esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this manuscript, the vibration test system was developed to acquire information on speed, load and vibration signal. The vibration characteristics of rolling bearing were analyzed, the identification and diagnosis of the fault characteristic frequency in rolling bearing were conducted under the time-varying slip conditions, and then the effect of multiple factors, such as speed, load, slip, and temperature on the skidding damage of rolling bearings under time-varying slip conditions was comprehensively discussed. This study can be useful for fault diagnosis of skidding damage in rolling bearings.  
(1) A vibration test system was designed to study the skidding damage of rolling bearings. By using FFT and DWT in the frequency-domain and time-domain, the abnormal vibration signal of the rolling bearing was identified and analyzed, the damage point was preliminary determined, and the actual fault characteristic frequency of skidding damage of the bearing was 16.44 Hz. This study also showed that there was an obvious difference between the starting time of the skidding damage and the maximum vibration time, and the starting time of skidding damage was ahead of the maximum vibration time.  
(2) The effects of the coupling factors, such as radial load, temperature, and slip rate on the skidding damage of the rolling bearing were comprehensively studied, and the internal relationships between the multiple coupling factors and the skidding damage of the rolling bearing were clarified. The results showed that the amplitude of vibration acceleration increases with the increase of the radial load and temperature. The integrated analysis of the vibration, load, temperature, slip rate and other multivariate information showed that the starting time of skidding damage was about 8.4 s under time-varying slip conditions.  
(3) Diagnosis and identification of skidding damage signals of rolling bearings under time-varying slip working conditions were realized.  
Conceptualization, J.L.; Data curation, W.C. and J.X.; Formal analysis, K.H. and Q.W.; Funding acquisition, J.L.; Investigation, J.X.; Methodology, W.C. and J.L.; Software, W.C. and K.H.; Writing--original draft, W.C. and J.L.; Writing--Review and Editing, J.L.  
This research was funded by The Program of State Key Laboratory of Mechanical Transmissions (No. SKLMT-KFKT-201808), National Natural Science Foundation of China (No. 51505361), Innovative Talents Promotion Plan in Shaanxi Province (No. 2017 KJX-58).  
The authors would like to thank [PERSON] for their valuable discussions on this article.  
The authors
# Ripple Suppression in Broadband Microwave Photonic Phase Shifter Frequency Response
## 2 Phase Shifter Amplitude Variation and Phase Deviation
This extra 8.3 dB loss is mainly due to the removal of one RF modulation sideband in the phase shifter structure. In general, MPPSs have a higher loss, which leads to a higher noise figure, than conventional microwave phase shifters. However, MPPSs are capable of realizing a continuous 0-360\({}^{\circ}\) phase shift while keeping the RF signal amplitude constant over a very wide frequency range that cannot be achieved by its electronic counterpart. The spurious free dynamic range (SFDR) of the MPPS and the conventional fiber optic link were also simulated by applying two RF signals with the frequencies of 10 GHz and 10.1 GHz into the modulator and measuring the output RF signal power at 10 GHz (or 10.1 GHz) and the third order intermodulation distortion component power at 9.9 GHz (or 10.2 GHz). The simulation results show that the two systems have a similar SFDR of around 100 dB Hz\({}^{2/3}\) when they have a shot noise limited performance with a noise floor of \(-\)161 dBm/Hz. However, in practice, an optical amplifier is required in the MPPS to compensate for the insertion loss of the integrated OFS and OPS structure, which is higher than the insertion loss of an MZM used in a fiber optic link. The use of an optical amplifier in the OFS and OPS-based MPPS introduces the signal-spontaneous beat noise, which is above the shot noise and is the dominant noise source in the system. This degrades the phase shifter dynamic range performance. A low insertion loss integrated OFS and OPS structures, and a low noise figure optical amplifier are required to minimize the degradation of the phase shifter dynamic range performance.
## 3 Experimental Results
### Experiment Settings
#### 3.1.1 Database
The sampling rate of air traffic control speech is 16-kHz, and it can be categorized based on the speaker's gender (male/female), signal-to-noise ratio (SNR), and speech rate, as shown in Table 5.  
To verify the quality assessment results and ensure a reliable comparison, 80% of this database is used for training the air traffic control speech quality assessment model, while 20% is used for testing the model.
# A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM
## 2 A Low-Complexity Digital Filter to Model Mobility  
In this section, a channel propagation model is enhanced to account for mobility. A relatively simple model is developed that creates dilation of the signal as it propagates along different paths, and as a function of the relative speed between the two nodes. A shallow water environment is assumed in which the water depth is much smaller than the distance between the nodes. This covers a wide range of challenging applications, e.g. in harbours, on the littoral, and long-range transmission in the Arctic. First, in Section 2.1, the physical effect of mobility on acoustic propagation is described. Then, in Section 2.2, a low-complexity time-variant filter is applied to accurately represent time-variant mobile conditions during data transmission.
# A Web Shell Detection Method Based on Multiview Feature Fusion
###### Abstract  
Web shell is a malicious script file that can harm web servers. Web shell is often used by intruders to perform a series of malicious operations on website servers, such as privilege escalation and sensitive information leakage. Existing web shell detection methods have some shortcomings, such as viewing a single network traffic behavior, using simple signature comparisons, and adopting easily bypassed regex matches. In view of the above deficiencies, a web shell detection method based on multiview feature fusion is proposed based on the PHP language web shell. Firstly, lexical features, syntactic features, and abstract features that can effectively represent the internal meaning of web shells from multiple levels are integrated and extracted. Secondly, the Fisher score is utilized to rank and filter the most representative features, according to the importance of each feature. Finally, an optimized support vector machine (SVM) is used to establish a model that can effectively distinguish between web shell and normal script. In large-scale experiments, the final classification accuracy of the model on 1056 web shells and 1056 benign web scripts reached 92.18%. The results also surpassed well-known web shell detection tools such as VirusTotal, ClamAV, LOKI, and CloudWalker, as well as the state-of-the-art web shell detectionmethods.  
web shell detection; multiview feature fusion; feature selection; large-scale experiments; machine learning +
Footnote †: journal: Applied Soft. 2020, 10, 6274; doi:10.3390/app10186274
# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 2 Methodology
### Loss Function  
The network model proposed in this paper consists of two processes: forward propagation and backpropagation. During the forward propagation process, the input image is reconstructed based on the output results from the depth estimation network and the pose estimation network. In the backpropagation process, the error between the reconstructed image and the original image is calculated using a loss function, which is then employed to train the network parameters. The loss function \(\mathbf{L}\) is a weighted sum of the re-projection loss \(\mathbf{L_{rep}}\) and the edge smoothing loss \(\mathbf{L_{smooth}}\) and can be expressed as follows:  
\[L=\mu L_{rep}+\lambda L_{smooth} \tag{8}\]  
where \(\mu\) and \(\lambda\) represent the weights assigned to the re-projection loss and edge smoothing loss, respectively. In the proposed method, their values are set to \(\mu=1\) and \(\lambda=0.04\) according to the values taken by _Monodepth2_.  
The re-projection loss \(\mathbf{L_{rep}}\) can be presented as follows [27]:  
\[L_{rep}=\frac{\alpha}{2}\left(1-\frac{2u_{\mathbf{I}}u_{\mathbf{J}}+C_{1}}{u_ {\mathbf{I}}^{2}+u_{\mathbf{J}}^{2}+C_{1}}\times\frac{2\sigma_{\mathbf{J}}+C_{ 2}}{\sigma_{\mathbf{I}}^{2}+\sigma_{\mathbf{  
value of the images **I** and **J**, respectively. \(\sigma_{\textbf{J}}\) is the covariance of the images **I** and **J**, and \(\sigma_{\textbf{I}}\) and \(\sigma_{\textbf{J}}\) are the standard deviation of pixel values of the images **I** and **J**, respectively. The constant \(\alpha\) is generally taken to be 0.85 [27].  
Using the edge smoothing loss \(L_{smooth}\) can effectively mitigate depth discontinuities, suppress noise, and enhance the clarity of edges in the depth map [28]. Its mathematical formulation is presented as follows:  
\[L_{smooth}=\frac{1}{MN}\sum_{i=1}^{N}\sum_{j=1}^{M}|\
abla\textbf{D}(i,j)| \cdot\left(e^{-|\
abla\textbf{I}(i,j)|}\right)^{T} \tag{10}\]  
The number of pixels in the input image is denoted by \(M\times N\). The equation above reveals that the edge smoothing loss consists of two components: the depth smoothing term \(\
abla\textbf{D}\), which mitigates local singular noise in the depth map and enhances its smoothness, and the edge perception term \(\
abla\textbf{I}\), which promotes feature learning at edges with significant depth gradients to produce clearer contours. During the training phase, a smaller sum of \(\
abla\textbf{D}\) and a greater amount of edge information \(\
abla\textbf{I}\) yield better results. Consequently, Equation (10) employs negative exponential functions to ensure that both the depth smoothing term \(\
abla\textbf{D}\) and the edge perception term \(\
abla\textbf{I}\) change in the same direction.
# Advancing OCR Accuracy in Image-to-LaTeX Conversion--A Critical and Creative Exploration
## 8 Leveraging Symbol Relationships for OCR Error Correction
Utilizing information about the mathematical domain and the relationships between the symbols also enables OCR models to enhance error correction accuracy [52]. Machine learning algorithms can be trained to explain symbol relationships for error correction. These models learn to recognize symbols and the correlation between them by analyzing large annotated datasets. These models can capture the dependencies and patterns in mathematical notation by incorporating symbol relationships into the trainingprocess to make accurate predictions and corrections during the OCR and conversion processes. Graph-based approaches can be used to leverage symbol relationships in mathematical expressions, in the form of graphs, to identify and correct errors [52]. For instance, a disconnected node in the graph can infer the missing symbol and restore the right relationship between symbols. The spatial arrangement of symbols within a mathematical expression may also provide important information about their relationships [13]. OCR models can examine symbols' relative positions, alignments, and distances to infer their roles and relationships. For instance, when two symbols are vertically aligned, they are likely to be related as a numerator and denominator in a fraction [43]. Leveraging symbol relationships can also be complemented with statistical concepts like probabilistic models. The models estimate the likelihood of certain symbol relationships based on the statistical properties of mathematical notation. OCR models can make informed decisions and prioritize the most likely corrections by incorporating statistical information into the error correction process. In summary, leveraging symbol relationships for OCR error correction requires using semantic information, context-based machine-learning algorithms, statistical techniques, graph-based approaches, and spatial relationships. The courses enable OCR models to examine the relationship between symbols and make accurate predictions and corrections during recognition and conversion [53]. The tools enable OCR systems to achieve higher accuracy, improve the structural integrity of mathematical expressions, and ensure the semantic fidelity of the converted LaTeX code.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 3 Principle and Method of Polar Code Parameter Recognition
### Decision Threshold
Similarly, when \(X_{i}=-1\), an odd number of errors occurs:  
\[E(X_{i}\cdot Y_{i},X_{i}=-1|H_{1})=-\sum\limits_{i=0}^{\lfloor w/2\rfloor-1}C_ {w}^{2i+1}p_{e}^{2i+1}(1-p_{e})^{w-2i-1}.\int\limits_{0}^{+\infty}xf_{T^{2i+1 }}(x)dx. \tag{69}\]  
It can be obtained from (67) and (69) that  
\[E(X_{i}\cdot Y_{i}|H_{1})=E(X_{i}\cdot Y_{i},X_{i}=1|H_{1})+E(X_{i}\cdot Y_{i},X_{i}=-1|H_{1}), \tag{70}\]  
\[Var(X_{i}\cdot Y_{i}|H_{1})=E(Y_{i}^{2})-E(X_{i}\cdot Y_{i}|H_{1})^{2}. \tag{71}\]  
It is easy to obtain the mean and variance of \(\text{AL}_{i,k}\) under the two assumptions:  
\[E(\text{AL}_{i,k}|H_{0})=E(X_{i}\cdot Y_{i}|H_{0})=0, \tag{72}\]  
\[E(\text{AL}_{i,k}|H_{1})=E(X_{i}\cdot Y_{i}|H_{1}), \tag{73}\]  
\[Var(\text{AL}_{i,k}|H_{0})=Var(X_{i}\cdot Y_{i}|H_{0}), \tag{74}\]  
\[Var(\text{AL}_{i,k}|H_{1})=Var(X_{i}\cdot Y_{i}|H_{1}). \tag{75}\]  
According to the statistical properties of random variables,  
\[\mu_{0}=E(\text{AL}|H_{0})=E(\text{AL}_{i,k}|H_{0})=0, \tag{76}\]  
\[p_{e}=0.5 erfc(\sqrt{1/(2\sigma^{2})}), \tag{68}\]0  
\[p_{e}=0.5 erfc(\sqrt{1/(2\sigma^{2})}), \tag{68}\]1  
\[p_{e}=0.5 erfc(\sqrt{1/(2\sigma^{2})}), \tag{68}\]2  
Then, the false alarm probability is  
\[p_{e}=0.5 erfc(\sqrt{1/(2\sigma^{2})}), \tag{68}\]3  
The probability of a false alarm is  
\[p_{e}=0.5 erfc(\sqrt{1/(2\sigma^{2})}), \tag{68}\]4  
It does not have any prior knowledge under non-cooperative conditions, so \(P_{f}=P_{a}\), and the average incorrect decision probability is  
\[p_{e}=0.5 erfc(\sqrt{1/(2\sigma^{2})}), \tag{68}\]5The minimum error decision threshold for solving (82) is  
\[p_{e}=0.5 erfc(\sqrt{1/(2\sigma^{2})}), \tag{68}\]6  
where \(\zeta=\sqrt{\left(\mu_{0}-\mu_{1}\right)^{2}+\left(\sigma_{1}^{2}-\sigma_{0}^{ 2}\right)\cdot\ln(\sigma_{1}/\sigma_{0})}\). When \(\text{AL}>\Lambda_{\text{opt}}\), \(H_{1}\) is true.
# Augmented Reality and the Digital Twin: State-of-the-Art and Perspectives for Cybersecurity
## 4 Perspectives for Cybersecurity
### Benefits for Cyber Security
#### 4.1.1 Contextualizing Physical Surroundings  
First of all, using AR to inspect physical systems by providing data from the respective DT enables novel security-related insights **contextualized on physical surroundings**. This is especially true since the data is imposed on the physical object by AR. Thus, security operators not only have the data at their hands but, instead, have a contextualized visual representation of the data. Therefore, they can directly associate data to the actual physical objects to which the data logically belongs. As mentioned in Section 3.3.2, this offers an immense advantage when dealing with cybersecurity in a CPS-dominant context. Cybersecurity breaches and threats in a cyber-physical system are often particularly hard to identify when the physical system seems to work correctly. There is no direct indication for operators that the respective industrial process might be compromised.  
For example, it might be easy for human operators to detect single cases of misbehavior regarding physical objects (e.g., bottles being overfilled in an industrial filling plant), which may occasionally occur. However, this malfunction might potentially not just be casual but could result from a cybersecurity breach where the perpetrator is slowly and cautiously moving towards doing actual physical harm. This behavior is common for Advanced Persistent Threats (APTs), where attackers refrain from significant physical impact for quite some time to remain undetected for longer within the infested time [69, 70]. They use this time for probing security measures and lateral movement towards systems where they can deliver significant damage. This so-to-speak final blow becomes apparent through over-rotating centrifuges [69], undesigned shut-down petro-chemical processes [70], or a spill of tremendous amounts of fluid within our exemplary filling plant. With DT information, especially system and sensor data collected from the real-world system, human operators using AR devices might detect suspicious patterns. For example, the overfilled bottles could be imposed with a warning, such as an AR-based overlay over the real-world bottle leveraging the pattern-detection capabilities of humans to identify the anomaly.  
As described within the qualitative results of our literature review (see Sections 3.3.1 and 3.3.2), similar approaches are, for example, becoming popular to detect manufacturing malfunctions. However, a contextualization of physical surroundings with primarily security-relevant information from DTs would benefit the detection of APTs within CPS.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 5 Conclusions  
This study proposed a hybrid fingerprinting-based localization method that integrates RSSI, AOA, and EOA features, combined with UAV trajectory optimization to enhance localization accuracy. Evaluation using ray tracing data in a realistic outdoor environment demonstrated the effectiveness of the HYBRID model over RSSI-only and AOA-only methods, particularly under NLoS conditions (Section 4.1.4). Additionally, the HYBRID approach showed robustness to changes in UAV trajectory geometry (Section 4.2). Sequential estimation was found to significantly improve accuracy when the initial modality was weak (e.g., RSSI; Section 4.3.1), but offered limited benefits when starting from a stronger estimate (e.g., AOA or HYBRID; Sections 4.3.2 and 4.3.3).  
In summary, this study revealed several key insights. First, the integration of RSSI, AOA, and EOA features in the HYBRID model consistently improved localization accuracy, especially under multipath and NLoS conditions. Second, UAV trajectory radius significantly affected RSSI-based localization, while AOA and HYBRID models showed resilience to geometric variations. Third, sequential estimation proved highly effective when the initial estimate was poor (e.g., with RSSI), but had limited benefit when starting from robust estimates (e.g., AOA or HYBRID). These findings were obtained through simulation-based evaluation using ray-tracing data within a specific urban area (Tokyo Tech's Ookayama campus) and assuming a stationary transmitter. While the results may not generalize directly to all environments or mobile-source scenarios, they provide meaningful implications for designing robust localization strategies in realistic settings. The proposed framework has the potential to contribute to future applications, such as illegal radio monitoring or emergency transmitter localization, where rapid and accurate estimation is critical.  
Writing--original draft, T.T.; Writing--review & editing, G.K.T.; Supervision, G.K.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
###### Abstract  
This paper proposes a nonlinear decoupled current control scheme for a grid-connected inverter with LCL filter. Decoupling the active and reactive current control channels is one of the main demands in the control of inverters. For inverters with an L filter, the decoupling can be achieved by a proper feed-forward of grid voltages. However, the coupling of channels is a complex issue for converters with LCL filters. The resonance mode of the LCL filter may cause instability, which adds more complexity to the analysis. In this paper, state equations of the system are provided, which highlight the coupling between active and reactive currents injected into the grid. Accordingly, a non-linear control scheme is proposed which effectively decouples the channels and dampens the resonant modes of the LCL filter. The stability of the proposed control method is verified by the Lyapunov criterion. Independency of the system stability to the grid-impedance is another feature of the proposed approach. Moreover, only grid-side currents are needed for implementation of the proposed scheme, avoiding the need for additional current sensors for the output capacitor and grid-side inductor. For accurate modelling of the inverter, the computation and PWM sampling delays are included in the controller design. Finally, various case studies are provided that verify the performance of the proposed approach and the stability of the system.  
decoupled current control; LCL-type filter; back-stepping control; renewable energy; voltage source inverter +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 4 Experiment and Evaluation
### Evaluation of Pose Estimation

Table 1: Pose estimation performance using Pascal3D+ (number of images is in parenthesis and bold font indicates the best performance in each column). Employing two evaluation metrics enables complementary analysis. Evaluation only using _Acc30_ might lead to misleading conclusions; for instance, an error of 31\({}^{\circ}\) would be classified as incorrect despite being very close to the reference angle. Consequently, incorporating _Median Error_ provides the distribution of errors, which is a more comprehensive evaluation for performance comparison.
## 4 Discussion of Experimental Observations
### Service Type and Slip Modulus  
In Figure 10, the average % variation of slip modulus \(K_{ser}\) is shown as a function of the moisture content, the bonding direction to the grain and the type of adhesive.  
The service class, as shown, generally resulted in severe modification of mechanical parameters for the tested specimens, both for epoxy or polyurethane bonded rods, with more pronounced effects for loading parallel to the grain (Figure 10a). In the perpendicular direction, a rather stable variation of average stiffness results can be observed in Figure 10b for both the adhesive types.  
Figure 10: Average experimental variation of slip modulus \(K_{ser}\) for specimens with (**a**) parallel or (**b**) perpendicular rod-to-grain arrangement.