esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Verification and Physical Experiment
### Simulation Analysis of Inverter Fault Diagnosis Under Noise Interference  
In industrial environments, inverter systems are inherently exposed to persistent stochastic noise during operation. Such noise interacts with fault signature signals through nonlinear coupling mechanisms, substantially complicating the diagnosis of OC faults in power switches. To quantitatively evaluate noise impacts on fault diagnosis, this study simulates practical interference using zero-mean Gaussian white noise with a variance of  
Figure 8: Simulation results of the \(T_{b2}\) and \(T_{b4}\) OC fault under voltage fluctuation. (**a**) Three-phase currents. (**b**) Fault detection. (**c**) Initial location. (**d**) Precise location.  
3. Taking \(T_{b1}\) and \(T_{b3}\) faults as representative cases, the figure demonstrates diagnostic outcomes under both ideal and noise-contaminated conditions.  
In Figure 10, \(T_{b-n}\), \(R_{b-n}\), \(W_{b-n}\) and \(\eta_{b-n}\) denote the diagnosis variables under practical operating conditions. As shown in Figure 10a, the adaptive threshold \(T_{b-n}\) oscillates around the ideal threshold \(T_{b}\) during normal inverter operation, yet its overall trajectory closely aligns with the ideal curve. Figure 10c reveals that the practical fault location variable \(\eta_{b-n}\) remains marginally below its ideal counterpart \(\eta_{b}\). Furthermore, the discrepancy in fault location time between practical and ideal conditions is merely 0.06 ms.  
Figure 10: Comparison of fault diagnosis results under ideal and noisy conditions. (**a**) Fault detection. (**b**) Initial location. (**c**) Precise location.  
Figure 9: Simulation results of the \(T_{c4}\) OC fault under load fluctuation. (**a**) Three-phase currents. (**b**) Fault detection. (**c**) Initial location. (**d**) Precise location.  
The proposed fault detection and location method extracts current root mean square (RMS) values to derive current characteristic values and compute location variables. This RMS-based processing approach inherently incorporates statistical smoothing properties, effectively suppressing noise interference and thus ensuring diagnostic accuracy and stability. Consequently, the method demonstrates strong noise robustness, enabling reliable fault diagnosis under practical operating conditions.
# SOH Estimation of Lithium-Ion Battery Pack Based on Integrated State Information from Cells
## 1 Introduction
Time series analysis models, such as recurrent neural network (RNN), do well in time-dependent relations model realization [22]. The historical data memory and relationship analysis ability of an RNN can be utilized to screen and retain informative the degradation process from the long-term data. With the help of deep learning, this type of network is expected to achieve a model description of the multi-degradation process and an accurate SOH prediction [23]. At present, neural networks have been increasingly used in SOH prediction and estimation of batteries. Take advantage of deep learning, in this research, we adopted a data-driven theory to propose a multi-cell degradation information fusion method for battery pack SOH prediction based on deep learning, making full use of the inter-degradation information of the cells. In the proposed method, there are three critical improvements to achieve our goal:  
1. Different with the existing studies, the status of battery pack is the object focused on to realize global assessment result of the power system instead of SOH of cells. 2. Multi-dimension SOH data of cells from different levels of structure are utilized as input to assess battery packs, instead of only pack indicators, which provide dependency information in interior. 3. To improve prediction performance, not only SOH information, but also exhaustive temperature data of battery cells is captured to offer status information of battery cells. To verify the performance and feasibility of the novel method, the experiments were carried out for battery packs. In the experiment, the electrical indicators of cells and packs were acquired as input for the model established based on long-short term memory (LSTM) basic structure and they were used to determine the interaction between the cells for SOH prediction. The main content of this paper is arranged as follows: Section 2 briefly introduces the basic method theories involved in this paper. Based on the basic theories, the main ideas of the technical method proposed in this paper is explained in Section 2. As described in Section 3, for the demands of SOH data collection, the cycle experiments of battery packs were designed for data acquisition. The data for typical battery packs were preliminary analyzed for subsequent research and the validation of the method. In Section 4, the data set collected in the experiments mentioned in Section 4 is used to verify the feasibility and advantages of the novel method. Section 5 summarizes the entire paper.
## 5 Verification Experiments and Analysis
### Verification of Planning Mechanism and GA Operators
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline _e.id_ & _e.Cid_ & _e.typ_ & _e.Cap_ & _e.id_ & _e.Cid_ & _e.typ_ & _e.Cap_ & _e.id_ & _e.Cid_ & _e.typ_ & _e.Cap_ \\ \hline
1 & 1 & 1 & 5 & 2 & 1 & 1 & 5 & 3 & 1 & 1 & 5 \\
4 & 1 & 1 & 5 & 5 & 1 & 1 & 5 & 6 & 1 & 2 & 6 \\
7 & 1 & 2 & 6 & 8 & 2 & 2 & 6 & 9 & 2 & 2  
Furthermore, the effectiveness of three operators are verified. The performance of selection operator is shown in Figure 8, where R_O, R_W, and R_A represents Optimal fitness, Worst fitness, and Average fitness of Binary Tournament selection respectively, and H_O, H_W, and H_A are that for hybrid selection. From Figure 8, we can observe that the hybrid selection operator performs better than traditional Binary Tournament selection. With this hybrid selection operator, more better solutions can be evolved, and the convergence speed is also faster. Figure 9 shows the compared results between traditional crossover denoted as (O_*) and designed adaptive crossover denoted as (A_*), with fixed parameters of Formula (6): \(P_{c1}=0.85\), \(P_{c2}=0.6\), and \(P_{c3}=0.45\). From these results, we can also find the new operator is effective to find optimal solutions, but the convergence speed is somewhat slow. This is mainly because such procedure has been artificially interfered, typically Formula (6). Figure 10 presents the effect of Effectiveness-oriented mutation, denoted as (ECM_*). We can find that this operator can promote the performance of both getting better fitness and accelerating the evolving speed, just similar to that of Figure 8. Figure 11 shows the final performance with the proposed mechanism combining all these new operators. The results of 100 experiments are shown in Figure 12. From this Figure, we know that feasible solutions can be obtained at every time, and in 97 experiments the fitness values approximating the optimal value 360 are achieved. Of course, there still exist three experiments that converge to some suboptimal solutions. This is a normal case for a genetic algorithm, and is acceptable because, primarily, this problem must be solvable at each time. Figure 6: The relation of Population Size and Fitness Value. Figure 7: The relation of Population Size and Convergence Speed. Figure 8: Compare of Roulette Selection and Hybrid Selection. Figure 10: Compare of Traditional Mutation and Effectiveness-oriented Mutation. Figure 9: Compare of Traditional Crossover and Constrained Adaptive Crossover.
## 4 Simulation and Results Analysis
### Discussion  
The simulation results indicate that for systems with nonlinear coupling characteristics, all three controllers (FLMPC, LMPC and AMPC) are able to achieve target control under constraints and disturbances. However, the LMPC method shows significant shortcomings in handling inter-axis coupling, particularly in rapidly changing environments where it fails to meet high precision requirements and performs poorly when disturbances are encountered. On the other hand, while AMPC is capable of handling nonlinearities effectively, its computational time per iteration is relatively long, posing challenges for real-time applications, especially in cases with limited computational resources.  
In contrast to the two methods mentioned above, the model predictive method combined with feedback linearization in this study offers distinct advantages in dealing with system nonlinearities and disturbances, along with outstanding computational efficiency. Due to its lower computational burden, FLMPC can update control inputs in real time, maintaining high-precision control performance. Furthermore, FLMPC exhibits excellent disturbance rejection capabilities, effectively mitigating the impact of external disturbances and ensuring system stability and accuracy. Thus, FLMPC is particularly well-suited for engineering applications that require high real-time performance when dealing with complex nonlinear systems.  
Overall, FLMPC strikes a balance between control performance and computational efficiency. It not only outperforms the other methods in simulations but also holds strong potential for practical applications. Particularly in high-dynamic environments, FLMPC offers significant advantages over traditional control methods, ensuring superior control performance while reducing computational overhead and enhancing system reliability.
# A Unified Denoising Framework for Restoring the LiDAR Point Cloud Geometry of Reflective Targets
## 2 Materials and Methods
### Methods
#### 2.2.1. Veiling Effect Denoising  
The original LiDAR point cloud data contain a large number of noise points induced by the veiling effect, which are usually located near the edges of the target. These noise points share a common characteristic of a relatively low reflectivity intensity. To remove these noise points, we employ an improved intensity-based pass-through filtering algorithm, as illustrated in Figure 3.  
The intensity threshold for the pass-through filter is determined by searching for the center of the peak probability density. First, the intensity of all points is statistically analyzed. Then, a local peak detection algorithm [39] is applied to identify the local peaks in the histogram, and the peak with the highest intensity is selected. Subsequently, Gaussian fitting [40] is performed on the data within the neighborhood of this peak to determine the center point of the local distribution. Finally, the intensity of the center point is used as the threshold to remove all points with an intensity below this value, leaving the remaining points as potential target points.
## 3 Proposed Hybrid Optimization Approach
### Updating the Individuals  
Finally, an individual \(X_{i}^{t}\) from the current population is replaced if and only if the value of the objective function of a potential individual \(P_{i}^{t+1}\) is smaller (i.e., minimization problem); otherwise, this individual remains in the population. This behavior is reflected in (13).  
\[X_{i}^{t+1}=\begin{cases}P_{i}^{t+1}&\text{if $F(P_{i}^{t+1})<F(X_{i}^{t})$}\\ X_{i}^{t+1}&\text{otherwise}\end{cases} \tag{13}\]  
In Equation (13), \(F(\cdot)\) represents the objective estimate of an individual in the slave stage. Once the individuals in the population have been updated, the best individual in the new population \(X^{t+1}\) is taken as \(X_{\text{best}}\).  
Algorithm 1 summarizes the DSCA's implementation to solve the D-STATCOM location problem. The first thing to do is store the information related to the optimization problem so that the initial report is generated from Equation (9). In line three, the time must be set to zero so that the value of the objective function of each individual in the slave stage can then be evaluated with the aim of selecting the best solution. However, in line six, by making the time equal to one so that the values are generated, the power of each individual is determined according to Equation (11), and the value of the objective function of the potential individual in the slave stage is evaluated. This process and the previous one are carried out to compare the results, and if a better solution is found, it is replaced, with the last value being the best solution obtained so far. Thus, the individual is retained in the population.  
```
1 Data: The information related to the optimization problem is stored, as well as the DSCA criteria; Generate the initial information from Equation (9);
2 Do \(t=0\);
3 Evaluate the value of each individual's objective function in the slave stage;
4 Select the best solution from the population as \(X_{best}^{t}\);
5 for\(t=1:t_{max}\)do
6 for\(i\)=\(N_{i}\)do
7 Generate the values \(r_{1}\), \(r_{2}\),\(r_{3}\), and \(r_{4}\);
8 Determine the potential individual \(P_{i}^{t+1}\) from Equation (11);
9 Evaluate the objective function value of the potential individual in the slave stage;
10 if\(F(P_{i}^{t+1})<F(X_{i}^{t})\)then
11 Replace the individual in the population with the potential individual;
12 else
13 Retain the individual in the population;
14
15 Select the best solution from the new population as \(X_{best}\);
16
17 Result: Take \(X_{best}\) as the result and solution for the optimization problem.
```  
**Algorithm 1**Pseudo-code for the proposed DSCA approach in optimization problems.
## 6 Current Research Focus
The TEMA-HF1 trial demonstrated that the use of telemonitoring during follow-up in patients with HF reduced all-cause mortality (an absolute reduction of 12.5 per cent), with a lower number of follow-up days lost to death, hospitalisation, or dialysis [72]. Interesting results are those coming from a study by [PERSON] et al., which demonstrated that the randomisationof diuretic regimens guided by a second-generation personalised AI algorithm improves the response to diuretic therapy with a significant reduction in NT-proBNP and serum creatinine values [73]. Therefore, a novel, machine learning-derived model was validated also by [PERSON] et al. to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2 DM). The cumulative 5-year incidence of HF increased in a graded fashion from 1.1 per cent in patients with WATCH-DM score of 7 to 17.4 per cent in patients with a WATCH-DM score of more than 14 [74]. Among ongoing studies, HeartMan project aims to develop a personal health system that would comprehensively address CHF self-management by using sensing devices and artificial intelligence methods with significantly improved self-care behaviour and reduction of depression and anxiety [75]. The role of deep-learning-based echocardiography in the diagnosis and evaluation of the effects of routine anti-heart-failure Western medicines was investigated in elderly patients with acute left heart failure (ALHF). The study demonstrated a reduction in rehospitalisation and mortality rate [76].
# Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System
## 3 Evaluation of the Bridge Condition Using SHM Monitoring Data
### Acceleration-Strain Frequency Domain Evaluation  
The frequency contents of strain and acceleration measurements for the mid-span monitoring points in the passage and opposite directions are illustrated in Figure 13. The cross spectrum density function in Matlab (Version 7.6, MathWorks, Natics, MA, USA, 2008) is used to calculate the frequency contents.Based on the FEM [5] analysis, the band-pass filters in between 1 to 45 Hz with 101 hamming window are used to filter the measured data to include the static and dynamic frequency contents of the bridge. From Figure 13, the frequency contents are 3.223, 3.906 Hz and 3.223, 4.199 Hz for 290 and 360 km/h at the opposite and passage directions, respectively. In addition, the frequency contents equal (4.297 Hz) for the 400 and 406 km/h at the two directions. From the comparison of the FEM frequency and real data, it is observed that the first dynamic mode changes increased with increasing the trains' speeds. The changes of passage frequency from the first bending FEM frequency mode are 18.5%, 24.2%, 25.8% for the speeds 290, 360 and 406 km/h, respectively. The strain frequencies contents at the two directions with the effect of all trains' speeds are similar. In addition, the static frequency contents are clearly shown with strain measurements only. The low frequencies are 0.781, 0.977, 1.172, 1.172 Hz and 0.879, 1.074, 1.172, 1.172 Hz of the opposite and passage directions for the 290, 360, 400, 406 km/h train speeds, respectively. It means that the strain measurements are enough to estimate the static and dynamic behavior in frequency domain. Moreover, from the comparison between the first mode contents of the measurements and the FEM calculations, it can be concluded that the bridge is safe under its current dynamic behavior with the development speeds of trains.  
The Matlab Spectrogram toolbox is used to extract the three dimensional time-frequency maps for the passage train at the mid-span point of the bridge at speeds 290 and 406 km/h, as shown in Figure 14. The results show that the power spectrum density (PSD) at 290 km/h speed is lower than the PSD amplitude at 406 km/h. The PSD frequency responses' amplitude differences between trains passage and departure (load and unload) show small values at 406 km/h. Therefore, it is concluded that the dynamic behavior of the bridge at train speeds of 406 km/h is greater than the 290 km/h. However, it is concluded that the bridge is safe at a speed of 406 km/h, but it should be continuously monitored if trains speeds are increased above this value. Moreover, the increase of PSD with train speeds indicates that the simple beam girders of steel bridges are very sensitive to train induced vibrations, and, therefore, may be not suitable for an increase in the speed of train traffic.  
Figure 12: Rain-flow matrix of mid-span stress (**a**) 360 km/h; (**b**) 406 km/h.
# A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis
## 3 Results and Discussion
### Comparison to the State of Arts  
We used eight types of models, including SVM, KNN, gradient boosting descent tree (GBDT), NN, the fuzzy c-means algorithm (FCM), CNN, LSTM and deep parallel diagnosis to diagnose the same dataset before and after the data augmentation in the same computer environment. In the SVM model, the kernel function is Gaussian, the kernel scale is 0.56, and the box constraint level is 1. In the KNN model, the number of neighbors is 1, the distance metric is Euclidean, and the distance weight is equal. In the GBDT model, the maximum number of splits is 20, the number of learners is 30, and the learning rate is 0.1. As for the structure of the NN model, it contains an input layer of five neurons, a hidden layer of 10 neurons, and an output layer of six neurons. The training epoch is 1000 and the learning rate is 0.01. In the FCM model, the exponent for the fuzzy partition matrix is 2.0, the maximum number of iterations is 1000, and the minimum improvement in objective function between two consecutive iterations is \(1\times 10^{-5}\).  
The comparison results are shown in Table 7. Due to the uncertainty of the results of some methods, the diagnostic accuracy of this article is the average result of five independent experiments. The diagnostic accuracy of all the eight diagnostic methods increased after the data augmentation. It can be observed that the imbalance of the former dataset limits the effectiveness of the fault diagnostic method to a certain extent.  
Moreover, the deep parallel diagnosis performs better than the traditional intelligent algorithms and single deep learning method in the transformer diagnostic scenario without conducting a complex feature extraction and selection process.
# Exploring ISAC: Information-Theoretic Insights
## 6 Secrecy of ISAC Systems
### Secrecy of Messages: Results  
Most of the results have been derived under the assumption of perfect feedback from both the Rx and Eve, i.e., \(Z=(Y_{1},Y_{2})\)[69]. Only the outer bounds in [69] apply for a slightly more general scenario where \(Z\) is a noisy version of \((Y_{1},Y_{2})\).  
Note that the optimal estimator at the Tx is the same as in the setup without a secrecy constraint, see (5). In the case of perfect output feedback \(Z=(Y_{1},Y_{2})\) and two states, these optimal estimators are:  
\[\hat{s}_{k}^{*}(x,y_{1},y_{2})=\arg\min_{s^{\prime}\in\mathcal{S}_{k}}\sum_{s \in\mathcal{S}_{k}}P_{S_{k}|XY_{1}Y_{2}}(s|x,y_{1},y_{2})d_{k}(s,s^{\prime}), \quad k\in\{1,2\}. \tag{60}\]  
Combined with these optimal estimators, the output statistics of random binning (OSRB) proof technique [113] allows the following result to be achieved [69].  
**Theorem 11** (Inner Bound).: _The secrecy-capacity-distortion region \(\mathcal{SCD}\) contains all secrecy-rate-distortion tuples \((R_{1},R_{2},D_{1},D_{2})\) that satisfy the following inequalities for some pmf \(P_{UVX}\):_  
\[R_{1} \leq I(U;Y_{1}S_{1}) \tag{61}\] \[R_{2} \leq\min\{[I(V;Y_{1}|S_{1}U)-I(V;Y_{2}|S_{2}U)]^{+}+H(Y_{1}S_{1} |Y_{2}S_{2}V),\quad(I(V;Y_{1}|S_{1})-R_{1})\}\] (62) \[D_{k} \geq\mathbb{E}[d_{k}(S_{k},s_{k}^{*}(X,Y_{1},Y_{2}))],\quad k\in \{1,2\}. \tag{63}\]  
**Theorem 12** (Outer Bound).: _The secrecy-capacity-distortion region \(\mathcal{SCD}\) is included in the union over all joint distributions \(P_{UVX}=P_{UV}P_{X|V}\) of all rate tuples \((R_{1},R_{2},D_{1},D_{2})\) satisfying. The bounds are slightly simpler and stronger than the bounds in [69] and can be proved using similar steps._  
\[R_{1}+R_{2} \leq I(V;Y_{1}|S_{1}), \tag{64}\] \[R_{2} \leq I(V;Y_{1}S_{1}|Y_{2}S_{2}),\] (65) \[D_{k} \geq\mathbb{E}[d_{k}(S_{k},s_{k}^{*}(X,Y_{1}Y_{2}))],\quad k\in \{1,2\}, \tag{66}\]  
_One can limit \(\mathcal{V}\) to \(|\mathcal{V}|\leq\min\{|\mathcal{X}|,\ |\mathcal{Y}_{1}||\mathcal{S}_{1}|,\ |\mathcal{Y}_{2}||\mathcal{S}_{2}|\}+1\)._  
The above results assume that only a part of the message (namely \(W_{2}\)) has to be kept secure from [PERSON]. Corresponding results where all messages have to be kept secure are easily obtained by setting \(R_{1}=0\) and interpreting \(R_{2}\) as the total rate of all communicated messages.  
Above inner and outer bounds do not coincide in the general case. They do in the case of degraded channels where \(P_{Y_{2}S_{2}|XS_{1}Y_{1}}=P_{Y_{2}S_{2}|Y_{1}S_{1}}\) and reversely degraded channels where \(P_{Y_{1}S_{1}|XS_{2}Y_{2}}=P_{Y_{1}S_{1}|Y_{2}S_{2}}\). They have also been specialized to several interesting and practical channels. In particular, the results for the Gaussian fading examples are worth being mentioned for binary states [111] as well as for Rayleigh fading states [112].  
Finally, note that a finite blocklength analysis for ISAC with security constraints has been performed in [85].