esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effect of Survey Length on Fault Imaging  
The inversions (Figure 3a-d) in both scenarios demonstrate that increasing the survey line length enhances the accuracy of fault imaging, particularly for faults F1 and F3. The geological context plays a significant role in these observations, as resistivity contrasts and fault geometries influence the magnetotelluric responses.  
In Scenario 2, which features wider faults, the imaging accuracy of F1 improves considerably when the survey line length is extended to 12 km. At this length, the low-resistivity anomalies associated with the F1 fault closely match their true lateral positions in the geological model, and the fault's localization accuracy surpasses that of Scenario 1.  
Figure 3: Inversion resistivity profile of different survey length. (**a**) Survey length is 8 km. (**b**) Survey length is 10 km. (**c**) Survey length is 12 km. (**d**) Survey length is 14 km.  
This improvement could be attributed to the wider fault, which allows for better signal penetration and easier detection, even in regions with varying geological conditions.  
Interestingly, in Scenario 2, the inversion accuracy of the F3 fault is already high at a survey length of 8 km, with no significant improvements when the length is further increased. This suggests that for wider faults with strong resistivity contrasts, a shorter survey line may be sufficient for accurate imaging. The geological implication here is that fault width and the associated lithological properties can reduce the need for extensive survey lengths in certain cases.  
In both scenarios, the imaging accuracy of the F2 fault remains consistently poor, regardless of the survey line length. The inversion depth for F2 is always shallower than the actual depth, resulting in smaller-scale low-resistivity anomalies. This persistent issue may be due to geological factors such as lower resistivity contrast or gradational boundaries between the fault and surrounding rock. These characteristics can weaken the electromagnetic response, making it difficult to accurately image the fault using standard MT inversion techniques. This suggests that additional geological information or advanced inversion methods may be required to resolve such features.
# Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism
## 6 Conclusions  
In this work, we proposed the Hypergraph-driven Knowledge Tracing (HGKT) framework. Extensive experiments on three diverse public datasets (ASSIST2012, ASSISChall, and Ednet-KT) demonstrate HGKT's state-of-the-art predictive performance. HGKT achieved significant improvements over baseline models, with average AUC gains of 6.71%, 8.82%, and 12.15% on ASSIST2012, ASSISChall, and Ednet-KT, respectively, alongside corresponding accuracy increases of 4.0%, 3.44%, and 3.96%. These results validate HGKT's efficacy in predicting student knowledge state.  
The key advancements stem from HGKT's novel two-layer convolutional architecture employing hypergraph neural networks. This design effectively captures intricate high-order correlations among exercises and knowledge concepts-a critical limitation in prior models. HGKT successfully integrates rich hypergraph-derived exercise representations with essential temporal features (answer time and interval time). Furthermore, our cognitively inspired dual-gating strategy dynamically balances learning and forgetting mechanisms, enabling realistic modeling of evolving knowledge states. Visualization analyses confirm the model's interpretability in tracing this evolution. The consistent performance gains across datasets with varying characteristics underscore HGKT's strong practical potential for real-world educational platforms.  
Future work will focus on employing knowledge distillation to develop lightweight HGKT variants, significantly reducing model complexity and inference latency to enable real-time deployment on large-scale platforms. Concurrently, we will explore integration with large foundation models (LLMs) to enable multimodal knowledge tracing, incorporating textual content, images, or audio prompts for richer contextual understanding. Building on this, further refinement of knowledge concept representation and interaction modeling will enhance generalizability across diverse educational domains and curricula. These combined enhancements aim to broaden HGKT's applicability and solidify its role within next-generation intelligent tutoring systems.  
Data curation, F.M.; supervision, C.Z.; methodology, F.M. and P.L.; writing--original draft preparation, F.M.; writing--review and editing, F.M. and P.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science and Technology Planning Project of Chengguan District of Lanzhou (2023 JSCX0004), the Outstanding Youth Fund of Gansu Academy of Sciences (2023 YQ-03), and the Higher Education Research Project of Lanzhou University of Technology (GJ2024B-45).  
Not applicable.  
All datasets are available from public websites.  
The authors declare no conflicts of interest.
## 1 Introduction  
Back pain is a leading cause of disability and a main contributor of disease burden worldwide [1; 2]. Non-specific back pain (also called mechanical back pain) is the most common clinical presentation of back pain and refers to conditions without a known etiology [1; 3]. Back pain is a complex condition that results from the interaction of multiple factors including physical issues (e.g., prolonged standing), unhealthy lifestyle (e.g., smoking and obesity), psychological factors (e.g., distress), manual tasks (e.g., heavy loads or lifting objects far from the body), awkward postures and genetic factors [4]. In addition, sitting issues (i.e., sitting duration, chair type, amount of spinal motion and spinal curvature during sitting) have been proposed as risk factors for the development of back pain [5]. The physiological sagittal spinal curvature represents a typical feature of good body posture [6]. As to standing, the presence of slight curves (thoracic kyphosis and lumbar lordosis) has been described as the \"ideal\" spinal posture on the sagittal plane [5; 6; 7; 8; 9]. On the other hand, despite the definition of the best sitting posture still being the object of debate, physiotherapists deem advantageous those sitting postures that match the natural shape of the spine [7; 8; 9]. One of the most common strategies used by physiotherapists for managing back pain is to provide some advice on spinal postures. In particular, addressing sitting spinal posture may help reduce symptoms of patients with back pain [8; 9]. Dynamic chairs have been proposed to address the negative effects of prolonged sitting by increasing the effort required to maintain sitting balance, stimulating posture variation, promoting lordotic lumbar postures and decreasing lumbar muscle tension [10].  
The \"EnergyBack\" device is a newly patented dynamic stool (Italian Registry no. 102016000118233) that aims to improve sitting posture (see Figure 1). To date, this novel device has not yet been validated.  
The main aim of this study was to compare dynamic sitting vs. conventional sitting as to trunk posture adaptations on the sagittal plane (thoracic kyphosis and lumbar lordosis) in healthy people. Our study hypothesis was that thoracic kyphosis and lumbar lordosis during dynamic sitting would greater match the natural shape of the spine (i.e., spinal curves on the sagittal plane evaluated during standing) than conventional sitting on a standard stool.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Test in Hardware Platform
#### 4.2.2 Test of Routing Protocol  
Next, nodes interact with each other and collect resources of the whole network by routing protocol.  
As shown in Figure 20, there are three LSAs in the LSDB, each of which is advertised by a node in the network. It can be seen that the LSDB has collected all resources in the network of three nodes.
## 4 Discussion
### Phonation Parameters
#### 4.2.3 Harmonic Measures  
The parameter CPP is currently one of the most applied parameters for characterizing dysphonia [68; 69; 70; 71; 72; 73]. It is a measure of the voice quality that reflects the degree of occurring harmonics in a voice sample [57; 74]. In addition, due to its calculation method, it is suitable for application to aperiodic signals, which is why it is of particular importance in the present study [75].  
Just like in [PERSON] et al. [39] the values of \(\mathrm{CPP_{A}}\) and \(\mathrm{CPP_{P}}\) decreased from \(\mathrm{GGI_{1-3}}\), and thus became worse (Tables 5 and 7), as the CPP describes the quality of the voice signal [57; 68; 74; 76; 77]. Compared to [PERSON] et al. [39] it is noticeable that the CPP values for the disordered \(\mathrm{GGI_{1,2}}\) are about 30% below the normal CPP values for \(\mathrm{GGI_{1,2}}\), while the disordered CPP value for \(\mathrm{GGI_{3}}\) is about 45% below the normal CPP values for \(\mathrm{GGI_{3}}\); see Table 7. Meaning that these deteriorations of CPP for \(\mathrm{GGI_{1-3}}\) are considerably stronger for the disordered oscillations. These findings apply both to \(\mathrm{CPP_{A}}\) and \(\mathrm{CPP_{P}}\). Deteriorations of \(\mathrm{CPP_{A}}\) and \(\mathrm{CPP_{P}}\) were also found from \(\mathrm{Group_{S}}\) to \(\mathrm{Group_{A}}\), indicating again that aperiodic oscillations (\(\mathrm{Group_{A}}\)) are worse for the acoustic quality compared to subharmonic oscillations (\(\mathrm{Group_{S}}\)). This deterioration was however not as strong as for \(\mathrm{GGI_{1-3}}\) (Tables 5 and 6).  
As Table 8 shows, \(\mathrm{GGI_{3}}\) is mainly composed of \(\mathrm{Group_{A}}\). So, the poor \(\mathrm{CPP_{i}}\) values for \(\mathrm{GGI_{3}}\) may be based on this over proportional composition. However, since the mean \(\mathrm{CPP_{A}}\) (15.4 dB) and \(\mathrm{CPP_{P}}\) (16.0 dB) values of \(\mathrm{Group_{A}}\) (Table 6) are above the \(\mathrm{CPP_{A}}\) (11.0 dB) and \(\mathrm{CPP_{P}}\) (14.0 dB) values of \(\mathrm{GGI_{3}}\) (Table 5), it is the combination of \(\mathrm{GGI_{3}}\) and irregularity that seems to be crucial for these poor CPP scores (i.e., reduced harmonics in the acoustic and subglottal signals).
## 6 Results and Discussion
### LPN with DBS Architecture
The DBS network's ability to capitalize on the spatial distribution of RRHs, reducing path loss and enhancing signal gain, contributes to the overall optimization of the 6G network performance, making it an attractive solution for future mmWave deployments. Figure 14: Spectral efficiency of CBS and DBS networks. Figure 15: Data throughput of CBS and DBS networks.
## 2 Data and Methods
### Methods
#### 2.3.4 Multivariate Regression Residual Analysis  
To quantitatively separate the effects of climate change and human activities on NPP variation, this study defined three categories of NPP: a multivariate regression model was constructed using solar radiation, temperature, and precipitation data to derive NPP predictions (PNPP). NPP values obtained from remote sensing imagery represented actual values (ANPP). NPP residual values (HNPP), which represent the impact of human activities on NPP, were calculated as the difference between ANPP and PNPP. When HNPP is greater  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Absolute Value of \(Z\)** & **Reliability** \\ \hline \(\geq\)1.65 & 90\% \\ \(\geq\)1.96 & 95\% \\ \(\geq\)1.96 & 99\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The reliability table.  
than zero, it indicates a promotional effect of human activities on NPP, while values less than zero indicate inhibition.  
\[\text{HNPP}=\text{ANPP}-\text{PNPP} \tag{8}\]  
\[\text{PNPP}\ =a\times P+b\times T+c\times S+d \tag{9}\]  
where P represents annual precipitation, T represents annual mean temperature, S represents annual total solar radiation; a, b, and c are regression coefficients; and d is the constant term.  
Linear regression is employed to calculate the change slopes for the three NPP types, elucidating vegetation growth factors and quantifying their relative contribution rates (Table 2).
## 4 Result and Discussion
### Computational Time  
Most of the computational time of the conventional ANN model is used for the BP training of the model, whereas the HS-ANN model needs computational time for finding the optimal initial weights using the HS algorithm and then for finding the global minimum of the error function by the BP training. [PERSON] [31] compared the computational time between the conventional ANN model and the HS-ANN models with various combinations of HMCR and PAR. Since the statistical characteristics of computational time do not show a big difference among different combinations of HMCR and PAR, here we only present the case of HMCR = 0.7 and PAR = 0.5 for which the HS-ANN model gives the most excellent predictive ability and stability. Table 6 shows the average and standard deviation (SD) of the computational times of the 50 runs of each model. The total computational time of the HS-ANN model is five to six times greater than that of the conventional ANN model. In spite of the greater computing time, it is worthwhile to use the HS-ANN model because it gives much more accurate and stable prediction than the conventional ANN model with a small number of simulations. It is interesting to note that the computing time for the BP training of the HS-ANN model is greater than that of the conventional ANN model probably because it takes a longer time to reach the global minimum which is smaller than the local minimums as shown in Figure 1. On the other hand, the standard deviation of the BP training of the HS-ANN model is smaller than that of the conventional ANN model because the HS-ANN model starts the BP training from the optimal initial weights whose variation is not so large. The standard deviation of the HS algorithm is very small because the maximum number of improvisation was set to 100,000.  
Figure 8: Same as Figure 7 but for Case 2 (HMCR = 0.1; PAR = 0.5).
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 4 Key Techniques for Voxel-Based Navigation
### Voxel Segmentation
#### 4.2.1 Segmentation of Static Objects  
Traditional voxel-based semantic-segmentation methods mainly rely on handcrafted and extracted features (such as geometric features, color information, texture, etc.) and are then used in machine-learning algorithms (such as Conditional Random Fields (CRF) [41]) for semantic segmentation. [PERSON]. [42] proposed a supervoxel-based 3D urban scene  
Figure 6: Voxelization of a surface: (**a**) With connectivity level 26. (**b**) With connectivity level 6 (from [30]).  
segmentation-and-classification method, where supervoxels are first clustered using a chained-linking method and then classified based on surface normals, geometric centroids, color intensity, and geometric shape. [PERSON]. [27] performed semantic segmentation of indoor spaces by combining histogram analysis and region-growing methods based on an octree indoor space model, ultimately identifying walkable paths for pedestrians. This is shown in Figure 7, which depicts the classification of different indoor components that are critical for determining navigable pathways. Traditional methods have strong interpretability but rely on expert knowledge for feature selection, making it challenging to handle high-dimensional features in complex and large-scale environments.  
With the rise of deep learning, more research is leveraging deep-learning techniques for voxel labeling. Through multi-layer structures and convolution operations, neural networks can automatically learn features from high-dimensional data and capture complex relationships. Ref. [43] introduced VoxNet, a framework based on Convolutional Neural Networks (CNNs) for real-time object recognition, converting 3D point clouds into voxel grids and using 3D CNNs for feature extraction. Ref. [44] proposed OctNet, a 3D CNN architecture utilizing an octree structure for sparse 3D data, improving computational efficiency and storage. Ref. [45] developed SpoxelNet, which transforms voxels into global descriptor vectors, enhancing feature capture and reducing errors in occluded directions, especially in complex indoor spaces. However, these methods perform less effectively in outdoor environments. Recently, new approaches combining voxel-based techniques with other methods have significantly improved handling of complex outdoor environments. Table 3 compares their performance on the SemanticKITTI test set:  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{
# Federated Learning for Surface Roughness
## 3 Methods
### Federated Learning Framework  
The federated learning architecture employed in this study is illustrated in Figure 7. During the federated training process, each edge computing device (Local IPC) at the machining site computes gradients locally from the collected training data without transmitting raw data externally. After local computations, the gradients are securely uploaded to a central server, which aggregates them using the Federated Averaging (FedAvg) algorithm. FedAvg was specifically chosen for its simplicity and effectiveness given the consistent precision and similar data volume from sensors installed on both machining setups, eliminating the need to assign differentiated weights to individual agents. Once aggregation is completed, the central global model is updated accordingly and redistributed to each local IPC. This iterative training mechanism allows for efficient and privacy-preserving collaborative learning, enhancing the generalization performance of the global model across diverse machine environments and rapidly accommodating newly integrated machines.
## 4 Performance Evaluation
### Performance Analysis
#### 4.3.1 Impact of 3D Spatial Reuse of mmWave Spectra  
Figure 3 shows SE and EE responses when applying 3D spatial reuse of mmWave spectra to in-building small cells for \(\varepsilon_{\text{RF}}=20\). It is clear from Figure 3a that with an increase in the number of buildings \(L\), SE increases significantly when employing 3D spatial reuse of spectra to small cells within each building as compared to when no reuse is considered. Employing only horizontal 2D spatial reuse of mmWave spectra, reusing any spectrum just once for each building containing small cells is not sufficient to gain high SE, due to the limitation of the maximum number of multistory buildings that can exist within each macrocell's coverage. To achieve ultra-high SE (e.g., 10 times the efficiency of 5G networks [1]) for future 6G mobile networks, in addition to the horizontal spatial reuse of spectra by more than one building containing small cells (i.e., \(L>1\)), the vertical (i.e., the \(\varepsilon\)D inter-floor- and intra-floor-level reuse of spectra within a building) spatial reuse of mmWave spectra within each building needs to be exploited.  
By exploiting the vertical spatial reuse of mmWave spectra by small cells within each building, an MNO can address the scarcity of spectrum demands without sharing the spectra of other MNOs or different systems, such as non-terrestrial satellite systems. This can result in saving additional costs and reducing the network complexities associated with sharing spectra with other systems. From Figure 3b, it can be found that EE improves exponentially with an increase in \(L\) and becomes almost  
Figure 3: Impact of applying 3D spatial reuse of mmWave spectra to in-building small cells: (**a**) system-level average SE; (**b**) system-level average EE.  
steady for large values of \(L\). Hence, unlike SE, 3D spatial reuse of spectra within each building of small cells improves EE by a decent margin of about 20 times in the steady-state of the EE response with \(L\) as compared to when no 3D spatial reuse of spectra is exploited. In short, exploiting 3D spatial reuse of mmWave spectra within multistory buildings of small cells is a very effective technique to address the expected SE and EE requirements for 6G mobile networks [1,2].
# GeoBERT: Pre-Training Geospatial Representation Learning on Point-of-Interest
###### Abstract  
Thanks to the development of geographic information technology, geospatial representation learning based on POIs (Point-of-Interest) has gained widespread attention in the past few years. POI is an important indicator to reflect urban socioeconomic activities, widely used to extract geospatial information. However, previous studies often focus on a specific area, such as a city or a district, and are designed only for particular tasks, such as land-use classification. On the other hand, large-scale pre-trained models (PTMs) have recently achieved impressive success and become a milestone in artificial intelligence (AI). Against this background, this study proposes the first large-scale pre-training geospatial representation learning model called GeoBERT. First, we collect about 17 million POIs in 30 cities across China to construct pre-training corpora, with 313 POI types as the tokens and the level-7 Geohash grids as the basic units. Second, we pre-train GeoBERT to learn grid embedding in self-supervised learning by masking the POI type and then predicting. Third, under the paradigm of \"pre-training + fine-tuning\", we design five practical downstream tasks. Experiments show that, with just one additional output layer fine-tuning, GeoBERT outperforms previous NLP methods (Word2 vec, GloVe) used in geospatial representation learning by 9.21% on average in F1-score for classification tasks, such as store site recommendation and working/living area prediction. For regression tasks, such as POI number prediction, house price prediction, and passenger flow prediction, GeoBERT demonstrates greater performance improvements. The experiment results prove that pre-training on large-scale POI data can significantly improve the ability to extract geospatial information. In the discussion section, we provide a detailed analysis of what GeoBERT has learned from the perspective of attention mechanisms.  
pre-training; grid embedding; point-of-interest; BERT Article
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 2 Methodology
### A Novel Damage Index: MS-LIE
The MS-LIE provides insight into multi-scale damage features, calculated as  
\[\text{MS-LIE}_{\sigma}(i)=-\frac{Y_{\sigma}(i)(log_{2}Y_{\sigma}(i)-log_{2} \Sigma_{j=-\frac{i}{2}-1}^{i+\frac{5+1}{2}}Y_{\sigma}(j))}{\sum_{j=-\frac{5+2} {2}}^{j+\frac{5+1}{2}}Y_{\sigma}(j)\;log_{2}S} \tag{13}\]  
where \(\text{MS-LIE}_{\sigma}(i)\) is the local information entropy of the \(i\)-th measurement point at the \(\sigma\) scale. In particular, where there are closely spaced multiple damages, the scale parameter should be set not exceeding the damage spacing, to obtain better detection results. In this case, the scale step size can be reduced to obtain more damage evidence in a limited scale range. On the other hand, for high noise scenarios, the scale parameter can be set appropriately high to obtain better noise robustness. To compare the damage characteristics for different scales, the MS-LIE can be processed by a normalization method:  
\[\text{MS-LIE}_{\sigma}^{\text{norm}}(i)=\frac{\text{MS-LIE}_{\sigma}(i)-min( \text{MS-LIE}_{\sigma}(i))}{max(\text{MS-LIE}_{\sigma}(i))-min(\text{MS-LIE} _{\sigma}(i))} \tag{14}\]