esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Model for Cantonese Rumor Detection on Twitter
###### Abstract  
The development of information technology and mobile Internet has spawned the prosperity of online social networks. As the world's largest microblogging platform, Twitter is popular among people all over the world. However, as the number of users on Twitter increases, rumors have become a serious problem. Therefore, rumor detection is necessary since it can prevent unverified information from causing public panic and disrupting social order. Cantonese is a widely used language in China. However, to the best of our knowledge, little research has been done on Cantonese rumor detection. In this paper, we propose a novel hybrid model XGA (namely XLNet-based Bidirectional Gated Recurrent Unit (BiGRU) network with Attention mechanism) for Cantonese rumor detection on Twitter. Specifically, we take advantage of both semantic and sentiment features for detection. First of all, XLNet is employed to produce text-based and sentiment-based embeddings at the character level. Then we perform joint learning of character and word embeddings to obtain the words' external contexts and internal structures. In addition, we leverage BiGRU and the attention mechanism to obtain important semantic features and use the Cantonese rumor dataset we constructed to train our proposed model. The experimental results show that the XGA model outperforms the other popular models in Cantonese rumor detection. The research in this paper provides methods and ideas for future work in Cantonese rumor detection on other social networking platforms.  
online social networks; rumor detection; Cantonese; XGA model +
Footnote †: journal: Applied Sciences
# Dry Printing of Ag-Ni Conductive Particles Using Toner-Type Printed Electronics
## 3 Results
The electrical resistance measurements were obtained using a 4-probe propber, as shown in Figure 7c. The obtained electrical resistivity of the Ag-Ni composite test piece was 6.89-9.43 \(\times\) 10\({}^{-8}\)\(\Omega\cdot\)m. However, the electrical resistivity of the Ag test piece was 3.41-4.52 \(\times\) 10\({}^{-8}\)\(\Omega\cdot\)m. The estimated electrical resistivity of the Ag-Ni composite test piece was approximately twice that of the Ag test piece. Furthermore, the electrical resistivities of Ag and bulk Ni were 1.59 \(\times\) 10\({}^{-8}\)\(\Omega\cdot\)m and 6.99 \(\times\) 10\({}^{-8}\)\(\Omega\cdot\)m, respectively. Therefore, the obtained resistivity of the Ag-Ni composite was comparable to that of the bulk Ni. Because the volume ratio of Ag in the Ag-Ni composite particles was approximately 80.9% and Ag is a soft metal that can be easily deformed [37; 38; 39; 40; 41; 42; 43; 44; 45; 46], compressed Ag-Ni particles formed direct  
Figure 5: (**a**) Schematic illustration of the electrophotographic printing equipment. The developer particle, comprised of the toner and carrier, forms a magnetic brush structure along the magnetic lines of the force, and the magnets move in a reciprocating motion by a motorized mechanism. (**b**) Photograph of the magnetic brush formed in the developer reservoir. Ag-Ag connection networks by being crushed and deformed under sufficient compression. As a result, a relatively low electrical resistivity was achieved, even in only the compressed Ag-Ni particles. This value is sufficiently low compared to other printing methods using ink. Printed electronic circuits using inks present a higher electrical resistivity owing to the residual chemical agents included. To remove the chemical components, the printed pattern should be thermally annealed at a temperature range of near or over 200 \({}^{\circ}\)C, where the plastic film substrates that endure this process are limited. Therefore, the Ag-Ni composite toner is expected to be a conductive toner material with sufficient fluidity and high conductivity for toner-type printing. Figure 6: (a) Photograph of the Ag–Ni toner pattern formed with our toner printing method. (b) Optical micrograph of the Ag–Ni pattern indicated by the dashed rectangle in Figure 6a. The bright particles are the Ag–Ni particles.
###### Abstract
Since the local variation of fluorophore concentration in a biological sample is expected to span over several orders of magnitude, the concentration window for which FCS is applicable should ideally be expanded to extend the practical implementation of FCS in combination with imaging MPM. At least the \"dynamic concentration range\" of the system should be exploited to validate the operational range before proceeding to applications. Since the requirement of low concentration is related to the inherent thermodynamic properties of local concentration fluctuations being Poisson distributed [13], an alternative approach to control the concentration would be to constrain the number of detected particles. In a two-photon excitation setting this can be achieved by controlling the laser excitation power [29]. Thus, in order to implement FCS in connection to MPM the effect of laser power will play an important role and should be further explored. The early FCS experiments were implemented using data acquisition through so-called hardware autocorrelators [11; 14; 18; 19]. In this setting, the signal out-put directly contained time correlated data. With the development of electronics for time-domain microscopy studies, such as fluorescence life-time imaging FLIM [30; 31; 32], the development of time-correlated single photon counting (TCSPC) technology has been expanded [32; 33]. This technology enables counting and timing single individual photons and is therefore preferable in the context of FCS. There are commercial data-analysis packages available to extract and perform FCS analysis based on TCSPS, but in our case this \"black-box\" solution has not demonstrated to be a reliable approach and consistent data have been lacking. Therefore, we in this paper take a \"ground-zero\" approach, developing a protocol starting from the raw data photon counts using TCSPC where particularly the choice of data binning is of importance. This practical guideline thus aims to clarify the conceptual principles and define experimental operating conditions in order to facilitate implementation of MPM-FCS using TSCPC. In particular, the influence of objective lens, concentration range and laser power are studied. Furthermore, a protocol for converting raw data photon counts to autocorrelated signal is presented, the effect of binning explored and the effect of excitation power on autocorrelation data is investigated.
# Wideband-Narrowband Switchable Tapered Slot Antenna for Breast Cancer Diagnosis and Treatment
## 2 Design of the Proposed Frequency Switchable Tapered Slot Antenna  
The configuration of the proposed system is presented in Figure 1. The system consisted of an external microwave radiator with an artificial cylindrical breast phantom. All simulation and experimental results were based on the configuration of Figure 1. To verify the electromagnetic effects of the proposed antenna on the human body, an artificial breast phantom was formed with homogeneous material that takes into account the fatty component (\(\varepsilon_{r}\) = 5.14, \(\sigma\) = 0.137 S/m) of the human breast [18]. The external radiator was in direct contact with the breast phantom to achieve maximum electromagnetic wave transmission.
## 5 Second Generation: Optimization of Virtual Inertia Systems  
The first generation of virtual inertia systems in the literature focused on developing novel topologies for emulation of inertia using power electronic converters. These topologies have matured since as pointed out in Section 3. Recently, the field is more focused towards improving and optimizing the performance of these topologies from the point-of-view of enhanced dynamics, stability, and minimizing energy storage requirements.
## Materials and Methods
### ATDC5 Cell Viability Study
#### 2.5.4 Gene Expression Analysis  
After 21 days of ATDC5 cells growth, the BNC and BNC/\(\kappa\)-Car scaffolds were transferred to Falcon test tubes and washed twice with 2 mL of phosphate buffer saline. Subsequently, 1.5 mL aliquots of 0.25% trypsin/EDTA solution were added to detach ATDC5 cells. The suspensions of cells were collected and the density of cells was determined. Templates for one step real-time RT-PCR, without RNA extraction process, were prepared from 1.0 \(\times\) 10\({}^{4}\) cells using a CellAmp Direct RNA Prep Kit (Takara Bio Inc., Kusatsu, Japan) according to the manufacturer's instructions. Primers (Table 1) were designed using the NCBI's Primer-BLAST software (National Center for Biotechnology Information) based on sequences from the GenBank database and purchased from Genomed, Poland.  
Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed using a One Step PrimeScript(tm) RT-PCR Kit (Takara Bio Inc., Kusatsu, Japan) and a CEX96(tm) detection system (Bio-Rad, Hercules, CA, USA). The process started with reverse transcription (5 min; 42 \({}^{\circ}\)C) and cDNA melting (10 s; 95 \({}^{\circ}\)C). 40 cycles of real time-PCR consisted of denaturation (5 s; 95 \({}^{\circ}\)C), annealing (30 s; 60 \({}^{\circ}\)C), and elongation (30 s; 72 \({}^{\circ}\)C). Nonspecific products and primer dimers were excluded by melting curve analysis performed by maintaining the temperature at 50 \({}^{\circ}\)C for 2 s, followed by a gradual increase in temperature to 95 \({}^{\circ}\)C. The final products were separated electrophoretically on a 2.7% agarose gel in TAE buffer (TRIS/acetate/EDTA) containing a nucleic acid stain (Midori Green Advance DNA stain, NIPPON Genetics, Tokyo, Japan). Constitutively expressed GAPDH gene was selected as an endogenous control. Expression of examined genes were normalized to GAPDH expression and control samples genes expression. The target gene expression levels were calculated using the [PERSON] and Schmittgen equations [PERSON] (1969):  
\[\Delta C_{t,sample}=C_{t,target\ gene\ for\ sample}-C_{t,GAPDG\ for\ sample} \tag{4}\]  
\[\Delta C_{t,\ control}=C_{t,target\ gene\ for\ control}-C_{t,GAPDG\ for\ control} \tag{5}\]  
\[\Delta\Delta C_{t}=\Delta C_{t,sample}-\Delta C_{t,\ control} \tag{6}\]  
\[Relative\ gene\ expression=\ 2^{-\Delta\Delta C_{t}} \tag{7}\]
## 2 Materials and Methods
### Experimental Methods
Data simplification and MCCV correction criteria of steady state databases were set through data row frequency analysis under the same conditions based on ME ORDER RPM and VOYAGE considering propulsion engine control and operation characteristics and used to improve the MCCV algorithm. The correction equation for calculating RMCCV, which is the correction value of the MCCV, is described in Equation (5) using the regression coefficient and correction standard coefficient of each correction factor derived. An improvement algorithm was built, and the results were analyzed. \[\text{RMCCV}=\left\{ \begin{aligned} & CET\\ &+\begin{bmatrix}(ELR*(LC-CL))\\ +(ESTR*(STC-CST))\\ +(ESPR*(SPC-CSP))\end{bmatrix}\end{aligned} \right\}-\left\{CCP+[CPLR*(LC-CL) ]\right\} \tag{5}\]  
Figure 3: Correlation analysis of cylinder 1. (**a**) All modes and (**b**) navigation full mode (source: own elaboration, Python 3.8.3). where LC is the load criterion, STC is the scavene temperature criterion, SPC is the scavene pressure criterion, CPLR is the combustion pressure/load regression coefficient, ELR is the exhaust temperature/load regression coefficient, ESTR is the exhaust temperature/scavenge temperature regression coefficient, ESPR is the exhaust temperature/scavenge pressure regression coefficient, CL is the collected load, CST is the collected scavene temperature, and CSP is the collected scavene pressure. The RMCCV is calculated as the difference between the CET correction value and the maximum CCP correction value. The CET correction value multiplies the difference between the correction reference coefficient (LC, STC, and SPC) of each of the three selected correction factors (engine load, scavenging air pressure, and scavenging air temperature) and the collected correction factors (CL, CST, and CSP) by the calculated correction factor regression coefficient (ELR, ESTR, and ESPR). The three derived correction values are calculated by combining them with the CET. The maximum CCP correction value is calculated by multiplying the difference between the LC and the CL by the CPLR and then combining it with the obtained CCP. The propulsion engine anomalistic symptom judgment algorithm built based on the MCCV and the RMCCV was verified using the entire dataset and average data, and the validity of the algorithm was confirmed by comparison.
## 3 Results and Discussion
### Characterization of [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM Radioconjugate  
The obtained [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate was purified by HPLC (gradient two) and tested for its lipophilicity, as well its stability in human serum. The lipophilicity (LogD7.4 value) of the [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate was determined to be \(-\)3.68 \(\pm\) 0.12, which proves its highly hydrophilic nature.  
Figure 8: HPLC analysis of reaction mixtures after synthesis of [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM (gradient 2) and the ‘cold’ reference compound Ga-NODAGA-Glu-CFM (270 nm, gradient 2).  
The electrophoretic diagrams of the [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate and gallium eluate [\({}^{68}\)Ga]GaCl\({}_{3}\) (with the [\({}^{68}\)Ga]Ga\({}^{3+}\) cation as a reference radiocompound) showed only single peaks. In both cases, the peaks were shifted toward the cathode. These results confirmed the expected positive charge of the [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate, which may have a beneficial effect on the interaction of the radioconjugate with bacterial cells. Based on the literature data, most bacterial cells possess an overall negative charge due to the presence of peptidoglycan, which is rich in carboxyl and amino groups [32; 33; 34]. For this reason, better binding of the designed radioconjugate to the wall of bacterial cells can be expected.  
The HPLC radiochromatograms of the isolated [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate recorded after 0.5 h and 2 h of incubation at 37 \({}^{\circ}\)C in human serum are shown in Figure 10. These studies showed that the tested radioconjugate is completely stable in human serum. The measurements of radioactivity of both the supernatant and precipitate fractions showed that the percentage of \({}^{68}\)Ga-radioconjugate bound to serum components ranged from 7% to 10% and was independent of incubation time.  
Figure 10: The HPLC radiochromatograms of the [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate recorded after (**A**) 0.5 h and (**B**) 2 h of incubation at 37 \({}^{\circ}\)C in human serum.  
Figure 9: ITLC analyses of free \({}^{68}\)Ga\({}^{3+}\) (**A**) and the reaction mixture after synthesis of the [\({}^{68}\)Ga]Ga-NODAGA-Glu-CFM radioconjugate (**B**) using citrate buffer as a developing solvent.
# Fatty Acid Composition and Volatile Profile of _M. longissimus thoracic_ from Commercial Lambs
###### Abstract  
Animal production factors can affect the fatty acid and volatile profile of lamb meat. The fatty acid and volatile composition of the _M. longissimus thoracis_ was evaluated from 150 lambs from 10 groups of commercial lambs that differed in age, sex, diet and breed, from three farms, which represent typical forage lamb production systems in New Zealand. The meat from 4-month-old composite lambs slaughtered at weaning had a similar polyunsaturated to saturated fatty acid ratio compared to 6- to 8-month-old composite lambs, but a greater ratio than that of 12-month-old Merino lambs (\(p<0.05\)), with all ratios being lower than the recommended \(\geq\)0.45. All lamb production systems produced meat with an omega-6 to omega-3 ratio below 1.5, well below the recommended ratio \(\leq\) 4.0. Meat from 4-month-old lambs had higher C12:0, C14:0 and C16:0 and lower C18:0, reflecting the composition of the milk diet, resulting in higher atherogenic index than meat from other animal groups, while meat from 12-month-old Merino lambs, with lower content of polyunsaturated fatty acids, showed higher thrombogenic index. Meat from lambs processed at weaning contained the greatest concentration of eicosapentaenoic and docosahexaenoicenoic acids, which would qualify as a'source' or 'good source' of these target fatty acids based on the Commission of Regulation of the European Union or the Food Standards Australia New Zealand guidelines, respectively. Volatiles were extracted from the headspace of raw lean meat and 36 volatile compounds were identified. The abundance of carbon disulphide, isododecane, heptanal, 2,5-hexanediol and 3-octanone and pentanoic, octanoic, nonanoic and heptanoic acids was similar between all groups of lambs. Meat from 12-month-old Merino lambs had low abundance of acetic, propanoic, butanoic and hexanoic acids, and hexanal, octanal and dimethyl sulphide. For 6- to 8-month-old composite lambs, hexanal, octanal and nonanal were present at higher relative abundance in meat from lambs that grazed on chicory than perennial ryegrass. The significant differences in the fatty acid and volatile profiles in meat from 12-month-old Merino lambs compared with lambs slaughtered at weaning or further grazed on red clover, chicory or mixed pasture may result in distinctive nutritional value and lamb flavour.  
meat; lipids; volatiles; nutrition; production system 2020  
9, 1885 doi:10.3390/foods9121885
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 2 Sensor Types
### Radar Sensors
#### 2.6.3 SAR Interferometry Technique  
The SAR interferometry technique is based on a normalized cross-correlation of two compound signals. The benefit of this method is the high interferometric consistency for stable characteristics, such as settlements, while it is low for unstable characteristics, such as irrigation water. In the past, this technique was adopted for digital elevation model production and surface movement analysis, while researchers have shown the unique potential of SAR interferometry, which is currently being used in data collection for a diverse set of crop parameters, including plant height, biomass density, and surface irrigation, among others [123].
# Fourier Neural Operator for Fluid Flow in Small-Shape 2D Simulated Porous Media Dataset
## 6 Conclusions  
Classical NNs attempt to learn mappings between finite-dimensional Euclidean spaces, making them confined to a particular discretization. On the other hand, the FNO, as a mesh-independent algorithm, tries to learn function-to-function mappings. This makes it possible for FNO to be trained on one mesh and subsequently assessed on another. This study further extended the capabilities of FNO by applying it to a new simulated dataset made up of small-shape samples. The generated dataset simulates single-phase fluid flow in a porous reservoir assessed by 1700 2D grid samples, each constructed as a unit square with a 30 \(\times\) 30 uniform mesh. The models of FNO and CNN are trained to predict the pressure distribution of each grid sample based on its permeability field. The statistical-graphical results confirm the good ability of the FNO to predict the pressure distribution based on the permeability field. The FNO model provided better prediction performance than the CNN model when applied to the testing dataset. Analysis of the results leads to three recommendations for future research. These are: (i) training the FNO models for fluid flow in porous media with more data covering a wider range of matrix and fracture permeabilities and a variable number of fractures in each small-shape grid sample, (ii) applying FNO to solve other types of small-shape data problems, and (iii) designing and testing novel more complex FNO architectures.  
Conceptualization, A.C. and J.C.; methodology, A.C.; formal analysis, A.C., J.C. and D.A.W.; data curation, A.C. and J.C.; writing original draft, A.C.; writing--review and editing by A.C., D.A.W. and F.C.; visualization, A.C.; supervision, J.C., F.C. and F.M. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the Key Program Special Fund in XJTLU (KSF-E-50), XJTLU Postgraduate Research Scholarship (PGRS1912009), and JYTLU Research Development Funding (RDF-19-01-15).  
Not applicable.  
The datasets generated and supporting the findings of this article are obtainable from the corresponding author(s) upon reasonable request.  
We would like to thank [PERSON] for clarifying some points related to FNO.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \(a_{0}\) & first term of \(a_{n}\) \\ \(a_{n}\) & Fourier series coefficient \\ BC & Boundary Condition \\ BN & Batch Normalization \\ \(b_{n}\) & Fourier series coefficient \\ CNN & Convolutional Neural Network \\ CONV & convolutional layers \\ DL & Deep Learning \\ \(\Delta p\) & Laplace pressure \\ \(f\) & source term \\ \(F\) & Fourier transform \\ \(F^{-1}\) & inverse Fourier transform \\ FC & Fully Connected \\ FEM & Finite Element Method \\ \end{tabular}
# Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback
###### Abstract
Notably, our work contributes significantly to the field as it is the first to evaluate the impact of telemanipulation assistance within telepresence systems, building on our previous work [9], which explored motion blending techniques to enhance task efficiency. This study introduces several key advancements. Firstly, it integrates multi-level haptic feedback with assistive manipulation methods to improve task performance and user embodiment. Secondly, we replace the five-finger robotic hand with a parallel gripper to reduce task complexity and improve usability for novice users. Lastly, this research expands experimental evaluation by exploring new metrics, including Illusion of Virtual Body Ownership (IVBO) components, to understand user experience comprehensively. IVBO refers to the phenomenon where individuals perceive a virtual body or body part as their own, integrating it into their self-representation. This illusion is typically induced through synchronized multisensory inputs, such as aligning visual and tactile stimuli, which convince the brain to accept the virtual entity as part of the physical self.