esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Constraints Applied to the Optimization Algorithm
### Constraints on the Values of \(I_{c77K_{i}}\)  
The workflow described in Section 4.2 for calculating \(c_{HTS_{i}}\)\({}^{\%}\) enables the determination of the critical current values at 77 K and the self-field (\(I_{c77~{}K_{i}}\)) for the HTS tapes used in each phase. Without constraints, the solver might produce impractical solutions, such as the use of extremely poor-performing (although extremely cheap) coated conductors or tapes with critical currents far exceeding current manufacturing capabilities, especially for long batches. Therefore, it is reasonable to restrict \(I_{c77~{}K_{i}}\) between realistic maximum and minimum values achievable by manufacturers \(I_{c~{}77~{}K_{max}}\) and \(I_{c~{}77~{}K_{min}}\), respectively, referred to here as 4 mm-wide tapes.
## 4 Simulation Results and Discussion
### Case 1: Sensitivity Analysis for Criterion Variation Within \(\pm 5\%\)
#### 4.2.4 Scenario 4: Variation in Upload Speed, Download Speed, Ping, and Packet Loss  
In this last scenario, we randomly vary the initial packet loss values, in addition to the previous variation in upload speed, download speed, and ping. Table 11 shows the random variation rates applied to the initial packet loss values of each satellite. The resulting decision matrix is shown in Table 12.
## 5 Performance of Terahertz Replicas Generation
The SOA-MZI introduces nonlinearity due to effects such as gain saturation and XPM. These nonlinear effects play a crucial role in processes like signal amplification and frequency mixing. High nonlinearities in SOAs are desirable for effective mixing, as they contribute to the generation of harmonics and facilitate the nonlinear phase shift required for signal manipulation. However, an excessively high level of nonlinearity may lead to signal distortion and other undesirable effects. On the other hand, having some degree of low nonlinearity is also important to maintain the integrity of the signals and prevent excessive distortion. Achieving the right balance involves optimizing the SOA parameters, such as bias current and input power levels, to harness the benefits of nonlinear effects for mixing while mitigating their negative impact on signal quality. Therefore, future work in SOA-MZI systems should explore methods to fine-tune and control the nonlinearity to achieve efficient mixing without compromising overall system performance. Figure 9: Experimental and simulated BER versus SR for 4- and 4096-QAM mixed signal. Sim: Simulation and Exp: Experimentation. To address existing constraints, future investigations could focus on optimizing the system's efficiency and performance through novel design approaches, exploring advanced materials, and implementing optimization techniques for the SOA-MZI architecture. Additionally, there is room to enhance data rate capabilities and extend transmission distances by employing advanced modulation techniques, signal processing methods, and innovative compensation techniques to mitigate signal attenuation and dispersion challenges. Integration with emerging technologies, such as machine learning and quantum communication, could be explored for improved adaptability and security. Furthermore, research could delve into practical applications in fields like telecommunications, medical imaging, and industrial sensing, ensuring the technology's viability in real-world scenarios. In the end, considerations for standardization, interoperability, and addressing security vulnerabilities are essential components for comprehensive future research directions in THz communication systems. Finally, a promising upcoming solution involves utilizing the OPC control signal within the attosecond domain, as opposed to the current 1.6 ps pulse width duration with an exceptionally high repetition rate. This solution may be used with the intention to overcome the dynamic behavior limitations associated with the use of SOA-MZI structures and enhance the overall mixing quality performance.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.2.3 Single Reduction Result Compared with Other Algorithm under Different SNR  
The results of the horizontal group with the largest dimensionality (four dimensions) reduction are selected in the comparison. To verify its robustness under noisy conditions, Philips and Shazam algorithms are applied as reference methods. As classical algorithms, they are widely used in audio fingerprint retrieval and have good retrieval performance. In Philips' algorithm, the spectrum of audio signal is divided into 33 bands and generates a 32-bit binary fingerprint by comparing the energies in adjacent bands. On the other hand, Shazam uses the peak information extracted in the frequency domain to construct a hash value with the storage size of 32 bits. In addition, a comparison of the original MASK audio fingerprint is added in the experiment to prove the feasibility of the dimensionality reduction. The original MASK size is 22 bits and the reduced fingerprint size is 18 bits. Figure 8 shows the retrieval performance of each method under different SNR.  
The comparison of the recall and precision rate of each method is shown in Figure 8,b. Both of them represent the retrieval performance and the performance of the horizontal dimension reduced fingerprint in this paper is basically the same as the original MASK, and slightly higher than that of Philips and Shazam algorithms. With the decrease of SNR, the retrieval performance of the method presented in this paper decreases slowly which indicates that it has stronger resistance against noise. To sum up, the method in this paper reduces the amount of fingerprint data which saves storage space and improves retrieval speed on the premise that the robust performance is almost not affected.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 2 Uplink and Downlink Channels in Satellite-Ground FSO Communications
### Satellite-Ground FSO Channel Environments  
Earth's surface warms by absorbing solar radiation, causing the adjacent layer of air to warm as well [18]. The warmer air rises above and turbulently mixes with the relatively cooler surrounding air, resulting in random temperature fluctuations. Turbulent inhomogeneities lead to the formation of turbulent cells, also known as eddies, of varying temperatures. These eddies behave like refractive prisms of varying sizes and refractive indices, interfering constructively or destructively with the propagating beam [2,18]. An optical beam spreads out due to diffraction when turbulent eddies are smaller than the transmitted beam. If turbulent eddies are significantly larger than the beam, it is deflected from its original path, causing it to wander and potentially miss the receiver. When a turbulent eddy's size is the same as the transmitted beam, the received signal intensity fades randomly, resulting in beam scintillation [2].  
Figure 1 illustrates the effect of turbulent eddies on satellite-ground FSO communications on both uplink and downlink. We can see from Figure 1 that the source of attenuation is closer to the transmitter on uplink. On downlink, the source of attenuation is closer to the receiver. Therefore, turbulent eddies of different diameters and refractive indices affect the FSO channel differently for uplink and downlink, respectively. Figure 2 illustrates beam divergence caused by the atmospheric channel for both uplink and downlink FSO channels. As shown in Figure 2, the optical beam starts to spread and acquires distortion the moment the optical signal is emitted from the ground station during uplink. However, on downlink, the optical signal passes through free space for much of the propagation path and is only disrupted by atmospheric turbulence for a short distance. Hence, optical signal attenuation due to the atmosphere on ground-satellite uplink is comparatively more than on satellite-ground downlink.  
In FSO link design, system designers should have a full understanding of beam propagation via random atmospheric conditions and the related losses in order to establish a reliable FSO communication link between the transmitter and the corresponding receiver. As mentioned earlier, due to the presence of turbulent eddies of varying temperatures, sizes, and refraction indices in the atmosphere, a series of turbulence effects is produced when the optical wave propagates through the atmosphere. Hence, the optical beam suffers a variety of losses when traveling through the atmospheric optical channel. In the following subsections, these losses are discussed.
## 6 Results and Discussion
### Applications, Limitations, and Future Developments  
The presented case study shows the potential of predictive models in road rehabilitation/maintenance management. In fact, the predictive model allowed the quantification of the benefits of the planned intervention in terms of accident reduction. It can be concluded that this method can be used in the decision-making process to identify the best intervention alternative to be implemented. To this end, it would be appropriate to extend the model to the level of the entire managed network to determine where, when and how it is most appropriate to intervene. Obviously, this requires a large database on the characteristics of the road infrastructure, traffic, and the number of accidents. In addition, the model should be recalibrated over a wider geographical area to be suitable for application on a larger scale. Conversely, the model calibrated on a section of road with sufficiently homogeneous characteristics can only be adopted on the road itself or on roads with similar characteristics.  
Figure 5: Effect of traffic variation on crash trend.
## 1 Introduction
The above studies have mainly focused on the uplift of underground structures due to soil liquefaction, with less analysis of the internal forces and deformations of structures with different burial depths in earthquakes. Increasing the burial depth will significantly change the state of the structural forces, so what will be the seismic response of the utility tunnel in non-homogeneous liquefiable soil at different burial depths? Flac3D numerical simulation is widely used to analyze the seismic response of underground structures in liquefiable or non-liquefiable soils [9, 18, 20, 21, 22], which provides a feasible means to study the above problems. This paper investigates the performance of utility tunnels with different burial depths in non-homogeneous liquefiable sites under seismic loading. The underground structure is simulated using Shell-Type structural elements in Flac3D, using plastic hinges at the members' joints to simulate their possible plastic behavior during the earthquake. Based on the common non-homogeneous sites in practice, a typical \"clay-liquefiable sand-clay\" stratigraphic model is established. The effect of burial  
Figure 1: Geological profile of a section of the utility tunnel at Beijing Daxing International Airport. depth on the uplift value of the structure is analyzed while considering its effect on the internal force and deformation of the structure. The second section details the process of numerical simulations in this paper, including geometric modeling, materials properties and parameters, boundary conditions, and the input ground motions. General results on the liquefaction characteristics of the soil and the seismic response of the structure for the numerical model with a 2 m burial depth are given in section three. The fourth section analyzes the internal forces, deformation, uplift, and structure-soil interaction of the utility tunnel under different burial depths to investigate the effects caused by the burial depth. At present, urban construction in China is increasingly using utility tunnels, while research in the field of seismic resistance of utility tunnels, especially under complex site conditions, is significantly lagging behind engineering practice. In this paper, detailed numerical analysis is carried out to reveal the force and deformation mechanism of the utility tunnels in non-homogeneous liquefiable sites under seismic action, analyze the effect of burial depth on the seismic response of the structure, and provide a basis for the structural design and construction of the utility tunnel in layered liquefiable sites.
# Anomaly Detection for Service-Oriented Business Processes Using Conformance Analysis
## 6 Setup for Conformance Analysis
### Fitness Calculation for Different Checkpoints  
As presented in an earlier section, the event log is divided into several sub-logs based on the time threshold using the cut-off time (25%, 50%, 75% and 100%). Each sub log contains traces of the length \(\frac{1}{m}\) of length \(L_{n}\) where \(n\) is the total length of the trace and \(m\) is the number of segments as shown in the Figure 9.  
In this work, for CDP the presented value of \(m\) is three as the domain experts recommend three logical portions. Each sub-log may contain a different number of events.  
Figure 8: Comparison of fitness and precision—process discovery by four techniques.  
Figure 9: Segmentation of the entire process trace into several sub logs.  
For instance, sub-log A contains traces with the start of the process till around 33% of the trace execution, sub-log B includes the middle portion of the trace, and sub-log C contains the final 33% of the trace. The goal is to identify the failure by earliest identifying the decreasing trend of the fitness score. The value of fitness is generated using the ProM tool through the following steps:  
1. Convert XES file for each of the sub-log. The plug-in used is the 'XES converter' in ProM Lite 1.2.
2. Use XES log to discover the process model using the Heuristic miner algorithm through the plug-in 'Interactive Heuristic Miner' in Prom 6.9.
3. Extract the data Petri net for each sub-log.
4. Use the 'Multidimensional Conformance Analysis' plug-in in ProM 6.10 to extract case-level fitness.  
Figure 10 shows the screenshot with each row presenting a sub-log, and the fitness score is mentioned at the left corner. Table 3 presents the fitness score of some traces in tabular form. Each row in the Table 3 presents the complete trace, divided into the three phases.
## 4 Verification of Simulations and Experiments
### Simulation Results
#### 4.1.1 No-Load Analysis  
Figure 9(a) shows the line voltage of back-EMF at 1000 rpm at no-load conditions. The back-EMF waveform is a major factor that affects the torque characteristics. As the shape of the back-EMF waveform resembles the sinusoidal wave with lesser distortion, a smaller torque ripple response is generated. The maximum value of the no-load back-EMF with harmonics is 38.557 V for the initial model and 40.566 V for the optimal model. At the maximum operating speed of the PMSM at 12,000 rpm, the maximum back-EMF is 486.792 V. It can be observed that it has a margin of 18.8% regarding the IGBT withstand voltage specification of 600 V used in this study. Figure 9(b) shows the distribution ratio of each harmonic order based on the total harmonic distortion (THD) analysis of the back-EMF waveform. When the fundamental component of the back-EMF waveform is increased, the efficiency is improved. It can be observed that the fundamental wave component of the optimal model is improved by 2% compared with the initial model.  
Figure 11 shows the comparison of the cogging torque characteristics of the initial and the optimal models. The optimum model's cogging torque is 0.2778 Nm, which is smaller than the constraint of the initial model of 0.3479 Nm. Based on FE analyses, the cogging torque of the optimal model was reduced by 20.2% compared with the initial model.  
Figure 10: Comparison of the back-electromotive force (EMF) characteristics between the initial and the optimal models at no-load conditions (rotation speed at 1000 revolutions per minute (rpm): (**a**) Waveform; (**b**) Harmonic.  
Figure 9: Comparison of initial and optimal models.
## 2 Methods
### Tracking Data Analysis Algorithm  
The quality of observations is the footstone of BDS-3 POD. The following is a brief description of some indices commonly used in tracking data quality analysis.  
1. C/N0 analysis  
C/N0 is mainly affected by parameters of antenna gain, multipath and the condition of correlator in receiver. It is an index reflecting the quality of carrier phase observations. C/N0 can be obtained through the observations [33; 34; 35; 36; 37].
2. Pseudo-range MP analysis  
When the GNSS observation signal is received by a station, it will generate a variety of reflected signals due to the interference of the surrounding environment, making the received signal deviate from true value, which is called MP. Compared with pseudo-range MP, carrier phase MP is negligible. The pseudo-range MP can be expressed as:  
\[MP_{i}=P_{i}-\lambda_{i}\varphi_{i}-\frac{2f_{i}^{2}}{f_{i}^{2}-f_{j}^{2}}\big{(} \lambda_{i}\varphi_{i}-\lambda_{j}\varphi_{j}\big{)}-B_{i} \tag{1}\]  
where \(i\) and \(j\) are frequency band with the value, \(i,j=1,2,3\ldots(i\
eq j)\), \(P_{i}\) and \(\varphi_{i}\) are the observations of pseudo-range and carrier phase, \(\lambda_{i}\) is the wavelength of frequency, \(B_{i}\) contains the linear combination of the phase ambiguities and the constant part of the hardware delays which can be obtained with the mean value within a continuous observation arc [38].
3. Pseudo-range noise analysis  
The pseudo-range noise can be expressed with difference between pseudo-range and carrier phase observations (code minus carrier phase, _CC_). Considering the high accuracy of carrier phase observations, the phase multipath is negligible, then _CC_ can be expressed as follows.  
\[CC_{i}=P_{i}-\lambda_{i}\varphi_{i} \tag{2}\]
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
## 2 Related Work
TEMPEST integrates various security measures, such as zoning, shielding, filtering, and masking, to protect sensitive data. Devices must be positioned at certain distances with additional built-in protection [4; 7]. Wires transmitting sensitive data should be isolated from those transmitting unclassified data through RED/BLACK separation [44]. Overlapping jamming signals can also be used to mask real data [1]. This study aims to improve the understanding of EM leakage from devices designed for general purposes, as there is increasing concern about security threats to sensitive data. Compromising security through EM leaks has become simpler and more cost-effective, so attackers can perform their malicious activities in shorter periods. This study also makes a valuable contribution to EM data leakage prevention research by providing insight into the latest trends and standards for effectively mitigating the potential risk of inadvertent EM emanation.
## 3 Solver Optimization
### Sub-Interval Predictive Integration
\[\mathbf{\varphi}_{k+1}=[T_{k+1}]^{-1}[M][T_{k+1}](T_{c}\mathbf{v}_{k}+\mathbf{\varphi}_{k}) \tag{22}\]0 \[\mathbf{\varphi}_{k+1}=[T_{k+1}]^{-1}[M][T_{k+1}](T_{c}\mathbf{v}_{k}+\mathbf{\varphi}_{k}) \tag{22}\]1  
Using \(\mathbf{v}_{k}^{r}=\mathbf{v}_{k}^{n}[T_{k}]\) and \(\mathbf{\varphi}_{i}^{r}=\mathbf{\varphi}_{i}^{n}[T_{k}]\):  
\[\mathbf{\varphi}_{k+1}=[T_{k+1}]^{-1}[M][T_{k+1}](T_{c}\mathbf{v}_{k}+\mathbf{\varphi}_{k}) \tag{22}\]2  
And then, (30) is derived where voltages and fluxes are both in the rotor reference frame. \[\mathbf{\varphi}_{k+1}=[T_{k+1}]^{-1}[M][T_{k+1}](T_{c}\mathbf{v}_{k}+\mathbf{\varphi}_{k}) \tag{22}\]3  
Equation (30) is a recursive function that takes the starting voltage, transports it into the rotor reference frame, sum and multiplies the fluxes for the next sub-iteration and finally brings it all back into the natural reference frame at each iteration. This double transformation can be avoided by leaving voltage and flux in the rotor reference frame during the \(m\) iterations and transforming just the last results by using the rotational matrix calculated for the cumulative angle increment. In this way (31) can be isolated from (30), showing just the recursive function for every sub-interval for the rotor flux. \[\mathbf{\varphi}_{k+1}=[T_{k+1}]^{-1}[M][T_{k+1}](T_{c}\mathbf{v}_{k}+\mathbf{\varphi}_{k}) \tag{22}\]4In (31) the integral iteration uses only rotor values (constant in the period \(T_{c}\)) and, being recursive, the sub-interval angle \([\Delta T_{i}]\) does not require elevation because it will be achieved by the multiplication by itself in the next sub-interval since the resulting \(\mathbf{\varphi}_{i+1}^{\prime}\) will become \(\mathbf{\varphi}_{i}^{\prime}\). To note how also the input voltage is rotated to account for the fact that, since in the stator reference frame it is assumed fixed and constant, in the rotor reference frame it is counter-rotating (because the frame rotates). The final rotation angle can be calculated recursively inside the integration, or outside using (32) depending on which is the less demanding option for the application. \[ \begin{split}[T_{k+1}]^{-1}&=[T_{k}]^{-1}([ \Delta T_{i}]^{-1})^{m}\\ &=([T_{k}][\Delta T_{i}]^{m})^{-1}\end{split}  \tag{32}\]  
Uniting (31) at the last step \(m\) and (32) yields to the last calculation (33) to get the final flux in the \(k\) step in the natural reference frame. \[\mathbf{\varphi}_{k+1}^{n}=[T_{k+1}]^{-1}\mathbf{\varphi}_{m}^{\prime} \tag{33}\]  
The final implementation of any of the above functions on the real motor control would, of course, require an error correction feedback to prevent dangerous integration offsets or erroneous parameters setup and update. Such a system will not be discussed because it is outside the scope of this paper. It is worth noting that the \([T_{k}]\) and \([\Delta T_{i}]\) matrices are almost diagonal with a very simple calculation of the inverse by changing the sign of the sine elements in position 12 and 21 of the rotational matrix (11).
## 2 Materials and Methods
### Main Parameters Configuration  
For the selection of hyperparameters of the Random Forest, the Leave-One-Out Cross-Validation (LOOCV) method was applied, which is a reliable evaluation technique for assessing unseen data. This method is feasible due to the relatively small sample size  
In developing the Random Forest model, the Leave-One-Out Cross-Validation (LOOCV) method combined with GridSearchCV was implemented, utilizing the scikit-learn library in Python. This approach was chosen to optimize and validate the Random Forest Regressor ([[https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomFores](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomFores)]([https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomFores](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomFores)) tRegressor.html) (accessed on 26 July 2024), allowing for a precise and thorough evaluation of each hyperparameter configuration.  
Leave-One-Out Cross-Validation (LOOCV) is a cross-validation technique where each sample in the dataset is used once as the test set, while the remaining samples are used to train the model. This process is repeated such that each sample in the dataset serves as a unique test case in a model trained on all other samples. LOOCV is particularly useful for small datasets as it maximizes the use of available data for training, providing a very detailed and accurate assessment of model performance.  
GridSearchCV facilitated a systematic and exhaustive exploration of hyperparameter combinations. The hyperparameters adjusted included the maximum depth of the trees (max_depth), the number of trees (n_estimators), and the minimum thresholds for splitting nodes and forming leaves (min_samples_split and min_samples_leaf).  
The application of GridSearchCV, in conjunction with LOOCV, enabled the identification of the optimal hyperparameter configuration that balances model complexity and generalization capability. The optimal hyperparameters identified were:  
* **max_depth**: 5
* **n_estimators**: 100
* **min_samples_split**: 3
* **min_samples_leaf**: 1