esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 2 Overview of Experimental PBR System  
### Geometry of the PBR System  
The experimental PBR system consists of a reference antenna, a surveillance antenna, and a receiver. As shown in Figure 1, the reference antenna points in the direction of transmitter and receives the direct wave signal and the surveillance antenna covers the surveillance area to receive the scattered signal simultaneously.  
As shown in Figure 1, the transmitter and receiver are separated by a distance noted as \(L\). Suppose that there is an aircraft target in the air, the distance between the target and transmitter is \(R_{t}\) and the distance between the target and receiver is \(R_{r}\). The azimuth of the target relative to the surveillance antenna is \(\theta_{r}\).  
When scattered off a target, the combined distance traveled by the signal can be calculated as  
\[R_{t}+R_{r}=L+c\Delta t \tag{1}\]  
where \(c\) is the velocity of light and \(\Delta t\) is the time delay between the target echo and the direct wave signal. After the range sum (\(R_{t}+R_{r}\)) is calculated, according to the triangular geometry, the distance between the target and receiver can be calculated as follows  
\[R_{r}=\frac{(R_{t}+R_{r})^{2}-L^{2}}{2(R_{t}+R_{r}+L\sin\theta_{r})}=\frac{(L+ c\Delta t)^{2}-L^{2}}{2(L+c\Delta t+L\sin\theta_{r})} \tag{2}\]  
Therefore, the target positioning can be realized by the PBR system as long as we get the time delay \(\Delta t\) and the azimuth of the target relative to the surveillance antenna \(\theta_{r}\).
## 1 Introduction
Adopting dense connections, as opposed to conventional linear connections, significantly enhances its effectiveness in identifying fire-related scenes, a critical challenge in fire detection systems. * Faster Convergence: To expedite the convergence process and mitigate issues related to slow convergence and increased training time, E-EFNet introduces a randomized weight initialization strategy that contributes to the model's efficiency and effectiveness. * Efficient Inferencing on Edge Devices: The study demonstrates that E-EFNet is adaptable for efficient inferencing on edge devices. It significantly contributes to the Internet of Things (IoT) and resource-limited environments, where efficient fire detection is crucial. * Comprehensive Evaluation: The research contributes by thoroughly assessing various deep models before ultimately selecting E-EFNet as the optimal solution for the pressing challenges in fire detection. It demonstrates a rigorous evaluation process and the model's superiority over contemporary benchmarks. Our proposed model was subjected to comprehensive experimentation, which revealed its superior performance in terms of increased recognition accuracy and reduced false alarm rates. We conducted benchmark testing and compared our results to those achieved by recent state-of-the-art models. The findings indicated that our model outperformed the existing alternatives in these critical performance metrics. In the remainder of this paper, we delve into the details of our proposed method. In Section 2, we comprehensively describe the approach we have developed. Subsequently, Section 3 is dedicated to presenting the results obtained through the application of our method, shedding light on the outcomes and findings of our study. Finally, in Section 4, we draw our conclusions, summarizing the key insights from our research and discussing their implications in the broader context of the study area.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 3 Classifications of Cyber-Attacks in SGs
### Distributed Denial of Service (DDos) Attacks  
DDoS aimed to disrupt the service or make it unable to provide any service like DoS. The attacker created it before the target's attack with the machine or computer community. However, the attacker can easily be concealed without revealing their identity. Unlike the DoS attack, many machines are used in a DDoS attack, and IP detection is more complicated than others. A firewall may not be sufficient, making DDoS more dangerous and effective than DoS attack. Moreover, Distributed Reflective Denial of Service (DRDoS) is similar to DDoS and uses additional networks to attack more frequently. Attacks on the protocol, grid operation control, communication infrastructure, bandwidth, consistency observation, and billing mechanisms are all possible forms of DDoS attacks in the SG environment [86; 93].
## 5 Attenuation Method Experiment  
The essential part of this paper is the experimental verification of the QMUX attenuation method on a real commercial QKD system Clavis[3] and the evaluation of its behavior at various optical route distances. The system parameters are provided in Table 1. The measurements were carried out in three separate phases.  
1. **Design and safety verification of the initial route--**In this phase, a potentially functional QKD polygon with a route length of 20 km was designed. The length of the fiber was sufficient to manifest nonlinear effects while not exceeding the maximum possible quantum channel attenuation. DWDM filters were used to merge the quantum and classical channels, and their properties were tested by using an optical spectrum analyzer (OSA). Based on the results, these filters were then incorporated at a suitable location in the scheme. The optical power of the originally used Small Form-factor Pluggable (SFP) modules and the attenuation of all the optical elements used were also measured. Based on the calculations, it was verified that there could be no damage to any detection devices in the topology, especially the quantum signal detectors.
2. **Attenuation of service channels--**The second phase consisted of finding the minimum attenuation of the service channels so that the quantum channel was still functional. Testing was performed by using two pairs of SFPs. The first pair was used only for testing, i.e., no useful signal was sent, and the second pair provided a direct service channel link. This was the only way to ensure the correct operation of the entire system.
3. **Building the final route and tuning--**The final step was to build and test a fully functional QKD polygon. Unlike the previous phase, attention was already paid to the service channels, which are also a limiting element when sufficiently attenuated. For this reason, several modifications were made to the route. In particular, the replacement of lossy multiplexers by more attenuation-friendly circulators or the addition of an EDFA at the end of both service channels was carried out. The functionality of this route was tested for distances of 0, 5, 10, 15 and 20 km.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Manufacturer** & \multicolumn{3}{c}{ID Quantique (IDQ)} \\
**QKD protocol** & \multicolumn{3}{c}{Coherent One-way (COW)} \\
**Pulse generation rate** & & & 1.25 GHz & \\
**Key generation rate** & & & 1.4 kbps & \\
**Dynamic range** & & & 10–14 dB & \\
**Photon number** & & & 0.03 & \\
**Quantum channel** & \(\longrightarrow\) & 1551.72 nm & CH32 & –83.2 dBm 1 & 1.4 kbps \\
**Service channel I** & \(\longrightarrow\) & 1553.33 nm & CH30 & –3.0 dBm 2 & 2.7 Gbps \\
**Service channel II** & \(\longleftarrow\) & 1554.13 nm & CH29 & –3.0 dBm 2 & 2.7 Gbps \\ \hline \hline \end{tabular}  
* **Arows indicate the direction of the emitted light. 1 Based on own calculation. 2 Based on own power measurements.  
\end{table}
Table 1: Selected parameters of the system Clavis[3] used for the final route.
# Thermodynamic Analysis of In-Cylinder Steam Assist Technology within an Internal Combustion Engine
## 2 Methodology
### Boundary Definition and Verification  
In the in-cylinder steam-assisted cycle, the effectiveness of the heat exchanger determines the upper-temperature limit of injected steam. Hence, the limitation on the injected steam's temperature is verified in this section. In the heat exchanger analysis, the LMTD method can be considered when the fluid inlet and outlet temperatures are available or calculated based on the energy equations. However, if the LMTD temperature conditions are insufficient, the number of transfer units (NTU) or effectiveness method is an alternative approach to estimating the heat transfer rate. The effectiveness of a heat exchanger is defined as the ratio between the actual and theoretical heat transfer rate that can be obtained in an ideal counter-flow heat exchanger of infinite length. Therefore, the effectiveness is also equal to the ratio between the actual and maximum possible temperature difference and can be expressed as follows:  
\[\varepsilon_{1}=\frac{q}{q_{\text{max}}}=\frac{T_{\text{g,in}}-T_{\text{g,out }}}{T_{\text{g,in}}-T_{\text{w,in}}} \tag{18}\]  
Additionally, the effectiveness can be calculated by the heat capacity ratio and a dimensionless parameter, as follows:  
\[\varepsilon_{2}=\frac{1-exp[-NTU\cdot(1-C_{r})]}{1-C_{r}\cdot exp[-NTU\cdot(1-C _{r})]} \tag{19}\]  
where the NTU is universally applied for heat exchanger analysis and can be expressed as:  
\[NTU=\frac{K\cdot A}{\big{(}\dot{m}\cdot c_{p}\big{)}_{\text{min}}} \tag{20}\]  
where the overall heat transfer coefficient \(K\) and heat exchanger area \(A\) are determined as in the previous section. Additionally, the heat capacity ratio \(C_{r}\) is calculated as:  
\[C_{r}=\frac{\big{(}\dot{m}\cdot c_{p}\big{)}_{\text{min}}}{\big{(}\dot{m}\cdot c _{p}\big{)}_{\text{max}}} \tag{21}\]  
The calculation of outlet temperature is conducted in two steps. Figure 3 shows a flowchart that verifies the maximum injected steam temperature under a specific steam-to-air ratio. The first step is to assume an outlet steam temperature, which falls between the inlet steam temperature and outlet exhaust temperature. The second step is to calculate the difference between the two effectiveness methods mentioned above until the error is <0.001.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Item** & **Value** \\ \hline Outer diameter of tubes (\(d_{\text{o}}\)) & 12 mm \\ Inner diameter of tubes (\(d_{\text{i}}\)) & 10 mm \\ Centre distance (\(Pt\)) & 17 mm \\ Number of tubes (N) & 43 \\ Length of tubes (\(L\)) & 450 mm \\ Baffle space (\(L_{\text{b}}\)) & 50 mm \\ Shell diameter (\(D_{\text{i}}\)) & 140 mm \\ Heat exchanger area (\(A\)) & 0.73 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Designed parameters of a heat exchanger.
## 3 Theoretical Model Verification
### Comparative Verification of Dynamic Calculation Models
This suggests that further research and development are needed for field testing techniques for the acoustic radiation of track structures. In this paper, the dynamic and acoustic solution methods have already been widely adopted and recognized. A large number of researchers have used vehicle-track-bridge interaction theory to solve the dynamic characteristics of tracks and bridges and the acoustic boundary element method to calculate the low-frequency structural noise. Therefore, the hybrid numerical integration method in this paper was able to accurately and effectively predict the structure noise of SSFSTs. However, in order to further verify the method's effectiveness, the acoustic radiation characteristics of the four short floating-slabs on the 24 m bridge in this paper are compared with the acoustic radiation characteristics of the 24 m long floating-slab in the literature [34]. Figure 5: Comparison of calculation results of two models: (**a**) The model calculation results of this paper; (**b**) The calculation results of UM software. Both adopt the SSFST on the 24 m BGB of URT, and the dynamic and acoustic analysis methods are the same. Meanwhile, the parameters of vehicle, track, bridge, and vehicle speed are consistent. Therefore, the effectiveness of the acoustic radiation prediction method adopted in this paper can be demonstrated by comparing the calculation results of the reference [34]. The sound field points of comparison and subsequent analysis are shown in Figure 6; the interval of the field points is 1 m. The noise caused by URT operations mainly affected the acoustic environment along the railway and in vehicles. Therefore, this paper selected 25 m from the longitudinal centerline of the track and the sound field point directly above the track as the main object of study, as shown in Figure 6.  
The comparison of sound radiation characteristics shows in Figures 7 and 8 that the sound power and sound pressure spectrum curves are very similar, and the frequencies of sound radiation peak are basically the same in most frequency ranges. The main difference is reflected in the 0-50 Hz frequency range; it can be seen that because the vibration of SSFST is larger through comparison of vibration, the sound radiation capacity is stronger. The vibration of the floating-slab is mainly manifested as the whole bending vibration below 15 Hz, and the acoustic radiation characteristics of the long floating-slab are stronger than those of the short floating-slab. Therefore, the comparison with the research results in the literature [34] can verify the effectiveness of the acoustic prediction method in this paper and can effectively predict the acoustic radiation characteristics of SSSFST. Figure 6: The research points to the selection of the track acoustic characteristics in the free sound field. Figure 7: Comparison of the sound power.
# Estimating the Thermal Conductivity of Unsaturated Sand
## 2 Heat Transfer Mechanisms of Sand  
As a kind of porous medium, the thermal properties of sand are determined by the volume fraction of each constituent material, such as soil particles, water, and air. Previous studies have revealed that conduction was generally the dominant heat transfer mechanism of sand produced by the presence of temperature gradients, and convection heat transfer undertook a significant role only in highly permeable soils. In this paper, the symbol \(\lambda\) refers to the thermal conductivity of sand.  
The thermal conductivity of the solid phase is on average approximately 6 times and 200 times that of water and air, respectively. Specifically, for completely dry sand, or under great matric suction, heat is mainly conducted through the solid phase and restricted by contact points between solid particles. When the water content increases, the initial water is in the lowest energy state, and water menisci form near the particle contact region where they are the most stable [31; 32]. In this paper, menisci are called transitory water bridges because the characteristic of the water bridge is connecting the gap between neighboring particles. The water bridges at the particle contacts increase the contact areas and thus remarkably enlarge the transfer paths of heat conduction in sand. Particle-particle conduction is altered to particle-water-particle conduction, which causes a rapidly increasing tendency in thermal conductivity, as shown in Figure 1. As the water content continues to increase, individual water bridges are gradually interconnected and form an evenly distributed water membrane coating on all particles. This phenomenon is generally referred to as the funicular regime, which is conducive to the further augmentation of the thermal conductivity of sand [33]. The contributions of particle-water-particle conduction in enhancing the thermal conductivity of sand achieve its maximum at this stage.  
Unsaturated sand gradually tends to saturate with increasing moisture content. At this point, the heat transfer mechanism of sand changes from solid phase conduction to joint conduction by the solid and liquid phases, with continuously increasing thermal conductivity due to increasing water content. However, the rates of increase in thermal conductivity decrease gradually as the sand tends to full saturation. Hence, the curve of thermal conductivity, as shown in Figure 1, approaches a plateau, which indicates that the thermal conductivity of sand reached its maximum value \(\lambda_{\text{sat}}\).
## 3 Experiments
### Comparison with Existing Methods
It is easy to see that Capsule-Transformer is characterized by better classification performance and high robustness compared to other methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Inputs** & **Valence** & **Arousal** & **Dominance** \\ \hline KNN & DE features & 73.21 \(\pm\) 4.88 & 73.28 \(\pm\) 5.13 & 74.40 \(\pm\) 5.29 \\ \hline SVM & DE features & 81.66 \(\pm\) 4.82 & 81.13 \(\pm\) 4.18 & 81.98 \(\pm\) 4.05 \\ \hline MLP & Raw EEG & 83.75 \(\pm\) 4.75 & 84.52 \(\pm\) 5.09 & 84.49 \(\pm\) 5.05 \\ \hline CNN-RNN & Raw EEG & 84.66 \(\pm\) 4.20 & 85.06 \(\pm\) 4.11 & 85.03 \(\pm\) 3.32 \\ \hline Conti-CNN & DE features & 86.63 \(\pm\) 3.20 & 87.22 \(\pm\) 3.37 & 86.61 \(\pm\) 3.16 \\ \hline DCoT & DE features & 90.77 \(\pm\) 3.09 & 90.19 \(\pm\) 3.12 & 90.62 \(\pm\) 3.12 \\ \hline SFCSAN & DE features & 92.85 \(\pm\) 2.23 & 92.68 \(\pm\) 2.54 & 92.43 \(\pm\) 2.15 \\ \hline Ours & DE features & 96.75 \(\pm\) 1.32 & 96.88 \(\pm\) 1.46 & 96.25 \(\pm\) 1.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average accuracy (%) of different methods on the valence, arousal, and dominance dimensions of the DEAP dataset (mean \(\pm\) std. dev. ).
## 2 Theoretical Background
### Statistical Features  
In the condition monitoring field, statistical features in different domains allow the identification of characteristics within signals with a low computational load. Time domain indicators analyze a time series signal based on the relative position, separation, distribution, or other data characteristics, giving information helpful for the discrimination of the different conditions under analysis. As mentioned in Section 1, the use of statistical indicators is common for condition monitoring and fault detection in bearings as they provide information related to the waveforms captured, allowing the characterization of the defects and avoiding the problem of similar amplitudes or the closeness of the components when using techniques such as the FFT. Table 2 presents a set of 15 indicators reported in the literature for fault identification with satisfactory results [30], which were employed in this research.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistical Features** & **Equation** & \\ \hline Mean & \(\mathfrak{F}=\frac{1}{n}\sum\limits_{i=1}^{n}\mathfrak{x}_{i}\) & (5) \\ Maximum value & \(\mathfrak{x}=\max(x)\) & (6) \\ Root Mean Square & \(x_{RMS}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(\mathfrak{x}_{i})^{2}}\) & (7) \\ Square Root Mean & \(x_{SRM}=\left(\frac{1}{n}\sum\limits_{i=1}^{n}\sqrt{|\mathfrak{x}_{i}|}\right)^ {2}\) & (8) \\ Standard Deviation & \(\sigma=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(\mathfrak{x}_{i}-\mathfrak{F})^ {2}}\) & (9) \\ Variance & \(\sigma^{2}=\frac{1}{n}\sum\limits_{i=1}^{n}(\mathfrak{x}_{i}-\mathfrak{F})^ {2}\) & (10) \\ RMS Shape Factor & \(SF_{RMS}=\frac{\mathfrak{F}_{RMS}}{\frac{1}{2}\sum\limits_{i=1}^{n}| \mathfrak{x}_{i}|}\) & (11) \\ SRM Shape Factor & \(SF_{SRM}=\frac{\mathfrak{F}_{SRM}}{\frac{1}{2}\sum\limits_{i=1}^{n}| \mathfrak{x}_{i}|}\) & (12) \\ Crest Factor & \(CF=\frac{\mathfrak{F}}{\mathfrak{F}_{RMS}}\) & (13) \\ Latitude Factor & \(LF=\frac{\mathfrak{F}}{\mathfrak{F}_{SRM}}\) & (14) \\ Impulse Factor & \(IF=\frac{\mathfrak{g}}{\frac{1}{2}\sum\limits_{i=1}^{n}|\mathfrak{x}_{i}|}\) & (15) \\ Skewness & \(S_{k}=\frac{\sum[(x-\mu)^{\mathfrak{g}}]}{\mathfrak{g}^{\mathfrak{g}}}\) & (16) \\ Kurtosis & \(k=\frac{\sum[(x-\mu)^{\mathfrak{g}}]}{\mathfrak{g}^{\mathfrak{g}}}\) & (17) \\ Fifth Moment & \(5 thM=\frac{\sum[(x-\mu)^{\mathfrak{g}}]}{\mathfrak{g}^{\mathfrak{g}}}\) & (18) \\ Sixth Moment & \(6 thM=\frac{\sum[(x-\mu)^{\mathfrak{g}}]}{\mathfrak{g}^{\mathfrak{g}}}\) & (19) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical features employed in the analysis.
## 5 Results
### Cutting-Edge Comparisons  
The following section presents the findings of the proposed CAD system in comparison to prior studies on breast cancer identification leveraging thermographic pictures. Table 8 demonstrates the effectiveness of the proposed CAD in breast cancer identification via thermal scans and provides an in-depth comparison with previous studies. The proposed CAD demonstrates superior results, achieving 99.9% accuracy, 99.8% sensitivity, 100% specificity, 100% precision, and 99.9% F1-score. These efficiency scores demonstrate the proposed CAD's capabilities to identify anomalous thermal patterns associated with possible tumors, with merely one instance of misclassification. Previous studies [29] employing VGG16 alongside a sequential classifier achieved an accuracy of 99.4% and a sensitivity of 100%, with a slightly diminished specificity of 97.5%. A method adopting ResNet34 [64] in conjunction with chi-square FS and SVM achieved an accuracy of 99.62%, but it fell short of the  
Figure 5: Sorted feature importance scores calculated using the MRMR feature selection approach by taking into account both the mutual information between each feature and the class labels (relevance) and the mutual information among features (redundancy). outstanding outcomes demonstrated by the presented CAD throughout all metrics. Other studies [27; 40; 42; 43] demonstrated significantly inferior performance relative to the presented CAD, as they relied solely on individual deep learning models. The CAD's flawless performance in sensitivity, precision, and specificity indicates a highly reliable detection method that eliminates false negatives as well as false positives. This outcome is essential for clinical use, as it reduces the probability of missed diagnosis while concurrently eliminating unnecessary follow-up testing. The effectiveness of the proposed CAD arises from the integration of attributes from multiple compact CNN designs (SuffleNet, MobileNet, and EfficientNetB0), the application feature transformation, and mRMR for FS, which adeptly indicates distinguishing thermal features relevant to breast cancer identification. Although research including [29; 36; 64] has demonstrated high accuracy with individual deep networks like VGG16 (99.4%), ResNet34 (99.62%), and custom CNN (99.33%), these approaches are predominantly dependent on end-to-end learning. This approach, while effective, frequently requires considerable computing power, a large number of parameters, and exhibits limited versatility in feature selection and dimensionality reduction. Therefore, these architectures are complex. On the other hand, the suggested CAD system mitigates such constraints by incorporating various compact CNN architectures--MobileNet, EfficientNetB0, and ShuffleNet--to create a more resilient and computationally effective system. This multi-architecture methodology improves feature diversity and