esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 State Evaluation and Fault Prediction of Protection System Based on Digital Twin Technology
### Fault Prediction of Protection System Based on Clustering Algorithm
#### 3.3.2 Clustering Center and Fault Prediction  
For the nine faults mentioned above, 30 groups of each fault characteristic sequence are selected in the historical database to form a sample set of 270 groups, which are normalized as shown in Table 6.  
Set the number of clusters to \(K=9\), and randomly select a set of sequences as the initial cluster center in the sample set. Iterate each group of sample data, calculate the Euclidean distance between each group of sample data and nine clustering centers, and mark the group of samples to the nearest category cluster. After all the marks are completed, the centroid of each cluster is recalculated as the updated clustering center, and the second iteration is carried out. Repeat the above steps until the data within the cluster is not changed for two consecutive iterations. Finally, the cluster centers of nine fault types are calculated, as shown in the Table 7.  
changes and environmental factors, so there is still some room for improvement in the control of errors. After this method is put into practical application, benefiting from the self-improvement ability of the closed-loop feedback of digital twin technology, the clustering centers of nine fault types can be further improved according to the actual results of defect handling.  
When the status of a device is assessed as \"severe\" at a certain moment, the Euclidean distance \(E_{i}(\mathbf{N})\) is calculated between the characteristic sequence of the protection system and each cluster center at that moment. The smaller the distance, the higher the correlation between the device and the cluster center at that moment. The final prediction fault type is the fault type corresponding to the cluster center with the highest correlation. For visual representation, the relative correlation degree can be calculated using the following equation.  
\[\overline{E}_{i}(\mathbf{N})=\frac{E_{i}(\mathbf{N})-minE_{i}(\mathbf{N})}{maxE_{i}(\mathbf{N })-minE_{i}(\mathbf{N})},\ (i=1,2,\cdots,n) \tag{14}\]  
where \(n\) is the number of cluster centers, \(maxE_{i}(\mathbf{N})\) and \(minE_{i}(\mathbf{N})\) are the maximum and minimum Euclidean distances between the protection system and each cluster center.  
The final prediction fault type is the fault type corresponding to the clustering center with relative correlation degree of one.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 3 Instruments for TD-NIRS
### Time-Correlated Single Photon Counting (TCSPC) Technique
#### 3.2.1 Principle, Components, Characteristics, and Operation of the TCSPC Technique
Therefore, it is important to choose a light source capable of emitting light pulses stably with a narrow pulse width and a high repetition rate. To shorten measuring times, [PERSON] and [PERSON] [42] devised a method correcting the pile-up distortion mathematically for the measured data with \(m\) larger than 0.1 (10%). In the TCSPC technique, there also exists a dead time when the detector system is unable to count photons due to processing signals after receiving photons. If the dead time is longer than the time period between two successive input pulses, counting losses take place. In standard TCSPC systems, the dead times are longer than the time period between two successive input pulses. Then the CW intensity, \(I\), given by integration of a TOF distribution with respect to time must be corrected for the counting losses by \(I_{true}=I_{meas}/(1-\alpha I_{meas})\) where \(I_{true}\) and \(I_{meas}\) [s\({}^{-1}\)] are the corrected and integrated intensities, respectively, and \(\alpha\) is the dead time [s] when the measuring systems are non-paralyzed models.
# Comparison of Hysteresis Based PWM Schemes \(\Delta\Sigma\)-PWM and Direct Torque Control
## 2 Model of a PMSM
### Equations of a PMSM  
With the stator resistance \(R_{s}\), the stator voltage vector \(\underline{u}_{s}\) is given by [27; 28]  
\[\underline{u}_{s}=R_{s}\cdot\dot{i}_{s}+\frac{\mathrm{d}\underline{\Psi}}{ \mathrm{d}\underline{t}}. \tag{5}\]  
The electromagnetic flux vector \(\underline{\Psi}\) can be expressed by [27]  
\[\frac{\mathrm{d}\underline{\Psi}}{\mathrm{d}\underline{t}}=L_{s}\frac{\mathrm{ d}\underline{i}_{s}}{\mathrm{d}\underline{t}}+\frac{\mathrm{d}}{\mathrm{d} \underline{t}}(\Phi_{F}\cdot e^{\jmath_{el}}). \tag{6}\]  
The stator inductance is named \(L_{s}\) and both expressions are valid for various types of three phase machines. In the following, (5) and (6) are adopted for a PMSM [27]. Substituting (6) for (5) and applying both coordinate transformations leads to the following result [27]  
\[u_{sd}=R_{s}\cdot i_{sd}+L_{d}\frac{\mathrm{d}\underline{i}_{sd}}{\mathrm{d} \underline{t}}-\omega_{el}\cdot L_{q}\cdot i_{sq} \tag{7}\]  
\[u_{sq}=R_{s}\cdot i_{sq}+L_{q}\frac{\mathrm{d}\underline{i}_{sq}}{\mathrm{d} \underline{t}}+\omega_{el}\cdot L_{d}\cdot i_{sd}+\omega_{el}\cdot\Phi_{F}. \tag{8}\]  
Besides, the flux components can be derived from (7) and (8) [29; 30]  
\[\Psi_{q}=i_{sq}\cdot L_{q} \tag{9}\]  
\[\Psi_{d}=i_{sd}\cdot L_{d}+\Phi_{F}. \tag{10}\]  
The electric angular velocity \(\omega_{el}\) is linked to the mechanical angular velocity \(\omega_{mech}\) by the pole pair number \(p\) and is given by [28]  
\[\omega_{el}=p\cdot\omega_{mech}. \tag{11}\]  
The dynamic of the mechanical part of the PMSM is described by [27; 28]  
\[T_{el}-T_{L}=J\frac{\mathrm{d}\omega_{mech}}{\mathrm{d}t} \tag{12}\]  
\[\omega_{mech}=\frac{\mathrm{d}\varepsilon_{mech}}{\mathrm{d}\underline{t}}. \tag{13}\]  
Both parts are connected by the electric torque  
\[T_{el}=\frac{2}{3}\cdot p\cdot\Big{(}\Phi_{F}\cdot i_{sq}+(L_{d}-L_{q})\cdot i _{sd}\cdot i_{sq}\Big{)}. \tag{14}\]  
This set of equations, i.e., (7), (8), (12)-(14) is referred to as the machine model.
# Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD
## 5 Discussion and Summary
### Summary: Which Is the Best Way?  
In the comprehensive experimental results, there is not a single best setup to boost all workloads, since each of these workloads has different characteristics, even in its job lifetime. However, we can still propose how to optimize the configurations of a distributed in-memory computing platform by considering the variety of target workloads as follows:  
* **Spark JVM heap configuration--shuffle area vs. storage area**: According to the experimental results of four different workloads, we can observe the performance differences depending on workload characteristics. For example, PageRank is a typical example of having a large amount of shuffle data so that allocating more memory to the shuffle portion improves the overall performance. However, in the case of k-means clustering, the more we allocate to storage memory, as opposed to shuffle memory, the less execution time is required. Therefore, if we can adjust the JVM memory allocation percentage dynamically according to the workload characteristics, we can optimize the total execution time. The Hadoop YARN [25] enables us to assign jobs to different types of clusters (configurations), so that we can apply this idea to a large sized Hadoop cluster to meet memory characteristics for various types of jobs.
* **RDD caching policy--memory vs. SSD**: In most cases, the SSD-backed memory caching shows the best performance unless all of the RDDs can fit within the actual main memory. Therefore, the SSD-assisted memory caching policy can be a viable choice for challenging workloads requiring substantial amounts of main memory that cannot be met by any single node in a cluster.
## 3 Materials and Methods  
In the proposed study, the publicly available UCI cardiac attack dataset is used and preprocessed for missing values, followed by normalization using the standard scalar technique. SMOTE was then applied to address the class imbalance, ensuring a more representative dataset for training ML models. The balanced dataset was subsequently used to train various models, including AdaBoost, RF, KNN, Bagging, SVM, and ANN. Following extensive testing, the ANN model is found to be the most accurate model for classification.  
To enhance the interpretability of the ANN model, XAI techniques like LIME and SHAP were applied. LIME explains individual predictions locally, while SHAP provides global insights into feature influence. Both techniques helped identify significant features in the heart attack prediction, enhancing clinical interpretability. This improved transparency aids in more accurate diagnoses and treatment planning, making AI-driven decisions more actionable for healthcare professionals. Figure 1 depicts the flow of the study.  
Figure 1: Proposed workflow of the experiment.
## 6 Conclusions  
In this paper, we establish a zero-carbon building system with integrated energy, and the main devices based on the analysis of electricity, heat, gas, and carbon flows are modeled. To achieve optimal energy dispatch in a zero-carbon building under uncertainty, the heat balance is described using chance constraints based on the occupants' comfort zone. Then, we employ a data-driven approach by clustering the prediction errors of PV&WPP outputs to construct uncertainty sets. Furthermore, the MRO approach is proposed for solving the optimization problem. We conduct numerical studies using a typical day chosen from summer and winter. Comparative analysis under different uncertainty settings shows that the proposed model can reduce energy costs while ensuring robustness. Additionally, the results show that carbon emissions in winter are approximately 1.85 times higher than those in summer, with CO2 produced by GT accounting for about 90% of the total. Relaxing the occupant comfort constraints leads to a reduction in energy costs, with a maximum decrease of 18.82% in summer and 27.22% in winter, respectively. Additionally, comparative analysis indicates that the proposed method can balance conservativeness and computational efficiency through the selection of the number of clusters.  
There are many types of uncertainties in building systems, including renewable energy output, market prices, and user demand. How to design zero-carbon building systems and dispatch energy under multiple uncertain parameters and even coupled uncertainties is the direction of our future research.  
Conceptualization, K.H. and Q.X.; methodology, K.H. and Q.X.; software, K.H. and S.L.; validation, L.F.; formal analysis, K.H.; investigation, S.L.; resources, L.F.; data curation, L.F.; writing--original draft preparation, K.H.; writing--review and editing, Q.X. and S.L.; visualization, K.H.; supervision, L.F.; project administration, Q.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
All authors of this manuscript declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CHP & Combined heat and power \\ GT & Gas turbine \\ CCS & Carbon Capture and Storage \\ P2G & Power to gas \\ PV\&WPP & Photovoltaic power generation and wind power plant \\ ES & Energy storage \\ SO & Stochastic optimization \\ SG & Scenario reduction \\ \end{tabular}
# The Aerodynamic Analysis of a Rotating Wind Turbine by Viscous-Coupled 3D Panel Method
## 2 Numerical Formulation
### Viscous-Inviscid Coupling  
The current method employs a very straightforward and intuitive method of coupling the inviscid and viscous solvers, by adopting the inviscid solution as an input for the viscous solver, and in turn incorporating a viscous correction, in this case a displacement of the inviscid surface streamline, back into the inviscid solver. However, these directly coupled models usually break down at separation due to the well-known Goldstein singularity, which is caused by a breakdown in the inviscid-viscous hierarchy, where the inviscid solution is no longer dominant.  
[PERSON] [33] overcame these hierarchal problems via computationally expensive merging of the viscous and inviscid equations into a large system of non-linear equations to be simultaneously solved by a global Newton-Raphson method. [PERSON] [34], to reduce this high computational cost, introduced the concept of an interaction law, which approximates the inviscid flow field. The interaction law and the viscous equations are solved simultaneously, and this solution is in turn input into the inviscid solver. Simultaneously coupled inviscid-viscous solvers, employing the \"double wake\" method, have more recently been applied to 2D yacht sails [35], unsteady pitching airfoils in stall conditions [36], and the quasi-3D (2D with rotational effects) analysis of rotating airfoils [37] and wind turbine rotors [38], while most of the recent work on the quasi-simultaneous approach has focused on deriving suitable interaction laws [39; 40; 41; 42].  
Notwithstanding the aforementioned convergence problems associated with direct coupling of the inviscid and viscous solvers, the current method was able to accurately simulate the severely separated flow around a rotating wind turbine rotor. \"By modelling the separated wake as an extension of the displacement body, the thick wake model is able to avoid the Goldstein singularity, allowing for a fast, robust CFD tool, for which the computational expense of simultaneously solving the inviscid and boundary layer equations is no longer necessary\" [6]. Furthermore, the speed and code-openness of the current method allow for ease of coupling for aeroelastic problems, optimisation algorithms, and actuator line models, all of which have recently found application in the modelling of full wind farms [43; 44].  
Figure 4: Single rotor blade showing airfoil sections, separated “thick” wake, and helical thin wake, with “trailing edge” highlighted at root and tip.
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 2 Hybrid FSO/RF Link System  
Firstly, we present a ML-based soft-switching model for hybrid FSO/RF links, as shown in Figure 1. The model comprises transceivers for FSO/RF links that are connected to the switching system. FSO and RF channels are established simultaneously. The ML algorithm is used to calculate the LB, which defines the range of switching thresholds between the FSO and RF links under diverse modulation schemes. In this switching mechanism, the FSO link is designated as the primary communication channel, with the RF link assuming an auxiliary role.
# Log-Based Fault Localization with Unsupervised Log Segmentation
## 2 Materials and Methods
### Context-Based Ranking  
Existing Log-based Fault Localization methods struggle when there are many previously unseen logs. This situation is common in large software systems where a single issue can cause a cascade of failures and generate thousands of related logs. Simply returning all of them may not be helpful.  
To address this issue, we propose a ranking based on context. Intuitively, unseen log lines appearing within the context of well-known log lines are more suspicious than unseen log lines grouped together. The reason is that grouped unseen log lines are often stack traces, crash dumps, or new functionalities, whereas a single anomaly midst well-known behavior can be the first symptom of a program going off the track. To calculate this metric, we first segment the log file using unsupervised log sequence segmentation, and then calculate the mean of suspicious scores of log templates in the segments where the most suspicious log templates are present. Context-based ranking was calculated using the following equation:  
\[Context\_ranking(Segm_{k})=1-\frac{\sum_{i_{l}\in Segm_{k}}{Susp(l_{i})}}{| Segm_{k}|}, \tag{5}\]  
where \(Segm_{k}\) denotes the \(k\)-th segment of log file. The greater the mean value of the segment, the less suspicious are the lines.  
In the example in Table 2, the fault is located in line 6. It appears in the context of successful connection responses and explains subsequent node failure. Let us assume that the first segment of the failed log is from lines 1 to 3. The sequences of the template IDs is \(4,5,5\), and all lines were ranked as suspicious. The context ranking of this segment was calculated as \(Context\_ranking(S_{1})=1-1=0\). The second segment is from lines 4 to 8 with a sequence of template IDs \(1,1,6,1,1\), and the context ranking of this segment is \(Context\_ranking(S_{2})=1-mean(4\times 0.36+1)=0.5\). The last segment is from lines 9 to 11, and its context ranking is \(Context\_ranking(S_{3})=1-mean(0.5+0.5+1)=0.3\).  
The segment with the highest context ranking was \(S_{2}\), which was the expected value. The third segment is lower in the hierarchy. The first segment is correctly the lowest in the ranking, as it is not related to the actual fault, but represents an additional logger turned on for the expected failure.
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 4 Proposed Algorithm
### UAV Dynamic Deployment Algorithm
The traditional strategy gradient approach leads to unstable training due to the excessive magnitude of strategy updates, while trust domain strategy optimization (TRPO) has a high computational complexity despite the introduction of constraints. PPO simplifies the implementation of strategy updates by introducing the clip mechanism, and at the same time effectively restricts the magnitude of the strategy updates to avoid strategy collapses during the training process. The PPO algorithm mainly employs the strategy gradient approach to avoid strategy collapses during the training process by maximizing the expectation of the dominance function to optimize the strategies, and to guide the intelligent agent to take actions that can improve the coverage and reduce the delay. In particular, it calculates the ratio of the probability of the old and new strategies and introduces the CLIP function to ensure that the new strategy improves within the scope of not deviating from the old strategy while limiting the update magnitude. The strategy objective function is computed as in Equation (41), and the ratio of the old and new strategy probabilities \(r_{t}\) is as in Equation (42), where \(\pi_{\theta}(a_{t}|s_{t})\) is the output probability of the strategy under the parameter \(\theta\), and \(\pi_{\theta_{\text{old}}}(a_{t}|s_{t})\) is the output probability of the old strategy. \[L(\theta)=\mathbb{E}_{t}\bigl{[}\min\bigl{(}r_{t}(t)A^{\pi_{\theta_{\text{old }}}}(s_{t},a_{t}),\text{clip}(r_{t}(t),1-\epsilon,1+\epsilon)A^{\pi_{\theta_{ \text{old}}}}(s_{t},a_{t})\bigr{)}\bigr{]} \tag{41}\]  
\[r_{t}(t)=\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta_{\text{old}}}(a_{t}|s_{t })} \tag{42}\]  
The input of the value network is the current state \(s_{t}\) and the output is the value of the current state. The loss function of the value is mainly used to train the value network to minimize the difference between the value function and the actual return. The closer its predicted state value is to the actual return advantage estimate \(A^{\pi_{\theta_{\text{old}}}}\), the more accurate it is, where the value loss function is as in Equation (43), and the dominance function \(A^{\pi_{\theta_{\text{old}}}}\) is as in Equation (44), where \(V_{\[S(\pi_{\theta})=-\sum_{a}\pi_{\theta}(a_{t}|s_{t})\log\pi_{\theta}(a_{t}|s_{t}) \tag{45}\]  
The total loss function of the whole algorithm is a weighted summation of three parts, namely, the strategy objective function, value loss, and entropy regularization term. By appropriately adjusting the weights of these three parts, the total loss function can guide the algorithm to find the optimal balance between convergence, accuracy, and the exploratory nature of the strategy, and its formula is shown in Equation (46).
## 3 Results and Discussion
### Influence of Temperature on the Migration Number of Y(III) Ions  
In this section, the AlF\({}_{3}\)-(Li,Na)F-3 wt.%Y\({}_{2}\)O\({}_{3}\) molten salt system with a (Li,Na)F/AlF\({}_{3}\) molar ratio of 2.5 is examined at a current of 3 A; cathode current density of 1.60 A/cm\({}^{2}\); electrolysis time of 1 h; and temperatures of 900, 920, 960, 980, and 1000 \({}^{\circ}\)C. The relationship between the electrolysis temperature and the active Y(III) ion mobility is displayed in Figure 3. In the temperature range of 900-1000 \({}^{\circ}\)C, the migration number of Y(III) ions increases with temperature from 0.70 to 0.80 almost linearly (Equation (13)). The results show that with an increase in temperature, the migration rate (mass transfer rate) of Y(III) ions dissolved in AlF\({}_{3}\)-(Li,Na)F molten salt system will increase, and the electroreduction reaction of Y(III) ions at the cathode will become faster and easier, which leads to the increase of the number of Y(III) ions in the system.  
\[N_{\rm Migrate}=-0.183+9.81\times 10^{-4}T \tag{13}\]
# Building Equi-Width Histograms on Homomorphically Encrypted Data
## 2 Equi-Width Histograms on Homomorphically Encrypted Data
### Detecting Outliers in Encrypted Data Using Private Histograms
For an empty list, the reduce-by-summing operation returns 0. ```
1:\(x[]\)\(\triangleright\) New sampled data stream from IoT devices
2:\(low[],high[]\)\(\triangleright\) Bucket bounds in histogram
3:\(count[]\)\(\triangleright\) Element counts per bucket
4:\(th\)\(\triangleright\) Frequency threshold for outlier detection
5:\(a[]\)\(\triangleright\) Vector of outliers detected per sample
6:\(a\leftarrow[\;]\)
7:for all\(x_{i}\in x\)do
8:\(r\leftarrow\) vectorialIsInAbnormalBucket(\(x_{i},low,high,count,th\))
9:\(a.\)append(\(\sum r\))
10:endfor
11:return\(a\)
```  
**Algorithm 4** Equi-width histogram-based outlier detection on homomorphically encrypted data. The vectorial approach of labeling an input value involves iteratively checking if the value is in each of the buckets defined by three lists: one for minimum values, one for maximum values, and one for the current element count in each bucket. When the bucket in which the value must be placed is determined, the number of values that it contains is compared to a given threshold. The result of the vectorial procedure is a list of the same size as the lists containing the range limits for the buckets. It contains at most one element set to 1, positioned at the index of the bucket where the value fits, but only if the bucket's element count is below the threshold. If no such bucket meets the condition, all elements in the output list are 0. This mechanism is depicted in Figure 3.