esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Beacon-Based Hazard Alarm System Design
### Structure of the System
#### 3.2.1 Sensing and Data Generation (Backend)  
The primary function of the BLE beacon is to keep broadcasting Bluetooth packages in the form of BLE signals to the surrounding environment. This research used an LTE  
Figure 1: Overview of the design rationale.  
Figure 2: The structure of the system.  
beacon manufactured by Estimote Inc. for the beacon signal generating component. A BLE package can contain several different types of data, depending on the type of the beacon sensor, and the origin of data is identifiable, as each beacon sensor has a unique ID number [51].  
The second part of generating proximity data is to transmit the BLE package. A BLE package is a type of Bluetooth signal which beacon signal-reading devices can receive. While the beacon sensor device continuously sends out Bluetooth packets, a corresponding receiving device can receive Bluetooth signals within a certain range. This means that in the proposed system design, two LTE beacon sensors are used: one as the BLE signal-generating unit and the other as the BLE signal-receiving unit. The distance between a beacon and a receiving device varies from 7 m to 200 m, depending on the different types of Estimote Bluetooth equipment [52]. The Bluetooth Special Interest Group (SIG)'s research [53] found that the factors that influence the transmission distance include the radio spectrum, physical layer, receiver sensitivity, transmit power, antenna gain, and path loss.  
The third part of data generation is to parse the Bluetooth package. Another LTE beacon sensor works as a BLE signal receiver and collects BLE packages when the sensor is located within the Bluetooth signal distance range. In this way, the LTE beacon sensor can generate proximity data as the person who holds the sensor gets close to the location of the beacon signal's origin, which is the location of a hazard in the context of this application. Another IoT application needs to be installed in the receiver device to extract the data within the package. The IoT application receives, parses, and analyzes the Bluetooth signals, translating the Bluetooth packages into data transmitted across the internet by mobile data. In this manner, the Bluetooth signal-receiving device works as a micro-Bluetooth signal processor. In addition to data transmission, the application also synchronizes the processed data with the server platform to trigger the hazard alarm process.
## 2 Basic Theory
### Probabilistic Neural Network (PNN)  
The Probabilistic Neural Network (PNN) [26] combines Gaussian probability-density-function estimation and Bayesian optimization rules to complete classification estimation based on probability density. Therefore, the PNN has the following advantages: a simple learning process and fast training speed, more accurate classification and good fault tolerance, etc.  
The network topology of the PNN is shown in Figure 1. Its structure mainly includes an input layer, a hidden layer and an output layer. Different from the traditional neural network, the output layer of the PNN uses the competitive output of the probability-estimation method to replace the previous linear output, so as to obtain an effective neuron through the probability, and the output of the neuron is used as the final classification result.  
The hidden layer and the output layer are connected by a radial basis function which is a Gaussian function, as shown in Equation (6). After the neural-network weights are obtained through training, the final result of the output layer can be obtained.  
\[y_{k}(x,\sigma)=\frac{1}{l_{k}(2\pi)^{n/2}\sigma^{n}}\sum_{i=1}^{l_{k}}\exp(- \sum_{j=1}^{n}\frac{\left(x_{ij}^{(k)}-x_{j}\right)^{2}}{2\sigma^{2}}) \tag{6}\]  
Among them, \(l_{k}\) represents the number of \(k\) categories; \(n\) represents the number of features; \(\sigma\) represents the smoothing parameter, generally between 0 and 1; \(x_{ij}^{(k)}\) represents the \(j\)-th data in the \(i\)-th neuron under the \(k\)-th category.
## 2 Related Works
### Resource Allocation Based on the Fractional Frequency Reuse  
Most studies of fractional frequency reuse are based on an inter-cell interference coordination scheme (ICIC) [30], where all frequencies are available in the central region of a cell and partitioned frequencies are allocated for cell-edge areas. It reduces inter-cell interference because cell border regions use orthogonal frequency to uplink. In [31], a directional FFR (D-FFR) is considered, where a cell is divided into three sectors based on the ICIC scheme. From optimal configuration, the study shows that the D-FFR increases 60% of the throughput capacity relative to the omnidirectional-FFR scheme. In [32], the authors adopt FFR with multiple-input-multiple-output (MIMO) and heterogeneous networks (Hetnets). The authors in [33] propose a resource allocation with FFR for Hetnets and LTE Femtocell systems, which employs very high number sectors in a cell. In the [34], a buffered FFR scheme is proposed for LTE-Advanced Hetnets. It also adopts a more complex cell partitioning scheme. The authors in [35] propose an automata-based FFR scheme to allow a self-organized network to emerge. As shown in the above studies, FFR is still considered for various wireless systems to management resources.
## 3 Results  
The means and SD values for the area of the enamel removed after the interproximal enamel reduction procedure at the mesial and distal interproximal enamel surfaces of the STL digital files are displayed in Table 1 and Figure 8.  
Figure 7: (**a**) Points randomly distributed at the inner surface of the interproximal enamel reduction mesh of the anterior tooth; (**b**) linear parallel projected to the tooth to the inner surface of the interproximal enamel reduction mesh of the posterior tooth; (**c**) points where the projected lines crossed the inner surface of the distal interproximal enamel reduction mesh of the posterior tooth; and (**d**) points at the inner surface of the distal interproximal enamel reduction mesh of the posterior tooth.  
The distal surface of the teeth, which underwent the interproximal enamel reduction procedure, showed a high enamel reduction area (3.53 \(\pm\) 3.08 mm\({}^{2}\)) compared to the enamel reduction area of the mesial surface of the teeth (2.97 \(\pm\) 3.05 mm\({}^{2}\)). The paired \(t\)-test did not show statistically significant differences between the mesial and distal areas of the removed enamel (\(p\) = 0.529).  
The mean and SD values for the volume of the enamel removed after the interproximal enamel reduction procedure at the mesial and distal interproximal enamel surfaces of the STL digital files are displayed in Table 2 and Figure 9.  
The distal surface of the teeth submitted to the interproximal enamel reduction procedure showed a high enamel reduction volume (0.32 \(\pm\) 0.22 mm\({}^{3}\)) compared to the enamel reduction volume of the teeth submitted to the mesial surface in the interproximal enamel reduction procedure (0.22 \(\pm\) 0.16 mm\({}^{3}\)). The paired \(t\)-test did not show statistically significant differences between the mesial and distal volumes of the removed enamel (\(p\) = 0.430).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Surface** & \(n\) & **Mean** & **Median** & **SD** & **Minimum** & **Maximum** \\ \hline Mesial & 14 & 2.97 & 3.05 & 1.78 & 1.17 & 6.86 \\ Distal & 12 & 3.53 & 3.08 & 2.25 & 1.47 & 9.55 \\ \hline \end{tabular}
\end{table}
Table 1: Descriptive analysis of the area (mm\({}^{2}\)) of the enamel removed after the interproximal enamel reduction procedure at the mesial and distal interproximal enamel surfaces of the STL digital files.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Surface** & \(n\) & **Mean** & **Median** & **SD** & **Minimum** & **Maximum** \\ \hline Mesial & 14 & 0.22 & 0.16 & 0.22 & 0.02 & 0.85 \\ Distal & 12 & 0.32 & 0.22 & 0.31 & 0.04 & 1.16 \\ \hline \end{tabular}
\end{table}
Table 2: Descriptive analysis of the volume (mm\({}^{3}\)) of the enamel removed after the interproximal enamel reduction procedure at the mesial and distal interproximal enamel surfaces of the STL digital files.  
Figure 8: Box plot of the area of the enamel removed after the interproximal enamel reduction procedure at the mesial and distal interproximal enamel surfaces of the STL digital files. “*” represent an outlier.
# Detection of Smoking in Indoor Environment Using Machine Learning
## 3 Results  
### Feature Extraction and Correlation-Based Feature Selection  
After performing EDA based on sensing data transmitted from CO2, PM 2.5, PM 10, and TVOC sensors extracted for indoor air quality monitoring, correlations between these values are derived. Basic data analysis is done using Python's Pandas library. Table 4 shows statistical information of CO2, TVOC, PM 2.5, and PM 10 among the sensor data used in the experiment. It has a total of 3516 data, and the sensor data measured every second is acquired and transmitted to the collection server.  
The correlation between the measured data was derived from two situations. First, we looked at the correlation in the overall situation (smoking, cooking, heating). In Figure 8, the correlation between TVOC and CO2 was 0.915857, showing a high correlation. There was a high correlation (0.999868) between PM 2.5 and PM 10, and the correlation between TVOC and PM 10 was 0. 197758, indicating a low correlation.  
Second, we analyzed the correlation in smoking situations. As a result, looking at Figure 9, the correlation between TVOC and CO\({}_{2}\) was 0.738492, and the correlation between PM 2.5 and PM 10 was 0.997929. The correlation between PM 10 and TVOC was 0.230974, showing the lowest correlation.
## 5 System Simulation  
### Simulation Setup  
In order to evaluate CENTERA and prove its correctness in providing routing information while creating a trusted environment and isolating the bad nodes, we have used the TOSSIM simulator to simulate a grid of Micaz sensors running TinyOS [25]. We used different topologies and different network sizes, a linear network of 30 nodes, a tree of 40 nodes (where each node has three daughters), and a grid network of 5 \(\times\)5, 9 \(\times\)9, 15 \(\times\)15, and 31 \(\times\)31 networks.  
As for the authentication technique to use, depending on our previous work [23], we choose the asymmetric key cipher technique PBC, which was found out to be the best authentication technique to be incorporated into CENTERA in a hostile environment. We used the TinyPairing library [26,27] and modified the BLS-SS (revised) and the BF-IBE (revised) to have an IBE-SS to sign a message using the private key and an IBE-Encryption to encrypt other node's signature using the private key; where the public key (ID) is used to verify and decrypt respectively.  
This way each receiving node can be sure that the source node is indeed the true sender of the packet and the BS can calculate the trust values for the nodes, and properly construct routing paths and detect and isolate malicious/malfunctioning nodes. The attacker model in this case can be assumed to be strong, with the power to take over a node and use it to send packets. With PBC, CENTERA can detect the malicious or compromised node and isolate it completely from the network, thus increasing the network lifetime.  
In the network tested, we varied the hop cost between 0, 0.25, 0.5, and 1 and tested the correctness of our protocol and the energy overhead imposed by the additional transmission/receipt and the cryptographic calculations namely signing/verifying and encryption/decryption.  
We also assigned several bad nodes to see the effectiveness of our protocol as follows: Node 12 partially non-cooperative dropping one out of every three packets forwarded through it. Node 7 is an outsider node not belonging to the system and node 23 is a partially malicious node sending one badpacket every three packets it sends. Node 17 is declaring sending packets through a non-DL neighbor. Node 3 is incrementing its DL counters and node 19 is incrementing one of its UL counters.
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
## 5 Proposed HLQR Controller Design
#### Adaptive Structure for Error Elimination Based on HLQR  
The adaptive structure of our HLQR highlights the phenomenon of error elimination, as shown in Figure 9. It takes \(u(t-1)\) as an input power that varies with time and results in Ym(t) as an intermediate output. It helps us to compute the inverse of \(u(t-1)\) via the Riccati equation, presented in Equation (19). This is a first-order non-linear differential equation in which complex higher order non-linear equations are avoided. Consequently, Equation (22) is used for the error eliminations in our controller.  
\[E(t)=R_{u}(t)-y_{o}(t)+y_{m}(t) \tag{22}\]
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
###### Abstract  
Emerging applications in fields such as extended reality require both a high throughput and low latency. The millimeter-wave (mmWave) spectrum is considered because of the potential in the large available bandwidth. The present work studies mmWave Wi-Fi physical layer latency management mechanisms, a key factor in providing low-latency communications for time-critical applications. We calculate physical layer latency in an ideal scenario and simulate it using a tailor-made simulation framework, based on the IEEE 802.11 ad standard. Assessing data reception quality over a noisy channel yielded latency's dependency on transmission parameters, channel noise, and digital baseband tuning. Latency in function of the modulation and coding scheme was found to span 0.28-2.71 ms in the ideal scenario, whereas simulation results also revealed its tight bond with the demapping algorithm and the number of low-density parity-check decoder iterations. The findings yielded tuning parameter combinations for reaching Pareto optimality either by constraining the bit error rate and optimizing latency or the other way around. Our assessment shows that trade-offs can and have to be made to provide sufficiently reliable low-latency communication. In good channel conditions, one may benefit from both the very high throughput and low latency; yet, in more adverse situations, lower modulation orders and additional coding overhead are a necessity.  
physical layer; latency; millimeter-wave; Wi-Fi; WiGig; IEEE 802.11 ad; time-critical applications; Pareto optimality +
Footnote †: journal: _electronics_  
_Acticle_  
Physical Layer Latency Management  
Millimeter-Wave Wi-Fi
## 1 Introduction
### MQTT Architecture  
MQTT is regarded as the most advantageous connection protocol for M2M and IoT. It employs the publish/subscribe approach for easy deployment and flexible transition see Figure 1 depicts the MQTT publish/subscribe pattern. MQTT is suitable for re-source-constrained devices used on low-bandwidth or unstable networks. MQTT, like the Hypertext Transfer Protocol, is built on top of the TCP protocol (HTTP). It is designed, therefore, to provide a lower communication cost than HTTP [12], however, it sends messages at three different QoS levels. MQTT is divided into two distinct standards. MQTT v3.1 is the first, followed by MQTT-SN, sometimes known as MQTT-S (V1.2) [13]. MQTT-SN, as opposed to MQTT, employs the UDP protocol. Amazon Web Services just began supporting the MQTT protocol [14]. MQTT is a numerous-to-many networking standard that sends messages among several devices via a central broker.  
MQTT devices communicated with the broker using a long-lived outbound TCP link, which was initially burdened by limited devices. To allow communication through MQTT, all devices need to be aware of the message formats in advance [15]. An MQTT broker is an intermediary entity that connects MQTT clients. Specifically, a MQTT broker receives messages generated by clients, filters them by subject, and spreads them to subscribers.
## 5 Conclusions  
The study provided useful insights into the EPS-synthetic potential of Antarctic yeasts _V. victoriae_ and _Tremellomycetes_ sp. To the best of our knowledge, there are no published reports on the production and characterization of EPSs from these yeast strains. Chemical, chromatographical, and spectral investigations indicated that yeast strains produced high molecular weight hetero-mannan and \(\beta\)-glucan type EPSs. In addition, yeast strains may be cultivated relatively easily in a bioreactor, which can guarantee the accumulation of EPS in a higher amount, taking a short time of cultivation. In conclusion, the key findings from this study may form a basis for a better assessment of the potential for the practical application of EPS in the food, cosmetic, and pharmaceutical industries and yeasts in biochemical engineering and industrial biotechnology.  
Conceptualization, S.R.-V., M.O. and Y.G.; methodology, S.R.-V., M.O. and Y.G.; formal analysis, M.O., Y.G. and M.K.; investigation, S.R.-V., M.O., M.K., A.A. and V.K.; resources, S.R.-V., M.O. and M.K; data curation, S.R.-V., M.O., Y.G. and M.K.; writing--original draft preparation, M.O., Y.G. and S.R.-V.; writing--review and editing, M.O., S.R.-V., Y.G. and M.K.; visualization, S.R.-V., M.O. and Y.G.; supervision, S.R.-V. and M.O.; project administration, S.R.-V.; funding acquisition, S.R.-V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Bulgarian Ministry of Education and Science through the National Centre for Polar Studies, and Sofia University \"St. Kliment Ohridski\" in the frames of the National Program for Polar Studies 2017-2021, grant number 70.25-173/22.11.2019.  
Not applicable.  
Data availability Statement: Data is contained within the article.  
We thank the staff of the Bulgarian Antarctic Expedition for their logistic support. M.O. would like to express his gratitude to [PERSON], M.Sc. for her irreplaceable technical assistance.  
The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 5 Conclusions  
This paper has presented a new fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques, where an innovative technique was proposed for contrast enhancement of fingerprint images, based on a variant of adaptive histogram equalization called CLAHE along with a combination of iterative FFT and Gabor filters. The minutiae features are then extracted from the thinned binary fingerprint image by the use of a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature descriptors of two feature points was used as a feature matching similarity metric of two fingerprint images. In addition, an iterative RANSAC algorithm was employed to refine the matched points. Experiments and evaluations on two benchmarking fingerprint databases showed that the proposed framework has achieved state-of-the-art performance in terms of accuracy and robustness while maintaining real-time performance. Future work will include larger experiments on publicly available low-quality fingerprint databases along with a quantitative comparison of the framework to additional state-of-the-art baselines.  
Conceptualization, S.B.; methodology, S.B.; software, R.Y. and S.B.; validation, S.B. and A.A.-H.; formal analysis, S.B. and A.A.-H.; project administration, A.A.-H.; funding acquisition, A.A.-H. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the Federal Ministry of Education and Research of Germany (BMBF) (Robo-Lab no 03 zz04x02b, HuBa no. 03 ZZ0470 367, RoboAssist no. 03 ZZ0448L) within the Zwanzig20 Alliance 3 Dsensation.  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
We are extremely grateful to the anonymous referees for their insightful comments and valuable suggestions which helped us to considerably improve the content of the paper. The authors declare no conflict of interest.
# The Evaluation Method of Particle Clustering Phenomena in RAP
## 4 Results and Discussion
### Other Attempts to Evaluate the Old material Clustering Degree  
The angularity results of all aggregates are shown in Figure 14. Compared with the new aggregates, the clustering of the old aggregates results in an increase of the edge angle, which makes the RAP aggregates more fragile. RAP aggregates subjected to a secondary crushing can reduce the edge angle to a certain extent, which also accounts for the relatively high clustering stability of the old aggregates. From the results of the angularity test, RAP material particles in a flat shape tend to have a higher edge angle and poorer cluster stability, and thus the edge angle could to some extent represent the clustering degree of RAP materials. The greater the edge angle, the larger the RAP material clustering degree. The worse stability of the cluster could cause gradation variation of rejuvenated RAP material.  
The sphericity results of RAP materials are shown in Figure 15. As is shown in Figure 15, compared with the new materials and the flat shape of the old materials, the sphericity of flat old material is obviously smaller. Given that the old flat particles clustering degree is higher and the clustering stability is poorer, the sphericity index might be used to represent the old materials clustering degree. The smaller the sphericity value is, the higher the RAP material clustering degree and the worse the stability of the cluster will be.
###### Abstract  
Satellite atomic clocks are the key elements for position, navigation, and timing services of the Global navigation satellite system (GNSS); it is necessary to research the characteristics of BDS-3 on-orbit satellite atomic clocks for their further optimization. In this study, clock offset data with a duration of 620 days since BDS-3 was officially commissioned were applied to long-term characteristic analysis. To begin with, the precision clock offset data of Deutsches geoforschungs zentrum (GFZ) processed by a MAD-based method were used as reliable test data. Herein, the working principle and main characteristics of satellite atomic clocks are analyzed and discussed, and thus, a comprehensive long-term characteristic analysis scheme is designed. On this basis, the performance indicators--mainly including physical parameters, periodic characteristics, frequency drift rate, frequency accuracy, frequency stability--were calculated and analyzed respectively, revealing the long-term characteristics of the BDS in orbit satellite atomic clocks during the test period. The results of experimental data testify that the performance of BDS-3 satellite atomic clocks is significantly superior to that of BDS-2, especially in terms of drift rate and frequency stability, and the performance of passive hydrogen maser (PHM) is generally superior to that of rubidium atomic frequency standards (RAFS). Within about half a year since BDS-3 was officially commissioned, the frequency stability of BDS-3 satellite atomic clock gradually improved and then reached the order of \(10^{-15}\), reflecting the effectiveness of system maintenance and inter-satellite link. Furthermore, some novel conclusions are drawn, such as the long-term period term of the fitting residual and drift rate, which may be caused by the earth's revolution.  
GNSS; BDS-3; satellite atomic clock; frequency stability; frequency drift rate +
Footnote †: journal: _Remote Sensing_, 2022, 14, 4535. [[https://doi.org/10.3390/rs14184535](https://doi.org/10.3390/rs14184535)]([https://doi.org/10.3390/rs14184535](https://doi.org/10.3390/rs14184535))