esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 5 Some Prospects of the Microseismic Monitoring Technique in Coal Mines
### Development of Microseismic Monitoring Equipment  
The microseismic monitoring technique has been extensively utilized in Chinese coal mines, and the microseismic monitoring equipment is mainly imported from abroad. The SOS microseismic system from Poland, the IMS microseismic system from South Africa, and the ESG microseismic system from Canada are famous in Chinese coal mines. The core technology of the imported microseismic equipment is closed, which inevitably limits the user to embedding the latest research results into the microseismic configuration and improving the equipment according to new requirements under different mining conditions. The Hubei Seaquake Technology Co., Ltd. (Hubei, China), in collaboration with the Institute of Rock and Soil Mechanics, Chinese Academy of Sciences and Northeastern University, started to research and develop microseismic monitoring equipment for rock fracturing in 2008. A high-precision microseismic monitoring system named SinoSeism was successfully created and applied to various deep rock excavations [127]. The SinoSeism system uses a  
Figure 13: A comprehensive method for estimation of water inrush risk, revised from [108].  
newly developed time arrival picker for seismic waves emitted from the stressed rocks and a sectional velocity model for positioning microseismicities.  
Due to the complicated environment of underground coal mining, microseismic monitoring equipment characterized by a simple structure, high precision, high sensitivity, anti-interference, and a wide transmission frequency band is needed in terms of long-term stability, accurately detecting coal/rock fracturing, and the efficient transmission of the microseismic data with low signal attenuation in coal mines. The all-fiber microseismic monitoring system meets the abovementioned requirements, which comprises the hardware of microseismic sensors based on fiber optic interferometry and signal transmission fiber, and the software of intelligent data processing and analysis methods. The microseismic sensors based on fiber optic interferometry are sensitive, have a wide bandwidth and dynamic range, and feature great performances in complicated coal mining environments in comparison with the traditional piezoelectric sensors. In addition, they are cheaper than sensors imported from abroad. Therefore, the development of the all-fiber microseismic monitoring system is meaningful for the mitigation of mining-induced disasters and improvements in safety conditions during deep coal mining.
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 6 Recommendations and Future Directions
### Security  
Securely integrating AI into the metaverse is not a simple task and is one that requires a multifaceted approach. First, data anonymization plays a role in protecting user identities by concealing the link between personal information and the user. Second, strong encryption protocols and schemes can ensure secure data transmission. In this context, lightweight cryptographic schemes [127] represent promising candidates for use in environments such as the metaverse, where computing resources are critical. These schemes are designed to perform encryption and decryption efficiently, thereby reducing the burden on processing power, memory, and energy consumption. Third, blockchain integration helps to ensure secure and transparent data management in AI-powered virtual environments. Fourth, regular security audits, combined with continuous monitoring systems, can help detect vulnerabilities and mitigate threats in real time [107]. In addition, AI can play a pivotal role in addressing security challenges. The integration of AI with other technologies such as blockchain and non-fungible tokens (NFTs) offers a robust framework for enhancing identity security and managing digital assets. Moreover, AI can analyze vast amounts of data to authenticate users' identities more effectively than traditional methods. This capability is particularly important in the metaverse, where digital interactions are frequent and require robust security measures to prevent identity theft and fraud. In this direction, distributed architectures supported by AI can facilitate secure interactions across 3D educational worlds, enhancing overall security [128]. Concerning authentication, AI algorithms can ensure seamless authentication by leveraging a multimodal approach that combines behavioral biometrics, such as typing patterns, voice recognition, gait analysis, etc. In the metaverse, particularly in educational settings, ensuring secure and seamless authentication is crucial for user experience and trust. Furthermore, developing AI algorithms to identify and counteract deep fakes is essential for maintaining the integrity of educational content and interactions in the metaverse [129]. Additionally, federated learning could usefully improve privacy by retaining user data on local devices.
# Lagrangian Modeling of Marine Microplastics Fate and Transport: The State of the Science
## 4 Numerical Lagrangian Models
### CaMPSim-3D
Furthermore, CaMPSim-3D employs an inverse distance-weighted method for the interpolation (or map) of hydrodynamic inputs in space, which is implemented for each particle [98].
## 3 System Model
### A Multi-Tier Energy-Efficient Task Offloading Algorithm  
In this section, a multi-tier energy-efficient task offloading algorithm is presented in which the derivation of the near-optimum task offloading decision for multi-tier environments is outlined as follows (Algorithm 1).  
First, the offloading decision for all computation tasks of mobile devices are initialized with \(\varphi_{i,j,0}=1\), which indicates the local execution. Then, the backbone router iterates over UAVs and gathers their available capabilities including storage and computational as well as the available bandwidth. Next, it receives the required information for the user's task, including \(\{b_{i,j},ci,j,\tau_{i,j},\zeta_{i,j},p_{i,j}\}\), as well as the local capabilities for each device \(f_{i,j}^{t}\), through the connected UAV. Further, the maximum allowable data rate for each mobile device is calculated according to Equation (1). Afterwards, the task offloading decision values for each computation task are calculated and derived by solving the formulated problem in Equation (12), and then said values for each device user are calculated, in which decreasing the consumption of energy for the system with a latency constraint is the main goal.  
Algorithm 1 describes the comprehensive steps of task offloading decision making, in which the computational complexity is \(\mathcal{O}(MN)\), where \(M\) denotes the number of UAVs and \(N\) denotes the total number mobile devices.
# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 1 Introduction
The benchmark datasets most used for research on medical image classification are the National Institutes of Health (NIH) Chest X-ray14 dataset and the International Skin Imaging Collaboration 2018 (ISIC2018) Skin Lesion Analysis dataset. The ChestX-ray14 dataset consists of 112,120 chest X-rays of 30,805 patients. 53.9% of the images are normal, and 46.1% are abnormal. The abnormal images are labeled with 14 different pathologies. Each image can have one or more labels, with 40.1% of abnormal images being multi-labeled, making the dataset suitable for multi-label classification. The ISIC2018 dataset consists of 10,015 skin lesion dermoscopy images labeled with seven types of skin lesions. Each image belongs to one class, making the dataset suitable for multi-class classification. One major issue facing medical image analysis is the lack of large datasets of high-quality labeled samples because manual labeling of images is a labor-intensive, expensive task [18]. However, obtaining unlabeled medical data from clinical sites is relatively easier [19]. Therefore, semi-supervised learning (SSL) is appropriate for medical image analysis because SSL uses a combination of both labeled and unlabeled data for learning. The goal of SSL is to use unsupervised learning with unlabeled data to improve the performance of the supervised model developed from labeled data [20]. This paper provides a survey of some of the latest SSL methods developed for the purpose of medical image classification. This paper is organized as follows: Section 2 discusses commonly used SSL approaches, Section 3 explains the survey methodology, Section 4 presents the review of recent SSL approaches, Section 5 offers a summary of the results, and Section 6 discusses gaps and future work.
# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
###### Abstract  
A good product-to-shelf assignment strategy not only helps customers easily find desired product items but also increases retailer profit. Recent research has attempted to solve product-to-shelf problems using product association analysis, a powerful data mining tool that can detect significant co-purchase rules underlying a large amount of purchase transaction data. While some studies have developed efficient approaches for this task, they largely overlook important factors related to optimizing product-to-shelf assignment, including product characteristics, physical proximity, and category constraints. This paper proposes a three-stage product-to-shelf assignment method to address this shortcoming. The first stage constructs a product relationship network that represents the purchase association among product items. The second stage derives the centrality value of each product item through network analysis. Based on the centrality of each product, an item is classified as an attraction item, an opportunity item, or a trivial item. The third stage considers purchase association, physical relationship, and category constraint when evaluating the location preference of each product. Based on the location preference values, a product assignment algorithm is then developed to optimize locations for opportunity items. A series of analyses and comparisons on the performance of different network types are conducted. It is found that the two network types provide variant managerial meanings for store managers. In addition, the implementation and experimental results show the proposed method is feasible and helpful.  
s +
Footnote †: journal: Applied Sciences
## 7 Conclusions  
This paper has presented a perceptual listening test in which participants were asked to evaluate both individual and non-individual binaural renderings of a jazz ensemble on four scales: brightness, richness, externalisation and preference. Results show significant differences in the ratings given to particular HRTF sets at the 95% confidence interval. An overall preference for the measurement set of the KU100 dummy head is seen, followed closely by the the measurement set of the KEMAR mannequin. A slight preference is shown for rich and external stimuli of a neutral/slightly bright timbre. Very little correlation is seen with respect to the responses given to stimuli generated with individual HRTFs.  
Details of the measurement and post processing of the SADIE II Database were also presented. Diffuse field equalised HRTFs, BRIRs and HpIRs data of 20 subjects (2 dummy, 18 human) are now available online for use in similar tests. The database represents the largest measured HRTF datasets for both human subjects and the KEMAR mannequin currently available. Furthermore, it is the only database which provides comparative echoic and anechoic measurements.  
The results of this paper lead to questions regarding the future of HRTF measurement and binaural rendering. Source localisation, shown to improve with individual measurements, must be carefully balanced against timbral and spatial qualities of competing measurement sets. For now, this paper serves to promote the significance of non-localisation based HRTF attributes and the compelling performance of the KU100 measurement set.  
Conceptualization, C.A. and G.K.; Data Curation, C.A. and L.T.; Formal Analysis, C.A.; Funding Acquisition, G.K.; Investigation, C.A.; Methodology, C.A. and G.K.; Project Administration, C.A.; Software, C.A. and G.K.; Supervision, D.M. and G.K.; Validation, C.A.; Visualization, C.A.; Writing--Original Draft, C.A.; Writing--Review and Editing, C.A., D.M. and G.K.  
This research is supported by a Huawei Innovation Research Partnership. The research was funded by the Engineering and Physical Sciences Research Council (EP/M001210/1).  
**Acknowledgments:** The authors would like to thank [PERSON] for his technical assistance in the construction of the measurement rig. They would also like to thank the measurement and listening test volunteers for their valuable time.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Detection of DDoS Attacks in Software Defined Networking Using Entropy
## 4 DDoS Attacks Detection on Simulated SDN Network  
### Experiment Environment Setup  
This experiment was carried out on an HP laptop with 8 GB RAM. The experimental operating system adopts Linux Ubuntu 20.04 with 20 GB memory and uses Ubuntu image files to run in Oracle VM VirtualBox software. The version of mininet is 2.3.0 and runs locally on Linux. The network topology shown in Figure 3 is created using mininet. The network is a tree structure with a depth of 2. The switch adopts OpenFlow switch, which can support OpenFlow protocol.  
In order to realize the function of the controller, it is necessary to turn on the controller to observe the changes of traffic in the network during simulation. In SDN, when a data flow arrives, the Openflow switch will parse the data packet and match it with the local flow entry. If the match is successful, the data flow will be processed according to the forwarding policy. The flow table counter corresponding to this flow table entry is increased by 1. If the match fails, the packet is encapsulated as a packet_in message and sent to the controller through the Openflow switch. Then, the controller sends the updated flow table information to the Openflow switch. The flow table includes three modules: matching domain, counter and forwarding policy.  
In the experiment, the destination IP address of the packet is extracted from the matching and the corresponding number of packets is obtained from the counter. Before the experiment, the window size and threshold size need to be set in advance. If the  
Figure 2: Flowchart of attack detection method.  
window setting is too small, not enough samples will be obtained, which will affect the accuracy of attack detection. If the window setting is too large, the computing load of the controller will increase.
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 3 Source Coding Protection
### Evaluation of Source Coding Protection
Figure 5: TLR obtained for every PLR for the AI and LPI4 encoding modes. Non-FEC bit streams. Figure 6: PSNR for all the til/frm layouts and all the encoding modes with 3% and 10% of PLR. Non-FEC bit streams. From these results, some conclusions can be drawn. Of the 9 encoding modes evaluated, there are three that do not have good error resilience properties. Two of them (LP, IPx) have shown the expected performance as they do not use any intra refreshing at all. However, the third one (IPx25 ptCTU), which refreshes one of every four CTUs, does not have the expected intra-refresh effect in the bit stream. For the rest of the modes, two of them (LPI4 and IPPP) stand out with respect to the others when the percentage of loss remains low, and another method (IPIP) has a good performance when the percentage of loss increases. With respect to the effect of frame partitioning, we have confirmed in all experiments that the higher the number of tiles per frame, the higher the final video quality. However, it is recommended to use no more than 6 tiles per frame, since the benefits in video quality are negligible for higher values. Although some of the methods exhibit good error resilience properties, this is not enough to guarantee robust video streaming. In the next section, we will evaluate RaptorQ codes, and will search for the best setup to provide the desirable protection to the video packet stream.
## 3 Comparison with Experimental Data
Figure 2: Comparison of computed and measured mass flow parameter against pressure ratio. Figure 4 compares the calculated effective area of the lower entry with the measured data under various degrees of admission. In comparison with the equal admission results, the CFD simulation of unequal admission had more discrepancies with experimental results.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 4 Proposed Architecture
### Proposed Algorithm
#### 4.2.2 Normalization  
Based on the experiments conducted in [30], the best-performing normalization variant across a wide array of scenarios was Gaussian scaling:  
\[Normgauss(o)=max[0,2\cdot cdfgauss(S(o))-1] \tag{3}\]  
where \(o\) is a network flow, \(S\) is an outlier score, and \(cdfgauss\) is the cumulative distribution function (CDF) of a Gaussian distribution with parameters estimated from the data.  
Figure 3: Horizontally and vertically sampled ensemble illustration.  
As can be seen, in Figure 4 the first term of the Taylor expansion of _erf_(\(z\)). When used as a transformation function, the linear approximation better preserves values far from zero than the _erf_(\(z\)) function.  
This can be rewritten using the Gaussian error function _erf_:  
\[Norm_{gauss}(o)=max\left\{0,erf\left(\frac{S(o)-u}{\sqrt{\pi\sigma}}\right)\right\} \tag{4}\]  
where \(\mu\), and \(\sigma\) are the estimated mean and standard deviation, respectively. For our purposes, however, a change to this equation is warranted, as the models in our ensemble are identical (only the data is not) and there is no need for the required result to be inside the [0, 1] interval, by removing the exponential decay towards 1 of the _erf_ function.  
This decay (illustrated in Figure 4) makes the scores of outliers less pronounced and could arguably lead to their dilution. When _erf_ is substituted by its linear approximation around the origin and the maximum function is removed, we get:  
\[Norm_{lin}\left(o\right)=\frac{2}{\sqrt{\pi}}\cdot\frac{S\left(o\right)-u}{ \sqrt{\pi\sigma}}\alpha\frac{S\left(o\right)-u}{\sigma} \tag{5}\]  
This coincides with the standardization procedure which is a frequently used technique in ensemble outlier detection [31]. This normalization function will be used as one choice for our experimental evaluation. The second choice will be the identity function: \(Norm_{id}(o)=S(o)\). This will provide a baseline for evaluation of how much (if at all) the results improve when normalization is employed.
## 1 Introduction
However, most of the current studies focused on separating and detecting a single range of particles, while few studies focused on multi-sized particles detection. [PERSON] et al. investigated the combination of cascade sampling and SAW delay lines for PM\({}_{10}\) and PM\({}_{2.5}\) monitoring [16]. [PERSON] et al. proposed a low flowrate Film Bulk Acoustic Resonator (FBAR) PM sensor using the thermophoretic sedimentary method and a double-stage designed virtual impactor to separate PM\({}_{10}\) and PM\({}_{2.5}\)[17]. These studies have attempted to measure two different ranges of particulate matter, extending the application scenario of mass-sensitive piezoelectric PM sensors. In this paper, we used SAWRs, which were highly sensitive to the particle mass loading effect, and a specially designed cascade impactor to separate various diameter ranges of airborne particles. We proposed a PM sensor's design, fabrication, and experiments based on SAWRs for multi-sized aerosol particles detection, including large coarse particles (LCP), PM\({}_{10}\), and PM\({}_{2.5}\). In Sections 2.1 and 2.2, an innovatively theoretical simulation of the SAWR was performed to extract an optimal response area for particle attachment, and the influence of the distance between the nozzle and the surface of the chip on the particle distribution was researched. We theoretically calculated and designed the 2 LPM cascade impactor we describe in Section 2.3. An experimental platform was built to conduct a series of experiments, including the sensor's response test for the combustion of leaf fragments and a repeatability test, and we verified the sampling performance of each stage of the impactor in the last section. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Complexity** & **Instrument** & **Accuracy** & **Resolution** &
\begin{tabular}{c} **Measurement Cycle** \\ **Time** \\ \end{tabular} \\ \hline Gravimetric & High & BETTERSIZE BTPM-AS1 & 10 \(\upmu\)g (electronic balance) & 0.01 L/min (Airflow) & 1–100 h \\ TEOM & Low & Thermo Scientific 1405-F TEOM & \(<\)\(\pm\)1 \(\upmu\)g/m\({}^{3}\) (24 h) & 0.1 \(\upmu\)g/m\({}^{3}\) & 1–24 h \\ \(\upmu\)-ray attenuation & Low & METONE BAM 1020 & \(<\)\(\pm\)2 \(\upmu\)g/m\({}^{3}\) & 0.1 \(\upmu\)g/m\({}^{3}\) & 1 h \\ Light scattering & Low & AeroQual Dust Sentry Pro & \(<\)\(\pm\)5 \(\upmu\)g/m\({}^{3}\) & 0.1 \(\upmu\)g/m\({}^{3}\) & 1 min–24 h \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of particulate matter monitoring equipment.
## 4 Results and Discussion
### Residue Analysis
#### 4.3.1 DRAGO 0511/DR04-15  
Figure 9 shows a micrograph obtained via SEM-EDS, used to identify elemental associations. The image reveals particles with Fe/S/O and Si/S/O associations, as well as impurities with Fe/O, Al/Si/O, and Mg/Si/O associations. The Si/S/O particles range in size from 29.0 \(\upmu\)m to 48.8 \(\upmu\)m in length, while the Fe/S/O particles range from 7.6 \(\upmu\)m to 30.1 \(\upmu\)m in length.  
Figure 8: Effect of potential and pH on the extraction of Mn and Co from ferromanganese crusts.  
Figure 9: Scanning electron microscopy (SEM-EDS) micrograph of residue of Drago 0511/DR04-15. Magnification 295\(\times\).  
Figure 10 depicts particles with Fe/S/O associations (orange in (1), green in (2), and gray in (3)) and Si/S/O associations (purple in (1), blue in (2), and pink in (3)). Additionally, impurities with Fe/O associations are shown (lemon green in (1) and (2), and light gray in (3)), as well as Al/Si/O associations (dark green in (1) and uncolored in (2) and (3)) and Mg/Si/O associations (pink in (2), and uncolored in (1) and (3)). The analysis confirms the absence of contaminating elements and demonstrates that no cobalt or manganese compound precipitation occurred (see Figure 11).  
Figure 11: Scanning electron microscopy elemental analysis for sample DRAGO 0511/DR04-15 (SEM-EDS).  
Figure 10: SEM micrograph with EDS analysis (color change) of residue Drago 0511/DR04 15. Magnification 295\(\times\) (100 \(\upmu\)m).