esa-sceva/satcom-chunk-collection huggingface dataset:
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 5 Performance Evaluation  
### Implementation  
We implemented FlexBFT using Python. Our implementation was mainly improved from BDT's open-source code, and naturally also used some methods used by BDT, such as basic cryptography libraries and threshold signatures. In addition to some components that are not open source or written in Python, including ABA by [PERSON] et al. [36], AVID-M is implemented in Golang. In addition, we know that Dumbo is no longer the most advanced MVBA, but we still use it as part of the back-up mode of FlexBFT. This is because our research is not optimistic about the performance of MVBA.  
In addition to FlexBFT, we have implemented Dumbo, HotStuff, and BDT as comparative algorithms. Each of these represents a distinct approach in the realm of Byzantine-fault-tolerant (BFT) consensus protocols: Dumbo is a next-generation classical asynchronous BFT protocol, HotStuff is renowned for its efficiency in a semisynchronous BFT protocol, and BDT stands as a state-of-the-art (SOTA) optimistic asynchronous BFT protocol. We believe that by comparing FlexBFT with these algorithms, we can effectively highlight the distinctive features and advantages of FlexBFT.  
We use Docker on a large computing server to simulate blockchain nodes, setting each node allocated 10 cores and 50 Mbps bandwidth and, similarly, operating the network delay through Docker's built-in network driver.  
During the implementation process, we will fill the buffer of each node with random strings (as dummy transactions). As consensus proceeds, logs are printed and the relevant parameters TPS and latency are calculated, as shown in Equations (1) and (2), where TPS evaluates the number of transactions that a replica can successfully record per second,and latency (default unit is milliseconds (ms)) measures the time cost from the proposed block to the replica record. In Equation (2), _recordTime_ represents the time when a replica appends a new block including transactions to its shared ledger, and requestTime represents the time when a client generates a transaction and gives it to the blockchain node. All performance assessments are conducted consecutively, and their results are averaged from five separate trials.  
\[TPS=\frac{success\ transactions}{totalTime} \tag{1}\]  
\[Latency=recordTime-requestTime \tag{2}\]
## 2 Antenna Designing and Operation Mechanism
### Design of Band Notch Elements  
The proposed antennas (Tachism) are designed for triple notching characteristics in WiMax, WLAN, and X-band for satellite communication. Interference is found in these bands due to the co-existence of UWB. Notching characteristics are introduced into the UWB characteristics by etching slits and slots on the patch to eliminate the interference within the entire range.
# Working Length Transfer in the Endodontic Clinical Practice: A Comparative Study
###### Abstract  
The present paper evaluated the accuracy of two different methods for transferring working length (WL) between manual endodontic instruments and nickel-titanium (NiTi) shaping files. Thirty root canals of extracted permanent teeth were used. Root canals were divided according to canal length (CL) and canal curvature (CC). The reference cusp and the root end were flattened to provide reproducible and accurate measurements. During shaping, the WL measurements were obtained with manual k-files (KF) and transferred to WaveOne (W1) NiTi reciprocating files using the traditional method with the endodontic ruler (method I) and an alternative clinical procedure based on the comparison of the instruments side by side from tip to shank (method II). For each file and each tested method, two measures were taken by two examiners using Rhino (ver. 4.0, McNeel, Seattle, WA, USA) software for a total of 360 (\(30\times 3\times 2\times 2\)) measures. Analysis of variance was performed by taking the difference in length (Delta WL, DWL) between files used for the same canal. The difference between methods I and II for WL transfer was found to be statistically significant (df = 1; F = 71.52; \(p<0.001\)). The DWL absolute values obtained with method II were found to be closer to 0 mm (i.e., same length as corresponding KF) than those obtained with method I. Both CL (df = 2; F = 1.27; \(p=0.300\)) and CC (df = 1; F = 2.22; \(p=0.149\)) did not significantly influence WL measurements. With respect to WL transfer, method II seemed to better preserve the correct WL transfer between instruments during the clinical endodontic procedures.  
working length transfer; NiTi reciprocating file; endodontic file; WaveOne; root canal shaping Article Working Length Transfer in the Endodontic Clinical Practice: A Comparative Study
Statistical Analysis of the Main Configuration Parameters of the Network Dynamic and Adaptive Radio Protocol (DARP)  
[PERSON]  
1 Department of Electrical Engineering and Computer Science, University of Applied Sciences of Munster, Stegerweldstr. 39, 48565 Steinfurt, Germany; [EMAIL_ADDRESS]  
2 Department of Computer Architecture and Technology, University of Granada, Periodista [PERSON] S/N, 18071 Granada, Spain; [EMAIL_ADDRESS] (J.G.); [EMAIL_ADDRESS] (I.R.)  
3  
[PERSON]  
2 Department of Computer Architecture and Technology, University of Granada, Periodista [PERSON] S/N, 18071 Granada, Spain  
3  
[PERSON]  
1 Department of Electrical Engineering and Computer Science, University of Applied Sciences of Munster, Stegerweldstr. 39, 48565 Steinfurt, Germany; [EMAIL_ADDRESS]  
[PERSON]  
3  
Footnote 1: email: {[EMAIL_ADDRESS]}  
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Footnote  
are defined: full-function devices (FFD), which are devices with all the routing and storing capabilities; and reduced-function devices (RFD), which are network devices like sensors or end-points that do not need to perform complex tasks.  
In cluster-based networks, the main challenge is the selection of the cluster-head. This process becomes critical due to the importance of cluster-heads in the network. Cluster-heads carry out the tasks of message distribution and cluster management.  
Among the Internet of Things (IoT), different new protocols have emerged, one of them and the focus of study of this work is the Dynamic and Adaptive Radio Protocol (DARP), which has several advantages and improvements as presented by [PERSON] et al. in [7; 11]. Based on the network layer and especially on network protocols, there is a problem that recurrently emerges, which is the problem of the proper configuration. Most of the modern protocols are able to perform a vast majority of operations in many different scenarios, but this is far from being optimal for a scenario or use case [12; 13; 14], so we have developed the present work in order to find an optimal configuration for DARP-based networks in Smart City scenarios,  
Due to its design, DARP relies on different key parameters, which greatly determine the behavior of the protocol and the performance in a certain scenario. These critical parameters are four timers and four thresholds, being the process of finding their optimal configuration the main goal of this work.  
The paper is structured as follows: Section 2 presents the DARP network protocol with its main characteristics. Section 3 presents the simulation framework, the results and the methodology applied to find the main configuration parameters. Section 4 delves into the statistical analysis carried out. Finally, Section 5 presents the most relevant conclusions of the present work.
###### Abstract  
Sound source identification technology based on a microphone array has many application scenarios. The compressive beamforming method has attracted much attention due to its high accuracy and high-resolution performance. However, for the far-field measurement problem of large microphone arrays, existing methods based on fixed grids have the defect of basis mismatch. Due to the large number of grid points representing potential sound source locations, the identification accuracy of traditional grid adjustment methods also needs to be improved. To solve this problem, this paper proposes a sound source identification method based on adaptive grid splitting and refinement. First, the initial source locations are obtained through a sparse Bayesian learning framework. Then, higher-weight candidate grids are retained, and local regions near them are split and updated. During the iteration process, Green's function and the source strength obtained in the previous iteration are multiplied to get the sound pressure matrix. The robust principal component analysis model of the Gaussian mixture separates and replaces the sound pressure matrix with a low-rank matrix. The actual sound source locations are gradually approximated through the dynamically adjusted sound pressure low-rank matrix and optimized grid transfer matrix. The performance of the method is verified through numerical simulations. In addition, experiments on a standard aircraft model are conducted in a wind tunnel and speakers are installed on the model, proving that the proposed method can achieve fast, high-precision imaging of low-frequency sound sources in an extensive dynamic range at long distances.  
sound source identification; sparse Bayesian learning; grid adaptive split refinement; far-field measurement +
Footnote †: journal: Medical Image Analysis
## 1 Introduction  
Unmanned vehicles are equipped with advanced technologies that integrate environmental perception, navigation, path planning, decision making, and control, combining many scientific fields, such as computer science, data fusion, machine vision, and deep learning [1; 2; 3; 4; 5]. The main advantage of UGVs is operation without the presence of a driver. Such mobile platforms are able to replace humans for a variety of applications, such as agricultural irrigation, logistics, or express delivery in civilian applications. In the field of military application, remote or autonomous operation modes allow for the completion of tasks such as monitoring and reconnaissance, transportation and logistics, detection of improvised explosive devices, provision of fire support, and rescue and evacuation on the battlefield [6; 7; 8; 9].  
Autonomous platforms represent the future direction in the development of vehicles, but the most immense challenge is to create control systems that ensure accurate detection of objects in the operational environment [10; 11; 12].  
Perception technology is related to simultaneous localization and mapping, as well as object recognition and classification. Commercial applications require proper detection of typical objects in traffic such as pedestrians, cars, bikes, etc., to reduce accidents. The military field is more complicated, in which special-purpose vehicles carry out missions, typically in unknown environments where objects can be hidden in vegetation, with irregular shape or structure of obstacles [13]. The effective operation of UGVs in such conditions allows for accurate attacks in modern warfare but also requires a collection of environmental information from high-precision and high-reliability sensors.  
Perception sensors can be divided into two categories: passive and active. The first type only receives the energy emitted from the environment and, on its basis, generates output signals such as through monocular cameras, stereo cameras, omnidirectional cameras, event cameras, and infrared cameras. Active sensors emit energy and measure the reaction of the environmental feedback represented by a portion of this energy such as LiDAR, radar, and ultrasonic signals [14].  
The main goal of this paper is to present the abilities and limitations of sensors used for object recognition, considering off-road environments. In this article, we introduce a newly developed unmanned wheeled platform equipped with perception sensors that can carry out special missions. Typical operational conditions focusing on terrain and vegetation are described. We highlight the types of objects that should be recognized by the vehicles in the off-road conditions, as well as the sensors' performance in such detection tasks.
## 3 Results and Discussion
### Comparison of the Performance of Different Object Detection Models  
The comparison between the YOLO-chili model and the currently mainstream object detection models, including Faster-RCNN, SSD, YOLOv7, YOLOv7-tiny, and YOLOv5, is presented in Table 3. All models use the default configuration after downloading, except for the YOLO-chili model presented in this paper, which is detailed in Section 3.1. The average precision mean of the YOLO-chili model is, respectively, 10.48, 2.87, 0.18, 0.49, and 3.09 percentage points higher than the other five models. Among them, the single-stage detection network model SSD has the lowest recognition accuracy, and the two-stage detection model Faster-RCNN has the largest number of parameters, thus leading to slower inference speed. The average precision mean and inference speed of YOLOv7 are improved compared to Faster-RCNN and SSD, but it still cannot meet the requirement of real-time detection for pepper fruits. Although the precision of the YOLO-chili model is only slightly higher than that of YOLOv7-tiny, the parameter count is much higher than that of YOLOv7-tiny. While it can meet the requirement for real-time detection of pepper fruits, further optimization is still necessary. In particular, the test time in Table 3 is the time it takes for the model to detect an image.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**YOLO-Chili**} & \multirow{2}{*}{**HFFN**} & \multicolumn{2}{c}{**Three-Channel**} & \multicolumn{2}{c}{**Resolution**} & \multirow{2}{*}{**AP (Average**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} \\  & & **Attention** & & **Network Module** & & \\ \hline ✓ & & & & 83.24 & 91.33 & 81.77 \\ ✓ & & ✓ & & 91.39 & 92.74 & 91.65 \\ ✓ & ✓ & & & 82.32 & 87.93 & 81.62 \\ ✓ & & & ✓ & 85.54 & 93.54 & 82.15 \\ ✓ & ✓ & & ✓ & 92.24 & 93.42 & 91.19 \\ ✓ & ✓ & ✓ & & 91.27 & 93.20 & 91.62 \\ ✓ & ✓ & ✓ & ✓ & 94.11 & 94.42 & 92.25 \\ \hline \hline \end{tabular} \(\checkmark\) Represents the Use of Such Modules.  
\end{table}
Table 2: Ablation Test Performance Comparison.  
As can be seen in Figure 10, the YOLO-chili model has the fastest fitting speed, while the curve change of Faster-RCNN is obviously unstable, which is due to the efficiency of the YOLO series model as a one-stage model itself. The YOLO-chili, on the other hand, is due to the possession of efficient computational power and the use of transfer learning to acquire sufficient prior knowledge. At the same time, traditional SSDs may not be able to adapt to the complex and changing environment of the chili dataset and the complexity of the model, thus making it the slowest to train. For the complex environment of the chili pepper dataset YOLOv7-tiny does not seem to outperform YOLO7, but both are using a migration learning approach and therefore both are slower to fit. It can be inferred from the results that the model performance of both YOLOv7 and YOLO-chili is suitable for the real-time detection of chili peppers.
## 6 Discussion
However, the edge infrastructure is still deployed on the factory premises so that the infrastructure capabilities can be adjusted to the specific requirements of the industrial applications. The option when the public network hosts the PNI-NPN could be suitable for such industry use cases when the service requirements are relaxed, and it is not critical if the data goes outside the factory premises. Furthermore, this scenario is suitable for such use cases where service continuity is essential, e.g., when the UE moves to a new location and a different edge server is selected, thus, the minimization of the service interruption is a crucial point for industry applications. However, if the footprint of the MNO enables the deployment of edge computing resources close enough to the factory premises, then applications with low-latency requirements can also be supported. In PNI-NPN deployment, both scalability and reliability can efficiently be ensured in the mobile network. If the related edge computing solution (deployed on a factory premises or provided by the MNO as a PaaS) can ensure these to the same extent, then the end-to-end solution can utilize these advantages. As discussed in Section 3.4, many business and technical alternatives are possible when the MNO and third-party providers could offer IaaS or PaaS for the enterprise customers. The PaaS solution enables less flexibility than an on-premises, private edge since the edge cloud infrastructure cannot be reached directly, which causes difficulties, e.g., configuring a seamless TSN FRER-edge reliability integration. On the other hand, this option facilitates the efficient management of such application deployment, where some application functions are deployed at the edge and others are deployed in a central cloud. In Table 2, the most important characteristics of the above-mentioned integration scenarios of the NPN deployment and edge computing options are summarized and compared. The customization term covers the flexibility of the application deployment in the current scenario as well as indicates how the specific features (e.g., direct hardware access of the edge infrastructure) can be realized. The maintenance term covers the management complexity of the current scenario. We also intend to give insights into the scalability, latency, and reliability capabilities of the edge deployment. Considering Kubernetes as the edge computing container orchestration platform: Currently, the standard Kubernetes distribution needs to be customized to some extent to fulfill the requirements dictated by demanding industrial applications. Many aspects involve the configuration and setup of the hardware resources hosting the cloud platform, in this case, Kubernetes. They can be adjusted if the hardware is in the supervision of the owner/integrator, and then custom options can be set, such as providing real-time support at the operating system level of the data center servers. At the Kubernetes-level configuration, it is recommended to define no CPU limits or disable enforcing CPU limits for supporting low-latency workloads. In addition to that, Kubernetes tools can support
## 4 Experiment Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Used** & **Machine Learning Algorithms** & **Attack Detection Rate ACCURACY** \\ \hline \multirow{4}{*}{UNBS-NB 15 Dataset [10]} & RF & 0.9867 \\ \cline{2-3}  & SVM & 0.9769 \\ \cline{2-3}  & ANN & 0.9478 \\ \cline{2-3}  & DT & 0.9323 \\ \hline \multirow{4}{*}{KDD99 Dataset [8]} & ANN & 0.9744 \\ \cline{2-3}  & SVM & 0.9155 \\ \cline{2-3}  & USML & 0.9808 \\ \cline{2-3}  & DT & 0.9333 \\ \cline{2-3}  & ANN & 0.6397 \\ \cline{2-3}  & SVM & 0.8432 \\ \cline{2-3}  & USML & 0.9478 \\ \cline{2-3}  & ANN & 0.9744 \\ \cline{2-3}  & DT & 0.9443 \\ \hline \multirow{4}{*}{UNBS-NB 15 Dataset [7]} & KNN & 0.9611 \\ \cline{2-3}  & RF & 0.9237 \\ \cline{2-3}  & XGBoost & 0.9835 \\ \hline \multirow{4}{*}{UNSW-NB 15 Dataset [6]} & DT & 0.9015 \\ \cline{2-3}  & ANN & 0.8411 \\ \cline{2-3}  & Proposed approach GIWRF-DT & 0.9301 \\ \hline \multirow{4}{*}{KDD99 Dataset [11]} & DT & 0.8100 \\ \cline{2-3}  & Proposed approach (GBM) & 0.9182 \\ \hline \multirow{4}{*}{UNBS-NB 15 Dataset [11]} & DT & 0.9570 \\ \cline{2-3}  & Proposed approach (GBM) & 0.9508 \\ \hline \multirow{4}{*}{CSE-CIC-DS-18 Dataset [9]} & ANN & 0.9997 \\ \cline{2-3}  & KNN & 0.9973 \\ \cline{1-1} \cline{2-3}  & RF & 0.9983 \\ \cline{1-1} \cline{2-3}  & SVM & 0.9980 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results compared from similar studies.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 1 Introduction
These matrices impose an upper limit on the interference temperature, short of the constraint on the amount of SUs. This optimization design is solved iteratively until the algorithm converges monotonically, where transmitters and receivers characterise the precoders and receiver subspaces by turns. The second paradigm considers the same CR network as the first paradigm, but the SUs are in closer proximity to the PU-Tx (in the manner of indirect spectrum sensing [8]). However, this paradigm proposes that under power-limitation, a PU that maximizes its own rate by using appropriate algorithms (usually water-filling algorithms) on its MIMO channel singular values might leave some of them unused, i.e., no transmission takes place along the corresponding spatial directions. These unused directions may be opportunistically utilized by one of the SU-Tx by designing an IA technique for the SUs, in which a linear pre-coder perfectly aligns the interference generated by the SU-Tx with such unused spatial directions, thereby enabling the SUs to share the licensed spectrum with zero interference to the PU transmission [24]. An optimization scheme can also be designed to maximize the transmission rates of the SUs. In this paper, our main objective is to provide a systematic overview on IA in CR communications and networking. We review the key system models/paradigms involved in the implementation of IA in CR networks and explain how these are crossly related. In particular, for each paradigm, we review various strategies for calculating alignment results and wide-ranging interference management techniques. It should be noted that these two paradigms are merely a representation of the direction of research on this subject because majority of the work done fall into either of the two. However, there are quite a number of other equally significant methods of implementing IA in CR that do not fall under the scope of either paradigm. Thus, this overview will include discussions on some of these other research endeavours, some practical implementations of this network model as well as future research challenges that remain in accomplishing IA in practical CR networks. The remainder of this paper is organized as follows: Section 2 gives a brief introduction of IA principles, techniques and applications. Section 3 focuses on a CR network having a number of SU pairs and a single PU-Tx and PU-Rx pair, where the PU-Tx is assumed far from its Rx and the SU-Rx's are considered not to be influenced by the interference from the PU-Tx. Section 4 considers the same CR network as the first paradigm in Section 3, but the SUs are in closer proximity to the PU-Tx in the manner of indirect spectrum sensing. Comparison of research literature and analysis, computational complexity of different methods, observations and recommendations are presented in Section 5. Section 6 presents open research challenges and lastly, Section 7 provides conclusions.