esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method for Fault Diagnosis in Milling Machines
### Gaussian Filter  
The Gaussian filter is a linear filter used in image processing to smooth images by reducing noise while keeping key structural features. It operates by applying a Gaussian function, defined as shown in Equation (2):  
\[\mathbf{G}(\mathbf{x},\mathbf{y})=\frac{1}{2\pi\sigma^{2}}\text{exp}\bigg{(}\frac{x^{2}+ \mathbf{y}^{2}}{2\sigma^{2}}\bigg{)} \tag{2}\]  
where \(\mathbf{x}\) and \(\mathbf{y}\) are pixel coordinates, and \(\mathbf{\sigma}\) is the standard deviation controlling the extent of smoothing. In this work, the Gaussian filter is applied to CWT scalograms generated from AE signals obtained from milling machines. These scalograms provide a detailed time-frequency representation of the AE signals, which is necessary for detecting and classifying faults. The application of the Gaussian filter smooths the scalograms, reducing noise and random pixel fluctuation while maintaining the essential energy patterns associated with different fault conditions [32]. Gaussian filtering is effective in reducing noise while preserving essential features within the scalograms. Unlike other filters, the Gaussian filter  
Figure 2: CWT scalograms of AE signals: (**a**) scalogram of the normal condition; (**b**) scalogram of a tool fault condition; (**c**) scalogram of the gear fault condition; and (**d**) scalogram of a bearing fault condition.  
smooths high-frequency noise without distorting the primary structure of the CWT images, which is important for accurate fault detection. This step improves the clarity of the CWT images, ensuring that further CNN processing focuses on meaningful data, leading to more accurate fault detection and classification. The visualization of CWT scalograms after applying the Gaussian filter is shown in Figure 3.
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 2 Methodology  
The scattering geometry is shown in Figure 1, where the receiver (R) and transmitter (T) lie in the YZ plane, and are at heights \(\mathrm{h_{R}}\) and \(\mathrm{h_{T}}\) over a flat surface. \(\mathrm{R_{T}}\) and \(\mathrm{R_{R}}\) are the distances to the origin of coordinates where the specular reflection point is, and \(\mathrm{\theta_{i}}\) is the incidence angle, which for the specular reflection point (origin of coordinates) is equal to the reflected angle \(\mathrm{\theta_{r}}\). The different disks represent the Fresnel zones, with the odd ones (1, 3, 5, etc.) colored dark gray, and the even ones (2, 4, 6, etc.) colored white.  
Figure 1: Global navigation satellite systems reflectometry (GNSS-R) scattering geometry showing the transmitter, receiver, specular reflection point (origin of coordinates), and the different Fresnel zones.  
In what follows, a few principles of electromagnetism are reviewed. They will be used to compute a closed-form solution of the GNSS-R response to a step function, such as in the transition between water and the ocean.
## 4 Experiments and Results
### Evaluation Metrics  
The mean average precision (mAP) and the precision-recall curves (PRC) commonly used in the field of target detection are used to compare the detection performance of different methods. The precision-recall curves and the mean average precision are described in detail below.  
The precision indicator can be seen as a measure of exactness or fidelity, and the recall indicator is a measure of completeness. The precision and recall indicators are formulated as follows:  
\[precision=\frac{\text{TP}}{(\text{TP}+\text{FP})},recall=\frac{\text{TP}}{( \text{TP}+\text{FN})} \tag{1}\]  
where, TP, FP, and FN represent the number of true positives, false positives, and false negatives respectively. The precision-recall curve commonly takes the recall as the transverse coordinates and the precision as the vertical coordinates. If the detection method can keep a high precision with the increasing of recall, it means that the detection method has good performance.  
The average precision (AP) calculates the average value of the precision over the interval from recall = 0 to recall = 1, which can be formulated as:  
\[\text{AP}=\int_{0}^{1}p(r)\text{d}x \tag{2}\]  
where \(p\) is the value of precision and \(r\) denotes the value of recall. Hence, the AP is equal to the area under the curve, the higher the AP, the better the performance of the detection method, and the mean average precision (mAP) represents the average value of all category AP.
## 3 Results and Discussion
Although, in theory, the notch affects the fatigue resistance of a composite material less significantly than traditional material, the value of the fatigue notch coefficient is still different from the unit (Table 3); in particular, they are higher than 1 except in one case: this means that the presence of a notch is not negligible in the short-life range i.e., at higher fatigue loads. Regarding the evaluation of the fatigue limit, it is observed that the specimens obtained from the first panel have greater resistance than those obtained from the second panel. This is due to the stacking sequence of the composite, which in the first laminate has two layers in the \(0^{\circ}\) direction, which is the main stress direction in the case of a tensile test. Given the absence of data on the fatigue limit in the literature, a traditional test was carried out to check on the results obtained and validate the used technique. In particular, the specimens of geometry V were tested, given the higher number of samples available. The Wohler curve is shown in Figure 9, obtaining a fatigue limit for N = \(10^{7}\) cycles equal to 60 MPa. It can be seen that the value differs from that obtained with the Risitano technique which provides the fatigue limit for _infinite_ life. In particular, the fatigue limit obtained with thermographic methods is 30% lower than that traditionally obtained. The fixed number of cycles is arbitrary since it depends on numerous factors, primarily based on the material. Obviously, the estimation improves by increasing this value. Finally, it is possible to demonstrate that what has been done through the limit temperature can also be obtained considering the temperature rise v\({}_{\text{T}}\), which is the slope of the initial trait of the temperature trend. Figure 10 shows the fatigue limit calculation based on temperature rate for notched specimens AA03 and A4, obtaining a fatigue limit equal to 79.1 MPa for AA03 and 45.8 MPa for A4. The last points are neglected since they deviate significantly from the previous ones; this indicates that the specimen is close to rupture. By comparing these values of the fatigue limits with values in Table 3, a negligible percentage difference, lower than 1%, was found. Figure 10: Fatigue limit identification for specimens AA03 (**a**) and A4 (**b**) based on temperature rise \(\rm{v_{T}}\). Figure 9: Wöhler curve obtained according to the traditional method.
## 3 Extraction of Echo Envelope Fluctuation Features
### Fluctuation Intensity  
Figure 8 is the comparison of fluctuation intensity for experimental and simulated results for a frequency of LFM 20-40 kHz and pulse lengths of 1 ms and 3 ms, respectively. Figure 8 shows that the tendencies of the experimental and simulated results are fundamentally similar; the value of the fluctuation intensity is at the minimum on the beam, and at the maximum on the keel-line.  
Figure 9 is the fluctuation intensity results of the scaled benchmark model with different carrier frequencies: the tested signal from Figure 9a is the broadband signal, and Figure 9b is a single frequency signal. From the results, we can see that the echo envelope fluctuation intensity greatly depends on the carrier frequency; when the carrier frequency increases, the echo envelope fluctuation intensity increases correspondingly. According to the theoretical analysis, when the carrier frequency increases, the time resolution of the incident pulse increases, the echo envelope presents a finer fluctuation, and the fluctuation intensity of the envelope increases correspondingly. This feature notably represents the influence of target shape and structure on the echo envelope.  
Figure 8: Theoretical and tested echo envelope fluctuation intensity with the frequency of LFM 20–40 kHz. (**a**) The pulse length is 1 ms; (**b**) the pulse length is 3 ms.  
Figure 9: (**a**) Fluctuation intensity comparison of the wideband echo envelope with different frequencies, the pulse length is 3 ms; (**b**) fluctuation intensity comparison of the CW echo envelope with different frequencies, the pulse length is 3 ms.  
From the comparison of Figure 9a,b, we can see that the echo envelope fluctuation intensity of the narrowband signal does not change markedly compared to that of the broadband. This is because the time resolution of the broadband signal is greater than that of the narrowband signal, thus, the envelope structure presents a higher frequency fluctuation.  
Figure 10a,b is the echo fluctuation intensities of the CW and LFM signals, with pulse lengths of 1, 3, and 10 ms, respectively. The results show that the magnitude of the fluctuation intensity changes as the pulse length changes; when the pulse length increases, the magnitude of the echo fluctuation intensity decreases. This is because a smaller pulse length has a greater time resolution, the echo envelope presents finer fluctuation, and, correspondingly, the fluctuation intensity of the envelope increases. This feature is consistent with the theoretical analysis. Also, as shown in Figure 9, the wideband signal provides better time resolution than that of the narrowband signal. Then, the envelope structure presents a higher frequency fluctuation, so the wideband pulse presents clearer results than the narrowband signal with the same pulse length.
# Quantum Private Comparison Based on Four-Particle Cluster State
## 4 Security Analysis
### Participant Attacks  
Participants can launch more sophisticated attacks compared to external eavesdroppers, as they have access to the quantum sequences containing encoded inputs. This access enables them to deduce the secrets of other participants with a higher probability of success. Unlike external eavesdroppers, if a quantum communication protocol can withstand participant attacks, it is inherently secure against external threats, since participants can employ similar quantum attack strategies as those used by external adversaries. The following analysis explores various strategies employed by internal participants to ascertain the secrets of others.
# Advanced Technology in ATSC3.0: Boosting Data Signal Rates Using Polarization Properties and FSS +
## 4 Computer Simulation Result
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Broadcasting** & **Data Communication** \\ \hline Modulation Scheme & QPSK & QPSK \\ Number of Subcarriers & 64 & 16 \\ Guard Interval & 16 & - \\ Spreading Code & - & Hadamard code \\ Spreading Length & - & 4, 16 \\ Data Length & 20 & 20 \\ Pilot Signals & 2 & 2 \\ Channel Model & 5 path Rayleigh fading & 5 path Rayleigh fading \\ Transmission Rate & 20 Mbps & 20 Mbps \\ Doppler frequency & 10 Hz & 10 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Table 2 clearly indicates that QPSK is employed for SNR values below 24 dB, while 16 QAM is used for SNR values of 24 dB or higher, reflecting the trade-off between data rate and signal robustness. These findings suggest that integrating SNR-based feedback into the system and adopting adaptive modulation with QPSK and 16 QAM can significantly improve the overall performance, particularly in challenging channel conditions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Modulation** & **QPSK** & **16 QAM** \\ \hline
**SNR [dB]** & \(0\leq\text{SNR}<24\) & \(24\leq\text{SNR}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Modulation switching table for adaptive modulation. Figure 8: Throughput performance of the proposed method. Figure 7: BER performance of the Proposed Method.
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 2 Related Work  
### Machine Learning Approaches  
Operations based on AI have received tremendous attention in research and technology industry. ML, that allows the applications to function in an \"intelligent\" manner, is a core tool of AI and plays a vital role in discovering insights from the data. Various types of ML algorithms such as supervised, unsupervised, semisupervised, and reinforcement learning exist in the area [11]. Categories of data being processed in ML also vary from unstructured, semistructured, well-structured, and metadata. Depending on the principles and targets of various ML algorithms, their applicability in various domains differs also (Figure 2). Unsupervised learning analyses unlabeled datasets which can be supplied with various input data and is used for identifying meaningful trends and structures. It requires a large amount of data to find hidden patterns from the given data, group the input data together, and create arbitrary categories. Alternatively, the supervised learning approach needs a small amount of data to estimate the necessary parameters and quickly achieve the expected result (e.g., cluster or classify data). This approach is typically used to map an input to an output based on showing the machine the connection between different variables and known outcomes. It uses labeled data collection and predicts an outcome based on a 'guide' supervising the learning process. It is preferable for solving basic or straightforward problems. Conversely, the reinforcement learning method automatically evaluates the optimal behaviors in a particular context or environment by insight into the related data based on the software-enabled agents that apply a reward/penalty rule based on the observation. The goal of this type of learning is to use insights obtained from the environment to take an action, e.g., to increase the reward or minimize the risk or failure. They are specifically considered a powerful tool for training AI models that facilitate automation or optimize the operational efficiency of sophisticated systems, and for solving numerous real-world problems in various fields [12].  
The classifications algorithms often instantiate hybrid methodologies. Nevertheless, the right approach selection, including the appropriate identifying and grouping of related data, is a key to build effective ML-based solution. In this way, the outcome of an ML method can be more accurate and meaningful.
# Assessment of BeiDou-3 and Multi-GNSS Precise Point Positioning Performance
## 1 Introduction
For the above reasons, this paper conducts GPS, BDS-2, BDS-2/BDS-3, GLONASS, Galileo, and multi-GNSS PPP tests and evaluates the performance of each system in term of positioning accuracy, convergence time, and ZTD accuracy based on international GNSS Monitoring and Assessment System (iGMAS) observation data and the precise products that were provided by Wuhan University (WHU). This paper makes comments for the development of multi-GNSS integration, global precise positioning, and the construction of iGMAS by evaluating the performance of GPS, BDS-2, BDS-2/BDS-3, GLONASS, Galileo, and multi-GNSS global PPP. The remaining paper is organized, as follows: PPP methods are first presented in Section 2. Experiment and data processing strategies are then described in Section 3. In Section 4, the Position Dilution of Precision (PDOP) was used to explore the service areas of the GPS, BDS-2, BDS-2/BDS-3, GLONASS, Galileo, and multi-GNSS and compare their differences. In Section 5, The GPS, BDS-2, BDS-2/BDS-3, GLONASS, Galileo PPP, and multi-GNSS PPP are used to verify the capacity of the global precise positioning in terms of positioning accuracy, convergence time, and ZTD accuracy. In the last section, the conclusions are drawn.
## 8 Cooperative Model-Predictive Control Methodology
### Non-linear MPC Formulation  
Equation (64) represents the non-linear problem that runs in NMPC where \(\mathcal{L}_{i}(\mathbf{x}_{i,k},\ \mathbf{u}_{i,k},\ \mathbf{x}_{i,ref})\) is the objective function, and \(\mathcal{C}_{eq}(\mathbf{x}_{i,k},\ \mathbf{u}_{i,k})\) and \(\mathcal{C}_{inq}(\mathbf{x}_{i,k},\ \mathbf{u}_{i,k})\) are equality and inequality constraints, respectively. \(M\) in (64) denotes the prediction horizon for the NMPC.  
\[\underset{\mathbf{x}_{i,k},\mathbf{u}_{i,k}}{\text{minimize}} \sum_{k=1}^{M}\mathcal{L}_{i}(\mathbf{x}_{i,k},\ \mathbf{u}_{i,k},\ \mathbf{x}_{i,ref})\] (64) s. t. \[\mathbf{x}_{i}(t)-\mathbf{f}_{i}(\mathbf{x}_{i}(t),\ \mathbf{u}_{i}(t))\ =\ \mathbf{0}\] \[\mathcal{C}_{eq}(\mathbf{x}_{i,k},\ \mathbf{u}_{i,k})\ =\ \mathbf{0}\] \[\mathcal{C}_{inq}(\mathbf{x}_{i,k},\ \mathbf{u}_{i,k})\ \leq\ \mathbf{0}\]  
The objective function implemented in this work has the following quadratic form.  
\[\mathcal{L}_{i}(\mathbf{x}_{i,k},\ \mathbf{u}_{i,k},\ \mathbf{x}_{i,ref})\ = \tag{65}\] \[\sum_{k=1}^{M}\mathcal{L}_{i,tp}(\mathbf{x}_{i,ref},\ \mathbf{x}_{i,k})+\mathbf{u}_{i,k}^{T}\mathbf{Ru}_{i,k}+\mathbf{x}_{i,k}^{T}\mathbf{Q}\mathbf{x}_{i,k}\]  
where \(\mathbf{Q}\in\mathbb{R}^{n_{x}\times n_{x}}\) and \(\mathbf{R}\in\mathbb{R}^{n_{u}\times n_{u}}\) are square matrices. This objective function drives the system to achieve the cooperative task defined by \(\mathcal{L}_{i,tp}(\mathbf{x}_{i,ref},\ \mathbf{x}_{i,k})\), which brings a nuanced cooperativeness with task prioritization to the tracking and is explained in Section 8.3.  
The equality and inequality constraints serve the following purposes. \(\mathcal{C}_{eq}(\mathbf{x}_{i,k},\ \mathbf{u}_{i,k})\) is introduced to ensure the initial conditions, which are \(\mathbf{x}_{i,c}\ =\ \mathbf{x}_{i}(t=0)\) and \(\mathbf{u}_{i,c}\ =\ \mathbf{u}_{i}(t=0)\), and \(\mathcal{C}_{inq}(\mathbf{x}_{i,k},\ \mathbf{u}_{i,k})\) is introduced to enforce states and inputs to stay in the predefined ranges. These constraints are provided in [45].
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 4 Network Resource Allocation Algorithm Design for the HSN
### Bursty Traffic Allocation Scheme  
Bursty traffic, \(Bur\_Traffic_{k}\), exhibits significant randomness and uncertainty. It can occur continuously within a specific time period or appear suddenly in an irregular manner. The data characteristics of bursty traffic, including timing, size, rate, source, and destination, are all uncertain. In space application networks, bursty traffic occupies a significant proportion of network bandwidth. As mentioned earlier in the traffic analysis, the sensitivity to the time and throughput requirements of bursty traffic can vary greatly. Therefore, in order to allocate and handle bursty traffic effectively, it is important to extract relevant features and characteristics.  
\[Bur\_Traffic_{k}=\left[s_{k},\;d_{k},\;\alpha_{k},\;\beta_{k},\;\gamma_{k},\;p _{k},\;b_{k},\;t_{k}^{-}\right] \tag{21}\]  
\(\alpha_{k}\) denotes the weight of the effective bandwidth, \(\beta_{k}\) denotes the weight of the average latency, and \(\gamma_{k}\) denotes the weight of the packet-loss ratio. These three factors, along with the parameters of the edge (\(s_{k},\;d_{k}\)), decide the physical channel of the chosen bursty traffic.  
\(p_{k}\) denotes the traffic priority level, \(b_{k}\) denotes the traffic buffer size waiting for transmission, and \(t_{k}^{-}\) denotes the countdown times. Once the bursty traffic, \(Bur\_Traffic_{k}\)is serviced, \(\mathrm{t}_{k}^{-}\) will be set back to the initial timeout value, or \(\mathrm{t}_{k}^{-}=\mathrm{t}_{k}^{-}-1\), until the value of \(\mathrm{t}_{k}^{-}\) is 0. When the value of \(\mathrm{t}_{k}^{-}\) becomes 0, the bursty traffic becomes preemptive traffic and has absolute priority to be serviced as fast as possible. For special bursty traffic that has high real-time requirements, the initial attribute \(\mathrm{t}_{k}^{-}\) can be set to 0 until the traffic is serviced (Figure 8).
# Iteratively Refined Multi-Channel Speech Separation
## 6 Conclusions and Future Work  
In this paper, an iteratively refined multi-channel method is proposed to improve the performance of speech separation in complex environments. Benefiting from the strength of neural beamforming and the multi-stage iteratively refined structure, the proposed method achieved outstanding performance. The experiments on the spatialized version of the WSJ0-2 mix corpus showed that the proposed method achieved significant improvements. Specifically, our method achieved an SDR improvement of 24.17 dB, demonstrating that our method not only provided good separation performance in a reverberant environment but also had significant advantages compared with current popular methods of speech separation. In addition, the method also achieved an SDR of 20.2 dB on joint separation and dereverberation tasks, further showing its promising capability. However, the dataset in this paper did not contain noise components. Therefore, exploring speech separation problems in noisy environments will be our future research direction, and the effectiveness of this method in realistic environments will be further validated by using noisy datasets such as LibriCSS [28] and WHAMRI [29].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**SDR**} \\ \cline{2-3}  & \(\hat{y}_{q,1}^{(n)}\) & \(\hat{z}_{q,1}^{(n)}\) \\ \hline Beam TasNet & 10.8 & 14.6 \\ Beam-guided TasNet & 16.5 & 17.1 \\ iBeam-TFDPRNN & 20.2 & 19.7 \\ Oracle mask-based MVDR & 11.4 & 12.0 \\ Oracle signal-based MVDR & \(\infty\) & 21.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance on joint separation and dereverberation tasks.  
**Author Contributions:** Conceptualization, X.Z. and C.B.; methodology, X.Z.; software, X.Z.; validation, X.Z., X.Y. and J.Z.; formal analysis, X.Z., X.Y. and J.Z.; investigation, X.Z.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z., C.B., X.Y. and J.Z.; visualization, X.Z.; supervision, C.B.; project administration, C.B.; funding acquisition, C.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61831019.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** The authors are grateful to the thorough reviewers.  
**Conflicts of Interest:** The authors declare no conflicts of interest.