table} 
Table 2: Descriptive values and interrater reliability of telerehabilitation and face-to-face clinical assessment of the Alberta Infant Motor Scale (AIMS). Figure 1: [PERSON]–Altman plot of the difference between the face-to-face assessment and tele-assessment for Alberta Infant Motor scale total score.
## 3 Result
### IoT attacks  
IoT architecture can be separated into a perception layer, a network layer, a processing layer, and an application layer [77]. There are different features for each IoT layer, so there are multiple threats for each layer [78]. IoT attacks can be detected in any layer of IoT architecture. In the perception layer, hardware components of IoT systems, such as zigbee, radiofrequency identification (RFID), wireless sensor networks (WSNs), and sensors, are vulnerable to various attacks. The network layer in an IoT system has substantial security measures, but certain issues still occur. There are various types of IoT system at-tracks, such as DoS attacks, viruses, man-in-the-middle attacks, and eavesdropping attacks that affect the network layer [79]. The processing layer contains various types of technology, such as data analysis and data storage. The most popular type of attack on the IoT processing layer is a cloud attack since the cloud receives data sent at this phase [80]. The attacker will use trojan worms, horse applications, spyware, malware, and malicious scripting software attacks that can damage IoT system devices in the application layer. Figure 4 illustrates the percentage of IoT attacks considered in primary studies.  
DoS attacks were frequently used in the studies we compiled. A DoS attack is a type of attack in which the attacker makes a service inaccessible and stops legal users of the service by sending floods of ICMP echo replies or SYN to port(s). U2R attacks are the second most frequent IoT attack. An U2R attack is when the attacker uses illegal techniques and methods (e.g., sniffing passwords or malicious injection) to gain access to devices or get access from a normal user account. R2L attacks are the third most frequent IoT attack. R2L attacks are exploitations in which the attacker identifies a security vulnerability in a network in order to enter it as a local user. Probing is the fourth most frequent IoT attack. Probing is an attack where the attacker attempts to gather information about the network to exploit its protection by sending an ipsweep-ping to several hosts to discover the IP address of the target and scan for ports to discover the services of the host. Reconnaissance attacks are the fifth most frequent IoT attack. In reconnaissance attacks the attacker collects  
Figure 4: Percentage of IoT attacks considered in primary studies.  
information for the system in order to observe it. Table 9 below summarizes the IoT attacks according to the layers.  
Other IoT attacks that have rarely been considered in primary studies are wormhole (4.8%), distributed denial-of-service (DDoS) (4.8%), backdoor (4.1%), analysis (3.4%), generic (2.7%), fuzzers (2.7%), shellcode (2.7%), sinkhole (2%), blackhole (2%), hello flooding (1.4%), SQL injection (1.4%), ARP cache poisoning (1.4%), malformed packets (1.4%), exploits (1.4%), and scanning (1.4%) attacks.
## 4 Results and Discussion
### A Few Remarks on Computational Complexity Comparison  
Last but not least, it is worth giving a few remarks on the computational complexity of various algorithms considered in this paper. With reference to [40], the computation requirements for the existing algorithms can be summarized as follows:  
* [PERSON]'s algorithm [24] requires \(2L+M+1\) multiplications per iteration, \(2L+M-2\) additions/subtractions per iteration, and 1 comparison operation.
* The Th-FxLMS algorithm [25] requires \(2L+M+1\) multiplications per iteration, \(2L+M-2\) additions/subtractions per iteration, and 2 comparison operation.
* The INSS-FxLMS algorithm [26] requires \(3L+M+4\) multiplications per iteration, \(2L+M+1\) additions/subtractions per iteration, and 1 division operation.
* The previous CCSS-NMFxLMS algorithm [39; 40] requires \(6L+2M+7\) multiplications per iteration, \(4L+2M+4\) additions/subtractions per iteration, 1 comparison operation, and 2 division operations.  
Figure 15: A typical alpha stable signal generated from changing characteristics.  
Here, \(L\) denotes length of the FIR ANC filter \(W(z)\), and \(M\) is the length of the secondary path modeling filter \(\hat{S}(z)\). Considering the execution summary of the proposed algorithms presented in Table 1, it is straightforward to determine that the proposed FSS-MFxRRLS algorithm (operations 1-11 in Table 1) would require \(4L^{2}+5L+2M+5\)  
Figure 16: Detailed simulation results for reference signals from non-stationary SatS process with varying \(\alpha\): (**a**) Sun’s algorithm [24]; (**b**) Th-FxLMS algorithm [25]; (**c**) modified FxLMK algorithm; (**d**) INSS-FxLMS algorithm [26]; (**e**) effect of step-size parameter in the proposed FxRLS-based impulsive ANC algorithms; (**f**) performance comparison between various algorithms: (1) Sun’s algorithm (\(\mu=1\times 10^{-8}\)); (2) Th-FxLMS algorithm (\(\mu=1\times 10^{-6}\)); (3) modified FxLMK algorithm (\(\mu=5\times 10^{-5}\)); (4) INSS-FxLMS algorithm (\(\mu=1\times 10^{-4}\)); (5) previous CCSS-NMFxLMS algorithm (\(\mu_{1}=1.0,\mu_{2}=1\times 10^{-2}\)); (6) FSS-MFxRRLS algorithm (\(\mu=0.1\)); (7) proposed CCSS-MFxRRLS (\(\mu_{1}=1.0,\mu_{2}=0.05\)).  
multiplications per iteration, \(3L^{2}+3L+2M-1\) additions/subtractions per iteration, and 4 division operations. In addition to these computations, the time-varying step size in the proposed CCSS-MFxRRLS algorithm (operations 12, 14-16 in Table 1) would require \(L+5\) multiplications per iteration, \(L+4\) additions/subtractions per iteration, and 1 division, 1 comparison, and \(1\)\(\exp\{\cdot\}\) operation per iteration of execution. It is obvious that the proposed algorithms, being based on an RLS-based adaptation, have an increased computation complexity. However, the performance of the proposed algorithm is far superior compared with the other LMS-algorithms considered in this paper for impulsive ANC systems, as demonstrated by the extensive simulation results presented earlier. Therefore, the increased computational cost may be considered as the price paid for an improved performance.