esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Ghost Imaging via Sparsity Constraints
## 3 Applications of GISC
### Motion Compensation Techniques  
Both the inertial navigation technique and the opto-electronic detection technique [55; 56] are suitable for motion compensation. The inertial navigation system mainly consists of a global positioning system (GPS) and an inertial measurement unit (IMU). It makes use of the GPS information of both the LiDAR system and the target, together with the IMU information of LiDAR system, to compensate the relative motion. While the traditional opto-electronic detection technique relies on conventional thermal or visible light images of reference markers. The optimization program for motion compensation is [49]  
\[X(\mu)=\arg\min_{X}||y-A(\mu)X||_{2}^{2},s.t.X\geq 0, \tag{6}\]  
Figure 1: (**a**) Schematic of GISC LiDAR system. BS: beam splitter; CCD: charge-coupled device; PMT: photomultiplier tube. (**b**) 3D GISC reconstruction of a static scene at about 1.2 km far away [19].  
with the discrepancy \(D(\mu)\) between the calculated signal and the recorded data as:  
\[D(\mu)=||Y-A(\mu)X(\mu)||_{2}^{2}/||Y||_{2}^{2}, \tag{7}\]  
where \(\mu\) is the unknown constant speed of the target, \(A(\mu)\) is the measurement matrix dependent on the relative speed \(\mu\) of the target.  
With the techniques mentioned above, an airborne 3D GISC LiDAR image with 0.48 m horizontal resolution as well as 0.5 m range resolution at approximately 1.04 km height is experimentally demonstrated in 2016 [57] (Figure 2). As shown in Figure 2c, this system has the capability of motion deblurring and it realizes successful 3D reconstruction on a high-speed platform.  
In the past decade, GISC LiDAR has experienced leapfrogging development from principle demonstration, static platforms and static targets to moving platforms and moving targets [19; 57], and is promising to be applied in many fields such as high resolution airborne remote sensing, underwater detection and imaging, traffic monitoring, and so on. However, further theoretical and experimental researches in many aspects still need to be performed. For example, the unified theory of GISC LiDAR based on information theory and light field encoding, 1-bit compressive sensing theory [58] for GISC with photon counting detection, and the image quality evaluation of GISC LiDAR are still open questions.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 2 Steam Trap Study
### Steam Trap Failures  
The probability of steam trap failure in a steam circuit is influenced by factors such as trap type, operating conditions, maintenance quality, and the environment [7]. Industry data indicate that annual failure rates range from 5% to 25%, depending on maintenance practices, with well-maintained systems exhibiting failure rates below 10%, while poorly maintained ones can exceed 25%. Common causes of failure include sediment or calcium buildup, internal leaks, insufficient discharge, mechanical wear, corrosion, and extreme operating conditions like incorrect pressure or temperature. Failure rates also vary by trap type: thermodynamic traps are robust but require regular maintenance to avoid failure rates of 10-15%; float traps are more susceptible to sediment accumulation and mechanical issues, with failure rates of 15-20%; and thermostatic traps are prone to debris and temperature control issues, resulting in failure rates of 15-25%.
# A Roadmap for the Integration of Active Solar Systems into Buildings
## 4 Roadmap Development
### Step 3--Integration of an Active System  
Step 3 of the roadmap deals with the integration of a selected active solar system and a simulation of its energy production in order to confirm that it may cover the energy needs of the building and has the expected passive behavior as determined in Step 2. The process starts with the integration of a system on a three-dimensional model of a building to identify possible geometrical as well as morphological issues that may be optimised at this early stage.  
Subsequently, the computational calculations of a system's energy production are performed using appropriate software. The software can be selected by the designer as per the requirements of the project. However, it is important to confirm the validity of the simulation results by performing pilot simulations using a well-established software, e.g., TRNSYS [64].  
In order to obtain accurate results, it is important to use the technical characteristics of the specific models of used active solar systems. It should also be noted that, for evaluation purposes, the energy production of all used systems should be given in the same unit notations. Similarly, all of the energy production and consumption values should be converted into primary energy values following the energy performance of buildings Directive (2018/844/EU) [9] in order to allow for comparability between buildings and help facilitate their subsequent certification. Similar work that calculates the energy production of building-integrated active solar systems via computational simulations has been performed by [PERSON] et al. [25; 26; 65; 66].  
Finally, any chosen design should be implemented without violating applicable regulations.  
The third methodological step of the roadmap described above is presented in Figure 5.
# Secure Control of Linear Controllers Using Fully Homomorphic Encryption
## 4 Control System with Encrypted Controller
### Attack Scenario and Security  
NCSs are at risk of eavesdropping attacks because plants and controllers communicate with each other over network links. Our proposed network control system with encrypted controllers is well protected against eavesdropping attacks, and we briefly describe it here. We consider the following attack scenarios.  
In our model, the attacker \(\mathcal{A}\) has mainly the following described capabilities.  
1. Adversary \(\mathcal{A}\) can collect data within the communication channel through an eavesdropping attack.
2. Adversary \(\mathcal{A}\) can collect data within the controller through an eavesdropping attack.  
Note: In addition to the capabilities listed above, the decryptor, encryptor, and actuator of the control system cannot be compromised by an attacker \(\mathcal{A}\).  
We say that the scheme is not resistant to eavesdropping attacks if the attacker \(\mathcal{A}\) can obtain the controller parameters \(A,B,C,\) and \(D\) or signals \(y\) in polynomial time; otherwise, we say that it is eavesdropping-resistant.  
The security of the control system in this attack scenario is analyzed. Attacker \(\mathcal{A}\) collects data in the controller and communication channel by eavesdropping. In this scenario, the data in the controller and the data in the communication channel are in the form of ciphertext, which is encrypted using the BFV encryption scheme. In order to obtain useful data, the attacker \(\mathcal{A}\) needs to reduce the ciphertext to plaintext. The BFV encryption scheme is based on the difficult problem of RLWE and hence cannot obtain useful plaintext data in polynomial time. Therefore, our scheme is resistant to eavesdropping attacks.
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
## 3 System Model
### Threshold Value Channel Estimation Method  
The OFDM symbols will be affected by the noise in the wireless channel during the transmission process inevitably. The noise will cause the estimation error of the estimated CIRs and thereby increase the bit error rate (BER) or symbol error rate (SER) performance of the system. Therefore, AWGN is an important factor leading to the decrease of channel estimation accuracy. To suppress the noise and improve the accuracy of channel estimation effectively, many literature works have studied the noise suppression channel estimation methods. The threshold noise suppression method is a commonly-used method in OFDM receivers. The channel estimation method that remains the most significant taps (MST) can suppress noise in the channel estimation results. However, when the number of channels is unknown, the threshold value needs to be set according to the known SNR, so this method is not easy to apply. In the literature [16], the wavelet transform is utilized to estimate the standard deviation of noise; it is used as the threshold value of noise suppression, and the threshold value varies with the change of SNR. The paper proposes the best threshold for computing the noise suppression by optimizing the minimum square error (MSE) of subcarriers, and the threshold value channel estimation results \(\hat{\mathbf{h}}_{\text{thre},i}(n)\) after noise suppression are expressed as [18]:  
\[\hat{\mathbf{h}}_{\text{thre},i}(n)=\left\{\begin{array}{ll}\hat{\mathbf{h}}_{\text {LS},i}(n),&|\hat{\mathbf{h}}_{\text{LS},i}(n)|\geq\lambda\\ 0,&|\hat{\mathbf{h}}_{\text{LS},i}(n)|<\lambda\end{array}\right.,\ 0\leq n<N. \tag{12}\]  
In Equation (12), \(\lambda\) is a threshold value that can be defined according to the noise variance \(\hat{\mathbf{\sigma}}_{n}^{2}\). Normally, \(\lambda\) is set in the threshold region of \([0.001,\ 0.2]\).  
The advantage of the threshold suppression method is that the calculation is simple, but the selection of the threshold will vary with the difference of SNR and channel characteristics. Therefore, the effect of noise suppression is very sensitive to the selection of the threshold. Its disadvantage is that it cannot separate the smaller energy paths from the AWGN in the channel estimation results. That is,it may restore the noise with the larger energy as paths or suppress the paths with small energy as noise. The method of averaging the mean value of the continuous multiple channel estimation results can effectively suppress the noise in the channel estimation results, and the channel estimation method based on the filter matching can also suppress the noise effectively.
## 2 Background and Related Work
### Background
eSIM, short for Embedded Subscriber Identity Module, is a technology that enables the use of a digital SIM card directly embedded within a device, such as a smartphone, tablet, or wearable, without needing a physical SIM card [23]. The 5G architecture is characterized by the following key components [24; 25; 26; 27]:  
* User equipment (UE): The mobile devices used by end-users, such as smartphones, tablets, and IoT devices. * Radio access network (RAN): The RAN connects UEs to the core network. It includes the base stations, antennas, and other radio equipment. * Core network (CN): The core network handles data processing, authentication, and service delivery. It is a virtualized network that can be optimized for different use cases. * Network slicing: 5G supports network slicing, allowing the creation of multiple virtual networks with specific performance characteristics to cater to different types of services. In the context of 5G, fog computing is integrated into the network through multi-access edge computing (MEC). MEC nodes are deployed at the edge of the 5G network, close to end-users [28]. These nodes can host applications, services, and virtualized network functions, enabling data processing and service delivery closer to the point of data generation. As shown in Figure 1, MEC serves as a critical intermediary component by positioning compute resources at base stations and roadside units, which provides low-latency services and supports real-time applications, which is a core characteristic of fog computing, particularly in applications such as augmented reality (AR), virtual reality (VR), autonomous vehicles, industrial automation, and real-time gaming [29; 30].The 5G network employs several authentication protocols to ensure secure communication between the user equipment (UE) and the network. Some of the key authentication protocols used in 5G are:  
* Authentication and Key Agreement (AKA) [31]: AKA is a challenge-response-based protocol used during the initial connection setup between the UE and the 5G core network. It ensures that only legitimate UEs are granted access to the network. * Extensible Authentication Protocol (EAP) [32]: EAP is an authentication framework that supports multiple authentication methods. It uses various authentication mechanisms based on the user's and network's specific needs. * Transport Layer Security (TLS) [33]: TLS is a cryptographic protocol that secures data transmission between the UE and network elements. It ensures privacy and integrity during communication. * Secure Authentication Vector (AV) [31]: This protocol generates authentication vectors used by the AKA protocol for mutual authentication between the UE and the network. * Subscription Concealed Identifier (SUCI) [31]: The SUCI protects the user's permanent identity by concealing it behind temporary identifiers during authentication procedures. These authentication protocols work together to establish a secure connection within the 5G network. They play a vital role in ensuring the integrity and security of the communication between the user and the network components.
## 3 Results
### Experimental Setup
#### 3.1.2 Metrics  
The main evaluation metrics of this study are listed in Table 3. First, the primary function of the UGSAM is to segment vegetation areas in streetscape images, so we need to evaluate the accuracy of image segmentation. The image segmentation metrics include OA (overall accuracy), mIoU (mean intersection over union), recall, and precision. Second, the function of the WRPM within the UGSAM is to dehaze urban streetscape images, and its performance needs to be assessed. The evaluation metrics for this include MSE (mean squared error), PSNR (peak signal-to-noise ratio), SSIM (structural similarity index measure), and LPIPS (learned perceptual image patch similarity).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Formula** \\ \hline OA & \(\frac{TP+TN}{TP+TN+FN+FP}\) \\ Recall & \(\frac{TP}{TP+FN}\) \\ Precision & \(\frac{TP+FN}{TP+FN}\) \\ mIoU & \(\frac{TP}{TP+FN+FN+FN+FP}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of calculation formulas for performance metrics.
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
## 4 Blockage Probability Analysis and Modeling
### Human Body Shadowing Calculation Method
If the distance between the transceiver is greater than \(d_{low}\) and less than \(d_{up}\), the receiver is located in the shadowing area. The coordinate value of point \(Q\), point \(Q_{N}\), and point \(Q_{F}\) can be calculated by the binary system of equations [35]  
\[\left\{ \begin{matrix}(x_{Q}-x_{H})^{2}+(y_{Q}-y_{H})^{2}=r^{2}\\ y_{Q}=Kx_{Q}\end{matrix} \right., \tag{25}\]  
where \(K\) is the slope of the line between the receiver and the origin in the LCS of \(P_{L}\). By solving the binary system of equations, we can get two groups of solutions  
\[\left\{ \begin{matrix}x_{Q1}=\frac{x_{H}+y_{H}K}{1+K^{2}}+H_{1}\\ y_{Q1}=Kx_{Q1}\end{matrix} \right., \tag{26}\]  
and  
\[\left\{ \begin{matrix}x_{Q2}=\frac{x_{H}+y_{H}K}{1+K^{2}}-H_{1}\\ y_{Q2}=Kx_{Q2}\end{matrix} \right. \tag{27}\]  
The expression of \(H_{1}\) can be given by  
\[H_{1}=\frac{\sqrt{[2(x_{H}-x_{L})+2(y_{H}-y_{L})K)^{2}]-4(1+K^{2})[(x_{H}-x_{L })^{2}+(y_{H}-y_{L})^{2}-R^{2}]}}{2(1+K^{2})}. \tag{28}\]  
For the case where the horizontal distance between the human body and the transmitter is less than \(R\), if the receiver is in the first quadrant or the fourth quadrant in the LCS of \(P_{L}\), then \(x_{Q}=x_{Q1}\). If the receiver is in the second quadrant or the third quadrant in the LCS of \(P_{L}\), then \(x_{Q}=x_{Q2}\). For the case where the horizontal distance between the human body and the transmitter is greater than \(R\), \(x_{QF}\) equals the larger absolute value of \(x_{Q1}\) and \(x_{Q2}\). \(x_{QN}\) equals the smaller absolute value of \(x_{Q1}\) and \(x_{Q2}\).
## 2 Materials and Methods
### Hemolytic Activity Assay  
The hemolysis test was utilized to assess how green ZnO NPs and the fruit extract from _C. Spinosa_ affected RBCs. Fresh human red blood cells were obtained from healthy 20-30-year-old donors. Centrifugation was used to prepare the blood sample, which required 8 min at 1500 rpm. A clean pellet was then obtained by washing three times with PBS. The collected RBCs were suspended in PBS (10% \(v/v\)). All samples were combined with various concentrations of _C. Spinosa_ L. and ZnO NPs (7.5, 15, 30, 60, and 120 \(\mu\)g mL\({}^{-1}\)) and incubated at 37 \({}^{\circ}\)C for 60 min. Following this, all samples were centrifuged at 5000 rpm for 4 min. The sample supernatant was then transferred to a 96-well plate. Using a plate reader, the absorbance values of the supernatants were calculated at 570 nm. The positive control was SDS in PBS (0.1%), and the negative control was PBS solution. The following formula was used to calculate the percentage of hemolysis in RBCs [25].  
\[H\%=\frac{S.a-N.C.a}{P.C.a-N.C.a}\times 100\%\]  
where \(H\) indicates hemolysis, \(S\) indicates sample, a indicates absorbance, \(C\) indicates control, \(N\) indicates negative, and \(P\) indicates positive.
## 3 Experimental Verification of the Modeling Method and Parameter Settings
### Comparative Analysis of the Experimental Results  
Due to the limitations of the experimental equipment, it was impossible to carry out a complete experiment with the cutting head cutting soft rock. A finite element simulation using the same constitutive model was therefore undertaken, as shown in Figure 7. This enabled us to verify the validity of the simulated model by comparing the experimental results with those generated by the simulation.  
The cutting force curves, according to different cutting depths for the physical experiment and the simulation, are shown in Figure 8, respectively. By comparing the two, it can be seen that both cutting force curves have a step change and maintain much the same amplitude, as shown in Figure 9. On top of this, it can be seen from Figure 9 that the average values for the cutting resistance and traction resistance display the same trends. Thus, the simulation results are consistent with the experimental ones. The maximum error between the two sets of results occurred when the cutting depth was 6 mm. At this point, the cutting resistance values for the physical experiment and the simulation were 14.5 kN and 13.9 kN, respectively. However, this difference is still less than 5%. Therefore, the experiment proved the effectiveness of the approach to simulation and the accuracy of the simulated model, allowing for a minimal error tolerance.  
Figure 6: Rotary cutting test bed: (**a**) The overall structure of the experiment bench, and (**b**) the rock-breaking part of the experimental bench.  
Figure 7: Experimental verification of the finite element simulation.
## 3 Results and Discussion
### Multi-Criteria Decision Making with TOPSIS
#### 3.6.5 Determination of the Positive and Negative Ideal Solutions
This outcome signifies that ABS-Injection is superior across multiple criteria, including high compressive strength, low surface roughness, high dimensional accuracy, and low cost. However, as the cost significantly increases to TRY 1200 and TRY 12,000, the TOPSIS score for the ABS-Injection alternative decreases. This indicates that, at higher cost points, its superior performance in other criteria is unable to counterbalance the increased expense. Conversely, the \"PLA-FDM\" alternative demonstrates the highest TOPSIS scores when the cost is TRY 1200 and TRY 12,000. This suggests that PLA-FDM offers a more balanced performance and becomes the preferable option when cost escalation is a primary concern. In conclusion, the ABS-Injection alternative is identified as the optimal choice in scenarios where cost reduction is a priority. Conversely, the PLA-FDM alternative emerges as the more favorable option in situations where cost escalation is a significant factor. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Alternatives** & **Filled** & **Filled** & **Filled** & **Filled** & **Filled** & **Filled** \\ \hline PLA-FDM & 2.97 & 3.05 & 0.0400 & 176 & 0.5836 & \\ ABS-FDM & 0.95 & 1.43 & 0.0025 & 176 & 0.1767 & 1000 \\ ABS-INJECTION & 3.93 & 0.16 & 0.0020 & 120 & 1.0000 & \\ \hline PLA-FDM & 2.97 & 3.05 & 0.0400 & 176 & 0.6751 & \\ ABS-FDM & 0.95 & 1.43 & 0.0025 & 176 & 0.4091 & 100 \\ ABS-INJECTION & 3.93 & 0.16 & 0.0020 & 1200 & 0.6134 & \\ \hline PLA-FDM & 2.97 & 3.05 & 0.0400 & 176 & 0.6939 & \\ ABS-FDM & 0.95 & 1.43 & 0.0025 & 176 & 0.4459 & 10 \\ ABS-INJECTION & 3.93 & 0.16 & 0.0020 & 12,000 & 0.5737 & \\ \hline \hline \end{tabular} Note: The cost values for ABS-Injection change with production volume. For example, the unit cost is approximately TRY 120 at 1000 pieces, TRY 1200 at 100 pieces, and TRY 12,000 at 10 pieces due to the influence of mold and machine depreciation.  
\end{table} 
Table 9: TOPSIS scores of different alternatives.
# Imbalanced Diagnosis Scheme for Incipient Rotor Faults in Inverter-Fed Induction Motors
## 4 Test Bench and Signal Acquisition  
The test bench designed for this research is shown in Figure 4. The IM is fed from the line supply and from inverters whose specifications are defined in the Appendix A. In both cases, the voltage main frequency is 50 Hz. The motor load is a magnetic powder brake with its control unit. A Hall Effect current transducer by LEM(r) (Meyrin, Switzerland) is used to measure the stator current. A National Instruments(r) (Austin, TX, USA) NI cDNA9174 base platform with an NI 9215 acquisition module is used for the signal collection. The sampling frequency is 50 kHz.  
The rotor fault is simulated by drilling a hole in one of the bars near the short circuit ring. Three rotor condition states are considered: (i) The rotor is healthy in the first tests. This condition is labeled as RI; (ii) After that, a 4.2 mm depth hole is drilled in one of the bars. This intermediate-severity rotor condition is labeled as R3; (iii) Finally, a more severe rotor fault is provoked by drilling the whole rotor bar. This rotor condition is known (RS). The stator current signal is collected while the motor is operating in a steady state for 10 s with a constant load torque. During these tests, the motor slip is in the range of 3-5%, according to the motor specifications mentioned in Appendix A.  
Different tests, considering the type of power supply, the state of health of the rotor, and the level at which the motor is loaded, have been developed to develop an automatic steady-state diagnosis tool.  
Figure 3: Demonstration of the operation of the MWMOTE algorithm. A dataset from an inverter\(-\)fed motor is used. The green markers represent the majority class (healthy motor), and the blue markers represent the minority class (motor with failure). (**a**) Case with imbalanced classes; (**b**) synthetically balanced case (synthetically created markers are shown in red).
## 5 Influence of Major Factors on the Performance of Building Settlement Prediction
### Location of Monitoring Points  
The model performance metrics for monitoring points F1-F3 were obtained using the proposed LSTM-RA-ANN model (shown in Table 4).  
As can be seen from Table 4, the greater the distance of the monitoring point from the edge of the foundation pit, the better the assessment indicators. Compared with monitoring point F3, when the distance from the edge of the deep foundation pit increased by 5 m (F2), the RMSE decreased by 5%, the MAE decreased by 6%, and the R\({}^{2}\) increased by 2%; when the distance from the edge of the deep foundation pit increased by 10 m (F1), the RMSE decreased by 26%, the MAE decreased by 17%, and the R\({}^{2}\) increased by 2%. This is because the greater the distance from the edge of the deep foundation pit is, the further it is from the deep foundation pit construction area, the less it is affected by the deep foundation pit when it is being constructed, and the higher the prediction accuracy of the model is.