esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 2 Morse Theory and Reeb Graphs
### Reeb Graphs  
Let \(M\) be a topological space and let \(f:M\longrightarrow\mathbb{R}\) be a scalar function defined on \(M\). The Reeb graph of the pair \(f\) and \(M\) gives a summary of the topological information encoded in by tracking changes occurring to the connected components of the level sets of \(f\). More precisely, the Reeb graph of \(M\) and \(f\) is the quotient space \(R(M,f)\) of \(M\) defined, as follows. We say that the \(x\) and \(y\) are equivalent in \(M\), and write \(x\sim y\), if and only if they belong to the same connected component of \(f^{-1}(r)\) for some \(r\in\mathbb{R}\). The quotient space \(M/\sim=R(M,f)\) with the quotient space topology induced by the quotient map \(\pi:M\longrightarrow R(M,f)\) is called the Reeb graph of \(M\) and \(f\). Here, recall that the map \(\pi\) takes a point \(x\) in \(M\) to its equivalence class \([x]\) in \(R(M,f)\). Given a point \(p\) in \(R(M,f)\), it is often important in practice to retrieve the set of points in \(M\) that map to \(p\) via \(\pi\). In this article, we provide an efficient method to retrieve the data in \(M\) that are associated with a point on a Reeb graph.  
The map \(\pi\) induces a continuous function \(\bar{f}:R(M,f)\longrightarrow\mathbb{R}\), where \(\bar{f}(p)=f(x)\) if \(p=\pi(x)\). This map is well defined, since \(f(x)=f(y)\), whenever \(\pi(x)=\pi(y)\).  
When \(M\) is a manifold and \(f\) is Morse, then \(R(M,f)\) exhibits certain additional properties. For instance, in this case, every vertex of \(R(X,f)\) arises from a critical point of \(f\) or a boundary component. Furthermore, every maximum or minimum of \(f\) gives rise to a degree \(1\)-node of \(R(M,f)\). Saddle points for a Morse function \(f\) defined on a \(2\)-manifold have degree \(3\)-node. This is not guaranteed if the scalar function is not Morse. See Figure 2 for an example of a Reeb graph.  
Figure 1: Minimum, Saddle, and Maximum, respectively.
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 2 Piezoelectric Materials
### Polymer Materials  
Single-crystal and perovskite materials are not flexible; therefore, polymer materials are often incorporated into PENGs. After the discovery of the polyvinylidene fluoride (PVDF) polymer, the applications of piezopolymers in sensors and actuators [53] have considerably increased owing to their excellent flexibility, low production cost, and fast synthesis compared with most piezoelectric materials [54]. The good biocompatibility of polymers, such as PVDF and polyvinylidene fluoride trichloroethylene P(VDF-TrFE), makes them promising candidates for piezo-biomedical applications [55]. Piezo-polymers are classified as bulk, void-charged, and composites [56]. Void-charged polymers are composed of thin layers with gas voids that generate internal dipoles on charging, which is essential for the piezoelectric effect. By contrast, bulk polymers are semi-crystalline polymers. For example, PVDF is a bulk semicrystalline polymer with five crystalline phases--\(\alpha\), \(\beta\), \(\gamma\), \(\delta\), and \(\epsilon\)--among which \(\alpha\) and \(\beta\) are highly piezoelectric [57]. Numerous studies have been reported on the fabrication of flexible PENGs.  
In the present section, different classes of piezoelectric materials, such as single crystals, perovskites, and polymers, were summarized. Every material has its own characteristic properties in terms of its piezoelectric response, synthesis process, flexibility, and biocompatibility. Thus, appropriate materials are selected according to the application requirements.
## 1 Introduction  
Cyanobacteria are ubiquitous in the environment, but arguably best recognized as the conspicuous \"blue-green algae\" in freshwater and coastal habitats, and particularly in association with eutrophication and episodic \"harmful algal blooms\" (HABs). This is reinforced by the recognized diversity of toxic, and more generally bioactive, metabolites by this phylum. Studies of the cyanobacteria have, in particular, focused on the role of several metabolites--the so-called _cyanotoxins_--with documented toxicity (e.g., human and livestock poisonings, chronic health effects, _etc._). In parallel, pharmacological investigations have sought to leverage this chemical diversity in regards to possible drug discovery leads. The chemical diversity of cyanobacteria with respect to both areas is more generally reviewed elsewhere [1; 2; 3; 4; 5].  
Among studies of bioactive metabolites from cyanobacteria, the order Stigonematales remains relatively underrepresented with regards to both environmental health (_i.e._, toxin) and biomedical (_i.e._, drug discovery) aspects. Lack of investigation, with respect to the former, arguably stems from the focus on a limited number of recognized toxic metabolites with established links to HAB events, or human health concerns (e.g., contamination of drinking water), and the thus taxa (_i.e._, Chroococcales, Notostcales, Oscillatoriales) which produce these compounds. Specifically,investigations of the toxic potential of cyanobacteria have consequently focused on well-described neurotoxins (e.g., savitoxin, anatoxin-a, BMAA) and \"Hepatotoxic\" metabolites (e.g., microcystins, nodularin, cylindrospermopsin), and therefore, the taxa most frequently associated with these compounds (e.g., _Microocystis, Nodularia, Cylindrospermopsis, Anabaena, Aphanizomenon, Lynygya)_[3; 5]. Similarly, the vast majority of investigations of cyanobacterial metabolites explored with respect to potential drug leads have been, likewise, largely biased toward--and presumptively due to the tremendous chemical diversity of--a rather small taxonomic subset of cyanobacteria, and most notably the polyphyletic genus, _Lyngbya_ (Order Oscillatoriales), and limited number of other genera (e.g., _Nostoc, Symploca_) [1; 2; 4].  
That said, an accumulated knowledge of the chemistry of the Stigonematales, from more than three decades of investigation, suggests that the order represents a unique repertoire of bioactive metabolites with relevance to both drug discovery and environmental health. In particular, a chemically diverse family of bioactive indole alkaloids is seemingly characteristic and, in fact, phylogenetically restricted to the order. Herein, we specifically review this family of unique indole alkaloids, and to a lesser extent other bioactive metabolites, from the Stigonematales with respect to their chemical diversity--including biosynthesis and pharmacology/toxicology--specifically as it relates to their potential as drug leads, possible ecological function and relevance to environmental health.
# Defacement Detection with Passive Adversaries
## 5 Results: Experiments, Full-Scale Deployment and Discussion
### Newspaper Scenario  
#### 5.1.1 Setup  
The test system has been used to monitor 10 selected URLs of a major news website. The URLs were chosen based on the likelihood of changes and expected update frequency.  
To simulate an attack, we used the Nginx web server, as a virtual host that responds to local requests. Nginx will respond to HTTP requests sent to the local domain with a PHP page that loads content from a substitution source, locally hosted. This substituted content can be (1) a modified version of the expected page, (2) a page from another news website or (3) a known defacement instance from Zone-H [16].  
Before some randomly chosen executions of the defacement detection tool, a script will add a line to the local _hosts_ file, in order to redirect HTTP requests to the local virtual host. On those random occasions, we will thus obtain locally substituted content, instead of the remote target page. Afterwards, the script deletes the line from the _hosts_ file, to restore normal operation. In this way, we use the modified page for virtually just one execution of the tool. When defacement detection is performed with a high frequency, the modified page can be found for more than one execution.  
A Python script runs daily, decides when to carry out the replacement of the page, and generates a configurable number of tasks, to be scheduled randomly during the day. The defacement examples used in the replacement are not used in the Machine Learning phase, in order to make the test as realistic as possible.
## 2 Related Work
### MEC/Cloud Computing Cooperation  
Sharing computing resources with MEC and cloud, in other words, when applications continue to be provided via MEC or cloud deployment without needing to be aware of their physical location, is a key technology. Thus far, various discussions have been held regarding offloading using MEC and cloud [36; 37; 38; 39; 40; 36; 37; 38]. References [36; 37; 38] have simulated a basic computational model that divides processing tasks between MEC and cloud to minimize latency or power consumption. A more complicated definition of objective function and analysis considering the queue of computing processing is performed in [39; 40; 41].  
On the other hand, distributed computing has been discussed by providing the MEC architecture in a hierarchical design [42; 43; 44]. In [42], the authors proposed a hierarchical edge cloud that distributes and deploys computing in order to reduce the amount of traffic on the backhaul side. Further, in [43], the data sent by User Equipment (UE) is first aggregated on MEC, and MEC performs the first-order analysis of the aggregated data. By transferring only the first-order analysis results to the cloud side, a hierarchical edge architecture is adopted to reduce the amount of traffic in the backhaul network and analyze unique data locally. In addition, data collected by MEC/cloud is stored in multiple different layers of information, such as a dynamic map in which essential map information and time-varying data are embedded. An architecture that links mapping information to applications that enables distribution and linkage of data from the cloud to each MEC has been being considered as well [44]. According to the related research mentioned above, the current deployment of applications in MEC or cloud has used the catalog of images. As micro-services that divide application functions have already attracted attention, in Beyond 5G, deploying what is needed at the required location and time will be necessary without being aware of the site (MEC or cloud) for each application function. Therefore, this paper clarifies how to deploy applications in MEC or cloud according to the user's registration information based on subscription.
## 3 Experiment and Analysis of the Benchmark Functions Test
### Performance on Uni-Modal and Multi-Modal Benchmark Functions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{Uni-Modal Benchmark Functions} & & & & \\ \hline  & \(F_{1}\) & \(F_{2}\) & \(F_{3}\) & \(F_{4}\) & \(F_{5}\) & \(F_{6}\) & \(F_{7}\) \\ \(Avg\) & 0 & 0 & 0 & 0 & 998.8497 & 0 & \(2.16\times 10^{-6}\) \\ \(Std\) & 0 & 0 & 0 & 0 & 0.0269 & 0 & \(2.36\times 10^{-6}\) \\ \(Med\) & 0 & 0 & 0 & 0 & 998.8519 & 0 & \(1.28\times 10^{-6}\) \\ \hline  & \multicolumn{8}{c}{Multi-Modal Benchmark Functions} & & & \\ \hline  & \(F_{8}\) & \(F_{9}\) & \(F_{10}\) & \(F_{11}\) & \(F_{12}\) & \(F_{13}\) \\ \(Avg\) & \(-\)22,604.3559 & 0 & 0 & 0 & 1.1482 & 99.9825 \\ \(Std\) & 2113.57601 & 0 & 0 & 0 & 0.0077 & 0.0050 \\ \(Med\) & \(-\)22,364.7032 & 0 & 0 & 0 & 1.1477 & 99.9835 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Benchmark functions with \(N=1000\). Figure 4: The comparison curves of some tested functions: (**a**) \(F_{3}\); (**b**) \(F_{5}\); (**c**) \(F_{9}\); and (**d**) \(F_{11}\). The results indicate that the BLSPIO outperforms the other algorithms for ten out of thirteen functions (\(F_{1}\) to \(F_{7}\) and \(F_{9}\) to \(F_{11}\)), achieving optimal solutions for seven uni-modal functions and three multi-modal functions. Notably, for functions \(F_{1}\) to \(F_{4}\), \(F_{6}\), and \(F_{9}\) to \(F_{11}\), the BLSSPIO attains optimal solution 0. In terms of accuracy, \(F_{7}\) can also be considered as achieving the optimal solution. Compared to the PIO and BLSSPIO, the improvement offered by the BLSSPIO is significant. Although the CFPSO and TVPSO demonstrate better performances for \(F_{8}\), \(F_{12}\), and \(F_{13}\), the BLSSPIO is still outstanding. Additionally, the BLSPSO retains fast convergence, particularly on uni-modal functions, while also enhancing performance on multi-modal functions, especially for \(F_{9}\) to \(F_{11}\). To validate the aforementioned conclusion, two typical uni-modal functions, \(F_{3}\) and \(F_{5}\), and two typical multi-modal functions, \(F_{9}\) and \(F_{11}\), are presented in Figure 4. It is evident that the BLSSPIO exhibits the fastest convergence speed, whereas the CFPSO, TVPSO, and PIO have comparatively slower convergence rates. To further assess the extensibility of the BLSSPIO, experiments are conducted on the same functions with \(N=1000\). The simulation results demonstrate that the BLSSPIO achieves optimal solutions for functions \(F_{1}\) to \(F_{4}\), \(F_{6}\), and \(F_{9}\) to \(F_{11}\). Under the condition of absolute precision, \(F_{7}\) is also regarded as having found the optimal solution. Although the BLSSPIO does not attain optimal solutions for functions \(F_{5}\), \(F_{8}\), \(F_{12}\), and \(F_{13}\), the BLSSPIO still demonstrates impressive performance even in high-dimensional scenarios.
# Experimental Study on High-Energy Gas Fracturing Artificial Coal
## 5 Conclusions  
In this paper, a high-energy gas fracturing agent was developed, and its performance, sensitivity, pressure, and temperature resistance were tested. High-energy gas fracturing simulated coal tests were carried out, measuring the pressure in the borehole wall and the strain in the test block, in addition to analyzing the fracturing effect of high-energy gas. The following conclusions were drawn:  
(1) Analyzed the gas production principle of fracturing agent and the fracturing mechanism of high-energy gas and developed a high-energy gas fracturing agent with high gas production, strong desorption effect, and excellent performance.  
(2) The pressure-time curve of the borehole wall under the action of high-energy gas can be divided into three stages, namely, the rapidly rising pressure-rising stage, the slowly rising pressure-stabilizing stage, and the non-linearly declining pressure-releasing stage. The pressure rise in the borehole is about a dozen us, the maximum pressure is tens to hundreds MPa, and the pressure distribution in the borehole is not uniform.  
(3) Under the action of high-energy gases, the stress wave in the simulated coal sample consists of two phases: compressional phase and tensile phase; the intensity of the stress wave is about several tens of MPa, which can only cause tensile destruction to the simulated coal sample but not compressional destruction. In addition, the duration of the stress wave under the action of high-energy gas is long (about 5 ms-15 ms), and the peak value of the stress wave decays slowly with the increase in distance (the decay coefficient \(\alpha=1.67\)).  
(4) High-energy gas fracturing technology is primarily based on the quasi-static action of the gas to cause tensile destruction to coal, and the stress wave energy utilization is high, so it has an excellent fracturing effect.  
In this work, the influence of ground stress is not considered. Ground stress may affect the fracturing effect of the fracturing agent. In the future, research on the fracturing agent fracturing coal considering ground stress can be carried out.  
For conceptualization, J.L. and M.Y.; methodology, G.L. and J.L.; formal analysis, Z.W. and G.L.; investigation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, M.Y. and J.L.; visualization, M.Y. and J.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Major Special Project of Shanxi Province, (No.20181101013), and Shanxi Major Science and Technology Special List Unveiling Project, (No.20201101003). The data in this manuscript are presented in Tables 1-6.  
The authors thank [PERSON], [PERSON], and [PERSON] for their guidance on the experiment.  
The authors declare no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could appear to have influenced the work reported in this paper.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
###### Abstract  
The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform.  
physical layer security; radio frequency fingerprint identification; 5G mobile device; PRACH preamble; differential constellation trace figure; convolutional neural network +
Footnote †: journal: _entropy_  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))).
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 8 Performance Improvement of IoB-DTN Based on Data Aggregation Mechanism
### Spatial Aggregation  
The aggregation area is the space where the sensed values by various sensor nodes are generated. We evaluated three values of the aggregation distances: 20 m, 50 m and 100 m.  
Figure 24 shows the average delivery rate for spatial aggregation. We observe that the increase of the aggregation area does not impact the delivery rate. More precisely, the forwarding rate raises slightly by increasing the aggregation distance. Moreover, the throughput is not impacted by the rise of the aggregation distance, as illustrated in Figure 25. This indicates that the dynamics in the network is more impacted than the aggregation area.  
Figure 26 depicts the average delivery delays of received data packets. Again, we notice that the delivery delays of the three parameters are almost the same. It is important to point out that using a small duty cycle is better since the packets are forwarded faster.  
To evaluate the energy consumption of IoB-DTN protocol with spatial aggregation, we appraise the protocol cost and the number of packets aggregated per packet. Figure 27 shows the average protocol cost of IoB-SA variant. It is calculated as the number of data packets forwarded in the network and is depicted in Figure 27. We can see two fields in each column: _NPSNG_ (number of packets sent to nodes and gateways) and _NASN_ (number of acknowledgments sent to nodes). It is clear that the increase of the aggregation area reduces the overall protocol cost in the network. This is an obvious consequence since more data packets are aggregated into same packets, thus achieving better communication cost. In addition, the use of a large value of duty cycle reduces the protocol cost since data packets will be stored for a longer period in the buffers.  
Figure 24: Average delivery rate for IoB-SA.  
Figure 25: Average throughput for IoB-SA.  
Moreover, to assess the communication cost as well as investigate the energy consumption of IoB-SA, we also evaluate the number of aggregated packets into the same packet for the three parameters, as illustrated in Figure 28. As cited above, when a node receives more than seven data packets to be aggregated into the same packet, it applies the aggregation functions. This can influence on the energy consumption of sensor nodes, therefore the overall network lifetime. It is important to notice that using a small aggregation distance (20 m) leads to aggregating fewer data packets into the same packet since the source node meets fewer neighbors nodes.  
Figure 28: _Cont._  
Figure 26: Average delivery delay for IoB-SA.  
Figure 27: Average protocol cost for IoB-SA.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
###### Abstract
The strategy prioritizes using poles with DA devices to enable, whenever possible, the positioning of routers and gateways in locations close to the backhaul network. This applies a grid-based heuristic to determine the candidate positions, minimizing the number of pole positions to be evaluated. In addition, a simplified mechanism for the multihop connectivity analysis based on a minimum spanning tree (MST) heuristic is employed to minimize the number of connections to be analyzed. The selected strategies aim to balance complexity and final solution quality. Different approaches are found in the literature to deal with the gateway positioning problem, some combining different techniques. However, using a grid-based candidate position selection that prioritizes the use of DA device positions, combined with an MST heuristic to explore multihop and minimize the number of connections to be analyzed, is not common. The remainder of this article is organized as follows: Section 2 describes the architecture of the AMI network and the application scenario. The related work is presented in Section 3. The proposed strategy is detailed in Section 4. Section 5 presents the results of the experiments, where different scenarios are used to evaluate the method's applicability. Section 6 reviews the research's main objectives, evaluates the results, and presents our future works regarding the method. Finally, the conclusions are drawn in Section 7.