esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DA-Based Turbo Equalization
### Soft Information-Based ADFE
#### 3.3.1 Signal Filtering  
Specifically, define  
\[\begin{array}{l}\mathbf{z}^{\alpha,m}(n)=[z^{\alpha,m}(n),\ldots,z^{\alpha,m} (n-(N_{f}-1))]^{\mathrm{T}}\\ \mathbf{s}^{\alpha,m}(n)=[\tilde{s}^{\alpha,m}(n),\ldots,\tilde{s}^{\alpha,m} (n-(N_{b}-1))]^{\mathrm{T}}\end{array} \tag{13}\]  
where \(N_{f}\) and \(N_{b}\) refer to the length of the feedforward filter and feedback filter, respectively; and \(\tilde{s}^{\alpha,m}(n)\) denotes the decision fed into the feedback filter. In the first iteration, the value of \(\tilde{s}^{\alpha,m}(n)\) is equal to the hard decision of filtered output signal \(\tilde{s}^{\alpha,m}(n)\), and in the subsequent iteration, \(\tilde{s}^{\alpha,m}(n)=\sum_{\delta\in S}sP_{\mathrm{in}}(s^{\alpha,m}(n)=s)\).  
The input vector of signal filtering is defined as  
\[\mathbf{u}^{\alpha,m}(n)=\left[\begin{array}{c}\mathbf{z}^{\alpha,m}(n)\\ \mathbf{\tilde{s}}^{\alpha,m}(n)\end{array}\right] \tag{14}\]  
The filtered output signal can be written as  
\[\tilde{s}^{\alpha,m}(n)=[\mathbf{f}^{\alpha,m}(n)]^{\mathrm{H}}\mathbf{u}^{ \alpha,m}(n)=\left[\begin{array}{c}\mathbf{f}^{\alpha,m}_{\mathrm{f}}(n)\\ -\mathbf{f}^{\alpha,m}_{\mathrm{b}}(n)\end{array}\right]^{\mathrm{H}}\left[ \begin{array}{c}\mathbf{z}^{\alpha,m}(n)\\ -\mathbf{s}^{\alpha,m}_{\mathrm{s}}(n)\end{array}\right] \tag{15}\]  
where \(\mathbf{f}^{\alpha,m}(n)\) is the filter coefficient vector, including feedforward-filter coefficients \(\mathbf{f}^{\alpha,m}_{\mathrm{f}}(n)\) and feedback-filter coefficients \(\mathbf{f}^{\alpha,m}_{\mathrm{b}}(n)\).
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 3 Results
### Determination of TPC and TFC  
As shown in Table S2, drying methods significantly affected TFC and TPC levels in AS fruits. VFD (131.9 mg/g) and ND (125.5 mg/g) retained the highest TFC without significant differences, followed by moderate retention in MD (90.0 mg/g) and HD (78.3 mg/g). VD (11.4 mg/g) exhibited the lowest TFC, highlighting its limited suitability for preserving heat-sensitive flavonoids.  
The superior flavonoid retention in VFD is attributed to its low-temperature process, which stabilizes tissue structure and minimizes flavonoid loss. Similarly, ND avoids high temperatures, preventing oxidative degradation. Flavonoids, containing phenolic hydroxyl groups, are highly susceptible to heat-induced oxidation [PERSON] et al. (2020), explaining the significant TFC reduction in HD and MD. VD's rapid heating and vacuum conditions likely accelerate degradation, resulting in minimal retention. This aligns with [PERSON], [PERSON] et al., 2013, who found that vacuum freeze-drying preserved higher TPC and antioxidant capacity in mulberry fruits compared to vacuum drying, highlighting the role of oxygen restriction in bioactive compound preservation [PERSON] et al. (2020).  
TPC followed a similar trend. VFD (23.62 \(\pm\) 1.57 mg/g) achieved the highest retention, followed by ND (19.60 \(\pm\) 1.22 mg/g), MD (15.26 \(\pm\) 3.36 mg/g), and HD (8.93 \(\pm\) 1.79 mg/g), with VG (0.93 \(\pm\) 0.11 mg/g) showing the lowest value. VFD further enhances TPC retention by restricting oxygen exposure during the drying process, significantly reducing oxidation. ND's low-temperature, gradual drying minimizes oxidative degradation, preserving phenolic compounds and maintaining cell structure integrity.  
In contrast, HD and MD promote degradation due to higher temperatures, while VG's extreme vacuum and thermal stress result in severe phenolic loss. [PERSON], [PERSON] et al. (2021) similarly reported significant TPC and antioxidant declines in Rhodomyrtus tomentosaberries following microwave and hot-air drying [33]. [PERSON], [PERSON] et al. (2017) found that drying generally reduces TPC and antioxidant activity in fruits, with ambient drying causing greater losses than oven drying, highlighting the importance of controlled drying conditions for bioactive compound preservation [34].
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 2 Basic Theory
### Introduction to CNN Principles  
CNN [18] is a representative deep learning network that is usually used in fields such as image recognition for processing two-dimensional signals, and its central role is to extract local features from data automatically. It is especially good at processing data with a grid structure. For time series data, CNNs can extract local patterns or trends in time through convolution operations.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods  
In the task of modulation format recognition, extracting unique features from received signals across different modulation formats is essential for achieving high-precision classification. Traditional feature-based methods focus on manually designing interpretable features. They enhance classification performance through optimized feature representation. Simple classifiers such as decision trees are commonly used. In contrast, deep learning-based methods, as shown in Figure 7, focus on automatic feature learning driven by the model. Automatic recognition is achieved through innovations in neural network architectures. Preprocessing steps, such as constellation diagram generation and processing, serve mainly as auxiliary means for data adaptation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **Main Design Scheme** & **Research Objective** & **Performance (\%)** & **SNR (dB)** \\ \hline
[19] & 2017 & Utilizing fourth-order cumulants for noise suppression and feature extraction, establishing classification thresholds through Monte Carlo simulations. & To distinguish high-order complex modulation formats and enhance noise robustness and modulation sensitivity. & 88.9 (Acc) & 15 \\ \hline
[20] & 2020 & Extracting constellation diagram features through clustering analysis and two-dimensional Gaussian models, combined with decision trees for M-QAM signal recognition. & To improve the recognition accuracy of M-QAM under low-SNR conditions. & 100 (Acc) & 18 \\ \hline
[21] & 2021 & Discrete Wavelet Transform for multi-scale noise suppression, integrating multi-dimensional features with supervised learning classification models. & To address the issue of decreased modulation recognition accuracy in time-varying noise conditions. & 96 (Acc) & 15 \\ \hline
[22] & 2023 & Designing time-domain integral windows for energy accumulation feature extraction for L-PMT signals, using multi-classifier comparison. & To achieve real-time recognition of multi-order PPM signals under complex channels. & 97.78 (Acc) & 25 \\ \hline
[23] & 2023 & Combining chaotic mapping and wavelet fusion to process constellations, generating encrypted templates and employing autocorrelation estimation for classification. & To efficiently classify eight modulation formats (PSK/QAM) under dynamic channels with low complexity. & 100 (AROC) & 5 \\ \hline
[24] & 2024 & Extracting frequency-domain histogram feature vectors and applying machine learning methods for classification. & To identify the number of activated subcarriers in OFDM-Md sub-blocks in dynamic underwater optical communication channels in real-time. & 100 (Acc) & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of feature-based modulation recognition schemes.  
Figure 7: Basic structure of modulation recognition based on deep learning models.
## 3 Analysis of Torque Load Transfer Mechanism
### Influence of Concrete Strength Grade  
Using the same analysis method, the torque load transfer process of specimen ZT-4, with a reduced concrete strength grade, was analyzed. Under various loads, the stress changes at the measurement points on the plane above or under the opening of specimen ZT-4 were obtained, as shown in Figure 14.  
Using the simplified analysis method, the force in the top radial reinforcement and the foundation ring of specimen ZT-4 during the whole loading process was analyzed, as shown in Figure 15.  
Figure 14: Torsional shear stress in the foundation ring of ZT-4.  
Figure 13: Torque load sharing ratio of specimen ZT-3.  
The torque load on specimen ZT-4 is mainly sustained by the foundation ring; within 0.2-0.6 \(T\), the foundation ring shares about 70%; after 0.8 \(T\), the region is stable, and the foundation ring shares about 85%.  
The concrete strength grade of standard ZT-1 was C35. After it was reduced to C25, as in specimen ZT-4, the load transfer process and the mechanism of torsional load in the foundation ring were not obviously affected, and the torque load was still mainly sustained and further transferred by the foundation ring. The top radial reinforcement tends to be stable after 0.8 \(T\), and the sharing ratio is about 15%.
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 5 BL0K Performance Measures
### Attack Model  
Of the four entities involved, CT is considered the most trusted entity. The cellular tower for each mobile phone is generally aware of the owner's name and location in real-time; therefore, no attempt is made to conceal the devices' locations from the networks. The security risks from LBS, SNS, and mobile users are of great concern.  
BL0K assumes that either LBS or SNS might become compromised and seized by an adversary seeking to link users' identities with their locations. However, the adversary cannot take control of both the LBS and SNS. Controlling the servers simultaneously is impossible, since they cannot collude with each other, preventing the adversary from controlling both servers simultaneously. In this case, the LBS server broadcasts the coverage region and the verifier connects. Then, the challenge/response cycle continues until a failed response is detected, or until the verifier chooses to discontinue. Note that the verifier needs to actively make a choice (by a button press, for example); the verifier may decide that it is sufficiently close to target (as given by a global positioning system (GPS), for example) so no further iterations are needed. The verifier may also coordinate responses given by more than one LBS server.  
The BL0K supposes that LBS and SNS are both honest but still curious, and scans them to retrieve additional privacy information. Location privacy is threatened by the LBS that is continuously searching for the user's location. In the BL0K framework, the private location information is protected from the LBS through the security property of the BF within the friends' locations query. The SNS does not directly release the friend list to LBS. Instead, the SNS builds a BF and then deposits each element from the friend list into BF, which is later sent to the LBS. Once received, the LBS can then discover the potential friend list shared by the SNS through the testing of every element, which satisfies some condition for membership in BF. Since the determined input is impractical to enumerate, the users cannot maliciously exploit the friend list, preventing a violation in location privacy. However, the BF can query the elements and determine which one belongs to their respective friend list. At the conclusion of the BF process, the individual elements are no longer preserved. This mechanism structure is equipped with sound security measures. The BF is composed of a simple structure that can be established quickly. This feature of the filter is due to the insert and test data both using a hash function test. BL0K requires less communication and computation overhead than previous methods [21; 27].  
Finally, the private location information is hidden from the LBS by the BL0K framework. Thus, we proved that the private location information is protected and secured by BL0K.
## 5 Future Expectations  
The future challenge and direction of development of GPR detection might be the following perspectives:  
* Deeper, larger-scale neural network models. With the continuous development of deep learning technology and the continuous upgrading of hardware equipment, GPR image recognition will use deeper and larger scale network models to improve performance.
* Classification of different subterranean targets. For example, how to distinguish underground voids from infrastructures such as sewers without a pre-data acquisition process in terms of these infrastructures.
* Dataset preparation. CNN and other deep learning methods usually need a large volume of data for training, and the lack of training dataset can lead to an over-training issue. The proper application of synthetic data generalization and data augmentation for GPR can be a direction of study.
* Adaptive performance. Factors such as soil type and soil layer thickness can affect the GPR echo image. How to make the system adapt to different working environments and working scenarios is also a problem worth studying.
* Real time. With the development of technology, GPR should achieve real-time performance improvement to meet the needs of real-time detection.
* Detection based on C-scan data. Though there were several studies discussing the feature extraction of C-scan data and achieving some promising progress, more effective methods remain to be discovered.
# Eulerian-Eularian Model for Agglomeration Behavior of Combusted Iron Particles
## 4 Results and Discussion
### Minimum Fluidized Velocity and Bed Conditions  
To validate the CFD model for solid hydrodynamics of combustion iron powder, the minimum fluidization velocity (\(\mathrm{u_{mf}}\)) must be estimated. Minimum fluidization velocity is the minimum velocity required to make the bed fluidized by keeping the upward drag force of the gas equal to the total solid bed weight. This is one of the most crucial elements in fluidized bed combustion. Numerical simulations were run to estimate the \(\mathrm{u_{mf}}\) with an \(\mathrm{N_{2}}\) environment by varying the flow rate at a constant temperature of 20 \({}^{\circ}\)C.  
Minimum fluidized velocity (\(\mathrm{u_{mf}}\)) for this simulation setup was found to be 0.01 m/s, while 0.15 m/s was found to be excessive based on the range of simulations carried out from 0.001 m/s to 0.15 m/s. Solids climbed to the top of the gasifier at a velocity of 0.15 m/s, suggesting that the velocity was too high. On the other hand, the velocity of 0.001 m/s resulted in non-fluidized behavior because the velocity was not enough to fluidize the bed. This result for minimum fluidization velocity estimation aligns with DEM simulations conducted by [PERSON] et al., indicating that 0.01 m/s is the minimal fluidization velocity for this arrangement, though there are some discrepancies with theoretical calculations derived using [PERSON]'s correlation [26]. The variation of the particle size distribution can be attributed to the slight variation in theoretical and numerical results [17; 30].  
Moreover, in the CFD-DEM study, 0.15 m/s is used to analyze the effect of fluidization velocity on particle agglomeration. However, 0.15 m/s was found to be excessive for this simulation setup. This distinction can be ascribed to the fact that these simulations were carried out using the Eularian-Eularian two-fluid approach, in which the solid is considered as a fluid, but the CFD-DEM method is based on the Lagrangian approach, which considers solid as discrete particles. However, in general, the simulation outcomes reasonably agree with the outcomes of the other referenced literature mentioned above, providing evidence for the applications of accurate hydrodynamic theories.
## 3 Results and Discussion
### Micro Hardness Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **O** & **Mg** & **Si** & **P** & **Ca** & **Phase** \\ \hline
1 & 61.51 & 13.20 & 13.22 & 0.99 & 11.08 & Monticellite \\
2 & 70.01 & 3.09 & 0.30 & 14.05 & 12.56 & Montetite \\ \hline \end{tabular}
\end{table} 
Table 7: EDS analysis (atom %) for directionally solidified rods grown at 50 mm/h after soaking in the SBF solution for 28 days. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Ca** & **Mg** & **P** & **Si** \\ \hline Before Bioactivity & 2.5 & 1.5 & 1 & - \\ After Bioactivity & 2.2 & 3.5 & 0.91 & 0.22 \\ \hline \end{tabular}
\end{table} 
Table 8: Ion concentration of SBF (simulated body fluid) before and after 28 days of sample immersion. Results in mM. There is no evidence that a silica-rich layer was formed. When this layer, due to the reaction of Si with OH\({}^{-}\), was formed on the sample, the surface acted as a nucleation agent for HAp formation. In this case, the increase in Mg in the fluid medium can reduce the rate of a stable apatite phase formation, as reported by [PERSON] (2018). Moreover, the decrease in P ion concentration in the fluid was consistent with the transformation of the Ca\({}_{2-x}\)Mg\({}_{x}\)SiO\({}_{4}\)\({}^{+}\)P phase to a P-rich and Si-depleted phase, while the monticellite phase remained unaltered. The longitudinal cross section and surface morphology of the sample grown at 100 mm/h after soaking in SBF for 28 days are shown in Figure 7a. The presence of a large number of pores at different sizes significantly increased the surface roughness and total porosity, as shown in Figure 7b. This morphology contributed beneficially to the process of scaffold integration and influenced the bone healing rate. The composition of the phases in the interaction zone with SBF is given in Table 9. In the active region pores, monticellite and a new phase with a composition compatible with monotite can be identified. A similar behavior was observed in samples grown at 300 mm/h. The FE-SEM micrograph of the polished longitudinal cross section of the sample after soaking in SBF for 28 days is shown in Figure 8a. The surface of the sample was eroded by the dissolution of the MgO phases in the SBF during the period of immersion forming a porous structure layer. The depth of the porous layer at this time was about 20-30 \(\upmu\)m.  
Figure 8: _Cont._  
Figure 7: SEM image of a sample grown at 100 mm/h after soaking in the SBF solution for 28 days: longitudinal cross section (**a**) and disc surface (**b**). The formation of a thin submicrometric layer was observed on the surface of the sample as shown in Figure 8b. This layer was determined to be bone-like apatite, although from EDS microanalysis the Ca/P ratio was 1.26, lower than that of the hydroxyapatite.
## 3 Simulation Studies
### Simulation Study 2
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Estimation** & \multicolumn{2}{c}{**Optimal Factor Settings**} & \multicolumn{1}{c}{**Process**} & \multicolumn{1}{c}{**Process**} & \multicolumn{1}{c}{**Process**} & \multicolumn{1}{c}{**EQL**} \\
**Model** & \(x_{1}\) & \(x_{2}\) & **Mean** & **Bias** & **Variance** & \\ \hline LSM & \(-1.000\) & \(-1.000\) & \(117.2490\) & \(10.7500\) & \(413.2320\) & \(528.8130\) \\ FFNN & \(-0.804\) & \(-0.561\) & \(127.2800\) & \(0.7190\) & \(359.3420\) & \(359.8600\) \\ CFNN & \(-0.383\) & \(-0.274\) & \(127.2120\) & \(0.7870\) & \(445.7840\) & \(446.4040\) \\ RBFN & \(-0.141\) & \(-0.190\) & \(127.0680\) & \(0.9310\) & \(422.7920\) & \(423.6600\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative results of simulation study 2. Clearly, the proposed NN-based modeling methods produced better solutions than LSM-based RSM in both simulation studies. Whereas RSM is generally used to estimate second-order functions, the proposed modeling methods can effectively estimate nonlinear functions.
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Modal Parameter-Based Methods
#### 5.1.1 Mode Shape-Based Methods  
[PERSON] et al. [122] applied the concept of the spectral element method to the derivation of the mode shape expression to establish the mathematical correlation between the mode shapes of damaged and healthy structures. The numerical results of a fourteen-storey shear building and a six-storey laboratory building model showed that the proposed method was effective in quantifying damage. [PERSON] et al. [123] proposed a new method to identify damage in plate-like structures. They established a damage index, namely the mode shape damage index (MSDI), based on the difference between the modal displacements before and after the structure was damaged. Experimental and numerical studies on reinforced concrete slabs showed that the MSDI was reliable in locating damage. However, the proposed method did not perform well in identifying the severity of the injury, and further research was needed. [PERSON] et al. [124] developed a simple and rapid SDI technique. Using the method of interval analysis, they deduced the interval conditions of each section of the beam according to the mode shape of the beam structure in a damaged and a healthy state. The possibility of damage was defined in each beam segment, and the product of it and the mode shape increment was taken as the damage measurement index. The numerical simulation results showed that the proposed method can identify the given damage rapidly and accurately.  
Passing vehicles will elicit responses from the bridge structure, so sensors can be installed on vehicles to monitor the response data. The modal parameters with a high spatial resolution can be extracted from the vehicle response. Recently, some scholars have paid attention to this. [PERSON] et al. [125] developed a two-stage bridge damage detection method based on the mode shape estimated by moving vehicles. They first identified the damage location using the damage location index defined in the regional mode shape curvature (RMSC). Then, the relationship between damage degree and RMSC was established in a finite element simulation. Numerical and experimental examples demonstrated the effectiveness of the proposed method. Under the condition of meeting the accuracy requirement, the method can locate and quantify the damage by using only one sensor response, which proves that the indirect recognition method has a good application prospect. [PERSON] et al. [126] improved the indirect SDI method utilizing vehicles. In their study, the filter in the traditional vehicle scanning method (VSM) was replaced by a self-made filter. Compared with the previous VSM, the mode shape extracted by this method was more obvious. Therefore, this improved VSM did not need to establish an additional damage index, which greatly reduced the post-processing workload.
# Plant Electrical Signal Classification Based on Waveform Similarity
###### Abstract  
(1) Background: Plant electrical signals are important physiological traits which reflect plant physiological state. As a kind of phenotypic data, plant action potential (AP) evoked by external stimuli--e.g., electrical stimulation, environmental stress--may be associated with inhibition of gene expression related to stress tolerance. However, plant AP is a response to environment changes and full of variability. It is an aperiodic signal with refractory period, discontinuity, noise, and artifacts. In consequence, there are still challenges to automatically recognize and classify plant AP; (2) Methods: Therefore, we proposed an AP recognition algorithm based on dynamic difference threshold to extract all waveforms similar to AP. Next, an incremental template matching algorithm was used to classify the AP and non-AP waveforms; (3) Results: Experiment results indicated that the template matching algorithm achieved a classification rate of 96.0%, and it was superior to backpropagation artificial neural networks (BP-ANNs), supported vector machine (SVM) and deep learning method; (4) Conclusion: These findings imply that the proposed methods are likely to expand possibilities for rapidly recognizing and classifying plant action potentials in the database in the future.  
plant action potential (AP); AP recognition; template matching; difference threshold; nonlinear features; AP classification +
Footnote †: journal: Accepted: 10 October 2016; Published: 15 October 2016