esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Fabric-Based Graphene ECG Electrodes
#### 2.2.4 Nylon-Textile-Based Graphene ECG Electrode  
It is time-saving and cost-effective to change insulation fabric materials into conductive materials using traditional cloth dyeing methods. Besides, the fabric ECG electrode realized by this method can be directly integrated into undergradments for daily ECG signal monitoring. Nylon has long been used as a reliable clothing material with low surface roughness, high strength, and excellent elasticity [52]. When impregnated and dried with a conductive dye, the conductive nylon composite can be used as a wearable fabric electrode. [PERSON] et al. proposed graphene-clad-nylon textile electrodes [53]. First, by using GO suspension as the dye and dipping the nylon textiles in it, nylon textiles fully impregnated with GO slurry were obtained. Then, the GO-impregnated nylon textile was thermally treated to form a uniform GO-nylon core-sheath structure. Finally, the GO-nylon fibers were reduced by hydrogen iodide (HI) to create a graphene-nylon conductive network (Figure 2d). To apply graphene-nylon electrodes to clothing and create healthcare garments, the graphene-nylon electrodes were cut into pieces and attached to clothing, including wristbands, neckbands, etc., to create daily, multi-lead ECG monitoring. In combination with wearable processing systems, real-time ECG signals were acquired by the nylon-textile-based graphene ECG electrodes and transferred to a backend system wirelessly for further analysis. This work created a complete prototype of the wearable garment based on fabric-graphene ECG electrodes.
## 4 Results
### Cross-Dataset MultiClass Classification
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **Class** & **Precision** & **Recall** & **F1 Score** & **Accuracy** & **Training Time (s)** & **Prediction Time (s)** \\ \hline \multirow{8}{*}{Random Forest} & 0 & 1.00 & 1.00 & 1.00 & & & \\  & 1 & 1.00 & 0.99 & 1.00 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 1.00 & 1.00 & 1.00 & & & \\  & 4 & 1.00 & 1.00 & 1.00 & & & \\  & 5 & 1.00 & 1.00 & 1.00 & & & \\  & 6 & 1.00 & 1.00 & 1.00 & & & \\  & 7 & 0.95 & 0.99 & 0.97 & & & \\  & 8 & 0.99 & 0.97 & 0.98 & & & \\ \hline \multirow{8}{*}{MLP} & 0 & 0.98 & 1.00 & 0.99 & & & \\  & 1 & 0.99 & 0.99 & 0.99 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 0.99 & 1.00 & 1.00 & & & \\  & 4 & 0.99 & 1.00 & 1.00 & & & \\  & 5 & 1.00 & 1.00 & 1.00 & & & \\  & 6 & 1.00 & 1.00 & 1.00 & & & \\  & 7 & 0.98 & 0.94 & 0.96 & & & \\  & 8 & 0.91 & 0.96 & 0.94 & & & \\ \hline \multirow{8}{*}{CNN} & 0 & 1.00 & 0.98 & 0.99 & & & \\  & 1 & 0.97 & 0.83 & 0.99 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 1.00 & 1.00 & 1.00 & & & \\  & 4 & 0.96 & 1.00 & 0.99 & 0.975160 & 5815.22 & 3514.5 \\  & 5 & 0.89 & 0.96 & 0.98 & & & \\  & 6 & 0.90 & 0.72 & 0.97 & & & \\  & 7 & 0.99 & 0.67 & 0.99 & & & \\  & 8 & 0.99 & 0.99 & 0.99 & & & \\ \hline \multirow{8}{*}{Ensemble Approach} & 0 & 1.00 & 1.00 & 1.00 & & & \\  & 1 & 1.00 & 1.00 & 1.00 & & & \\  & 2 & 0.99 & 0.99 & 0.99 & & & \\ \cline{1-1}  & 3 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 4 & 1.00 & 1.00 & 1.00 & 0.9967 & 9232.4 & 3201.12 \\ \cline{1-1}  & 5 & 0.99 & 0.99 & 0.91 & & & \\ \cline{1-1}  & 6 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 7 & 1.00 & 1.00 & 1.00 & & & \\ \cline{1-1}  & 8 & 1.00 & 1.00 & 1.00 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._20 features also provided significant results. Overall, the findings show that, in terms of scores, training times, and prediction times, utilizing 20 features is the best choice.
# The Cell Membrane of _Sulfolobus_ spp.--Homeoviscous Adaption and Biotechnological Applications
## 2 Cell Membrane and Lipids of _Sulfolobus_ Spp.
### Cell Membrane Structure
\begin{tabular}{c c c c c} \hline \hline
**Membrane Lipids** & _Sulfolobales_ & _Natronococcus_ & _Methanobacterium_ & **Reference** \\ \hline
**C\({}_{\text{2D-2D}}\) DGD** & \(\boldsymbol{\Sigma}\) **15\%-30\%** & \(\boldsymbol{\Sigma}\) 57\%-77\%** & \(\boldsymbol{\Sigma}\) **12.8\%–50\%** & [40; 41; 42; 43; 44] \\ \hline DGD & -5\% & n.d. & **+** & [38; 42; 43] \\ \hline IP-DGD & 10\%-30\% & n.d. & **+** & [38; 42; 43] \\ \hline PG & - & 8-26\% & **-** & [44] \\ \hline PGP & - & 49–51\% & **-** & [44] \\ \hline PGP-Me & - & **+** & **-** & [45] \\ \hline
**C\({}_{\text{2D-2D}}\) DGD** & **-** & \(\boldsymbol{\Sigma}\) **22\%-44\%** & **-** & [44] \\ \hline PG & - & 8\%–9\% & **-** & [44] \\ \hline
**PGP** & - & 14\%–35\% & **-** & [44] \\ \hline
**C\({}_{\text{4D-4D}}\) TEL (GDNT/GDGT) \({}^{*}\)** & \(\boldsymbol{\Sigma}\) **70-85\%** & **-** & \(\boldsymbol{\Sigma}\) **50\%–83\% (GDGT only)** & [40; 41; 42; 43] \\ \hline TEL & 7–9\% & **-** & n.d. & [42; 43] \\ \hline Hex2-TEL & 57.8\% & **-** & n.d. & [42; 43] \\ \hline IP-TEL & 3–36\% & **-** & n.d. & [42; 43] \\ \hline Sulfono-Hex3-TEL-IP & 1–11\% & **-** & n.d. & [42; 43] \\ \hline Hex-TEL-IP & **+** & **-** & n.d. & [9] \\ \hline Hex2-TEL-IP & 4\%–82\% & **-** & n.d. & [42; 43] \\ \hline C\({}_{\text{4D-4D}}\) GDNT & \(\boldsymbol{\Sigma}\) 68–80\% & **-** & - & [34; 46; 47; 48; 49] \\ \hline Hex-GDNT & **+** & **-** & **-** & [46; 47] \\ \hline IP-GDNT & **+** & **-** & **-** & [46; 47] \\ \hline \hline \end{tabular}  \({}^{*}\) Recent publications [42; 43], that investigated the different head groups of TELs from the _Sulfolobus_ genus (_S. acidocaldarius_, _S. s.
## Appendix A
\begin{array}{cc}\frac{ \widetilde{M}}{M}&0\\ 0&\frac{\widetilde{M}}{M}-\frac{\widetilde{\varphi}_{2}}{M(k_{1}+k_{2})} \end{array} \right]\left[ \begin{array}{cc}1&0\\ 0&1\end{array} \right]=\mathbf{E}\
abla\mathbf{F}\] (A10)  
and:  
\[\
abla^{T}\
abla<\mu\mathbf{I}\] (A11)  
where \(\mu\) is a known positive constant. Furthermore, considering the following inequality:  
\[ \begin{array}{ll}\Delta\mathbf{A}^{T}\mathbf{P}+\mathbf{P}\Delta\mathbf{A}&= \mathbf{F}^{T}\
abla^{T}\mathbf{E}^{T}\mathbf{P}+\mathbf{P}\mathbf{E}\
abla \mathbf{F}\\ &\leq\epsilon\mathbf{F}^{T}\mathbf{F}+\frac{1}{\epsilon}\mu\mathbf{P}\mathbf{E} ^{T}\mathbf{P}\end{array}  \tag{10}\]  
where \(\epsilon>0\). Based on (10), (11) is guaranteed as long as:  
\[\mathbf{A}^{T}\mathbf{P}+\mathbf{P}\mathbf{A}+\epsilon\mathbf{F}^{T}\mathbf{ F}+\frac{1}{\epsilon}\mu\mathbf{P}\mathbf{E}^{T}\mathbf{P}+\mathbf{Q}<0 \tag{11}\]  
is attained. As a result, it can be concluded that the robust stability can be guaranteed provided the following inequalities  
\[\left[ \begin{array}{cc}\mathbf{A}^{T}\mathbf{P}+\mathbf{P}\mathbf{A}+ \epsilon\mathbf{F}^{T}\mathbf{F}+\mathbf{Q}&\sqrt{\mu}\mathbf{P}\mathbf{E}\\ \sqrt{\mu}\mathbf{E}^{T}\mathbf{P}&-\epsilon\end{array} \right]<0 \tag{12}\] \[\mathbf{P}^{T}=\mathbf{P}>0,\;\;\;\mathbf{Q}^{T}=\mathbf{Q}>0, \;\;\;\epsilon>0\]  
are feasible. Based on (12), the Lyapunov function is re-written by:  
\[ \begin{array}{ll}\dot{V}_{\epsilon}\leq-\mathbf{e}^{T}\mathbf{Q}\mathbf{e}+ 2\mathbf{e}^{T}\mathbf{P}\mathbf{D}\leq-\lambda_{\min}(\mathbf{Q})\|\mathbf{e }\|^{2}+2\lambda_{\max}(\mathbf{P})\|\mathbf{e}\|\|\mathbf{D}\|\\ \leq-\lambda_{\min}(\mathbf{Q})\|\mathbf{e}\|\Big{(}\|\mathbf{e}\|-2\frac{ \lambda_{\max}(\mathbf{P})}{\lambda_{\min}(\mathbf{Q})}\|\mathbf{D}\|\Big{)} \end{array}  \tag{13}\]  
which indicates that:  
\[\|\mathbf{e}\|\rightarrow\mathbf{\Omega}:=2\frac{\lambda_{\max}(\mathbf{P})}{ \lambda_{\min}(\mathbf{Q})}\|\mathbf{D}\| \tag{14}\]  
eventually. As a result, a robust stability can be guaranteed, even when \(w=0\).
# Investigation of Zero Moment Point in a Partially Filled Liquid Vessel Subjected to Roll Motion
## 4 Conclusions  
The stability of dynamic fluid cargo for autonomous vehicles and liquid-handling robots poses a significant hurdle for the advancement of robotics. The indeterministic nature of sloshing liquids that obstruct development of rigorous motion planning has been addressed in this work by a novel approach that incorporates the Zero Moment Point. Computational fluid dynamics (CFD) were employed to capture the displacements of the Center of Mass of a rectangular liquid domain subjected to roll motion by solving the Navier-Stokes equations coupled with the Volume of Fluid (VOF). The computational results were first validated against experimental data prior to the estimation of ZMP. Analysis of the results shows a strong agreement between the fluid's sloshing motion, the CoM and its ZMP. This enables us to determine the stability of partially filled liquid containers. Further research can be carried out on determining support polygons for complex liquid transport problems involving humanoid robots or autonomous vehicles.  
Conceptualization, M.S. and Y.A.; Methodology, M.U., M.S., Y.A. and E.U.; Software, M.U. and M.S.; Validation, M.U. and M.S.; Investigation, M.U.; Resources, M.S. and E.U.; Data Curation, M.U. and M.S.; Writing--Original Draft Preparation, M.U.; Writing--Review & Editing, M.S.; Supervision, M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 12: Displacement of horizontal component of ZMP vs. time at different fill levels.
## 3 Results  
### Study Selection  
The search yielded a total of 1107 studies. Of these, 472 duplicate records were excluded by the reference manager software. A further 621 studies were excluded after title and abstract screening because they did not meet the eligibility criteria. The remaining 14 studies were selected for full-text review, which concluded with the exclusion of 1 study because it compared only two conventional GICs. Then, 1 study was added from citation research and 1 study from a Google Scholar search. Finally, 15 articles were included in this study for qualitative synthesis. The PRISMA 2020 statement flow diagram summarizing the selection process is shown in Figure 1 [36].
# Evaluating the Effectiveness of Natural Carbon Sinks Through a Temperature-Dependent Model
## 14C Absorption vs. Sink effect
### Comparing the Absorption Rate of the Extended Sink Model with the Decay Rate of the Bomb Test Data  
According to Equation (20), the extended sink model has the result that the yearly absorptions are rather large, approximately 5% of the CO\({}_{2}\) concentration beyond the equilibrium concentration, which is partly compensated by a significantly large effect of temperature dependence on natural emissions, approximately 3.6 ppm/\({}^{\circ}\)C (7.7 Gtc/\({}^{\circ}\)C) per year. The 95% confidence interval of the absorption rate is [0.042, 0.058].  
The decay rate of bomb test data with the Suess effect correction is 5.1% per annum. This rate matches very closely the determined absorption rate by the extended sink model within the statistical error boundaries. This supports the ability of the extended sink model to separate natural absorptions from natural emissions instead of mixing them as in the simple sink model. The absorption rate is, therefore, only controlled by the CO\({}_{2}\) concentration, while natural emissions are controlled by sea surface temperature.
# Deepwater 3D Measurements with a Novel Sensor System
## 5 Conclusions  
The presented 3D sensor system has high potential for use in the inspection of technical underwater structures, as well as for the detailed documentation of flooded cultural heritage sites or sunken shipwcks. Further potential lies in the large-scale mapping of the seabed at depths of up to 1000 m in conjunction with the detailed recording of individual objects with high measurement accuracy.  
In its current form, however, several improvements are desirable, primarily to make the system more manageable. This includes reducing both dimensions and mass to make the system mountable to inspection-class ROVs. Furthermore, the preparatory effort before a measurement operation, which currently requires up to a week of prearrangement time, should be reduced. In addition to structural changes to the overall system, further detailed test operations in the offshore area are necessary. Approaches have been developed by the authors to address these needs, and further development projects are being planned.  
Conceptualization, C.B.-B.; methodology: C.B.-B., C.M. and M.B.; software: M.H., C.M., A.B. and M.B.; validation: C.B.-B.; formal analysis: C.B.-B.; investigation: C.B.-B. and I.G.; resources: I.G. and M.H.; data curation: C.B.-B. and M.H.; writing--original draft preparation: C.B.-B.; writing--review and editing: C.B.-B., C.M. and M.B.; visualization: C.B.-B. and C.M.; supervision: P.K. and G.N.; project administration: C.B.-B. and P.K. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the German Federal Ministry for Economic Affairs and Climate Action, grant number 03 SX482.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to a confidentiality agreement.  
The authors would like to thank the enterprises SeaRenegy Offshore Holding GmbH and Cie. KG and Oktopus GmbH, who were involved in the research project and took part in the conception and construction of the sensor system.  
Author [PERSON] was employed by the company 3 plusplus GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationship that could be constructed as a potential conflict of interest.
# A Green AI Methodology Based on Persistent Homology for Compressing BERT
## 3 Our Proposal
### PBCE: Using Persistent Homology to Compress BERT
#### 3.2.4 Evaluation of the Compressed Model Through the GLUE Benchmark  
The evaluation of the simplified model through the General Language Understanding Evaluation (GLUE) benchmark involves assessing the model's performance on a set of diverse natural language understanding tasks. GLUE is a benchmark that consists of multiple downstream NLP tasks, such as text classification, sentence similarity, and question answering. It serves as a standard evaluation suite for assess the generalization and performance of language models.  
Here is how the evaluation process generally works:  
* Fine-tuning process:The simplified model is fine-tuned on the GLUE benchmark tasks. The tasks are MNLI [39], QQP [40], QNLI [41], SST-2 [42], CoLA [43], STS-B [44], MRPC [45] and RTE [46].
* Model evaluation: Use the fine-tuned simplified model to make predictions on the GLUE benchmark tasks. For each task, the model will generate predictions.
* Evaluation metrics: Calculate task-specific evaluation metrics for each GLUE task. These metrics can vary depending on the task but often include accuracy, F1 score, or other relevant measures. GLUE provides a standard evaluation script for each task (Table 1).
* Comparison: Compare the performance of the simplified model to the performance of the original, more complex BERT model and other simplified approaches from the literature. This will give an indication of how much simplification impacted the model's ability to perform various NLP tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Train** & **Evaluation** & **Metric** \\ \hline CoLA & 10 K & 1 K & Matthew’s Correlation \\ \hline SST-2 & 67 K & 872 & Accuracy \\ \hline MRPC & 5.8 K & 1 K & F1/Accuracy \\ \hline STS-B & 7 K & 1.5 K & Pearson–Spearman Correlation \\ \hline QQP & 400 K & 10 K & F1/Accuracy \\ \hline MNLI & 393 K & 20 K & Accuracy \\ \hline QNLI & 108 K & 11 K & Accuracy \\ \hline RTE & 2.7 K & 0.5 K & Accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 1: GLUE tasks, training and evaluation sizes, and metrics.  
The GLUE benchmark provides a standardized way to assess the trade-off between model complexity and task performance. It helps determine if the simplified model retains sufficient performance on a range of NLP tasks while being computationally more efficient than other BERT models.
# Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
## 6 Results  
### Simulation Parameters  
In this section, several parameters are list in Table 1 for simulation. As for the flow model, it is assumed that the packet burst obeys a 0-1 distribution, which represents whether the new packets are produced within each TTI. According to 3 GPP TS 22.104, time-sensitive applications always have small packets, but video streams always generate large packets, so the packet size of time-triggered flows and video streams are set to 200 Bytes and 800 Bytes, respectively. The EMLR of time-triggered applications and the deadline of video flow are 6 ms and 20 ms, respectively. The 5G radio channel model follows a Rayleigh distribution with a standard deviation of 1.0, and the distance between gNB and mobile terminal should increase or decrease according to a 0-1 distribution [26]. In addition, the transmission power of the base station is 20 dbm, and the noise power of the channel is \(-90\) dbm. \(\kappa_{t}\) indicates total resource bands. The subcarrier interval used in this paper is 15 kHz, indicating that the scheduling cycle interval is 1 ms. \(\Delta\) is a small positive constant in Equation (3). \(R_{j}^{\min}\) indicates the minimal throughput requirement of the corresponding video flow in Equation (4).  
In order to improve the learning rate and convergence rate, the structure of the neural network is designed as in Table 2. Both actor and critic have two hidden layers, and each hidden layer consists of a fully connected layer, a RELU activation function and a normalization layer (LN). LN is used to address the problem that the distribution of output data changes with the updating of neuron parameters [27]. Moreover, \(\alpha\) and \(\beta\) indicate the learning rates for actor and critic, respectively. \(\mu\) is the discount factor in Equation (18).
###### Abstract  
A MedRadio RF receiver integrated circuit for implanted and wearable biomedical devices must be resilient to the out-of-band (OOB) orthogonal frequency division modulation (OFDM) blocker. As the OFDM is widely adopted for various broadcasting and communication systems in the ultra-high frequency (UHF) band, the selectivity performance of the MedRadio RF receiver can severely deteriorate by the second-order intermodulation (IM2) distortion induced by the OOB OFDM blocker. An analytical investigation shows how the OFDM-induced IM2 distortion power can be translated to an equivalent two-tone-induced IM2 distortion power. It makes the OFDM-induced IM2 analysis and characterization process for a MedRadio RF receiver much simpler and more straightforward. A MedRadio RF receiver integrated circuit with a significantly improved resilience to the OOB IM2 distortion is designed in 65 nm complementary metal-oxide-semiconductor (CMOS). The designed RF receiver is based on low-IF architecture, comprising a low-noise amplifier, single-to-differential transconductance stage, quadrature passive mixer, trans-impedance amplifier (TIA), image-rejecting complex bandpass filter, and fractional phase-locked loop synthesizer. We describe design techniques for the IM2 calibration through the gate bias tuning at the mixer, and the dc offset calibration that overcomes the conflict with the preceding IM2 calibration through the body bias tuning at the TIA. Measured results show that the OOB carrier-to-interference ratio (CIR) performance is significantly improved by 4-11 dB through the proposed IM2 calibration. The measured maximum tolerable CIR is found to be between \(-40.2\) and \(-71.2\) dBc for the two-tone blocker condition and between \(-70\) and \(-77\) dBc for the single-tone blocker condition. The analytical and experimental results of this work will be essential to improve the selectivity performance of a MedRadio RF receiver against the OOB OFDM-blocker-induced IM2 distortion and, thus, improve the robustness of the biomedical devices in harsh wireless environments in the MedRadio and UHF bands.  
RF receiver; blocker; second-order intermodulation (IM2); orthogonal frequency division modulation (OFDM); CMOS; MedRadio; medical implanted communication service (MICS); biomedical device; biosensors +
Footnote †: journal: Sensors  
1
## 4 Results and Discussion
### Stress of the Anchorage Zone at the Top Slab
#### 4.1.2 Stress Distribution of Concrete  
Cracking was observed at the top surfaces of anchorage block at Sections A-A and I-I during the loading process. With the increase of load, the cracking gradually extended along the longitudinal direction. Cracking was also observed at the surface of concrete at Sections B-B and H-H when the load was equal to the prestressing force of 100%. The transfer path of the local stress inside anchorage block in the anchorage zone increased due to the development of cracks. The concrete was under compression condition at Sections C-C through G-G during the loading process. The stress level at the top surface of the concrete of section was higher than that at the bottom surface of concrete due to the eccentricity of the prestressing tendons.  
It can be concluded that the compressive stress level and effect of local bearing pressure at Sections C-C and G-G gradually decreased when the distance from tension and anchorage sections progressively increased along the longitudinal direction of the slabs. The compressive stress gradually extended to the entire section, which caused the stress distribution to gradually become uniform. The stress level at Section E-E was the lowest. The value of stress gradient near the anchorage section was higher when compared with that near the tension section. In the same length scale, the stress dispersion was more remarkable. It can be concluded that the effect of local bearing pressure was more obvious at the tension section.  
Figure 11: The relationship between load and stress of the steel bars on the top slab specimen: (**a**) the upper layer steel of Section A-A; and (**b**) the bottom layer steel of Section A-A. MP, measuring point.