esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The mechanical properties of glasses are of great importance in practical applications. The possibility of glass strengthening (which is inherently a fragile material) plays an important role in the production of: display windows, in mobile personal electronic devices, cockpit windshields for aircrafts and vehicles, windows in architecture, panels for large displays, chemical equipment (pharmacy) and safeguard protections of all kinds [1]. In these cases, it is important to be able to modify the hardness of the glass surface. For this purpose, different technological methods are used [2]. In addition to the commonly used thermal method, the chemical ion exchange method is also utilized [3; 4; 5; 6; 7] at temperatures below the glass transition temperature. This method can be applied to finished glass products. The effects of the ion exchange process are the stresses created in the surface area of the glass. They have the character of compressive stresses and reach the maximum value (in the order of hundreds of MPa) at the glass surface [8]. The stresses fade away at depths from a few to tens of \(\upmu\)m (depending on the depth of the ion exchange area) [9; 10]. The resulting stresses are examined using various methods [2]. In addition to mechanical methods (determination of the modulus of rupture, impact testing), optical methods are also used [11; 12]. Among them the waveguide method deserves more attention [13]. It is a non-destructive method, used for flat forms of glass, based on measurements of propagation constants of waveguide modes for TE and TM polarization [14]. Its advantage is the possibility of determining the function of tension distribution from the surface into the glass. For its application, it is necessary to know the elasto-optic constants of a given type of glass.  
The paper presents the use of this method for determining the function of stress distribution generated by K\({}^{+}\)\(\leftrightarrow\)Na\({}^{+}\) ion exchange in BK7 glass. A method of monitoring the occurring stresses (resulting from the ion exchange, based on the determination of diffusion profiles of dopant ions introduced into the glass) in the real time of the process [15] is also presented. The results obtained from the monitoring were compared with the results of optical measurements.
## Underlying Theories
### 5G Private Network for Industrial Areas  
The 5G private network, also known as the 5G local network or 5G non-public network, is a local area network that underlies 5G technology, having all the characteristics of a 5G network, including a lower latency and faster speeds. It is believed that 5G private networks will provide better overall connectivity, with a variety of benefits and enhanced services for users of the limited geography network. The 5G private network is likely to become the best choice for many of the world's corporations, especially for industrial sectors, such as manufacturing plants, remote offices, and terminals. Figure 2 illustrates the 5G private network [16].  
One significant aspect of 5G private networks is their ability to improve automation in the industry by using certain advanced technological platforms, such as artificial intelligence technologies (AI), Internet of Things (IoT), and machine learning [17]. Thus, 5G private networks may become essential to creating smart factories, including industrial campus network solutions, smart industrial areas, and smart manufacturing companies [18].  
One function that sets 5G private networks apart from other services is that they allow a company to work as a mobile private 5G network operator (MPNO). With this function, the company can set up its 5G private network infrastructure and have absolute authority over its private network, including its configuration, procedures, technical performance, and other related network management issues. Alternatively, the company can subscribe to public service operators who also run public 5G private network services. Under this conception, the private network is only part of the business model run by the 5G service operator, in which the company is simply classified as a corporate user [19].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Freq Band 26/28 GHz**} & \multicolumn{4}{c}{**Modulation**} \\ \cline{2-5}  & **QPSK** & **16 QAM** & **64 QAM** & **256 QAM** \\ \hline
100 MHz & 2.16 Gbps & 4.31 Gbps & 6.47 Gbps & 8.62 Gbps \\
400 MHz & 8.62 Gbps & 17.24 Gbps & 25.86 Gbps & 34.48 Gbps \\ \hline \end{tabular} Source: Authors’ calculations.  
\end{table}
Table 1: Peak data rate vs. Modulation for 26/28 GHz.  
Figure 1: 5G Usage Scenario.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
###### Abstract  
The aim of the study was to develop a method for automatic classification of the three spatial audio scenes, differing in horizontal distribution of foreground and background audio content around a listener in binaurally rendered recordings of music. For the purpose of the study, audio recordings were synthesized using thirteen sets of binaural-room-impulse-responses (BRIRs), representing room acoustics of both semi-anechoic and reverberant venues. Head movements were not considered in the study. The proposed method was assumption-free with regards to the number and characteristics of the audio sources. A least absolute shrinkage and selection operator was employed as a classifier. According to the results, it is possible to automatically identify the spatial scenes using a combination of binaural and spectro-temporal features. The method exhibits a satisfactory classification accuracy when it is trained and then tested on different stimuli but synthesized using the same BRIRs (accuracy ranging from 74% to 98%), even in highly reverberant conditions. However, the generalizability of the method needs to be further improved. This study demonstrates that in addition to the binaural cues, the Mel-frequency cepstral coefficients constitute an important carrier of spatial information, imperative for the classification of spatial audio scenes.  
Keywords:binaural audio; machine-listening; machine-learning; spatial audio scene classification +
Footnote †: journal: Applied Soft 2019, 9, 1724; doi:10.3390/appj09091724
## 3 Results and Discussion
### Evaluation of Environmental Effects
#### 3.3.2 Environmental Effects of Double-Axis Loading  
Table 6 shows the comparison of temperature stresses and coupling stresses in different seasons under duplex loading. As can be seen from Table 6, the temperature-load coupling stresses show an increase and then a decrease in the depth direction in spring and winter, and a gradual decrease in the fall. The degree of influence of temperature stresses on temperature-load coupling stresses shows a gradual decrease in winter along the depth direction. The effect of temperature stress on coupling stress at the road surface can reach 92%. The spring and fall seasons showed a decrease followed by an increase, with the least effect at 9 cm. Overall, the degree of temperature influence on the road surface along the depth direction is 76.3%, 45.7%, 30.0%, and 26.3%, in that order.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 1 Introduction
While used extensively in the past, they are becoming less relevant due to increases in threat variability, pervasive use of encryption and other evasive techniques. Anomaly-based detection systems rely on anomaly detection, which is the problem of finding patterns in data called \"outliers or anomalies\" that do not conform to expected behavior [8]. The major benefit of anomaly-based methods is the ability to detect previously unknown threats or whole classes of attacks based on broad behavioral characteristics rather than precise signatures. This often comes at the expense of higher false-positive rates and greater complexity. High false-positive rates cause a need for manual sifting through the data to extract actionable knowledge, which limits the utility of anomaly-based IDSs in practice. In this scheme, we focus on improving an anomaly-based IDS. The major challenges and characteristics of such systems are described in the following subsection.
## 2 Materials and Methods
### System Design
#### 2.1.3 Design of Satellite Communication Network Terminals  
The satellite communication network terminal integrates BeiDou multi-frequency antenna, radio frequency, baseband, and master control and other functional units, which can realize self-test and initialization, short-message communication, satellite precision timing, and other functions. Each BeiDou satellite short message is categorized into either  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Logical Role of Node** & **Connected Sensors** & **Parameters Monitored** \\ \hline Coordinator Node & – & Summarize all parameters \\ \hline End Device Node A & Input-level transmitter & Water remaining in tank \\  & Infrared temperature sensors & Water temperature in tank \\ \hline End Device Node B & Temperature and humidity sensors & Temperature and humidity in environment \\  & Wind speed and direction sensors & Instantaneous wind speed and direction \\ \hline  & Carbon monoxide concentration sensor & Carbon monoxide concentration \\ End Device Node C & Smoke concentration sensor & Smoke concentration \\  & Sulfide concentration sensor & Sulfide concentration \\ \hline \hline \end{tabular}
\end{table}
Table 2: Types of sensors connected to each node of local self-organizing network and parameters monitored.  
Figure 2: (a) Distribution of Zigbee channels over 2.4 GHz band. (b) Four-layer structure of Zigbee protocol stack. (c) Logical structure between nodes of local self-organizing network.  
a RSMC or a global short message (GSMC), and short messages are coded by using mixed coding or code coding mode [14; 38].  
The RSMC service used in this system utilizes the GEO satellite to provide RSMC to users in China and the surrounding areas, using mixed codes to encode the RSMC to be sent. Statements begin with the statement start delimiter $ and end with the statement terminator <CR> <LF>.  
The BeiDou satellite navigation system integrates the Regional Navigation Satellite System (RNSS) and RDSS, i.e., both RNSS and RDSS services are integrated in the navigation satellites of the system as well as the operation and control of the system [39; 40]. The positioning service provided by the BeiDou RDSS system is active, with positioning accuracy of 100 m, whereas the positioning provided by the RNSS system is passive, with positioning accuracy of within 5 m. Therefore, this system adopts the short-message communication service provided by the BeiDou RDSS system and the positioning service provided by the BeiDou RNSS system. Figure 3 shows the signal processing flow of the satellite communication network terminal.
## 4 Results and Discussion
### Importance of the Soil Descriptors and Incidence Angle in Backscattering Estimates
Regardless of the considered satellite configuration, the two roughness variables derived from the profiles measured in the parallel direction are associated with levels of importance higher than those measured in the perpendicular direction (averages of 7.0 and 4.3%, respectively). Finally, the texture components show limited importance, which reaches 18.0% in the case of the C-band with VH polarization. This level is lower for signals acquired with HH polarization, that is, 6.2% and 9.2% for the X- and C-bands, respectively. The values of importance associated with the clay content are higher (mean of 7.2%) than those of the sand content (5.3%), regardless of the satellite configuration considered. The moderate influence of texture on signals acquired in the X- and C-bands is thus consistent with the results presented in [68], with the effects of the fractions of sand, silt and clay being more pronounced on signals acquired at lower frequencies (e.g., in the L-band).
## 5 Results and Discussion
### Results and Discussion of Base Classifier Models
#### 5.3.2 UNSW_2018_IoT_Botnet Dataset  
The Bot-IoT dataset was developed in 2018 and published in 2019 by the New South Wales University (UNSW). It is a contemporary and authentic dataset for training machine learning models to effectively identify and mitigate Botnet attacks within Internet of Things (IoT) networks. The dataset comprises 72 million instances, consisting of three dependent and forty-three independent features. The dataset encompasses various cyber-attacks, such as OS and Service Scan, DoS, DDoS, Data exfiltration, and Keylogging. Additionally, the DoS and DDoS attacks are further categorized based on the specific protocol. At first, we extract packet fields from the DDoS attack SDN dataset. The number of samples used is 100000. The extracted feature appears in Figure 8.  
The UNSW 2018 dataset involves pkSeqID to represent the row identifier, Proto is the representation of textual for transaction protocols that are resent in the network flow, saddr is the IP address of the source, sport is the port number of the source, daddr is the IP address of the destination, dport is the port number of the destination, seq is the argus sequence number, stddev is the aggregated records of the standard deviation, min is the minimum duration of the standard deviation, state number represents the feature state numerical representation, mean is the aggregated records of the average deviation, date is the packets per second of destination-to-source, srate is the packets per second of source-to-destination, max is the aggregated records' maximum duration, attack is the class label where 0 represents normal traffic and 1 represents the attack traffic, category is the category of traffic, subcategory is the subcategory of traffic, and dbytes is the byte count of destination-to-source.  
Hence, like the previous dataset, we split the data into 80% for training and 20% for testing. Attack traffic is labeled 1, the normal traffic is labeled 0, and we train the model for 500 epochs. The classification results appear in Table 3 and Figures 9 and 10.  
Figure 8: Extracted feature from the original UNSW 2018 dataset packets.  
For further checking, we utilize the standard deviation metrics to quantify the extent to which the attribute value of a feature deviates from its mean value. The standard deviation categorization aids in the identification of features that deviate from the average value by highlighting values that are both above and below the mean. Figure 12 shows the standard deviation result for the proposed model for two datasets.  
As shown in Figure 12 and Table 4, the proposed model has a lower variance. As a result, the proposed approach outperforms in terms of accuracy and reliability, and the learning curves are smoother, indicating that the proposed model is consistent. As a result, it is not only more accurate, but it is also more robust and consistent.
## 3 Pseudolite-Based Positioning System for Legacy GNSS Receivers
### User Algorithm Concept  
In this system, pseudolites simulate GPS signals received at a particular receiving point \(R_{{}_{\tau}}\), and each pseudolite corresponds to the simulated GPS satellite. If the distances between the user terminal and each pseudolite are identical, as in Figure 5a, the output of the GPS receiver in the user terminal becomes the receiving point \(R_{{}_{\tau}}\). The GPS receiver regards the received signals transmitted from not the pseudolites but the GPS satellites, as shown in Figure 5b, and the additional signal delay \(d_{{}_{0}}\) due to the use of pseudolites becomes an additional common bias that does not affect the position result.  
If the distances between the user terminal and each pseudolite are different from each other, as in Figure 6a, the output of the GPS receiver in the user terminal will also differ from the receiving point. From the perspective of the GPS receiver, the signals from Satellite 1 are delayed by \(d_{{}_{0}}+d_{{}_{1}}\), and signals from Satellite 2 are delayed by \(d_{{}_{0}}+d_{{}_{2}}\). Therefore, the output of the GPS receiver is neither the receiving point \(R_{{}_{\tau}}\) nor the user position.  
Figure 4: Block diagram of user terminal.  
Although the output of the GPS receiver is not the desired one, it contains some information about the delays \(d_{1}\), and \(d_{2}\). Therefore, using the output position of the GPS receiver and the information about simulated GPS satellites and pseudolites obtained from the data server, it is possible to estimate \(d_{1}\) and \(d_{2}\) and use this information to re-estimate the desired user position.
## 2 Results and Discussion
### Optimization of Headspace Solid-Phase Microextraction and Desorption  
In the SPME method, fiber coatings with high distribution coefficients must be used to increase the extraction efficiency of the compounds. To select an SPME fiber, the four different commercially available fibers (PDMS/DVB, DVB/CAR/PDMS, CAR/PDMS, and polyacrylamide) were tested for extraction of 100 ng of the 2-nonenal standard solution by HS-SPME. Of these fibers, the 65 uM StableFlex PDMS/DVB fiber extracted 2-nonenal most efficiently (Figure 2A), and the fiber was used in subsequent experiments. To optimize HS-SPME extraction conditions, 100 ng of 2-nonenal standard solution in a 40 mL vial was exposed to 65 uM StableFlex PDMS/DVB fiber at various temperature and times. These experiments showed that 2-nonenal could be efficiently extracted and concentrated by exposing the fiber to the headspace at 50 \({}^{\circ}\)C (Figure 2B) for 45 min (Figure 2C). Although the extraction efficiency was highest at 30 \({}^{\circ}\)C, the extraction temperature set at 50 \({}^{\circ}\)C was less affected by the outside air temperature and could be kept. On the other hand, the time of the fiber exposition in the GC sample vaporization chamber at 230 \({}^{\circ}\)C was tested to optimize the desorption condition. The 2-nonenal extracted on the fiber was easily desorbed within 5 min by heating in the GC sample vaporization chamber, and carryover was not observed because the fiber was washed during exposition.  
The absolute amount of 2-nonenal extracted by the HS-SPME method was calculated by comparing peak height count with direct injection onto the GC column. From the sample of 20 ng, 8.0 ng (40%) of 2-nonenal was extracted onto the PDMS/DVB fiber by HS-SPME under optimized conditions. Although the extraction yield was low, the reproducibility was good (relative standard deviation: RSD = 4.7%, \(n\) = 3). Figure 3 shows the chromatograms obtained by direct injection and HS-SPME techniques. The sensitivity of the SPME method showed a 40-fold increase in sensitivity compared with when a solution of the same concentration was directly injected (1 uL).  
Figure 2: Effects of (**A**) fiber coatings, (**B**) temperature, and (**C**) time on the HS–SPME of 2-nonenal. For HS–SPME, 100 ng of 2-nonenal in gauze was extracted by (**A**) fiber exposition at 50 \({}^{\circ}\)C for 45 min, (**B**) for 45 min with PDMS-DVB, and (**C**) at 50 \({}^{\circ}\)C with PDMS-DVB.
## 4 Calculation Example of Slope Stability under Freeze-Thaw Conditions
### Analysis of Calculation Results
#### 4.4.3 Effect of Slope Coefficient on Slope Stability under Freeze-Thaw Conditions  
We usually call the ratio of the horizontal width of the slope to the vertical height of the slope as the slope coefficient \(m\). The slope coefficient \(m\) and the slope are reciprocal to each other. In the spring melting season, when the resistance of the slope is greater than the sliding force, the shallow debris on the slope is stabilized at a certain place on the slope after sliding down a certain distance. After a period of accumulation and consolidation, the slope safety factor \(Fr\) is improved. When the slope is graded, the slope safety factor \(Fr\) increases most obviously. When there are progressively more shallow slump deposits, and the sliding force exceeds the friction resistance, then the second slip begins. Repeatedly, the safety factor \(Fr\) of the slope changes in stages, and the lower the slope factor \(Fr\), the more obvious the characteristics, as shown in Figure 5. When the other conditions remain unchanged, and the sliding amount of the shallow slope reaches a certain value, then the slope stabilizes and the slope safety factor \(Fr\) remains at a certain value.  
It can be seen from Figure 5 that when the gradient coefficient \(m\) increases, the slope safety factor \(Fr\) increases accordingly after freeze-thaw cycles. When \(m=0.75\), \(Fr=0.87\); when \(m=1.0\), \(Fr=1.06\); and when \(m=1.5\), \(Fr=1.27\). The reason for the change in the slope safety factor is that when the slope coefficient \(m\) increases, the slope slows down, the shallow slip of the slope decreases after freeze-thaw, then the safety of the slope increases, and the safety factor \(Fr\) of the slope increases accordingly. Therefore, the reasonable reduction of slope rate is a countermeasure to prevent freezing and thawing disasters in cold regions.  
Figure 4: Trend graph of slope safety factor \(Fr\) with initial moisture content w.
## 5 Discussion
### Comparison with Existing Methods
The advantage of this method is that it can segment the trees with obvious trunk characteristics well. However, the trunk points scanned by airborne laser lidar in forests with high canopy density are sparse, which leads to the inaccurate center points found by the DBSCAN algorithm, and then affects the final segmentation results. HAIS is a deep learning method for instance segmentation. This method can achieve good results when segmenting indoor datasets. However, indoor objects are far apart from each other, and individual trees are close to each other. If HAIS is directly applied to ITS, it may cause serious over-segmentation and under-segmentation problems. The Improved DBSCAN and K-means method (Improved DK) is an improved strategy that integrates extreme offset deep learning and the DK method. Experiments show that the introduction of extreme offset makes the segmentation accuracy of the DK method significantly improved in high-canopy-density forests. As shown in Figure 12a, the overall \(p\), \(r\), and \(F\) of the proposed method in the four plots are higher than other comparison methods. As shown in Figure 12b, the F-score of the proposed method is only slightly higher than the improved DK method in U1 with low canopy density. However, in the actual forest resource survey, the difficulty is usually the forest with high canopy density. Although the F-score of the proposed method is only slightly higher than the Improved DK method in U1, the F-score of the proposed method is much higher than the other comparison methods in other plots with high canopy density, which indicates the superiority of the proposed method. In addition, the average F-score of the Improved DK method (85%) is much higher than the average F-score of the DK method (68%). Although the proposed method consumes slightly more time (13.5 min total) than the DK method (12.5 min total) in the four plots, the segmentation accuracy of the proposed method is significantly higher than the DK method. This indicates that the proposed method is effective, and the extreme offset deep learning can significantly improve the segmentation accuracy of the bottom-up method and has good extendibility in the high-canopy-density woodland of the ALS point cloud.