esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sec-Malls Analysis of Saccharide Fractions
As a result, increasing the hydrolysis reaction time is required to generate oligomers from _C. myriophylloides_ alginate. The presence of sulfates contamination in ALCM could be the factor influencing the degradation process. [PERSON] et al., have suggested that the presence of sulfate could protect molecules against ROS hydrolysis [55]. Under the same conditions, oligomers produced by the degradation of alginate extracted from _B. bifurcata_, exhibited an M\({}_{\text{w}}\) of 5.5 kDa and 2 \(\leq\) DP \(\leq\) 24 after 6 h of treatment and 3.5 kDa after 10 h [26], which are different from values found in this study. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fractions** & **Source** & **M\({}_{\text{n}}\)\({}^{8}\) (g/mol)** & **M\({}_{\text{w}}\)\({}^{9}\) (g/mol)** & **D\({}^{10}\)** & **Intrinsic Viscosity (mL/g)** & **DP\({}^{11}\)** \\ \hline ALCM \({}^{1}\) & _C. myriophylloides_ & 59,000 (\(\pm\)1.3\%) & 76,000 (\(\pm\)0.9\%) & 1.3 (\(\pm\)1.6\%) & 224.2 (\(\pm\)1.5\%) & 352 \\ \hline ALSM \({}^{2}\) & _S. muticum_ & 173,000 (\(\pm\)1.7\%) & 202,000 (\(\pm\)1.7\%) & 738.6 (\(\pm\)2.3\%) & 738.6 (\(\pm\)1.0\%) & 935 \\ \hline OACM \({}^{3}\) & _C. myriophylloides_ & 11,000 (\(\pm\)4.9\%) & 19,000 (\(\pm\)5.6\%) & 1.7 (\(\pm\)7.4\%) & 35.8 (\(\pm\)6.0\%) & 88 \\ \hline OASM \({}^{4}\) & _S. muticum_ & 2000 (\(\pm\)9.8\%) & 3000 (\(\pm\)10.8\%) & 1.04 (\(\pm\)14.5\%) & 8.4 (\(\pm\)3.1\%) & 14 \\ \hline FUCCM \({}^{5}\) & _C. myriophylloides_ & 37,000 (\(\pm\)3.0\%) & 148,000 (\(\pm\)1.0\%) & 4.0 (\(\pm\)3.1\%) & 84.4 (\(\pm\)3.7\%) & Nd\({}^{12}\) \\ \hline AGP \({}^{6}\) & _C. decoricatum_ & 1,295,000 (\(\pm\)0.9\%) & 2,042,000 (\(\pm\)0.7\%) & 1.6 (\(\pm\)1.2\%) & 560.6 (\(\pm\)4.\%) & Nd \\ \hline AGB \({}^{7}\) & _C. decoricatum_ & 711,000 (\(\pm\)1.4\%) & 2,173,000 (\(\pm\)0.4\%) & 3.1 (\(\pm\)1.4\%) & 266.7 (\(\pm\)3.0\%) & Nd \\ \hline \hline \end{tabular}  
* ALCM: alginate extracted from CM; \({}^{2}\) ALSM: alginate extracted from SM; \({}^{3}\) OACM: LMW ablginates obtained from ALCM; \({}^{4}\) OASM: LMW ablginates obtained from ALSM; \({}^{5}\) FUCCM: fucoidan extracted from CM; \({}^{6}\) AGB: sulfated glycoprotein (crude extract) extracted from CD; \({}^{7}\) AGP: sulfated glycoprotein (purified extract) extracted from CD; \({}^{8}\) M\({}_{\text{n}}\): number average molecular weight; \({}^{9}\) M\({}_{\text{w}}\): weight average molecular weight; \({}^{10}\) D: index of Polydispersity; \({}^{11}\) DP: degree of polymerization; \({}^{12}\) Nd: Not determined.  
\end{table} 
Table 2: Average molecular weight determination and DPs distribution of extracted saccharides.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 5 A Deadline-Aware Scheduling and Forwarding Scheme
### Group-Based Forwarding  
To begin with, we consider node \(A\) in group \(j\) and assume that it has \(m\) potential forwarder(s), whose group ID is less than node \(A\)'s. The transmitter selects the first active node among \(m\) potential forwarders. Assume that node \(A\) has three potential forwarders (e.g., nodes \(B\), \(C\) and \(D\)). Suppose that node \(A\) has a packet to send to the sink at time \(t_{0}\). On receiving a beacon message, node \(A\) knows that a sender of this message is active. If node \(A\) receives a beacon message first broadcast by \(C\) among three potential candidates, node \(A\) will select node \(C\) as the next forwarder.  
Let random variable \(M\) denote the number of potential forwarders of a node in group \(j\). Since one-hop delay and E2E delay depend on the value of \(M\), we need to estimate the distribution of \(M\). After that, based on the forwarding algorithm, we present the distribution of the one-hop delay and E2E delay.
## 5 Conclusions  
A reactive power optimization method in distribution network based on EWOSM is presented. The proposed method is tested on several systems with different scales and comparison has been made with GA method and SQP method. The results have proved the validity and effectiveness of the proposed method EWOSM. The contributions and the novelties can be concluded as follows:  
1. The proposed EWOSM can rapidly and accurately select out the optimal scheme from large amount of historical data. And the advantage in computation time is remarkable than existing methods.
2. The proposed EWOSM can be used in combination with existing methods to speed up the convergence and ensure the global optimization.
3. As the proposed EWOSM is based on the analysis of large amount of historical data, it is more suitable for the distribution system that has relatively stable load and complete historical database; otherwise the proposed EWOSM needs to cooperate with existing methods. The application of big data theory and method in reactive power optimization needs to be further improved and perfected.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **EWOSM** & **The Hybrid Method** & **SQP Method** \\ \hline Network loss/kW & 631.40 & 632.02 & 632.75 \\ Node voltage offset & 18.40 & 17.18 & 16.71 \\ Minimum module-eigenvalue & 0.00569 & 0.00568 & 0.00567 \\ Convergence algebra & / & 9 & 42 \\ Computation time/s & 4.37 & 7.41 & 19.50 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of computation time and results with three methods.  
**Acknowledgments:** This work was supported by Science and Technology Project of State Grid Corporation of China (EPRIPDKJ (2015) 1495), and Beijing Natural Science Foundation (3172039).  
[PERSON] and [PERSON] conceived, designed and performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed the power load data and provided some suggestions on the experiments and manuscript; [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Use Cases
### Problem Formulation  
Our objective was to design a demodulator that could successfully recover the transmitted symbols \(s\) with the help of only a few pilots. Therefore, we assume that the BS can use the information from the previous transmissions of \(K\) devices and their meta-training dataset (\(D_{k}^{tr}\)), which consists of a \(P\) pair of pilots, \(s_{k}\) and \(y_{k}\), such that \(D_{k}^{tr}=\{(s_{k}^{(p)},y_{k}^{(p)})\) for \(p=1,\ldots,P\}\). Here, \((s_{k}^{(p)},y_{k}^{(p)})\) are the \(p\) pilot received signal pairs for the \(k\) meta-training device. \(N\) pilots are collected from the meta-test device in set \(D_{T}=\{(s^{(p)},y^{(p)})\) for \(p=1,\ldots,N\}\), where both datasets \(D_{T}\) and \(D_{k}^{tr}\) are used for training the demodulator. The demodulator (\(\hat{y}\)) is then defined by a distribution that depends on the vector \(\phi\) i-e \(\hat{y}=p(s|y,\phi)\).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **IoT** & **MCS** \\ \hline Modulation & 16 QAM & OOK \\ \hline Number of iterations & 5000 & 10,000 \\ \hline Average SNR & 30 dB & 15 dB \\ \hline Number of meta-training devices & 10, 1000 & 10, 1000 \\ \hline \(\eta\) & 0.1 & 0.1 \\ \hline \(\kappa\) & 0.0001 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters for IoT and MCS.  
To train the demodulator, (\(\hat{y}\)), we assume that it is defined by a multilayered neural network, with \(L\) layers, expressed as  
\[\hat{y}=\frac{exp([f_{\phi^{L-1}}(f_{\phi^{L-2}}(\cdots f(f_{\phi^{1}}(y))))]_{s} }{\sum\limits_{s\
eq s}(exp([f_{\phi^{L-1}}(f_{\phi^{L-2}}(\cdots f(f_{\phi^{1}}( y))))]^{\prime}_{s}} \tag{13}\]  
Here, \(f_{\phi}^{(l)}(x)\) is the non-linear activation function, which is given by the weight matrix (\(W^{(l)}\)) and bias factor \(b^{(l)}\) such that \(f_{\phi}^{(l)}(x)=\sigma(W^{(l)x+b^{(l)}})\). \(\sigma(.)\) is a non-linear function, \(\phi\) is the vector of trainable parameters, and \([.]_{s}\) represents the elements belonging to the symbol \(s\).  
The problem can now be formulated as a typical meta-learning problem by assuming a demodulator with a context-variable \(\varphi\) and a shared parameter \(\omega\) such that \(\hat{y}=p(s|y,\varphi,\omega)\).  
During meta-training, we used two state-of-the-art meta-learning algorithms, namely model-agnostic meta-learning for fast adaptation of deep networks (MAML) [41] and Caviia [50].
# A Novel Approach to Incremental Diffusion for Continuous Dataset Updates in Image Retrieval
## 5 Proposed Methodology
\begin{cases}\text{sim}(\mathbf{c}_{i},\mathbf{c}_{j})&i\
eq j,\mathbf{ c}_{i}\in\text{NN}_{l}(\mathbf{c}_{j}),\mathbf{c}_{j}\in\text{NN}_{l}( \mathbf{c}_{i})\\ 0&\text{otherwise}\end{cases} , \tag{10}\]  
where \(\text{NN}_{l}(\mathbf{c}_{i})\) denotes \(l\) nearest neighbors of \(\mathbf{c}_{i}\). This affinity matrix is normalized to the block \(\mathbf{S}_{cc}\) in the following transition matrix:  
\[\mathbf{S}^{\prime}= \begin{bmatrix}\mathbf{S}_{qq}&\mathbf{S}_{qc}\\ \mathbf{S}_{cq}&\mathbf{S}_{cc}\end{bmatrix}  \tag{11}\]  
Regardless of the mutual reciprocal check and the normalization applied to obtain the transition matrix, the key is that the values of its elements are proportional to the pairwise similarities between image features. Given the following facts:  
\[\mathbf{X}_{\mathcal{G}}\mathbf{X}_{\mathcal{G}}^{\top}\approx(\mathbf{Z} \mathbf{X}_{\mathcal{C}})(\mathbf{Z}\mathbf{X}_{\mathcal{C}})^{\top}=\mathbf{ Z}\mathbf{X}_{\mathcal{C}}\mathbf{X}_{\mathcal{C}}^{\top}\mathbf{Z}^{\top},\]  
\[\mathbf{X}_{\mathcal{G}}\mathbf{X}_{\mathcal{Q}}^{\top}\approx\mathbf{Z} \mathbf{X}_{\mathcal{C}}\mathbf{X}_{\mathcal{Q}},\]  
\[\mathbf{Z}\mathbf{Z}^{\top}\approx\mathbf{I},\]  
where the approximation signs can be replaced by the equal signs when \(k=n\), it can be derived that \(\mathbf{S}_{gg}\approx\mathbf{Z}\mathbf{S}_{cc}\mathbf{Z}^{\top}\) and \(\mathbf{S}_{gg}\approx\mathbf{Z}\mathbf{S}_{cq}\). Based on the above analyses, Equation (4) is approximated by  
\[\mathbf{f}_{g}^{\infty}\propto\mathbf{Z}(\mathbf{I}-\alpha\mathbf{S}_{cc})^{-1 }\mathbf{S}_{cq}\mathbf{f}_{q}^{0}. \tag{13}\]  
Note that the right side of Equation (7) is essentially an inner product between the weight matrix \(\mathbf{Z}\) and the diffusion result on the anchor graph. As illustrated by the right side of Figure 1, for any new images inserted into the gallery, we only have to reconstruct them by existing anchor points and update the weight matrix \(\mathbf{Z}\) without building new graphs. In the next sections, we show that our proposed approach is able to handle new data while ensuring efficiency and effectiveness.
## 2 Bottom-Up Assisted Pressure Drive Mechanism
### Dilation (Start-Up)  
An effective start-up is essential for the following production stage. Dilation start-up technology utilizes a short period of high-pressure fluid injection to establish a high porosity and high permeability dilation zone connecting the injector and producer. A similar technique is applied in the SAGD start-up process to vertically and uniformly connect the injector and producer [30].  
Since 2013, a geomechanical dilation mechanism, termed as fast and uniform SAGD start-up enhancement (FUSE), has been applied successfully as a technique to reduce the start-up time and enhance thermal conformance along the horizontal well and thus eventually increase the initial oil production. FUSE technology is based on a geomechanical dilation mechanism. By utilizing a short period of high-pressure water injection, the FUSE process creates a high porosity dilation zone vertically connecting a SAGD well pair. Meanwhile, the FUSE process can also overcome the inherent geological heterogeneities along the well length. The newly formed high porosity zones serve as a preferred conduit for the initially injected steam to travel through. Therefore, the more efficient convective heat transport mechanism becomes active between the SAGD well pair allowing for earlier inter-well communication. Another benefit is its ability to break through the mud/shale stingers near the SAGD wells. Therefore, uniform dilation and consequently uniform communication along the horizontal well length is achieved. Since 2013, FUSE technology has been applied to more than 60 SAGD well pairs, and good results have been achieved. Significant experience has been gained in site execution [31].  
The geomechanical dilation mechanism can alter rock structure by creating new pore spaces and enhancing permeability in the reservoir. Dilation creates the preferred conduits for steam to travel through and distribute in the reservoir, contacting the heavy oil in large volumes.
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 5 Discussion
### Impact of Cementation on Reservoir Quality  
The analysis of cementation revealed that the calcareous and siliceous cement formed in the IIA-IIB stages of diagenesis primarily fills the intergranular dissolved pores, intragranular dissolved pores, and melodic pores formed in the IB-IIA stages of diagenesis. Cementation results in a reduction in the pore space, where the volume of cement is equivalent to the reduced volume. Cementation (calcite, silica, and iron oxide) typically reduces porosity in proportion to the cement volume [59]. Consequently, the remaining intergranular pores in sandstone today (\(V_{3}\)) have undergone compaction and cementation during the burial process in clastic reservoirs, and the calculated formula is as follows:  
\[V_{3}=\frac{P_{1}}{P_{t}}\times Q_{m} \tag{5}\]  
\[V_{cc}=V_{2}-V_{3} \tag{6}\]  
In the above formula, \(V_{3}\) represents the porosity after compaction and cementation, expressed in %; \(V_{cc}\) represents the porosity reduction caused by cementation, expressed in %. The calculation results show an average decrease of 11.8% in the porosity of the Enping Formation, ranging from 0 to 11.1%. This study indicates that cementation significantly influences the reservoir in the Enping Formation, resulting in a primary pore loss rate ranging from 2.1 to 28.7% based on the observed relationship between the intergranular volume (IGV) and cement content in sandstone reservoirs (Figure 8).
# Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
## 4 Experimental Results
### Evaluation of Performance for the Hyperspectral Image Application
#### 4.2.2 Automatic Extraction of the Light-Intensity Values Within the Steady Period  
In order to facilitate the analysis of the data for the quantification of different parameters of the light sources, it would be beneficial to have an automatic extraction of the different time stages of the light intensity. It is interesting to automatically locate the transient and steady periods within the light-intensity profile (Figure 13a). Taking advantage of the different light intensities for the different stages of the proposed experiments, we made use of a histogram in order to automatically identify such areas. In the histogram shown in Figure 13b, it can be observed that there are two regions with a high presence of pixel counts. The low intensity values of such histogram represent the period when the light is turned off, while the high light-intensity values correspond to the period during which the light is on (steady period). The intermediate light-intensity values correspond to the transient period. Using this information and a thresholding method, it was possible to automatically identify the different temporal stages of the experiment, as shown in Figure 13c.  
Once this process was applied to every image in the dataset, there were different light-intensity profiles for each power supply model (PPS, CPS1, and CPS2) and for each illumination mode (reflectance and transmittance), as shown in the examples of Figure 14. The specific details concerning the features of the different power supplies based on their light-intensity profiles are discussed later in the manuscript. However, some differences  
Figure 12: Automatic identification of the spatial location of the light source in transmittance mode. (**a**) Derivative extracted from the spatial profile. (**b**) Selection of the region of interest (red crosses) within the spatial profile using the information from the derivative. (**c**) Spatial profile from the region of interest.  
Figure 13: Automatic identification of the transient region for the experiments. (**a**) Light profile. (**b**) Histogram from the light profile. (**c**) Identification of the steady period (blue) and the transient period (red) from the light profile (black).  
can be directly observed from Figure 14. The transient period is shorter for CLS-1 compared to PLS and CLS-2. Meanwhile, although the light intensity seems to remain constant during the steady period for PLS and CLS-2, the light profile of CLS-1 shows a slight decrease over time.
## 1 Introduction
The proposed method fills critical gaps in the prediction of the compression index (\(C_{c}\)) of fine-grained soils by addressing the limitations of traditional consolidation testing and empirical models. Conventional methods for determining \(C_{c}\) are time-consuming, requiring up to 15 days for test preparation, execution, and parameter calculation, which can significantly delay construction projects. Empirical formulas, while useful for initial estimates, often fail to generalize across diverse datasets due to their reliance on simplified assumptions and limited variables. These challenges underscore the need for advanced, efficient methodologies capable of delivering accurate and reliable \(C_{c}\) predictions while reducing time and resource demands. To bridge these gaps, this study aims to develop and validate a novel hybrid machine learning model combining Genetic Programming (GP) and XGBoost. This hybrid approach uses the interpretability of GP and the computational efficiency of XGBoost to accurately predict \(C_{c}\) using easily measurable soil properties such as the liquid limit (LL), the plasticity index (PI), the initial void ratio (\(e_{0}\)), and the water content (\(w\)). The research seeks to overcome the shortcomings of traditional methods by creating a model that not only delivers superior predictive accuracy but also generalizes effectively across diverse soil profiles. By validating the hybrid model against standalone GP and XGBoost models, as well as traditional empirical approaches, the study provides a robust and adaptable tool for geotechnical engineers, enabling faster, more cost-effective, and more reliable predictions of soil behavior.
## 3 Data Reduction Methodologies
### Response Surface Model (Interpolation of Boundary Layer Profile)  
Figure 7 shows the response surface model being used to interpolate the dataset. The model was reconstructed based on the flight Mach number and total pressure. The original flight experimental  
Figure 6: Sensitivities of each variable on \(C_{p}\) (at outmost probe height): (**a**) angle of attack; (**b**) yaw angle; (**c**) flight Mach number; (**d**) unit Reynolds number; (**e**) total and static pressures; (**f**) total and static temperatures.  
data are superimposed as black-colored dots for a comparison. The experimental data indicate the case for targeted flight Mach number of 0.65 and measured at the probe numbers 5 (: near the wall surface) and 45 (: near the outer edge of the boundary layer). The corresponding response surface models for probe numbers 5 and 45 are presented. The response surface model reconstructs the \(C_{p}\) distribution through the interpolation technique using RSM as a function of the flight Mach number and total pressure with fine accuracy, the details of the uncertainty in reconstructing the boundary layer profile by using the response surface model will be presented in a later section. It should be noted that the response surface model must not involve any extrapolations as the extrapolated data point could cause a massive error. In order to ensure that the generated response surface does not involve any extrapolations, constraints were added. The constrains were defined based on the minimum and maximum values of primary variable \(C_{p}\) obtained in the actual flight data at targeted steady cruise flight conditions. If the reconstructed dataset in the response surface curve, which is shown in Figure 7, exceeded the constrained area, the exceeded data points were constrained to be a boundary value of the constrained area. Thus, the reconstruction of the profile ensured the use only of the interpolation and did not involve any extrapolation. This process strengthens the robustness of the interpolation as it ensures using only interpolation and avoid potential errors by using the extrapolation.
# Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols
## 2 Description of the Energy Model
### Limitations of the Energy Model  
This subsection describes some of the limitations of the proposed energy model. This model comprises the main types of energy required for a node to perform generic functions within the network. However, this model does not describe specific energies related to the anomalous behavior of the nodes, yet it is capable of predicting strange behaviors in the network due to the alteration of typical node functions. These behaviors are due to situations of stress, such as node connections and disconnections due to channel intermittence. Likewise, these situations can be represented as a result of other types of energy. For example, in network areas with intermittent nodes or links, nodes frequently disconnect. This situation reflects in increased retransmissions, channel auditing retries, and packet collisions. These changes increase the transmission, reception, switching, and energies of the CSMA/CA algorithm. Thus, the proposed model can identify anomalous behavior observed in these types of energy and draw conclusions regarding risk areas or potential attacks.  
The main function of a WSN is sending information to one or more collector nodes and detecting the conditions of the channel to find the optimal route. Then, in these networks, it is possible to implement different sleeping node techniques. That is to say, there are periods of time in which a certain number of nodes enter into passive mode, consuming less energy than they normally do. In this study, we do not look into sleeping node energy. However, the model can be extended, adding this energy equation, if the voltage and current of a sensor in a given time interval are available. For these reasons, the proposed model is simple, easy to implement, and quite scalable as per analysis requirements.