esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optimization Algorithms  
Three algorithms were selected for this study, all of which fall within the domain of population-based stochastic genetic optimization techniques. These methods, namely Particle Swarm Optimization (PSO) [42], Artificial Bee Colony (ABC) [43] and Fireworks Algorithm (FWA) [44; 45], are tailored to the optimization of nonlinear functions within multidimensional spaces, as described earlier. The variables to be optimized are the positions of the sensors, which form a multidimensional search space whose dimensions vary depending on environmental factors and the properties of the sensors used.  
The choice of the PSO algorithm stems from its status as one of the most widely used swarm optimization algorithms, which has proven itself in various optimization problems. Alongside PSO, the ABC algorithm is utilized, as a representative of more modern optimization techniques, which is characterized by its effectiveness in certain optimization scenarios [43]. Additionally, the inclusion of the FWA algorithm demonstrates a recent advancement in swarm optimization techniques, presenting notable improvements over the PSO algorithm, according to [44]. In particular, the combination of these algorithms, as used in [46] for the path planning of mobile robots, highlights their suitability for tackling optimization problems in continuous space, and a number of other applications [47; 48; 49].  
Since there are no known solutions to the sensor placement problem and it cannot be solved analytically, the optimization cannot be directed toward a fixed target. Therefore, we use a \"fixed-cost\" approach, where the number of evaluations of the optimization functions for each algorithm within a given test case is limited [50]. Specifically, the number of evaluations is set to \(E=E_{v}\cdot D\), where \(E_{v}\) is the number of evaluations of the optimization function per independent variable and \(D\) is the number of independent variables. In this study, the influence of the number of evaluations of the optimization function per independent variable \(E_{v}\) is analyzed.  
For the placement of the single isotropic sensor in the two-dimensional environment, \(D=2\), which corresponds to the positions \(x\) and \(y\) of said sensor. In the case of a three-dimensional environment, \(D=3\) because an additional axis \(z\) is added.
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
## 2 Oropharynx and Esophagus
### Diagnostics
Magnetoencephalography (MEG) has also been used to monitor cortical processes during swallowing. Sophisticated analytical techniques such as synthetic aperture magnetometry can further dissociate the cortical contributions of each separable component of swallowing in the sensorimotor sequence and identify the corresponding spatio-temporal characteristics of cortical activity [33]. By utilizing this technique, a study showed that a reduction in swallowing related cortical activation and a disease related shift of hemispheric lateralization were observed in ALS patients with dysphagia compared to healthy controls [34]. These findings have shown how cortical analysis can be used to investigate and monitor the presence of dysphagia and, with refinement, have the quantitative potential to identify dysfunctions at a primary level for greater precision in developing and prescribing therapies.
## 3 Results and Discussion
### Colour and Shear Force Value  
As shown in Figure 2A, compared with CT, the \(L^{*}\) value (lightness) of the PT15 and PT30 samples had no significant (\(p>0.05\)) differences, but it was significantly (\(p<0.05\)) higher than PT45, while the \(L^{*}\) value of NT significantly (\(p<0.05\)) increased. In addition, there were no significant (\(p>0.05\)) differences between the plasma treatment group and the control group with respect to the value of yellowness (\(b^{*}\)) presented in Figure 2C, but the \(b^{*}\) value of NT samples decreased. For the value of redness (\(a^{*}\)), NT samples increased 30.87% compared with the CT groups (Figure 2B) and increased the roasted  
Figure 1: Changes in the content of sulfhydryl (**A**) and TBARS value (**B**) of roasted lamb with different treatments. \(a,b,c\) Mean values with different letters are significantly different (\(p<0.05\)). Control group (CT): without sodium nitrite and plasma treatment; NT: with 0.005% sodium nitrite; PT15: with plasma treatment for 15 min; PT30: with plasma treatment for 30 min; PT45: with plasma treatment for 45 min.  
lamb's redness, which was also an important reason for adding nitrite in the process of roasted lamb. Nitrite can decompose into nitric oxide (NO), after which NO reacts with myoglobin to form pink nitrosomyoglobin during the heating process in meat products [34]. Interestingly, the _a_\({}^{*}\) value of the roasted lamb increased as plasma treatment time increased. The redness (_a_\({}^{*}\) value) of the roasted lamb prepared by plasma treatment for 15 min, 30 min, and 45 min increased by 9.30%, 14.65%, and 31.40% compared with the control treatment, respectively. Meanwhile, the redness of the PT45 samples was similar to that of the NT samples. Similarly, [PERSON] et al. [17] also observed that the _a_\({}^{*}\) value of the 60 min plasma-treated pork jerky increased significantly (_p_ < 0.05), while the _a_\({}^{*}\) value of the 40 min plasma-treated jerky was comparable with the sodium nitrite-treated ones. It has also been reported that the _a_\({}^{*}\) value and nitrite content of ground ham and meat batter also increased with the atmospheric nonthermal plasma treatment time [18; 32].  
As shown in Figure 2D, the shear force value of the samples showed no significant (_p_ > 0.05) differences among all treatment groups, indicating that the tenderness of the samples was not influenced by the plasma treatment. A previous study also reported that the atmospheric pressure plasma treatment had no influence on the textural properties of the canned ground hams [16]. The results in this study clearly showed that plasma treatment can enhance the redness of roasted lamb while having no influence on its tenderness.
## 5 Performance Evaluation
### Temperature-Sensitive Variables Selection  
Prior to establishing the thermal error prediction model, the temperature-sensitive variables were chosen based on the experimental data at 12,000 rpm. Sixteen temperature transducers were arranged in five different heat source regions in the experiment.  
According to the KR-BPNN method, different \(K\) values will produce different clusters. Considering that there are five thermal source regions, and both the ambient temperature transducer and the spindle region transducer are essential, the \(K\) value is set between [3; 8]. Seventy percent of the experimental data are utilized to train the model, and the remaining sample data are used to test the model.  
Based on Equations (3) and (4), the absolute mean correlation coefficient \(\overline{R}\) is obtained, as shown in Table 3.  
Table 4 shows the clustering results of different \(K\) values, and the temperature-sensitive variables sets under different \(K\) values are shown in Table 5.  
The set of temperature variables under different \(K\) values is used as the temperature input of the BP neural network to setup the thermal error model. The predictive perfor  
Figure 6: Flow chart of establishing thermal error model based on IPSO-RBFNN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature Variables** & \(\mathbf{R}\) & **Temperature Variables** & \(\mathbf{R}\) \\ \hline T1 & 0.8540 & T9 & 0.7415 \\ T2 & 0.7845 & T10 & 0.7345 \\ T3 & 0.7858 & T11 & 0.7341 \\ T4 & 0.8289 & T12 & 0.7714 \\ T5 & 0.8475 & T13 & 0.8228 \\ T6 & 0.7937 & T14 & 0.7968 \\ T7 & 0.7345 & T15 & 0.2276 \\ T8 & 0.7441 & T16 & 0.7616 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Absolute mean correlation coefficient between temperature variables and thermal errors.  
mance of the model is evaluated according to Equation (5), and the results are shown in Figure 7.  
It can be seen from Figure 7 that the values of \(MAE\), \(MSE\), and \(RMSE\) are all smallest when \(K=5\). Obviously, the BP neural network established by the combination of sensitive temperature points corresponding to \(K=5\) has the best prediction performance. Hence, the combination of sensitive temperature points [T1, T6, T9, T13, T16] is determined as the combination of sensitive temperature points of the overall machine tool.
## 3 Results
### Impact of the NR Network on the Environmental RF-EMF Exposure  
As shown in the previous section, the exposure levels induced by the commercial NR network are low in comparison to the ICNIRP reference level at the considered frequency for the general public of 10 W/m\({}^{2}\) (equivalent to 61 V/m in the far-field region) [3]. However, to assess the impact of the NR network on the existing environmental RF-EMF exposure caused by wireless telecommunications networks, additional SRM measurements were conducted at 15 positions to assess the contributions of the different frequency bands used by these networks.  
Without any induced downlink traffic--assuming the presence of 5G NR users on the network was negligible--the contribution of the commercial NR network to the environmental exposure was negligible (Table 1, values are in agreement with Figure 7) as was expected from a technology with such sparse broadcast signaling [2] and possibly PDSCH signals beamsteered towards other users. Nonetheless, even with 100% downlink traffic at the evaluation point, which is the extreme case, the additional exposure remained limited: with a maximum value of 0.4 V/m--also in agreement with Figure 7--the largest contribution of the 3.5 GHz band was only 9.5% of the total field; and the average contribution of the NR network was just 2% (Table 1).
## 4 Results and Analysis
### Results and Analysis of Experiments  
#### 4.2.1 Effects of Feedback and Contexts in Real Environments  
Using [PERSON]'s exact test, RQ1 and RQ2 in real HRI experiments were analyzed, and the results are shown in Table 5. The meaning of \(n\) is the same as in Figure 2b. Since all \(p\)-values for contexts shown in Table 5 are larger than 0.05, this reveals that contexts do not show significant differences in user emotions in real environments. In addition, feedback shows significant differences in the cases of first and second interaction errors but no significant differences in the case of third interaction errors. Comparing the adjusted residuals between IF and CF, statistical differences exist. Relatively, IF tends to evoke happiness and surprise, and CF tends to evoke sadness and anger. The effects of feedback on user emotions in the CAIR and the CPIR were analyzed, respectively, and the results are shown in Table 6. It shows significant differences when an interaction error occurred for the first time in the CAIR. It also reveals that, for feedback, the magnitude of the effect size in the CAIR and the CPIR reduces with the number of times of interaction errors.
# Complete Genome Report of a Hydrocarbon-Degrading _Sphingobium yanoikuyae_ S72
## 4 Discussion
For example, COG category K, 307 genes belonging to this category were present in S72. The genes encoding the LTTR family are part of the genes in this category, LTTR are important regulators in bacteria helping them to cope effectively in their environment. A total of 49 LTTR encoding genes were identified in S72; this observation is similar to the findings from the study of [PERSON] et al. on Franconibacter, another hydrocarbon-degrading gene which possess a higher number of LTTR family proteins in its genome [8]. [PERSON] et al. had previously reported that the LTTR family plays important role in regulating genes responsible for aromatic compound catabolism, motility of cell, and quorum sensing [44]. The abundance of the LTTR gene family in S72 implies that it is highly metabolically active and may employ these genes in the detection of and regulation of its response to hydrocarbons. Twenty-one proteins of the OmpR family transcriptional regulator of the two components system, six Mer family regulator protein, twenty-one proteins belonged to the multiple antibiotic resistance regulator (MarR) family (COG1846) [45]. S72 has 41 TetR regulation protein (COG1309 and COG3226), and 14 GntR family transcriptional regulators protein (COG1167, COG1802, COG2186 and COG2188). These transcription regulators are known to play essential roles in the degradation of aromatic hydrocarbon [38]. Other transcriptional regulating protein in S72 include 23 AraC family (COG, 2207, COG4977, COG1609) known to be involved in the metabolism of different sugars [46], 8 proteins of the L6I family transcriptional regulator (COG1414), 2 proteins in the AsnC transcriptional regulator, and 8 proteins of the ArsR family transcriptional regulator. S72 also has genes in the two-component system which are responding to the limitation of phosphate in the environment. It has a Pho R and Pho B genes which are phosphate regulon regulator in the OmpR family which can play an essential role in the phosphate transport system [8]. S72 also has in abundance genes in the COG category for mobilome: prophages, transposons (COG X). This COG category comprises genes that were acquired via horizontal gene transfer. Some studies have shown that bacterial adaptation to polycyclic aromatic hydrocarbon could be mediated by an acquired mobile genetic element such as transposons, integrin, plasmids, or even prophages [47]. There are about 80 genes in this COG category. The high abundance of genes from the COG X category could be an indication that certain genes, which are essential for the degradation of hydrocarbons, may be part of the mobilome, prophage, or transposon.
## 5 Experiments and Discussion
### Early Fake News Detection Performance  
* To answer **EI4**, we sorted all comments (or forwarded posts) according to their publishing time and evaluated the changes in TRSA's detection performance by changing the number of posts received (0%, 20%, 40%, 60%, 80%, 100%). Figure 8 presents the early detection results of the model for both datasets. One can see that when only the first 40% of comments were considered, the accuracy of the proposed model  
Figure 7: Visualization of word-level attention weights in propagations of three fake (0–2) and three true (3–5) source tweets.  
could reach 85.2% and 91.2% on the two datasets, which was superior to the results of the baseline models. This indicates that our model performed well in terms of early detection. Additionally, we observed that the accuracies of the GLAN, BiGCN, and DDGCN models increased slowly over time, whereas the proposed model exhibited significantly improved performance over time. This is because the dispersion network structure of information becomes more complex and the types of posts become more diversified over time. The proposed model has a module for filtering noise posts. Therefore, they had good robustness.
## 3 Results and Discussions
### Permanent Deformation
#### 3.1.2 Impact of Moisture Content  
The illustration in Figure 7 presents an analysis of how cumulative axial strain correlates with the number of loading cycles, with consideration given to various moisture content levels. With an initial stress ratio \(\eta_{0}\) of 0.5, an initial pressure \(\text{p}_{0}\) set at 0.9 MPa, a temperature maintained at \(-5\)\({}^{\circ}\)C, and a moisture content of 11%, Figure 7a presents a visual representation of how cumulative axial strain varies with the increasing number of loading cycles, each pattern reflecting a unique dynamic stress amplitude. Under dynamic stress amplitudes of 0.8 MPa and 1.2 MPa, respectively, the cumulative axial strain exhibits a gradual and progressive increase as the number of loading cycles advances, ultimately exhibiting a stabilizing trend, the respective \(\varepsilon_{\text{acc,20000}}\) values, corresponding to these conditions, are 0.45% and 0.83%. These curves are classified as the first type, resembling line 1. Under dynamic stress amplitudes of 2.0 MPa, 2.3 MPa, and 2.6 MPa, the cumulative axial strain accelerates its growth, attaining \(\varepsilon_{\text{acc,20000}}\) values of 2.19%, 2.97%, and 6.43%, respectively. The pattern of these curves, which indicate an inclination towards creep damage as the number of loading cycles increases, places them in the second category, akin to line 2. Upon attaining a dynamic stress amplitude \(\sigma_{\text{d}}\) of 4.8 MPa, the cumulative axial strain escalates rapidly with each subsequent loading. The termination of the test occurred at the 115 th loading iteration, marking the fulfillment of the criterion for reaching 20% of the maximum vertical strain. The observed curve's behavior aligns it with the third type, displaying characteristics comparable to those of line 3. Similar curve classifications can be made in Figure 7b-d, etc. corresponding to moisture contents of 12.5%, 18.5%, and 15.5%, respectively.  
Based on the previously mentioned method for characterizing the results of accumulated strain deformation, Figure 8 illustrates the correlation between the vertical cumulative strain rate and the vertical cumulative strain, considering varying temperature and moisture content conditions. The figure classifies the prototypical patterns of permanent strain accumulation into three distinct types, designated as regions A, B, and C, mirroring the previously mentioned first, second, and third types of variation curves. To ensure comprehensiveness, curves positioned between the first and second types are cautiously grouped within the ambit of region B. Each of these regions possesses unique characteristics, delineated by boundaries that will be delved into more thoroughly in conjunction with Figures 9 and 10.  
Figure 8: The interplay between the vertical cumulative strain rate and the vertical cumulative strain, influenced by varying temperatures or moisture contents.  
Figure 9: The vertical cumulative strain rates for regions A, B, and C, corresponding to varying numbers of loading cycles, are depicted at different temperatures.
# Lattice Structure for Improving Cooling Uniformity in HPDC Mould Corners
## 1 Introduction and Literature Review
This can cause early solidification of the flowing metal, potentially leading to incomplete filling of the die cavity. Additionally, residual humidity from incomplete evaporation of water in the cooling lubricant can result in porosity in the casting. Utilising noncircular and nonuniform cross-sectional profiles in conformal cooling channels can enhance cooling uniformity at corners [14], though this approach may not be effective for cooling small features. The design of internal cooling channels with nonuniform cross-sections is related to the study of surface sensitivity response [3]. In a previous study [15], the design of conformal cooling channels for cooling corners using the adjoint optimisation method demonstrated that the standard deviation of temperature variation on the mould cavity surface could be reduced from 19 \({}^{\circ}\)C to 6 \({}^{\circ}\)C for internal corners and from 26 \({}^{\circ}\)C to 2 \({}^{\circ}\)C for external corners. This method employs mesh deformation to adjust the positions of mesh nodes within the cooling channel, resulting in changes to the channel's surface location and cross-sectional shape. These modifications are made to optimise an objective function, which is defined as the minimisation of the standard deviation of the temperature on the mould cavity surface. However, this method involves extensive computational time to iteratively adjust the cooling channels' location and cross-sectional shape to achieve the desired objective function. Consequently, for complex geometries with multiple corners, this approach may be impractical. In this study, three lattice structures previously tested by [15] were utilised to surround the cooling channels on either side of a 90\({}^{\circ}\)-angled cast section in order to limit heat transfer and increase the thermal resistance. A three-dimensional thermal model was subsequently developed to evaluate the contribution of these lattice structures to achieving uniform cooling of the 90\({}^{\circ}\) section corner. This method can be integrated with existing techniques to improve cooling efficiency at corner regions. Additionally, lattice structures can be incorporated into any location within a 3D-printed insert or tool to reduce heat transfer in specific areas. Lattice structures have also been recommended for use in high-pressure die casting tools to reduce weight [16].
# On-Orbit Polarization Calibration for Multichannel Polarimetric Camera
## 4 Experimental Verification  
### Measurement for Polarization Parameters  
In the verification experiment, the off-axis sub-aperture of a coaxial reflecting optical system was used to simulate the off-axis reflecting optical system of the polarimetric camera. There were a rotating linear analyzer and an ASD detector (single pixel) [23] on the back focal plane. The native polarization sensitivity of the ASD spectrometer has been researched and calibrated [24]. Thereby, an off-axis reflection polarization detection system was simulated. A standard diffuse panel of the Labsphere company is placed between an integrating sphere light source and the simulated polarization detection system for simulating the on-orbit polarization calibration. The linear analyzer was precisely rotated with 10\({}^{\circ}\) steps from 0\({}^{\circ}\) to 340\({}^{\circ}\) by a Newport rotation stage. The measurement system is shown in Figure 9.  
The experimental data and fitting curve are shown in Figure 10.  
Figure 8: Analysis result of the relative deviation of the radiance intensity between an ideal Lambertian source and an actual aluminium solar diffuser. We can see that the biggest relative deviation is 4%, when the polarization degree of the solar diffuser \(DoLP_{0}\) changes from 0 to 4%. The polarization degree of the solar diffuser should be as low as possible for on-orbit polarization calibration.  
Figure 9: Photograph of the setup of the measurement system for polarization parameters.  
The fitting results show that the polarization parameters (670 nm) of the simulated polarimetric camera are obtained: the extinction ratio \(E=1/200\), the diattenuation of the optical system \(\epsilon_{1}\)=7.97% and its angle of linear polarization \(\theta_{1}=36^{\circ}\).