esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
## 2 Rough Surface Matching Based on Octree and Edge Feature Point Algorithm
### Point Cloud Feature Point Extraction  
The accuracy of feature extraction is related to the speed and accuracy of subsequent registration. Figure 2 is a schematic diagram of the original point cloud data, which can be used to extract feature points. Using a single feature point extraction method such as the voxel down sampling method, although the point cloud data are simplified, it cannot keep the local features of the point cloud. The internal shape signatures (ISS) feature point extraction method can preserve the local features around the point cloud data; however, the internal points are missing due to the calculation method, as shown in Figure 3. Due to the principle of simplification, the simplified curvature algorithm has low efficiency and a large amount of calculation. In this paper, octree sampling and ISS feature point extraction are combined to extract feature points. Octree is an efficient way to organize point cloud data. The data are divided by cyclic recursion until the maximum recursion depth is reached. Figure 4 is the schematic diagram of the point cloud octree, Figure 5 is a schematic diagram of octree sampling results. In this paper, the octree sampling method is used to simplify the point cloud data for the first time, and the important data such as the shape and features of the point cloud are retained. Then, the ISS feature point algorithm is used to further retain the surrounding data so that the local features of the point cloud can be well preserved. Stanford bunnies were sampled according to this method, and the results are shown in Figure 6. The flow of the improved algorithm is as follows:  
1. Hierarchical processing of octree is performed. The set depth of this paper is 4, that is, divide the largest unit into eight parts, and repeat four times until the empty node skips.
2. Search the neighborhood. Take a point in the neighborhood as the root node, judge whether there is an intersection with the set unit, and if there is an intersection, continue to search for its child nodes, and repeat the above process.
3. Stop the calculation when the child node meets the conditions, and take this point as the nearest neighbor.
4. Check whether there is a closer point around the child node, if so, define it as the new nearest neighbor, otherwise, remove it.
5. Repeat the above process, define the searched nearest neighbor as set Q, and carry out the next optimization calculation.
6. After all the nearest neighbors are obtained, ISS feature extraction is performed, and all of the points in the neighborhood that can reflect local features are screened. Those that meet the conditions are reserved as feature points, and the optimization is finished.  
Figure 1: The rough registration process of the point cloud.  
Figure 4: Schematic diagram of octree with a depth of 4.  
Figure 3: ISS feature point extraction.  
Figure 2: Point cloud raw data.
# Power Control Techniques for Interference Management-
## 3 Analysis and Findings
### Dataset
#### 3.1.2 Annual Scientific Production  
An accurate indicator of the current publication trend in a particular field of study is the number of articles published yearly. The trend analysis of the number of publications provides information on future research trends. Table 6 displays the distribution of publications that received power control over time and the total number of publications.  
For ease of analysis, we present Figure 3 to illustrate the annual scientific production of the journal articles. We observe similar patterns from the WoS and IEE Xplore databases. The WoS database follows the IEEE Xplore trend; however, its response has been delayed by a few months. Considering this, we may predict an increase in the trend of WoS beyond 2023. In comparison with other databases, Scopus indicates a slight increase in the number of articles. Several factors can cause these publication trends. This could result from changes in peer review processes or an increase or decrease in open access journals. Furthermore, there is a shift in funding when some journals receive more funding than others. A high impact factor and the number of citations could also be reasons for database publication trends.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Scopus** & **Web of Science** & **IEEE Xplore** \\ \hline
2018 & 22 & 19 & 61 \\
2019 & 15 & 29 & 95 \\
2020 & 12 & 83 & 84 \\
2021 & 9 & 94 & 68 \\
2022 & 14 & 59 & 40 \\
2023 & 17 & 40 & 44 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Annual scientific publication for each database.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Scopus** & **Web of Science** & **IEEE Xplore** \\ \hline Timespan & & 2018–2023 (until October 2023) & \\ Documents & 54 & 324 & 392 \\ Sources (journal articles) & 27 & 31 & 27 \\ Keywords plus (ID) & 499 & 505 & 381 \\ Author’s keywords (DE) & 181 & 1305 & 1302 \\ Average citations per document & 14.89 & 17.32 & 14.79 \\ Annual growth rate \% & \(-\)2.33 & 16.05 & \(-\)6.32 \\ Authors & 200 & 1195 & 1339 \\ Authors of single-authored documents & 0 & 3 & 6 \\ Single-authored documents & 0 & 5 & 9 \\ Co-authors per document & 3.89 & 4.44 & 4.32 \\ International co-authorship \% & 40.74 & 47.22 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overview of the main information about the data.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 1 Introduction
This method has to effectively distinguish between nodes that leave the monitoring area and faulty nodes that fail to provide service while (physically) within the monitoring area. Specifically, in this work, the classification method uses trends of the neighbor count sequences and link quality statistics for the suddenly disconnected (due to any reason) mobile nodes and also the same information from (previously connected) neighboring boundary nodes in the monitoring area. These collected trends are monitored to distinguish nodes that naturally leave the monitoring area from the nodes that fail to provide satisfactory service. Figure 1: Mobile medical assets with mobile nodes. (**a**) IV Pole; (**b**) Syringe Pump; (**c**) Ventilator; (**d**) Wheel Chair [17]. (With the permission of IEEE publisher). The evaluations using real traces collected from a real hospital environment show that the system successfully tracks and distinguishes the mobile assets with 100% accuracy, and the system reduces about 97% of the asset management time. Specifically, the contributions of the paper can be summarized in four-fold. * This work develops a framework to classify failed nodes from mobile nodes, which can leave a monitoring region without failures using a sequence of neighbors of them. This method can be applied to many mobile asset-tracking services. * This work proposes a node state classifier using trends extracted from the neighbor counts and the ratios of the boundary nodes included in the neighbor counts. To the extent of our knowledge, the proposed method is the first state classification technique, for mobile nodes, that does not use heartbeat messages or routing protocols. * This work classifies a mobile node disconnected from a wireless sensor network as either a faulty node within a monitoring region or a node that leaves (or already left) the region without failures based on the trends of connectivity metrics. * This work presents simulations and experiments using a network simulator, and real datasets with the proposed classifier are used. Their results indicate that this state classifier can detect most failed nodes that cannot be identified properly by conventional methods. The rest of this paper is organized as follows. The related work and background of this study are presented in Section 2. The architecture of the mobile asset tracking system and its components are presented in Section 3. The failure detection method is explained in Section 4. The experimental environment and the experiment results are presented in Section 5. Finally, conclusions are given in Section 6.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 4 Secure IoT Communication and Attack Mitigation
### Modeling IoT Attacks and Defenses
#### 4.3.2 Defense Strategies for IoT Networks  
Along with attack modeling, effective defense mechanisms are required to protect IoT systems. SDN and machine learning are two emerging paradigms for securing IoT environments [10]. SDN provides centralized control and management of the network operations by decoupling the control and data planes [24]. This allows dynamic security policies to be enforced network-wide. The SDN controller monitors traffic, detects anomalies, and modifies routing to counter the attacks.  
We utilized SDN concepts to defend against the NS flooding attacks in IoT networks, as shown in Figure 4. The victim and attacker nodes connect through an SDN-enabled switch. The SDN controller monitors traffic statistics such as flow table usage, unique source IP addresses, and NS message rates. These metrics are analyzed by a controller-based machine-learning-based attack detector, which looks for anomalies that point to an NS flooding attack. Effective threat identification and mitigation are made possible by the combination of SDN and machine learning. SDN enables dynamic network reconfiguration in reaction to the identified risks and offers fine-grained visibility into network traffic patterns. Real-time machine-learning algorithms are capable of effectively identifying anomalies and malicious behaviors since they have been trained on historical attack data and typical traffic patterns. SDN's centralized control and programmability let the machine-learning-based attack detector identify NS flooding threats rapidly. It may also instruct the SDN controller to create flow rules that filter hostile traffic while letting legitimate traffic through. The effect on normal network operations is lessened, and the availability of essential IoT services is ensured by using a customized mitigation strategy [17].  
Combining SDN with machine learning has many advantages for IoT network security detection and mitigation. First and foremost, SDN allows the gathering of extensive traffic and network telemetry data, resulting in a large dataset that machine-learning algorithms may evaluate to detect anomalies. Second, since SDN has a centralized control plane, countermeasures may be implemented quickly if an attack is identified. The SDN controller may dynamically adjust the network's flow rules to isolate the impacted devices and block malicious traffic. Third, machine-learning algorithms can continually discover and adapt to new attack patterns, hence boosting threat detection accuracy and efficiency over time. When IoT attackers change their tactics, machine-learning models may be retrained on fresh data to keep up with the developing threats. However, the controller may interfere with performance. The efficacy of attack detection is determined by the precision of the machine-learning algorithm.  
Figure 4: Using SDN and machine learning for defense against NS flooding.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 4 Discussions  
This paper focuses on researching the denoising method to attenuate random noise in micro-seismic data.  
According to the characteristics of micro-seismic signals, the random noise in micro-seismic signals is mainly composed of high-frequency signal components. However, the frequency of the effective signal is relatively lower. For these non-stationary signals, time-frequency analysis is undoubtedly a powerful tool. When the signals are distributed linearly in time, the instantaneous frequency can be estimated by the peak value of the time-frequency distribution. The noisy signals are modulated into analytical signals by a frequency modulation factor. TFPF introduces PWVD (WVD with time window) to realize local linearization. The instantaneous frequency is estimated by the peak value of the analytical signal of PWVD. In this way, an unbiased estimation can be realized. As a novel time-frequency algorithm, the advantage of TFPF is that it can better recover the effective signal under the conditions of low SNR and fewer constraints. However, the selection of window length is very important. A fixed window length cannot achieve a balance between amplitude preservation and random noise suppression. The advantage of CEEMDAN is that it can decompose non-stationary signals into several stationary IMFs with different dominant frequencies. CEEMDAN can overcome the modal aliasing caused by EMD, and reconstructs the signals accurately as the sum of all IMFs.  
Based on the advantages of TFPF and CEEMDAN, the CEEMDAN-TFPF method is researched in this paper. However, the TFPF method cannot be performed on all IMFs after CEEMDAN. The reason is that low-frequency IMFs are barely associated with random noise. The effective signals will be destroyed when denoising these IMFs.  
In order to solve the problem, the discriminant factor is introduced in this paper. The noisy IMFs to be processed are screened out through the discriminant factors. The window lengths of the TFPF method performed on these IMFs are flexibly selected according to the selection rule. In this paper, four discriminant factors are discussed. Among them, through introducing the concept of fuzzy membership degree, FE is designed more scientifically. The variation trend of FE has more obvious inflection points. Therefore, the denoising effect of FE is also the best. The experimental results of synthetic data show that the SNR has been significantly improved by CEEMDAN-FE-TFPF.  
With the depth study of the signals, more indicators can reflect the stability of a signal. In recent years, with the idea of multi-scale and multivariable entropy being put forward, the CEEMDAN-TFPF method still has room for improvement.  
Figure 14: Random noise of the actual micro-seismic data.  
Figure 15: Frequency spectrum of random noise of the actual micro-seismic data.
## 3 Results
### Component Analysis of Calcium Carbonate Scale Samples  
The three types of calcium carbonate crystals are aragonite, calcite, and vaterite. Among them, calcite scaling is the most stable, aragonite is a high-pressure crystal, and vaterite has unstable thermodynamic properties [2]. In this experiment, we found two types of calcium carbonate, aragonite and calcite, in the XRD results of non-adherent scale samples and calculated the relative mass fraction. The adherent scale attached to the pipe wall cannot be easily scraped and has a small quantity. The non-adherent scale intercepted by the sieve in the water collecting tank was easy to collect. Hence, the non-adherent scale was conducted by XRD analysis.  
The non-adherent components of the experimental groups are shown in Table 4. Among the six groups of non-adherent scales, the largest difference in the relative content of aragonite and calcite was observed in the No. 2 experimental group, and the other groups exhibited only slight differences. In heat exchangers, aragonite tends to become dense, is highly adherent and scale-removing, whereas the calcite is granular, with poor adhesion, and is easily mobilized by the shearing force of flowing water [3; 4]. The high content of aragonite in non-adherent indicates that the aragonite has a great chance of adhering to the copper tube wall of the heat exchanger. Many aragonite feature structures were also observed on the SEM photograph of the scale of the No. 2 experimental group.  
Figure 6: Experimental groups calcium ion concentration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Group** & **Non-Adherent Scale (g)** \\ \hline
1 experimental group & 27.80 \\
2 experimental group & 25.21 \\
3 experimental group & 32.39 \\
1 control group & 31.53 \\
2 control group & 36.93 \\
3 control group & 20.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Non-adherent quality.  
[PERSON] et al. [20] believed that high concentrations of magnesium ions inhibit the growth of calcite, and a similar phenomenon was observed in the non-adherent scale of the heat exchanger of the control group. Compared with the No. 1 control group, the No. 2 and No. 3 control groups with magnesium ions added have lower calcite content and higher aragonite content. This may be that during the growth of calcite, magnesium ions are more likely to adhere to its growth surface. Thereby, magnesium ion enters the lattice structure of calcite and occupies the original position of calcium ions in the calcite lattice to inhibit the continuous growth of calcite crystals [19; 20]. For the No. 2 experimental group, this effect is significantly enhanced under the action of high-voltage electric field. However, there is no similar effect on aragonite.
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## Data Pre-Processing  
### Data Balancing  
The dataset used in this study suffers from an imbalanced class distribution over several fault types, including Valve, Pump, and Accumulator components with a ratio of normal and fault class at least 1:2 or more. An imbalanced class dataset can result in our model ignoring the learning of a minority class, thereby resulting in biased model performance [30]. Thus, we perform data balancing using a random over-sampling to duplicate samples randomly from the minority class. As a result of this pre-processing, the number of component samples becomes 741 \(\times\) 3 for the Cooler, 1125 \(\times\) 4 for the Valve, 1221 \(\times\) 3 for the Pump, and 808 \(\times\) 4 for the Accumulator.
# Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues
## 2 Key Features in the Energy Internet
### Energy Management Systems in IoT framework
In contrast, these systems have a more complex communication system architecture, as sub-systems need to transact information with each other. Distributed EMS is implemented to decentralized system category in order to have more reliability of the system in some cases [84]. In distributed EMS, each node shares specific information with the neighboring nodes using a bidirectional communication system, yet they act independently and decide to optimize their performance based on the shared information. In this manner, more complex communications are needed (compared to other schemes), but more controllability can be achieved over a system wide operation. In each of the mentioned schemes, different communication technologies such as microwave (uW), power line carrier (PLC), fiber-optics, infrared, and/or wireless radio networks (such as global system for mobile (GSM) communications and code division multiple access (CDMA)) can be effectively used and integrated into the existing infrastructure. On the other hand, self-autonomy of operation for a local controller without having information from neighboring nodes is the main idea of a passive power/energy management scheme (PP/EMS). In this structure, it is assumed that making an information sharing mechanism is too costly or not viable, thus independent operation of energy sources is required. In this realization, it is important to clearly define the control objective of each node to assure reliable operation of the system. It should also be mentioned that this category includes limited accessibility and communication links. Mostly, it includes simple energy management in which local optimization is needed. This category of energy management may also be used for very low harvested power levels [85]. As the level of energy is very low, for instance in sensors, it is logical to observe energy from sensors' environment and then use it for any kind of applications. Therefore, there may be the maximum level of energy to be managed [86].
# Design of an Intelligent Vehicle Behavior Decision Algorithm Based on DGAIL
## 4 Discussion
### Straight Road Scene
#### 4.1.3 Effectiveness of Driving Strategies  
Comparison of decision-making model based on DGAIL and A3C in the same simulation scenes and parameter settings. The training results and vehicle motion parameter curves of the two different algorithms for the vehicle in the straight road scene during simulation progress are shown in Figures 10 and 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Reward Value** & **Round Duration** & **Training Times** \\ \hline DQN & 30.8 & 24 & 2231 \\ GAIL & 5.2 & 24 & 3000 \\ A3C & 32.3 & 40 & 1853 \\ DGAIL & 35.5 & 40 & 1651 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training time cost for straight road.  
Figure 9: Convergence of the vehicle driving on the straight road.  
Figure 11: Comparison of two methods’ motion curves for the straight road.  
Figure 10: Comparison of two methods’ training results for the straight road.  
As shown in Figure 10, in the 1000 rounds, both algorithms were in the initial learning stage, and the decision-making model had high randomness in the selection of actions. In the 1000-1600 rounds, the DGAIL algorithm started to make correct decisions based on expert knowledge, and the success rate increased rapidly to 94% and the reward value increased to 32.25. In contrast, the A3C algorithm was still in the exploration stage, during which the randomness of the action selection strategy was larger, and the success rate and reward value were lower. In the 1600-1800 rounds, the DGAIL algorithm continued to learn the empirical knowledge that was generated from the interaction with the environment, the decision-making model was approaching the expert level, and the success rate and reward value were in the stable rising stage. At this time, the A3C algorithm also began to learn the correct action decision, the randomness of the action selection strategy was reduced, and the success rate and reward value continued to rise. In the 1800-2000 rounds, both algorithms had learned the optimal strategy, the decision-making model entered a stable period, and the driving decision obtained was able to meet the needs of the straight road scene. As shown in Figure 11, compared with the A3C algorithm, the DGAIL algorithm had a slightly higher average vehicle speed, fewer lane changes, more frequent acceleration and deceleration, and higher total rewards value considering the influence of the reward guidance factor.
# 3-D Numerical Study on Progressive Failure Characteristics of Marbles under Unloading Conditions
###### Abstract  
3-D particle-based discrete element method (PB-DEM) was employed to numerically study the mechanical and progressive failure characteristics of pre-fissured marble specimens under conventional triaxial unloading conditions. The microscopic parameters of PB-DEM for marble materials were calibrated using comparison with the previous experimental data. To systematically investigate the mechanical properties and the progressive failure characteristics of pre-fissured marble specimens under the unloading conditions, a series of numerical specimens were simulated. The effects of fissure geometric conditions, initial confining pressures, and unloading rates on the mechanical and failure behaviors were explored via simulations. The present numerical results indicate that peak strength increased as the initial confining pressures increased or the unloading rate decreased. Crack coalescence types and the ultimate failure modes in the pre-fissured marble specimens were significantly affected by the unloading stress paths. The present numerical results provide a better understanding of unloading mechanical and failure characteristics to scientists and engineers in rock mechanics and rock engineering.  
pre-fissured marble; confining pressure unloading; mechanical behaviors; failure characteristics; loading stress paths +
Footnote †: journal: Applied Sciences