esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model Description
### System Durability Model  
After prolonged operation, adverse conditions such as idling, start-stop cycles, load variations, and high power loads can lead to voltage degradation in the fuel cell system. Similarly, the power battery undergoes irreversible degradation due to repetitive charge and discharge cycles over its operational lifespan. The modeling of system durability focuses on the degradation of both power sources.  
Following the empirical formula presented by [PERSON] in Pei (2016), the degradation of performance in an FCS \(\Delta\mathcal{O}_{FC_{degrad}}\) is described as follows:  
\[\Delta\mathcal{O}_{FC_{degrad}}=Kp(k_{1}t_{1}+k_{2}n_{1}+k_{3}t_{2}+k_{4}t_{3}) \tag{9}\]  
where \(\Delta\mathcal{O}_{FC_{degrad}}\) represents the performance degradation due to adverse operating conditions. \(t_{1}\), \(t_{2}\), and \(t_{3}\) are the duration of adverse operating conditions and \(n_{1}\) is the number of complete start-stop cycles. Other parameters are degradation coefficients, with specific meanings and values detailed in Table 2.  
The battery also incurs performance losses during operation, and the performance degradation of the lithium battery is defined as the percentage of capacity loss. According to the study in (2016), the formula for calculating the percentage of battery loss is given by Equation (10), as follows:  
\[\mathcal{O}_{BAT_{degrad}}=Ae^{(\frac{-E_{t}+\text{BC}_{g}}{K_{g}T})(A_{h})^{ z}} \tag{10}\]  
where \(E_{a}\) is the activation energy, \(C_{R}\) is the current flux, \(R_{g}\) is the gas constant, \(T\) is the absolute temperature, Ah is the Ah-throughput, and \(Z\) is the power law factor.  
In simulations, the inherently discrete nature of the data introduces deviations between the simulated Ah values per unit time and the experimental results obtained using the continuous formula. To enhance computational accuracy, the continuous equation is reformulated into a discrete form suitable for numerical analysis. The discretized model is expressed through Equations (10)-(12), as follows:  
\[\Delta\mathcal{O}_{BAT_{degrad}}=\Delta A_{h}z\Delta^{\frac{1}{z}}.e^{(\frac{ -E_{t}+\text{BC}_{g}}{\text{dB}T})}\mathcal{O}_{BAT_{degrad}}\frac{z-1}{z} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Coefficient** & **Values (Unit)** & **Definitions** \\ \hline \(k_{1}\) & 0.00126 (\%/h) & Output power less than 5\% of max power \\ \(k_{2}\) & 0.00196 (\%/cycle) & One full start-stop \\ \(k_{3}\) & 0.0000593 (\%/h) & Absolute value of load variations rate is larger \\ \(k_{4}\) & 0.00147 (\%/h) & Higher than 90\% of maximal power \\ \(Kp\) & 1.47 & - \\ \hline \end{tabular}
\end{table}
Table 2: Coefficients for performance degradation model.  
\[\Delta A_{h}=\frac{1}{3600}\int_{t_{p}}^{t_{p+1}}|I_{bat}| \tag{12}\]  
where \(\mathcal{O}_{B,AT_{dep}}\) is treated as a constant, and temperature \(T\) is assumed to remain constant during calculations.
# An Exploit Traffic Detection Method Based on Reverse Shell
## 3 Methodology
### Traffic Feature Analysis
#### 3.1.3 Analysis of Two-Stage Session Association Features of Vulnerability Exploitation  
Logically, the shellcode delivery phase and shellcode execution phase have a causal relationship, causing two-stage sessions to have a temporal and spatial correlation. Therefore, this section mainly analyzes the spatiotemporal association features of two-phase sessions and explains how to preliminarily filter out suspicious shellcode delivery phase traffic based on traffic features in Section 3.1.2.  
1. According to the reverse shell session features described in Section 3.1.2, we can identify the IP address of the suspicious target machine. As shellcode delivery sessions must be binding with the target, the first step is to filter out all the packets that have the target's IP address as the recipient;
2. From the chronological analysis, the shellcode execution phase always occurs after the shellcode delivery phase, which means the message carrying the shellcode must appear before the first SYN message of the reverse shell session three-way handshake. The interval between the execution of the shellcode and the establishment of the reverse shell connection is usually less than 0.1s. Considering the delay caused by network transmission problems, we filter out all the packets based on timestamp and further analyze the suspicious session.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 4 Results
### Analysis of Single Evaluation Indicator
#### 4.2.4 Results of Repeatability Indicator  
Figure 8 displays the repeatability results of all sensors to the experimental gases calculated according to Equation (4). The results confirmed that the repeatability values of different sensors in different gases were quite different. In order to compare the repeatability of each sensor, we calculated their average values (see Figure 9). It can be seen that the three sensors with the largest discrete values were S5 (0.27), S7 (0.26), and S1 (0.24), indicating that the experimental repeatability capabilities of these sensors were relatively low. In contrast, the three sensors with the smallest discrete values were S4 (0.05), S3 (0.1), and S8 (0.12). Therefore, in order to improve the stability of the E-nose performance, sensors S5, S7, and S1 were discarded from the initial sensing array.  
According to the analysis results of the four evaluation indicators, if the initial sensor array was optimized by removing the three sensors with the worst performances, the optimization results are summarized in Table 3. Both the sensitivity indicator and the selectivity indicator evaluated S1, S2, and S3 as the sensors with the worst performance, which indicates that there may be a certain correlation between the two evaluation indicators, that is, when a sensor has a low selectivity, its sensitivity may also be low. However, the correlation and repeatability evaluation results indicated a completely different situation. Therefore, according to these two evaluation indicators, deleting S5, S8, and S9, or S3, S4, and S8, respectively, can realize the optimization of the initial sensor array.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Indicator** & **Sensors Retained after Optimization** & **Sensors Recommended for Removal** \\ \hline Sensitivity & S4, S5, S6, S7, S8, S9, S10 & S1, S2, S3 \\ Selectivity & S4, S5, S6, S7, S8, S9, S10 & S1, S2, S3 \\ Correlation & S1, S2, S3, S5, S6, S7, S8 & S5, S8, S9 \\ Repeatability & S4, S5, S6, S7, S8, S9, S10 & S3, S4, S8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimization of the initial sensor array based on different evaluation indicators.  
Figure 7: The average value of the correlation coefficient between sensors.
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 3 Quantum Computing/Quantum Gate
### Quantum Superposition and Quantum Entanglement  
Quantum superposition is a state of having multiple possibilities at the same time. This state exists as a probability until measurement, which allows them to perform operations as fast as \(2^{\text{Qubit}}\) times compared to classical computers. Quantum entanglement is a phenomenon in which a particle is split into two and placed at a very long distance. When one side's state is determined, the other side's state is also determined. For example, assume that you put one quantum on Earth and the other one entangled to it in a very distant universe. If a quantum state on Earth is determined, a quantum in the distant universe is also determined. Quantum gates are built on these properties, and some quantum gates can replace classical logic gates.
# A New Road Damage Detection Baseline with Attention Learning
## 4 Our Proposed Baseline with Attention Learning  
In the labeling process of the CNRDD dataset, it was found that road damage detection is far more difficult than conventional object detection. The main reason behind this is that the damage foreground pixels are sparse, and the proportion of pixels in the corresponding bounding box is also relatively low. While other example objects (e.g., animals or buildings) have abundant and dense pixel areas in the bounding box. This leads to strong similarity between different types of damage marked with bounding boxes. If the bounding box is changed to pixel-level annotation and the detection is transformed into a segmentation task, this problem can be alleviated. This is bound to increase labor and time costs, and does not promote the development of road damage classification. Therefore, we still maintain bounding box annotation on the published CNRDD dataset and propose a damage detection method based on attention learning. Its outline is shown in Figure 5.  
As shown in Figure 5, the method takes raw images as input and realizes the prediction of road damage through an attention fusion module and a salient feature learning module. The attention fusion module exploits edge detection to achieve the location cues for road damage and then guides the network to pay attention to the salient area through multiplication from a self-attention mechanism. The salient feature learning module introduces attention normalization for spatial pyramid pooling. It not only strengthens the learning of salient features but also suppresses the weight of insignificant features. It is helpful to alleviate the interference of pixel dispersion and approximate appearance on road damage detection. In the following subsections, each component of the proposed method will be introduced in detail.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 5 Discussion and Future Outlook
### Security of the BFL  
As a new privacy-preserving machine learning technology, federated learning security is important. Due to its own centralized central server, there is a risk of privacy disclosure. Furthermore, the defense mechanism of federated learning also has notable defects which are prone to poison attacks and defense obstacles in the process of model training [134]. However, the distributed storage and transparent switching node single-point scheme provided by blockchain can effectively solve the problems arising in federated learning. The BFL framework mitigates external attacks and internal malicious servers to a certain extent, and there may be problems such as model efficiency degradation. Many scholars have suggested that the security of BFL supported by federated cryptography technology needs to be further developed.
## 4 Conclusions  
In conclusion, we have thoroughly completed the comparison between self-Raman Nd:YVO4 lasers and Nd:YVO4/KGW Raman lasers at lime and orange wavelengths. By using KGW crystal to accomplish the SRS process, the thermal loading can obviously decrease in the Nd:YVO4/KGW Raman lasers than the self-Raman Nd:YVO4 lasers, especially in the high pump power. Furthermore, employing the highly reflective coating for the first Stokes field on the facet of KGW crystal not only improved the beam quality, but also reduced the lasing thresholds. For the self-Raman Nd:YVO4 lasers, the output powers of 6.54 W and 5.2 W at 559 nm and 588 nm have been achieved at the incident pump power of 40 W. Moreover, for the Nd:YVO4/KGW Raman lasers, the output powers at 559 nm and 589 nm have been up to 9.1 W and 7.5 W. We believe that the high-power Nd:YVO4/KGW Raman lasers at the 559 nm and 589 nm are available commercially for many applications.  
Conceptualization, C.-C.L. and C.-H.T.; validation, C.-M.C. and C.-C.L.; formal analysis, H.-Y.H. and C.-Y.H.; resources, H.-Y.H. and C.-Y.H.; writing--original draft preparation, C.-C.L.; writing--review and editing, C.-C.L., C.-M.C. and C.-H.T.; supervision, C.-H.T. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Ministry of Science and Technology of Taiwan (contract number 109-2112-M-009-015-MY3).  
Not applicable.  
All of the data reported in the paper are presented in the main text. Any other data will be provided on request.  
Figure 4: (**a**) The output power at 588 nm and 589 nm versus the incident pump power. Optical spectra of 588 nm and 589 nm wavelengths and corresponding transverse mode patterns in the (**b**) self-Raman Nd:YVO4 laser and (**c**) Nd:YVO4/KGW Raman laser.
## 3 Results
### Time Interval Test Results  
The compilation results show that one TDC channel consumes 35 DFFs, 31 LUTs, and 16 CARRY4 logics, with a less than 0.02% resource occupation rate. We conducted a time interval test to evaluate the RMSE performance of one TDC channel. In the time interval test, we used two channels of the arbitrary waveform generator to provide the start and stop signals. Their time interval was selected by adjusting the start and stop signal phases. To reduce the effect of statistical errors, the sample number for each experiment was 500,000. First, we tested a moderately long time interval; its distribution histogram of measurement results is shown in Figure 15, with a mean value of 15,521 ps and an RMSE of 18.9 ps.  
Figure 14: Flow chart of the timestamp calculation.  
In addition, a time interval test every 1.375 ns in the range of 0 to 30 ns was conducted to observe the performance of the TDC when measuring time intervals of different lengths. The test results are shown in Figure 16, and both the resolution and RMSE could reach the level of 15-20 ps when using the new methods presented in this paper.  
The effect of temperature is almost negligible for the acceleration module [20]. Additionally, for the Vernier-measuring module, its Vernier-type delay line is affected by the temperature to a very small extent, approximately 0.1 ps/\({}^{\circ}\)C [21]. Therefore, it is unnecessary for this design to adopt any corrections that balance the measurement errors caused by different operating temperatures. To verify the reliability of the experimental results, we add the performance analysis of the proposed method when mounted on different FPGA models (Artix-7). Additionally, we also provide the performance comparison to similar works as shown in Table 1.  
Figure 16: Measured RMS precisions of different time intervals.  
Figure 15: Histogram of a measured time interval.
# Stabilization of Pickering Emulsions by Hairy Nanoparticles Bearing Polyanions
## 3 Results and Discussion
### PS@PSS Concentration  
The concentration of PS@PSS nanoparticles is another vital factor that can affect the stabilization of Pickering emulsion. Optical images and microscope images of Pickering emulsions stabilized by PS@PSS with different concentrations were shown in Figure 9. When PS@PSS concentration varied from 1.02 wt.% to 0.34 wt.%, a reduction of emulsion fraction and an increase in hexadecane bulk phase volume were observed in Figure 9a, which was also shown in previous work [41]. Figure 9b shows that the amount of emulsion droplets had an obvious decline and the homogeneousness of droplets was getting worse with the decrease in PS@PSS concentration. In addition, the size analysis in Figure 9c indicated that the droplet size became smaller, with mean diameter from 12.0 \(\upmu\)m to 2.2 \(\upmu\)m. When particle concentration was quite low, there was not enough PS@PSS nanoparticle anchoring at all the water/oil interfaces. So, a layer of oil phase could be observed in Figure 9a when PS@PSS concentration was 0.34 wt.% and 0.51 wt.%, and oil phase volume became larger when particle concentration was lower (0.34 wt.%). However, when PS@PSS concentration increased to 1.02 wt.%, more particles migrated to anchor at the interface of hexadecane and water under ultrasonic treatment.  
Figure 8: (**a**) Optical images of hexadecane/H\({}_{2}\)O Pickering emulsions stabilized by PS@PSS under different water:oil volume ratios after standing for 2 h; (**b**) Schematic illustrations of hexadecane/H\({}_{2}\)O Pickering emulsions stabilized by PS@PSS under different water:oil ratios; (**c**) Microscope images of hexadecane/H\({}_{2}\)O Pickering emulsion stabilized by PS@PSS under different water:oil ratios. Here the ultrasonic power is 150 W, salt concentration is 0.1 mM, and ultrasonic time is 3 min. The bar is 10 \(\upmu\)m. (**d**) Droplets size distribution corresponding to microscope images.
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
###### Abstract  
Nowadays, more and more online content providers are offering multiple types of data services. To provide users with a better service experience, Quality of Experience (QoE) has been widely used in the delivery quality measurement of network services. How to accurately measure the QoE score for all types of network services has become a meaningful but difficult problem. To solve this problem, we proposed a unified QoE scoring framework that measures the user experience of almost all types of network services. The framework first uses a machine learning model (random forest) to classify network services, then selects different nonlinear expressions based on the type of service and comprehensively calculates the QoE score through the Quality of Service (QOS) metrics including transmission delay, packet loss rate, and throughput rate. Experiment results show that the proposed method has the ability to be applied on almost all the types of network traffic, and it achieves better QoE assessment accuracy than other works.  
Quality of Experience; QoE assessment; network traffic classification +
Footnote †: journal: Applied
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Internet of Things (IoT) and Industrial Internet of Things (IIoT) Technologies
#### 2.2.1 IoT the Enabling Technology for IIoT  
Cloud computing, Machine-to-Machine (M2M), Artificial Intelligent (AI), and Blockchain are among the large group of technologies that enables effective and reliable IIoT [52], with IoT forming the backbone. Figure 6 shows the structure of a typical IoT technology. The _things_ here are regular objects, such as a VR headset, watch, electric cooker, parking lot or garage, security cameras, shopping cart, etc., that are made smart because of the sensor nodes embedded in them. This sensing ability enables these things to interact through a common infrastructure, the Internet, with a management center for decision making. The interaction is possible through the various wireless communication technologies available. For the protection of data, a firewall is designed to secure the information exchange between the objects and the center. Communication device manufacturers have provided solutions to protect this data. The network layer provides the platform on which different communication technologies can function. These include long-range licensed and unlicensed wireless bands. The licensed wireless technology, such as 2G/3G and LTE involves, the use of existing telecommunication infrastructures, while the unlicensed wireless (LoRa, ZigBee, etc.) is an ad hoc setup with Internet capability (through an Internet modem or a gateway). The smart objects, set up in this fashion, have various applications such as in homes, city environment, traffic, etc. Their management is also done through any internet-empowered device such as smartphones, laptops, tablets, etc. Figure 7 represents the application of IoT in the industrial environment, the IIoT. In industrial applications, the sensor nodes are embedded in the industrial equipment for monitoring, operations, and management. The communication technologies remain the same; however, the processes that are managed are of critical nature. Applications include oil and gas industries, chemical, automobile, food processing industries, etc. While IoT could be less stringent in terms of criticality, IIoT is critical with timing, reliability, privacy, and security of data being gathered in operation or monitoring.  
Figure 6: A typical structure of consumer-based IoT showing basic layers.
## 2 Materials and Methods
### Gaussian Calculations  
_Ab initio_ quantum chemistry calculations were performed using density functional theory (DFT). The geometry optimizations and subsequent NMR calculations of the fluorobenzene compounds were both performed using the 6-311G** basis and the unrestricted functional of Truhlar and Zhao (uM06) [47]. Other functionals (B3 LYP, CAM-B3 LYP, wB97 XD, M05, PBE, M11, TPSSH) and basis sets (STO-3G, 6-31G*, aug-cc-pVDZ, aug-cc-pVTZ, TZVP, DGTZVP) were studied, but did not yield more accurate results when compared to experimental observations, and they proved to be more central processing unit (CPU)-intensive. All quantum calculations used Gaussian 16 software (v16, Gaussian, Wallingford, CT, USA, 2016), revision A.03 [48]. A two-step spin-spin coupling calculation was used to provide the higher accuracy Fermi contact interaction, which represents the dominant contribution to the \(J\)-coupling [49; 50].
## 3 Results and Discussion
### Experimental Results
The results demonstrate the constraining mathematical model that aids GWO control action by reducing the rapid fluctuations; however, the probabilistic selection of weak solutions for maximal search space exploitation constrains the quicker settling time. The PSO's power tracking is displayed in Figure 15, where 400 ms is used. Low waves are seen after settling at the GM, as seen in the zoomed-in picture. With less effective control action, less power is generated, increasing the power loss. The experimental data cause the impact of the exploratory, hybrid, and exploitative phases to be very evident. In the MRA, strong exploration behavior is shown in the beginning of the iterations, and, after a few iterations, few particles start exploitation, which reduces the oscillations. The exploitation begins at the conclusion of the iterative procedure, causing the MRA to settle at the GM without oscillations. The experimental findings support the MRA's better performance in all the testing configurations.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 3 Robust Matrix Factorization with Robust Adaptive Structure Learning
### Model Optimization
#### 3.2.2 Fix \(S\) and \(W\); Update \(A\)  
For Equation (12), removing the irrelevant term with \(A\) yields the following objective function:  
\[\begin{array}{l}\varphi(A)=||Z-ZSA||_{2,1}\\ \text{s.t.}\quad A\geq 0\end{array}. \tag{21}\]  
Through a series of algebraic formulas [29], Equation (21) can be simplified as  
\[\begin{array}{l}\varphi\Big{(}A)=||Z-ZSA||_{2,1}=tr((Z-ZSA)^{T}U(Z-ZSA))\\ =tr(Z^{T}UZ-2A^{T}S^{T}Z^{T}UZ+A^{T}S^{T}Z^{T}UZSA)\\ \text{s.t.}\quad A\geq 0\end{array}. \tag{22}\]  
Likewise, the Lagrangian multiplier \(\varpi\) with a non-negative constraint \(A\geq 0\) is introduced to solve Equation (22), and the Lagrangian function \(\varphi(A,\varpi)\) can be expressed as follows:  
\[\varphi\Big{(}A,\varpi)= tr(Z^{T}UZ-2A^{T}S^{T}Z^{T}UZ+A^{T}S^{T}Z^{T}UZSA)+tr(\varpi A). \tag{23}\]  
The partial derivative of Equation (23) with respect to \(A\) is defined as follows:  
\[\frac{\partial\varphi(A,\varpi)}{\partial A}=-2S^{T}Z^{T}UZ+2S^{T}Z^{T}UZSA+ \varpi=0. \tag{24}\]  
As the complementary relaxation of the KKT condition leads to \(\varpi_{ij}A_{ij}=0\), we can get Equation (25).  
\[(-2S^{T}Z^{T}UZ+2S^{T}Z^{T}UZSA+\varpi)A_{ijij}=0. \tag{25}\]  
According to Equation (25), the update rule of the matrix \(A\) is as follows:  
\[A=A\odot\frac{S^{T}Z^{T}UZ}{S^{T}Z^{T}UZSA}. \tag{26}\]