esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Summary of Optimization Procedure  
While the coefficients presented in Figures 2 and 4 can be used to implement the approximated fractional-order elliptic responses in this work, to realize responses with different ripple characteristics requires calculation of different sets of coefficients. To support the adoption of the process used in this work to calculate further sets of coefficients for different characteristics, the steps are summarized below:  
1. Select desired second-order elliptic magnitude characteristics to approximate with fractional-order filter: 1. Passband ripple (5 dB in this work). 2. Stopband attenuation (\(-50\) dB in this work).
2. Select target frequency band to use for procedure fitting.
3. Implement objective function given by Equation (9) using desired elliptic response and fractional-order transfer function given by Equation (7).
4. Apply optimization solver to objective function to evaluate coefficients for target filter order (\(1+\alpha\)).
5. Evaluate stability of fractional-order filter with solved coefficients using W-plane transformations.
6. Select appropriate circuit topology to realize fractional-order transfer function.
7. Calculate necessary component values to realize target coefficients for desired center frequency.
# Machine Learning Methods in Weather and Climate Applications: A Survey
## 7 Discussion
### Overall Comparison
Medium-to-long-term climate predictions, however, face challenges related to the scarcity of quality long-term datasets and the intricacies associated with interdependent climatic variables. Yet, there are challenges that are common to both, exemplified by the nonlinearity inherent in weather and climate prediction models, which underscore the complex dynamic relationships among atmospheric variables, necessitating techniques adept at capturing such intricate interactions. Furthermore, the assessment of model uncertainties is arduous as they emanate from various facets, demanding algorithms that can quantify, accommodate, and ideally mitigate these uncertainties to augment the reliability and accuracy of predictions. **Machine-learning models vs. traditional models. ** In terms of computational speed, machine learning algorithms--particularly those based on deep learning--have the capability to process extensive datasets at a far quicker rate compared with traditional methodologies. When it comes to prediction accuracy, the machine learning algorithms stand out for their superior feature extraction capabilities, often yielding more precise outcomes in short-term weather forecasting scenarios. Additionally, the adaptability of machine learning models enables them to evolve and improve over time. This flexibility makes them particularly useful tools that can be fine-tuned as climate data and observational technologies continue to advance. While machine learning models can excel at generating rapid and sometimes more accurate forecasts, their lack of interpretability can be a barrier to gaining deeper scientific insights. Machine learning models, especially complex ones like deep neural networks, are often considered \"black boxes\", meaning their internal workings are not easily understandable. This is a significant drawback during meteorological application. Understanding the underlying mechanisms of weather and climate variability is crucial across all temporal scales, serving as the bedrock upon which all predictive methods are built. For instance, in short-term weather forecasting, an in-depth grasp of these mechanisms assists researchers in selecting the most relevant datasets. For example, when forecasting precipitation, it would be ineffective to merely input precipitation data as a training set. Instead, one must understand the specific meteorological factors that influence precipitation in a given region. This necessity becomes even more pronounced for medium-to-long-term forecasts, which are inherently more complex. To construct accurate and reliable models, it is imperative to identify the factors that interact with each other, eventually leading to variations in the target predictive elements for a particular region. Thus, a nuanced understanding of these mechanisms not only enhances the precision of our models but also broadens the scope for comprehensive climatic analysis and future scenario planning.
## 5 Experimental and Simulation Setup Analysis  
We used high-computing hardware and software in the experimental setup to run simulations and observed the results to predict intrusion attacks. So, we used a high-memory computer processor, intel Xeon W1370 (Intel Corporation, Santa Clara, CA, USA), and GPU RTX3072 (NVIDIA Corporation, Santa Clara, CA, USA) on the Windows 11 platform (Microsoft Corporation, Redmond, WA, USA). The Windows 11 platform had 16 GB RAM and SSD. We used Python (Version 3.10) and Jupiter Notebook software (Version 7.3.3) current versions to conduct the experiment. Our proposed method used a sample batch size of 256 in the experiment, along with the Adam optimizer, SoftMax, and Relu activation functions. We ran a maximum number of 25-80 epochs. We also used binary classification with Cross Entropy loss.  
Our research method values and parameters used in the experimental setup are in Table 3. We present an extensive experiment examining the performance of our proposed DLMIDPSM algorithm and the ML and DLMIDPSMs. The evaluation process included using deep learning-based and other machine learning-based classification models. The criteria for assessing our performance metrics were accuracy, precision, recall, and F1-score. These were based on the performance evaluation of the simulation hyperparameter parameters and their values, as shown in Table 4 below.  
Furthermore, we showed our proposed machine learning-based (ML) model, as well as the deep learning multilayer perceptron (DLMLP) intrusion detection and prevention  
Figure 19: The ML and DLMIDPSMs pipeline used in this research.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Learning Table Rate & \(10^{-3}\) \\ Sample Batch Size & 256 \\ Optimizer & Adam \\ Activation Functions & SoftMax and Relu \\ Number of Epochs & Max 100 \\ Binary Classification & Cross Entropy \\ GPU & RTX3072 \\ Processor & Intel Xeon W 1370 \\ Windows Platform & 11 has 16 GB RAM, SSD \\ Language Platform & Python, Jupiter Notebook \\ Classification Method & Binary \\ Libraries & Pandas, Scikit, Tensor flow, Keara \\ Dataset Used & CICIoT2023 dataset \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental simulation setup hyperparameters.  
system (IDPS)-based models. We also showed the combined machine learning and deep learning models as ML and DLMIDPSMs. The proposed ML and DLMIDPSMs provided an outstanding performance in terms of precision, recall, f1-score, and support, as shown in Table 3 above. In addition, we presented the confusion matrix of our proposed exceptional combined ML and DLMIDPSMs, shown in Figure 20. Based on the confusion matrix performance, we could determine that the models were more effective in detecting the DDoS, DoS, and Mirai intrusion attacks, which depict the actual botnet attacks for intrusion detection.
# A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images
## 2 Basic Model of DCBCD  
### The Dual-Channel Image Module  
During the imaging process of dual input channels, it is assumed that the images are blurred with their blur kernel, which is from the different input channels. The blur model of a two-channel image can usually be written as:  
\[\begin{split} Y_{1}^{Total}&=X\mathbin{\hbox{ \hbox to 0.0 pt{\hbox{\lower 4.0 pt\hbox{$\sim$}}}\hbox{$<$}}}H_{1}^{Total}+n_{1}\\ Y_{2}^{Total}&=X\mathbin{\hbox{\hbox to 0.0 pt{\hbox{ \lower 4.0 pt\hbox{$\sim$}}}\hbox{$<$}}}H_{2}^{Total}+n_{2}\end{split} \tag{1}\]  
where \"\(\mathbin{\hbox{\hbox to 0.0 pt{\hbox{\lower 4.0 pt\hbox{$\sim$}}}\hbox{$<$}}}\)\" denotes the discrete convolution operator, \(Y_{1}^{Total}\) and \(Y_{2}^{Total}\) are the two-channel input images, \(X\) is the ideal image, \(H_{1}^{Total}\) and \(H_{2}^{Total}\) are different blur kernels, and \(n_{1}\) and \(n_{2}\) denote the additive noises incurred in the imaging processes of channel-1 and channel-2, respectively; both of which are zero-mean Gaussian noises by default. Takingthe example of recovering an image of channel-1, the deconvolutional algorithm model under the dual-channel framework can be written in the following form:  
\[\begin{split} H_{1}^{Total}=\underset{\forall H_{1}^{Total}\in \mathcal{H}_{1}}{\text{argmin}}\left\|H_{1 ter}^{Total}\right.\oplus X_{1}^{Total}-Y_{1 }^{Total}\right\|_{2}^{2}(\text{\emph{Estimate Kernel}})\\ X_{1}=\underset{\forall X^{Total}\in\mathcal{X}}{\text{argmin}} \left\|H_{1}^{Total}\right.\oplus X_{1 ter}^{Total}-Y_{1}^{Total}\right\|_{2}^{2}( \text{\emph{Estimate Original image}})\end{split} \tag{2}\]  
where \(\|\quad\|_{2}\) is the norm operator, and \(\mathcal{H}_{1}\), \(\mathcal{X}\) denote the pool of allowed \(H_{1}^{Total}\) and \(X_{iter}^{Total}\). \(X_{1}\) is the final restoration image of channel-1. \(H_{1 ter}^{Total}\) is the iter-kernel in the process of channel-1, and \(X_{iter}^{Total}\) is the iter-image in the process of channel-1. Therefore, it is the same with channel-2.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 2 Materials and Methods
### Spectral Data  
In order to model observed temperatures based on LULCs, we used spectral data from the Sentinel-2 satellite constellation, provided by the European Space Agency. Sentinel-2 provides 13 separate bands, which provided information between 433-2280 nm on the electromagnetic spectrum (Table 2); [21]). The satellite had a temporal resolution of approximately 5 days and spatial resolutions of 10 m\({}^{2}\) and 20 m\({}^{2}\) for visible light and infrared, respectively. In addition, Sentinel-2 provided three 60 m\({}^{2}\) bands which were optimized for atmospheric interference detection (bands 1, 9, and 10)--these were not included in the analysis due to their limited relationship to LULC [22]. Notably, Sentinel-2 did not capture thermal information, such as the commonly-used Landsat or ASTER platforms [23]--in other words, all spectral information in our analysis was based purely off of the surface reflectance of different LULCs within different bandwidths of the visible/infrared spectrum.  
Often, analyses using satellite imagery combine several bands to create an index that describe specific LULC attributes, and categorize them such as 'impervious surface' or'vegetation' [22]. Our analysis abstracted this classification by one level and used the raw spectral bands themselves, which were used to calculate different indices and land covers. Within the Washington, D.C. and Baltimore, MD study areas we conducted a brief analysis between two common satellite imagery indices and the bands of Sentinel-2. The first index used was the Normalized Difference Vegetation Index (NDVI), an index with values scaled from \(-1\) (less vegetation) to 1 (more vegetation) [24]. NDVI is common in remote sensing literature and is often used for vegetation detection and change analyses [25; 26]. The second index tested was the Normalized Built-up Area Index (NBAI), an index which associates higher values to impervious surfaces (including buildings) and lower values to water, soil, and vegetation [27].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Band** & **Purpose** & **Wavelength (nm)** & **Spatial Resolution (m\({}^{2}\))** \\ \hline
1 & Coastal aerosols & 443 & 60 \\
2 & Blue & 490 & 10 \\
3 & Green & 560 & 10 \\
4 & Red & 665 & 10 \\
5 & Vegetation red edge & 705 & 20 \\
6 & Vegetation red edge & 740 & 20 \\
7 & Vegetation red edge & 783 & 20 \\
8 & NIR * & 842 & 10 \\
8A & Narrow NIR & 865 & 20 \\
9 & Water vapour & 940 & 60 \\
10 & SWIR **/Cirrus & 1375 & 60 \\
11 & SWIR & 1610 & 20 \\
12 & SWIR & 2190 & 20 \\ \hline \hline \end{tabular}
*: Near infrared.; **: Shortwave infrared.  
\end{table}
Table 2: Sentinel-2 bands.
# Approaching Breakthrough: Resource-Efficient Micropolitan Removal with MBR-GAC Configuration
## 2 Materials and Methods
### Large-Scale Membrane BioReactor and Activated Carbon Filtration Pilot (MBR-GAC)  
A large-scale pilot plant (Qdim of 12 m\({}^{3}\)/h) with the MBR-GAC configuration (Figure 1) was established in 2020, with the MBR process representing a copy of the designated full-scale process [36]. The dynamic load to the pilot is linked to the actual inflow to the main WWTP. The biological process consists of three cascades with both anoxic and oxic zones fed with approximately the same load to utilize the carbon in the incoming wastewater for nitrogen removal and to avoid nitrate circulation flows. However, an external carbon source is added in the last cascade when necessary. For phosphorus removal, precipitation chemicals are added in the RAS/deoxy. The membranes used in the pilot are Zeeweed 500D (Veolia) ultrafiltration (UF) hollow fiber membranes (nominal pore size of 0.04 \(\upmu\)m) installed in two parallel membrane tanks (Figure 1). This corresponds to the same membrane units as designed for the full-scale WWTP.  
The design of the GAC filters was based on previous research collaborations between Syvab and IVL Swedish Environmental Research Institute [37; 38; 39]. The two GAC filter pilot lines, GAC L1 and GAC L2, operate as 2-stage filters in a lead/lag configuration to maximize the overall utilization of the carbon. The operation of the GAC filters, each with a filter bed depth of 1.9 m, commenced in the fall of 2020 (calendar week 40) and has continued, with a brief maintenance break for the MBR pilot, up to the present. Both GAC lines utilize Filtrasorb 400 (Chemviron) and are dynamically operated with equal share of the flow in accordance with the inflow to the MBR pilot.  
Each filter achieves an Empty Bed Contact Time (EBCT) of 10 min, corresponding to an average surface loading rate of 10 m/h. While GAC L1 operates with conventional backwash intervals, GAC L2 employs a need-based backwash strategy determined by the clogging level in each filter. For more detailed information about the pilot configuration, see [36].
## 4 Review of the Routing Protocols
### QoS-Based Routing Protocol Comparison  
Table 9 shows a comparison between QoS routing protocols. The comparison parameters include QoS, mobility, scalability, energy efficiency and the used methodologies.  
Figure 8: Categorisation of WBASN routing protocols.  
The aforementioned QoS-based routing protocols show the following aspects:  
* WBASNs require prioritised QoS mechanisms at the network layer to handle the heterogeneous nature of various body sensors.
* Geographical position and residual energy are the most important metrics for next-hop selection.
* End-to-end delay, reliability and packet delivery ratios are the most considered network performance parameters.
## 2 Materials and Methods  
### Plant Material and Collection of Fruit Samples  
Two _C. annuum_ L. cultivars previously released by the New Mexico State University (NMSU) Chile Pepper Breeding and Genetics Program, viz., 'NuMex Pumpkin Spice' (a jalapeno type) [22] and 'NuMex LotaLutein' (a serrano type), [23] were used in this study (Figure 1). The 'NuMex Pumpkin Spice' originated from the hybridization between the 'Permagreen' bell pepper and 'Early Jalapeno' and was developed using a pedigree breeding approach with three generations of backcrossing to a jalapeno fruit type, followed by multiple cycles of single plant selections [22]. 'NuMex LotaLutein' is a biofortified serrano pepper with improved lutein content and was derived from a segregating F\({}_{2}\) population resulting from selfing a commercial F\({}_{1}\) hybrid serrano cultivar [23]. 'NuMex Pumpkin Spice' has a characteristic deep orange (i.e., 'pumpkin'-like) color, whereas 'NuMex LotaLutein' has a distinct yellow color.  
The cultivars were transplanted at the Amy Goldman Flower Chile Pepper Institute Teaching Garden, Fabian Garcia Science Center, Las Cruces, NM, USA. Three mature fruits from three individual plants (representing three biological replicates) from each cultivar were sampled ~130 days after transplanting (~190 days after initial swing in the greenhouse). Fruit samples were subsequently freeze-dried for ~48 h using a Labconco
# Smooth Information Criterion for Regularized Estimation of Item Response Models
## 3 Simulation Study 1: Differential Item Functioning  
In the first Simulation Study 1, the assessment of differential item functioning (DIF; [30; 31; 32]) is considered as an example. DIF occurs in datasets with multiple groups if item parameters are not invariant (i.e., they are not equal) across groups. In this study, the case of two groups in the unidimensional 2 PL model is treated. The IRF is given by  
\[\mathsf{P}(X_{i}=1|G=g,\theta)=\Psi(a_{i}\theta-b_{i}-\delta_{i}\boldsymbol{1} (G=2))\text{ for }g=1,2\, \tag{14}\]  
where \(\delta_{i}\) indicates the DIF in item intercepts, which is also referred to as uniform DIF. The item parameters of item \(X_{i}\) are given by \(\boldsymbol{\gamma}_{i}=(a_{i},b_{i},\delta_{i})\). The mean and the standard deviation of \(\theta\) in the first group are fixed for identification reasons to \(0\) and \(1\), respectively. Then, the mean \(\mu_{2}\) and the standard deviation \(\sigma_{2}\) of \(\theta\) in the second group can be estimated.  
It has been pointed out that additional assumptions about DIF effects \(\delta_{i}\) must be imposed for model identification [33; 34; 35]. Assuming a sparsity structure on the DIF effects may be one plausible option. To this end, DIF effects \(\delta_{i}\) (\(i=1,\ldots,I\)) are regularized in the optimization based on the regularized log-likelihood function (3) or the minimization of the SIC (13). Regularized estimation of DIF in IRT models has been widely discussed in the literature [36; 37; 38; 39; 40; 41; 42].
# Distributed Coordination of Space-Ground Multiresources for Remote Sensing Missions
## 2 System Model
### Network Model
#### 2.2.1 Ordinary Vertices  
The ordinary vertices of TEG represent the temporal replicas of the observation objects, RSSs, DRSs, and ground stations in each slot of SGCN, of which the set is denoted by \(V_{OD}=V_{o}\cup V_{s}\cup V_{r}\cup V_{g}\), where \(V_{o}\), \(V_{s}\), \(V_{r}\), and \(V_{g}\), respectively, represent the set of vertices representing the temporal replicas of the observation objects, RSSs, DRSs, and ground stations in each time slot [18; 19]. For example, \(V_{o}=\{o_{n,i}^{k}|1\leq k\leq K,1\leq i\leq|OM_{n}|,1\leq n\leq N\}\) denotes the set of observation object vertices in TEG, where \(o_{n,i}^{k}\) is the temporal replica of the observation object \(o_{n,i}\) in the \(k\)-th time slot. Similarly, \(s_{n,i}^{k}\), \(r_{i}^{k}\), and \(g_{n,i}^{k}\) represent the temporal replica of RSS \(s_{n,i}\), DRS \(r_{i}\), and ground station \(g_{n,i}\) in the \(k\)-th slot, respectively.  
Figure 2: Time-expanded graph.
###### Abstract  
In order to study the effect of initial defects on fatigue crack propagation law, a test method to identify fatigue crack propagation rate and path based on load waveform variation was presented, and a new test device was designed to apply fatigue pulsation loads to multiple wires for bridge cables simultaneously in this paper. To simplify the corrosion defect formation process, a machine-cut notch was used to describe the initial defect on the steel wire surface. Firstly, fatigue crack propagation tests were conducted on the surface notched steel wire specimens. By using crack front marking technique, the \"beach-like patterns\" visible to the naked eyes on the cross sections of the steel wires were formed, and the process of fatigue crack propagation can be tracked and reproduced. Then Autodesk Computer Aided Design (AutoCAD) software was used to describe the morphology of \"beach-like patterns\" and accurately measure the depth and width of cracks. Finally, the influence of initial defect morphology on fatigue crack propagation rate was investigated according to the relationship between fatigue cracks depth and cyclic loading numbers. The results show that the test device designed in this paper can effectively realize the synchronous fatigue crack propagation test of multiple wires, and significantly shorten the fatigue test period. By observing and analyzing the change of load waveform, the moment of fatigue crack propagation can be directly and accurately determined. The larger the depth, the smaller the width and the sharper the morphology of initial defect, the faster the crack propagation rate and the shorter the life of notched wire specimens under the combined action of fatigue loads and corrosive medium.  
high-strength steel; corrosion defect; fatigue cracks; crack propagation; notched specimen +
Footnote †: journal: Applied Mechanics and Physics
## 3 Results and Discussion  
### Weight Analysis  
After updating the fully connected BP neural network for 20,000 epochs, the weight of the influence of 10-dimensional parameters on the mixture ratio was learned and fitted. As seen in Figure 3a, for all of the enhanced data, 60.41% of the fitting results were completely accurate, and 37.39% had a small classification deviation of only 1, which was also acceptable. The predicted results of the mixture ratio close to the critical point could indeed fall into an adjacent classification, so a total sum of 97.8% of the fitting results was acceptable. When focusing on the raw data of the 480 thrusters, as shown in Figure 3b, the accuracy of the fitting could be further improved. The percentage of the accurate fitting results was 81.01%, and 18.35% obtained a classification deviation of 1. This means that only 0.63% was unacceptable with a large deviation. Therefore, the results of the historical data fitting were reliable.  
Through normalization, the weight of 10 characteristic features, which means the extent of influencing the mixture ratio, is shown in Figure 4. The contribution of the SMD value and flow rate after injector welding in the NTO flow channel was shown to effect the mixture ratio more significantly than that of the other features. Compared to the thruster flow rate with the orifice, these two reasons can play more important roles, which are the main hidden reasons why there is often such a large difference between the liquid-flow test results and the firing test data.  
Among all the flow rate related characteristics, the influence of the NTO flow results after injector welding was the largest. This shows that the injector welding process has a great effect on the NTO flow channel. As shown in Figure 5, compared to the MMH flow channel with a circular nozzle, the NTO flow channel was more sensitive due to its annular nozzle structure. In particular, at welding temperatures of more than 900 degrees, a small deformation of the NTO nozzle gap can significantly affect the subsequent spray atomization and flow characteristics. Therefore, the micro deformation and flow rate change in the NTO flow channel after welding need to be given more attention in the manufacturing process.  
Except for the flow rate related features, the SMD value is the factor that has never been previously considered in the mixture ratio adjustment, but has the greatest weight of influence. It shows that atomization and combustion also significantly determine the deviation of the mixture ratio, however, this effect cannot be verified in the flow rate test, but only in the firing tests in the past.  
Figure 4: A comparison of the influence of the normalized weight of each factor.  
Figure 3: The accuracy of the mixing ratio classification fitting: (**a**) 24,000 enhanced data statistics; (**b**) 480 raw data statistics.  
Therefore, these two features were regarded as the sequence features, together with the final flow rate with orifice of the MMH and NTO, and were carried out in the RNN for four-to-one classification prediction of the mixture ratio.