esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Genotoxicity and Pulmonary Toxicity Study of Modified SiO2 Nanomaterials
## 3 Results  
### Ames Test  
In the Ames test, five different concentrations of SA, mA, SB, and mSB were tested on TA98 and TA100 strains with and without S9 metabolic activation. For the TA98 strain, no tested samples increased the number of revertant colonies with or without S9 fraction. For the TA100 strain, SA and mSB did not increase the number of revertant colonies with or without S9 fraction. In the 0.4713 mg/plate concentration of mSA without S9 metabolism, the number of revertant colonies was significantly increased with 1.05 times the negative control. The number of revertant colonies in the mSA group was not more than twofold of the negative control, indicating a negative result in the Ames test. In the 0.4713, 0.9425, and 1.885 mg/plate concentrations of SB without S9 metabolism, the number of revertant colonies was significantly increased, with 1.57, 1.75, and 2.47 times that of the negative control, respectively. Due to the number of revertant colonies in the SB group increasing in a dose-dependent manner to over twofold that of the negative control at the 1.885 mg/plate concentration, SB induced a positive result in the Ames test with S9 metabolism. However, the number of revertant colonies of the TA100 strain did not significantly increase in the mSA and SB group with S9 metabolism (Tables 4 and 5). Overall, the data of the Ames tests showed that SB might induce genotoxicity in the TA100 strain. However, genotoxicity might be reduced with S9 metabolism.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Sample**} & \multicolumn{1}{c}{**SA**} & \multicolumn{1}{c}{**mSA**} & \multicolumn{1}{c}{**SB**} & \multicolumn{1}{c}{**mSB**} \\ \cline{3-6} \multicolumn{2}{c}{} & \multicolumn{1}{c}{NC 1  } & \multicolumn{1}{c}{**Name of Revertants (Colony, CFU/Plate)**} \\ \hline \multirow{4}{*}{4 NQO  } & 22.7 \(\pm\) 2.1 & 28.0 \(\pm\) 5.1 & 25.0 \(\pm\) 3.7 & 25.0 \(\pm\) 3.7 \\  & (positive control) & 213.7 \(\pm\) 5.7* & 586.3 \(\pm\) 75.9* & 468.3 \(\pm\) 7.0* & 468.3 \(\pm\) 7.0* \\  & & 0.0295 & - & 31.3 \(\pm\) 0.9 & - & 20.7 \(\pm\) 1.2 \\  & & 0.0589 & 22.0 \(\pm\) 1.4 & 26.3 \(\pm\) 5.4 & 25.0 \(\pm\) 6.4 & 22.7 \(\pm\) 3.1 \\  & & 0.1178 & 20.3 \(\pm\) 0.5 & 23.7 \(\pm\) 1.7 & 23.0 \(\pm\) 2.9 & 21.7 \(\pm\) 2.5 \\  & & 0.2356 & 23.0 \(\pm\) 1.4 & 27.3 \(\pm\) 1.2 & 21.0 \(\pm\) 0.8 & 21.0 \(\pm\) 2.2 \\  & & 0.4713 & 23.7 \(\pm\) 2.6 & 37.7 \(\pm\) 3.7 & 25.3 \(\pm\) 0.9 & 22.7 \(\pm\) 1.2 \\  & & 0.9425 & 22.7 \(\pm\) 1.9 & - & 20.3 \(\pm\) 0.5 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Revertant changes of SA, mSA, SB, and mSB in _Salmonella_ TA98 mutagenicity test.
# A Novel Method for Detecting Advanced Persistent Threat Attack Based on Belief Rule Base
###### Abstract  
Advanced persistent threat (APT) is a special attack method, which is usually initiated by hacker groups to steal data or destroy systems for large enterprises and even countries. APT has a long-term and multi-stage characteristic, which makes it difficult for traditional detection methods to effectively identify. To detect APT attacks requires solving some problems: how to deal with various uncertain information during APT attack detection, how to fully train the APT detection model with small attack samples, and how to obtain the interpretable detection results for subsequent APT attack forensics. Traditional detection methods cannot effectively utilize multiple uncertain information with small samples. Meanwhile, most detection models are black box and lack a transparent calculation process, which makes it impossible for managers to analyze the reliability and evidence of the results. To solve these problems, a novel detection method based on belief rule base (BRB) is proposed in this paper, where expert knowledge and small samples are both utilized to obtain interpretable detection results. A case study with numerical simulation is established to prove the effectiveness and practicality of the proposed method.  
advanced persistent threat; belief rule base; attack detection; network security +
Footnote †: journal: Applied  
Article
# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 5 DT Solutions and Future Directions
### Intrusion Detection Systems for EV and AV  
Data attacks are greatly mitigated with DT technology, which provides greater security for the mechanism against attacks and protects the privacy of EV users. The DT enables the development of high-accuracy models for real-time systems using massive quantities of operational data with expert observations. The DT architecture could be built to effectively detect intrusions or anomalies that behave abnormally inside the vehicular (EV and AV) network. Available studies have explored this technology for industrial applications using data analytics and ML technologies, such as the one-class support vector machine, local outlier factor, and deep unsupervised learning. For example, in [83], [PERSON] et al. proposed DT architectural enhancements to improve the security of unmanned aerial systems. [PERSON] et al. [84] introduced an anomaly detection system to monitor abnormal behaviors in DT-based cyber-physical systems.  
[PERSON] et al. [85] investigated how the DT model and security architecture can share data and control critical security processes. They introduced a new security framework that will provide the foundation for future research work in automation and control systems. [PERSON] et al. [86] presented a two-phase digital-twin-assisted fault approach based on deep transfer learning; it detects faults during the development and maintenance of a vehicle's body-side production line. [PERSON] et al. [87] used the convolutional neural network model to improve the predictive power of the DT for cyber-physical energy systems. The behaviors of ten batteries were predicted using real-world data. They concluded that ML for DT can aid in maintaining a heterogeneous energy ecosystem. [PERSON] et al. [88] presented cluster centers (a clustering-based method) and Siamese autoencoders (a neural architecture designed for weakly supervised environments with few labeled data samples). The methods utilize a DT model to create a training dataset that replicates the machinery's usual operation. The above research employed DT to detect intrusions and abnormal behaviors in unmanned aerial systems, manufacturing, and energy systems utilizing ML and DL. In conclusion, the same technologies could be applied to EVs and AVs to obtain benefits.
# The Influence of Co-Firing Coal with Biomass Syngas on the Thermodynamic Parameters of a Boiler
## 4 Results and Discussion
### Effect of Co-Firing on \(\text{CO}_{2}\) Emissions  
The \(\text{CO}_{2}\) emissions are related to the content of carbon and the calculation coal consumption rate [24]. Therefore, the \(\text{CO}_{2}\) emissions descend gradually due to the drop of the calculation coal consumption rate with ascending biomass syngas consumption rates. Then, the \(\text{CO}_{2}\) emission intensity of power generation and the \(\text{CO}_{2}\) annual emission (\(m_{\text{t}}\), t) decline according to Equations (21) and (22), the result is displayed in Figure 11a. Consequently, an obvious growth of the \(\text{CO}_{2}\) annual emission reduction (\(m_{\text{r}}\), t) for different biomass syngas consumption rates under BMCR can be seen in Figure 11b. The results in Figure 11a,b are the same as those in our previous study [24], which could confirm that co-firing the fuels, such as biomass syngas and hydrogen-derived fuel, with the coal is significantly beneficial to \(\text{CO}_{2}\) emission reduction. The \(\text{CO}_{2}\) annual emission reduction equals 0.001 to 0.095 million tons for the palm syngas, 0.005 to 0.069 million tons for the straw syngas, and 0.013 to 0.107 million tons for the wood syngas.  
Figure 9: Acidic dew point, total air flow rate, and total flue-gas flow rate with various biomass syngas consumption rates (BMCR, standard condition). (**a**) Acidic dew point; (**b**) total air flow rate of air heater inlet; (**c**) total flue-gas flow rate of air heater outlet.  
Figure 11: CO\({}_{2}\) emission intensity and CO\({}_{2}\) annual emission reduction for various biomass syngas consumption rates (BMCR). (**a**) CO\({}_{2}\) emission intensity; (**b**) CO\({}_{2}\) annual emission reduction.  
Figure 10: Acidic dew point, total air flow rate, and total flue-gas flow rate comparison with various boiler loads (wood syngas, 450 \({}^{\circ}\)C, standard condition). (**a**) Acidic dew point; (**b**) total air flow rate of air heater inlet; (**c**) total flue-gas flow rate of air heater outlet.  
As 3 \(\times\) 10\({}^{4}\) m\({}^{3}\) h\({}^{-1}\) of wood syngas is entrained, the CO\({}_{2}\) emission intensity decreases and the CO\({}_{2}\) annual emission reduction rises compared with the standard condition. It can be seen from Figure 12a that the decrement of the CO\({}_{2}\) emission intensity increases with descending boiler loads, which could achieve 13.2% under 30% THA. It is apparent from Figure 12b that the CO\({}_{2}\) annual emission reduces more under a low load, while the introduction of wood syngas would exacerbate the reduction.
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 3 Acknowledgement-Based Opportunistic Routing Protocol
### Queue Overflow Avoidance  
Queue overflow leads to packet loss that reduces the delivery ratio. ABORt avoids queue overflow at each node. Nodes constantly monitor their queue occupancy level. When a critical occupancy threshold based on the difference between the arrival rate and departure rate of packets is reached in a queue, the node has to alert its transmitting neighbors using an alert message. When the neighbors receive the alert message, they have to _abort_ using this overloaded node as a next hop and find an alternative next-hop.  
Figure 2: Node delay \(d\) and path delay \(D\) presented for each node on its left \(d/D\). Node 9 top-list neighbors are 7, 8 and 4 encircled in red.
## 1 Introduction
### Bluetooth and Wi-Fi Wireless Communication Technologies  
Wi-Fi is local wireless network technology using Wireless Local Area Network (WLAN) based on IEEE 802.11 standards. Wi-Fi uses unlicensed industrial, scientific and medical (ISM) frequency bands: (2400-2483.5) MHz--known as Wi-Fi 2G, and (5150-5350) MHz and (5470-5725) MHz--known as Wi-Fi 5G. The recently introduced Wi-Fi HaLow is a Wi-Fi extension that enables the low energy connectivity required for sensor and wearable applications, smart homes, autonomous vehicles and smart cities. Wi-Fi HaLow is based on the 802.11 ah standard and operates in the 900 MHz frequency band. The maximum operational range of Wi-Fi is 70 m indoors and 1000 m outdoors. The limits of equivalent isotropically radiated power (EIRP) for Wi-Fi devices used without special administrative permission are (1) 100 mW (20 dBm) for Wi-Fi 2G (European Telecommunications Standards Institute (ETSI) EN 300-328 v2.2.2 (2019-07)); (2) 200 mW (23 dBm) for (5150-5350) MHz (ETSI EN 301 893 v2.1.1 (2017-05)), and (3) 1000 mW (30 dBm) for (5470-5725) MHz (ETSI EN 301 893 v2.1.1 (2017-05)) [18; 19].  
Bluetooth is a technology for short-range wireless communication between various electronic devices. It is an open standard described in the IEEE 802.15.1 specification that includes three transmission power classes with a range of 100 (class 1), 10 (class 2) and 1 (class 3) metre in open space. According to ETSI EN 300 328 V2.2.2 (2019-07), the limits of EIRP from Bluetooth devices are 100 mW (20 dBm), 2.5 mW (4 dBm) and 1 mW (0 dBm) for class 1, class 2 and class 3 devices, respectively. The standard uses the same ISM frequency band as Wi-Fi 2G. For IoT applications, e.g., medical, security-related, remote meter reading and position determination, Bluetooth Low Energy (LE) technology has been developed, in which the power consumption and data transmission speed have been significantly reduced, while increasing the range up to 400 m indoors (1000 m outdoors).
# Integrating Digital Twins of Engineering Labs into Multi-User Virtual Reality Environments
## 3 Workflow for the Development of a New Crawling Engine Experience
### Development of the Technological Solution
#### 3.2.2 PLC-Server-Unity Communication  
The third stage consisted of establishing a communication system to send the signals acquired by the PLC to a server, to later integrate them into Unity. Node-RED was used as a visual development tool to connect the PLC with an IoT server based on Blynk [36; 37; 38; 39].  
Blynk was selected as the IoT platform because of its ability to manage devices remotely and provide real-time communication. Its integration allowed PLC signals to be monitored and stored centrally, facilitating their later use in Unity by converting raw sensor data into structured parameters compatible with the Unity environment for real-time visualization and interaction [34; 40; 41; 42; 43].  
Figure 5 illustrates the communication scheme between the PC, the Blynk server, and the Node-RED development environment, used to process and visualize in real time the signals acquired from the PLC, ensuring minimal latency and data consistency across platforms.  
The data flow starts with the reception of the signals coming from the PLC, which are processed in Node-RED. In this environment, the first nodes of the scheme are assigned to each of the signals, allowing their capture directly from the PLC by defining individual processing pipelines for temperature, RPM, and other key variables. Subsequently, through an output node, the converted data are sent to a server via a WebSocket connection which ensures low-latency transmission and seamless data synchronization with Blynk.  
This process is repeated for each monitored signal, ensuring its efficient transmission to Blynk, where it is finally visualized within an interactive dashboard capable of displaying  
Figure 4: Programming of pressure and temperature signals.  
Figure 5: Signal flow.  
real-time trends, historical data analysis, and alert notifications for critical values. In this way, a clear and accessible representation of the variables in real time is achieved, facilitating their analysis and allowing remote supervision of the system while also enabling potential future integration with machine learning models for predictive maintenance.
# Flexible Antennas: A Review
## 9 Challenges and Future Prospects of Flexible Antennas
Another challenge of designing flexible antennas is identifying suitable conducting materials that sustain different bending and twisting conditions and have reasonable resistance value as not to affect the antenna radiation efficiency. Various methods have been considered to find the conductive substrates, such as chemically modifying fabrics surface [252] or physical mixing of several conductive materials [253; 254; 255]. Future flexible antennas should feature low profile, low loss, easily integrable with RF front end system, ability to control/manipulate radiation pattern, and eventually circular polarization for wider bandwidth. Regardless of the shape of the flexible antenna, one way to downsize the antenna structure is to exploit the higher frequencies of V (40-75 GHz) and W bands (75-110 GHz) [256]. This high-frequency operation will ensure a high-performance data connection. Materials withhigh dielectric constants are used to miniaturize the size of the antenna [257; 258]. Most common elastomeric materials have low dielectric constants. This low value can be increased by mixing the substrate with high dielectric constant materials such as ceramics like Ba\({}_{\mathrm{x}}\)Sr\({}_{1-\mathrm{x}}\)TiO\({}_{3}\)[258], BaTiO\({}_{3}\)[257], NdTiO\({}_{3}\)[259], MgCaTiO\({}_{2}\)[259], CNTs [260], and nanoparticles [261]. Metamaterial based flexible antenna is a relatively new development and has found its way in the commercial market because of its characteristics like lightweight, robustness, and reconfigurability [262; 263; 264; 265; 239]. It has the potential to be cheaper and smaller. The co-design of antenna and RF system on a flexible substrate has made breakthroughs in biomedical implantable devices [122]. Improvement in the design techniques and introduction of new materials will help make this co-design system more viable for many other applications. Metamaterial has a natural ability to couple with the radiation and converts it from one type of energy to another. This feature of the metamaterial can be used to flexible rectenna for energy harvesting [266; 267].
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 3 Feature Extraction and Decision-Making Parameters
### Communication Decision-Making Parameter
#### 3.2.2 Power, Modulation and Code Decision-Making Parameters  
The power adaptive decision-making parameter is transmitted power. Modulation and code decision-making parameters are modulation order and channel coding rate. In practical applications, we first select modulation order and channel coding rate according to the requirements, and then determine the reliable transmission power. Therefore, these two decision-making parameters can be discussed together.  
Communication transmission requirements can be divided into two categories: minimum transmission power consumption and maximum transmission rate. For minimum transmission power consumption, we adopt the lowest-order modulation and the minimum channel coding rate. For maximum transmission rate, we adopt maximum order modulation and maximum channel coding rate. The transmitted power is determined according to the following formula:  
\[P_{\Delta}(\mathrm{dB})=P_{S}(\mathrm{dB})-P_{J}(\mathrm{dB})-SINR_{\mathrm{ th}}(\mathrm{dB}) \tag{7}\]  
where \(P_{J}\) is the jamming signal power, \(P_{s}\) is the communication signal power, \(SINR_{\mathrm{th}}\) is the signal-to-interference ratio threshold of the receiver under modulation and coding, and \(P_{\Delta}\) is the part where the JSR exceeds the threshold. According to [27], the jamming effects when the \(JSR\) is less than 0 dB. With the purpose of reliable communication transmission, it is necessary to make \(P_{\Delta}\) more than 0 dB.
## 2 Materials and Methods
### Implementation Details  
All signals were sampled with a sampling rate of 16 kHz. Initially, the input signals were transformed to the subband domain by uniform 32-band single-sideband filter banks [21]. Each subband consisted of 150 taps. These were the equivalent of filters in the time domain with 2400 taps and of length 150 ms.  
For the RES model, the transformation of the input signals to the T-F domain was achieved by a 512-point STFT, resulting in 257 frequency bins. The STFT window length was 25 ms, and the hop length was 6.25 ms. The number of convolution kernels for the different encoder layers was \([16,32,64,128,256,256]\). The LSTM had two layers with a 128 hidden size. The model comprised 2.07 M parameters. The Adam optimizer [31] was employed for model optimization. Training started with a learning rate of \(5\times 10^{-4}\). The learning rate was decreased by a factor of 2 if the validation loss did not improve after 3 consecutive epochs. The mini-batch size was 16, and the training continued for a maximum number of 100 epochs.  
The number of encoder and decoder blocks for the speech-denoising model was 5. The number of the first encoder block's output channels was 64, and each encoder block doubled the number of channels. Subsequently, each decoder block halved the number of channels, where the output of the last decoder block consisted of 1 channel. The convolution kernel size was 8, and its stride was 4. The LSTM consisted of two layers, and its hidden size matched the number of channels of the last encoder block (and the number of channels of the first decoder block). The input to the model was normalized by its standard deviation. The model was pre-trained using the Valentini dataset [26] and the INTERSPEECH 2020 DNS challenge dataset [27]. The model comprised 18.87 M parameters. For a fair comparison with the RES model, the causal version of the denoiser was employed. For both linear AECs, the model was fine-tuned using the same data used to train the RES model. Training continued for 20 epochs with a learning rate of \(3\times 10^{-4}\) using the Adam optimizer [31]. Further details regarding the model architecture can be found in [17].
# Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability
## 3 Example Application
### Probabilistic Analysis  
The number of simulations \(N\) is a critical parameter in performing probabilistic slope stability analysis. Sixty stability calculations were performed. Figure 5 shows the fluctuation of \(\mu_{Fs}\) (the mean of \(Fs\)) and \(\sigma_{Fs}\) (the standard deviation of \(Fs\)) with the increase in \(N\). In general, a proper \(N\) can be obtained when \(\mu_{Fs}\) and \(\sigma_{Fs}\) approach a steady level. As seen in Figure 5, the values of \(\mu_{Fs}\) vary between 1.75 and 1.78, and the values of \(\sigma_{Fs}\) range from 0.121 to 0.146 when \(N>25\). In other words, the fluctuation of \(\mu_{Fs}\) and \(\sigma_{Fs}\) became steady when \(N>25\). As a result, 30 realizations are adequate for the underlying rock slope stability analysis, representing a tradeoff between the accuracy and calculation efficiency [24]. For this level of discretization, each realization required at least two hours of computing time on a 3.6 GHz dual processor 64 GB RAM desktop computer for the above slope.  
The histogram of \(Fs\) values for 60 realizations is shown in Figure 6. From the picture, the \(Fs\) values are in the range of 1.45-2.08 and are concentrated between 1.55 and 1.90. All of these \(Fs\) values are greater than 1 (the critical value for slope instability), which indicates that the rock slope has a better stability and the likelihood of instability damage is very low. Further, long-term field monitoring results show that the rock slope has been stable, which is consistent with the numerical modelling results. In addition, these 60 \(Fs\) values follow a normal distribution, with the \(\mu_{Fs}\) and \(\sigma_{Fs}\) being 1.78 and 0.146, respectively. Further, the coefficient of variance (COV) is 8.2% (according to the formula \(\text{COV}=\sigma_{Fs}/\mu_{Fs}*100\%\)).  
Although the \(Fs\) of rock slope can be obtained by deterministic analysis (e.g., \(Fs\) = 1.45 or \(Fs\) = 1.73 in Figure 4), the result of one-time numerical sim  
Figure 5: Influence of the number of realizations on \(\mu_{Fs}\) and \(\sigma_{Fs}\) (**(a)**—the fluctuation of \(\mu_{Fs}\) with \(N\) increases; **(b)**—the fluctuation of \(\sigma_{Fs}\) with \(N\) increases).  
Figure 6: Histogram of the \(Fs\) with 60 realizations.  
due to the randomness of the fracture arrangement. However, by means of probabilistic analysis (i.e., multiple simulation realizations), we can obtain the mean, standard deviation, and distribution pattern of _Fs_. Now, the obtained \(\mu_{F_{S}}\) (equal to 1.78) can better reflect the true stability state of the fractured rock slope. Moreover, it is more accurate and practical to use \(\mu_{F_{S}}\) rather than _Fs_ to investigate the effect of variability in fracture geometrical characteristics on the slope stability.