esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
GC-MS, GC-O analysis and sensory evaluation were successfully used to investigate aroma compounds of RRT samples. A total of 37 compounds were identified according to GC-O analysis. These volatile compounds were quantitated according to the GC-MS data. Furthermore, 23 compounds were detected as important odorants according to their OAVs. Additionally, the omission tests confirmed that fruity, sour, green, and woody were the key notes for the aroma of RRT sample. The phenomenon of interactions among different notes was also evaluated in this experiment. The result noted that a synergistic effect was occurred between fruity and woody notes.  
The specific feature of the RRT odor is based on the composition of the volatile fraction, which contains both odorants exhibiting RRT notes and a large range of odorants with various qualitative properties. Assessing the typicality of the odor is a direct and efficient way to study the impact of odor categories. It is a straightforward evaluation of the positive or negative impact of the category. The woody note has a positive effect when the fruity note is also in the mixture but tends to show a negative effect otherwise. This study showed that all the notes contribute to the RRT typicality due to a complex balance between fruity, sour, green, and woody and RRT notes arising from the presence of esters, carboxylic acids, aldehydes, ketones, and carbocyclic compounds.  
Conceptualization, Y.N.; methodology, R.W.; software, X.S.; validation, P.W.; formal analysis, J.Z.; investigation, R.W.; resources, R.W.; data curation, R.W.; writing--original draft preparation, R.W.; writing--review and editing, Y.N.; visualization, X.C.; supervision, Z.X.; project administration, Z.X.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program Nanotechnology Specific Project, 6 YFA0200304, National Natural Science Foundation of China, 31972196 and Capacity building project of local universities Science and Technology Commission of Shanghai Municipality [21010503900].  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.  
Samples of the compounds are available from the authors.
## 4 Results
### Classification Accuracy
Models trained with 50 epochs reach optimal performance (78.4% at \(-\)10 dB), with diminishing returns observed for higher epochs (100), indicating overfitting in low-SNR conditions. These findings highlight that selecting SELU as the activation function, a batch size of 256, and training for 50 epochs are the most effective configurations for achieving robust performance under diverse SNR conditions. Figure 5: Comparison of (**a**) accuracy, (**b**) F1 score, (**c**) Jaccard index, and (**d**) [PERSON]’s correlation coefficient of different models in the RadioML2016.10b dataset.
# Deep Learning Method for Fault Detection of Wind Turbine Converter
###### Abstract  
The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOCetConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC-ResNet50). It is found that the algorithm based on AOC-ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC-ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC-ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct-ResNet50 networks. Therefore, the effectiveness of the AOC-ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC-ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.  
AOC-ResNet50 network; converter; deep learning; fault detection; radar chart +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Methods
#### 2.3.3 Detection Process  
1. Traditional ELISA Method:  
IGF-1 standard solution was diluted sequentially with dilution buffer to obtain five IGF-1 standards at concentrations of 0, 6.25, 12.5, 25, 50, and 100 ng/mL, respectively. A total of 50 \(\mu\)L of IGF-1 standards (biotinylated antibodies included) and 50 \(\mu\)L of enzyme-labeled reagents were added to the coated wells. As the blank control, 100 \(\mu\)L of dilution buffer was added to the wells instead. After incubation at 37 \({}^{\circ}\)C for 30 min, the plates were washed four times with 300 \(\upmu\)L of wash buffer. A total of 100 \(\upmu\)L of the working solution prepared in Section 2.3.1 was then added to each well and the reaction was carried out at 37 \({}^{\circ}\)C for 15 min in a dark environment. Finally, 50 \(\upmu\)L of termination solution was added to stop the reaction. The OD value of each well was measured at 450 nm by the multimode microplate reader. Error bars were obtained according to the standard deviation and mean value obtained from three experiments. The LOD of the method was calculated from the standard deviation of the OD value of the blank well (\(\sigma_{\text{a}}\)) and the slope of the standard curve (s) according to Equation (1).  
\[\text{LOD}=\frac{3\sigma_{\text{a}}}{\text{s}} \tag{1}\]  
1. Fluorescent sandwich ELISA method:  
Similarly to the procedure above, 0, 0.1, 1, 5, 15, and 30 ng/mL IGF-1 standards, biotinylated antibodies, and enzyme-labeled reagents were sequentially added to the wells; then, the plates were incubated at 37 \({}^{\circ}\)C for 30 min and washed with 50 mM PBS (pH = 7) buffer. The optimal fluorescence working solution prepared in Section 2.3.2 was added to the wells and placed in a dark environment at 37 \({}^{\circ}\)C for 15 min before 100 \(\upmu\)L of termination solution was added. Finally, the mixture of fluorescence working solution and termination solution was transferred to a microcuvette for testing. The fluorescence intensity was measured by the fluorescence spectrometer at an excitation wavelength of 570 nm and emission wavelength of 585 nm [33]. The LOD of the fluorescent sandwich ELISA was calculated from the method reported by [PERSON] et al. [32]:  
\[\text{y}=\text{background}+\text{3 SD} \tag{2}\]  
where background is the mean value of the background fluorescence intensity and SD is the standard deviation of the background fluorescence intensity. The LOD of the fluorescent sandwich ELISA method corresponds to the IGF-1 concentration at the above calculated fluorescence intensity.
## 1 Introduction  
\(\gamma\delta\) T cells are the prototype of 'unconventional' T cells, containing a T cell receptor (TCR) composed of \(\gamma\) and \(\delta\) chains with diverse structural and functional heterogeneity. With both innate- and adaptive-like properties, \(\gamma\delta\) T cells bridge innate and adaptive immunity [1] and participate in various immune responses to overcome a myriad of challenges. The innate like properties of \(\gamma\delta\) T cells mirror those of natural killer (NK) cells, expressing the NK receptor NKG2D, and showing cytotoxicity against stressed and abnormal cells, such as viral infected and tumor cells [2]. Interestingly, in most target-response processes, the highly variable \(\gamma\delta\) TCR is involved which is a distinct feature of adaptive immunity. Human \(\gamma\delta\) T cells normally comprise only 1-5% of circulating T lymphocytes but undergo rapid expansion in response to tumor, inflammation, and invading pathogens such as cytomegalovirus (CMV) [3] and malaria [4]. In addition, \(\gamma\delta\) T cells demonstrate cytotoxic activity via the granzyme-perforin axis and antibody (Ab)-dependent cellular cytotoxicity (ADCC) via FcyRIII (CD16) expression [5]. \(\gamma\delta\) T cells release cytokines such as TNF-\(\alpha\), IFN-\(\gamma\), and IL-17 [6], prime T cells at the tumor site by playing the role of antigen presenting cells (APC) [7], as well as interacting with B cells in promoting immunoglobulin (Ig) class switching. Several studies have shown that \(\gamma\delta\) T cells can robustly kill a wide range of tumor cells from both solid and hematopoietic malignancies [8; 9] and that their infiltration into the tumors is the most positive prognostic marker in many cancers [10].  
\(\gamma\delta\) T cells are known to recognize stress induced molecules, typically expressed by malignant cells [11]. Ligand recognition by the \(\gamma\delta\) TCR often requires expression of accessory cost stimulatory stress molecules on both T lymphocytes and target cells which, therefore, provides a mechanism preventing harmful self-reactivity [12; 13]. Unlike conventional \(\alpha\beta\) T cells, \(\gamma\delta\) T cells recognize their target antigens irrespective of major histocompatibility complex (MHC) haplotype, and mediate anti-tumor response without causing graft versus host disease (GvHD) [14]. These combined characteristics render \(\gamma\delta\) T cells ideal candidates for cancer therapy with exciting possible application as a third-party product [15]. Emergence of synthetic biology and novel engineering approaches has provided ample opportunity to genetically modify and manipulate immune cells. These strategies can be employed to optimize the unique anti-tumor function of \(\gamma\delta\) T cells for future cancer therapies.
## 3 Results
### CS Estimation for Artificial Light Sources
The measurement accuracy for \(\text{CS}_{2018,\text{origin}}\) is of the order of \(\pm 8\%\) due to the measurement uncertainties of the used spectroradiometer. Additionally tabulated are the final \(3\times 3\) transformation matrix, as well as the functional relationship between the illuminance and the sensor’s green channel output \(G_{i}\). The annotation “\((Meas.-Calc. )\)” in the “Compared parameters” section of the table serves as an indication that the accuracy estimate \(|\Delta\text{CS}_{2018}|\) for a given light source is determined by the absolute difference between [PERSON] et al.’s and [PERSON] et al.’s corresponding CS predictions as derived from the light source’s radiometric measurements (i.e., “_Meas._”) and calculated from the resulting sensor readouts (i.e., “\(Calc.\)”), respectively. The same holds true when applying the proposed CS estimation to a similar, yet different selection of artificial light sources that were not included in the training dataset used for the determination of the transformation matrix. Their SPDs constituting the validation data are illustrated in Figure 9b. The corresponding model prediction errors \(\Delta u^{\prime}v^{\prime}\) and \(\Delta\)CS were again calculated and are summarized in Table 4 for an illuminance of 750 lx. As can be seen, even smaller errors are observed for the validation than for the original training data, emphasizing that the discussed method including the optimized matrix transform to perform the colorimetric mapping of the sensor responses is most suitable for the determination of sufficiently accurate CS values over a broad variety of lighting technologies usually found in the indoor lighting context. To ease implementation and to give a better overview of the proposed methodology for the CS estimation of artificial light sources from color sensor responses, Figure 10 depicts the necessary computational steps as a flowchart. Figure 10: CS estimation from \(RGB\) sensor responses for artificial light sources. The 3 \(\times\) 3 transformation matrix given in Table 3 is used to map \(RGB\) sensor responses to \(XYZ\) tristimulus values. The function for the calculation of \(E_{\text{vprocessed}}\) from \(G\) channel responses is also given in that table. [PERSON] et al.’s CS approximation formula is given by Equation (1). Figure 9: Selection of artificial light sources used in this work (**a**) for determining the \(RGB\) to \(XYZ\) transformation matrix and (**b**) for model validation.
###### Abstract  
Visible light communication is an emerging high-speed optical wireless communication technology that can be a candidate to alleviate pressure on conventional radio frequency-based technology. In this paper, for the first time, the advanced modulation format of probabilistic shaping (PS) bit loading is investigated in a high data rate visible light communication system based on a 450-nm Gallium Nitride laser diode. The characteristic of the system is discussed and PS bit loading discrete multi-tone modulation helps to raise the spectral efficiency and improve the system performance. Higher entropy can be achieved in the same signal-to-noise ratio (SNR) and modulation bandwidth limitation, comparing to bit and power loading. With PS bit loading, an available information rate (AIR) of 10.23 Gbps is successfully achieved at the signal bandwidth of 1.5 GHz in a 1.2 m free space transmission with normalized generalized mutual information above 0.92. And higher AIR can be anticipated with an entropy-loading strategy that fixes the channel characteristic. Experimental results validate that a PS bit loading scheme has the potential to increase the system capacity.  
laser diode; visible light communication; probabilistic shaping; bit loading +
Footnote †: journal: Sensors
# Wear Properties of Sc-Bearing Zr-Based Composite BMG with Nano-CuZr\({}_{2}\) under Lubrication
## 2 Experimental Procedures  
A bulk amorphous (Zr\({}_{55}\)Cu\({}_{30}\)Al\({}_{10}\)Ni\({}_{5}\))\({}_{99.98}\)Sc\({}_{0.02}\) matrix/CuZr\({}_{2}\) nanocrystal composite BMG (Sc-bearing Zr-based composite BMG) was prepared using a tilt-casting method to produce 3-mm-diameter samples. The advantage of this casting methodology can prevent the flow of molten alloy from cold shuts and suppressing the initiation of crack and crack propagation [16]. The size of composite BMG specimens were 3 mm in diameter and 5 mm long. The end of a pin was mounted this composite BMG. The dimension of 440C steel (0.95-1.20 wt.% C, 1.00 wt.% Mn, 1.00 wt.% Si, 16-18 wt.% Cr, 0.04 wt.% P, 0.03 wt.% S, and 0.75 wt.% Mo and Fe in balance) disk was 36 mm in diameter and 5 mm thick. Before testing, the disks were polished using 0.3 \(\upmu\)m Al\({}_{2}\)O\({}_{3}\) powders to reach the average surface roughness of approximately 0.02-0.015 \(\upmu\)m. Wear tests of sliding were conducted using the apparatus of pin-on-disk (see Appendix A Figure 11) according to the American Society for Testing and Materials (ASTM) G99 [17] and International Organization for Standardization (ISO) VG68 oil lubrication (ISO viscosity classification ISO 3448, 1975). The load at 20 N was applied normally coupling diverse sliding velocities of 0.043-0.128 m/s. The average value and standard deviation of the wear rate and friction coefficient were evaluated based on five test results.  
To investigate the microstructure of the (Zr\({}_{55}\)Cu\({}_{30}\)Al\({}_{10}\)Ni\({}_{5}\))\({}_{99.98}\)Sc\({}_{0.02}\) composite BMG before and after the wearing tests, X-ray diffraction (XRD) was conducted using a Bruker-D2-Phaser (Billerica, MA, USA) at 300 V with Cu K\(\alpha_{1}\) (\(\lambda\) = 0.154 nm) and scanning from 20 degrees to 80 degrees. The thin foils of Zr-based composite BMG were examined by using a field-emission gun transmission electron microscope (FEI Tecnai G2 F30 TEM) operated at 300 kV for high-resolution microstructure observation. The test-specimen and disk both surfaces were ultrasonically cleaned by using acetone to remove the detrimental effect of residual debris from earlier experiments for each test. A load of dead weights pressed the stationary composite BMG sample onto the rotating disk. The frictional force, which was exerted on the test specimen during sliding, was measured by means of a force transducer placed on the pin-holder arm. Before the wear test, the hardness of 440C steel (a high hardenability martensitic stainless steel with high carbon and straight chromium) and composite BMG was measured under a 300 g load to compare the relative hardness. Before and after wear tests, the mass loss in each test was measured by weighing the tested samples by using an electronic balance with \(\pm\)0.0001 g precision. The ratios called wear rates, which were defined as the mass loss to the total sliding distance were determined.
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 5 Future Research Directions
### Reliability  
Reliability in WSNs means the accurate transfer of data between entities. It also means less delay. A WSN is reliable if all the sensor nodes are able to process data correctly and the duties by CH and UAV are performed well. Data dissemination should never be a problem when it comes to WSNs. Reliability is also ensured when the packet delivery ratio (PDR) is improved [68]. The work by [69] develops a procedure that is cost friendly and reliable. Their tree-based method is used to establish connections between any two nodes as a result of a weighting method. The authors in [70] have proposed a packet reporting and packet forwarding mechanism for UAV path planning. According to [71], clustering leads to a more scalable network and increases network reliability by reducing end-to-end delay of packet delivery.
## 3 Structural and Gas Distribution Characteristics of the Kaiping Syncline Turning End
### Gas Emission Characteristics at the Turning End  
The gas emission volumes on the northern and southern limbs of the Kaiping syncline turning end differ significantly. According to data from 2001 to 2014 on gas emissions in the Zhaogezhuang mine, the relative gas emission ranged from 5.68 m\({}^{3}\)/t to 12.63 m\({}^{3}\)/t, with an average relative gas emission of 7.41 m\({}^{3}\)/t. The absolute gas emission ranged from 19.19 m\({}^{3}\)/min to 35.1 m\({}^{3}\)/min, with an average absolute gas emission of 25.68 m\({}^{3}\)/min (Figure 5). According to gas emission data from the Linxi mine between 2012 and 2022, the absolute gas emission ranged from 9.272 m\({}^{3}\)/min to 14.864 m\({}^{3}\)/min, with an average of 12.25 m\({}^{3}\)/min. The relative gas emission ranged from 3.736 m\({}^{3}\)/t to 6.865 m\({}^{3}\)/t, with an average of 5.13 m\({}^{3}\)/t. Both the relative and absolute gas emissions in the Linxi mine were lower than those in the Zhaogezhuang mine.  
Figure 4: Gas content contour map of the Kaiping syncline turning end. (**a**) No.7 coal seam gas content contour map; (**b**) No.9 coal seam gas content contour map; (**c**) No.12 coal seam gas content contour map.  
To provide a clearer representation of the distribution patterns of gas emissions on both limbs of the turning end, data on gas emissions from the No. 7, No. 9, and No. 12 coal seams in the Zhaogezhuang mine were systematically collected. These data were then combined with gas emission data from the Linxi mine to create a contour map of gas emissions at the turning end for both mines (Figure 6). In the study area, the relative gas emissions from the No. 7, No. 9, and No. 12 coal seams generally increase with burial depth, consistent with trends in gas content. Gas emissions are relatively lower near the axis of the Kaiping syncline, but increase with distance from the axis on both limbs. A comparison of relative gas emissions on both limbs of the turning end shows that emissions in the Linxi mine are generally lower than those in the Zhaogezhuang mine. At the same burial depth, the relative gas emissions in Zhaogezhuang are typically higher than in Linxi. Among the coal seams, the relative gas emissions from the No. 12 seams are higher than those from the No. 7 and No. 9 seams. The relative emissions from the No. 7 and No. 9 seams are generally consistent, with only slight local variations, likely due to geological structural differences and other factors.  
Figure 5: The absolute gas emission and relative gas emission line diagram of the mine. (**a**) Zhaogezhuang mining area; (**b**) Linxi mining area.  
Figure 6: The contour map of gas emission at the turning end of the Kaiping syncline. (**a**) No. 7 coal seam gas emission contour map; (**b**) No. 9 coal seam gas emission contour map; (**c**) No. 12 coal seam gas emission contour map.
# The Status of Space Weather Infrastructure and Research in Africa
## 2 Distribution of Space Weather Infrastructure in Africa
### Space Weather Monitoring  
In monitoring space weather, ground-based instruments and satellites are used to monitor the Sun for any changes and issue warnings and alerts for hazardous space weather events.  
Solar activities, such as solar flares, coronal mass ejection, and moon shadow of an eclipse, induce the rapid change of ionosphere morphology, so-called ionospheric weather, which significantly impacts radio communication and navigation systems. Ground-based GNSS receivers can measure the ionospheric total electron content (TEC) and the ionospheric electron density (through ionospheric tomography). This tool allows continuous monitoring of ionospheric weather and modelling of ionospheric climate. Magnetometers make it possible to follow the regular variations of electric currents in the ionosphere and magnetosphere caused by solar wind dynamics. During magnetic storms, the magnetic variations are affected by the disturbance of ionospheric and magnetospheric currents generated by solar events. Thus, GNSS receivers and magnetometers are very useful in monitoring the impact of solar disturbances on the Earth's environment and in the development of space weather science in Africa.  
Many other instruments are used for space weather monitoring, including very-high-frequency (VHF) and very-low-frequency (VLF) receivers, ionosondes, incoherent scatter radar, all-sky cameras, and Fabret-Perot interferometers, among others. Some of these are discussed below.