esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of the Heat Treatment on Crack Growth Behaviour  
#### 3.3.1 Fatigue Crack Growth Rate  
The fatigue crack growth rates (CGR) of identical-size CT specimens subjected to three different heat treatment processes were measured. The seven-point incremental polynomial method in ASTM E647 was used to calculate CGR curves in the linear Paris zone (zone II).  
Figure 11a shows the CGR curves of the AT specimens obtained by the incremental polynomial method. In the AT specimens, different heat treatment temperatures show the same trend. When the stress intensity factor \(\Delta\)k \(<\) 20 MPa m\({}^{0.5}\), the CGR curve of 1050 FC is the lowest while 950 FC is the highest, but it is not much different from that of 850 FC. When 20 MPa m\({}^{0.5}\) \(<\)\(\Delta\)k \(<\) 30 MPa m\({}^{0.5}\), the difference between the CGR curves of all heat-treated specimens gradually becomes smaller, and the CGR of the as-built specimen remains similar to those of the heat-treated specimens. When \(\Delta\)k continues to increase, the CGR of the as-built specimens is higher than that of the heat-treated specimens. In the low \(\Delta\)k area, there is a certain fluctuation in the CGR. Overall, 1050 FC has higher crack growth resistance, and the as-built specimen has the lowest.  
Figure 11b shows the CGR curve of SAT specimens obtained by the incremental polynomial method. Compared with the AT specimens, the difference in CGRs of the SAT specimens is more obvious. When \(\Delta\)k \(<\) 20 MPa m\({}^{0.5}\), the CGR curve of 1050 WAC is the lowest, followed by 950 WAC. And 850 WAC has the highest CGR. When 20 MPa m\({}^{0.5}\) \(<\)\(\Delta\)k \(<\) 30 MPa m\({}^{0.5}\), the difference between 850 WAC and 950 WAC gradually decreases, and the CGR curve of the constructed specimen remains similar to that of 850 WAC. When \(\Delta\)k continues to increase, the CGR of the as-built specimen is higher than that of the heat-treated specimens, and the CGR of 1050 WAC is gradually higher than 950 WAC. Overall, 1050 WAC has higher crack growth resistance, and the as-built specimen has the lowest crack growth resistance.  
Figure 10: Macroscopic fatigue fracture surface and the crack initiation zone of different specimens: (**a,b**) 850 FC (3,961,000 cycles), (**c,d**) 1050 WAC (2,175,400 cycles).  
The linear zone (Zone II) satisfies the Paris equation:  
\[da/dN=C(\Delta k)^{m} \tag{1}\]  
The parameter m represents the slope of the scatter plot, and C and m comprehensively represent the value of the CGR. We fitted the CGR curves and calculated the parameters C and m, which are listed in Table 3. By comparison, it is found that in the Paris zone (zone II), 1050 FC and 1050 WAC have excellent resistance to fatigue crack growth. When T\({}_{\alpha}\) < T\({}_{\beta}\), the impact of the microstructure's change on the CGR is not obvious. When T\({}_{\alpha}\) > T\({}_{\beta}\), the presence of the large \(\alpha\) colony can greatly improve the fatigue crack resistance of the specimens. large colony significantly increases the fatigue crack resistance of the specimens.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 2 System Model and Performance Metrics
### Beam Deployment
\(L_{i,h}\) is expressed as  
\[L_{i,h}=\bigg{(}\frac{4\pi d_{i,h}f}{v}\bigg{)}^{2}X_{\sigma}, \tag{5}\]  
where \(d_{i,h}\) is the slant distance between user \(i\) and the HAP \(h\) in km, \(f\) is the carrier frequency in GHz, \(v\) is the speed of light in m/s and \(X_{\sigma}\) is a log-normally distributed random variable with zero mean and standard deviation \(\sigma_{x}\) of 4 dB, representing fading due to shadowing [19]. Small-scale fading is not considered in this line-of-sight (LoS) scenario, since the focus is on cellular structure in general and the long-term mutual interference effects of the cells on each other. It is also because the HAP is assumed quasi-stationary and users are fixed; therefore, no small-scale fading is experienced. This is validated by results from a practical HAPS flight test reported in [27]. Cell pointing to achieve good tessellation depends initially on the radius of the SPPC (i.e., the HAP antenna broadside cell), which is defined by the angle \(\rho\) subtended by the edge of the SPPC to a plane vertical to the HAP. This depends on the antenna mainlobe beamwidth and the minimum CNR \(\Gamma\) requirement at the edge of the SPPC. Here, \(\rho\) is defined such that at all locations within a cell, \(\Gamma_{p}\geq 9\) dB. The HAP antenna array forms multiple beams, which are pointed at a set of coordinates \(\mathcal{C}\), such that the footprints create a regular tessellated structure of contiguous cells over the entire HAP service area. The cell centre coordinates are supplied by the proposed beam-pointing algorithm discussed in  
Figure 2: Antenna element excitation for an \(M\times N\) antenna array. Elements in rows and columns are referred to as \(x\)- and \(y\)-axes elements with \(d_{x}\) and \(d_{y}\) distances apart, respectively. There is proportionality between the excitation amplitudes of the elements in both \(x,y\) axes. The \((m,n)\)th element excitation amplitude is expressed as \(I_{mm}=I_{mj}|j_{m}\)[24]. detail in Section 4. The resulting cells in the extended service area are disproportionate in size and shape due to the effect of broadening mainly at low elevation angles.
# Travel-Time Inversion Method of Converted Shear Waves Using RayInvr Algorithm
###### Abstract  
The detailed studies of converted S-waves recorded on the Ocean Bottom Seismometer (OBS) can provide evidence for constraining lithology and geophysical properties. However, the research of converted S-waves remains a weakness, especially the S-waves' inversion. In this study, we applied a travel-time inversion method of converted S-waves to obtain the crustal S-wave velocity along the profile NSS. The velocities of the crust are determined by the following four aspects: (1) modelling the P-wave velocity, (2) constrained sediments Vp/Vs ratios and S-wave velocity using PPS phases, (3) the correction of PSS phases' travel-time, and (4) appropriate parameters and initial model are selected for inversion. Our results show that the vs. and Vp/Vs of the crust are 3.0-4.4 km/s and 1.71-1.80, respectively. The inversion model has a similar trend in velocity and Vp/Vs ratios with the forward model, due to a small difference with \(\Delta\)Vs of 0.1 km/s and \(\Delta\)Vp/Vs of 0.03 between two models. In addition, the high-resolution inversion model has revealed many details of the crustal structures, including magma conduits, which further supports our method as feasible.  
converted S-waves; travel-time inversion; RayInvr; ocean bottom seismometer (OBS)
# A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants
## 2 Materials and Methods
### One-Class Techniques
#### 2.3.2 Gaussian Model  
An alternative method for tackling anomaly detection, particularly in facial recognition, involves employing one-class techniques that rely on density functions. One straightforward approach within this paradigm is to apply a normal or Gaussian distribution function to the target dataset [38]. This function is derived from the same dataset used for training purposes.  
After establishing the mean vector and covariance matrix, the method for identifying the anomalous status of a test sample relies on its evaluation within the Gaussian function. This straightforward approach offers a low computational overhead, making it advantageous, particularly for large datasets with a normal distribution shape.  
In Figure 5, a simplified representation of a Gaussian function (depicted by the blue line) with a one-dimensional set is illustrated. The determination of whether a test instance is anomalous or not is based on comparing it to a predefined threshold (indicated by the red lines), which is set during the training phase.
## 7 Summary and Conclusions  
The addition of metallic particles as fillers to PLA-based biocomposites is an ongoing research trend owing to their numerous advantages, including enhanced properties and wider application spectrum of the composites. The purpose of this research was to see whether adding silver particles to PLA changed the surface properties of the printed components compared to using regular PLA. Researchers subjected 3D-printed specimens of commercial PLA with silver particles added for antibacterial qualities to experiments that included SEM and image analysis, FTIR, and surface wetting. The data revealed no discernible alterations in surface morphology or chemistry, despite the addition of silver particles causing an uneven layer thickness in the part's structure [178]. This review article gives the in-depth analysis of PLA with metal nanoparticles printed by fused deposition modeling. Filament extrusion for various PLA-metal-printed specimens was discussed along with printing parameters. Several properties like tensile, flexural, compression, Young's modulus, impact, hardness, ductility, ultimate tensile strength, and wear are discussed here.  
Various optimizations like ANOVA, Grey analysis, X-ray microscopy, scanning electron microscope, thermal stability, and biodegradation of PLA Metal composites are also discussed here. Three-dimensional printing with PLA-metal composite filaments is not always simple because the two materials have different characteristics. Filament fragility, increased brittleness, and an uneven distribution of metal particles could cause problems with nozzle clogging and breakage. Metal particles are abrasive, which wears down nozzles and stresses extruder components. Additionally, controlling flow and temperature becomes more complicated due to different thermal properties. The print speed is affected by the extra weight, and warping could occur due to differences in cooling rates. The material is expensive and scarce, and post-processing necessitates extra attention to surface quality. Specialized hardware, optimized print settings, and a supervised printing facility might help mitigate these challenges. The patent landscape reveals the significant growth for this composite material with the potential ability for manufacturing.  
Writing--Original Draft, Data collection, S.S. and V.B.; Writing--Review & Editing, Supervision, L.R.; Conceptualization, D.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
No new data generated or created.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Descriptive Analysis (EDA)
#### 4.1.3 Relationship Between Waiting Time and Other Variables  
An examination of waiting times in relation to key variables (Figure 3) revealed the following:  
* Start and end times: The longest waits (over 30 min) occur between 11:00 and 21:00, when demand peaks and operational capacity tends to be lower.
* Travel duration: On very long routes (>200 min), waiting time variability decreases slightly, suggesting some degree of consistency in long-distance travel, though outliers persist.
* Walking minutes: Users who walk more than 20 min show a greater dispersion in waiting times, potentially due to distant stops or lower service frequencies in those areas.
* Final income per person: The scatter plot shows no linear trend; nevertheless, lower-income strata display greater dispersion and contain most extreme waits (>40 min). These long upper tails point to localized accessibility gaps that merit targeted operational measures.
* Number of persons/vehicles in the household: No strong correlation was found; however, a lack of a private vehicle can heighten dependence on public transport modes with longer waits.  
Figure 2: Boxplot of origin commune and mode of transport by waiting times.  
These findings underscore the need to address periods of high demand and to focus on areas with fewer stops or lower-income populations in order to mitigate excessive wait times and improve equity in public transport services.
## 4 Equivalent State-Space Model  
We shall show that Equation (4) is equivalent to two equations (subsystems). Based on [3; 31] we obtain the following. To simplify the notation we introduce  
\[\bar{A}_{1}=E^{D}\bar{A},\quad\bar{B}_{1}=E^{D}B,\quad\bar{B}_{2}=(\mathbb{I}_ {n}-\bar{E}E^{D})\bar{A}^{D}B,\quad\bar{N}=(\mathbb{I}_{n}-\bar{E}E^{D})\bar{A }^{D}\bar{E}. \tag{20}\]  
**Lemma 2** ([3]).: _Let_  
\[x_{1,i}=\bar{E}\bar{E}^{D}x_{i},\quad x_{2,i}=(\mathbb{I}_{n}-\bar{E}\bar{E}^{ D})x_{i}, \tag{21}\]  
\[x_{1,i}+x_{2,i}=x_{i}. \tag{22}\]  
_Equation (4) can be decomposed into the following equations:_\[\Delta^{\alpha}x_{1,j+1}=\bar{A}_{1}x_{1,i}+\bar{B}_{1}u_{i}, \tag{23}\] \[\bar{N}\Delta^{\alpha}x_{2,i+1}=x_{2,i}+\bar{B}_{2}u_{i}. \tag{24}\]  
Substituting (2) into (23)-(24) and introducing the length \(L\) of practical implementation (the constraint on the upper limit of the summation), as in the case of (9), gives  
\[x_{1,i+1}=\bar{A}_{1a}x_{1,i}+\sum_{j=1}^{L}c_{j}\bar{E}E^{D}x_{1,i-j}+\bar{B} _{1}u_{i}, \tag{25}\]  
\[\bar{N}x_{2,i+1}=(\mathbb{I}_{n}+\bar{N}\alpha)x_{2,j}+\sum_{j=1}^{L}c_{j}\bar {N}x_{2,i-j}+\bar{B}_{2}u_{i}, \tag{26}\]  
where \(\bar{N}\) is a nilpotent matrix with the nilpotency index \(q\) and  
\[\bar{A}_{1a}=\bar{A}_{1}+\bar{E}E^{D}\alpha. \tag{27}\]  
It is not difficult to verify that the solution (16) is the sum of solutions to Equations (25) and (26) for \(L=i\).
## 1 Introduction
[17] introduced an enhanced dual-attention mechanism on top of the SE-ResNet-50 model to learn the dependence of pixel characteristics and the intercorrelation between channels, demonstrating that this method can improve the accuracy of the recognition results. [PERSON] et al. [18] utilized the polar coordinate variation method to preprocess vibration signals and combined it with the ResNet-50 network to eliminate the effects of changing cutting conditions. The above results demonstrate that neural network models can effectively monitor tool wear and outperform traditional monitoring methods, particularly when using the ResNet network model. This model enables the extraction of more complex features with deeper layers but typically requires significantly more hardware and runtime as the network depth increases. In this study, we propose a monitoring method that combines the KANS network with the ResNet-34 network. Using continuous wavelet transform (CWT), the signal is transformed into a CWT time-frequency map as the input for the network model. A ResNet-34 convolutional neural network, based on the PyTorch platform, was used to build the KANS network as the initial training model. The structure of the KAN network was optimized to reduce its runtime, resulting in the new network structure known as KANS. The \"KANS\" algorithm was employed to optimize the ResNet-34 neural network, and the publicly available PHM Society 2010 dataset from the Health Prediction Competition organized by the American Society for Prediction and Health Management was used for model training. Feature extraction was performed using the convolutional and pooling layers, while nonlinear fitting of various signal types and output was conducted in the fully connected layer. Through the network's unique residual block for continuous iterative operations, we constructed the KANS-optimized ResNet-34 tool wear state identification model. Using KANS to optimize ResNet has higher accuracy and faster speed compared to existing combined ResNet neural network models, which can ensure higher accuracy while saving a lot of hardware resources and time costs.
## 2 Materials and Methods
### Alpha Analysis  
ITS DNA gene sequencing libraries were used for constructing high-throughput sequencing. The purified PCR products were sequenced on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) at Personal Biotechnology Co., Ltd. (Shanghai, China) [13]. The double-end sequences that were primed by mass were paired according to overlapping bases using FLASH software (version 34.0.0.277) to obtain the effective sequence of each sample [14]. Raw sequencing reads were quality-filtered and analyzed using QIIME (version 1.7.0). Ambiguous and chimera sequences were eliminated using the method of UCHIME in MOTHUR (version 1.31.2). High-quality sequences were clustered into operational taxonomic units (OTUs), defined by a sequence similarity threshold of 97%, using the UCLUST method embedded in QIIME (version 1.7.0) [15]. The sequence with the highest abundance in each OTU was designated as the representative sequence. These representative OTUs were then classified into various taxonomic levels (phylum, class, order, family, and genus) via the Ribosomal Database Project (RPD) [16]. Alpha diversity indices, including Chao richness indices, observed species (Sobs), Shannon, and Simpson, were calculated using the summary single command in MOTHUR to analyze species diversity and richness. The corresponding rarefaction curves were drawn using QIIME (version 1.7.0).
## 4 Proposed System Design
### OAM/OCDMA-FSO Transmitter  
As shown in Figure 3, the transmitter of our system consists of three parts: optical source, information, and SAC-OCDMA encoder. The optical source is the spatial laser (SL), which generates two distinct OAM beams (\(LG_{0,0}\), \(LG_{0,10}\)).  
Figure 3: Block diagram demonstrating the transmitter of the OAM/OCDMA-FSO system (MZM: Mach–Zehnder modulator; PRBS: pseudorandom bit sequence).  
As for the information bits, a pseudorandom bit sequence (PRBS) generator is used for generating 10 Gbps for each channel, while a non-return to zero (NRZ) modulator is used to encode the binary data. In the FRS encoder, specific wavelengths (optical carriers) that are generated from the optical source are combined according to the assigned FRS code using an optical coupler according to the assigned code.  
The wavelengths that are assigned to the channels used in the proposed model are given in Table 1.  
The Mach-Zehnder modulator (MZM) is used to modulate the electrical signal that carries 10 Gbps optical signal modulated over the two distinct OAM beams (\(LG_{0,0}\), \(LG_{0,10}\)).  
The resulting modulated signal from the MZM is further combined with the other signals from the other channels before transmitting to the free-space channel.
## 2 Related Works
### Industrial Control Device Fingerprinting Extraction Methods
#### 2.1.1 Fingerprint Extraction Method for Industrial Control Equipment  
At present, the extraction of ICS device fingerprints primarily relies on asset detection technology. The detection of assets within an ICS network involves tracking and understanding the status of connected industrial control devices, including network topology recognition, device role determination, and device information extraction. However, due to the sensitivity of ICSs to detection activities and the ongoing development of the Industrial Internet of Things (IIoT), ICSs now exhibit new characteristics such as a wide variety of devices, increasingly complex processes, and expanding control scales [9]. These advancements necessitate higher standards for the effectiveness and safety of asset detection methods. Fingerprint extraction methods are mainly categorized into two types: passive detection and active detection.  
(1) Passive Detection  
Passive asset detection utilizes network sniffing tools to capture data packets without interfering with the target, obtaining communication and hierarchical information [10]. Research focuses on basic information extraction, fingerprint recognition, and device information identification. Tools like NetworkMiner, GRASSMARLIN, and Ettercap assist in network topology recognition and device role determination.  
Fingerprint recognition establishes a database to identify devices with abnormal status. [PERSON] et al. [11] leveraged hardware heterogeneity and clock skew for device recognition, while [PERSON] et al. [12] used enhanced hybrid fingerprints for intrusion detection. [PERSON] et al. [13] monitored communication patterns and memory addresses of PLCs to identify potential cyberattacks, utilizing timing and process behavior analysis.  
Device information identification aims to extract manufacturer, type, and version information, crucial for vulnerability analysis. [PERSON] et al. [14] combined Time to Live (TTL) values, IP ID variation, and MAC address prefixes to improve device recognition accuracy, proposing a framework for identifying ICS assets. [PERSON] et al. [15] utilized passive fingerprinting techniques with network features like MAC addresses.  
(2) Active Detection  
Active detection involves active network communication to identify devices, offering completeness and real-time data acquisition. Combining active and passive methods maximizes information while minimizing system impact. Advances in scanning tools like Nmap, Zmap, and Masscan have improved detection efficiency [16]. [PERSON] et al. [17] used a probabilistic model based on Naive Bayes classifiers to identify industrial control honeypots by analyzing common protocols. [PERSON] et al. [18] proposed behavior-based fingerprinting for IoT devices, achieving over 99% accuracy in identifying device types. [PERSON] et al. [19] analyzed IoT device traffic using machine learning to achieve device identification across different network environments.