esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
###### Abstract  
With the rapid development of earth-observation technology, the amount of remote sensing data has increased exponentially, and traditional relational databases cannot satisfy the requirements of managing large-scale remote sensing data. To address this problem, this paper undertakes intensive research of the NoSQL (Not Only SQL) data management model, especially the MongoDB database, and proposes a new approach to managing large-scale remote sensing data. Firstly, based on the sharding technology of MongoDB, a distributed cluster architecture was designed and established for massive remote sensing data. Secondly, for the convenience in the unified management of remote sensing data, an archiving model was constructed, and remote sensing data, including structured metadata and unstructured image data, were stored in the above cluster separately, with the metadata stored in the form of a document, and image data stored with the GridFS mechanism. Finally, by designing different shard strategies and comparing MongoDB cluster with a typical relational database, several groups of experiments were conducted to verify the storage performance and access performance of the cluster. The experimental results show that the proposed method can overcome the deficiencies of traditional methods, as well as scale out the database, which is more suitable for managing massive remote sensing data and can provide technical support for the management of massive remote sensing data.  
remote sensing data; MongoDB; data management; sharding technology; GridFS mechanism +
Footnote †: journal: _Geo-Information_  
1
Footnote 1: [[https://www.mdpi.com/journal/ijgi](https://www.mdpi.com/journal/ijgi)]([https://www.mdpi.com/journal/ijgi](https://www.mdpi.com/journal/ijgi))
## 5 Limitations and Future Work  
This study proposed an AI-based prototype model for recognizing Korean traditional dance movements, focusing on five fundamental movements. However, several limitations must be addressed to advance this research further.  
First, although the established classification framework includes 36 traditional dance movements, metadata was created for only 13 movements due to time constraints, and the current model was trained on five movements. Expanding the dataset to include all 36 movements is essential for comprehensive recognition.  
Second, the current AI model is trained on a limited number of samples, which may reduce its ability to generalize across diverse or modified movement patterns. Increasing both the quantity and diversity of the training data, including different dancers, styles, and recording conditions, is critical to improving model robustness and performance.  
Third, this study primarily focused on developing an initial prototype, and real-time performance was evaluated in controlled environments with limited testing under varied conditions. Future research should include extensive testing in real-world environments with varying lighting, backgrounds, and occlusions to enhance the system's adaptability and accuracy.  
To overcome these limitations, future work will involve designing a more sophisticated AI model to capture both the spatial and temporal features of dance movements. Specifically, the integration of ResNet for stable feature extraction and Gated CNN for selectively emphasizing relevant information will be explored. Additionally, incorporating 3D CNNs or LSTM-based temporal modeling techniques will allow the model to learn the sequential flow and interdependencies between movements more effectively.  
Further efforts will focus on expanding the dataset through advanced data augmentation techniques and detailed labeling, enabling the recognition of diverse movement styles and regional characteristics. A comprehensive recognition system encompassing all 36 movements will ultimately be developed.  
Finally, a real-time recognition and analysis platform will be proposed to facilitate practical applications, such as educational tools for Korean traditional dance and systems for the digital preservation and transmission of cultural heritage. By advancing movement recognition technology, this research aims to enhance both the preservation and contemporary utilization of traditional arts.
###### Abstract  
To consider the influence of material parameter uncertainty on the structural deformation of a dam effectively and to establish a reasonable and reliable safety monitoring index for the displacement of a rockfill dam, a method for determining the displacement monitoring index of a rockfill dam based on stochastic finite element analysis is proposed in this paper. Firstly, uncertainty in the mechanical parameters of the rockfill material is simulated via the correlation log-normal random field, and the statistical characteristics of the dam displacement under the stability of the resultant distribution are obtained through several structural analyses, thus constructing a stochastic finite element method-based monitoring model (SFEMM model); subsequently, the boundary values of the water pressure component are determined based on the statistical characteristics of the displacement at different water levels, and the displacement monitoring index is determined by inputting it into the SFEMM model. Finally, the index is applied to the actual panel rockfill dam project. Finally, the method is applied to the actual concrete-face rockfill dam project. The results show that the SFEMM model achieves higher prediction accuracy and stability than other monitoring models, with the relative error lower than 4.7% and the correlation coefficient higher than 0.96, and the monitoring index is accurate and reasonable. This method provides a scientific and reliable new idea for the safety monitoring of rockfill dams.  
rockfill dam; material uncertainty; stochastic finite element method; dam safety monitoring; monitoring index +
Footnote †: journal: Applied Sciences  
Article
## 2 Calcium Leaching Model
### Pore Parameter Evolution Equation  
#### 2.3.1 Kozeny-Carman Equation  
The Kozeny-Carman (KC) equations are the best known semi-empirical formulation in the field of percolation and they have been adopted extensively in the simulation of permeability coefficient. Considering the effects of porosity, pore internal surface area, and distortion, the permeability coefficient of cement-based materials can be described by the KC equation in Equation (3).  
\[K=\frac{\varphi^{3}}{c(1-\varphi)^{2}S^{2}} \tag{3}\]  
where \(K\) means the permeability coefficient of cement-based material; \(\varphi\) is the porosity; \(c\) denotes the Kozeny-Carman constant; and \(S\) represents the specific surface area of the solid phase.  
The parameter \(c\) in the KC equations can be replaced by \(\tau^{2}F_{s}\) for simplifying the effect of pore shape and degree of distortion. The expression is given in Equation (4).  
\[\left\{\begin{array}{l}\Omega=\frac{1}{\tau^{2}F_{s}}\\ \Omega_{0}=\frac{1}{\eta}\Omega_{I}\\ \Omega=\Omega_{0}-(\Omega_{0}-\Omega_{I})d_{I}^{2}\end{array}\right. \tag{4}\]where \(\Omega_{0}\) and \(\Omega_{l}\) denote the set total term for undissolved and dissolved material, respectively; \(\tau\) is the tortuosity; \(F_{s}\) stands for the shape factor; \(n\) means the lumped term increased times; \(d_{l}\) represents the leaching degree. The leaching degree is given as Equation (5).  
\[d_{l}=\left\{\begin{array}{cc}1&C_{CH}=0\\ \frac{C_{CH}}{C_{CH}^{0}}&C_{CH}>0\end{array}\right. \tag{5}\]  
where \(C_{CH}^{0}\) and \(C_{CH}\) denotes the initial and current concentration of CH, respectively.
## Materials and Methods
### Experimental Procedures
#### 2.4.4 Calibration Set and Validation Set  
Before the modeling, the samples needed to be grouped. One group, called the calibration set, was used for the establishment of the models, and the other group, called the validation set, was used to test the predictive ability of the models. In this study, 92 soil samples were collected from the Honghu and Daye study areas. Nineteen calibration samples and 10 validation samples were selected in the Honghu area, and 41 calibration samples and 22 validation samples were selected in the Daye area. Referring to the _Soil Environmental Quality Standards GB15618-1995_ released by the Ministry of Environmental Protection of the People's Republic of China [29], the average value for the Honghu area exceeds the third-class standard (the critical value of soil for the protection of agricultural and forestry production and normal plant growth), and the Honghu area belongs to the polluted area category. The average value for the Daye area is lower than the first-class standard (the limit for soil quality that protects the natural ecology of the area and maintains the natural background), and the Daye area belongs to the unpolluted area category. The difference between the mean, standard deviation, and coefficient of variation of the calibration sets and verification sets is small [30; 31]. Therefore, this division can be considered as reasonable and can be used for subsequent modeling. The statistical descriptions of the As concentrations in two study areas are given in Table 1.
## 4 3D Integral Imaging Display Experimental Results
From left to right, the images represent (i) the display result with the original object parameters, where the depth range remains 425 mm; (ii) the display result after rescaling the depth from 425 mm to 100 mm along the z-axis, without scene resizing; and (iii) the display result after combining depth rescaling (from 425 mm to 100 mm along the z axis) with scene resizing on the x-y axis using a factor of 0.35. Figure 10b shows the five different viewpoints results for the real 3D scene. By applying the depth rescaling process, the 3D images can be displayed within the depth range of the display system. Due to the use of multiple reference planes that better align with the object's depth during the depth rescaling process (as discussed in Section 2), the 3D display demonstrates improved viewing quality. Specifically, in the computer-generated integral imaging group, the box and pyramid maintain clearer details in the reconstructed 3D image, as shown in Figure 9a(iii). In contrast, with the conventional SPOC method, which uses a single reference plane, both the box and the pyramid appear with lower viewing quality--surface features, such as numbers and letters, are lost, as shown in Figure 9a(i). For the real 3D scene group, the clarity and structural details of the bear toy and the globe are well preserved in the 3D display when using the MP-POC method, as shown in Figure 10a(iii). Conversely, under the SPOC method, both the bear toy and the globe appear blurred, with poorly defined edges and shapes, as shown in Figure 10a(i). Additionally, the scene resizing process (as discussed in Section 3) ensures that the field-of-view of the 3D image matches the display device, allowing the entire 3D  
Figure 9: Computer generated experimental group: (**a**) front view of the 3D display results showing (i) the original object parameters, (ii) the effect of the depth rescaling process only, and (iii) the combined effect of depth rescaling and scene resizing. (**b**) Display results from multiple perspectives, including front view, top view, bottom view, right view, and left view, after applying both depth rescaling and the scene resizing processes. scene to be displayed without cropping. The display results show improved quality while maintaining the relative positions of objects in the 3D image without distortion. In this section, we have presented experimental results of a 3D optical display using both a computer-generated (CG) 3D scene and a real-world captured 3D scene. The results confirm the feasibility of the proposed methods discussed in Sections 2 and 3.
## 4 Materials and Methods
### HPLC-MS/MS Conditions  
Chromotographic analysis was performed using Varian 1200/1200 LC (Varian Iberica, Spain) with a ProStar 410 autosampler (100 \(\mu\)L loop), two ProStar 210/215 solvent delivery modules, and a thermostatized space for the column. Separation was carried out following our previous work [30]. A C18 Kromaphase 100 column (150 mm \(\times\) 2.0 mm, 3.5 \(\mu\)m particle size; Scharlab, Barcelona, Spain) with a C18 Kromaphase guard column (10 mm \(\times\) 4.0 mm, 5 \(\mu\)m particle size; Scharlab, Barcelona, Spain) was used at 30 \({}^{\circ}\)C. The injection volume was 10 \(\mu\)L (partial injection), and the flow rate was set at 0.25 mL/min. The mobile phases consisted of solvent A (Milli-Q water) and solvent B (ACN), both containing 0.1% formic acid. The separation was in gradient elution started at 90% A and then decreased linearly to 30% in 10 min and returned to 90% in 1 min, holding these conditions for 4 min. The total run time of the method was 15 min.  
For detection, a triple quadrupole (1200 L TQ) mass spectrometer detector with an electrospray ionization (ESI) ion source operating in positive mode with the multiple reaction monitoring (MRM) mode (mass peak width Q1 2.5, mass peak width Q3 2.5, scan width in MRM 0.70) was used. The data acquisition system was MS Workstation version 6.8. N\({}_{2}\) was used as a drying gas and as a nebulizer gas. Argon was used as a collision gas. The conditions were as follows: N\({}_{2}\) drying gas (350 \({}^{\circ}\)C, 22 psi), nebulizer gas pressure (58 psi), capillary voltage (5000 V), and shield (600 V). Argon was set at 1.90 mTorr and the detector voltage at 1535 V. The TAs were monitored at a cone voltage of 70 V and a dwell time of 0.25 s following the transitions shown in Table S2. Quantification was performed using the next product ions: 124.1 \(m/z\) for atropine, 127.2 \(m/z\) for (\(\pm\))-atropine-D3, 156.0 \(m/z\) for scopolamine, and 159.0 \(m/z\) for (\(-\))-scopolamine-D3. The retention time was 6.3 min for scopolamine and (\(-\))-scopolamine-D3 and 6.7 min for atropine and (\(\pm\))-atropine-D3.
# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 3 Dynamap Approach
### The Dynamap Traffic Noise Sensors Network  
The main issue of Dynamap is to accurately estimate the variations \(\delta^{(\tau)}(t,s(g_{i}))\) (Equation (8)) in real time, by relying on the assumption that they do not depend, in a first approximation, on the location \(s\) but only on the group \(g_{i}\), i.e., \(\delta^{(\tau)}(t,s(g_{i}))\cong\delta^{(\tau)}(t,g_{i})\). This is based on the empirical fact that the variations \(\delta^{(\tau)}(t,s(g_{i}))\) do not change appreciably from site to site inside a given group. This means that the group variations \(\delta^{(\tau)}(t,g_{i})\) can be estimated from a mean value taken over a'reduced' number of sites \(s_{j}(g_{i})\) within \(g_{i}\), i.e.,  
\[\delta^{(\tau)}(t,g_{i})\cong\frac{1}{N(g_{i})}\sum_{j=1}^{N(g_{j})}\delta^{( \tau)}(t,s_{j}(g_{i})), \tag{9}\]  
where \(N(g_{i})\) is the number of road stretches in group \(g_{i}\) considered. In practical circumstances, \(N(g_{i})\) can be taken to be much smaller than the total number of sites in \(g_{i}\), allowing us to employ a rather small number of noise sensors to directly measure the noise level variations in real time, where now the sites \(s_{j}(g_{i})\) represent the locations of the Dynamap noise sensors. Finally, the Dynamap prediction for the local equivalent noise level becomes  
\[L_{\text{eq}}^{(\tau)}(t,s)=10\cdot\log_{10}\sum_{i=1}^{6}10^{[\delta^{(\tau )}(t,g_{i})+L_{\text{eq}}^{(1h)}(t_{\text{ref}}s(g_{i}))]/10}. \tag{10}\]For simplicity, we take the same number of sensors within each group, i.e., \(N(g_{i})=N_{g}\), so that the whole Dynamap network is built upon \(N_{D}=6N_{g}\) noise sensors. In our case, we use \(N_{g}=4\) so that our Dynamap network has just \(N_{D}=24\) fixed stations, which amounts to about 1.2% of the total number of road stretches in Z9 [69, 70, 82, 83]. The problem that arises is how to find a suitable set of locations so that the network can work more efficiently. This question will be considered later below.
## 3 Results and Discussion
### Consumer Preference and Freshness Evaluation of Raw Samples
Questions posed were about liking on a 9-point scale and about consumption and purchase intention on a binary (yes/no) scale. The results proved the value of using consumers in freshness evaluations in a multivariate approach together with, but not limited to, analytical tools. Our study is the second one that has used consumers to test the freshness of real fish products and the first one that has used them to test the potential of MAP-PAD packaging on the market.
## 3 Results and Discussion
### Physico-Chemical Properties
#### 3.2.3 Solubility Ratio  
The drying technique (either VOD or OD) itself influenced the solubility ratio (%) of the _Kurut_ samples significantly (_p_ < 0.05). Among the samples, KV35 and KV45 were less soluble than K35 and K45 (Figure 3). Vacuum drying causes more an elastic and compact structure in dried products. These changes cause difficulties in rehydrating and also in milling [16]. The solubility of dairy products varies depending on the degree of heat exposure before or after drying [23]. However, the drying temperature was found to be insignificant for the samples (_p_ > 0.05). In whole fat milk powders, a solubility rate below 50% indicates poor quality [58]. For the samples treated by both VOD and OD techniques, solubility ratios were higher than that ratio (Figure 3).  
Figure 2: Derived color indices of strained yogurt and _Kurut_ samples dried by VOD and OD techniques at 35 °C and 45 °C (_BI_—Browning Index; _\(\Delta\)E_—Total color difference; _WI_—Whiteness Index; Y_—Yellowness Index; Strained yogurt—raw material of _Kurut_ samples, K35—_Kurut_ sample OD applied at 35 °C; K45—_Kurut_ sample OD applied at 45 °C; KV35—_Kurut_ sample VOD applied at 35 °C; KV45—_Kurut_ sample VOD applied at 45 °C (Means \(\pm\) SD, n = 2)).  
In dairy powders, there is a strong relationship between solubility and _HMF_, of which an increase in _HMF_ causes a decrease in solubility [59; 60]. The solubility of KV35 and KV45 was lower (despite their low _HMF_ content) than that of the K35 and K45. Protein denaturation, drying temperature and also the presence of hydrophilic components (sugar or salt presence) in the system are also affective on solubility [26]. At higher temperatures, either in processing or storage, protein denaturation occurs [61]. High protein denaturation has been observed in vacuum dried (at 60 \({}^{\circ}\)C, at 85 kPa for 48 h) lentil protein isolates with lower solubility [62]. _Kurut_ samples were not investigated in terms of protein denaturation. However, KV35 and KV45 dried under vacuum pressure of 7 kPa (abs) at 35 \({}^{\circ}\)C and 45 \({}^{\circ}\)C, respectively. These temperatures are well below that required for protein denaturation.
# Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images
## 5 Algorithm
### Distribution Modeling and Non-Uniform Quantizer Design  
When the resolution of the quantizer is high, non-uniform quantizers demonstrate a heightened ability to closely align with an input pdf. This enhanced alignment results in a notably reduced average distortion, thereby offering a more efficient quantization performance [32]. Leveraging the high-resolution assumption in our mathematical derivation, we obtain a non-uniform quantization table for each of \(KL^{2}\) quantizers using [PERSON] algorithm [33] based on the assigned bits and the corresponding coefficients.  
In the absence of knowledge about the pdf, we resort to employing empirical data and numerical integration techniques. Consider the \(l\)-th scalar quantizer at latitude \(k\), the bits allocated to this quantizer are \(b_{k,l}\). Assuming the existence of a batch of images with size \(Z\), we obtain \(2 KZ\) coefficient data for this quantizer. These data serve as direct random samples in a Monte Carlo integration. With the resolution \(r=2^{b_{k,l}}\) specified, the quantizer designed for the empirical distribution can be implemented by the [PERSON] algorithm. Repeatedly applying the Lloyd algorithm to all \(KL^{2}\) quantizers, we obtain the corresponding quantization tables.
## 3 Proposed Method
### Loss Function  
In the weld seam recognition task under strong noise, the weld seam features exhibit multi-scale and multi-semantic complexity. Detailed areas may be disturbed by noise, or the edges of the weld seam may not be clear. Such tasks require the model to capture subtle local details while also accurately understanding the global semantic information. However, traditional single loss functions often struggle to find an optimal balance between these two aspects, especially in the presence of complex weld seam structures and background noise. To address this issue, we propose a comprehensive solution that introduces a multi-loss function framework. This framework aims to enhance the model's ability to learn features at different scales and improve its robustness under noisy conditions. The total loss function, L, is defined as follows:  
\[\mathcal{L}=\lambda_{1}\mathcal{L}_{Dice}+\lambda_{2}\mathcal{L}_{CE} \tag{17}\]Here, the Dice loss function \(\mathcal{L}_{Dice}\) is used to measure the overlap between the weld seam region and the predicted region. It helps the model focus on these key detail areas, ensuring that the detailed features of the weld seam are accurately recognized by optimizing the overlap region's precision. The cross-entropy loss function \(\mathcal{L}_{Dice}\), on the other hand, emphasizes the overall classification accuracy. Optimizing the classification of each pixel helps the model maintain consistency in global semantic information and has a faster convergence rate. Therefore, we combine the Dice loss function \(\mathcal{L}_{Dice}\) and the cross-entropy loss function \(\mathcal{L}_{CE}\) with weighted parameters as the optimization objective to improve the model's performance in the recognition task.  
By combining Dice loss with cross-entropy, we achieve a more flexible, partially class-imbalanced loss function. This hybrid approach allows us to balance the model's sensitivity to false predictions, ensuring that both local details and global semantic information are effectively captured [37].  
In our experiments, we set the weights \(\lambda_{1}\) and \(\lambda_{2}\) to 0.5 to achieve a balance between local details and global semantics. This weight assignment ensures that the model can accurately recognize the detailed structures of weld seams while maintaining good classification consistency across the entire image. Experimental validation has shown that this weight distribution is optimal for the current task, enhancing the model's robustness and recognition accuracy. Consequently, the model performs more stably and reliably in complex industrial environments.
## 3 Results
### Real Time (RT) Analysis
#### 3.2.3 Heart Rate  
Differences were found at RT1 (F(2, 18) = 5.914, \(p\) = 0.011), RT5 (F(2, 18) = 4.693, \(p\) = 0.023), and RT6 (F(2, 18) = 11.442, \(p\) = 0.001) between conditions. Higher HR values were observed in the NC (145 \(\pm\) 10 ppm) compared to the FC (141 \(\pm\) 9 ppm, \(p\) = 0.032, d = 0.42) and the SC (141 \(\pm\) 7 ppm, \(p\) = 0.043, d = 0.46) at RT1. At RT5 and RT6, significantly higher values were observed in the FC (RT5, HR = 166 \(\pm\) 8 ppm, \(p\) = 0.043, d = 0.63 and RT6, HR = 172 \(\pm\) 7 ppm, \(p\) = 0.004, d = 1) than in the SC (RT5, HR = 161 \(\pm\) 8 ppm, and RT6, HR = 165 \(\pm\) 7 ppm) (see Figure 4B).