esa-sceva/satcom-chunk-collection huggingface dataset:
# Fall Recognition Based on Time-Level Decision Fusion Classification
## 2 Related Work
### Accelerometer-Based Methods  
Some studies aim to detect falls by attaching wearable accelerometers to the body and utilizing the values obtained from these sensors [6; 7; 8; 9]. Ref. [6] compared numerous previous studies that used accelerometers for fall detection. In [7], a triaxial accelerometer was worn on the head to analyze acceleration patterns, and if the threshold was exceeded, it was classified as a fall. In [8; 9], features were extracted from the values obtained from a triaxial accelerometer, and machine learning-based classifiers were employed to detect falls. In [8], an accelerometer was attached to the chest, and its values were used as inputs for a classifier to classify falls and activities of daily living (ADL). The authors compared the outcomes by employing various machine learning-based classifiers. In [9], a pendant-style triaxial accelerometer that can be worn around the neck was proposed. The authors used long short-term memory (LSTM) to detect falls. Using an accelerometer for fall detection can capitalize on the sensitivity of the sensor itself, resulting in a high recall performance, which classifies actual falls as falls. However, when using an accelerometer attached to a limited body area, it may be challenging to reflect the overall body movement accurately, leading to a higher likelihood of misidentifying activities similar to falls as falls. Additionally, the requirement for wearing an accelerometer necessitates user participation and consent, thereby introducing potential constraints on user convenience.
## 3 Probabilistic Topology Control Algorithm
### Transmission Power Adjustment Probability Calculation
When the transmission power adjustment probability has been calculated, the AUVs adjust their transmission power according to this probability. For evaluating the effectiveness of the PTC algorithm, we define the transmission power adjustment ratio for underwater cooperation AUVs network as follows. **Definition 3**. : _The transmission power adjustment ratio is defined as the ratio of the number of AUVs which adjust their transmission power to the total number of AUVs in network, which can be expressed as:_  
\[R_{p}=\frac{\text{AUVs adjust the transmission power}}{\text{total AUVs in the network}} \tag{20}\]  
According to the PTC algorithm, not all the AUVs change their transmission power when the current transmission power does not equal the optimal one. Therefore, we can conclude Theorem 1 as follows. Figure 9: The membership functions of input (**a**) and output (**b**). **Theorem 1**: _The PTC algorithm can reduce the transmission power adjustment ratio greatly._  
According to Equation (19), in PTC algorithm, the transmission power adjustment probability of AUV \(s\) is \(\rho_{s}\). Assuming that there are \(n\) AUVs in network and the number of AUVs which the transmission power does not equal to the optimal one is \(n_{s}\); therefore, the average number of AUVs which adjust their transmission power can be calculated as:  
\[E(n_{s})=\sum\limits_{i=1}^{n_{s}}\rho_{i}, \tag{21}\]  
Then according to Definition 2, the transmission power adjustment ratio of the PTC algorithm can be calculated as:  
\[R_{p}=\frac{E(n_{s})}{n}=\frac{\sum\limits_{i=1}^{n_{s}}\rho_{i}}{n}, \tag{22}\]  
However, in traditional topology control algorithms, once the transmission power does not equal to the optimal one, the AUVs need to adjust their transmission power. Since the number of AUVs which the transmission power does not equal to the optimal one is \(n_{s}\), the transmission power adjustment ratio of the traditional topology control algorithm can be calculated as:  
\[R_{p}=\frac{n_{s}}{n}, \tag{23}\]  
Since \(\rho_{i}<1\), so the \(R_{p}\) in Equation (23) is larger than that in Equation (22); moreover, the smaller \(\rho_{s}\), the smaller \(R_{p}\) is. Therefore, the transmission power adjustment ratio in PTC algorithm is smaller than that in traditional topology control algorithm.
## 2 Methodology
### Description of the Algorithms  
Bio-optical algorithms are designed to estimate the near-surface Chlorophyll-\(a\) concentration from satellite data by using a functional relationship between remotely sensed reflectance data, R\({}_{\mathrm{rs}}(\lambda)\), and modeled Chlorophyll-\(a\) concentration, mChl-\(a\). These algorithms, developed since the 1970s, predominantly use empirical equations [9]. Among the plethora of available algorithms, the NASA Ocean Color website predominantly employs a combination of the O 'Reilly band ratio (OCx) and the Hu Color index algorithm (CI) [10]. In addition to these global algorithms, there are specialized algorithms tailored for the California Current System, such as CalCOFI 2 [24]. This study will evaluate the efficacy of all the aforementioned algorithms within the IMECOCAL region.
## 4 Discussion
The MTT data, however, further reveal that cell growth significantly slowed down when cells were cultured with SrHA or MgSrHA, at least at specific concentrations (Figure 4B,C), as opposed to HA samples, in which cells only showed a moderate and non-significant tendency to a decreased proliferation rate (Figure 4A). Noteworthily, a slower proliferation is consistent with an increase in cell differentiation, as it is known that primary osteoblasts gradually lose their duplication potential as they mature. Furthermore, the difference in protein in HA samples vs. SrHA and MgSrHA is not consistent with the increase in cell number unless the Lowry assay is mostly picking up matrix proteins. Taken together, the data in Figure 4 are consistent with an increase in differentiation and matrix deposition in SrHA and MgSrHA samples. When cultured in the presence of material granules, cells grew along and in close contact with the modified granules, exhibiting similar behavior to what was observed on HA. This behavior is desirable, as cell colonization is the first necessary step for scaffold integration, and the ability of cells to attach and grow along a biomaterial indicates that the biomaterial has promising properties as a scaffold. As composition and surface chemistry have been shown to affect initial cell attachment to biomaterials [30], our findings again point to the high compatibility of the tested granules. Moreover, cells appeared to form a higher number of nodules in the presence of substituted granules (Figure 5), which is a sign of cell differentiation in osteoblast cultures and is again consistent with the MTT and protein results (Figure 4). Interestingly, cell differentiation as assessed by alkaline phosphatase expression was increased in cells growing in contact with substituted HAs (Figure 5). This is compatible with literature showing that Mg and Sr-doped hydroxyapatite and cements increase osteoblast differentiation [31, 32, 33]. The increase in ALP expression was observed both along modified granules and all over the culture plate, indicating that the cellular effects were likely to be mediated by the release of bioactive ions into the culture medium. These results provide evidence supporting the potential of these granules for bone regeneration and a rationale to further investigate sintered scaffolds, as outlined in [PERSON] et al. [11].
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 3 Materials and Methods
### Self- and Cross-Layers  
We applied attention layers in three key modules: FusionBlockR, FusionBlockP, and Self-Cross Layer. By innovatively integrating the effective design originally used for 2D image matching tasks in LoFTR [26] into RGB-D feature matching tasks, we significantly enhance system performance. This improvement not only distinguishes our pipeline from PointMBF [24] but also plays a crucial role in achieving state-of-the-art results.  
For the self-attention layer, the inputs of query (Q), key (K), and value (V) all originate from the same variable, as illustrated in Figure 2b. In contrast, for the cross-attention layer, the Q is derived from the primary features, whereas the K and V inputs come from auxiliary information, which serves to enhance the representation of the primary features, as shown in Figure 2c.  
After obtaining the queries \(\mathbf{Q}\), keys \(\mathbf{K}\), and values \(\mathbf{V}\), these tensors are processed through a Transformer-based attention module, as depicted in Figure 2d. The module first computes pairwise relevance scores between \(\mathbf{Q}\) and all \(\mathbf{K}\) vectors via a _similarity metric_ (detailed mathematical formulation provided in the following paragraph). These scores determine the relative importance of each \(\mathbf{V}\) vector in the sequence. Formally, the final output is generated as a weighted sum of \(\mathbf{V}\), where higher similarity between \(\mathbf{Q}\) and a specific \(\mathbf{K}\) amplifies the contribution of its corresponding \(\mathbf{V}\). This mechanism allows the model to dynamically prioritize contextually relevant information.  
To address the \(O(N^{2})\) computational complexity inherent in standard Transformer attention, we propose a _linear attention variant_, which is illustrated in Figure 2e. Our improvement replaces the conventional softmax-activated dot-product operation with a kernelized similarity function:  
\[\sin(\mathbf{Q},\mathbf{K})=\phi(\mathbf{Q})\cdot\phi(\mathbf{K})^{\top},\quad \text{where $\phi(\cdot)=\text{elu}(\cdot)+1$}.\]  
This reparameterization eliminates the need for row-wise normalization while preserving the attention mechanism's ability to model long-range dependencies, thereby reducing computational overhead to \(O(N)\) without sacrificing expressiveness.
# Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
## 5 Simulation Analysis
### Performance Analysis of the Stackelberg Game Solution Scheme  
#### 5.1.1 Simulation Setting  
Considering IEEE 802.11p communication protocol is used in the IoV, including the V2V and V2R, the transmission distance of RSU does not exceed 1000 m [19]\(R^{R}\)= 1000 m. Thus, the simulation network area is set as 600 m \(\times\) 600 m, with five equally spaced roads from east-west and north-south, all in four lanes. Considering that in the IEEE 802.11p communication protocol followed by the RSU, the transmission distance does not exceed 1000 m [19]\(R^{R}\) = 1000 m. To evaluate the Stackelberg game solution scheme performance, the proposed BCB scheme and the following two baseline schemes are analyzed, as:  
1. **SPRS (Set Price by Response Time Scheme)**: After the RSU sends the request from the buyer to the vehicle, the shorter the feedback time of the vehicle giving priority to trade with the buyer, and the buyer tends to purchase all the data provided by it \(D\). The shorter the vehicle feedback time, the higher the unit price of the data, and the price can be obtained. Prices can be determined by \(P_{SPRS}=\frac{\mu p_{avg}}{t_{ms}}\). Here, \(\mu\) represents the coefficient of adjusting the price, \(t_{ans}\) is the response time of vehicle. Under the scheme, when the seller tends to give feedback faster, the buyer can obtain the data in a shorter time period. In the process of simulation analysis, the response time of the vehicle is set randomly to imitate the feedback situation of each vehicle, which is more consistent with reality;
2. **POS (Purchase from Other Data Providers Scheme)**: The buyer trades the data at the market price \(p_{avg}\) directly, with other institutions.
# Study on Improvement Characteristics of a Novel Geotextile with Stitched Transverse Ribs
## 3 Experimental Results  
### Comparison of Reinforcement Effect of Different Reinforcement Materials  
The relationship curves between shear stress and displacement of the four geotextiles and geogrids obtained in the tests are shown in Figure 5. It can be seen that the shear stress increases with the increase of shear displacement at the beginning of the test. Generally, the shear stress reaches the maximum value when the shear displacement reaches 5-15 mm. The maximum stress value increases with the increase of vertical stress. The curve is flatter, and the peak point appears earlier when the normal stress is small. The position of the peak point is delayed when the normal stress increases. Under normal stress of 150 kPa, the peak shear stress of woven geotextiles A and B is about 20% higher than that of unreinforced geotextiles, and the uniaxial geogrid improved 28% in shear strength.  
The relationship between the maximum shear stress and normal stress obtained from each test is shown in Figure 6. It can be seen that there is an excellent linear relationship between the maximum shear stress and the normal stress, and all of them are in accordance with the Mohr-Coulomb strength criterion. According to Figure 6, the cohesion and friction angle between the interface of the reinforced soil under different reinforcing materials can be obtained, as shown in Table 4.  
It can be seen from Table 4 that the cohesion and friction angle of reinforcement materials except Geotextile D increased compared with those of pure sand; the cohesion and friction angle of the geogrid reinforced structure interface is the largest. Among the four kinds of geotextiles, B has the best reinforcement effect, compared with geotextiles C and D. Its surface is rough enough to provide greater friction, forming an interlocking effect with sand. Compared with the woven geotextile A, B is thicker and more robust. Therefore, geotextile B was selected for subsequent tests.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Groups** & & **Reinforcing** & **Shear Rate** & **Reinforcement** & **Normal Stress (kPa)** \\  & & **Material** & **(mm/min)** & **Method** & **Normal Stress (kPa)** \\ \hline Without reinforcement & I & None & 1 & flattening & 25, 50, 100, 150 \\  & II & Geotextiles A & 1 & flattening & 25, 50, 100, 150 \\ Conventional geotextiles & III & Geotextiles B & 1 & flattening & 25, 50, 100, 150 \\  & IV & Geotextiles C & 1 & flattening & 25, 50, 100, 150 \\  & V & Geotextiles D & 1 & flattening & 25, 50, 100, 150 \\ Geogrid & VI & Uniaxial geogrid & 1 & flattening & 25, 50, 100, 150 \\  & VII & Geotextiles B & 1 & stitching 1 rib & 25, 50, 100, 150 \\ Novel geotextiles with stitched transverse ribs & VIII & Geotextiles B & 1 & stitching 2 ribs & 25, 50, 100, 150 \\  & IX & Geotextiles B & 1 & stitching 3 ribs & 25, 50, 100, 150 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic physical parameters of geosynthetics.  
Figure 5: Shear stress–displacement curves for different geosynthetics.
# Long-Term Creep Mechanical and Acoustic Emission Characteristics of Water-Immersed Coal Pillar Dam
## 3 Results and Discussion
### Instantaneous Loading Modulus  
During loading, to investigate the time-dependent damage characteristics of water-immersed coal-rock under long-term creep deformation, we measured the loading modulus via instantaneous unloading-reloading cycles. Since unloading/reloading beyond the rock's long-term strength may induce damage [34], the unloading magnitude was controlled to minimize impacts on time-dependent mechanical properties. Specifically, after sustained loading, specimens were instantaneously unloaded by 1 MPa and then reloaded to original stress levels while monitoring modulus evolution.  
Figure 6 shows the loading modulus variation curves of rock during loading/unloading for natural and water-immersed coal samples. During the primary creep stage, the loading modulus increased for both, but the mechanisms differed: For the natural coal sample, in the early loading stage, pre-existing microcracks within the coal sample gradually closed under sustained load, increasing the contact area between particles and enhancing frictional resistance, thereby improving resistance to deformation [35; 36]. This reflects the strain hysteresis of viscoelastic deformation, which gradually equilibrates over time, exhibiting hardening characteristics [37]. For water-immersed coal samples, during the primary creep stage, water's lubricating effect further promotes the re-interlocking of fracture surfaces after slippage, accelerating crack closure [38].  
During the steady-state creep stage, the loading modulus of both water-immersed and natural coal samples remained within a certain range. Comparing the steady-state creep stage, the loading modulus of the natural sample was larger than that of the water-immersed sample. The loading modulus of the natural sample was approximately 2300 MPa, while that of the water-immersed sample was approximately 2037 MPa. This indicates that for the Daliuta Coal Mine in the Shendong Mining Area, long-term water immersion reduces the mechanical performance of coal-rock by approximately 11.4%.  
Figure 6: Loading modulus variation curves under both natural and water-immersed conditions.
## 5 Short-Baseline Instantaneous RTK Performance with Smartphones in Lying Down and Upright Positions
### Instantaneous RTK Positioning Performance for Short-Baselines with External and Internal Antennas
In Figure 10a, we again see a poor fit of the formal and empirical confidence ellipses and intervals when the smartphones are lying down, whereas when the smartphones are in an upright position, in Figure 10b, they fit each other very well. The S20 smartphones achieve an instantaneous ILS SR of 64.4% when the smartphones are in an upright position (Figure 10b), which decreases to 27.5% when they are lying down (Figure 10a). Moreover, now we can see a decrease in the number of tracked satellites for GP4 when the smartphones are lying down (cf. Table 4). This is possibly due to the internal GP4 GNSS data logger that removes low-quality raw GNSS measurements more consistently with the more multipath-sensitive internal antennas used. Nevertheless, the consequence of having the GP4 smartphones in an upright position increases the ILS SR from 63.1% in Figure 10a and the left column, to 98.7% ILS SR when the GP4 smartphones are in an upright position in Figure 10b. These results are important, as they show that the GP4 smartphones can provide for close to 100% (98.7%) availability of mm-cm level short-baseline positioning precisions, even when internal antennas are used. Figure 10: **Short-baseline with internal antennas and smartphones in a (a) lying down and (b) upright position (cf. Figure 4): Horizontal (N, E) positioning scatter (first row) and corresponding vertical (U) positioning error time-series (second row) for 3.5 h of data (cf. Table 2) with GP4 (left column) and S20 (right column). In all RTK models GPS + Galileo + QZSS + BDS L1 + L5, E1 + E5a, L1 + L5, B1 observations have been used for GP4, except for S20 in (**b**) that did not have more than one QZSS satellite to formulate the DD observations. A zoom-in window is shown to depict the two order of magnitude improvement when going from incorrectly fixed (red dots) and ambiguity-float (grey dots), to correctly fixed solutions (green dots). The 95% empirical and formal CIs and ellipses for the correctly fixed solutions are shown as blue and red lines, respectively. The number of visible satellites and the correctly fixed formal Up STDs (bottom row) are shown as black and green lines, respectively. (**a**) Smartphones used in lying down positions: with GP4 (left): ILS SR 63.1% and S20 (right column): 27.5% (internal antennas). (**b**) Smartphones used in upright positions: with GP4 (left): ILS SR 98.7% and S20 (right column): 64.4% (internal antennas).
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 4 Results and Discussion  
### xLSTM Results for Wind Energy Forecasting  
#### 4.1.1 Model Evaluation Criteria  
The experimental validation used a substantial SCADA dataset following preprocessing and outlier removal procedures. After removing 3214 anomalous entries from the initial 50,530 data points, the analysis proceeded with 47,316 valid SCADA measurements. To ensure robust model evaluation, these measurements underwent systematic partitioning: 80% of the seasonal data were allocated for training the xLSTM model, while the remaining 20% were reserved for independent validation purposes. This deliberate segregation of training and validation sets facilitated a comprehensive assessment of the xLSTM model's predictive capabilities across different seasonal patterns and operational conditions.  
Multiple evaluation metrics were implemented to systematically assess the xLSTM model's capability in predicting wind power output based on SCADA data. These criteria comprehensively evaluate the xLSTM model's performance across different operational conditions and temporal scales. The mean absolute percentage error (MAPE) quantifies the average percentage deviation between predicted and actual wind power outputs:  
\[\epsilon=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{P_{i}-\hat{P}_{i}}{P_{i}}\right| \times 100\%, \tag{21}\]  
where \(P_{i}\) represents the actual power output recorded in SCADA data, \(\hat{P}_{i}\) denotes the xLSTM-predicted power value, and \(n\) indicates the total number of SCADA measurements in the validation dataset. The root mean square error (RMSE) measures the standard deviation of power prediction residuals:  
\[\xi=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(P_{i}-\hat{P}_{i})^{2}}. \tag{22}\]  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Value** & **Description** \\ \hline Input Size & 1 & Dimension of input features \\ Head Size & 32 & Dimension per attention head \\ Number of Heads & 4 & Parallel attention mechanisms \\ Projection Factor & 2.0 & Matrix memory expansion ratio \\ Matrix Memory Dim & 128 & Dimension of matrix memory \\ Batch Size & 32 & Training batch size \\ Learning Rate & 0.01 & Initial learning rate for optimization \\ \hline \end{tabular}
\end{table}
Table 4: Hyperparameter Configuration for mLSTM.  
For real-time SCADA-based monitoring applications, the mean absolute error (MAE) calculates the average magnitude of power prediction errors:  
\[\sigma=\frac{1}{n}\sum_{i=1}^{n}|P_{i}-\hat{P}_{i}|. \tag{23}\]  
The coefficient of determination (\(R^{2}\)) evaluates how effectively the xLSTM model captures variations in wind power output:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(P_{i}-\hat{P}_{i})^{2}}{\sum_{i=1}^{n}(P_{i}-\hat{ P})^{2}}, \tag{24}\]  
where \(\hat{P}\) represents the mean of actual power measurements from SCADA data.