esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Sensing Using Fractal Dimension and Wavelet
## 3 The Improved Fractal Box Dimension (IFBD) Recognition Algorithm
### The Improved Fractal Box Dimension (IFBD) Recognition Algorithm  
According to the improved box dimension of Equations (6)-(8), we calculated the improved box dimension features of the short circuits. Based on the calculation results, we found the relationship between the fault classes and the IFBD features, shown in Table 2 below. We found that the improved box dimension features have a similar relationship in the cases of the two-phase grounded faults and the two-phase-between faults. After the three phase currents or voltages were collected, we could use the relationship to classify the fault.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Types of Short Circuit Faults** & **Feature Relationship** \\ \hline Three-phase faults & \(F_{c}>F_{a}>F_{b}\) \\ Single-phase faults & \(F_{b}>F_{a}>F_{c}\) \\ Two-phase faults of \(A\) and \(B\) & \(F_{a}>F_{b}>F_{c}\) \\ Two-phase faults of \(A\) and \(C\) & \(F_{a}>F_{c}>F_{b}\) \\ Two-phase faults of \(B\) and \(C\) & \(F_{c}>F_{b}>F_{a}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relationship between the fault classes and the IFBD features.  
Figure 3: Fault feature histogram of the 10 types of short circuit faults by using (**a**) the traditional box dimension; and (**b**) the improved box dimension.  
The three phase currents were chosen as the original signals of the online power cable. The IFBD recognition algorithm could be described as follows.  
1. Input the three phase currents.
2. Extract the current signals of any phase in fault state, and calculate the traditional box dimensions \(Dim(S_{A})\), \(Dim(S_{B})\), and \(Dim(S_{C})\) of the fault components of phase \(A\), \(B\), and \(C\) using Equation (3).
3. Substitute the traditional box dimensions \(Dim(S_{A})\), \(Dim(S_{B})\), and \(Dim(S_{C})\) respectively into Equations (6)-(8) to calculate the improved box dimension features \(F_{A}\), \(F_{B}\), and \(F_{C}\) of the fault components of the three phase currents.
4. Classify the short circuit fault according to Table 2.
## 3 The Radiative Transfer Equation
Since the composition of natural water bodies varies with location and time \(t\), so does the IOP. The RTE exposes the underlying processes that influence the energy conservation flows in an electromagnetic beam passing through a medium [88]. RTE can be expressed in scalar and vector variable formats, including the derivation [81]. Selecting the relevant RTE form depends on the UOWC work scenario approach to model the light's temporal dispersion and polarisation state.
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 2 Materials and Methods
### Statistical Analysis  
All plant spectral image data were initially subjected to a two-way analysis of variance (ANOVA) to examine statistical significance in the effects of two factors, herbicides and time, on the spectral responses of crabgrass to the herbicides tested. The normalized spectral parameters were then subjected to a principal component analysis (PCA) to test if the spectral responses can be separated and clustered depending on the herbicide mode of action. Initially, a correlation-based PCA was performed for each time point (3, 6, 24, 48, 72, and 120 HAT) separately and then for pooled data with default settings. All statistical analyses were conducted using R 3.2.3 (R Foundation for Statistical Computing, Vienna, Austria).  
Figure 1: The overall process of multi-well plate assay combined with spectral image analysis. (**A**): Plant preparation and herbicide treatment, (**B**): Spectral image acquisition, (**C**): Process of image analysis, (**D**): Segmentation of acquired images.
## 2 Methods  
### Silicon Micromodels  
#### 2.1.1 Lithography  
Single-side polished, P-type, \(<\)1,0,0\(>\), four-inch silicon wafers with a thickness of 525 \(\upmu\)m, procured from LUVA System Inc. (Santa Clara, CA, USA), were used in this study. First, the silicon wafers were spin-coated with a positive photoresist, AZ 5214, procured from Merck Performance Materials GmbH, at the speed of 2800 r/min for 40 s, to make the photoresist reach a thickness of around 1.6 \(\upmu\)m. Second, the coated wafers were prebaked on hot plates at the temperature of 110\(\,{}^{\circ}\)C for 2 min. Third, the Heidelberg \(\mu\)PG 501 was used to direct-write custom patterns, shown in Figure 1A, on the silicon wafers using a laser with 390 nm wave-length and exposure time of 55 ms. Finally, the wafers were developed in AZ 726 MIF, procured from Microchemicals GmbH, for 1 min.
## 5 Plant Growth-Promoting Metabolites for Soil Remediation and Crop Improvement
### Bacterial Nitrogen Recycling for Soil Maintenance and Crop Improvement  
Nitrogen makes up 78% of the atmospheric volume and constitutes a major limiting factor for many physiological processes in soil. Molecular nitrogen cannot be assimilated by photosynthesizing plants or the majority of microorganisms [206]. The inter-conversion between nitrogen forms (nitrogen fixation, mineralization, nitrification and denitrification) represents the biogeochemical cycle of nitrogen, mainly made of biological processes in which microorganisms play a predominant role. Metabolically, microorganisms preferentially uptake ammonium while plants and some microorganisms assimilate nitrate [207].  
Nitrogen fixation is the first step in the process of making nitrogen usable by plants, where nitrogen-fixing bacteria play a crucial role in changing dinitrogen into ammonium via nitrogenase activity. After that, the produced ammonium needs to be converted into nitrates before assimilation by plants. This nitrification process, in which bacteria play an important role, allows nitrogen assimilation by plant roots to be used in amino acids, nucleic acids and chlorophyll composition. When a plant or animal dies, decomposers such as fungi and bacteria turn nitrogen back into ammonium so it can reenter the nitrogen cycle through denitrification [208].  
Unfortunately, the excessive use of fertilizers to supply nitrogen in soil and nitrous oxide emission in the atmosphere due to other human activities results in the unbalancing of the nitrogen cycle in both the atmosphere and soil [209]. To avoid such exaggerated application of chemical nitrogen to soil, N-fixing bacteria such as _Azospirillum_[210; 211; 212; 213; 214; 215], _Pseudomonas_[210; 211; 216; 217], _Bacillus_[218], _Herbspirillum_[215; 219], _Azotobacter_[210; 220], _Rhizobium_ and _Enterobacter_[221], _Klebsiella_[222] and _Burkholderia_[223; 224] have proved their efficiency in reducing or replacing chemical fertilizers for wheat and rice crop enhancement.  
In addition to nitrogen fixation, the presence of a positive correlation between bacterial denitrification and the rhizosphere colonization potential has permitted researchers to consider denitrification as an important trait in isolating and selecting efficient PGPR. In this context, a fluorescent PGP-_pseudomonas_ associated with rice was isolated and selected by [PERSON] et al. [177], considering the denitrification potential as an important trait in selecting competitive PGPR. Moreover, [PERSON] et al. [225], considered denitrification in the PGP-_Pseudomonas fluorescens_ F113 as an important character. The NO produced by the PGP-_Azospirillum brasilense_ Sp245 via denitrification could be a major signal implicated in wheat root branching stimulation when the dissimilatory nitrite reductase gene (nirK) is upregulated [226].
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 3 Methods
### Multi-Layer Protocol Hierarchical Relationship Extraction  
Wireshark maintains multiple dissector tables dynamically to establish relationships between different protocol dissectors. In this process, lower-layer protocols first create a sub-table within a dissector table, and then upper-layer protocols add entries to this sub-table. For instance, during initialization, the TCP dissector creates a \"TCP port\" sub-table within the \"Integer Tables\" dissector table, and other upper-layer protocols subsequently populate this \"TCP port\" sub-table with corresponding entries. These entries are typically associated with specific TCP port numbers, so when the TCP dissector processes packets with special TCP port numbers, it searches and invokes the appropriate upper-layer protocol dissectors based on these port numbers within the \"TCP port\" sub-table. Additionally, lower-layer dissectors can explicitly specify upper-layer dissectors by invoking specific functions like \"find_dissector()\". According to these two rules, the hierarchical relationship between different protocols can be deduced and extracted by analyzing all Wireshark protocol dissector files.
## 6 Simulation Verification and Analysis
### Response Characteristic Analysis of the TDC-VSG Adaptive Control Strategy  
Figure 13 illustrates the simulation results of the power and frequency curve for the TDC-VSG adaptive control strategy under given power and grid frequency disturbances,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(J_{0}\) & Virtual inertia & 0.9 \\ \(D_{\omega}\) & Damping coefficient & 7.6 \\ \(T_{\rm J}\) & Inertia decision threshold & 2 \\ \(K_{\rm J}\) & Inertia compensation coefficient & 0.2 \\ \(T_{\rm D}\) & Damping decision threshold & 0.1 \\ \(K_{\rm d}\) & Damping compensation coefficient & 10 \\ \(T_{\rm j}\) & Inertia decision threshold & 0.2 \\ \(D_{0}\) & Adaptive damping coefficient & 10 \\ \(K_{\rm j}\) & Inertia compensation coefficient & 0.9 \\ \(K_{\omega}\) & Primary frequency modulation factor & 7.6 \\ \(D_{\rm T}\) & Transient damping coefficient & 17.32 \\ \(T_{\rm T}\) & Lag time constant & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters of different control strategies.  
Figure 12: Simulation results of the TVSG with different damping coefficients \(D_{\omega}\).  
utilizing various adaptive compensation coefficients \(K_{\rm j}\) with \(D_{\rm T}=17.32\). As evidenced by Figure 13, when \(K_{\rm j}\) is small, the TDC-VSG closely approximates the traditional virtual inertia control strategy, exhibiting pronounced transient oscillations during given power and grid frequency variations. As \(K_{\rm j}\) increases, the performance of the TDC-VSG in suppressing transient oscillations is significantly enhanced, further reducing the frequency variation rate and the frequency deviation of the TDC-VSG system.  
Figure 14 illustrates the simulation results of the power and frequency curve for the TDC-VSG adaptive control strategy under given power and grid frequency variations, utilizing various \(D_{\rm T}\) with \(K_{\rm j}=0.9\). This figure indicates that introducing transient damping significantly mitigates power and frequency oscillations of the TDC-VSG system while ensuring that the steady-state deviation remains unchanged during grid frequency variations. At \(D_{\rm T}=17.32\), the active power is stable and exhibits no oscillation. Based on this, the simulation findings are in agreement with the theoretical analysis presented in Figure 6, validating the effectiveness of the proposed TDC-VSG adaptive control strategy.
## 3 Measurement Results
### Measurement Results of Loss Characteristics at Higher Frequencies  
Figure 6 shows the loss characteristics of the 0.10 mm UTGO silicon steel and FBA alloy at 200-400 Hz frequencies.  
It can be seen from the diagram that, in the high-frequency range, the loss difference between the 0.10 mm UTGO silicon steel and the FBA alloy is more obvious. With the increase in frequency, the growth rate of the loss curve of the 0.10 mm UTGO silicon steel is greater than that of the FBA alloy. When the frequency is 200 Hz and the magnetic induction intensity is 1.0 T, the iron loss density of the 0.10 mm UTGO silicon steel is 1.95 W/kg, and the iron loss density of the FBA alloy is 0.66 W/kg. When the frequency is 400 Hz and the magnetic induction intensity is 1.0 T, the iron loss density of the 0.10 mm UTGO silicon steel is 4.81 W/kg, and the iron loss density of the FBA alloy is 1.75 W/kg.  
Figure 5: Loss characteristics at low frequencies.  
Figure 6: Loss characteristics at high frequencies.  
The loss characteristics of the FBA alloy at 200 Hz are lower than those of the 0.10 mm UTGO silicon steel at 100 Hz, but higher than those of the 0.10 mm UTGO silicon steel at 50 Hz. The change rate of the 0.10 mm UTGO silicon steel's loss curve at 200 Hz increases rapidly. With the increase in frequency, the losses of the 0.10 mm UTGO silicon steel and FBA alloy increase, with the largest increase occurring between 100 Hz and 200 Hz.  
The above results show that the 0.10 mm UTGO silicon steel has a lower loss at higher frequencies, and has better high-frequency magnetic characteristics. The FBA alloy shows the lowest loss at both low and high frequencies, and has the best magnetic properties.
## 1 Introduction
In this paper, an event-triggered MFAC is developed for the LFC, and the proposed design alleviates the communication and computation burden of the system compared to existing model-free adaptive control (MFAC) methods in the reference [22]. In addition, existing MFAC system stability analyses use the shrinkage mapping technique tool. However, in this paper, stability analysis is given using the Lyapunov theory approach.
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 7 Challenges of FAST
### IR Drop Effect  
Another major challenge during the FAST is IR drop effects due to high switching activity. The high switching activity leads to a voltage drop, affecting the path delay. To address the issue, a novel work has been proposed in [23]. The method analyses the effect of IR drop on the path delay of each pattern applied at the particular FAST frequency. Based on the observation, the effective pattern and frequency are selected for testing the SDDs. If the IR drop effect is not addressed, the good chip will be falsely identified as defective. This is because the increased path delay is due to the IR drop effect and not due to the SDD effect. The switching cycle average power (SCAP) is the metric that is used to evaluate the effectiveness of pattern and frequency by considering the IR drop effect. Figure 9 shows the IR drop-based impact on the delay of paths due to the application of fast clocks [23]. Here, the worst delay of patterns due to IR drop effects is obtained by adding the worst performance degradation-based delay due to IR drop to the maximum path sensitization delay of a pattern. It shows that the delay of the paths increases due to FAST clocks. This IR drop effect may cause false alarms; that is, it can classify good chips as bad. Hence, the FAST clocks must be selected in such a way to avoid false alarms. The impact of IR drop is a significant issue and must be addressed during the FAST-based pattern generation.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 3 Inception-TCN-Attention Model Design  
The analysis of multidimensional time series is more difficult than for one-dimensional time series data because of the need to consider the differences and connections between multiple variables and to extract the features of each variable, as well as to fuse multiple features effectively. For this reason, we proposed the Inception-TCN-Attention model to extract the complex time series features of data packets and use them as device fingerprints for device identification. Inception is a special structure in neural networks that was designed by Google's DeepMind team [15] to improve the performance and efficiency of convolutional neural networks, allowing the network to learn features at different scales simultaneously. A temporal convolutional network (TCN) is a deep learning model specifically designed to process time series data. It mainly consists of convolutional layers that can effectively capture local and long-term dependencies in time series data. An attention mechanism is a technique that has been widely used in the field of deep learning in recent years, especially in the fields of natural language processing (NLP) [16], computer vision (CV) [17], and speech recognition (SR) [18], which enable models to be trained to focus more on features that are critical to the recognition task, thus improving the efficiency and accuracy of feature extraction.  
The model can be divided into two stages: the stage of extracting the temporal features of the packet based on the Inception structure of the TCN model, and the stage of fusing and classifying the features using the multi-attention mechanism. The overall framework of the model is shown in Figure 3.
_Applied_  
_sciences_  
_Article_  
**A Box-Behnken Design for Optimal Green Extraction**  
**of Compounds from Olive Leaves That Potentially**  
**Activate the AMPK Pathway**  
**[PERSON] 1, [PERSON] 1,2, [PERSON] 1,**  
**[PERSON] 1,2,*** and [PERSON] 1,2**  
1 Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Avd. Fuentenueva s/n, 18071 Granada, Spain; [EMAIL_ADDRESS] (B.M.-G.); [EMAIL_ADDRESS] (S.P.-M.); [EMAIL_ADDRESS] (A.M.G.-C.); [EMAIL_ADDRESS] (A.S.-C.)
2 Functional Food Research and Development Center, Health Science Technological Park, Avd. del Concolimention, Bioregion Building, 18100 Granada, Spain
* Correspondence: [EMAIL_ADDRESS]; Tel.: +34-685814310  
**Abstract:** Olive leaves contain bioactive compounds that have been shown to activate AMP-activated protein kinase (AMPK), which decreases intracellular lipid accumulation. Microwave-assisted extraction (MAE) is a green extraction technique that is frequently used in the recovery of phenolic compounds from plants. Thus, in this study, a Box-Behnken design was used to optimize MAE conditions such as temperature, percentage of ethanol and extraction time to obtain the maximum content of total compounds and compounds that activate AMPK. To this end, all extracts were characterized by High-Performance Liquid Chromatography Coupled to Electrospray Ionization Time-of-Flight Mass Spectrometry (HPLC-ESI-TOF-MS). The optimum conditions to obtain the highest content of total compounds were 123 \({}^{\circ}\)C, 100% of ethanol/water (_v_%) and 23 min, whereas the optimum conditions for the highest amount of compounds that activate AMPK were 111 \({}^{\circ}\)C, 42% of ethanol/water (_v_%) and 23 min. Thus, a multi-analysis by desirability was carried out to establish MAE optimal conditions for both responses. The optimum conditions were 111 \({}^{\circ}\)C, 100% EtOH and 23 min with a desirability of 0.97, which means that the responses are close to their individual optimal values. As a result, the olive leaf extract obtained at these optimal MAE conditions has great potential to be effective in the treatment of obesity.  
**Keywords:** microwave-assisted extraction; olive leaves; HPLC-ESI-TOF-MS; AMPK; Box-Behnken design
## 2 Literature Review
### Data Driven Process Improvement
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Industry** & **Purpose** & **Methodology** \\ \hline
[17] & Semiconductor industry &
\begin{tabular}{c} Reduce electrical defects during \\ manufacturing circuit cartridges for \\ inkjet printers \\ \end{tabular} & DMAIC \\
[16] & Forging manufacturing plants & Improve dents from forging in parts \\
[18] & Precision tools and dies & Improve heat treatment process and \\ manufacturer & \
\begin{tabular}{c} hardness quality of tools and dies \\ \end{tabular} &
\begin{tabular}{c} Value stream mapping \\ and DMAIC \\ \end{tabular} \\
[19] & Plastic injection & Reduce the scrap rate in the plant \\
[20] & IT industry & Reduce support issues with the \\ IT infrastructure &
\begin{tabular}{c} DMAIC \\ \end{tabular} \\
[21] & Software industry & \
\begin{tabular}{c} Develop a new model to bridge the gaps, \\ such as inefficient methods or tools, in \\ process improvement methodologies \\ \end{tabular} &
\begin{tabular}{c} IDDOV (Identity, Define, \\ Develop, Optimize, Verify) \\ \end{tabular} \\
[22] & Corrugated boxes &
\begin{tabular}{c} Reduce waste during production \\ \end{tabular} & DMAIC \\
[23] & Automobile industry &
\begin{tabular}{c} Emphasize the importance of Six Sigma \\ experts in enhancing the productivity, \\ customer satisfaction, and savings \\ \end{tabular} & Questionnaire \\
[24] & Thermal power plant &
\begin{tabular}{c} Reduce expensive de-mineralized water \\ consumption to compensate for the losses \\ during power generation \\ \end{tabular} & DMAIC \\
[25] & Aerospace industry &
\begin{tabular}{c} Reduce non-value added activities and \\ improve delivery times \\ Reduce the defect rates \\ during production \\ \end{tabular} & DMAIC \\
[26] & Garment industry & \
\begin{tabular}{c} Detect process anomalies before \\ failure occurs \\ \end{tabular} &
\begin{tabular}{c} Measure–validate–predict \\ (MVP) and statistical process \\ control (SPC) in Six Sigma \\ \end{tabular} \\
[27] & Oil and gas industry &
\begin{tabular}{c} Reduce the repeated complaint tickets \\ issued through customer service \\ Evaluate a framework by solving \\ organizational IT problems \\ \end{tabular} & DMAIC and CRISP-DM \\
[28] & Telecommunication providers & \
\begin{tabular}{c} Reduce the repeated complaint tickets \\ issued through customer service \\ Evaluate a framework by solving \\ organizational IT problems \\ \end{tabular} &
\begin{tabular}{c} DMAIC and CRISP-DM \\ Lean and Robotic Process \\ Automation (RPA) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of literature review in application of Lean Six Sigma and its tools in industries.