esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Characteristics of the Contributions of LPRO's Elements  
#### 3.3.1 Spatial Pattern of the Contributions of LPRO's Elements  
Based on the modeling results of the quantitative assessment of LPRO's macro effects and the screened explanatory indicators, this study proportionally selected representative indicators from different links of the LPRO system. These include the distance to the city center, the scale of public management land, and the land price in the transfer link; the level of advanced utilization in the utilization link; and the land marketization level, along with the establishment of the upper and lower limits on the FAR, in the supervision link. These indicators were used to reveal the spatial variations in the role of LPRO's elements in influencing its macro effects (Figure 6).  
Specifically, the distance to the city center exerts a negative driving influence on LPRO's macro effects, and its intensity gradually decreases from the eastern coastal area to the northeastern and northwestern inland areas. The transfer scale of public management purposes positively affects LPRO's macro effects, with the influencing intensity continuing to decrease in a circle centered on China's southeastern coastal area. The land price has positive promoting effects on LPRO's macro effects, with the intensity being strong in the northeastern and southwestern areas and weak in the central area. The degree of intensity of the promoting effects of the advanced utilization level is greatest along the southeast coast, followed by the east coast, and continues to decrease toward the northeastern and western inland areas. The land marketization level has a positive driving effect, and its intensity gradually decreases from west to east; both setting the upper and lower limits on the FAR and setting the lower limit of the FAR play positive roles in realizing LPRO's macro effects, but the former is strong in northeastern and northwestern areas and gradually weakens in southwestern areas, and the latter is strongest in the southwest and then continues to decrease from the southeast to the northwest.
## Materials and Methods
### Assimilation and Validation Datasets
#### 2.5.2 TIP-FAPAR Dataset  
In the assimilation experiments, we used FAPAR data (16-day resolution) derived from the Joint Research Center with a Two-stream Inversion Package (JRC-TIP) [44]. The JRC-TIP FAPAR product was derived by the JRC-TIP algorithm from MODIS 0.01-degree albedo data [45]. A quality assurance was performed at two steps in the FAPAR retrieval algorithm. First, the MODIS broadband albedo product comes with a quality flag and low-quality input albedos were discarded before the JRC-TIP was applied. Second, dubious retrieval results were detected and if possible, improved in an iterative procedure [46]. Uncertainties for the FAPAR data were derived from the MODIS input albedo uncertainty by error propagation [47]. For more detailed information on the uncertainty propagation, we refer to the related papers [48].  
We added an extra uncertainty of 0.05, taking into account very sparse observations (every 16-days). The total uncertainty of the FAPAR data was calculated as:  
\[\sigma_{FAPAR}=\sqrt{\sigma_{data}^{2}+0.05^{2}} \tag{6}\]  
where \(\sigma_{data}\) is the uncertainty of the product. \(\sigma_{FAPAR}\) is the FAPAR uncertainty used in the assimilation. A similar bias correction for the soil moisture observations (Equation (5)) was done for the JRC-TIP FAPAR product.
# Multi-Label Weighted Contrastive Cross-Modal Hashing
## 4 Experiment
### Evaluation Metrics  
To objectively evaluate the performance of our proposed method and compare it with the baseline methods, two frequently used cross-modal hashing evaluation protocols, i.e., Hamming ranking and hash lookup [69], are utilized in our experiments. The former ranks samples in the retrieval set by their Hamming distance to the query in ascending order, while the latter retrieves samples within a certain Hamming radius from the query [43]. The mean average precision (MAP) is used to measure the accuracy of the Hamming ranking protocol, while precision-recall curves (PR curves) are commonly used to measure the accuracy of the hash lookup protocol. Given a query \(\mathbf{q}_{k}\), the AP score of top \(n\) results is calculated by  
\[AP(\mathbf{q}_{k})=\sum_{i=1}^{n}\frac{\mathcal{I}(i)}{N}\sum_{j=1}^{i}\frac{ \mathcal{I}(j)}{i}, \tag{33}\]  
where \(\mathcal{I}(i)\) is an indicator function; if the \(i\)th retrieved sample is similar to the query, i.e., sharing at least one common category with the query, \(\mathcal{I}(i)=1\); otherwise, \(\mathcal{I}(i)=0\). \(N\) denotes the number of relevant samples in the returned top \(n\) samples. MAP is the average of APs for all queries:  
\[MAP=\frac{1}{K}\sum_{k=1}^{K}AP(\mathbf{q}_{k}), \tag{34}\]  
where \(K\) is the size of the query set.  
Moreover, during evaluation, an image \(\mathbf{v}_{i}\) and a text \(\mathbf{t}_{j}\) will be treated as a similar pair if they share at least one common label.
## 2 Trajectory Generation
### Closed-Form Local Trajectory Replanning
This steep trajectory induces trajectory error, and this could cause the quadrotor to fail to navigate, depending on the scale of the error.To prevent this, we add a smoothing process. The process is shown in Figure 4. Moreover, we considered a dynamically feasible trajectory. From [PERSON]'s equation and differential flatness property, the quadrotor's thrust (\(u_{1}\)) value can be expressed as follows. \[u_{1}=m\sqrt{\tilde{v}_{1}^{2}+\tilde{v}_{2}^{2}+(\tilde{v}_{3}^{2}+g)^{2}} \tag{26}\]  
where \(\tilde{v}_{1},\tilde{v}_{2},\tilde{v}_{3}\) are the acceleration of the quadrotor in the \(x\), \(y\), and \(z\) axes. \[u_{1}\leq u_{max} \tag{27}\]  
\[u_{max}=4\cdot k_{F}\cdot\Omega_{max}^{2} \tag{28}\]  
where \(u_{max}\) is the maximum thrust, \(\Omega_{max}\) is the maximum rotor speed, and \(k_{F}\) is a thrust coefficient. The relationship is in quadratic form.If the \(u_{1}\) is greater than the maximum input, we add some waypoints to slow down the acceleration. The required force (the input of the quadrotor) can be reduced by adding smoothing waypoints, as shown in Figure 5.  
In summary, the local trajectory is safe from a collision and has real-time computation speed. Because the trajectory uses a polynomial, it satisfies kinematic constraints by differentiating the polynomial and guaranteeing continuity. Additionally, the trajectory satisfies dynamic constraints by applying a differential flatness property. ```
1:n1 and n2 are normal vectors of waypoints. 2:\(\theta\) is the angle between the normal vectors of successive waypoints. 3:\(cos\theta=\frac{\mathbf{n_{1}}\cdot\mathbf{n_{2}}}{\|\mathbf{n_{1}}\|\mathbf{ n_{2}}\|}\)
4:if\(\theta>\frac{\pi}{2}\)then
5:\(\mathbf{n_{2}}=-\mathbf{n_{1}}\)
6:endif
```  
**Algorithm 1** Replanning waypoints. Figure 4: Smoothing trajectory. Figure 3: Trajectory replanning.
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### Future Development Potential of Blockchain-Based Data Storage Systems  
Blockchain-based data storage methods, particularly through the integration of on-chain, off-chain, and hybrid models, have the potential to revolutionize data management in the future [16; 37]. While on-chain methods provide high security and data integrity, they also face significant challenges related to cost and scalability [2; 55]. Off-chain solutions, on the other hand, enable the cost-effective and efficient storage of large datasets but require secure integration with blockchain to maintain data verifiability and protection [10; 77]. Hybrid models, by combining these two approaches, offer a strategic balance between security and performance, making them a highly viable alternative for scalable blockchain applications [46; 101].  
Scalability improvements like sharding, rollups, and plasma can enhance blockchain data processing efficiency [18; 19; 52]. Innovative cryptographic methods like ZKP and homomorphic encryption will boost data privacy and access control [109; 110]. The seamless integration of these innovations in sectors such as healthcare, finance, and IoT will facilitate the secure storage, processing, and exchange of sensitive data, further expanding the applicability of blockchain-based storage solutions [101; 102; 103; 104; 108].  
However, several key challenges must be addressed to ensure the widespread adoption of these systems. Hybrid models, while offering flexibility and scalability, require complex infrastructure and significant cost investments for seamless integration of on-chain and off-chain data processing workflows. Additionally, the lack of regulatory frameworksand standardization poses major compatibility and compliance issues for sector-specific applications [107; 111]. Furthermore, the dual objective of enhancing data security while minimizing energy consumption and transaction costs remains a critical research focus in blockchain-based solutions [97; 112]. Successfully overcoming these barriers will accelerate the sectoral adoption of blockchain-based data storage systems and unlock the full innovative potential of distributed ledger technology in data management.
## 3 Materials
### Feature Extraction
#### 3.2.8 Mel-Frequency Cepstral Coefficients  
MFCCs are the most popular features for recognizing human speech. In 1980, [PERSON] and [PERSON] brought a representation of the approximate structure of the human vocal tract system in which MFCCs accurately describe the system's shape in the short-time power spectrum.  
First, the Hamming window splits speech signals into frames of 25 ms with an overlap of 10 ms, and then a fast Fourier transform is utilized to acquire the power spectrum of each frame. Finally, DCT is applied to the logarithmically transformed spectrum to obtain MFCCs. The entire frequency range is divided into \(n\) mel filter banks, as shown in Equation (2).  
\[c(n)=\sum_{k=1}^{K}{(logS_{k})cos[n(k-\frac{1}{2})\frac{\pi}{K}]} \tag{2}\]  
where \(s_{k}\) denotes the output of the k-channel filter bank, and \(n\) represents the index of mel cepstral coefficients.
## 4 Results
### Monitoring of the RCA and Calibration Bias
#### 4.1.2 Monitor the Antenna Pointing  
The dMedian reflects the change in the 50 th percentile of \(\text{Z}_{\text{c}}\). The dMedian is more sensitive to change in the distribution of \(\text{Z}_{\text{c}}\) than RCA which reflects the change in the 95 th percentile of \(\text{Z}_{\text{c}}\). So, the dMedian was used to determine the change in antenna pointing. Figure 8 shows the time series of RCA and dMedian for KWK radar from 0000 KST 1 June 2020 to 0000 KST on 31 June 2020 (Figure 8a) and PPIs of \(\text{Z}_{\text{H}}\) at an elevation angle of \(-\)0.2\({}^{\circ}\) of KWK radar at 1 June 2020, 1100 KST (Figure 8b), 10 June 2020, 1100 KST (Figure 8c), and 25 June 2020, 1100 KST (Figure 8d). The number of GCs was significantly reduced during P2. The RCA and dMedian of KWK greatly changed at the end time of P1 due to an elevation angle offset problem. Also, the RCA fluctuations during P2 was considerably larger than those during P1 and P3 due to the pointing problem along the elevation angle. The antenna pointing was reset at the end of P2. The number of GCs in Figure 8d is similar to that in Figure 8b. The RCA difference between P1 (\(-\)0.11 dB) and P3 (\(-\)0.59 dB) was due to the slight change in the transmitted power.  
Figure 9a shows the RCA and dMedian for the MYN radar from 0000 KST 1 June 2020, to 0000 KST 31 June 2020. The MYN radar was shut down on 0845 KST on 4 June 2020, for routine inspection. The \(\text{Z}_{\text{C},50\text{th}}\) significantly changed when the radar restarted operation (at 1840 KST, 4 June 2020). Figure 9b and c show the PPIs observed at the MYN radar at an elevation angle of \(-\)0.8\({}^{\circ}\) on 0845 KST and 1840 KST on 4 June 2020. The location of the strong GC with a \(\text{Z}_{\text{H}}\) of more than 50 dBz rotated slightly in a counterclockwise direction.  
Figure 8: Example of elevation angle slip issue. (**a**) Time series of RCA (blue) and dMedian (black) for MYN radar from 0000 KST 1 June 2020 to 0000 KST 31 June 2020. The black and blue dots represent the RCA and dMedian. PPIs of ZH at the elevation angle of \(-\)0.2\({}^{\circ}\) of KWK radar at (**b**) 1 June 2020 1100 KST (P1), (**c**) 1 June 2020 1100 KST (P2), and (**d**) 25 June 2020 1100 KST (P3).  
The dMedian was significantly changed during P2 because of the rotation of the azimuth angle, while the RCA slightly changed (approximately 1 dB).
## 4 Backgrounds and Methods
### Backgrounds
#### 4.1.4. Handover Self-Optimization Related Works  
To minimize the oscillations of the HOs by the TTT and HO margin, parameters are optimized in [19]. Another oscillation-focused scheme is also proposed in [20]. The proposed scheme adjusts the TTT and HH to minimize the number of RLFs. In [21], an algorithm that finds the best HH and TTT combination, which serves to improve the HO performance, is presented. In the proposed algorithm, a weighting HPI function that includes the ping-pong HO, HO failures and dropped calls is defined. The HO parameters of individual HPIs are also weighted in order to reduce the number of HOFs, HPPs and RLFs [18]. In [22], the focus is on UE speed-based mobility robustness optimization, which involves optimized HH and classified UE speed. Simulation results are presented for cases in which TTT and CIO are constant for all users; however, each cell has its own CIO.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 3 Methodology
### Keypoint Representation and Coordinate Extraction  
The network predicts the location of each keypoint (\(k\)) as a dense heatmap, which is a part of the tensor \(H_{pred}=\psi_{c}(\phi(I))\) for an input image (\(I\)) of class \(c\). Higher values in the heatmap \(H_{pred,k}\) indicate a higher probability of the keypoint \(k\) being present at that location. To obtain differentiable keypoint coordinates (\(P\in\mathbb{R}^{8\times K\times 2}\)) from these heatmaps (\(H_{pred}\)), we employ the spatial softmax operation [31]. Unlike simple non-differentiable peak-finding methods (such as finding the argmax location), the spatial softmax computes a weighted average (center of mass) over the heatmap, providing continuous, sub-pixel-precise, and, crucially, differentiable coordinates. This differentiability is essential for the self-supervised learning framework, as it allows gradients from the downstream losses to flow back through the coordinate prediction process, enabling the model to learn accurate and distinct keypoint locations without manual supervision and preventing keypoint collapse.  
The extraction of the coordinates involves the following two steps:  
1. **Probability Calculation:** Each keypoint heatmap (\(H_{pred,k}\)) is normalized to a spatial probability distribution (\(p_{k}(h,w)\)), using the softmax function \[p_{k}(h,w)=\frac{\exp(H_{pred,k}(h,w))}{\sum_{h^{\prime}=0}^{H_{pred,k}}\sum_{ w^{\prime}=0}^{W_{pred,k}}\exp(H_{pred,k}(h^{\prime},w^{\prime}))}\] (1) where \(H_{pred,k}(h,w)\) is the predicted heatmap value for keypoint \(k\) at spatial location (\(h,w\)), \(H_{out}\) and \(W_{out}\) are the height and width of the heatmaps;
2. **Expected-Coordinate Calculation:** The continuous coordinate (\(x_{k},y_{k}\)) for keypoint \(k\) is computed as the expected value (center of mass) over this probability distribution as follows: \[x_{k}=\sum_{w=0}^{W_{out}-1}\sum_{h=0}^{H_{out}-1}p_{k}(h,w)\cdot w\] (2) \[y_{k}=\sum_{h=0}^{H_{out}-1}\sum_{w=0}^{W_{out}-1}p_{k}(h,w)\cdot h\] (3) These coordinates are calculated for each keypoint (\(k\)) and each image (\(b\)) in the batch, resulting in \(P_{b}=\{(x_{k},y_{k})\}_{k=1}^{K}\). The coordinates lie within the heatmap grid, \(x_{k}\in[0,W_{out}-1]\) and \(y_{k}\in[0,H_{out}-1]\). This coordinate range reflects that in our experimental setup, the visual elements of interest are consistently contained within the boundaries of the input image.  
Both the heatmaps (\(H_{pred}\)) and the derived coordinates (\(P\)) are used in the loss calculations, forming the basis for the self-supervised training signals. The choice of the heatmap resolution factor (e.g., \(H_{in}/4\times W_{in}/4\)) in this work was primarily made for experimental simplicity, allowing for the demonstration of the concept, with the potential for exploring other resolutions in future work.
# Assessing Voided Reinforced Concrete by Numerical Modelling of Impact-Generated Rayleigh Waves
## 1 Introduction
For example, a wave propagates faster in high-density materials, and steel reinforcement will guide the wave. Different defects, such as cracks and voids, reflect, refract and scatter the propagating wave, which results in more complex wave behaviour. R-wave-based detection methods are effective in the characterisation of near-surface defects. For defects located deeper under the surface, the method is limited by the R-wave propagation depth. This limitation of the method can be addressed by analysing the penetration depth of the R-wave. Numerical models help to simulate numerous combinations of parameters, such as wave frequency and defect properties. Thus, numerical models are helpful for investigating changes in R-wave properties against different defect characteristics. Correlations can be established by investigating the interaction between the wave and the defect, providing practical guidance on defect characterisation and further assessing the suitability of R-wave-based detection methods. Current approaches to inspecting concrete structures through non-destructive methods struggle to identify internal voids correctly because these defects detrimentally affect structural stability. The existing ultrasonic testing technique for void characterisation requires specialised instrumentation because it fails to detect subtle void variations properly. This investigation fills the current research gap by studying the appropriate use of R-wave properties to characterise void concrete in the form of low-density honeycombs. Specifically, the novelty of the work presented in this paper lies in the development of a simple yet practical 2D finite element model to simulate the propagation of R-waves in reinforced concrete that contains internal honeycombs, which are not often visible from the exterior. The numerical model was calibrated and validated with physical measurement data before being employed to study the influence of different honeycomb configurations on the propagation behaviour of R-waves derived from different excitation frequencies. An analysis of simulated wave data was carried out in the time and frequency domains, and simple correlations between R-wave velocity were developed to quantify changes with the density of honeycomb.
## 2 Materials and Methods  
### Study Site  
The Selva Maya is Mesoamerica's largest contiguous tropical forest, covering 100,000 km and extending from southern Mexico to northwestern Belize and northern Guatem. Within the Selva Maya, in the centre of the Yucatan Peninsula, is the Bala'an Ka'ax Flora and Fauna Protected Area (BKFFPA), covering 128,390.16 hectares with an area of influence of 230,552.6 ha (19\({}^{\circ}\)19\({}^{\prime}\) N-89\({}^{\circ}\)03\({}^{\prime}\) W) (Figure 1). The BKFFPA forms part of the Sian Ka'an-Calakmul Biological Corridor encompassing four regions across three states in the Yucatan Peninsula: Hopelchen in Campeche; Bacalar and JMM in Quintana Roo; and Tekax in Yucatan. Within the BKFFPA are 37 ejidos, and conservation efforts are led by the National Commission of Natural Protected Areas (CONANP) in coordination with community rangers made up of residents of the ejidos within the area of influence of the reserve. Our study focused on communities belonging to JMM (19\({}^{\circ}\)14\({}^{\prime}\)-88\({}^{\circ}\)24\({}^{\prime}\), 20\({}^{\circ}\)19\({}^{\prime}\)-89\({}^{\circ}\)19\({}^{\prime}\)). Jose Maria Morelos has a population of 39,165 inhabitants (49.3% females and 50.7% males), and 31% are between 0 and 14 years old. Jose Maria Morelos has a great ethnic diversity, with the Maya, Ch'ol and Mam being the most represented indigenous groups. Fifty-seven per cent of the population is moderately poor, and 25.1% is extremely poor, with low levels of formal education (29.5% with 1 to 6 years of schooling, 37.8% with 6 to 9 years and 20.5% with 10 to 12 years) [30]. The primary economic activities in the communities surrounding the BKFFPA include agriculture through the traditional slash-and-burn system (milpa), timber harvesting, beekeeping and, to a lesser extent, cattle ranching [32]. Subsistence hunting remains an essential cultural and livelihood practice in the region. Over the past decade, a Mennonite community has settled near the BKFFPA, resulting in approximately 1000 ha of forest being deforested.
## 3 Preliminary Optimal Design Method
### Empirical Models  
Four types of models are applied in the preliminary design phase:  
1. Flow loss, which is twofold, i.e., shock loss and profile loss: \[\overline{\omega}=\overline{\omega}_{\text{s}}+\overline{\omega}_{\text{p}},\] (6) where \(\overline{\omega}\) is total pressure loss coefficient; subscript s and p denote shock loss and profile loss, respectively.
2. Incidence angle,
3. Deviation angle,
4. Aerodynamic blockage.  
Profile loss was calculated by [PERSON]'s model [24]. The effects of inlet Mach number, Reynolds number, and annulus area contraction were revised by the method presented by [PERSON] and [PERSON] [26]. [PERSON] [27] derived a simple but reasonable shock loss correlation, which was applied in the present study. The incidence angle model proposed by [PERSON] [28] is widely used and also recommended in NASA report [29]. This model was applied here for subsonic NACA 65-series airfoils. [PERSON]'s minimum loss suction incidence angle model was adopted [27] for DCA profiles. The deviation angle models proposed by [PERSON] [28] for both of the subsonic NACA 65-series airfoils and transonic DCA airfoils were employed. The aerodynamic blockage correlation developed by [PERSON] [30] is used to properly match stage flow. A comprehensive estimation of these popular empirical models was given in our previous work [7].