esa-sceva/satcom-chunk-collection huggingface dataset:
# Pushing Mechatronic Applications to the Limits via Smart Motion Control
## 5 Pilot Applications and Key Results
### Smart Machining Tools and Milling Machines (Pilot 4)  
Smart milling machines (CNC-based) are optimized for high-productivity domains such as energy, aeronautics, automotive, mold, and dies [61; 62; 63]. The milling head is one of the most important modules having direct impact on production quality. Smart wireless nodes able to collect RT data about a machine's milling head performance were developed. At system level, the sensors follow the structure of BB2. This set of sensors includes accelerometer-based vibration sensors, proximity sensors, wireless temperature probes and embedded electronics data pre-processing.
## 2 Materials and Methods
### Effect of Temperature and Ionic Contamination  
Figure 9 shows the sampling values for different ionic contamination concentrations at different temperatures at relative humidities of 75% and 85%. The sampling values at different humidities show the same trend with temperature. The greater the change in the sampling value as the temperature increases leads to a smaller surface insulation resistance (SIR).  
Similarly, the boundary point of the discharge is also related to temperature. For example, at a relative humidity of 95%, a sample of 50 \(\upmu\)g/cm\({}^{2}\) discharges at 45 \({}^{\circ}\)C, but at 85 \({}^{\circ}\)C, a discharge also occurs at 10 \(\upmu\)g/cm\({}^{2}\). This suggests that the applied voltage of the discharge is different. Figure 10 shows the effect of temperature on discharge boundaries.  
Figure 8: Variation law of sampling value of 7 kV at constant temperature and different humidities. (**a**) Temperature of 30 \({}^{\circ}\)C; (**b**) Temperature of 45 \({}^{\circ}\)C; (**c**) Temperature of 65 \({}^{\circ}\)C; (**d**) Temperature of 85 \({}^{\circ}\)C.
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 4 System Model
### Beamformer Architecture at the BS  
Each mmWave F-RAN is equipped with a digital beamformer to allow for communication with multiple MSs and provide spatial multiplexing for an increased link capacity. Hence, the RRH are equipped with a UCA composed of \(\psi_{\text{RH}}\) antennas that are equally spaced along circular ring. In contrast to the MS design, each antenna here is connected to one RF chain. Note that the total number of antennas is equal to the number of RF chains, i.e., \(A_{\text{RH}}\)= \(\psi_{\text{RH}}\). Additionally, the overall radiated pattern from \(r_{\text{BS}}\) represents a beamforming vector, \(p_{\rm RH}\), in the beamforming matrix, \(P_{\rm RH}=p_{\rm bb}p_{\rm an}\), where \(P_{\rm bb}\) and \(p_{\rm an}\) represent the baseband and analog beamforming stages, respectively.
## 3 Optimization Empowerment Combined Model Based on Sliding Window
### Improved Coefficient of Variation Weighting Method  
The traditional coefficient of variation method for weighting the combined model uses the standard deviation and mean of the data, but these two statistics are easily influenced by outliers, leading to unstable weights. Furthermore, the coefficient of variation weighting method only considers the data's variability and neglects the correlations between data, making it necessary for improvement. An improved approach is to use the standard deviation of prediction accuracy to address the shortcomings of the traditional coefficient of variation weighting.  
When employing the improved coefficient of variation method for weighting, the relative prediction error (\(\widetilde{e_{rt_{i}}}\)) and prediction accuracy (\(A_{rt_{i}}\)) of the \(r\)-th prediction method at time \(t_{i}\) are determined as follows:  
\[\widetilde{e_{rt_{i}}}=\frac{W(t_{i})-\hat{W}_{rt_{i}}}{W(t_{i})} \tag{17}\]  
\[A_{rt_{i}}=\left\{\begin{array}{l}1-|\widetilde{e_{rt_{i}}}|,|\widetilde{e_ {rt_{i}}}|\leq 1\\ 0,|\widetilde{e_{rt_{i}}}|>1\end{array}\right. \tag{18}\]  
Let \(A_{rt}=[A_{rt1},A_{rt2},\ldots,A_{rtN}]^{\mathrm{T}}\), then the mathematical expectation \(E(A_{rt})\), standard deviation \(\sigma(A_{rt})\), and coefficient of variation \(d_{r}\) of the prediction accuracy series of the \(r\)-th prediction method are:  
\[E(A_{rt})=\frac{1}{N}{\sum\limits_{i=1}^{N}A_{rt_{i}}} \tag{19}\]\[\sigma(A_{rt})=\sqrt{\sum\limits_{i=1}^{N}{(A_{rt_{i}}-E(A_{rt}))^{2}/N}} \tag{20}\]  
\[d_{r}=\frac{\sigma(A_{rt})}{E(A_{rt})} \tag{21}\]  
The formula for the weight \(Q_{r}\) can be found in Equation (16). This method uses the coefficient of variation of prediction accuracy to determine the weight vector, improving the traditional coefficient of variation weighting. It effectively reduces the impact of outliers on weight allocation, considering both the variability of data indicators and their contribution to the entire model. This leads to enhanced stability and accuracy in the prediction results.
## 3 Experiment Two: Verification of the Localisation Thresholds
### Materials and Methods
#### 3.1.3 Test Method  
The test was completed by the same 10 subjects who participated in Experiment One. Localisation judgments were made using the LED strip located directly in front of the listening position. For each test, subjects were provided with a handheld knob, which controlled which LED on the strip was turned on. Subjects were required to adjust the knob until the position of the active LED matched the perceived location of the focal point of each stimulus. This method was chosen following research conducted by [PERSON] et al. [27], who found that it was faster and produced results with greater accuracy and consistency compared to the numbered scale method, which had been used in a number of previous vertical localisation studies [11; 28; 29]. The position of the LED selected for each stimulus was converted into an elevation angle. The heads of subjects were not fixed, however they were instructed to sit up and face forwards at all times, using only their eyes to look at the light strip. To help maintain the correct seating position, a small headrest was positioned behind the head of each subject. The test was completed four times by each subject, with each sitting containing all 30 stimuli and taking around 10 min to complete. The presentation order of stimuli was randomised for each test.  
Figure 8: Physical setup for the localisation threshold verification test.
## 4 Discussion
### Future Opportunities  
The focus of this study was to determine the minimum size of the chemical, flight parameters like altitude, speed, image overlap, which can be controlled by the operator directly, and that would directly impact the construction of the hyperspectral cube and accuracy of the model in identifying the traces of the chemical. It was beyond the scope of the study to assess the impact of temperature, wind speed, cloud cover conditions and mass of the chemical. In the study conducted by [20] the relationship between different weather conditions and image construction has been discussed. The study shows that wind speed has a negligible impact whereas cloud cover has an influence in the quality of the image. Since weather conditions cannot be controlled, we conducted experiments in sunny conditions around 11:00 a.m., with minimum wind speeds and placed the chemicals with different areal dimensions. In this study, we created the hyperspectral cube and processing approach with only CubeCreator, which is commercial software of BaySpec. The image construction algorithm might have a different level of performance if tested on different software's in future. This software has been used and demonstrated in different studies, and thus, it has a strong baseline which can be used for comparisons.  
The hyperspectral image was constructed using image to image reconstruction solutions. GNSS was not used in the process of reconstruction of the image and the whole process was dependent on the image features and position of the camera. A better image could be constructed in future using a hyperspectral sensor along with GNSS. Storage and disposal of the explosive chemicals has led to environmental issue, endangering health and safety of the public making it a major concern worldwide [21; 22]. Similar HSI can also be used in the future to find sites of illegal waste disposal and leakage of hazardous chemical in industries.
## 2 Materials and Methods
### Determination of Editing Efficiency  
To confirm the restoration of the target sequence, PCR was performed with specific primer (Supplementary Table S3) and 50 ng of each well cDNA as template by using Go Taq Flexi DNA polymerase (Promega, Tokyo, Japan) to obtain the DNA region containing the target base in EGFP W58X. PCR products were purified using a NucleoSpin Gel and PCR Clean-up Kit (MACHEREY-ANGEL, Shiga, Japan).  
Sanger sequencing was performed on purified products by Eurofins Genomics (Tokyo, Japan). The sequencing primer was the forward primer that had shown in Supplementary Table S3. Raw sequence data were analyzed by the SnapGene Viewer (Dotmatics, San Diego, CA, USA). When edited and unedited products were present together, a dual peak for G (edited) and A (unedited) was observed at the target site. Peak height was measured using ImageJ software (NIH, MD, USA). Editing was quantified based on the maximum peak height ratio of edited and unedited products using the formula [32]:  
\[\text{Editing efficiency}=\bigg{\{}100\%\ \times\bigg{(}\frac{\text{G height}}{\text{A height }+\text{G height}}\bigg{)}\bigg{\}}. \tag{1}\]  
Then, we obtained editing efficiencies by calculating the average of three individual transfection experiments.
# A Personalised Emotion-Based Model for Relaxation in Virtual Reality
## Methodology
### Heuristic Adaptor  
The adaptor is the component in charge of updating the adaptation intensities in real-time. It observes the current state of the VE and the user's emotional data. Based on this, it decides which adaptation intensity should be changed next. The adaptor updates one adaptation each time step. It calculates which action is best, performs this action, and waits for the user's emotional state to update. This process is repeated until the user reaches his optimal emotional state, at which point no more adaptations are necessary. Finding this optimal state and the maximal relaxation linked to it can be formulated as an optimisation problem. The emotional model is non-linear and can be solved using Mixed Integer Non-Linear Programming (MINLP). As MINLP problems are not easy to solve, the problem (and thus, the model) need to be linearised. This results in a linear problem that can be solved using Mixed Integer Linear Programming (MILP) [36]. However, this optimisation problem cannot be solved in real-time. Therefore, a heuristic approach is used.  
The heuristic approach first determines the absolute maximum achievable relaxation for the environment and user profile. It achieves this by sampling the adaptation intensities and comparing all possible combinations, i.e., a brute force approach. The combination that leads to the maximal relaxation value is the combination the heuristic optimiser should work towards. As it can only change one adaptation intensity per step, it should choose an order in which to change the intensities.
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 3 Deep-Coupled Navigation System Design  
This section will discuss the joint L1 C/A and L2 vector-tracking-based GNSS/grid SINS deep-coupled navigation system. The GPS L1 C/A and L2 civil moderate (L2 CM) signal model will be reviewed firstly. Then, the joint vector-tracking-based system structure will be proposed using the L1 C/A and L2 CM dual-frequency signals. Finally, the joint prefilter, the integrated navigation filter, and numerically controlled oscillator (NCO) feedback control will be designed in the remaining subsections.
## 2 Mathematical Modeling and Simulating
### Modeling
#### 2.1.3 Heat Pipe Flat Plate Solar Collectors  
The mathematical formulation was based on the works of [28; 32; 46]. This formulation relies on a set of coupled heat transfer equations to identify temperature distributions in various parts of the collector. At each time step, the model can predict the spatial temperatures on the glass cover, the absorbing surface, the evaporator, the condenser, and the water flowing inside the collector, as shown in Figure 4.  
The heat flow lost by the condenser towards the manifold liquid, is determined as follows:  
\[Q_{c}=A_{c}h_{c}\left(T_{C}-T_{w}\right), \tag{31}\]where \(T_{c}\) is the condenser temperature, equal to the evaporator temperature, and \(\overline{T}_{av}\), the average temperature, is determined as follows:  
\[\overline{T}_{w}=(T_{iw}+T_{wi})/2, \tag{32}\]  
The heat flow in the manifold is the useful heat of the collector and is determined as follows:  
\[Q_{u}=Q_{c}=\dot{m}_{w}Cp_{w}(T_{iw}-T_{wi}) \tag{33}\]  
The instantaneous efficiency of a solar collector as a function of the number of its heat pipes is determined as follows:  
\[\eta_{c}=\frac{\dot{m}_{w}C_{w}(T_{iw}-T_{wi})}{I\;l_{ce}N_{p}W}\;, \tag{34}\]  
where \(l_{ce}\) is the length of the evaporator, \(N_{p}\) is the number of tubes, \(W\) is the space between tubes.  
The convective heat transfer coefficient between the condenser and the work fluid in the manifold \(h_{c}\), for both laminar flow and turbulent flow, taken from [26], was determined as follows:  
\[h_{c}=\frac{k_{l}}{D}0.3+\frac{0.62 Re^{1/2}\text{Pr}^{1/3}}{[1+\left(\frac{0.4 }{7r}\right)^{\frac{2}{3},\frac{1}{4}}}[1+(\frac{Re}{282,000})^{5/8}]^{4/5}, \tag{35}\]  
where the dimensionless numbers are as follows:  
\[Re=\frac{4\dot{m}}{\pi D\mu N_{t}} \tag{36}\]  
\[Pr=\frac{\mu Cp}{k} \tag{37}\]
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 5 Experimental Results and Discussion  
### Baselines  
The primary objective of this paper is to assess the potential of active learning for achieving comparable performance to models trained on the entire dataset while using a significantly smaller amount of data. To establish a performance baseline, we employed a model trained on the complete training dataset.  
In this case, we random partitioned the preprocessed dataset into a training dataset comprising 70% of the whole dataset and a test dataset representing the remaining 30%. The machine learning models in our experiments underwent initial training using the training dataset and subsequent evaluation on the test data. The reported results are an average of the outcomes from ten rounds of experimentation.  
The results are reported in Table 5. Notably, we observed that RFC consistently demonstrated superior performance across both datasets. Machine learning models such as MLP, AdaBoost, and KNN exhibited higher performance compared to LR, DTC, and NB. This observation can be attributed to the constrained flexibility of the LR, DTC, and NB models, which tend to establish a linear decision boundary between the two classes. This inherent constraint can impede their ability to effectively distinguish between rumors and non-rumors.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**PHEME**} & \multicolumn{3}{c}{**RUMDECT**} \\ \cline{2-7}  & **Baseline** & **Best Accuracy** & **Best Strategy** & **Baseline** & **Best Accuracy** & **Best Strategy** \\ \hline LR & 0.725 & 0.726 \(\pm\) 0.01 & ERR & 0.889 & 0.864 \(\pm\) 0.01 & Unc (Lc) \\ LR (SGD) & 0.755 & 0.676 \(\pm\) 0.03 & QBC & 0.889 & 0.856 \(\pm\) 0.01 & QBC \\ NB & 0.407 & 0.553 \(\pm\) 0.08 & Unc (Ma) & 0.876 & 0.851 \(\pm\) 0.01 & QUIRE \\ GP & 0.726 & 0.710 \(\pm\) 0.01 & Unc (Lc) & 0.892 & 0.865 \(\pm\) 0.02 & QBC \\ DTC (Gini) & 0.679 & 0.690 \(\pm\) 0.01 & ERR & 0.874 & 0.859 \(\pm\) 0.02 & QBC \\ DTC (Entropy) & 0.677 & 0.709 \(\pm\) 0.02 & ERR & 0.872 & 0.860 \(\pm\) 0.02 & QBC \\ LDA & 0.771 & 0.673 \(\pm\) 0.03 & DW & 0.892 & 0.875 \(\pm\) 0.02 & QBC \\ QDA & 0.589 & 0.627 \(\pm\) 0.10 & DW & 0.881 & 0.876 \(\pm\) 0.02 & DW \\ Ada & 0.736 & 0.677 \(\pm\) 0.02 & QBC & 0.903 & 0.883 \(\pm\) 0.01 & QBC \\ KNN & 0.771 & 0.706 \(\pm\) 0.01 & QBC & 0.895 & 0.849 \(\pm\) 0.01 & QBC \\ MLP & 0.778 & 0.706 \(\pm\) 0.01 & Unc (Lc) & 0.904 & 0.875 \(\pm\) 0.01 & Unc (Lc) \\ RFC & 0.805 & 0.719 \(\pm\) 0.01 & Unc (Lc) & 0.915 & 0.902 \(\pm\) 0.01 & Unc (Ma) \\ SVM (RBF) & 0.778 & 0.714 \(\pm\) 0.01 & Unc (En) & 0.895 & 0.884 \(\pm\) 0.02 & QUIRE \\ SVM (Linear) & 0.775 & 0.719 \(\pm\) 0.03 & QBC & 0.895 & 0.879 \(\pm\) 0.02 & QBC \\ SVM (SGD) & 0.765 & 0.685 \(\pm\) 0.03 & QBC & 0.894 & 0.854 \(\pm\) 0.03 & QBC \\ \hline \hline \end{tabular}
\end{table}
Table 5: Baseline accuracy score, best-performing accuracy score, and corresponding strategy name among all model and strategy combinations.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 2 ECM Components That Contribute to Tissue Engineering Strategies
### Elastin  
The specific components of the ECM that are accountable for elasticity and expandability are the elastic fibres, which play crucial biological functions in many organs and tissue types such as arteries, skin and tendons [63]. Elastic fibres contain two structurally distinct components: a group of aligned microfibrils based on fibrin, and a dense network of crosslinked elastin, which normally contributes around 90% of total fibre content [63]. The microfibril component of the elastic fibres are usually 10-12 nanometres (nm) in width, which provides tissues with extensive elasticity. Elastin is an insoluble, 70 kilodalton (kDa) protein component derived from the more soluble precursor tropoelastin [63]. Elastin primarily serves to promote recoil in tissues that are exposed to repeated stretching during  
Figure 3: Cruciform/cross structure of laminin containing \(\alpha\), \(\beta\) and \(\gamma\) chains. This formation is maintained through the presence of an \(\alpha\) helical region. Created with BioRender.com.  
Figure 2: The fibronectin monomer contains several functional domains and alternative splicing regions that allow interaction of fibronectin with many other proteins of the ECM, along with cell surface receptors. These in turn promote the array of biological functions associated with the fibronectin protein. At the N-terminal region of the protein, type I repeats are responsible for producing fibronectin dimers, as well as promoting interaction with other molecules such as fibrin. Downstream of this region is the collagen-binding domain, made up of both type I and type II repeats, promoting attachment between fibronectin and type I collagen. Early type III repeats are important for fibronectin fibril formation, while other type III repeats prioritise the interaction of fibronectin with cell surface receptors, e.g., the integrin-binding domain which contains the RGD motif. Binding sites for other cell surface receptors can also be found within specific regions, such as the HepII C-terminal domain [60]. The fibronectin C-terminus acts as a site for antiparallel disulphide bonds during the formation of the fibronectin dimer [54]. Created with BioRender.com.  
normal tissue function [55]. It also has excellent durability attributed to its ability to resist proteolytic degradation [63]. The formation of mature elastin results from a multistep process involving intracellularly synthesised tropoelastin, elastin-binding proteins (EBP) and microfibrils. Elastin fibre assembly is then initiated, which eventually results in the formation of the final elastin product (Figure 4) [64].
## 3 Results and Discussion
### Effect of Different CR Content on the Performance of AR
#### 3.1.2 70# Matrix Asphal  
Similar tests were conducted with SK70# matrix asphalt for comparison purposes. The upper limit ranges of CR contents were expanded to 16%, 18%, 20%, 22%, 24%, 26% and 28%. The preparation and test methods were the same as previously described. The tests results were shown in Table 7, as following:  
Compared with the test results of 90# matrix asphalt, the trend of SK70# matrix asphalt with CR content between 16% and 26% was basically the same as that of 90# with CR content between 15% and 25% when it comes to rotational viscosity and elastic recovery (Figure 3d,e compared to Figure 4d,e respectively). It is worth noting that the 90# matrix asphalt did not show peaks in 15-25% range, while the 70# matrix asphalt had peaks in the penetration, elastic recovery and ductility index when the CR content was between 24% and 26%. When the CR content continued to increase to 28%, the softening point and viscosity index kept increasing, however, the penetration, elastic recovery and ductility index all showed a downward trend. This indicated that there was an optimum dosage in the range of 22-26% that the AR decreased after the index reached the peak.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ProportionProperties** & **Penetration** & **Softening Point** & **Ductility** & **Elastic Recovery** & **180 \({}^{\circ}\)C Rotational** \\
**(0.1 mm)** & **(\({}^{\circ}\)C)** & **(cm)** & **(25 \({}^{\circ}\)C)** & **Viscosity (Pa-s)** \\ \hline
16\% & 46.5 & 62 & 6.7 & 75.00\% & 0.8 \\
18\% & 42.6 & 64.5 & 7.4 & 78.30\% & 1.4 \\
20\% & 44.1 & 63.5 & 7.7 & 83.70\% & 1.8 \\
22\% & 39.3 & 65.5 & 8.2 & 82.30\
# Large Scale Imaging by Fine Spatial Alignment of Multi-Scanning Data with Gel Cube Device
## 3 Results
### Self-Fluorescent Microstructure for Spatial Alignment of Multi-Directional Images  
Accurate spatial alignment is the key to acquiring high-resolution images from multiple scanning data from six sides. Because alignment error may be high when the sample structure does not have a unique shape, spatial reference points are required to align the image data from different sides. The alignment marks were placed onto the polymer frame of the hybrid gel cube (Figure 2A). Negative permanent photoresist (SU-8; MicroChem Corp, MA, USA) was used to fabricate the alignment mark because of self-fluorescent ability of SU-8 as well as precision of fabrication by photolithography (Figure 2B,C). The triangular prism shape was designed for easy identification of the single point made by the vertex. Figure 2D shows the alignment error when two fabricated microstructures were used to align image data obtained from three sides. The average errors using 4\(\times\) and 10\(\times\) magnification lens were 8.7 \(\upmu\)m and 3.1 \(\upmu\)m, respectively. Relative to view range and sample size, the error in alignments does not result in deterioration of image quality.  
The fabrication process is shown in Figure 2E. First, sacrificed layer, such as LOR (Nippon Kayaku, Japan), was spin coated onto a silicon wafer, followed by spin coating of SU-8 photoresist and UV exposure of the designed photomask. The SU-8 was subsequently developed with 2-methoxy-1-methylethyl acetate (Wako Pure Chemical Industries, Ltd., Osaka, Japan), and the sacrificed layer was dissolved in isopropyl alcohol to lift off the self-fluorescent microstructure. The fabricated microstructure was then attached to the polymer cubic frame at its vertexes using polydimethylsiloxane (PDMS; Dow Corning, MI, USA). Agarose was attached, and a bio-sample was placed in the cubic device with ECM as described [15]. The size of the alignment mark has a known distance and can be used as a calibration reference for  
Figure 2: Self-fluorescent microstructure for fine alignments of scanning images obtained from multi-directional scanning. (**A**) Overview of the SU-8 microstructure attached to the vertex of a polycarbonate frame. The larger structure was used for easier visualization. (**B**) Bright field image of the SU-8 microstructure. (**C**) Fluorescent image of the SU-8 microstructure. (**D**) Magnitude of alignment error using a prism-shaped alignment mark. For both 4\(\times\) and 10\(\times\) magnification lens, the average errors were within 2 pixels (\(n>\) 15). (**E**) Process of fabricating a gel cube device with an SU-8 alignment mark. The lift off process was followed by photolithography.  
\(z\)-length, under conditions where the optical length can differ from stage movement, depending on the refractive index of the surrounding environment [22].