esa-sceva/satcom-chunk-collection huggingface dataset:
# Software-Defined Networking: Categories, Analysis, and Future Directions
## 8 SDN Controller Platforms
### Summary and Lessons Learned  
The summary of SDN controller platforms is presented in Table 7. These controllers manage flows to the switches/routers via SBI and the applications/business logic via NBI to deploy intelligent networks. The controllers install flow rules at the data plane devices (switches/routers) to perform required functionalities, such as forwarding, dropping, and so on. There are different kinds of SDN controllers (centralized and distributed), which are developed to perform various functionalities in different programming languages (Python, C, C++, Java, etc.). Some of the controllers are java-based (i.e., Beacon [212], Disco, Opendaylight, SMaRtLight [241], etc.). Some of them are based on distributed approaches. Ryu is very easy and straightforward to program. Beginners can easily deploy and use this controller for their network. Opendaylight is complex, and it is difficult to model new ideas. For experts, it is good option to use because it provides a dozen southbound APIs and protocols, such as NetConf, OVSDB, and PCEP, for managing and configuring forwarding devices. If someone want to rank these controllers on the basis of simplicity, the order would be as follows: Ryu, Floodlight, ONOS, ODL. The scalability, consistency, reliability, and security are very important to consider in designing an efficient and robust SDN controller. The current SDN controllers lack standard data models, anomaly detection, and security mechanisms. It is observed that developing a brand new SDN controller may not be the best solution; however, the existing SDN control frameworks need to be enhanced, refined, and improved to address the above-mentioned issues. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Studies** & **Techniques** & **Description** & **Strengths** & **Weaknesses** \\ \hline Beacon [212] & Java & Uses ad hoc NBI and SBI with OpenFlow 1.0 & Offers high-performance flow processing capabilities & No consistency and limited scalability \\ \hline Beehive [213] & GO language & Distributed control plane that utilizes REST northbound API and southbound API with OpenFlow specification & Supports multi-threading and good consistency & Weak documentation and reliability \\ \hline DCFabric [214] & C and JavaScript & Supports Linux platform along with CLI and WebUI. & Supports multi-threading and has a modularity functionality & Limited scalability \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of SDN controller platforms.
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## IVv6 Routing Protocol for Low Power and Lossy Networks (RPL)
### Support of Routing Metrics and Constraints  
The routing metrics can be a node or link-based. In general, the node-level metrics take the current state of the node into account. Typically, this state includes resources, like remaining energy, available memory, etc. As RPL specification does not have any obligation for using metric, rather it is open to choose the metrics that best suit the application requirements. Therefore, it is undesirable to adopt the same single metric or a combination of metrics for all applications, because, in the IoT network, diverse applications may exist with different requirements. Furthermore, a constraint in RPL, as an additional criterion, can be used for the pruning of links or nodes from a path, for instance, ignoring the nodes that have remaining energy below a certain threshold.
## 2 Proposed Method
### Data Preprocessing (Time-Frequency Domain Imaging)  
First, an accelerometer is used to collect vibration signals as work progresses under various circumstances with several different process conditions. The raw vibration signal is converted into a time-frequency domain through various signal processing methods to obtain useful information. The purpose is to extract the characteristics of the frequencies in a specific time domain from time-series sensor data composed of signals from various kinds of components, which are used for diagnosis. The short-time Fourier transformation (STFT), wavelet packet decomposition (WPD), and WPD with spectral subtraction are used to convert signals into the time-frequency domain [34]. The parameters are adjusted in detail using a 50% overlap to increase the resolution of the time domain. Each of these different spectrogram images extracted by the signal processing methods is treated as input to the model.  
Figure 1: Detailed procedure of the proposed multi-objective instance weighting-based deep transfer learning network (MOTL) method.
## 3 Inherent Optical Properties (IOPs)
### Snow and Ice IOPs  
#### 3.5.1 General Approach  
Assuming that snow grains and sea ice inclusions have spherical shape, we may obtain their IOPs from Mie computations, which require the refractive index and the size distribution of the particles as input. Then, the IOPs, i.e., the absorption and scattering coefficients and the scattering phase function, \(\alpha_{\rm p}(\lambda)\), \(\beta_{\rm p}(\lambda)\), and \(p_{\rm p}(\lambda,\Theta)\), can be obtained from Equations (58)-(60). This approach leads to computed snow albedo values that agree surprisingly well with available observations [46; 47]. The following reasons why large errors are not incurred by assuming spherical shape have been advocated by [PERSON] as quoted elsewhere [48]: _The orientationally averaged extinction cross section of a convex particle that is large compared with the wavelength is one-half its surface area. The absorption cross section of a large, nearly transparent particle is proportional to its volume almost independent of its shape. The closer the real part of the particle's refractive index is to 1, the more irrelevant the particle shape.  
_The asymmetry parameter of a large particle is dominated by near-forward scattering, which does not depend greatly on particle shape._  
Hence, we may assume that snow grains and ice inclusions (air bubbles and brine pockets) consist of homogeneous spheres with a single-mode lognormal volume size distribution [see Equation (66)], and use the refractive index data base for ice compiled by [49]. Specifying the effective radius \(r_{\rm eff}\) and the width of the distribution \(\sigma\), one obtains the geometrical mean radius \(r_{\rm n}\) from Equation (74). Since the complex refractive index is prescribed, \(r_{\rm n}\) and \(\sigma\) constitute the only input required for a Mie code (see Section 3.4.2), which can be used to compute absorption and scattering coefficients as well the scattering phase function. One may choose to use only the first moment of the scattering phase function in conjunction with the Henyey-Greenstein scattering phase function because the Mie scattering phase function is unrealistic for non-spherical snow grains and ice inclusions.
## 2 Engineering Background
### On-Site Measurement  
In order to visually analyze the ventilation situation in the double-hole tunnel, the TSI air flow velocity measuring instrument was used to measure the air flow velocities of the six measuring points shown in Figure 2, and air flow velocities were recorded once every 20 m, so as to obtain the velocity distribution law of the double-hole tunnel. During the measurement process, the slag is transported in the advance side tunnel, and the forced fan outside the tunnel is turned on at high velocity. The undercut side tunnel is lined twice, and the ventilator outside the tunnel is turned on at low velocity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Exhaust the Blasting Gases** & **Maximum Number of Workers** & \
\begin{tabular}{c} **The Minimum** \\ **Permissible Air Flow** \\ **Velocity** \\ \end{tabular} &
\begin{tabular}{c} **Dilution of Internal** \\ **Combustion Engine** \\ **Exhaust Gases** \\ \end{tabular} \\ \hline Calculations & \(Q_{\rm y}=\frac{7.8}{t}\sqrt[3]{G\left(AL_{\rm p}\right)^{2}}\) & \(Q_{\rm p}=k_{\rm f}mq_{\rm f}\) & \(Q_{\rm w}=60\
u A\) & \(Q_{\rm f}=q\Sigma\) w \\ air flow demand & 1769.4 m\({}^{3}\)/min & 202.4 m\({}^{3}\)/min & 796.5 m\({}^{3}\)/min & 1824.6 m\({}^{3}\)/min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation of air flow demand of working face under different working conditions.  
Figure 1: Schematic diagram of on-site construction.  
Figure 3 shows the air flow velocity distribution at the measurement points of the double-hole tunnel; Figure 3a is the air flow velocity distribution at measurement points of the undercut side tunnel; and Figure 3b is the air flow velocity distribution of the advance side tunnel. It can be seen from Figure 3a that in the range of 60 m\(\sim\)120 m from the working face, the air flow velocity at the measuring points in the undercut side tunnel show a downward trend as a whole. In the range of 120 m\(\sim\)180 m, the air flow velocity at the measuring points rise first and then stabilize. As can be seen from Figure 3b, the air flow velocity at the measurement points of the advance side tunnel show an overall downward trend. It is worth noting that the distance between the central axis of the cross passage and the working face of the advance side tunnel is 130 m, and the air flow velocity fluctuates around the cross passage in Figure 3a, indicating that the cross passage may have an impact on the flow field of the double-hole tunnel.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## Appendix A
### The Variance of the Random Variable \(r\) Defined in Equation (4)  
To obtain the variance of \(r\), we first derive \(\mathrm{E}(r^{2})\):  
\[\begin{split}\mathrm{E}(r^{2})&=\int_{-\infty}^{0} y^{2}\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(y-1)^{2}}{2\sigma^{2}}}\mathrm{d}y\\ &=\int_{-\infty}^{-\frac{1}{\sigma}}(\sigma t+1)^{2}\frac{1}{ \sqrt{2\pi}}e^{-\frac{t^{2}}{2}}\mathrm{d}t\\ &=\int_{-\infty}^{-\frac{1}{\sigma}}(\sigma^{2}t^{2}+2\sigma t+ 1)\frac{1}{\sqrt{2\pi}}e^{-\frac{t^{2}}{2}}\mathrm{d}t\\ &=\left(\frac{\sigma}{\sqrt{2\pi}}e^{-\frac{t^{2}}{2}}+\sigma^{2 }\mathrm{Q}\left(\frac{1}{\sigma}\right)\right)+\left(-\frac{2\sigma}{\sqrt{ 2\pi}}e^{-\frac{t^{2}}{2}}\right)+\mathrm{Q}\left(\frac{1}{\sigma}\right)\\ &=(1+\sigma^{2})\mathrm{Q}\left(\frac{1}{\sigma}\right)-\frac{ \sigma}{\sqrt{2\pi}}e^{-\frac{1}{2\sigma^{2}}}\end{split} \tag{11}\]  
Since the variance of a random variable equals the expectation of the square of this random variable minus the square of the expectation of this random variable, we obtain  
\[\begin{split}\mathrm{Var}(r)&=\mathrm{E}(r^{2})- \mathrm{E}(r)^{2}\\ &=(1+\sigma^{2})\mathrm{Q}\left(\frac{1}{\sigma}\right)-\frac{ \sigma}{\sqrt{2\pi}}e^{-\frac{1}{2\sigma^{2}}}-\left(-\mathrm{Q}\left(\frac{1} {\sigma}\right)+\frac{\sigma}{\sqrt{2\pi}}e^{-\frac{1}{2\sigma^{2}}}\right)^ {2}\end{split} \tag{12}\]
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 2 Materials and Methods
### Machine Learning Methodology
#### 2.3.4 Hyperparameter Optimization Strategy  
To maximize model performance, an advanced hyperparameter optimization strategy was implemented using the Optuna framework [84] for the top-performing algorithms identified during the initial screening phase. This optimization focuses specifically on maximizing the accuracy of central UCS predictions, which serves as the foundation for the subsequent uncertainty quantification framework. Unlike traditional grid search or random search methods, Optuna employs a Bayesian optimization approach that adaptively explores the hyperparameter space based on previous trial results.  
The optimization strategy recognizes the hierarchical nature of the two-stage modeling approach: optimal performance in uncertainty quantification depends fundamentally on achieving the highest possible accuracy in the primary prediction model. Therefore, hyperparameter optimization concentrates exclusively on maximizing central prediction performance, with R\({}^{2}\) score serving as the primary optimization objective.  
For each algorithm in the refined candidate set, custom search spaces were defined based on their specific hyperparameters and their known influence on model performance for regression tasks. Random Forest optimization focused on the number of estimators, maximum tree depth, and node splitting criteria, as these parameters significantly impact the model's ability to balance bias and variance in small-to-medium datasets. GradientBoosting optimization centered around learning rate, maximum tree depth, subsampling rate, and the number of estimators, which govern the model's capacity to learn from sequential errors without overfitting. XGBoost optimization explored additional parameters including minimum child weight and column sampling rates to optimize feature selection during training.  
The optimization process incorporated efficiency-enhancing features including MedianPruner for early termination of unpromising trials, balanced exploration through 50 trials per fold with computational time limits, and systematic tracking of performance across different hyperparameter configurations. This comprehensive approach ensured that the selected primary model achieved optimal accuracy, providing the strongest possible foundation for the uncertainty quantification framework.  
Importantly, the uncertainty model employs a separate, simpler hyperparameter configuration optimized specifically for error prediction rather than primary UCS prediction. This separation allows each component of the two-stage system to focus on its specialized task without compromising overall system performance.
# Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera
## 3 Methodology and Design
### Point Cloud Data Preprocessing
#### 3.6.3 Statistical Filtering Denoising (SFD)  
After removing outlier points, sparse noise remains in the tread surface point cloud, primarily originating from minor uncalibrated information at the edges of the rubber tread. This noise is influenced by camera resolution and calibration errors, with higher-resolution cameras being more susceptible due to their greater pixel density. The tread point cloud data obtained through the RANSAC algorithm fall into the category of conventional point clouds, typically filtered using digital image processing filtering algorithms for noise removal operations. Failure to filter effectively can introduce bias errors in subsequent geometric analysis [36; 37].  
The distance threshold can be established as depicted in Equation (4):  
\[d=\lambda+r\sigma \tag{4}\]  
where \(\lambda\) is the mean value of the average distance \(\overline{d}\) between each point and its surrounding \(i\) neighbors in the point cloud data traversing the upper surface of the tread, and \(r\) and \(\sigma\) are the scale factor and the mean square deviation, respectively.  
By traversing the surface point cloud data on the tread again, it is sufficient to remove the anomalies that have an average distance \(\overline{d}\) from \(i\) neighboring points. The filtering uses the statistical filtering algorithm in the Open3d library to configure the values of \(i\) and \(r\): \(i\) specifies the number of neighbors to be considered and determines the average distance [38], which affects whether or not most of the noise points can be filtered out, while \(r\) controls the threshold level. After experimental trial and error, when \(i\) and \(r\) are taken as 15 and 1.5, respectively, they can effectively remove the noise points in the point cloud data, and the filtering results are shown in Figure 11:  
Figure 10: Flow chart of RANSAC algorithm.
# High-Quality Classroom Dialogue Automatic Analysis System
## 5 Experiments
### Research Findings  
Here, we present key research findings from the large-scale deployment of our system, summarizing its impact and potential insights. In the initial round of evaluation, we analyzed 1008 lessons, offering a comprehensive overview of its effectiveness.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**IRE-9**} & \multicolumn{3}{c}{**Gagne**} \\
**Categories** & **P** & **R** & **F1** & **Categories** & **P** & **R** & **F1** \\ \hline \(I_{1}\) & 0.8232 & 0.9125 & 0.8655 & Gain Attention & 0.6244 & 0.8175 & 0.7058 \\ \(I_{2}\) & 0.8782 & 0.9226 & 0.8997 & Inform Learners of Objectives & 0.7564 & 0.7765 & 0.7663 \\ \(I_{3}\) & 0.6903 & 0.5822 & 0.6316 & Stimulate Recall of Prior Learning & 0.7382 & 0.6590 & 0.6963 \\ \(R_{1}\) & 0.8522 & 0.5690 & 0.6824 & Present the Content & 0.8934 & 0.8710 & 0.8821 \\ \(R_{2}\) & 0.8442 & 0.9077 & 0.8748 & Provide Learning Guidance & 0.8082 & 0.7312 & 0.7678 \\ \(R_{3}\) & 0.7213 & 0.6418 & 0.6792 & Elicit Performance & 0.8671 & 0.8687 & 0.8679 \\ \(E_{1}\) & 0.9512 & 0.8543 & 0.9001 & Provide feedback & 0.7986 & 0.8369 & 0.8173 \\ \(E_{2}\) & 0.7243 & 0.7141 & 0.7192 & Assess Performance & 0.7480 & 0.6601 & 0.7103 \\ \(E_{3}\) & 0.6837 & 0.6114 & 0.6455 & Enhance Retention and Transfer & 0.7125 & 0.6550 & 0.6825 \\ \hline Marco avg & 0.7965 & 0.7462 & 0.7664 & Macro avg & 0.7719 & 0.7639 & 0.7653 \\ Weighted avg & 0.8308 & 0.8339 & 0.8280 & Weighted avg & 0.8487 & 0.8459 & 0.8465 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance for each category of IRE-9 and Gagne. (P, R, and F1 stand for Precision, Recall, and F1-score, respectively).
# Metal-Organic Frameworks Toward Electrocatalytic Applications
## 4 Recent Progress in Utilizing MOFs for Electrocatalysis and Relevant Applications
### Oxygen Evolution  
Although the main purpose of water splitting is to produce hydrogen fuel instead of the oxygen gas, the design of better electrocatalysts for oxygen evolution is still crucial due to the higher kinetic barrier and thus higher overpotential in general for oxygen evolution compared to those for hydrogen evolution [87]. The typical heterogeneous electrocatalysts for oxygen evolution are IrO\({}_{2}\) and RuO\({}_{2}\), which can reveal remarkable electrocatalytic activity at relatively low overpotentials in both acidic and alkaline electrolytes [87]. Cobalt phosphate-based electrocatalyst, usually termed \"Co-Pi\", is another common candidate that can generate oxygen from water in neutral solutions [88]. Besides these options, most electrocatalysts for oxygen evolution such as nickel hydroxide, cobalt oxide, and nickel-iron oxide require alkaline electrolytes [87; 89]. As the number of MOFs that are stable in strong alkaline solutions is quite limited, examples of stable MOFs providing electrocatalytic activity for oxygen evolution are relatively rare. A variety of nickel-based, cobalt-based, nickel-iron-based, and cobalt-iron-based MOFs have been applied for electrocatalytic oxygen evolution in strong alkaline media previously [90; 91; 92; 93; 94; 95; 96]. The transformation of MOFs into metal oxides and/or (oxy)hydroxides that truly provide electrocatalytic activity during electrocatalysis in alkaline solutions has been also reported [94]. In 2016, [PERSON] et al. reported the use of a Zr-MOF constructed from nickel (II) porphyrinic linkers for electrocatalytic oxygen evolution in the electrolytes with neutral pH [97]. In addition, the same group also demonstrated the incorporation of electrocatalystally active ruthenium-based complex into a Zr-MOF; the obtained MOF thin films exhibit activity for oxygen evolution in neutral electrolytes (see Figure 6) [41]. Similar concepts also got reported by [PERSON] and coworkers at the same time [98]. On the other hand, [PERSON] et al. reported the spatially well-separated cobalt oxide nanoclusters decorated in a Zr-MOF, NU-1000, which can be used for electrochemical oxygen evolution in weakly alkaline solutions [99]. A recent finding reported by [PERSON] and coworkers shows that the Zr-MOF incorporated with electrocatalystally active iridium-based complex can catalyze oxygen evolution at neutral pH [100]. Very recently, [PERSON] et al. designed the highly active electrocatalyst for oxygen evolution based on a nickel-iron-based MOF, NH\({}_{2}\)-MIL-88B, which was found to exhibit superior electrocatalytic activity in strong alkaline electrolytes and maintain its crystallinity after long-term electrolysis [101].  
Figure 5: The NU-1000 thin film electrodeposited with a layer of nickel sulfide for electrocatalytic hydrogen evolution [83]. Lime green: Zr; red: O; grey: C. Hydrogen atoms are not shown for clarity.
## 3 Results
### Nanowire-Based Sensors
#### 3.3.2 Sensing Mechanisms and Performance Evaluation  
Nanowire-based sensors operate based on different sensing mechanisms to detect and measure various parameters relevant to bridge maintenance. One common mechanism is the piezoresistive effect, where the electrical resistance of the nanowires changes with mechanical strain or pressure [55]. This change in resistance can be correlated with the applied strain or pressure, providing valuable information on structural behavior and deformation.  
Nanowires can also function as field-effect transistors (FETs), where the conductance or current flow through the nanowire is modulated by the presence of target analytes [56]. This enables the detection of chemical species, gases, or biomolecules. Functionalizing the nanowires' surface with specific receptors enhances selectivity and sensitivity in chemical sensing applications.  
Performance evaluation of nanowire-based sensors involves assessing their sensitivity, response time, and stability [57]. Sensitivity refers to the ability of the sensor to detect and measure changes in the target parameter. Response time indicates how quickly the sensor reacts to changes in the measured parameter. Stability evaluation involves long-term monitoring of the sensor's performance under various environmental conditions, ensuring its reliability and durability.  
Additionally, nanowire-based sensors can be evaluated for their selectivity, cross-sensitivity to other parameters, and limit of detection. Selectivity refers to the sensor's ability to distinguish the target parameter from other interfering factors [58]. Cross-sensitivity is the sensor's response to parameters other than the target, which should be minimized to ensure accurate measurements [59]. The limit of detection defines the lowest concentration or level of the parameter that can be reliably detected by the sensor.  
Hence, nanowire-based sensors offer unique sensing capabilities for bridge maintenance. Growth techniques such as the VLS method and molecular beam epitaxy enable the fabrication of nanowires with controlled properties. These sensors operate based on piezoresistive effects, FET configurations, or other sensing mechanisms, allowing detection of parameters such as strain, pressure, or chemical species. Performance evaluation involves assessing sensitivity, response time, stability, selectivity, cross-sensitivity, and limit of detection, ensuring reliability and accuracy.