esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
\begin{tabular}{l l} AMP & Adenosine 5\({}^{\prime}\)-monophosphate \\ BCAA & Branched-chain amino acids \\ BCAT & Branched-chain aminotransferase \\ BCKDH & Branched-chain keto-acid dehydrogenase \\ C4H & Cinnamate 4-hydroxyase \\ CCL & Carboxyl CoA ligase \\ CHI & Chalcone isomerase \\ CHIL & Chalcone isomerase-like proteins \\ CHS & Chalcone synthase \\ DMAPP & Dimethylallyl diphosphate \\ DXR & 1-deoxy-d-xylulose-5-phosphate reductoisomerase \\ DXS & 1-deoxyxylulose-5-phosphate synthase \\ GES & Geranil synthase \\ GGPP & Geranylgeranyl pyrophosphate \\ GPP & Geranyl diphosphate synthase \\ GPDS & Geranyl diphosphate synthase \\ HDR & 4-hydroxy-3-methylbut-2-enyl diphosphate reductase \\ HDS & 4-hydroxy-3-methylbut-2-enyl diphosphate synthase \\ HIBCAT & Branched-chain aminotransferase in _Humulus lupulus_ \\ HICCL & Carboxyl CoA ligase gene in _Humulus Lupulus_ \\ HICHL & Chalcone isomerase-like proteins in _Humulus Lupulus_ \\ HIMPS & Monoterpene synthases in _Humulus Lupulus_ \\ HIPT & Prenyltransferase in _Humulus Lupulus_ \\ HMBPP & 4-hydroxy-3-methylbut-2-enyl diphosphate \\ ID & IPP/DMAPP isomerase \\ IPP & Isopentenyl diphosphate \\ IspG & Isoprenoid synthesis G \\ IspH & Isoprenoid synthesis H \\ LIS & linalool synthase \\ MEP & Methylerythntol phosphate \\ MTS & Monoterpene synthase \\ MVA & Mevalonate \\ OMT1 & O-methyltransferaseerase \\ PSBD & Peripheral subunit binding domain \\ PT & Prenyltransferase \\ VPS & Valerophenone synthase \\ \end{tabular}
## 3 Electrodeposition
### Properties Relevant to Applications
#### 3.3.2 Catalytic Activity  
High-entropy alloys have recently gained attention as promising electrocatalysts due to their unique surface chemistry [23,37,42], multielement synergies [23,37,42,53], and structural stability [22,23,24,25,51]. Their catalytic activity has been explored in various electrochemical reactions, particularly in water splitting processes such as the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) (Table 3). Effective catalysts for such reactions require not only high activity (low charge transfer overpotential) but also good electrical conductivity and chemical stability (corrosion resistance) under reaction conditions. Additionally, a high surface area with abundant active sites is crucial to enhance catalytic performance by facilitating efficient reactant access and product release. The largest group of high-entropy systems investigated for water splitting is represented by non-noble metal alloys, including FeCoCrNi [24], NiFeCoMnSn [23], FeMnCuCo [60], FeCoNiCuMn [51], FeNiCoCrMn [42; 52], NiFeCoMnV [25], FeCoNiMnW [54], NiFeCuCoW [21], CuMoNiCoFe [53], and NiFeCuCoCe [61]. To achieve a high reaction surface area, these alloys are often deposited on porous substrates such as nickel [23; 42; 61] and copper [52] foams, or their surfaces are purposely structured during electrodeposition to form porous morphologies [25; 51]. [PERSON] et al. [53] investigated the effect of individual elements on the catalytic activity of CuMoNiCoFe alloys by synthesizing (PD, sulfate-citrate-oxyanion bath) several dozen ternary and quaternary compositions. They found that for the hydrogen evolution reaction in alkaline solution, the overpotential decreased in the order Mo \(>\) Ni \(>\) Co \(>\) Cu, indicating that molybdenum was the most beneficial element for enhancing catalytic activity. This improvement was attributed to molybdenum's ability to modulate the binding energies of hydrogen and hydroxyl groups, thereby accelerating water dissociation. In contrast, for the oxygen evolution reaction (OER), the overpotential decreased in the order Ni \(>\) Fe \(>\) Mo \(>\) Cu, highlighting nickel as the key contributor. No correlation was observed between HER or OER overpotentials and the mixing enthalpy of the alloys, although catalysts with the lowest \(\Delta\)S\({}_{\rm mix}\) of 1.59R exhibited the highest bifunctional activity for both reactions involved in water splitting. [PERSON] et al. [42] synthesized an FeCoCrMnNi bifunctional electrocatalyst with a gradient composition (PP, organic electrolyte). The gradient profile featured a decreasing concentration of iron-group metals and a simultaneous increase in chromium and manganese content across the coating thickness. The elements' influence on HER performance followed the order Fe \(>\) Co \(>\) Mn \(>\) Cr \(>\) Ni, while for OER performance, the order was Fe \(>\) Co \(>\) Ni \(>\) Mn \(>\) Cr. The high electrocatalytic activity of these HEAs was attributed to the self-sacrificial oxidation of chromium, which exposes additional active sites, and
# One-Dimensional Fluid-Structure Interaction Models in Presurized Fluid-Filled Pipes: A Review
## 4 Engineering Applications
### Vibration Damping and Noise Reduction  
Pipe vibration may induce audible noise and FSI analyses are required for the assessment of such noise. The vibrating modes that produce sound were investigated by [162]. Experimental analyses were carried out by [163; 164], who developed a technique to reduce noise generation by the specific positioning of pipe clamps. Ref. [165] suggested that, for the full description of sound generation in pipe-systems, seven degrees-of-freedom are required. This statement was verified in [166]. A pump-induced fluid-borne noise investigation is carried out by [167] by means of a distributed-parameter transfer-matrix model in the frequency-domain. It was claimed that the method could be used as well for structure-born noise as long as Fluid-Structure Interaction was taken into account.  
Experimental water-hammer tests on a steel pipe containing a short segment of ABS were presented in [168]. MOC was successfully used to reproduce the experiments and they concluded that the vibration could be adapted and modified in the functioning of the segment material and geometry. A similar conclusion was reached by [169], who used an aluminium pipe set-up with a short segment of PVC. The analysis was carried out in the frequency-domain. Related with the previous subsection, Ref. [170] proposed a methodology to reduce vibrations by the installation of intermediate supports.
# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 4 Experimental Results and Discussions
### Performance Evaluation
#### 4.3.3 Comparison on the CEC 2005 Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{4}{c}{**GAP**} & \multicolumn{4}{c}{**Merit**} \\ \cline{2-7}
**FEs** & **1000** & **10,000** & **100,000** & **1000** & **10,000** & **100,000** \\ \cline{2-7}  & **Min./Avg.** & **Min./Avg.** & **Min./Avg.** & **Min./Avg.** & **Min./Avg.** & **Min./Avg.** \\ \hline CFA & 572.3/751.2 & 317.6/554.4 & 212.4/441.1 & 1.0/1.0 & 1.0/1.0 & 1.0/1.0 \\ CSA & 382.0/665.9 & 291.3/506.6 & 234.5/432.6 & 1.498/1.128 & 1.090/1.094 & 0.906/1.020 \\ STS & 616.1/759.4 & 348.9/576.6 & 198.3/413.4 & 0.929/0.989 & 0.910/0.961 & 1.071/1.067 \\ G-CMA-ES & 269.7/542.0 & 260.0/419.4 & 256.0/265.3 & 2.122/1.386 & 1.222/1.322 & 0.830/1.663 \\ EDA & 669.7/1059.1 & 287.1/335.1 & 269.4/300.6 & 0.854/0.709 & 1.106/1.654 & 0.788/1.467 \\ BLX-MA & 456.7/711.1 & 315.5/445.1 & 306.2/430.1 & 1.253/1.056 & 1.007/1.246 & 0.694/1.026 \\ SPC-PNX & 621.7/750.3 & 279.6/391.0 & 206.0/309.9 & 0.921/1.001 & 1.136/1.418 & 1.031/1.423 \\ BLX-GL50 & 676.0/716.3 & 272.8/341.0 & 257.2/319.0 & 0.847/1.049 & 1.164/1.626 & 0.826/1.383 \\ L-CMA-ES & 289.0/825.7 & 225.9/655.8 & 202.7/411.1 & 1.980/0.910 & 1.406/0.845 & 1.048/1.073 \\ DE & 715.4/914.1 & 396.7/492.4 & 228.8/272.0 & 0.800/0.822 & 0.801/1.126 & 0.928/1.622 \\ K-PCX & 671.0/968.5 & 488.0/564.4 & 257.4/475.6 & 0.853/0.776 & 0.651/0.982 & 0.825/0.927 \\ Co-EVO & 672.6/799.0 & 437.5/623.5 & 268.3/465.4 & 0.851/0.940 & 0.726/0.889 & 0.792/0.948 \\ \hline \multicolumn{7}{c}{Merit Product} & \multicolumn{1}{c}{} & 2.833/0.663 & 0.949/5.493 & 0.238/8.195 \\ \multicolumn{7}{c}{Geometric Mean} & \multicolumn{1}{c}{} & 1.099/0.963 & 0.995/1.167 & 0.878/1.211 \\ \hline \end{tabular}
\end{table} 
Table 10: Min./Avg. GAP and Merit over the CEC dataset with \(n=10\).
## 4 Results
### Evaluation Result of Design Concept Scalability
#### 4.5.1 Count Analysis  
In the count analysis, only the primary structural parts, such as rails, plates, rods, spacers, and fasteners, are considered. They are subcategorized accordingly in a similar way for the 3U models, as shown in Table 3.  
Figure 19: Liaison diagram of 1U BIRDS-3 conventional model.  
Figure 20: Liaison diagram of the 1U slot-based STM.  
Figure 18: Slot-based 1U model.  
A similar result is obtained for the 1U-size design. The total number of screws is reduced by 33.33% for the slot structure, whereas a 50.98% reduction is achieved in the total part count, which includes structural parts and spacers. The BIRDS-3 CubeSat uses 51 structural parts, including the long mounting rods, whereas the total number of structural parts of the slot-based design is only seven. This significant reduction is due to the withdrawal of the staking rods and the associated support frames.
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 2 Types of Fault in the Li-Ion Battery System
### Internal Battery Faults
#### 2.1.2 Overdischarge  
Overdischarge, similar to overcharge, can be caused by incorrect voltage and current measurements as well as inaccurate SOC estimation [13]. In [17], scanning electron microscopy and X-ray diffraction showed that overdischarge might be caused by copper deposition on the electrodes of the battery. Electrochemical impedance spectroscopy (EIS) studies also indicate that during battery overdischarge, the impedance of the anode is much smaller than that of the cathode, which means that the SEI change on the anode is much larger than the cathode, leading to capacity loss and current collection corrosion [14]. Overdischarge can impact the lifespan and thermal stability of a Li-ion cell, and result in considerable swelling of the cell [16]. The anode potential also increases abnormally during overdischarge, which can cause the Cu in the cell to oxidize to Cu\({}^{2+}\) ions. The dissolved Cu\({}^{2+}\) ions may result in migration through the separator and, ultimately, an internal short circuit [18].
Sensors_**2012**, _12_, 11113-11153; doi:10.3390/s1208111113  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**A Survey on Clustering Routing Protocols in**  
**Wireless Sensor Networks**  
**[PERSON]**  
School of Electronic and Information Engineering, South China University of Technology,  
Guangzhou 510641, China; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-20-8711-2490  
_Received: 2 May 2012; in revised form: 26 July 2012 / Accepted: 6 August 2012 / Published: 9 August 2012_  
**Abstract:** The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in a wide range of applications and it has become a hot research area. Based on network structure, routing protocols in WSNs can be divided into two categories: flat routing and hierarchical or clustering routing. Owing to a variety of advantages, clustering is becoming an active branch of routing technology in WSNs. In this paper, we present a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. We outline the advantages and objectives of clustering for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In particular, we systematically analyze a few prominent WSN clustering routing protocols and compare these different approaches according to our taxonomy and several significant metrics. Finally, we summarize and conclude the paper with some future directions.  
**Keywords:** wireless sensor networks; clustering routing; cluster construction; data transmission; taxonomy
# EffShuffNet: An Efficient Neural Architecture for Adopting a Multi-Model
## 4 Experimental Results
### Results
As a result, the model size was reduced to the smallest at 1.92 M and the computational amount was reduced to the lowest at 10.53 G, with an improved classification accuracy of 91.68%, which was the highest among the models except for the EffShuffDenseNet. The EffShuffDenseNet improved the accuracy to 91.08% compared to the EffShuffNet. Nevertheless, the EffShuffNet and EffShuffDenseNet still had smaller model sizes than the MobileNetV2 and ShuffleNetV2 models, which are representative of lightweight convolutional neural networks. In all of the image classification experiments conducted, the EffShuffNet, EffShuffDenseNet, and EffShuffNet-Plus were not found to outperform other pre-trained image classification models. It is expected that higher classification accuracy will be achieved through transfer learning, by pre-training the EffShuffNet and EffShuffDenseNet with the ImageNet dataset. Figures 8 and 9 depict the experimental results for the EffShuffNet and EffShuffDenseNet, which are lightweight and exhibit good classification performance in the proposed multi-model for age and gender classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Top-1 Accuracy** & **Params** & **Flops** \\ \hline ResNet50 & 93.18 & 79.28 M & 248.67 G \\ (PreTrained) & 90.42 & 79.28 M & 248.67 G \\ (Scratch) & 95.22 & 70.53 M & 276.79 G \\ \hline DenseNet201 & 91.47 & 70.53 M & 276.79 G \\ (Scratch) & 87.41 & 38.85 M & 26.01 G \\ \hline EfficientNetB0 (Scratch) & 90.63 & 38.85 M & 26.01 G \\ \hline MobileNetV2 (PreTrained) & 94.17 & 37.06 M & 20.00 G \\ \hline MobileNetV2 (Scratch) & 90.60 & 37.06 M & 20.00 G \\ \hline ShuffleNetV2 (Scratch) & 87.41 & 2.51 M & 11.73 G \\ \hline EffShuffNet & 88.26 & 1.64 M & 9.70 G \\ \hline EffShuffNet-Plus & 91.68 & 1.92 M & 10.53 G \\ \hline EffShuffDenseNet & 91.08 & 1.69 M & 14.89 G \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of NA Birds classification. Figure 8: Accuracy comparison of EffShuffNet, EffShuffDenseNet and other models in validation scheme. (**a**) The Audience dataset: age prediction; (**b**) the Audience dataset: gender prediction; (**c**) the Butterfly & Moths dataset: class prediction; (**d**) the NA Birds dataset: class prediction. Figure 9: _Cont._
## 2 Fundamental Principles of Optical Backscatter Reflectometry
The impurities change their location when the sample is loaded, either mechanically or thermally. A light pulse sent into the loaded fiber creates a new record of the backscattered light. This new record has a changed amplitude pattern because the impurities changed locations. By comparing the actual record with the reference record, the mechanical strains can be calculated. Thus, a sequence of local strain values along the fiber is the result. This is called here a measurement. Under ideal conditions a measurement has a spatial resolution of under 1 mm and the strain resolution can be as good as 0.001% [26]. Please see the Appendix B regarding the spatial and strain resolution and, e.g., [PERSON] et al. [27] or [PERSON] et al. [28] for additional detailed information about the processes involved using this technique. The Rayleigh backscattered light is of relatively weak intensity which under certain (rather extreme) circumstances acts as a drawback. Such conditions are high strain gradients, or if the fiber experiences microbending or pinching. In curing epoxy resin, the optical fiber is subject to such actions, which will lead to a diminished detected signal. In turn, the above described traditional method how strain values are obtained using OBR will fail to return meaningful strain values after a certain degree of cure (or conversion) is reached. However, using the so called running reference method for evaluating the OBR-data, these challenges can be overcome. In this method, each measurement is compared to the previous measurement, in contrast to conventional OBR where all measurements are compared to one reference. That means the reference is different each time and thus the name of this technique. In contrast to traditional OBR this new method yields strain differences. The actual strain value at a given time is then calculated by adding up these strain differences. The running reference method was used to obtain the strain values presented in this work and the method is to be published [29].
# Optimizing Nigella Oil Extraction Temperature for Sustainable Production
###### Abstract  
Understanding the extraction process of Nigella oil is crucial due to its significant impact on yield, quality, and therapeutic effectiveness. This study explored the effects of various screw press temperatures (40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, 70 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C) on the quantity and quality of Nigella oil to optimize conditions that maximize yield while maintaining its nutritional and therapeutic attributes. Our findings indicate a linear increase in oil yield as screw press temperatures rose from 40 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. There were no significant differences observed in total phenolic content (TPC), cupric reducing antioxidant capacity (CUPRAC), or the composition and ratio of fatty acids across oils extracted at different temperatures. However, the ferric-reducing antioxidant power (FRAP) was highest in oils extracted at 60 \({}^{\circ}\)C, while the thymoquinone (TQ) content peaked between 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C. These results underscore the importance of optimizing screw press temperatures to strike a balance between maximizing oil yield and preserving its valuable therapeutic and nutritional properties  
Nigella sativa; screw-press extraction; temperature effects; oil yield; phenolic content; antioxidant capacity; thymoquinone; fatty acid composition
# Influence of Cargo Luggage on the Vertical Drop Crashworthiness of Aircraft Mid-Fuselage Section
## 1 Introduction
A numerical simulation was performed, and the FEM and the analysis/experiment correlation were described. In addition, suggestions for improving the model to increase its relevance were also proposed. [PERSON] et al. [14] studied the effect of the foam filling of the cargo floor substructure on the acceleration and failure mode of the fuselage structure, and it was reported that the foam filling of the cargo floor substructure could significantly reduce the acceleration pulse transmitted to the passenger compartment floor. [PERSON] et al. [15] evaluated the effect of the outer reinforcing plate on the initial stiffness, ultimate strength, and failure mechanisms of tubular K-joints under axial load under ambient and different fire conditions and proposed a highly precise, practical design equation based on the yield volume model for determining the ultimate strength. [PERSON] et al. [16] studied the impact of cargo luggage on the crashworthiness of the fuselage section. The influences of the presence and absence of luggage in the cargo hold on the deformation mode, acceleration response, and energy absorption characteristics of the fuselage structure were obtained. However, the luggage in the cargo hold was simplified as a rigid body model, and the impact of cargo stiffness and cargo energy absorption was not considered; therefore, it is necessary to further establish a more realistic model of luggage in the cargo hold to study its impact on the crashworthiness of the fuselage section. [PERSON] et al. [17] studied the energy absorption and collapse behaviors of composite-coated corrugation-reinforced cylindrical absorbers and found that the composite coating could enhance the energy absorption by up to 44% by retarding the initial peak force. [PERSON] [18] analyzed the crash test data of a crash test on a B737 containing cargo luggage as the standard and verified the effectiveness of the crash simulation model considering cargo luggage from three perspectives: cargo cabin crush stroke, luggage energy absorption characteristics, and acceleration response at the seat rail. To summarize, although cargo luggage has been researched, such research is not sufficiently systematic, and more in-depth research is necessary. In this study, compression tests on three different materials of cargo compartment luggage were performed, and a simulation model of cargo luggage was established. Based on the dynamic simulation software LS-DYNA 971, an FEM of the fuselage segment with cargo luggage was established. The impact of cargo luggage on the crashworthiness of the fuselage segment was further studied by comparing the deformation, acceleration, displacement, and energy absorption of the fuselage frame during the vertical drop of the fuselage segment models with and without cargo luggage; moreover, the overload response of the passenger compartment seat rails was also compared.
# Edge-Preserving Image Denoising Based on Lipschitz Estimation
## 6 Conclusions  
This paper introduced a method to remove the noise from the signals or images during the acquisition phase of theNDT process. The method preserved the edges and transitions that corresponded to the most significant information about the nature of the signals or images while denoising. The algorithm removed the noise in the homogeneous areas but preserved all structures like the edges or corners by computing the Lipschitz exponents estimated from the modulus maxima lines.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sigma** & **Input** & **SURE-LET** & **Bishrink** & **NeighSure** & **SSplit** \\ \hline
10 & 0.56 & 0.73 & 0.49 & 0.57 & 0.69 \\ \hline
20 & 0.37 & 0.61 & 0.41 & 0.43 & 0.58 \\ \hline
30 & 0.27 & 0.54 & 0.37 & 0.38 & 0.53 \\ \hline
40 & 0.21 & 0.49 & 0.33 & 0.35 & 0.44 \\ \hline
50 & 0.16 & 0.44 & 0.31 & 0.32 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed methods with the other wavelet-based image denoising methods on the ’Pepper’ image in terms of the structural similarity (SSIM) for a range of input noise levels \(\sigma=10\) to \(\sigma=50\)[22].  
Figure 20: (**a**) Binary image, (**b**) Dilated image with the addition of Gaussian white noise (Input PSNR = 18.59 dB), (**c**) SURE-LET method (Output PSNR = 32.21 dB), (**d**) SSplit method (Output PSNR = 33.23 dB).  
It is shown that the Lipschitz estimation of the natural image lay within a certain range of values even in the presence of noise and also did not change significantly even in the presence of affine deformation. Based on the detection of edges from the modulus maximas, we classified each image into the edge and background pixels--the edges where the intensity changed abruptly preserved their Lipschitz estimation even with the addition of noise.  
The statistical results illustrated that the proposed method had improved PSNR and maintained the sharpness of the edges without introducing any artifacts. The Lipschitz estimation can highlight the discontinuities present in the images, and in more practical applications (e.g., NDT, medical applications), it is of extreme importance to localize them. The results are encouraging to further improve this method by working on multi-scale basis and employing practical examples e.g., surface inspection.  
Methodology: B.J.; Z.J.; E.F. and O.L.; project administration: B.J.; Software: B.J. and Z.J.; Supervision: E.F. and O.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.