esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Attenuation Zones of Combined Layered Periodic Foundations
## 2 Computational Methods
### Frequency Response Functions of CLPFs  
A traditional LPF with I-type unit cells and that with II-type unit cells, whose geometric parameters of unit cells are different, are used to construct CLPF, as shown in Figure 2, where \(a_{1}\) and \(a_{2}\) represent the thicknesses of the concrete and rubber layers of the I-type unit cells, respectively; \(b_{1}\) and \(b_{2}\) represent those of the II-type unit cells, respectively. The CLPF consists of a layer of concrete pad, \(N_{1}\) I-type unit cells and \(N_{2}\) II-type unit cells, as shown in Figure 3, where the periodic boundary conditions are unapplicable since unit cells in the CLPF are not identical.  
For simplicity, the top boundary of the CLPF is assumed to be stress free. A harmonic displacement excitation \(\mathbf{\Psi}=\delta e^{\mathrm{i}\omega t}\) is applied to the bottom boundary of the CLPF, where \(\delta\) is the amplitude of the excitation. By using the weak form quadrature element method, dynamic governing equations of the CLPF can be written as  
\[\left[\mathbf{\overline{K}}+\mathbf{\overline{K}}^{0}-\omega^{2}\mathbf{ \overline{M}}\right]\left[\begin{array}{c}\overline{d}_{2}\\ \overline{d}_{i}\\ \delta\end{array}\right]=\left[\begin{array}{c}0\\ 0\\ \overline{F}_{1}\end{array}\right] \tag{7}\]  
where \(\mathbf{\overline{K}}\), \(\mathbf{\overline{K}}^{0}\) and \(\mathbf{\overline{M}}\) are the stiffness matrix, geometric stiffness matrix and mass matrix of the CLPF, respectively; \(\overline{d}_{i}\) is the nodal displacement amplitude vector of interior nodes of the CLPF and \(\overline{d}_{2}\) is the nodal displacement amplitude of top boundary, respectively; \(\overline{F_{1}}\) is the nodal load force amplitude of bottom boundary of the CLPF. \(\mathbf{d}_{i}\), \(\overline{d}_{2}\) and \(\overline{F}_{1}\) can be obtained from Equation (7) for specified excitation angular frequency \(\omega\) and excitation amplitude \(\delta\).  
Figure 3: Combined layered periodic foundation with \(N_{1}\) I-type and \(N_{2}\) II-type unit cells.  
Figure 2: (a) I-type unit cell with thickness \(a\) and (b) II-type unit cell with thickness \(b\).  
To quantify the wave attenuation in the CLPF, the frequency response function (FRF) of the CLPF can be defined as  
\[\text{FRF}=20\log_{10}\left|\frac{\overline{d}_{2}}{\delta}\right| \tag{8}\]  
Negative FRF means that the displacement amplitude of the top boundary of the CLPF is less than the excitation displacement amplitude. For a traditional LPF with identical unit cells, results in previous studies showed that frequency ranges corresponding to the negative FRFs match well with the AZs [16; 17]. In Section 3.2, the FRFs of CLPFs will be calculated to identify CAZs since the CAZs cannot be directly obtained by applying periodic boundary conditions.
## 3 Results and Discussion  
### Mesh Sensitivity Analysis  
The results for the maximum stress convergence are similar to that seen in the literature, with about 5% variation [15]. This discrepancy could be due to the difference in geometry, as the current work takes an idealized circular shape instead of a scanned pear, and small parameter variances. Figure 4 shows the energy and stress convergence during the drop test simulation study. At lower mesh sizing, there tends to be an underreporting of energy, force, and stress values, also seen in the literature [15]. The program-controlled mesh degenerates for sizing below 1.5 mm and provides a structured but lower-quality mesh that must be adjusted using a patch-conforming tetrahedral technique. After 2.5 mm mesh sizing, the computation time increases almost exponentially while offering very little in terms of convergence. This computation time trade-off will become more expensive for the lower mesh sizing when also investigating the response and reaction of the elastic behavior of the floors (instead of the rigid support used in the mesh sensitivity analysis). Therefore, a mesh sizing of 1.5 mm will be used in the current simulations to balance the computationally expensive simulations and the accuracy of the results. A total of 139,716 nodes and 602,897 elements were obtained after meshing the pear. The model validation incorporates experimental methods and results from previous work to explore the relationship between drop height, material surface, and bruise severity.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 6 Key Enablers of High-Speed Networks Technology
### High-Speed Optical Interconnects  
Recent developments in silicon photonics-based high-speed optical interconnects by industry leaders such as Intel, Cisco, IBM, and NVIDIA are significantly advancing Fog and Cloud computing by addressing bandwidth, latency, and energy-efficiency challenges. Intel's Optical Compute Interconnect (OCI) chiplet enables direct ultra-high-bandwidth data transfer within computing modules, which is essential for Fog environments requiring fast edge processing [3]. Cisco's 1.6 Tbps OSFP-XD transceivers support efficient, high-speed interconnects in dense Cloud data centers, reducing power usage while boosting scalability for rapidly growing AI and data workloads [55]. IBM's co-packaged optics (CPO) with polymer waveguides and NVIDIA's Quantum-X800 InfiniBand switch, which leverages silicon photonics, both enhance throughput and reduce latency for AI-driven, hybrid Cloud-Fog platforms [56; 57]. Together, these technologies highlight the foundational role of optical interconnects in shaping next-generation distributed computing systems.  
Figure 6 illustrates the full assembly of a co-packaged optical switch system, highlighting sixteen transceiver modules closely co-packaged with a central switch application-specific integrated circuit (ASIC). This compact arrangement significantly enhances bandwidth density, reduces electrical link lengths, and minimizes power consumption, which are essential for addressing escalating demands in data center environments. By directly integrating optical transceivers and electronics into a unified assembly, this CPO architecture mitigates traditional interconnection bottlenecks and enables a scalable, energy-efficient interconnect solution suitable for future high-capacity switch deployments [58].  
The rising demands of Fog and Cloud infrastructures necessitate fast, low-latency, and energy-efficient data links--needs well met by PIC technologies. Broadcom's 51.2 Tbps CPO switch, with eight integrated optical engines, boosts bandwidth density while reducing energy use [59]. Marvell's 1.6 Tbps silicon photonics engine, demonstrated at OFC 2025, integrates drivers and amplifiers for compact, low-power interconnects in AI networks [60]. Infinera's multi-functional PICs, MACOM's L-PIC platform, and Lightmatter's Passage M1000 superchip further reduce power and cost while supporting high-speedworkloads [61; 62; 63]. Additionally, Ranovus' Odin platform and DustPhotonics' Tamar200 module offer monolithic, multi-terabit PIC solutions for next-generation hyperscale deployments [64; 65]. Collectively, these innovations emphasize the strategic importance of photonic interconnects in scaling Cloud and Fog computing architectures.
# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 4 Application to Industrial Data
### Pattern Detection  
The patterns determined in this way and their distortions could be found in raw signals using the DTW algorithm. An exemplary adjustment of the B pattern and its two modifications to the signal using the DTW method is shown in Figure 15. It can be seen that the DTW distance signal for distorted patterns changes slightly, but the detection of the moment of pattern occurrence is still possible (the fragment is marked on the signal with an ellipse). This shows that the discussed method also allows the detection of path patterns when applied to industrial data. Additionally, it is resistant to different signal lengths, which is a key problem in the case of time series. However, this method would require more comprehensive tests of real conditions, strictly in order to confirm its effectiveness.
## 1 Introduction
Thus, the applicability and usefulness of the existing review-based control charts are likely to be limited in real-world cases, especially when conducting attribute-level complaint monitoring. A new review-based control chart is required to assist in the accurate judgement of attribute quality. The present study aims to develop a novel approach for analyzing customer complaints using review-based control charts. The study proposes a bi-level control chart (BLCC) in combination with dynamic importance-performance analysis (DIPA) as a new tool for monitoring complaints and prioritizing attributes for quality improvement. The proposed method is distinguished from the existing studies on review-based control charts in three ways. First, the proposed BLCC is the first review-based control chart that segregates the monitoring of attribute importance and performance, which facilitates a fair comparison of multiple attributes and an accurate diagnosis of quality issues. Second, this approach is the first study to apply the \(g\)-chart to customer review analytics. It can address the challenges of the small number and inconsistent content of reviews in real-world applications. Third, to the best of our knowledge, this approach is the first attempt to integrate review-based control charts with importance-performance analysis (IPA), a widely used technique for establishing product improvement and investment strategies [13]. The proposed method is well-suited to the natural characteristics of online customer reviews and can be applied to a variety of products, including both tangible goods and intangible services. It is expected to support quality monitoring and complaint management in various industries, even in cases where it may be challenging to collect a sufficient number of customer reviews. To illustrate the proposed method and to demonstrate its application and effectiveness, this article presents an empirical case study conducted on online hotel reviews. The remainder of this article is organized as follows: Section 2 presents a survey of related studies; Section 3 describes the BLCC and DIPA methods; an illustrative case study is presented in Sections 4 and 5, which discuss the implications of the proposed method; and Section 6 concludes the paper with future research directions.
## 4 Experiments and Results
### Analysis of File Size Change  
Although the invisible characters are not visually perceptible, embedding them as watermark information can increase the size of the BIM data due to the additional characters. This increase in size can result in information redundancy, which may reduce the usefulness of the model, especially in BIM data transfer, use, and copy scenarios. Thus, it is important to evaluate the file size change resulting from the watermark embedding. To quantify this, we calculated the ratio of the incremental size of the redundancy to the original data size as a quantitative index, which is shown in Equation (12).  
\[Ratio(A,B)=\frac{Size(B)-Size(A)}{Size(A)}\times 100\% \tag{12}\]  
where \(Ratio(A,B)\) is the data size change ratio. \(A\) and \(B\) are the original BIM data and the BIM data after watermark embedding, respectively. \(Size()\) is the data size. After fully analyzing the actual application scenarios of BIM data, we can usually consider that when the change in the size of the BIM data after certain operations is less than 0.5%, it can be assumed that these operations have almost no effect on the size of BIM data, i.e., they do not affect the use of the data.  
The result of the file size transformation is shown in Table 4 and Figure 7.  
As presented in Table 4, the redundancy increment ratios for the gymnasium and structural model were both less than 0.1%, owing to the high proportion of movable elements in the models, which resulted in a low embedding of invisible characters. In contrast, the majority of the elements in the office building were immovable due to restrictions on the elevation and neighboring elements, resulting in a high proportion of embedded invisible characters and a relatively high redundancy increment ratio of 0.14%. Overall, the redundancy increment ratio for all three watermarked BIM data files was less than 0.5%, suggesting that the increase in BIM data redundancy after watermark embedding was small, and that the imperceptibility of the watermarking algorithm was excellent. The embedding of watermarks has essentially no impact on the BIM data's size or application.  
\begin{table}
\begin{tabular}{c c c c} \hline
**BIM Data** & **Hausdorff (m)** & **SNR (dB)** & **PSNR (dB)** \\ \hline Office building & \(2.24\times 10^{-4}\) & \(127.11\) & \(114.15\) \\ Gymnasium & \(2.56\times 10^{-5}\) & \(118.44\) & \(111.29\) \\ Structural model & \(4.32\times 10^{-5}\) & \(103.49\) & \(107.09\) \\ \hline \end{tabular}
\end{table}
Table 3: Results of quantitative analysis of imperceptibility.
## 3 The Experimental Results
### Average End-To-End Message Latency
Table 5 summarizes the compliance of each investigated communication technology in the context of the selected urban scenario with the networking and connectivity requirements of the most popular V2I services. Figure 13: End-to-end delay obtained for the traffic intensity of 1500 vehicles per hour.
### Calculation Method for the Absorbed Energy \(E_{a}\) of the RC Beam  
\(E_{\rm a}\) is calculated as the area under the load-deflection curve obtained by the fiber model, considering the plane conservation of the beam section.  
\[E_{a}=\int_{0}^{\delta}P(\delta)d\delta \tag{10}\]  
The calculation procedure is outlined as follows: (i) Divide the height of the section into 5 mm intervals and along the span direction into 100 mm intervals, considering the solution stability; (ii) increase the upper-edge strain by 10 \(\upmu\) to determine the cross-sectional neutral axis at each stage, and determine the curvature-bending moment relationship; (iii) determine the bending moment distribution along the span direction at each load step and the corresponding curvature distribution; and (iv) calculate the deflection of the span center using the elastic load method. Figure 3 shows the concept of the fiber model.  
The constitutive material laws for concrete and reinforcing bars were determined, as shown in Figure 4, following the JSC  
Figure 3: Calculation concept of the fiber model.  
The compressive stress and strain relationships of concrete were defined as follows:  
\[\rho E_{c}=E_{0}K\Big{(}\varepsilon\prime_{c}-\varepsilon\prime_{p}\Big{)} \tag{11}\]  
\[E_{0}=\frac{2\cdot f\prime_{c}}{\varepsilon\prime_{peak}} \tag{12}\]  
\[K=\exp\biggl{\{}-0.73\,\frac{\varepsilon^{\prime}\max}{\varepsilon^{\prime}\, \mathrm{peak}}\biggl{[}1-\exp\left(-1.25\,\frac{\varepsilon^{\prime}\max}{ \varepsilon^{\prime}\,\mathrm{peak}}\right)\biggr{]}\biggr{\}} \tag{13}\]  
\[\varepsilon\prime_{p}=\varepsilon\prime_{max}-2.86\cdot\varepsilon^{\prime} \,\mathrm{peak}\biggl{[}1-\exp\biggl{(}-0.35\frac{\varepsilon^{\prime}\max}{ \varepsilon^{\prime}\,\mathrm{peak}}\biggr{)}\biggr{]} \tag{14}\]  
Material test results were used to establish the compressive strength of concrete and yield strength of the reinforcing steel. The tensile strength \(f_{\mathrm{t}}\) of concrete was estimated using the following equation, according to JSCE [20]:  
\[f_{t}=0.23\,f\prime_{c}\tfrac{3}{3} \tag{15}\]  
As the tensile fracture energy of concrete is substantially smaller than the absorbed energy owing to the bending plastic deformation of the RC beams, the tension softening of concrete was not included in the scope of this study. Moreover, perfect bonding was assumed between the rebar and concrete.  
In this calculation, the strain rate cannot be considered because it changes significantly in time and space after a weight collision. It is difficult to apply it to a simple estimation formula. In addition, the applicable range of absorbed energy calculated under the assumption of plane conservation has not been clarified yet. This should be confirmed through a comparison with the numerous existing experimental results.
## 4 Experiments  
### Datasets  
**NTU RGB+D 60 dataset**. The NTU RGB+D 60 [44] is a large-scale 3D action recognition dataset that contains around 56,880 RGB+D action samples. It uses Microsoft Kinect V2 to capture 60 action categories performed by 40 subjects. The dataset follows two evaluation principles. In the case of cross-view, the samples captured by camera 1 were used as the testing set, and cameras 2 and 3 are regarded as the training set. That is, the number of testing samples is 18,960, and 37,920 samples are used for training. In the case of cross-subject, the data are divided into a testing set of 16,560 samples and a training set of 40,320 samples based on the subject's ID.  
**NTU RGB+D 120 dataset**. NTU RGB+D 120 [45] is an extensive dataset for 3D action recognition, consisting of 114,480 samples and 120 action categories completed by 106 subjects. This dataset contains daily actions, medical-related actions, and two-person interaction actions. The samples are collected in various locations and backgrounds, which are denoted as 32 setups. In addition to the general cross-subject settings, the cross-setup  
Figure 4: Illustration of the additional interval-frequency descriptor. It contains start timestamps, end timestamps, and the overall occupancy frequency of the voxels and is denoted as \((o,f,l)\) in Section 3.4; thus, the 3D depth spatial information is transformed into features of six channels.  
evaluation is introduced, where the training set comes from samples with odd setup IDs, and the testing set comes from the rest.
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 4 Experimental Setup
**Scale-Invariant Signal-to-Distortion Ratio (SI-SNR)**[43]: This metric usually reflects the degree of artifact distortion between the processed utterance \(\$\) and the clean counterpart \(\$\), which is formulated by\[\text{SI-SDR}=10\log_{10}\left(\frac{\|\mathbf{s}_{target}\|^{2}}{\|\mathbf{e}_{ noise}\|^{2}}\right),\] (16) where \[\mathbf{s}_{target}=\frac{\langle\mathbf{\hat{s}},\mathbf{s} \rangle\mathbf{s}}{\|\mathbf{s}\|^{2}},\] (17) \[\mathbf{e}_{noise}=\mathbf{\hat{s}}-\mathbf{s}_{target},\] (18) with \(\langle\mathbf{\hat{s}},\mathbf{s}\rangle\) being the inner product of \(\mathbf{\hat{s}}\) and \(\mathbf{s}\). 5. **Composite Measure for Overall Quality (COVL)**[44]: This metric is for the Mean Opinion Score (MOS) prediction of the overall quality of the enhanced speech. It reflects how listeners perceive the overall quality of the speech signal. COVL scores range from 0 to 5, where a higher score indicates better overall speech quality. COVL is commonly used in the development of SE systems to quantify improvements in speech quality as perceived by human listeners. 6. **Composite Measure for Signal Distortion (CSIG)**: [44]: This metric refers to the MOS prediction of signal distortion, which ranges from 0 to 5, with higher scores indicating lower distortion and better quality of the speech signal. It evaluates how much distortion has been introduced during the enhancement process. CSIG is useful for assessing the effectiveness of SE algorithms in preserving the integrity of the original speech while reducing noise and other distortions.
###### Abstract  
The precise prediction of natural gas pipeline corrosion rates holds great significance for pipeline maintenance and corrosion control. Existing prediction methods, especially traditional models, often fail to adequately consider noise interference and the strong nonlinear characteristics of corrosion data, resulting in insufficient prediction accuracy. To enhance predictive performance, a hybrid prediction model based on machine learning is been proposed. This model consists of three main components: data processing, model optimization, and prediction performance evaluation. In this model, data decomposition algorithms and principal component analysis are employed to eliminate redundant noise from the original data and capture their primary features. A stratified sampling method is utilized to divide the data into a training set and test set, avoiding biases caused by random sampling. A modified particle swarm optimization algorithm is applied to optimize the parameters of a back propagation neural network. The model's predictive performance is assessed using various indicators, including R\({}^{2}\), MAPE, RMSE, MAE, U1, U2, RE, forecasting effectiveness, comparing the results with existing literature, Grey Relational Analysis, and interpretability research. The proposed prediction model is compared with eight advanced prediction models using data from a natural gas pipeline in western China. This study reveals that the developed model outperforms the others, demonstrating excellent prediction accuracy and effectively guiding the formulation of corrosion control measures.  
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# Status of Phenological Research Using Sentinel-2 Data: A Review
## 5 Phenology of Other Land Classes  
### Mapping of Wetland Vegetation  
Sentinel-2 data have also been used to map the phenology of complex and ecologically important wetlands. [PERSON] and [PERSON] (2019) [123] compared the classification accuracies of a support vector machine classifier using data from Sentinel-2, Landsat-7 and Landsat-8. This study demonstrated that, when mapping _Phragmites australis_ (common need), the overall accuracy was highest for Sentinel-2 across all seasons. The lack of red-edge bands and the omission of the Landsat SWIR bands was responsible for the reduction in accuracies (up to 3-8% across sites). In another study by [PERSON] et al. (2019) [124], different types of wetlands were found to be mapped at an accuracy of 83% using multi-year composites of visible bands and vegetation indices derived from Sentinel-2 images for the summer season. It was found that bogs, fens and marshes that were poorly separable in the visible bands were discriminable using vegetation indices such as the NDVI and modified soil-adjusted vegetation index (MSAVI2). The combined use of Sentinel-1 and Sentinel-2 data further improved the discrimination of wetlands and led to overall accuracies of 88%. The improved performance of combined Sentinel-1 and Sentinel-2 time series data was also confirmed by [PERSON] et al. (2020) [125], who found a 10% increase in overall accuracy of mapping different land covers in wetlands through the combined used of the data sets. The different land covers were found to have unique time series curves in both the optical and SAR domains, which led to improved discrimination of land cover types. The addition of phenological metrics such as SOS, EOS, length of season (LOS), maximum NDVI and the seasonal amplitudes led to an additional improvement of 4% in the overall mapping accuracy. A review of the literature suggests that the variability in NDVI and NDWI time series pairs is directly proportional to the separability between the various land covers in wetlands, and hence critical in improving classification accuracy.
# Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning
## 2 Prognostics Method Based on Manifold Learning and a Wavelet Neural Network  
A flow chart of the proposed prognostics method based on manifold learning and a wavelet neural network is shown in Figure 1. First, common feature extraction methods were used in the time domain signals to extract features to construct high-dimensional feature signal sets. Next, methods based on manifold learning were adopted to reduce the dimensionality and obtain effective feature parameters. Lastly, wavelet neural networks were set up to take feature parameters as inputs and fault types as outputs, and train networks until the required errors were reached and the prognostics tested.