esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### XRD Assay  
Figure 2 displays the XRD results, which demonstrate the crystal structure of AgNPs produced using flaxseed extract. It can be attributed the planes (1 1 1), (2 0 0), (2 2 0), and (3 1 1) to the diffraction peaks at 2 of 38.06, 44.24, 64.45, and 77.61, respectively, which indicated the face-centered cubic (fcc) structure of flaxseed-mediated silver nanoparticles as reported in the reference (JCPDS: file No-087-0720). The diameters of particle size of silver nanoparticles were determined using Scherrer's Equation:  
\[D=K\lambda/\beta cos\theta\]  
where \(D\) is the crystallite size, \(k\) is the shape factor, which is assumed to be 0.89 for silver, X is the X-ray wavelength (1.5406 A), is the half-height width of the XRD peak, and \(\theta\) is the diffraction angle.  
AgNPs' predicted average crystallite size was estimated to be around 15.07 nm. Researchers can follow the guidelines of green chemistry and use less energy for synthesis by operating at temperatures, pressures, and pH that are close to those found in the ambient environment.  
This method can improve nanoparticles in some ways, including crystallinity, stability, size, and shape. Biomolecules (large macromolecules such as proteins, polysaccharides, lipids, and nucleic acids, and small natural molecules such as primary and secondary metabolites) produced by living organisms can convert the positive oxidation state of silver ion Ag\({}^{+}\) into the zero-oxidation state of atoms Ag0 and form the nanosilver particles in the biosynthesis of colloidal dispersions of AgNPs [18].  
Figure 2: XRD of the biosynthesized AgNPs.
## 4 Discussion
Adopting systematic approaches, such as the Label Recovery and Trajectory Designable Network (LRTDN) proposed by [PERSON] et al. [33], could alleviate this challenge. The LRTDN aims to address the noise in pseudo-labels by incorporating a robust annotation check module that identifies and corrects label anomalies, thereby enhancing the reliability of the training data. This ensures that the model learns from more accurate labels, ultimately improving its performance in the presence of annotation noise. In agricultural fields, pixel-level annotation poses unique challenges. The presence of many small regions of interest, such as wheat heads, necessitates meticulous annotation. Moreover, the lack of contrast between these regions and the background further complicates the task. For instance, accurately differentiating wheat heads from the background requires significant attention and accuracy during manual annotation. Consequently, pixel-level accurate annotation of a single image may take several hours, making developing large-scale, pixel-level, accurately annotated images costly and tedious. Furthermore, agricultural data are substantially variable due to variations in environmental conditions, phenotypic varieties, and crop growth stages. As such, it is impractical to develop a large-scale annotated dataset that represents all these variations across the crop's lifecycle. By utilizing only a small number of manually annotated images from the target domain, the proposed method accelerates the development of deep learning models for various fields. Additionally, the resulting model could facilitate the development of further annotated samples from the target domain by making predictions using the initial model for samples from the target domain and then applying corrections if needed. This semi-automated approach substantially accelerates the creation of annotated samples from the target domain that could be used to further improve the performance of the developed model in the target domain. In this research, we adopted an extreme strategy by utilizing only two annotated images for training and one for validation. However, we recommend incorporating more manually annotated samples from diverse domains to further enhance the robustness of the proposed approach. Additionally, in this research, we primarily relied on default hyperparameters. Tuning the model hyperparameters could lead to further improvements in model performance.
## 1 Introduction
They concluded that CV method can filter out inaccurate surrogates well and may identify the best surrogate if sample points which are used to build surrogate models are enough. Later [PERSON] and [PERSON] [20] concluded how CV is applied to consistently choose the best method and addressed several seemingly common misconceptions on CV, such as better estimation of prediction error by CV does not mean better model selection. In previous work, we have proposed a hybrid surrogate model, named the extended hybrid surrogate model (E-AHF) [17]. In the process of constructing the E-AHF model, the LOO-CV was used to select the best surrogate model and filter out worse ones. The best surrogate model is considered as the benchmark model for others; worse ones are removed, finally the rest surrogate models are saved to build the final surrogate library. In the E-AHF model, the key step is to construct the library of surrogate models and determine the best surrogate. Therefore, the criterion for modeling rank, i.e., the LOO-CV, is significant to E-AHF. However, there still exists a problem: the LOO-CV method cannot always filter out inaccurate models exactly and select accurately the best one without enough sample points. In the LOO-CV method, the sample points are divided into two parts, i.e., training points and one validation point. We modified the LOO-CV by moving a validation point around random normal distributions rather than leaving it out, named move-one-away cross validation (MOA-CV). In this paper, four surrogate techniques are used to construct models; errors generated by MOA-CV and LOO-CV methods, i.e., MOA-CVerror and LOO-CVerror, respectively, were used to select the best model from those four surrogate models through four standard mathematical functions and one engineering problems. A higher MOA-CVerror/LOO-CVerror indicates worse performance of surrogate models. However, how can we know exactly which best model is the true best model? Hence, we used the coefficient of determination (\(R\)-square, \(R^{2}\)) which is reliable to evaluate the accuracy of a model to be a calibration of MOA-CV and LOO-CV. Additionally, extra testing points need to be generated randomly for \(R^{2}\). The accurate rate of selecting the best surrogate model is used to assess and compare the performance of MOA-CV and LOO-CV. To explain the MOA-CV method clearly, a 1-dimensional (1D) function was used to demonstrate the operation process first. The remaining of this paper is organized as follows. The LOO-CV method is briefly introduced in Section 2, followed by the introduction of the MOA-CV method in Section 3. Results and discussions through four standard mathematical functions and one engineering problem are conducted in Section 4. Conclusions are presented in Section 5.
## 3 Proposed Research Methodology
### Models
#### 3.3.2 Random Forest  
Random forest (RF) is an ensemble model derived from decision trees, applicable for both classification and regression tasks. Decision trees function by recursively constructing a search tree that best divides the data into distinct response classes according to their predictor variables. An example of this process is outlined by [PERSON] et al. in their discussion of the ID3 algorithm [58], which employs a greedy method [59]. For the root node, \(p\), Entropy (\(E3\)) is computed for each response class \(i\).  
\[E(p)=-\sum_{i=1}^{n}p_{i}\times\log(p_{i}) \tag{3}\]  
Dividing the dataset using each predictor, the Entropy of the resultant child nodes is determined, and the information gain--which signifies a decrease in Entropy--is then calculated (Equation (4), where \(p\) represents the parent node and \(p_{j}\) represents the child nodes from splitting via predictor \(T\)). The predictor that results in the highest information gain is chosen for the split. This iterative procedure continues until the response classes are entirely segregated, there are no remaining predictors to split on, or a predetermined depth limitation is reached.  
\[Gain(p,T)=E(p)-\sum_{j=1}^{n}p_{j}\times E(p_{j}) \tag{4}\]  
Decision tree models are straightforward and easy to interpret; however, their inherent low bias renders them prone to overfitting unless measures such as pruning or limiting their depth are implemented [60].  
Random forest addresses these limitations by integrating multiple decision trees to improve model robustness and accuracy. The original RF algorithm by [PERSON] [61] constructs an ensemble of decision trees, each developed from a bootstrapped sample of the data. Unlike decision trees that consider all the predictors for splitting at each node, the RF selects a random subset of predictors at each node, increasing the diversity among the trees. This method, known as 'bagging' (bootstrap aggregating), significantly reduces the risk of overfitting by ensuring that most trees are uncorrelated [62] while preserving the model's interpretability and computational efficiency [63]. Ensemble models have been shown to perform better than individual models do if each model in the ensemble is better than random [64]. However, the RF may underperform when predicting values outside the training data's range [65], and it can still overfit if low-information predictors are abundant in the feature space [66]. Careful hyperparameter tuning and validation can alleviate this [67].
## 2 Structure Prototype and Finite Element Model
### Probabilistic Distribution Function Using Fragility Curve  
[PERSON], et al. [46] proposed the concept of seismic fragility analysis to evaluate the probabilistic seismic performance of a nuclear power station, and it has since evolved into a sophisticated analytical tool for analysing the seismic risk of various structures. With a particular intensity measure of ground motions, seismic fragility is defined as the conditional probability of attaining or exceeding a specified damage state. Fragility assessment is a technique of estimating the risk of a seismic event on a structural model, and it is a useful method for estimating the likelihood of structural damage. For far-field and near-field ground movements, the fragility curve represents the probability of collapse with regard to the IM. Along with the ISDR, it is used as a damage measure to detect serious deterioration that could lead to structural collapse under seismic scenarios.  
In seismic risk assessment, fragility functions are powerful statistical tools that can be used to understand the dynamic response of transmission towers. They are the link between a given seismic IM and the likelihood of reaching or exceeding a specific damage state. In this study, Sa(T1) is used as the seismic IM to develop the fragility curves. There are two processes involved in the formation of the fragility curves. The mean and standard deviation of the collapse capacity is computed at the initial stage. The second stage involves the development of the fragility curve from the obtained values of mean and standard deviation using the lognormal Cumulative Distribution Function (CDF).  
Figure 9: Selection of repeated ground motion successive seismic events.  
Since Sa(T1) is used in this work as the seismic IM for the IDA, the same parameter is also employed in the development of the fragility curves. Suppose the fragility curve is being drawn for the Collapse Prevention (CP) state. The Sa(T1) values that lead to the collapse state recorded from IDA would be used in the calculation of the fragility curve. In this work, a variety of Sa(T1) values are recorded under a variety of various ground motion records. Then, the mean and standard deviation of these values are calculated and used in Equations (1) and (2). Using the established fragility curves for all permutations of the transmission tower, the seismic performances of the structural models are evaluated in terms of IO, LS, and CP performance limit states.  
\[\mathrm{P}[\mathrm{Damage}/\mathrm{Sa(T1)}]=\phi\bigg{(}\frac{\mathrm{ln}( \mathrm{Sa[T1]})-(\mu)}{\sigma}\bigg{)} \tag{5}\]  
where \(\phi\) is the standard normal cumulative distribution function, \(\mu\) and \(\sigma\) are the mean and standard deviation of logarithm Sa(T1).
## 3 Air Data System Description and Functions  
As described in Figure 2b, the air data system partially based on synthetic sensors is made up of three main parts: (1) air data computer (ADC), (2) external Pitot probe (or Prandtl tube), and (3) external total air temperature probe. The TAT probe is equipped with dual sensing elements and anti-icing heaters. For the sake of generality, the TAT probe can be replaced with an Outside Air Temperature (OAT) if the operating Mach number is lower than 0.3. The choice of two sensing elements is not crucial but it is exploited to detect sensing element failures at the ADS level. All other failures (cables, heaters, etc.) can only be detected at the aircraft level with ADS redundancy.  
The Pitot probe is equipped with anti-icing heaters, whereas the sensing elements, i.e., absolute and differential pressure transducers, are housed in the ADC. Moreover, the Pitot probe has a very short pneumatic (less than 10 cm), metallic connection ducts from the probe. The TAT probe is equipped with a redundant analog sensing elements connected to the ADC by means of wires. Both the Pitot and TAT probes have power connections for  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Tag** & **Description** \\ \hline In-flight & Climb/Cruise/Descent \\ T/O—ground & Ground/Taxi/Takeoff (before the \(V_{1}\) speed) \\ T/O—in-flight & (after the \(V_{1}\) speed) \\ LAND—ground & Ground roll/Brake/Taxi \\ LAND—in-flight & Landing (before the touch down) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flight phases.  
Figure 1: Rationale of the failure rate calculation for the “top-down” approach.  
anti-icing purposes. Figure 2b describes the interactions among the main ADS sub-parts. The Pitot probe is made up of (1) the total pressure tube with static holes and (2) integrated heaters for anti-icing purposes. The TAT is made up of (1) the total temperature probe, (2) an integrated heater for anti-icing, and (3) two integrated sensing elements.  
The ADC is split into the following sub-parts: (1) two boards with hardware (HW) components (resistors, capacitors, etc.), (2) absolute pressure transducers, and (3) differential pressure transducers. The ADC board based on FPGA [33] encompasses all the necessary hardware and software functionalities compliant with applicable airworthiness standards [5]. For example, the ADC shall be able to apply pressure and temperature calibration algorithms in order to convert the local measurements into freestream measurements with the required accuracy.
## 4 Research Clusters: Content Analysis
### Cluster 4: Environmental Sustainability in Maritime Shipping
#### 4.4.2 Subcluster 2: Technological Feasibility and Energy Efficiency Analysis  
This subcluster comprises two articles that focus on the technological and operational aspects of achieving environmental sustainability through improved energy efficiency and waste heat recovery. It emphasizes quantitative performance analyses and feasibility studies that support the adoption of innovative technologies in maritime applications.  
[PERSON] and [PERSON] (2015b) addresses the critical need for improved energy efficiency in shipping by analyzing the feasibility of installing waste heat recovery (WHR) systems on vessels [52]. It introduces a methodology that calculates the available energy and exergy for WHR systems, comparing these with the vessel's propulsion and auxiliary power demandsbased on its operational profile. The proposed method serves as a decision support tool for shipowners by linking expected fuel savings to payback time and required capital investment. The case study results indicate that, depending on the waste heat sources and WHR system efficiency, realistic fuel savings of 5-15% can be achieved, highlighting both the environmental and economic benefits of such technologies.  
[PERSON] et al. (2018) state that the complexity of a cruise ship's energy system makes it a prime subject for energy and exergy analysis, as demonstrated by a one-year study of a Baltic Sea cruise ship [54]. The energy analysis revealed that propulsion consumes 46% of the ship's total energy, while both heat and electric power generation each account for 27%. The exergy analysis identified that 76% of energy is lost through combustion processes, with further losses occurring in equipment like turbochargers, heat recovery steam generators, steam heaters, preheaters, sea water coolers, and electric generators, especially in engines without heat recovery devices. By combining direct onboard measurements with computational models, the systematic approach used in this study provides a realistic representation of a ship's energy behavior and identifies key areas for further detailed analysis, which can support efforts to reduce the environmental impact of ship energy systems.  
Key considerations within this subcluster include the evaluation of emerging technologies that can improve energy efficiency and performance metrics as robust tools for measuring system performance.
# A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection
## 4 Experiments and Discussion
### Experiment Setup  
We evaluated two methods, DBFAD [23] and RD4 AD [24], chosen based on their availability as open-source code ([[https://github.com/[PERSON]/DBFAD](https://github.com/[PERSON]/DBFAD)]([https://github.com/[PERSON]/DBFAD](https://github.com/[PERSON]/DBFAD)) accessed on 2 April 2024) and their suitability for a one-class classification approach ([[https://github.com/hq-deng/RD4](https://github.com/hq-deng/RD4) AD.git]([https://github.com/hq-deng/RD4](https://github.com/hq-deng/RD4) AD.git) accessed on 2 April 2024) for the proposed fabric dataset.  
To evaluate the efficiency of the selected methods, we adopted a systematic approach. Initially, we divided the entire training dataset into six distinct parts, with data volumes of 1k, 2k, 4k, 8k, 16k, and 32k normal images. We enhanced the quality of our input data by cropping the images to a standard dimension of \(2000\times 1000\) pixels (width \(\times\) height) from the centre, addressing the noise present around the image corners, a common issue in our dataset. This resizing process was applied to the testing images as well, ensuring consistency throughout our evaluation process.  
Following this preprocessing phase, we trained each chosen method on each dataset partition and tested each iteration on the testing dataset to measure performance.  
For the RD4 AD [24] method, we configured the parameters as follows: 200 epochs, a learning rate of 0.005, a batch size of 16, an image size of 256 \(\times\) 256 pixels, a patience of 5. For the DBFAD [23] method, the configuration was as follows: 100 epochs, a learning rate of 0.005, a batch size of 16. an image size of 256 \(\times\) 256 pixels, a patience of 10.  
We utilized the Nvidia RTX A6000, Santa Clara, CA, USA for both training and testing purposes. These implementation details were consistent across all dataset splits, ensuring a fair and comprehensive evaluation of our methods. This systematic approach allowed us to rigorously compare the performance of DBFAD and RD4 AD under varying data volumes, ensuring robust and reliable results for fabric defect detection.
# A Survey on Change Detection and Time Series Analysis with Applications
## 1 Introduction
In many practical applications, researchers have to deal with non-stationary and unequally spaced time series. This paper starts by reviewing the most popular and traditional time series analysis methods and continues by reviewing some of the most recent methods of analyzing non-stationary and unequally spaced time series. More specifically:  
* In Section 2, several popular frequency decomposition methods are briefly reviewed, such as the Fourier transform and least-squares spectral analysis along with their modifications. * In Section 3, several popular time-frequency decomposition methods are discussed, including the short-time Fourier and wavelet transforms, Hilbert-Huang transform, constrained least-squares spectral analysis, and least-squares wavelet analysis, and then two methods of analyzing multiple time series together are reviewed. * In Section 4, several change or breakpoint detection methods within non-stationary time series are studied. * In Section 5, many applications of the methods in applied sciences along with other techniques of time series analysis are briefly mentioned. * Finally, the conclusion, findings, and limitations of this investigation are briefly summarized in Section 6. The advantages and weaknesses of each method are briefly discussed herein. For the sake of consistency and fluency, all the methods presented herein are described in terms of time and frequency. In general, all the methods can be applied to analyze any data series, e.g., distance and wavenumber instead of time and frequency, respectively. Herein, we discuss how one may: (1) extract useful information from a time series theoretically and empirically, (2) attenuate noise and regularize time series, (3) detect and classify changes in the time series components, and (4) analyze unequally spaced time series without any interpolations while considering the observational uncertainties.
###### Abstract  
Proximity-based content sharing between nearby devices in cellular networks using device-to-device (D2D) communications--without routing through the base station--has emerged as a promising solution for offloading traffic from the core cellular network and reducing network congestion, especially when the users requesting content can tolerate some delay before receiving it. Although several analytical models have been developed to derive theoretical performance bounds of D2D-based offloading schemes under different user mobility patterns and routing algorithms used for content dissemination, how to jointly analyse time-limited caching and forwarding policies with both constant and asynchronous timeouts remains still an unsolved problem. To address this issue, we propose a novel fluid model based on ordinary differential equations (ODEs) for the performance analysis of a general D2D-based mobile data offloading scheme, called OORS, which considers both content delivery guarantees and time limitations for storing content copies in local device caches, making it more practical for real-world applications. We also formulate an optimisation problem to maximise the utility of the content dissemination process through a simplified analysis of the stationary regime of the ODE model. Simulation results validate the accuracy of our model predictions, in terms of both aggregate statistics and the temporal evolution of the system state, using both synthetic and real-world mobility datasets. Finally, we compare OORS--optimally tuned with respect to protocol parameters--to two state-of-the-art content offloading schemes, Push-and-track (PAT) and SNSNI, a seed node selection algorithm based on node influence. Our results show that OORS achieves similar offloading efficiency to the benchmarks while reducing the number of content copies by at least 50%.  
mobile data offloading; D2D communications; fluid models; ODE; performance analysis Article