esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance Analysis
#### 4.5.4 K-Fold Cross Validation  
The suggested EDRL approach applies the K-fold cross-validation (K = 30) technique and analyzes performance metrics, such as accuracy for attack classification. The analysis of K-fold cross-validation is provided in Table 5. A 30-fold cross-validation creates 30 random-fold integrations for training, validation, and testing data. The EDRL approach is iterated thirty times and trained, tested, and validated on the employed dataset. Furthermore, the suggested classifier is turned on to facilitate learning for all-fold cross-validation. Cross-validation involves folding the input dataset into a 30-fold set. In the proposed IDS, the suggested EDRL approach outperforms the current approaches for 30-fold cross-validation. Depending on the type of approach, each fold cross-validation has a distinct performance.
## 2 Methodology
### Data Pre-Processing Module  
The Electricity Generating Authority of Thailand (EGAT) collects the load data from five different regions, which includes the Central area, Bangkok, South, North, and North-East regions in Thailand. The collected load data have been recorded every 30 min from 2009 to 2021. In this paper, the net peak load for the whole country from 2019 to 2021 is applied to test our prediction models. As indicated in Figures 2 and 3, the variations in peak loads react time to time and day to day so that our load data are time series seasonal variation repeating regularly over time. A better insight into the seasonal component in the time series load data could also improve the performance of ML modeling. Consequently, the historical data from the previous week, previous day and previous time are chosen to train the forecasting models. Moreover, temperature data are used as an input because it is one of the external factors affecting loads associated with the meteorological situation in Thailand. The data pre-processing module is further divided into three processes: data cleaning, data segmentation, and data arrangement.  
Figure 1: Overview of the integrated system for STLF, including 1. data pre-processing module, 2. training module, and 3. forecasting module.
## 2 Materials and Methods  
The cases were retrieved from the archives of the Surgical Pathology and Cytopathology Unit of Padua University Hospital (Padua, Italy). The original diagnosis was confirmed by two hematopathologists (MP, LS), who also reviewed the immunophenotype of the lesions. Immunohistochemistryal analysis was performed in an automated immunostainer (BOND-MAX; Leica Biosystems, Milan, Italy) using primary antibodies against CD20 (clone L26, Dako, Glostrup, Denmark), CD79a (clone JCB117, Novocastra -Leica Biosystems, Milan, Italy), PAX5 (clone DAK-Pax5, Dako), CD3 (clone LN10, Novocastra), Bcl6 (clone LN22, Leica Biosystems), CD10 (clone 56C6, Dako), MUM1 (clone MUM1p, Dako), Bcl2 (clone 124, Dako), CD23 (clone 1B12, Cell Marque, Rocklin, CA, USA), c-Myc (clone EP121, Biocare, Pacheco, CA, USA), CD30 (clone JCM182, Leica Biosystems), CD15 (clone Carb-3, Dako), and Kief7 (clone Mib-1, Dako). EBV status was assessed by chromogenic in situ hybridization (CISH; Leica Biosystems) for EBV-encoded small RNAs (EBER).  
All cases were also tested for _MYC_, _BCL2_ and _BCL6_ rearrangements by fluorescence in situ hybridization (FISH), using break apart probes (ZytoLight SPEC MYC, SPEC BCL2 and SPEC BCL6 Dual Color Break Apart Probe; ZytoVision GmbH, Bremerhaven, Germany). Cut-off value for all markers was 20%. Clonality tests were not performed due to the large amounts of necrotic tissue and due to the clear-cut neoplastic nature of the process.
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 2 Unstructured Grid Finite Volume Method (FVM) CFD Solver
### The Inherent Sequential Properties of Implicit Algorithms  
The LU-SGS scheme is widely used in CFD because of its low numerical complexity and modest memory requirements. The LU-SGS scheme is based on the factorization of Equation (3) into the following form  
\[(D+L)D^{-1}(D+U)\Delta\vec{W}^{m}=-\vec{R}_{I}^{n} \tag{4}\]  
Figure 1: Data structure of cell-centred FVM scheme for unstructured grid.  
where \(L\) is the lower triangular matrix of system matrix, \(U\) is the upper triangular matrix, \(D\) is diagonal terms. Equation (4) is then solved in two steps, a forward and a backward sweep, that is,  
\[\begin{split}(D+L)\Delta\vec{W}^{*}=-\vec{R}_{I}^{n}\\ (D+U)\Delta\vec{W}^{n}=D\Delta\vec{W}^{*}\end{split} \tag{5}\]  
The process of LU-SGS solving is inherently serial. In solving a triangular system through a forward or backward sweep, the solution depends on pre-updated solutions whose elimination order is in the front. Take forward sweeping as an example, as shown in Algorithm 2. The outer loop corresponding to the variable i is sequential. Parallelizing the sparse dot product in the inner loop according to the variable j is also inefficient because the length of the vector involved in the dot product is typically short [22].  
```
1:for\(i=1\) to \(nCells\)do
2:\(\Delta W_{i}=-R_{i}\)
3:for all j in L(i)do\(\triangleright\) L(i) : the lower neighbour cells of cell i.
4:\(\Delta W_{i}=\Delta W_{i}-L_{ij}\Delta W_{j}\)
5:endfor
6:\(\Delta W_{i}=\Delta W_{i}/D_{i}\)
7:endfor
```  
**Algorithm 2** Forward sweeping of LU-SGS
## 3 Results
### Application in Residue Study from Unmanned Arial Spraying  
The study successfully applied the established analytical method to the residue trials (Figure 2e,f), allowing for the quantification of tricyclazole residues after unmanned aerial spraying in paddy fields. This application demonstrated the method's robustness and adaptability to real-world conditions.  
The findings revealed that the type of adjuvant used significantly affected the residue levels of tricyclazole (Table 4) at 0 DAT. The highest mean residue in rice panicle was observed in Plot C (1.60 mg/kg), where Gondor was used as the adjuvant, while Plot A, with no adjuvant, showed the lowest residue levels (0.35 mg/kg). Plot B, with the adjuvant Cares, exhibited moderate residue levels (0.68 mg/kg). These results underscore the importance of selecting appropriate adjuvants to achieve optimal pesticide efficacy and adherence, which, in turn, influences residue levels. There have been reports indicating that adjuvants can significantly impact droplet size, thereby contributing to the reduction of off-target drift [30,31]. This directly affects the efficiency of pesticide delivery and the residue levels on crops, highlighting the critical role of adjuvants in precision agriculture.  
Residual concentrations of tricyclazole in brown rice at 10 DAT were assessed, as illustrated in (Table S3). Plot A, applied according to the established safety usage standards, demonstrated residues below the MRL of 0.7 mg/kg, affirming the effectiveness of the pesticide application while maintaining regulatory compliance. Plots B and C also stayed well within the MRL. The investigation highlighted that while adjuvants increased initial attachment of tricyclazole at 0 DAT, ultimately, at the point of harvest (10 DAT), residue levels remained safely within the established MRL. This suggests that the use of adjuvants can be considered safe, since they do not cause residue levels to exceed regulated limits, and  
Figure 2: Chromatograms for tricyclazole. The orange triangle symbols indicate the retention time (t\({}_{\text{R}}\)) at 2.0 min. (**a**) Control sample, (**b**) solvent standard at 0.01 mg/kg, (**c**) matrix-matched standard at 0.01 mg/kg, (**d**) recovery sample at 0.01 mg/kg, (**e**) panicle sample in Plot A, and (**f**) brown rice sample in Plot A.  
that they potentially contribute to environmental safety by minimizing off-target drift. The major components of Cares and Gondor are natural paraffin and soy lecithin, respectively, both of which are recognized as safe for agricultural use. Thus, their use is considered safe for human health when applied to agricultural pest controls.
## 3 Analysis and Results of CGAN-IFCM
### Performance Evaluation of CGAN-IFCM
#### 3.1.2 Multi-Class Detection Model using CGAN-IFCM
Table 5 presents the TNR and TPR of the selected scenarios with \(N_{c}=[3,10]\), \(\omega_{n},\omega_{0}=[2,2.4]\), and a step size of \(1\) and \(0.1\), respectively. In addition to the first two points stated in Table 2, there are two other observations. 1. The performance of CGAN-IFCM is better when \(N_{c}=[6,8]\) compared to \(N_{c}=[3,5]\) and \(N_{c}=[9,10]\). It achieves lower performance when \(N_{c}<6\) and \(N_{c}>8\), as either an insufficient number of clusters or redundant clusters are considered. 2. The multi-class detection model is formulated using an equal sample size in each class after the adoption of CGAN. The difference between TNR and TPR is not significant, and there is no bias in TNR, which is either always higher than TPR, or TPR is always higher than TNR.
###### Abstract  
The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable technology to deploy wide-scale Wireless Sensor Networks (WSNs). These networks are usually designed to support convergecast traffic, where all communication paths go through the PAN (Personal Area Network) coordinator. Nevertheless, peer-to-peer communication relationships may be also required for different types of WSN applications. That is the typical case of sensor and actuator networks, where local control loops must be closed using a reduced number of communication hops. The use of communication schemes optimised just for the support of convergecast traffic may result in higher network congestion and in a potentially higher number of communication hops. Within this context, this paper proposes an _Alternative-Route Definition_ (ARounD) communication scheme for WSNs. The underlying idea of ARounD is to setup alternative communication paths between specific source and destination nodes, avoiding congested cluster-tree paths. These alternative paths consider shorter inter-cluster paths, using a set of intermediate nodes to relay messages during their inactive periods in the cluster-tree network. Simulation results show that the ARounD communication scheme can significantly decrease the end-to-end communication delay, when compared to the use of standard cluster-tree communication schemes. Moreover, the ARounD communication scheme is able to reduce the network congestion around the PAN coordinator, enabling the reduction of the number of message drops due to queue overflows in the cluster-tree network.  
Keywords:IEEE 802.15.4; ZigBee; Wireless Sensor Networks; cluster-tree; ARounD; wide-scale; message streams +
Footnote †: journal: Sensors
# Alkylimidazolic Ionic Liquids Absorption and Diffusion in Wood
## 2 Experimental Section
### Methods  
Before the ionic liquids sorption and diffusion experiments, the wood samples were weighed, oven-dried at 103 \({}^{\circ}\)C for 7 h, re-weighed (\(m_{od}\)), and conditioned in a desiccator over a supersaturated Mg(NO\({}_{3}\))\({}_{2}\) solution (54% relative humidity) for a week at 22 \({}^{\circ}\)C, to attain an average equilibrium moisture content (_EMC_) of 9.18% (spruce), respectively 8.75% (beech). The average densities of the two types of conditioned wood samples were 0.425 g/cm\({}^{3}\) (Norway spruce) and 0.713 g/cm\({}^{3}\) (beech), respectively.  
The conditioned wood specimens were weighed (\(m_{wood,0}\)) and submerged in 50 mL of IL solutions in stoppered glass vials. Periodically, the wood samples were taken out from the IL solutions (from 10 to 10 min in the first 200 min, and from thereon hourly, from 3 to 3 h, or from 12 to 12 h until sorption equilibrium, depending on IL type and wood species), the superficial amount of liquid present on their surface was removed by blotting with filter paper, after which they were weighed (\(m_{wood,IL,t}\)). The weighing operation was performed until the absorption equilibrium was reached (on average between 500 and 6800 min). The relative amount of absorbed ILs (\(w_{IL,t}\)) was calculated with Equation (1) [16]:  
\[w_{IL,t}=\frac{m_{wood,ILL}-m_{wood,0}}{m_{wood,0}}\cdot 100 \tag{1}\]  
After reaching the ILs' absorption equilibrium (corresponding to the value \(w_{IL,eq}\)), the wood samples were dried for 7 h until constant mass (\(m_{wood,IL}\)). The drying was performed in a vacuum drying oven (Memmert, Germany) at 45 \({}^{\circ}\)C and 10 mbar to avoid the dissolution/leaching/degradation of wood components from the IL-loaded wood, which could occur under normal drying conditions.  
The weight percent gain (WPG) of the IL-loaded wood at equilibrium was calculated with Equation (2) [26]:  
\[WPG=\frac{m_{wood,IL,d}-m_{od}}{m_{od}}\cdot 100 \tag{2}\]  
The unidirectional (radial -R-, longitudinal -L-, or tangential -T-) ILs uptake were determined by coating the other sections of the wood samples apart from the two faces corresponding to the direction of interest through brushing with an epoxy resin [27; 28] and immersion in the IL solutions, following the same protocol as described above. Prior experiments indicated that there was no detectable dissolution or swelling of the resin in the studied ILs.  
The diffusion coefficients in the longitudinal, radial, and tangential direction (\(D_{x}\), where \(x=L\), \(R\) or \(T\)) were determined from Newmann's form of [PERSON]'s law (Equation (3)) [9]:  
\[D_{x}=\frac{\pi\cdot d^{2}\cdot E^{2}}{16\cdot w_{IL,eq}} \tag{3}\]where \(d\) is the diffusional thickness of the specimen (i.e., the distance between the two uncoated faces of the sample for each unidirectional case), \(E\) is the slope of the IL uptake kinetic for the first 66% of the data, and \(w_{IL,eq}\) is the relative IL uptake ratio at equilibrium.  
Five parallel samples were used for each type of experiment, and the average values were presented in the paper.
## 5 Simulation Results  
In order to test the performance and efficiency of the proposed control strategies, two trajectory-tracking scenarios were used to simulate the performance of the proposed controllers.  
First, the hierarchical controller and the trajectory tracking via attitude stabilization controller were tested in the absence of disturbances. In these simulations, the two proposed controllers were examined for a set point hovering flight, and then for a flight following a circular trajectory. The gains of the PD/PID controllers were calculated using the RM for the first time, and then optimized by utilizing GA optimization techniques, so that a comparative study between these two methods of synthesis can take place.  
Second, the robustness of the hierarchical controller and the new control strategy proposed in this work were approved in an environment with disturbances.  
Initially, the values of all the flight parameters are set to zero. The quadcopter parameter values used for simulation are mentioned in Table 5.
## 5 Simulation Experimental Results and Discussions  
### Simulation Protocol Based on the Orthogonal Experiment  
To further examine the weight of various factors on the compliance and computation time, the maximum stress after geometric reconstruction, and the weight reduction ratio, an appropriate experimental design is necessary. If each factor is set with three levels, a full factorial design requires 27 experiments, which is both time-consuming and labor-intensive. To optimize the experimental process and enhance efficiency, commonly used experimental design methodologies include the Plackett-Burman method [49], the response surface method [50], and an orthogonal experimental design [51]. This study adopts the orthogonal experimental design method, which is suitable for multi-factor multi-level analysis, to reduce the number of experiments, decrease the computational time for statistical analysis of the experimental data, and quickly identify the optimal combination and structure of the experiment. In an orthogonal experimental design, the notation \(L_{t}(l^{n})\) is commonly utilized to represent the orthogonal table, where \(L\) represents the code for the orthogonal table, \(t\) denotes the total number of experiments, \(l\) is the number of factor levels, and \(u\) signifies the number of factors.  
Based on the analysis in Section 3, the main factors influencing the TO structural configuration are mesh size, filter radius, and penalty factor, and the recommended standards for each factor have been defined. Therefore, three levels for each factor are set (as illustrated in Table 7), and a three-factor, three-level \(L_{9}(3^{3})\) orthogonal table is designed (see Table 8). Here, C represents the mesh size; D denotes the filter radius; E stands for the penalty factor, and C1 signifies the first level of the mesh size factor C. Other symbols can be identically defined.
## 4 CNN Architecture and Training Parameters  
The network architecture utilized in this paper was based on the one proposed by authors in their previous article [23]. The earlier proposed architecture was modified within the activation layer, the first convolution layer and the amount of data given in training batches. The final network architecture is presented in Table 3 and the training parameters in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Size** & **Number of Filter** & **Stride** \\ \hline Input & \(145\times 94\times 1\) & N/A & N/A \\
2D Conv & \(75\times 75\) & 128 & \(1\times 1\) \\ Batch Normalization & N/A & N/A & N/A \\ GELU Activation & N/A & N/A & N/A \\
2D Average Pooling & \(8\times 8\) & N/A & \(3\times 3\) \\
2D Conv & \(4\times 4\) & 128 & \(3\times 3\) \\ Batch Normalization & N/A & N/A & N/A \\ GELU Activation & N/A & N/A & N/A \\
2D Average Pooling & \(1\times 1\) & N/A & \(2\times 2\) \\ Fully Connected Layer & N/A & N/A & N/A \\ Softmax Layer & N/A & N/A & N/A \\ \hline \hline \end{tabular} N/A—not available.  
\end{table}
Table 3: CNN architecture.  
In the used deep CNN, the activation layer type was changed from ReLU to GELU. Changing the activation layer function enables improving accuracy due to non-linearities in the activation function, as presented in article [43]. The activation function of GELU includes the properties of dropout [44] (deactivation of certain neurons during training) and zoneout [45] (stochastically preserving previous values in hidden units) and ReLU.  
Based on the experiments performed in article [23], the same training algorithm--RMSProp-as well as the same initial learning rate value, 0.005, were used. Additionally, a decrease in the learning rate every 10 epochs by a value of 0.1 was applied. The number of epochs was set to 80 and the batch size to 256. Using the above architecture and training parameters, all CNNs were trained, and the results are presented in the next sections of this article.
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 4 Proposed Frequency Contour-Strip Method with Superiority in Characterizing Damage  
A frequency contour-strip method, acting as an upgradation of the frequency contour-line method, is formulated by addressing the aforementioned limitations of inefficiency in establishing the eigen-frequency panorama and incompetence to identify a crack in noisy conditions in the latter method.
###### Abstract  
The novel combustion concept Oxygen Carrier Aided Combustion (OCAC) is realized by addition of an active oxygen-carrying bed material to conventional fluidized bed boilers. The active bed material is meant to become reduced in fuel-rich parts of the boiler and oxidized in oxygen-rich parts, thus potentially providing advantages such as new mechanisms for oxygen transport in space and time. In this study, oxygen-carrier particles prepared from so called Linz-Donawitz (LD)-slag are examined as active bed material in a 12 MW\({}_{\text{th}}\) Circulating Fluidized Bed (CFB) boiler. LD-slag is the second largest by-product in steel making and is generated in the basic LD oxygen converter process. The experimental campaign lasted for two full weeks. The fuel was wood chips. LD-slag worked well from an operational point of view and no problems related to handling, agglomeration or sintering were experienced, albeit the production of fly ash increased. The boiler temperature profile suggested that fuel conversion in the main boiler body was facilitated, but the effect did not readily translate into reduced emissions from the stack. Spraying an aqueous solution of ammonium sulphate directly into the cyclone outlet with the aim of rejecting alkali metals as alkali sulphates was found to solve the problems related to carbon monoxide emissions, suggesting that the problems could be due to the poor ability of LD-slag to absorb certain ash components. Use of a mixed bed consisting of 10-50 wt% LD-slag, with the remaining part being silica sand for ash absorption, also worked well. It is concluded that LD-slag could be a very cheap and readily available oxygen-carrying bed material for use in fluidized bed applications.  
Oxygen Carrier Aided Combustion; fluidized bed combustion; Chemical Looping Combustion; oxygen carriers; Bio energy; LD-slag; slag utilization Article
## 2 Theoretical Model
### Spectrum Average Mutual Coherence Function of Localized Wave of Bessel-Gaussian Amplitude Envelope
#### 2.2.2 Spectrum Average Mutual Coherence Function  
By Equation (3), the approximation: \(\omega w_{0}^{2}/2c=\pi w_{0}^{2}/\lambda\gg z\), the paraxial approximate and the approximate relationship: \(I_{m_{0}}[x(1+\mathrm{i}y)]\sim\frac{[x(1+\mathrm{i}y)/2]^{m_{0}}}{\Gamma(1+ \mathrm{i}w_{0})}\), (\(|x(1+\mathrm{i}y)|\ll 1\)), and by ignoring the turbulence effects, we can express the TFMCF of BG vortex beam as [13; 18]  
\[\begin{split}\Gamma_{2}^{0}(\rho,\theta_{1},\theta_{2};\omega_{1},\omega_{2})=& E_{0}^{2}\pi T_{0}^{2}\exp\left[-2\frac{z^{2}}{w_ {0}^{2}}\left(\sin^{2}\alpha+\frac{\rho^{2}}{z^{2}}\right)-\frac{\omega_{1}^{2 }+\omega_{2}^{2}}{4}T_{0}^{2}\right]\\ &\times\left|I_{m_{0}}(2z\frac{\sin\alpha}{w_{0}^{2}}\rho)\right| ^{2}\left[\left(\frac{w_{0}^{2}}{2 zc}\right)^{2}\omega_{c}^{2}+\mathrm{i} \frac{w_{0}^{2}}{2 zc}\omega_{d}\right]^{m_{0}}\\ &\times\exp\left[\mathrm{i}\frac{z}{c}\left(1-\frac{\sin^{2} \alpha}{2}\right)\omega_{d}+\mathrm{i}m_{0}(\theta_{1}-\theta_{2})\right]. \end{split} \tag{14}\]  
where the marrow-band approximate: \(\omega_{c}^{2}\gg\omega_{d}^{2}\) has been applied.  
Figure 1: Spectrum average coherence length of spherical wave versus the propagation distance \(z\) for different “temperature structure” constant \(C_{m}^{2}\) under (**a**) \(\omega=-4.5\), \(T_{0}=20\)fs, \(\zeta=2\) and different half-modulated pulse width \(T_{0}\) under (**b**) \(\omega=-4.5\), \(C_{m}^{2}=10^{-14}\mathrm{m}^{-2/3}\), \(\zeta=2\).  
Combined with Equations (8), (9), (13) and (14), the SAMCF of output BGLW can be written as [13; 18]  
\[\begin{split}\langle\mathcal{W}(\rho,\varphi_{1},\varphi_{2},z;t) \rangle=& E_{0}^{2}\exp\left[-\frac{2z^{2}}{w_{0}^{2}}\left(\sin^{ 2}\alpha+\frac{\rho^{2}}{z^{2}}\right)-\frac{\xi^{2}}{T_{0}^{2}}\right]\\ &\times\left|I_{m_{0}}\left(2z\frac{\sin\alpha}{w_{0}^{2}}\rho \right)\right|^{2}\times\sum_{q=0}^{m_{0}}\frac{m_{0}!}{(m_{0}-q)!q!}2^{q-1}\\ &\times\left(\frac{w_{0}^{2}}{2 zcT_{0}}\right)^{2m_{0}-q}(2m_{0}- 2q-1)!!D_{q}\left(2\frac{\xi}{T_{0}}\right)\\ &\times\exp\{-\frac{2\rho^{2}}{\rho_{oc\xi}^{2}}\left[1-\cos( \varphi_{1}-\varphi_{2})\right]+\mathrm{i}m_{0}(\varphi_{1}-\varphi_{2})\}. \end{split} \tag{15}\]  
where \(\xi=t-z/c\left[1-\sin^{2}\alpha/2\right]\), \(D_{q}(.)\) denotes parabolic cylinder function, and \(N!!\) means taking the double factorial of \(N\).
## 3 Collaborative Secure Localization Algorithm  
In this section, we first introduce the network model, related assumptions and definitions, then describe collaborative secure localization mechanisms and present the CSLT algorithm, which includes the following five sub-processes: (1) trust evaluation of anchor nodes; (2) initial localization of unknown nodes; (3) trust evaluation of reference nodes; (4) selection of reference node; and (5) secondary localization of unknown node.