esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Evaluation of Response Time  
The response time of large language models (LLMs) is a critical factor influencing the overall user experience, particularly in real-time training environments such as WarehouseGame Training. The response time directly affects the fluidity of interactions, as excessive delays may lead to user frustration and disrupt the learning process. Table 14 presents the response time statistics (in milliseconds) for each LLM evaluated. Grob demonstrated the best performance, in terms of response speed. With a mean response time of 1924.86 ms and a median of 1852.0 ms, the model responded consistently and quickly. Its standard deviation (299.42 ms) indicated minimal variability across interactions, and the 95% confidence interval (1883.10 ms to 1966.61 ms) confirmed the model's stability. Furthermore, its minimum (1384 ms) and maximum (3235 ms) response times remained within acceptable thresholds, reinforcing its suitability for real-time interaction. ChatGPT provided moderate performance. Its mean response time was 5823.45 ms, with a median of 5578.0 ms. The standard deviation (1415.13 ms) suggested higher variability, occasionally resulting in longer waiting times. The 95% confidence interval (5626.12 ms to 6020.77 ms) showed general consistency, but the range between its minimum (3313 ms) and maximum (11,956 ms) values highlights the potential for noticeable delays. While ChatGPT can still be considered viable for real-time training, these peaks in response time may interrupt the user experience in more dynamic scenarios. DeepSeek exhibited the longest response times among the evaluated models. With a mean of 10,213.75 ms and a median of 10,098.5 ms, the model consistently responded more slowly. Although the standard deviation (718.28 ms) was smaller than ChatGPT's, its base latency was significantly higher. The 95% confidence interval (10,113.59 ms to 10,313.91 ms) indicated consistent delay, and the minimum (8513 ms) and maximum (12,836 ms) response times confirmed performance issues that could compromise real-time usability in an interactive learning environment. When comparing the three models,  
* Grob clearly outperformed the others, offering the lowest latency and highest consistency, making it ideal for seamless integration in real-time educational platforms.
* ChatGPT provided acceptable performance with occasional delays, suitable for applications where slight lags are tolerable.
* DeepSeek, while functional, presented response times that might hinder interactivity and user engagement in scenarios requiring prompt feedback. Overall, Grob emerged as the most efficient model in terms of response time, delivering fast and reliable outputs suitable for interactive training applications. Its combination of lowlatency, minimal variance, and consistency underlines its advantage for real-time adaptive learning systems like WarehouseGame. While ChatGPT remains a strong alternative, further optimization would be required to match Grob's responsiveness. DeepSeek, on the other hand, would need substantial improvements in response latency to meet the demands of such environments.
## 3 Efficient Hierarchical Co-Occurrence Graph Convolution Networks
### Spatio-Temporal Graph Convolution  
A skeleton sequence can be regarded as a graph structure of all body joints in each frame, as shown in Figure 2(a). Due to the fixed structure and function of body joints, the ST-GCN algorithm [9] extracts joint features by constructing a spatio-temporal graph. Specifically, it includes a spatial graph convolution (SGC) and a temporal convolution (TC). Figure 2(b) shows the input feature map of the SGC operation, which can be represented as \(f_{in}\in\mathcal{R}^{\mathcal{C}_{in}\times T_{in}\times V_{in}}\). Figure 2(c) shows the adjacency matrix between joints, which can be represented as A + I, where A denotes the connection matrix by the natural structure of body joints, and I represents the connection relationship of each joint to itself. Moreover, since each edge connecting joints has different importance, a learnable edge weight matrix M is introduced. Finally, the SGC operation is expressed in Equation (3) as follows:  
\[f_{\mathrm{out}}=\sum_{d=0}^{D}M_{d}\otimes\Lambda_{d}^{-\frac{1}{2}}(A_{d}+I )\Lambda_{d}^{-\frac{1}{2}}f_{\mathrm{in}}\,W_{d}, \tag{3}\]  
where D is the predefined maximum spatial graph distance (set to 2 in this paper) used for normalizing the adjacency matrix, and W represents the weight parameters. After SGC operation, the resulting output feature map is \(f_{out}\in\mathcal{R}^{\mathcal{C}_{out}\times T_{out}\times V_{out}}\).  
Figure 3: Spatial graph convolution operation representation.  
In order to capture the temporal information of joints, convolution operations are applied to the temporal dimension. Since the same nodes in all frames can naturally be arranged into a 2D sequence, the \(\Gamma\times 1\) convolution operation is performed on the output feature map of the SGC operation, where \(\Gamma\) is the size of the TC kernel (set to 5 in this paper).
###### Abstract  
Recently, guided wave (GW)-based non-destructive evaluation (NDE) techniques have been developed and considered as a potential candidate for integrity assessment of wood structures, such as wood utility poles. However, due to the lack of understanding on wave propagation in such structures, especially under the effect of surroundings such as soil, current GW-based NDE methods fail to properly account for the propagation of GWs and to contribute reliable and correct results. To solve this critical issue, this work investigates the behaviour of wave propagation in the wood utility pole with the consideration of the influence of soil. The commercial finite element (FE) analysis software ANSYS is used to simulate GW propagation in a wood utility pole. In order to verify the numerical findings, the laboratory testing is also conducted in parallel with the numerical results to experimentally verify the effectiveness of developed FE models. Finally, sensitivity analysis is also carried out based on FE models of wood pole under different material properties, boundary conditions and excitation types.  
Keywords:non-destructive evaluation (NDE); wood utility pole; guided wave propagation; finite element modelling; sensitivity analysis +
Footnote †: journal: Applied
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 4 Ground Subsidence Risk Prediction Model Based on Machine Learning
### Map of the Ground Subsidence Risk  
A map predicting the ground subsidence risk of the target area was generated from the results of the ground subsidence risk prediction model developed in this study, and it is shown in Figure 4. The risk map was visualized using the inverse distance weighting (IDW) technique in ArcGIS. IDW is primarily used in spatial analysis and estimates the value at a specific location using the values of sample points [39]. In the risk map, red indicates the highest risk level, which is level 3, yellow indicates level 2, and green indicates level 1. The points marked on the map indicate the locations where actual ground subsidence has occurred.  
The ground subsidence risk map indicates that the model accurately predicts the areas with a high density of past subsidence occurrences as high-risk zones. However, the risk level of some locations was predicted to be level 1 despite actual ground subsidence in the past. Therefore, it is necessary to include additional influencing factors and conduct analyses to improve the model accuracy and reliability.
## 5 Parametric Study on the Thermomechanical Performance
### Influence of Face-Sheet Thickness  
Another structural parameter of the sandwich structure is the thickness of the face sheets. Here, we also performed the effect of different face-sheet thicknesses on the thermomechanical of the UCS and BCS. The thickness of the core was kept the same (0.2 mm). The thicknesses of the face sheet are varied,  
Figure 21: Von Mises stress at 5000 s. Core thickness variation \(t_{c}\) = 0.1 mm (**a**) unidirectional core, (**b**) bidirectional core; \(t_{c}\) = 0.3 mm (**c**) unidirectional core, (**d**) bidirectional core, unit: MPa.  
Figure 22: Equivalent plastic strain (PEEQ) at 5000 s. Core thickness variation for the UCS (**a**) \(t_{c}\) = 0.1 mm, (**b**) \(t_{c}\) = 0.3 mm; for the BCS (**c**) \(t_{c}\) = 0.1 m, (**d**) \(t_{c}\) = 0.3 mm.  
Figure 20: Deflection of sandwich structures: (**a**) UCS and (**b**) BCS.  
as given in Table 6. The results of maximum deflection are given in Table 5. The maximum deflections satisfied the deflection limits for the face-sheet thicknesses from 0.1 mm to 0.4 mm. Note that the honeycomb sandwich structure used in the metallic panel of NASA's research had an area weight of 4.4 kg/m\({}^{2}\), a face-sheet thickness of 0.15 mm, and a maximum deflection of 6.4 mm that also satisfied the deflection limit of less than 1% of the diagonal length (646 mm) [4; 12]. The proposed corrugated core sandwich structures with a face-sheet thickness of 0.15 mm were an excellent structure in a comparison with the honeycomb sandwich structure in terms of the area weight and deflection limit. However, the increase of the face-sheet thickness of the sandwich structure was necessary to improve the high-speed impact resistance [54]. Therefore, the results of the influence of the face-sheet thickness in this study might be useful for future impact investigations.
## 3 Data Analysis, Experimental Design, and Method Validation  
### Chemometrics, Preprocessing, and Data Fusion  
Effectively employing vibrational spectroscopy for food fingerprinting within the context of foodomics depends heavily on the incorporation of chemometric modeling. In authentication tasks, spectral data, which are typically in the form of wavelength-dependent signals, serve as input variables, while corresponding output targets are defined during model construction, whether for categorical classification or quantitative regression purposes. Once the model is trained, new input data can be processed to identify clusters, patterns, or concentrations. The model's accuracy is heavily reliant on the quality of the input variables, including the wavelength range, the signal-to-noise ratio, and the way the samples are presented [44; 45].  
Two primary approaches are used in applying these techniques: targeted and untargeted analyses. Targeted methods focus on identifying specific properties or characteristics in food, often to detect known ingredients or contaminants. On the other hand, untargeted analysis involves interpreting signals from various instrumental techniques to gain a comprehensive understanding of the sample, without any predefined assumptions about its composition [46; 47].  
A wide range of algorithms is available to support various types of analysis, many of which have been extensively used and documented. These algorithms include principal component analysis (PCA), partial least squares (PLS) regression, support vector machines (for both classification and regression), artificial neural networks, K-nearest neighbors, and convolutional neural networks [48; 49]. Among multivariate analysis methods, PCA serves as a fundamental tool for data exploration and dimensionality reduction. It allows researchers to visualize variance patterns and identify outliers in high-dimensional spectral data. PLS, on the other hand, is especially valuable for developing regression models that correlate spectral data with physicochemical properties. Variants such as PLS discriminant analysis (PLS-DA) extend these capabilities to classification tasks. The interpretability and predictive strength of these methods make them indispensable in chemometric workflows involving vibrational spectroscopy.  
Recent developments in food fingerprinting have emphasized the potential of data fusion techniques, which have shown promising results in creating models that integrate data from multiple instruments or methods [50]. Although this approach appears relatively simple, it continues to pose significant challenges in data analysis within food chemometrics. Moreover, various methods and algorithms are available for analyzing data from IR sensors,and preprocessing steps, such as baseline correction and derivative calculations, are crucial for developing accurate calibration and classification models [51; 52; 53; 54].
# Characteristic Analysis of Compact Spectrometer Based on Off-Axis Meta-Lens
## 3 Results and Discussion
### Two Practical Device for Different Applications  
Consider the fact that the detector always has limited element sizes and array numbers, there will be a trade-off between the effective spectral range and spectral resolution in designing spectrometer. A high-resolution spectrometer with a wide spectral range is ideal but not realistic for the grating based species. Alternatively, we propose two practical configurations to satisfy the actual requirements: a wideband spectrometer with medium spectral resolution and a narrowband spectrometer with high spectral resolution. In practice, the meta-lens based approach has the advantages to integrate these two or more components into a single package.  
The structural parameters for the wideband spectrometer with medium spectral resolution are \(f=30\) mm, \(D=2\) mm, \(\alpha=15^{\circ}\), \(\beta=-89^{\circ}\) at \(\lambda_{\text{d}}=1550\) nm. Its focal line along the \(u\)-axis and spectral resolution as a function of wavelength are shown in Figure 5a,b, respectively, indicating a remarkably wide spectral range. This device will work at the band 800-1800 nm with the spectral resolution of 2-5 nm. The dimension of this configuration is estimated as 70 \(\times\) 20 \(\times\) 10 mm\({}^{3}\). This configuration is compact and its spectral range and spectral resolution is comparable to the traditional commercial mini-spectrometer that has the similar working parameters, for example, Hamamatsu Photonics C11482 GA [44]. The structural parameters for the narrowband spectrometer with high spectral resolution are \(f=30\) mm, \(D=6\) mm, \(\alpha=45^{\circ}\), \(\beta=-65^{\circ}\) at \(\lambda_{\text{d}}=850\) nm. Its focal line along the \(u\)-axis and the spectral resolution as a function of wavelength are shown in Figure 5b,c, respectively, indicating a narrow spectral range 780-920 nm. Within this spectral range, the spectral resolution is within 0.15-0.6 nm. Its dimension is approximately 40 \(\times\) 30 \(\times\) 10 mm\({}^{3}\). This results are also comparable to the traditional commercial spectrometer such as Hamamatsu Photonics C13054 MA [44].  
Figure 5: _Cont._  
In practice, one can determine the initial parameters of the meta-lens according to Equation (8) to meet the requirements of the spectral resolution. The effective spectral range could be broadened obviously by adjusting the orientation angle of the OP \(\beta\) according to the ray tracing method. These parameters together give the freedoms to design spectrometer for various application requirements.
# A New Top-Mounted Shear-Hinge Structure Based on Modal Theory and Rubber-Pad Damping Theory
###### Abstract  
Steel-spring floating-slab tracks (SSFSTs) are widely used as efficient vibration-damping beds, and in China, they are mainly used in subways and municipal railroads. The shear hinge is an important component that improves the stability of the line, and field research has found that the top-mounted shear hinge (TMSH) undergoes varying degrees of damage, which indirectly affects the safety and stability of line operation. In this work, we studied the causes of damage to TMSHs, designed a new TMSH structure with a rubber-pad layer installed based on modal theory and rubber-pad vibration-damping theory, and proved that the new structure can reduce the occurrence of damage by comparing it with the original TMSH structure. The main aspects of this study are as follows: Firstly, the ultimate load capacity of the existing and new TMSH structures was checked by establishing a refined finite-element model. Then, modal analysis and frequency-response function analysis were carried out based on modal theory and frequency-response function theory to reveal the causes of TMSH damage and prove that the new structure can effectively delay damage. Finally, the modal and vibration patterns of the two structures were obtained via indoor hammering tests and compared with the simulation results. The results show that the two TMSH structures are in line with the strength requirements, and the existing TMSH damage mainly results from the resonance between its natural frequency and the high-excitation frequency of the floating slab under long-term cyclic train loading, causing high-frequency vibration fatigue damage. It is also demonstrated that the new structure can effectively reduce the natural frequency of the TMSH so that its value is located in the region of low vibration on the floating slab. The excitation vibration levels of the TMSH mounted on the curved section of the 4.8 m floating slab and the 3.6 m floating slab were reduced by 9 dB and at least 3 dB, respectively. After adding rubber pads located in the 400-3000 Hz floating-slab high-vibration-level region of the TMSH damage-prone parts, the amplitude reduction, including lateral excitation of damage-prone parts, resulted in a vibration amplitude reduction of more than 30 dB. However, the vertical excitation of the mid-end and rear-end bolts slightly increased their amplitudes, whereas the shear-rod amplitude was reduced by 48 dB, and the front-bolt amplitude was reduced by 5.28 dB. The natural frequency and vibration pattern obtained from the hammering test were consistent with the simulation results, and the reliability of our conclusions was verified from both experimental and simulation perspectives.  
top-mounted shear-hinge damage; modal analysis; frequency-response analysis; resonance; top-mounted shear-hinge structure optimization +
Footnote †: journal: Applied Sciences
## 3 Results
### Experiment 2: Evaluation of the CAIS with Single Irrigation Point  
The aim of this experiment was to retain the moisture of the 3 cm depth layer soil at level 690. The parameters of air humidity and the air temperature were the same as those used to build the SMF model. The sensor captured the moisture value from VPA#4 of the experimental plot. The vertical dashed lines in Figure 9 indicate the time when the CAIS performed the irrigation task. The experiment started at p.m. 02:00 on Day 1. The initial soil moisture sampled by the sensors at 2 cm, 3 cm, and 4 cm depth layers, denoted as S2, S3, and S4, was 601, 624, and 649, respectively. The irrigation intervals for selection were 2 h, 3 h, and 4 h.  
The first irrigation task was at p.m. 03:00 on Day 1. At this time, the CAIS determined the next irrigation interval was 3 h based on the data from the SMF model. The next irrigation time was at p.m. 06:00 on Day 1. At this time, the moisture level of S3 was 625, which was well below the desired 690 level and the CAIS shortened the irrigation interval to 2 h. From then on, the CAIS actively increased the moisture level by maintaining the 2 h irrigation interval until p.m. 12:00 on Day 2.  
Figure 9 also shows the moisture level of the 3 cm depth layer in VPA#0, denoted as VPA#0 3 cm. The moisture level fell continuously and became stable after p.m. 03:00 on Day 2. This status continued until a.m. 01:00 on Day 3 and then grew moderately until the end of the experiment. The location of VPA#0 was far from the irrigation point and its moisture level was much lower than the required level 690. The average error bias of the moisture level was higher than 150. As the water continuously evaporated from the media and VPA#0 was not directly resupplied by irrigation water, the moisture levels fell. The increasing trend of the moisture levels after a.m. 11:00 on Day 3 came from water under the soil gradually infiltrating from VPA#4. However, the speed of evaporation was higher than that of infiltration. Thus, the VPA#0 moisture level maintained a relatively stable state with slight growth.  
Figure 8: The continuous forecasting errors of the SMF model with the real sample results. The forecasting error is evaluated by moisture level, which is ranged from 0 to 1023. (**a**) The forecasting errors of 2 h irrigation interval. (**b**) The forecasting errors of 3 h irrigation interval. (**c**) The forecasting errors of 4 h irrigation interval.
## 4 Case Studies of Real Wind Power Data
### Experimental Setup
#### 4.1.3 Parameter Tuning  
As discussed in Section 3, the forecast performance of NNs greatly depends on the quality of training datasets, which hinges on the parameters \((l,d,q^{up},q^{down})\) and \(Lag\). To find the best \((l,d,q^{up},q^{down})\), Algorithm 2 is run with 200 attempts.  
To optimize \(Lag\), we evaluate MAE under different values of \(Lag\) (see Figure 9) and pick the one with the lowest MAE. It is observed that the lowest MAE is achieved when the feature dimension is 9 (i.e., \(Lag=7\)).
## 3 Methods
### Overall Framework
#### 3.2.1 Data Preprocessing Module  
The original time\(-\)domain vibration signal is easily disturbed by background noise, which affects the feature extraction work. The spectral lines in the spectrum are often used as an important basis for fault feature extraction [8]. For this reason, we use FFT to preprocess the original data and convert the time domain signal into a frequency domain signal to provide reliable data for the subsequent data generation module and fault diagnosis module. First, the discrete Fourier transform formula of the signal \(x_{n}=x_{0},\cdots x_{N-1}\) is as follows:  
\[x_{k}=\sum_{n=0}^{N-1}x_{n}e^{-i2\pi kn/N} \tag{5}\]  
where \(x_{k}\) is the result of DFT. To reduce the calculation time of Formula (5), Formula (5) can be converted to Formulas (6) and (7):  
\[x_{k}=\sum_{m=0}^{N/2-1}x_{2m}e^{-i2\pi k(2m)/N}+\sum_{m=0}^{N/2-1}x_{2m+1}e^{- i2\pi k(2m+1)/N} \tag{6}\]  
\[x_{k}=\sum_{m=0}^{N/2-1}x_{2m}e^{-i2\pi k(m)/(N/2)}+\sum_{m=0}^{N/2-1}x_{2m+1} e^{-i2\pi k(m)/(N/2)} \tag{7}\]  
\(e^{i2\pi/N}\) is a primitive \(N\)-th root of 1 [26]. Formula (6) is the FFT result corresponding to the odd point, and Formula (7) is the FFT result corresponding to the even point. Therefore, the computational complexity is not reduced, which consists of \(2\times[(N/2)\times N]\), for a total of \(N^{2}\). By executing Equations (5)-(7) on the original signal, the frequency domain signal of the original signal can be obtained.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 4 Black Silicon for Light Trapping in Photodetectors
Techniques like electrochemical etching offer a balance of efficiency and simplicity, while laser ablation is ideal for applications requiring highly tailored surface morphologies.