esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results  
The setup of the gantry-type platform is shown in Figure 2. The PMLSMs are CPC-CLS-PM4 from Chieftek Precision Co., Ltd. and are actuated by the COR-5/230, which is a PWM digital servo driver manufactured by Elmo Motion Control. The peak and continuous force of the PMLSM are 74 N and 18.5 N. Its magnetic pole pitch and continuous current are 15 mm and 5 A, respectively. The platform consists of two parallel motors on the Y-axis and another one on the X-axis. A mechanical coupling happens on the Y-axis for these two motors. Optical encoder sensors provide the measurement of the displacement on each axis. The signals are transferred with an encoder card for position feedback while a D/A converter card charges for control. The optical encoders are set up to measure the displacements with the resolution of 1 \(\upmu\)m. The sampling rate of the practical controller is 1 KHz. Both the X- and Y-axes possess a stroke of 225 mm.
# Gaze Behavior and Positioning of Referee Teams during Three-Point Shots in Basketball
## 3 Results
### Primary Areas of Responsibility  
A 3 (Role: Lead, Center, Trail) \(\times\) 2 (Relation to the ball: nearby, far away) \(\times\) 3 (Time: phase 1, phase 2, phase 3) repeated measures ANOVA was conducted to compare the effects of the referees' role, relation to the ball and time covering the primary areas of responsibility. A main effect was found for the relation to the ball, \(F(1,\,177)=136.50\), \(p<0.001\), _[PERSON]'s f_ = 0.435. A position near the ball (\(M=0.89\), \(SD=0.26\)) was associated with higher coverage rates of the primary area of responsibility compared to a farther away position (\(M=0.51\), \(SD=0.41\)). There was no effect of the factor role, \(F(2,\,177)=2.50\), \(p=0.085\), but the interaction between these factors (relation to ball \({}^{\star}\) role) revealed a significant effect, \(F(2,\,177)=17.06\), \(p<0.001\), _[PERSON]'s f_ = 0.422. Post-hoc Tukey HSD tests found a significantly higher percentage of covering the primary areas of responsibility when the Center or the Trail referee were near the ball compared to percentages of covering the primary areas of responsibility from a position farther away from the ball, regardless of the referee's role (all \(p\)s \(<0.001\)). The Lead referee covered the primary areas of responsibility more often when being positioned near the ball, in comparison to a Center and/or Trail referee farther away from the ball (all \(p\)s \(<0.001\)). All other post-hoc comparison showed no significant differences (all \(p\)s \(>0.05\)).  
The ANOVA also showed an effect of time, \(F(2,\,354)=5.57\), \(p=0.004\), _[PERSON]'s f_ = 0.16. Post-hoc Tukey HSD tests showed no difference between phase 1 and phase 3 (\(p=0.24\)) as well as phase 2 and phase 3 (\(p=0.20\)). A significantly better coverage of the primary visual field was observed for phase 2 compared to phase 1 (\(p=0.003\)) though. No significant interaction was found for time and role, \(F(4,\,354)=1.83\), \(p=0.122\), or time, relation to the ball and role, \(F(4,\,354)=0.293\), \(p=0.883\), whereas the interaction between time and relation was significant, \(F(2,\,354)=10.06\), \(p<0.001\), _[PERSON]'s f_ = 0.225. Subsequent univariate analyses showed that being positioned far away from the ball in phase 1 was associated with worse coverage of the primary areas of responsibility by a referee than all other positions and phases combinations. Moreover, the coverage of the primary area of responsibility was significantly worse in phase 1 compared to phase 2 and 3 for a far-away position (all \(p\)s \(<0.001\), Figure 3).  
Figure 3: Mean coverage (error bars indicating standard errors) of the areas of responsibilities as a function of phase of the basketball shot and distance to the ball.
## 5 Simulation Results
### Validation of the Reassignment  
In this section, we aim to validate the ability of the proposed PIDSM algorithm to handle unexpected new targets by reassigning them. We utilize the 12 combinations obtained from Table 4, each with a low target distribution density and short-term observation time, and then generate 10 testing cases for each combination. After obtaining the initial assignment results of each testing instance using the PIDSM, we add five random targets to each  
Figure 8: The impact of variation of the target distribution density on the performance of the algorithms.  
Figure 9: The impact of the variation of target observation time on the performance of the algorithms.  
testing instance and execute the PIDSM again to obtain new assignments. Subsequently, we conduct an overall assignment with both original and random targets to verify the quality of the reassignment solution.  
Figure 9(a) presents the analysis of running time. It can be observed that compared to the overall assignment, the PIDSM's reassignment time increases less with an increase in instance size. This is because our proposed PIDSM algorithm can make full use of the consensus results of the original assignments, significantly reducing the number of iterations required for the consensus after adding new targets. Furthermore, it is worth noting that the sum of the original assignment and the reassignment time of our algorithm is equal to the time taken for the overall assignment. This demonstrates the high efficiency of our algorithm in the reassignment, without introducing any additional computational burden when handling dynamic new targets.  
Figure 9(b) presents the fitness value analysis, which shows that the solutions obtained through reassignment have almost the same fitness values as those obtained through the overall assignment for all testing instances. This demonstrates that our proposed PIDSM algorithm can handle dynamic new targets without sacrificing the quality of the solutions.
# Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting
However, the commonly used methods still rely on experience to determine the parameters, which lacks scientific basis. The second problem is the lack of high-precision fault diagnosis methods. The existing fault diagnosis classification methods and models are highly complex in principle and have a large number of parameters; some of them require a lot of experience to construct and train. Therefore, it is difficult to achieve high diagnostic accuracy in practical applications. The third problem is the simplification of fault diagnosis results. Most fault methods only diagnose a specific fault or two to three faults, but they require a variety of complex algorithms, so they have little significance in actual fault diagnosis of wind farms. Aimed at the current problems of wind turbine fault diagnosis, this paper proposes a fault diagnosis method for key parts of wind turbine based on the ReliefF feature selection algorithm and eXtreme Gradient Boosting (XGBoost) classification algorithm [18; 19; 20]. This method takes the gearbox and generator, which cause the longest shutdown time of the wind turbine as the research object, and uses the SCADA system data to diagnose the faults related to the gearbox and generator of the wind turbine, so as to ensure that the maintenance personnel can timely and accurately judge whether the faults occur and the specific fault types during the operation of the wind turbine. First, we obtained the multi-class fault data sets and normal data sets from the SCADA system, and then used the Relieff algorithms to complete the feature selection of multi-class faults. Finally, we used the XGBoost multi-class model to identify different fault types, so as to complete the fault diagnosis of the wind turbine. The rest of this article is organized as follows. In Section 2, this paper introduces the principle of the Relieff algorithm and the principle of the XGBoost algorithm. In Section 3, the fault diagnosis method used in this paper is described in detail. In Section 4, the feasibility of the method proposed in this paper is verified through actual experimental comparison. Finally, Section 5 summarizes the main conclusions of this paper.
## 6 Conclusions  
A new compact left-handed meta-atom for S-, C- and Ku-band applications has been designed, investigated, fabricated and measured in this paper. The unit cell and array structure exhibited left-handed characteristics with wide bandwidths in the major portions of S-, C- and Ku-bands at 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\) rotations. For the design, simulation and calculation of the S-parameters of the proposed meta-atom prototype, CST Microwave Studio has been used. Both the horn antenna and waveguide methods have been used for measurement purposes to obtain greater accuracy in the measured results. In addition, S-band is applicable for weather radar monitoring and microwave  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Resonant Frequency (f\({}_{0}\))** & **Quality Factor (Q)** \\ \hline [PERSON] et al. [17] & 2.74 GHz & 14 \\ [PERSON] et al. [23] & 7.53 GHz & 31 \\ Proposed Meta Atom & 3.53 GHz & 82 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quality factor at the resonant frequency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Year** & **Dimensions (mm\({}^{2}\))** & **Frequency Band** & **EMR** \\ \hline [PERSON] et al. [16] & 2013 & 25 \(\times\) 25 & C-Band & 1.99 \\ [PERSON] et al. [17] & 2014 & 30 \(\times\) 30 & Multi Band & 3.64 \\ [PERSON] et al. [19] & 2015 & 8.5 \(\times\) 8.5 & X-Band & 4.80 \\ [PERSON] et al. [5] & 2016 & 5.0 \(\times\) 5.0 & X-Band & 6.0 \\ [PERSON] et al. [21] & 2016 & 10 \(\times\) 10 & C- and X-Band & 4.0 \\ [PERSON] et al. [22] & 2016 & 5.0 \(\times\) 5.0 & X-Band & 5.45 \\ [PERSON] et al. [23] & 2017 & 10 \(\times\) 10 & X-Band & 2.5 \\ Proposed Structure & 2017 & 10 \(\times\) 10 & S-, C- and Ku-Band & 8.50 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison of the proposed meta-atom with the reported previous research.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Configuration** & **Array Structure** \\ \hline [PERSON] et al. in 2013 [16] & U-Shaped & 2 \(\times\) 2 orthogonal array \\ [PERSON] et al. in 2014 [17] & H-Shaped & 1 \(\times\) 2, 2 \(\times\) 2 array \\ [PERSON] et al. in 2015 [18] & G-Shaped & 2 \(\times\) 2 open and interconnect array \\ Proposed Structure & I-Shaped & 1 \(\times\) 2, 2 \(\times\) 2, 3 \(\times\) 3, 4 \(\times\) 4 arrays at 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\) rotation. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Array structural comparison with the reported references.  
devices. C-band is relevant in radio telecommunication and electromagnetic cloaking, and the Ku-band is significant for satellite applications.  
This work was supported by the Research Universiti Grant, Arus Perdana Code: AP-2015-007.  
**Author Contributions:** [PERSON] made substantial contributions to the conception, design and analysis. [PERSON] participated in revising the article critically for important intellectual contents. [PERSON] provided necessary instructions for experimental purposes.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work  
Typical scene applications of virtual reality audio and video communication technology include VR conferencing and panoramic live broadcast. Panoramic live broadcast was developed as early as 2016, which is also considered the \"first year of VR\". Now, 5G, VR, and 8K have come into the public eye. With the rapid development of VR softwareand hardware technology, panoramic live broadcast has become increasingly widely used in various industries. In recent years, due to the impact of COVID-19, researchers and the industry have begun to pay a greater attention to the development, design, promotion, and application of VR conferencing to improve the experience of remote collaboration. A VR conferencing room is one such typical application scenario. In addition, the international standards organizations Moving Picture Experts Group (MPEG) and 3 rd Generation Partnership Project (3 GPP), as well as the Audio and Video Coding Standards Workgroup of China (AVS), have been carrying out standardization research and development work related to immersive media, involving transmission protocols, audio and video coding and decoding, immersive communication systems, and other aspects, especially in the VR field, where the goal is to achieve a six-dof (degree of freedom) immersive experience. The research state of the art of VR conferencing and panoramic live broadcast in the academic research field is discussed next.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 3 A Fog-Based Management and Orchestration Framework for 5G Smart Cities
### Challenges of a Fog-Based MANO Architecture
#### 3.1.2 Security Challenges  
Existing security solutions are designed for protecting enterprise networks and data centers by providing perimeter-based protections. These security services are no longer adequate for addressing the new security challenges in the emerging IoT systems. Fog computing architectures will enable a wide range of security functions, such as distributed malware monitoring for the multiple IoT devices to compensate these devices' limited security and primarily take advantage of the gathering of local information in order to detect threats and attacks in a timely manner.
# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 3 Hardware Implementation and Computational Method
### Working Modes  
The IP core perform two different working modes:  
* _Initialization mode_. The initialization of the IP core consists of the load of the matrices \(\mathbf{x}_{\mathit{t}max},\mathbf{x}_{\mathit{t}min},\mathbf{W}_{h},\mathbf{ b}_{h},\mathbf{W}_{o},\mathbf{b}_{o},\mathbf{x}_{\mathit{t}max},\mathbf{x}_{ \mathit{t}min},y_{\mathit{t}min}\), and \(y_{\mathit{t}max}\), using the external signal interface. These matrices are essentially weights and bias of the neural network, along with normalization and denormalization values and vectors. All together, these matrices allow the IP core to perform a proper classification. Note that the calculation of these matrices is achieved out of this IP core, and the results are transferred to it during this initialization process. Once the initialization is complete, the core can change to another working mode, never before.
* _Run mode_ or _on-line mode_. In this mode, the input data \(\mathbf{x}\) is fed into the IP core. Then, several features are extracted, and in turn, fed into the neural network system. The output is computed according to the initialized network topology. The IP core, when running in this mode, computes and serves the corresponding output before accepting a new input.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Technical Considerations
#### 3.3.3 Unstable Communication  
BC is built on the assumption of steady network connections that is not viable for IoT, which frequently has poor network IoT device connectivity or network instability owing to node failure. In most situations, the state of IoT devices cannot be determined until they are tested, while, in others, the devices function normally for a length of time before changing due to a variety of factors such as disconnection, short-circuit, and program obsolescence [11]. IoT devices, unlike computer nodes on the Internet, are influenced by the transfer of physical underlying data. Wireless access allows IoT devices to connect, such as distributed drones and automobile network connections. If the wireless link's quality is inadequate, the node's information transfer will become unstable, which results in a change in the BC network's topology [76]. Some device information would be unavailable to the verification node, which would lead to incomplete data processing. Because of the enormous number of interconnections and broadcasts between nodes, broadcast storms can quickly occur, consuming too much network capacity. As a result, the IoT network's performance may be impaired, if not completely shut down.  
Edge computing, as a form of cloud computing, can handle dispersed node communication. The challenge of network bandwidth requirements may arise [76]. Side-chains, notary systems, hash locking, and standardization are all possible answers [44]. Moreover, inter-BC communication protocols can be utilized to address projects that are focused on interoperability [44]. Limiting the consensus over various network sections or linking nu merous BC by establishing inter-BC communication represents two more viable BC-based solutions [13].
# H-KPP: Hypervisor-Assisted Kernel Patch Protection
## 5 Results  
### Effectiveness  
This section evaluates the effectiveness and the performance of H-KPP. In order to assess the effectiveness of H-KPP, we have developed a kernel module that mimics some aspects of the malicious behavior of various attacks. It may seem more natural to run an actual attack; unfortunately, the attacks we have found target older versions of the Linux kernel.  
Our kernel module performs a sequence of operations that H-KPP should recognize as malicious. To demonstrate a successful recognition, we performed the following steps for each operation:  
* rebooted the system;
* requested the kernel module to perform a specific operation;
* verified that a special message appeared in the hypervisor's log.  
Regarding the last step, we note that the hypervisor outputs its log messages to the serial port in our current implementation, where these messages can be observed. We chose this communication channel only due to its simplicity. If needed, other communication channels, like Ethernet or USB, can be implemented. The following list summarizes the operations implemented by our kernel module.  
1. The 1 st operation demonstrates code injection, as performed by multiple attacks. While injecting a new code to various memory regions is possible, our kernel module demonstrates an injection to the stack. Then, the kernel module jumps to the injected code. The hypervisor reacts to the execution of unfamiliar code by writing a message to its log and resetting the system.
2. The 2 nd and the 3 rd operations demonstrate patching of the interrupt descriptor table (IDT) and the system call table, as performed by rootkit [19]. Specifically, the kernel module patches the keyboard interrupt entry of the IDT, thus allowing it to act as a keylogger. In the system call table, the kernel module patches the getdents system call, which allows it to hide files. The hypervisor reacts to both of these patches by writing a message to the log.
3. The 4 th and the 5 th operations demonstrate patching of the /proc inode's inode.f_op.read field and the patching of the tcp4_seq_ops.show field, which can enable an attacker to hide processes and network connections. In both cases, the hypervisor writes a log message.
4. The 6 th operation demonstrates unlinking a given process from the process list, thus enabling an attacker to hide a process, even from the kernel itself. The hypervisor detects this attack using information from other sources and writes a log message.
5. The last operation demonstrates a privilege escalation attack by copying the credentials of a privileged process. Specifically, this operation alters the task_struct.ced field. The hypervisor recognizes that a particular credentials structure is referenced more than once and writes a log message.
# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 4 Experimental Analyses
### Experimental Results in Learning Based Models  
Currently, the learning-based method is a research hotspot and has good application in the field of recommendations. Here, two classic learning-based methods, i.e., ConsisRes [32] and MF [33], are selected to evaluate the performance of our natural noise management scheme. We first test the results generated by ConsisRec and MF in the three original datasets, Movielens-100K, Yahoo Music, and Epinions, then generate the results in the same way in these datasets after denoising by our scheme. All the results are listed in Table 7.  
Figure 10: The \(F1\) values on Yahoo Epinions.  
Figure 9: The \(F1\) values on Yahoo Music.  
According to Table 7, we can see that (i) regardless of whether the original dataset or the denoised dataset is used, the ConsisRec model shows a very tiny difference in MAE, RMSE, and F1 results; (ii) the results of MF are also similar to those of ConsisRec, that is, the changes in MAE, RMSE, and F1 between the original datasets and their denoised datasets are very small. The test results in Table 7 show that our proposed method of removing natural noise does not significantly improve the recommendation quality of the two benchmarks. The main reason can be summarized as follows: our scheme detects and corrects natural noise mainly based on the rating bias of users. It can be regarded as a typical natural noise management scheme based on the explicit features of users and items. However, the learning-based models (such as ConsisRec and MF) utilize both explicit and implicit features of users and items to generate recommendation results. For ConsisRec and MF, the implicit features have more effect on the recommendation results than the explicit features. Since our noise management scheme only considers the explicit features of users and items, the improvement in recommendation quality caused by our scheme is not obvious in ConsisRec and MF. On the other hand, since similarity-based benchmarks, such as PCC, only rely on explicit features to generate recommendation results, our scheme can bring more obvious improvements in recommendation quality in such benchmarks. Therefore, it can be concluded that our scheme can improve the recommendation results of the similarity-based models, but the improvement is not significant for learning-based models.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
###### Abstract  
Beyond Fifth Generation (B5G) networks are expected to be the most efficient cellular wireless networks with greater capacity, lower latency, and higher speed than the current networks. Key enabling technologies, such as millimeter-wave (mm-wave), beamforming, Massive Multiple-Input Multiple-Output (M-MIMO), Device-to-Device (D2D), Relay Node (RN), and Heterogeneous Networks (HetNets) are essential to enable the new network to keep growing. In the forthcoming wireless networks with massive random deployment, frequency re-use strategies and multiple low power nodes, severe interference issues will impact the system. Consequently, interference management represents the main challenge for future wireless networks, commonly referred to as B5G. This paper provides an overview of the interference issues relating to the B5G networks from the perspective of HetNets, D2D, Ultra-Dense Networks (UDNs), and Unmanned Aerial Vehicles (UAVs). Furthermore, the existing interference mitigation techniques are discussed by reviewing the latest relevant studies with a focus on their methods, advantages, limitations, and future directions. Moreover, the open issues and future directions to reduce the effects of interference are also presented. The findings of this work can act as a guide to better understand the current and developing methodologies to mitigate the interference issues in B5G networks.  
B5G; interference; HetNet; D2D; UDN; UAV +
Footnote †: journal: Electronics Letters