esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
## 3 Background Knowledge
### Software-Defined Networking  
A new technology called SDN uses programmable interfaces to seamlessly integrate network and cloud application provisioning [7]. In light of layered engineering, the Open Systems Interconnection (OSI) concept was applied to communication. This model approved level-independent advancements and established prohibitive linked problems in heterogeneous frameworks. The major objective of SDN is to revive this theory-based approach to create the organization necessary to contain new settlements and boost creative practices at the same time [5; 26; 27]. SDN is a revolution that suggests numerous web-based applications or perhaps only indications. Managers who use massive volumes of data, such as Google and Facebook, are switching to SDN for their fundamental systems. Similarly, large ISPs see SDN as a suitable response to their current communications [6].  
Network communication management and design have been completely transformed by the SDN network architecture. Three layers make up the SDN architecture, and each one serves a unique purpose. The network applications that deliver services to end users are housed in the application layer, which is the first layer. Firewalls, intrusion detection systems, and load balancing are a few examples of these applications. The control layer, which is in charge of managing the network's resources and setting up network gadgets, is the second layer. It has a central controller that uses protocols like OpenFlow to talk to network devices. The controller gathers information about network traffic from the data plane and decides how to send it in accordance with the policy guidelines set forth by the applications running at the application layer. The data layer, which is the third layer overall, is in charge of forwarding network traffic. It is made up of network components such as switches and routers that the controller in the control plane uses to send forwarding instructions. SDN offers a network design that is more adaptable, scalable, and manageable by distinguishing the data place from the control plane. It allows network administrators to quickly react to shifting business requirements and take immediate action in response to security risks [28; 29].
## 2 The IDIVS Concept and Application Domains
### Capabilities  
An IDIVS performs data fusion to produce an output. Data fusion aims to integrate and aggregate data from multiple sensors to generate more accurate and complete information than would be possible when the sensors were used individually. The IDIVS may incorporate different data fusion types:  
* Redundant/competitive fusion: multiple sensor data (of the same type) representing the same information area are integrated to increase the accuracy of the information about the state of an environment;
* Cooperative fusion: multiple sensor data (of different types) representing the same information area are integrated to obtain more information about the state of an environment;
* Complementary fusion: multiple sensor data representing different environments are integrated to obtain complete information about a state. This information is usually provided by multiple IDIVSs connecting to different environments. The relevant capabilities of IDIVS are to:
* Fuse sensor data to produce a sensor-like output (numerical or categorical) about the state of a specific environment based on sensor-like input (numerical or categorical) relevant for the environment;
* Adapt to changes in the set of sensors, including integrating new relevant sensor values at run-time;
* Use labelled data to improve accuracy by machine learning, e.g., based on user feedback relevant for the current IDIVS output, a batch of pre-labelled data, or a model or partial model of IDIVS (e.g., by transfer learning);
* Support self-assessment of the accuracy of its output;
* Learn to detect new states of its environment (not specified at design time);
* Provide information about the sensor input it uses with respect to how important different sensor values are for generating the output (e.g., a type of information gain);
* Anomaly detection and diagnosis, e.g., detecting tampering attempts on input sensor streams.
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 3 MPR Selection Algorithm Based on Connection Necessity
```
1:\(FN=FN[\text{MPR\_Sel}]\), \(\text{Avail\_FN}=FN[\text{MPR\_Sel}]\), \(SN=SN[\text{MPR\_Sel}]\), \(\text{Avail\_SN}=SN[\text{MPR\_Sel}]\)
2:\(\text{Sel\_MPRS}\)
3:for each \(i\in[1,\text{FN}[\text{:}]_{size}]\)do
4:for each \(j\in[1,\text{SN}[\text{:}]_{size}]\)do
5:if\(\text{ACC}_{uni}=1\)then
6:\(\text{Sel\_MPRS}=\text{Sel\_MPRS}+\text{FN}[j]\)
7:\(\text{Avail\_FN}=\text{Avail\_FN}-\text{Sel\_MPRS}\)
8:\(\text{Avail\_SN}=\text{Avail\_SN}-\text{Cov}[\text{MPR\_Sel}]\)
9:elseif\(\text{Cov}[\text{FN}[\text{i}]]_{size}=\text{Max\_Cov}[\text{FN}]_{size}\) &&\(\text{Dep}[\text{FN}[\text{i}]]_{size}=\text{Max\_Dep}[\text{FN}]_{size}\)then
10:\(\text{Sel\_MPRS}=\text{Sel\_MPRS}+\text{FN}[j]\)
11:\(\text{Avail\_FN}=\text{Avail\_FN}-\text{Sel\_MPRS}\)
12:\(\text{Avail\_SN}=\text{Avail\_SN}-\text{Cov}[\text{MPR\_Sel}]\)
13:endif
14:endfor
15:if\(\text{Cov}[\text{Sel\_MPRS}]=SN\)then
16:break
17:else
18:continue
19:endif
20:endfor
21:continue
22:endif
23:endfor
24:return\(\text{Sel\_MPRS}\)
```  
**Algorithm 1**MPR selection algorithm based on connection necessity.
# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
## 2 Materials and Methods
### Hyperspectral Image Preprocessing  
Hyperspectral remote sensing imagery has the characteristics of extremely high spectral resolution. At the same time, it also brings about the problem of high data dimension and a large amount of redundant information between adjacent bands [27]. Therefore, in order to avoid redundant information, data dimensionality reduction is required [28]. Considering that adjacent bands have similar spectral characteristics, the median value is calculated every 9 bands and used as the representative band, which greatly reduces the amount of experimental data. The pixel values of all bands of the image are normalized so that all pixel values are limited between 0-1. This is to eliminate the adverse effects of singular sample data and reduce training time. The normalized conversion function is as follows:  
\[X=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{1}\]  
Among them, \(x\) represents the pixel value in the original image, and \(X\) represents the pixel value in the normalized image.
## 4 Rheological Properties  
### Strain Amplitude Sweep Test  
Figure 5 shows the storage modulus and loss factor of the MRE samples as a function of the strain amplitude under magnetic flux and constant frequency (1 Hz). The static oscillatory strain had been applied to the sample parallel with the particles alignment of anisotropic sample MRE. The strain amplitude depends on both the elastic and viscous behavior of the material. It can be observed that the storage and loss modulus had decreased with increasing strain amplitude for all sample. This phenomenon was strain-dependent behavior. Besides that, the strain amplitude had increased with magnetic field for all the samples. The anisotropic sample in on-state condition shows the highest storage and loss modulus values, which are 0.48 MPa and 0.038 Mpa. Meanwhile, isotropic MRE stated the lowest storage and loss modulus in an off-state condition, which is 0.25 Mpa and 0.017 Mpa. From the storage modulus, which was related to the stiffness behavior from the MRE point of view, the isotropic sample shows more stiffness than the anisotropic sample for both storage and loss modulus. The anisotropic stiffness sample is at 0.1% strain, while the isotropic sample is at 10% strain.  
This phenomenon demonstrated a common payne effect behavior for MRE, where it indicates the reliance of storage modulus on strain amplitudes, which is caused by the devastation and restructuring of a cobalt particle bonding in the rubber [22]. The performance during the on-state condition, the Payne effect, had been more obvious. That is why the storage modulus of the on-state condition is higher than that of the off-state condition. In addition, the linear viscoelastic (LVE) region has a tendency to reduce by the external magnetic field. For the anisotropic sample, the application of a magnetic field of 0.8 T caused the particles that had been aligned to be repelled, which is why the anisotropic curve shows a  
Figure 5: (**a**) Storage modulus and (**b**) loss modulus of MRE under different magnetic flux densities as a function of strain amplitude.  
lower LVE as compared to the isotropic curve. When there is a higher magnetic field, the particle chain tended to be easily interrupted, which led to the shorter linear region of the MRE sample. The matrix is reinforced by the particles because of the physical interactions among the particles and the matrix, and subsequently, the LVE region is reduced [23]. The morphological properties are strongly supported, as observed in Table 4. Those chain structures were easily broken at higher strain during the oscillatory test due to the particles attracting edge to edge. Particle chains and the movement of the particles within the silicon rubber structure affect the loss modulus. To sum up, the storage and loss modulus increase with the increasing magnetic field strength can be attributed to the stronger network structure at a higher magnetic field. It indicates that both the anisotropic and isotropic curves present significant magnetorheological effects.
# An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
## 3 The ECG Circuit Design
### The Schematic Diagram of the ECG Circuit
#### 3.2.3 The High Pass Filter  
The high pass filter shown in Figure S1a (in Supplementary Material) is a second order active filter that was designed to pass a frequency range above 0.08 Hz. The values of resistors and capacitors were calculated based on the following equation that is used to determine the cutoff frequency/\(f_{\rm C}\)[36]:  
\[f_{\rm C}=\frac{1}{2\pi\sqrt{R_{1}C_{1}R_{2}C_{2}}} \tag{1}\]  
By setting \(R_{1}=R_{2}=R\) and \(C_{1}=C_{2}=C\), then \(f_{\rm C}=1/2\pi RC\), We have designed the HPF at \(f_{\rm C}\) = 0.08 Hz, then \(R\) = 200 k\(\Omega\), and \(C\) = 10 uF. The buffers were added to reduce the loading effect between cascaded stages which highly improves the measured signal.
## 1 Introduction
For instance, with one input and one output variable, an ANN with one hidden layer and one hidden node has twice as many parameters to be estimated as a linear regression model. When the number of hidden nodes increases to two, the complexity of the ANN rises to 3.5 times that of the linear regression model. Notably, most ANN variations contain multiple hidden layers and numerous hidden nodes, significantly amplifying their complexity. In this paper, a practical prognostic method that employs only a linear regression model is presented to achieve significantly lower complexity and computational cost than an ANN while maintaining prediction accuracy comparable to or even better than that of an ANN. Linear regression is a fundamental method based on basic algebra and linear algebra, and it generally requires less specialized knowledge and expertise compared to the theories required for an ANN. Therefore, the proposed method is straightforward to understand and implement, and it significantly reduces the computational burden. As a strategy to improve prediction accuracy using a linear model, a stepwise linear approximation method is developed, and its prediction accuracy and computational efficiency are compared to those of a typical ANN on the same dataset. This paper is organized as follows: Section 2 briefly introduces linear regression; Section 3 explains the concept of the proposed method, including how to divide the regression steps and predict the RUL; Section 4 compares the results of the proposed method with those of a typical ANN across 100 different datasets; and finally, Section 5 provides the conclusions, while Nomenclature provides a list of notations used in this paper along with their descriptions.
# Fi-Fo Detector: Figure and Formula Detection Using Deformable Networks
## 4 Experimental Results
### Dataset
#### 4.1.2 ICDAR-2017 POD (Corrected)  
While evaluating the ICDAR-2017 POD dataset, the Fi-Fo detector highlighted the missing, confused, and irregular labels. As the Fi-Fo detector is a data-driven approach, it relies not only on the quantity of data but also the quality of data is very important. Confusions in the given data penalize the system resulting in degrading of performance, as is the case in the ICDAR-2017 POD dataset. Therefore, we decided to inspect the entire dataset manually to update missing annotations and fine-tune the confusing ones. While updating the ICDAR-2017 POD dataset, we did not add or remove any image from the dataset. Rather, we only updated the annotations to minimize the ambiguities and inconsistencies present in the dataset following the existing labeling conventions, as shown in Figure 4e. In the ICDAR-2017 POD dataset, some instances of decorative graphics are annotated as figures, and the rest were ignored. So, the only thing we completely changed is the removal of decorative graphics as figures from the original dataset. Figure 5c is a perfect example to demonstrate where the header-rule line was annotated as a figure, but the footer-rule line was ignored. All these problems contribute to the performance degradation of any data-driven system.  
Bringing consistency and clarity in the annotations will help in achieving generalized systems for page object detection. A total of 273 annotation files were updated. The update included the removal of false labels, updating missing labels, and bringing uniform labeling conventions. An overview of the ICDAR-2017 POD (corrected) dataset along with the ICDAR-2017 POD dataset is presented in Table 1. The updated annotations have been publicly released as the ICDAR-2017 POD (corrected) dataset to aid future research in this direction ([[https://bit.ly/2](https://bit.ly/2) AUSlzI]([https://bit.ly/2](https://bit.ly/2) AUSlzI)).  
Figure 3: Annotated examples from ICDAR-2017 Page Object Detection (POD) dataset (formulas are labeled as brown color and figures as green color).  
Figure 4: Problems with ICDAR-2017 POD dataset (1 st row) in comparison to ICDAR-2017 POD (corrected) dataset (2 nd row) (green, blue, and brown color represent figures, formulas, and tables, respectively, thanks to Fi-Fo Detector).  
Figure 5: Fi-Fo evaluation results on ICDAR-2017 POD dataset with original annotations (green color represents annotated objects; while red color highlights the detection by Fi-Fo Detector).
## 4 Performance Evaluation
### Performance Evaluation  
In order to assess the performance of the routing methods, we have selected three network metrics: the (1) packet delivery ratio (PDR), (2) end-to-end delay (E2E), and (3) energy  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value/Description** \\ \hline Traffic type & UDP \\ Learning rate \(\alpha\) & 0.3 \\ Number of RNs & 30, 45, and 60 \\ Discount factor \(\delta\) & 0.85 \\ Packet size (Bits) & 512 \\ Simulation Time (s) & 200 \\ Number of source nodes & 2, 4, 6, 8, and 10 \\ Deployment of sensor nodes & Random \\ Packet generation rate (pkts/sec) & 10 \\ The initial energy of sensor nodes & 5 Joules \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 4: Performance comparison of different RL versions on a Dynamic GridWorld environment.  
efficiency (EE). These metrics hold significant importance when evaluating the network, particularly in terms of the impact caused by path selection on the energy consumption of the wireless nodes. We define these metrics as follows:  
1. _PDR_: This refers to the ratio of successfully delivered packets to the total number of packets sent within the network. It is a measure of the effectiveness of the routing protocols and communication infrastructure in ensuring that packets reach their intended destinations without loss or errors. A higher PDR indicates a more reliable and efficient network, whereas a lower PDR suggests potential issues such as packet loss, congestion, or faulty routing. Monitoring and optimizing the PDR is crucial in evaluating and improving the overall performance and reliability of the WSNs.
2. _E2E_: This is the time it takes for a packet to travel from the source node to the destination node. It includes processing, queuing, transmission, and propagation delays. A lower delay is better for faster and more efficient communication. Minimizing the end-to-end delay is crucial in the WSNs to ensure timely and reliable data delivery.
3. _EE_: This refers to the ability of the network to perform its intended tasks and communication while utilizing minimal energy resources. We defined this metric as the ratio of the PDR to the average energy consumed by the RNs. It is a critical consideration in the WSNs due to the limited and often non-rechargeable energy sources available to the sensor nodes. By enhancing energy efficiency, the WSNs can achieve a longer network lifetime, extended monitoring capabilities, and reduced maintenance requirements.  
In the following subsections, we will compare and discuss the performance of the proposed scheme alongside its counterparts: the traditional OSPF and SDN-Q based on the network metrics.
# Experimental Study on Damage Constitutive Model of Rock under Thermo-Confining Pressure Coupling
## 4 Damage Constitutive Model of Rock  
### Rock Damage Theory Based on the Drucker-Prager Criterion  
Based on the Drucker-Prager strength criterion, the microelement strength of the rock \(F^{*}\) is defined as:  
\[F^{*}=f(\sigma^{*})=\alpha I_{1}+\sqrt{J_{2}} \tag{6}\]  
where \(\alpha\) represents the strength parameter of the rock microelement; \(I_{1}\) denotes the first invariant of stress; \(J_{2}\) signifies the second invariant of deviatoric stress.  
Expressing the above \(I_{1}\) and \(J_{2}\) in terms of stresses gives:  
\[I_{1}=\frac{E_{\varepsilon_{1}}(\sigma_{1}+2\sigma_{3})}{\sigma_{1}-2\
u \sigma_{3}} \tag{7}\]  
\[\sqrt{J_{2}}=\frac{E\varepsilon_{1}(\sigma_{1}+2\sigma_{3})}{\sqrt{3}(\sigma_ {1}-2\
u\sigma_{3})} \tag{8}\]  
Substituting Equations (7) and (8) into Equation (6), the microelement strength of the rock under confining pressure can be obtained as:  
\[F^{*}=\frac{E\varepsilon_{1}\bigg{[}\sin\varphi(\sigma_{1}+2\sigma_{3})+\sqrt {3+\sin^{2}\varphi}(\sigma_{1}-\sigma_{3})\bigg{]}}{\sqrt{9+3\sin^{2}\varphi} (\sigma_{1}-2\
u\sigma_{3})} \tag{9}\]  
Substituting Equation (9) into Equation (14), the rock damage evolution equation under the coupled effect of temperature and confining pressure is obtained as follows:  
\[D_{s}=1-\frac{E_{T}}{E_{0}}\exp\left\{-\left\{\frac{E_{T}\varepsilon_{1}\bigg{[} \sin\varphi(\sigma_{1}+2\sigma_{3})+\sqrt{3+\sin^{2}\varphi}(\sigma_{1}-\sigma _{3})\bigg{]}}{F_{0}\sqrt{9+3\sin^{2}\varphi}(\sigma_{1}-2\
u\sigma_{3})} \right\}^{m}\right\} \tag{10}\]
###### Abstract  
To explore the temporal and spatial evolution of carbon sinks in state-owned forest regions (SOFRs) and the efficiency of increased carbon sinks, this study used panel data from 19 periods in 40 key SOFRs in Heilongjiang Province from 2001 to 2019. Additionally, combined with geographic information system (GIS) and remote sensing (RS) technology, the individual fixed-effect model was used to estimate the number of forest management investment (FMI) lagging periods, and the panel threshold model was used to investigate the differences in the FMI efficiency in various forest regions. From 2001 to 2019, the carbon sink of key SOFRs in Heilongjiang Province showed an upward trend over time, with a growth rate of 20.17%. Spatially, the phenomenon of \"increasing as a whole and decreasing in a small area\" was found, and the carbon sink of each forest region varied greatly. The standard deviation ellipse of the carbon sink presented a \"southeast-northwest\" pattern and had \"from southeast to northwest\" migration characteristics. The FMI amount from 2001 to 2019 showed an upward trend, with a total of CNT 46.745 billion, and varied greatly among forest regions. Additionally, the carbon sink amount in each SOFR affected the FMI efficiency. The threshold of the model was 5,327,211.8707 tons, and the elastic coefficients of the impact of FMI below and above the threshold on the carbon sink were 0.00953 and 0.02175, respectively. The latter's FMI efficiency was 128.23% higher than that of the former. Finally, the increase in FMI to a carbon sink followed the law of diminishing marginal benefits. Therefore, the government should rationally plan the level of FMI in each SOFR to improve the FMI cost-effectiveness and help achieve the goal of \"carbon neutrality\".  
SOFRs; FMI; carbon sink; efficiency; GIS; RS; carbon neutrality Article
## 4 Discussion
### Limitations  
This was a relatively small, exploratory study. Our final sample exceeded the minimum requirements for the Kruskal-Wallis comparisons but represents a limitation for the more complex classification modeling. To address this limitation, we employed structural regularization techniques by constraining model complexity--limiting the depth of decision trees and using ensemble averaging in the random forest. These approaches are intended to reduce over-fitting and emphasize the most stable predictors across validation folds. Because the depth = 3 decision tree underlying the RF ensemble contains only seven splitting parameters, the effective model dimensionality is 7 rather than the full set of 16 predictors. Using the conservative rule of \(\geq\)5 observations per parameter for classification models, a minimum of 35 athletes is required; our training sample of 37 (plus 9 in the external hold-out) therefore satisfies the current recommendations while limiting the risk of over-fitting despite four outcome classes [40]. This work addresses two common limitations in sport-performance machine learning research--the lack of external validation and poor model interpretability--by providing both cross-sport testing and feature-level transparency via SHAP analysis. Despite promising overall model performance (macro F1 = 0.71, \(\kappa\) = 0.71), the sample size per class was limited, particularly for Strength- and Stability-Deficient profiles. Bootstrapped precision, recall, and F1-score estimates revealed wide confidence intervals in these under-represented categories (e.g., F1: 0.286-1.000 for Strength-Deficient; 0.000-0.909 for Stability-Deficient) (Table S2). This underscores the need for cautious interpretation and highlights the importance of larger, more balanced datasets in future validation studies. The rule-based hierarchy introduces boundary effects; in rare cases, athletes with mixed deficits but normal FMS could be misclassified. While such cases did not appear in our data, they remain theoretically possible due to the use of mutually exclusive categories. This study was cross-sectional. We did not track athletes longitudinally to assess how profile-based interventions affect injury risk or performance outcomes. While the rationale for individualized targeting is strong, its real-world impact remains to be tested. Lastly, our test battery did not include markers such as speed or power, which could enhance profiling but would increase burden in field settings. Future studies with larger, multi-club datasets can revisit higher-capacity algorithms (e.g., XGBoost, SVM) once sample size is sufficient to avoid over-fitting. Because test performance varies with maturation and sex and two deficit categories were under-represented, there is a need for broader recruitment. Longitudinal implementation is also recommended to assess how well the profiling system supports training adaptations and injury prevention over time. Collaborative data collection with other academies or federations could strengthen the model, allow for recalibration of thresholds, and improve robustness for applied use.