esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comprehensive Performance Evaluation  
The evaluation and comparison of the purposed SLAM algorithms can be divided into two categories: localization and mapping in an indoor environment and localization and mapping in an outdoor environment. The organization aims to compare the overall performance of SLAM algorithms in two environments. There are five outdoor datasets and five indoor datasets, including 3D LiDAR data, RGB-D images, and the corresponding benchmarks. In this paper, the trajectory paths for the self-recorded indoor dataset are about 90 m, while the outdoor dataset has a trajectory path of about 40 m. Additionally, when running SLAM results (both trajectory and mapping), programs (Ros-noetic-rviz 1.14.20 on ROS Netics and debugging messages) are employed to ensure that both SLAM methods in every running achieve loop detection and result in the best SLAM outcomes. During the recording of datasets in this paper, the mobile robot moved along the same route five times with the same starting and ending positions each time. Therefore, in Table 1, for example, I1 represents the first recording in an indoor environment, while O5 represents the fifth recording in an outdoor environment. The numbers in the descriptors only indicate the order of dataset recording in each environment. In the indoor environment, we used the third dataset (I3) for our implementation. In the outdoor environment, we used the fourth dataset (O4) for our implementation
# Performance of Tilting Pad Journal Bearings with the Same Sommerfeld Number
## 2 Numerical Modeling of Tilting Pad Journal Bearing
### Energy Equation  
The energy equation governs the fluid film temperature by accounting for fluid film pressure distribution, velocity, and viscosity as shown in Equation (6).  
\[\rho\bigg{(}u\frac{\partial T}{\partial x}+w\frac{\partial T}{\partial z}\bigg{)} =k\bigg{(}\frac{\partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T}{ \partial y^{2}}+\frac{\partial^{2}T}{\partial z^{2}}\bigg{)}+\mu\bigg{[}\bigg{(} \frac{\partial u}{\partial y}\bigg{)}^{2}+\bigg{(}\frac{\partial w}{\partial y }\bigg{)}^{2}\bigg{]} \tag{6}\]  
The heat conduction is considered along the x, y, and z directions, while the velocity profiles (\(u,w\)), due to the convection, have x and z components. The viscosity term is a function of \(u\) and \(w\) across the bearing film y. The finite model solves the energy equation utilizing the up-winding scheme and adopts a modified mixing temperature theory [6; 21].  
To solve the energy equation, the thermal boundary conditions need to be defined. Equations (7) and (8) show the thermal flux and temperature conditions at the bearing pad and lubricant.  
\[k_{L}\left.\frac{\partial T_{L}}{\partial r}\right|_{(r=R+H)} =k_{B}\left.\frac{\partial T_{B}}{\partial r}\right|_{(r=R+H)} \tag{7}\] \[T_{L}|_{(r=R+H)} =T_{B}|_{(r=R+H)} \tag{8}\]  
The boundary condition between the rotating shaft and lubricant is defined by orbit averaged thermal boundary condition [16].  
\[k_{I}\left.\frac{\partial T_{I}}{\partial r}\right|_{(\theta=0,r=R)} =k_{L}\left.\frac{\partial T_{L}}{\partial r}\right|_{(\theta=at,r= R)} \tag{9}\] \[T_{I}|_{(\theta=0,r=R)} =T_{L}|_{(\theta=at,r=R)} \tag{10}\]  
The inlet temperature (\(T_{in}\)) to the bearing pad largely affects the performance of the TPJB because the fluid viscosity is a function of temperature which affects the pressure generation of the bearing. Hence, it is important to account for any possible change in the inlet temperature of the bearing. Figure 1 shows how the oil is transferred from one pad to another. In most cases, not all the lubricant exiting from the previous bearing pad goes into the next pad, i.e., \(Q_{in}<Q_{out}\). The mixing coefficient (\(\eta\)) is used to account for the change in temperature due to the occurrence of mixing in between the pads. It is important to emphasize that the mixing coefficient (\(\eta\)) is assumed to be 0.8 in the analysis.  
\[T_{in}(z)=\left\{\begin{array}{ll}\frac{Q_{out}^{i-1}(z)\cdot T_{out}^{i-1}( z)+\big{(}Q_{in}^{i}(z)-Q_{out}^{i-1}(z)\big{)}\cdot T_{supply}}{Q_{in}^{i}(z)}& \text{if }\eta\cdot Q_{in}^{i}>Q_{out}^{i-1}\\ \frac{\eta Q_{in}^{i}(z)\cdot T_{out}^{i-1}(z)+\big{(}Q_{in}^{i}(z)-\eta Q_{in }^{i}(z)\big{)}\cdot T_{supply}}{Q_{in}^{i}(z)}&\text{if }\eta\cdot Q_{in}^{i}\leq Q_{out}^{i-1} \end{array}\right. \tag{11}\]  
Figure 1: Schematic of oil inlet groove and boundary conditions at pad trailing/leading edges.
# Online Foot Location Planning for Gait Transitioning Using Model Predictive Control
## 5 Results
### Simulation Setup
Therefore, flight trotting and walking transitioning at this speed are equally relevant for applications. For walking, 1.2 m/s and 1.6 m/s are already quite large speed of movement, very close to the maximum speed in that gait, which itself is already not moving stably enough. Feet in swing may collide with feet lifting off the ground sometimes. Furthermore, the COT in this gait is already much larger than trotting and flight trotting. Therefore, transitioning between trotting or flight trotting and walking at this speed lacks practical meaning. Only the gait transitioning between trotting and flight trotting is considered. To eliminate the effect of incidental factors, multiple gait transitioning is performed at a given speed during the transitioning process. During the motion of a quadruped robot, the posture change is closely related to the stability of the control. With small variations in posture, the quadruped robot performs better in actual motion. Ideally, a quadruped robot is most stable when the posture is at the desired value and does not change during motion. Therefore, comparing the change in posture at the time of gait transitioning and throughout the gait transitioning process gives a good indication of how well the foot location planning method works. With the control algorithm unchanged, the choice of foot location affects the planning result of MPC in gait transitioning, which in turn influences the posture control of the quadruped robot. The posture is the result of the combined effect of force and foot location. The advantages and disadvantages of the two methods can be determined by posture changes and foot-end forces. Because the selected gaits are all symmetrical, the force change at the foot end of one leg can better reflect the planning result of MPC. The effectiveness and superiority of the UPMPC can be better demonstrated by integrating the results of force planning and posture change. Finally, the posture changes in trotting are shown to demonstrate that UPMPC can also get good results in a single gait.
## 2 Results
### Antitumor Activity of DSF/Cu against NPC In Vivo
In addition, the apoptosis of fibroblasts was observed in tumor tissues through TUNEL assay. Compared to the control and CDDP groups, it can be obviously seen that there were TUNEL-positive cancer-associated fibroblasts (CAFs) in the DSF/Cu and CDDP/DSF/Cu groups (Figure 9A). The apoptotic indices were significantly increased in the DSF/Cu and CDDP/DSF/Cu groups compared to the control group (57.5% vs. 16%, \(p<0.001\)) and the CDDP group (48% vs. 13.3%, \(p<0.001\)). What is more, IHC analysis of CAFs by \(\alpha\)-SMA showed that CAFs were obviously activated in the control group, and some activated CAFs (\(\alpha\)-SMA-positive) could also be seen in the CDDP group. In contrast, the expression of \(\alpha\)-SMA in CAFs was negative in the DSF/Cu and CDDP/DSF/Cu groups (Figure 9B). Moreover, double immunofluorescence staining with \(\alpha\)-SMA and TUNEL further exhibited that CAFs were inactive and there was apoptosis in the DSF/Cu group and CDDP/DSF/Cu group (Figure 9C). Taken together, these results suggest that DSF/Cu could induce CAF apoptosis and inactive CAFs in vivo. Figure 8: DSF/Cu inhibits the growth of 5-8F xenografts via targeting tumor cells. (**A**) Schematic outline for in vivo drug treatment. (**B**) Photographs of subcutaneously growing human 5-8F xenografts extracted from mice at day 13. (**C**) The growth curves of tumors in mice treated by the indicated drugs. Data are shown as means \(\pm\) SD. *** \(p<0.001\), \(n=5\). (**D**) Time-course diagram of mice weight. Data are shown as means \(\pm\) SD. *** \(p<0.01\), \(n=5\). (**E**) Representative hematoxylin and eosin (H&E) staining on tumor biopsies. Scale bar: 100 \(\upmu\)M. (**F**) Immunochemical staining of kio7 in tumor tissue slices from the indicated formulation-treated groups. Scale bar: 50 \(\upmu\)M.  
Figure 9: DSF/Cu inhibits the growth of 5-8F xenografts via targeting cancer-associated fibroblasts. **(A)** The TUNEL assay on tumor biopsies. Cancer-associated fibroblasts (CAFs) and the tumor cells in the middle are shown separately. Scale bar: 100 \(\upmu\)M. The apoptotic indices (percentages of apoptotic nuclei) are shown as means \(\pm\) SD. ***\(p<0.001\). **(B)** Immunochemical staining of \(\alpha\)-SMA in tumor tissue slices from the indicated formulation-treated groups. Scale bar: 100 \(\upmu\)M. **(C)** Double immunofluorescence staining of a-SMA (red color) and TUNEL (green color) and counterstraining of chromatin with DAPI (blue color). Scale bar: 50 \(\upmu\)M.  
Moreover, no obvious damage could be observed in the lung, kidney and liver sections with H&E staining among the four groups (Figure S7). Meanwhile, both biochemical and blood routine indices showed that no significant difference was found in the treatment groups compared to the control group (Tables S2 and S3). This means that DSF/Cu was well tolerated in vivo. Collectively, these data demonstrate that DSF/Cu was safe and can target both tumor cells and CAFs in vivo. What is more, combing it with CDDP treatment could improve the therapeutic effect in NPC mice.
## 4 Conclusions  
In this paper, two kinds of metamaterial structures are proposed to realize an electromagnetic-like induced single transparent window, which are unilateral symmetric and unilateral asymmetric metamaterials. Various size parameters of the metamaterials were optimized to form EIT-like windows as well as modify them. By analyzing the distribution of electromagnetic field and surface current together with the transmission spectra of EIT-like window, the mechanism of EIT-like effect was investigated. In addition, the modulation effects of transparent windows of the two metamaterials were discussed, including both the passive modulation (changing the coupling distance) and active modulation (changing the conductivity of the photosensitive Si buried in the metamaterials). The tunability of EIT-like window under both modulation modes shows great potential for THz switches. Furthermore, the results show that the metamaterials of both structures can achieve high sensitivity refractive index response, up to 102.3 GHz/RIU, which can be applied for high sensitivity sensing purposes.  
Conceptualization, Z.W. and X.Y.; methodology, Z.W. and X.Y.; software, Y.Q., D.L. and L.Z.; validation, D.L., L.Z., S.H. and X.Y.; formal analysis, Y.Q. and M.D.; investigation, P.A., Z.W. and M.D.; resources, Z.W. and C.C.; data curation, Z.W., C.C., Y.Q., L.Z., S.H. and D.L.; writing--original draft preparation, M.D. and P.A.; writing--review and editing, Z.W., M.D. and P.A.; visualization, Z.W. and S.H.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. and X.Y. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Structure**} & **Bandwidth** & **Sensitivity** & \multirow{2}{*}{**Q-Factor**} & \multirow{2}{*}{**FOM**} \\ \cline{2-2} \cline{4-5}  & **(THz)** & **(GHz/RIU)** & & \\ \hline This work (unilateral symmetric) & 0.6–1.2 & 84.4 & 33.67 & 2.8 \\ This work (unilateral asymmetric) & 0.6–1.2 & 102.3 & 51 & 5.115 \\ Ref. [28] & 0.15–0.85 & 54.18 & – & – \\ Ref. [10] & 0.6–1.2 & 102 & 20 & 2.99 \\ Ref. [29] & 1.05–1.17 & 49.3 & 65 & – \\ Ref. [30] & 0.4–1.0 & 180 & 0.22 & – \\ Ref. [11] & 0.8–1.6 & 163 & 7.036 & 2.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of various terahertz metamaterial sensors.  
**Funding:** This research was funded by National Key Research and Development Program of China (2021 YFE0102900), Fundamental Research Funds for the Central Universities (DUT21 GF402).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** [PERSON] and X.Y. would like to acknowledge the financial support from the National Key Research and Development Program of China and Fundamental Research Funds for the Central Universities.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Emerging Roles of RNA Modifications in Glioblastoma
## 3 RNA Modifications in GBM
### RNA m\({}^{6}\)A Modification in GBM
#### 3.1.2 RNA m\({}^{6}\)A Erasers in GBM  
Like the writers, m\({}^{6}\)A erasers also play essential roles in GBM. Inhibition of FTO, an m\({}^{6}\)A eraser, also impedes the self-renewal ability and tumorigen of GBM stem cells both in vitro and in mice models [59]. Other studies also show that FTO functions as an oncogenic role via maintaining the stability of transcripts of avian myelocytomatosis viral oncogene homolog (c-Myc) and CCAAT enhancer binding protein alpha (CEBPA) in glioma, especially IDH1/2 mutant glioma [64]. Besides, inhibition of FTO by its specific inhibitor, MA2, significantly renders the tumorigen in GSC-grafted mice [59]. These evidences suggest that the hemostasis of m\({}^{6}\)A modification in cells is important and FTO may be a promising drug target of GBM.  
Another m\({}^{6}\)A demethylase, ALKBH5, is also highly expressed in patient-derived GSCs. Silencing ALKBH5 inhibits cell proliferation of GSCs. An integrated transcriptome and m\({}^{6}\)A-seq analysis identified several ALKBH5 target genes, including forkhead box M1 (FOXM1), a transcription factor that is critical for GSCs. ALKBH5 demethylates FOXM1 nascent transcripts, thereby enhancing its expression. A lncRNA forkhead box M1-antisense RNA (FOXM1-AS), also enhances the interaction of ALKBH5 with FOXM1 nascent transcripts [65]. These results show that m\({}^{6}\)A erasers may also play pro-oncogenic roles in GBM and may be potential drug targets for GBM treatment. If both m\({}^{6}\)A writers and erasers are confirmed to be upregulated in GBM, it is probable that the modulatory capacity of m\({}^{6}\)A in GBM is enhanced, indicating that GBM may be more flexible to response to complex extracellular or intracellular stresses.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 7 Conclusions  
This paper has presented and analyzed the evaluation results of wireless low-power links (compliant with the IEEE 802.15.4 standard) in two in-city forests and one on-mountain forest. The evaluation uses the PRR, the RSSI and the LQI as the basic metrics for link quality and investigates the in-forest low-power link characteristics from the following aspects: the effects of link distance and transmit power level on the link quality, the time-varying characteristics of link quality and link asymmetry, the empirical radio propagation model and the correlation between incident links. This paper also summarizes the observations and gives some open research issues in deploying wireless sensor network in forest environments. As suggested in [51; 52], the weather conditions or seasonal factors possibly affect the performance of the low-power wireless link, which are not involved in this paper. Our future work is to deploy and operate a large-scale low-power link wireless sensor network in a forest for a longer term and to conduct more comprehensive investigations.  
The authors were supported, in part, by the NSF of China with Grant No. 61300180, and by the Fundamental Research Funds for the Central Universities of China with Grant No. TD2014-01.  
[PERSON] and [PERSON] conceived of and designed the experiments. [PERSON], [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] contributed to the result demonstration with the use of R; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 2 Literature Review
* Earlier researchers used PSO, variants of PSO, DPSO, and the hybridization of PSO with MA for solving NWFSSP with TFT as an optimality criterion; however, the development of an efficient algorithm using the hybridization of PSO with SA and TFT as an optimality criterion for NWFSSP has not been reported, neither for small-sized jobs (n = 20, 50, and 100) nor for large-sized jobs (n = 200 and 500). * Investigations done by most of the researchers for solving NWFSSP have been limited to 100/200 jobs [3; 9; 15; 16; 17]. Recently, [PERSON] et al. [10] tried to solve large-sized problems up to 500 jobs for the Permutation Flow Shop Scheduling Problem (FSSP) and [PERSON] et al. [31] considered NWFSSP for solving large-sized problems up to 500 jobs. Hence, the scope for further research can be clearly sensed to develop improved metaheuristics. This provided the impetus to study the hybridization of PSO with SA in order to solve NWFSSP optimally. To know the effectiveness and efficiency of the newly-proposed algorithm, the results produced by earlier researchers, [PERSON] and [PERSON] [3], DPSO [9], [PERSON] and [PERSON] [10], and HPSO [31], are used for comparison with the PHPSO algorithm.
_Article_  
Elastohydrodynamic Performance of a Bio-Based, Non-Corrosive Ionic Liquid  
[PERSON] 1,*\({}^{\ast}\)\({}^{\Box}\), [PERSON] 2, [PERSON] 3, [PERSON] 3 and [PERSON] 1  
1 Division of Machine Elements, Department of Engineering Science and Mathematics, Lulea University of Technology, SE-97187 Lulea, Sweden; [EMAIL_ADDRESS]  
2 Georgia Institute of Technology, Centre for High Pressure Rheology, G.W. Woodruff School of Mechanical Engineering, Atlanta, GA 30332-0405, USA; [EMAIL_ADDRESS]  
3 Intelligent Composites Laboratory, Department of Chemical and Biomolecular Engineering, The University of Akron, Akron, OH 44325, USA; [EMAIL_ADDRESS] (L.M.); [EMAIL_ADDRESS] (J.Z.)
*Correspondence: [EMAIL_ADDRESS]; Tel.: +46-920-491281  
Received: 30 August 2017; Accepted: 25 September 2017; Published: 27 September 2017  
To improve performance of machine components, lubrication is one of the most important factors. Especially for use in extreme environments, researchers look for other solutions rather than common lubricant base stocks like mineral oils or vegetable oils. One such example is ionic liquids. Ionic liquids have been defined as molten salts with melting points below 100 \({}^{\circ}\)C that are entirely ionic in nature, comprising both cationic and anionic species. The industrial use of ionic liquids is mostly as solvents, electrolytes, extractants and catalysts. In tribological applications, ionic liquids are mainly studied in boundary lubrication and in pure sliding contacts. In this work, the elastohydrodynamic performance of a bio-based, non-corrosive, [choline][L-proline] ionic liquid is evaluated in terms of pressure-viscosity response, film forming capability and friction. The results show a pressure-viscosity coefficient of below 8 GPa\({}^{-1}\) at 25 \({}^{\circ}\)C, among the lowest reported for any ionic liquid. The ionic liquid generated up to 70% lower friction than a reference paraffin oil with a calculated difference in film thickness of 11%. It was also shown that this ionic liquid is very hygroscopic, which is believed to explain part of the low friction results, but also has to be considered in practical applications since the water content will influence the properties and thus the performance of the lubricant.  
Ionic liquid; EHL; friction; film thickness; bio-based; non-corrosive; pressure-viscosity; elastohydrodynamic
## 3 Results and Discussion
### Electroluminescence Properties
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Materials** & **V\({}_{\text{on}}\) (V)** & **LE (cd/A)** & **PE (Im/W)** & **CIE (x, y)** & **EL\({}_{\text{max}}\) (nm)** & **FWHM** (nm)** \\ \hline TP-AFF-TP & 8.41 & 2.55 & 1.03 & (0.25, 0.45) & 504 & 96 \\ \hline TP-ASF & 7.50 & 5.17 & 2.39 & (0.17, 0.31) & 484 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: EL performances of the fabricated OLED devices at 10 mA/cm\({}^{2}\). following configuration of WOLED device was fabricated using TP-ASF: ITO/PEDOT:PSS (40 nm)/TP-ASF: 0.2 wt% or 0.4 wt% rubrene (50 nm)/TPBi (30 nm)/LiF (1 nm)/Al (200 nm) at Al electrodes were prepared under vacuum evaporation except PEDOT:PSS. The electroluminescent characteristics of the white OLED produced through solution processing are outlined in Table 3 and depicted in Figure 6. Operating voltages of the two devices were similar to 4.08 and 4.12 V because these two devices had the same structure except for the rubrene concentration (see Figure 5b). The white OLED devices of the 0.2 wt% rubrene (WOLED I) and 0.4 wt% rubrene (WOLED II) were showed LE of 3.13 cd/A, 4.24 cd/A and PE of 2.69 Im/W, 3.60 Im/W, respectively. Also, WOLED I and II showed EQE of 1.42 and 1.67%, respectively. The EL efficiency of WOLED II was improved more than that of WOLED I due to a high concentration of rubrene as the used dopant. The maximum values of EL spectra for the white OLED devices WOLED I and II were identical, measuring 463 nm and 549 nm, respectively. This means that the recombination zone was well generated within EML for both devices. However, WOLED I and II exhibited the CIE values of (0.30, 0.34), (0.36.0.40), respectively. These results mean that in terms of efficiency, WOLED II achieved high efficiencies, but in terms of color coordinates, WOLED I demonstrated a more suitable implementation for the ideal WOLED. In essence, WOLED I might be more applicable than WOLED II for commercial lighting display applications. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Materials** & **V\({}_{\text{on}}\) (V)** & **LE (cd/A)** & **PE (lm/W)** & **CIE (x, y)** & **EL\({}_{\text{max}}\) (nm)** \\ \hline TP-ASF: & & & & & \\
0.2 wt\% Rubrene & 4.08 & 3.13 & 2.69 & (0.30, 0.34) & 463, 549 \\ \hline TP-ASF: & & & & & \\
0.4 wt\% Rubrene & 4.12 & 4.24 & 3.60 & (0.36,0.40) & 463, 549 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: EL performance of white OLED device using synthesized material: ITO/PEDOT:PSS (40 nm)/TP-ASF: 0.2 wt% or 0.4 wt% rubrene (50 nm)/TPBi (30 nm)/LiF (1 nm)/Al (200 nm) at 10 mA/cm\({}^{2}\). Figure 6: White OLED device performance with TP-AFF-TP and TP-ASF Emitters: (**a**) current-voltage-luminescence curve, (**b**) current efficiency and power efficiency according to the current density, (**c**) EQE and (**d**) EL spectra at 10 mA/cm\({}^{2}\); and device photos of 0.2 wt% rubrene (top) and 0.4 wt% rubrene (bottom).