esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Environmental Impact on Temperature Distribution of Thin-Walled Concrete
### Correlation between Temperature Distribution and Environmental Factors  
#### 4.2.1 Concrete Mean Temperature and Air Temperature  
As per the sensitivity analysis results, air temperature was the main factor that influenced \(T_{\rm m}\). Thus, the correlation between the mean concrete temperature and the average air temperature should be clarified. The correlations between the average value, as well as the extreme values of \(T_{\rm m}\), and the average air temperature were drawn, as shown in Figure 11, and the superscripts min, avg, and max represent the minimum, average, and maximum value within a day, respectively. \(T_{\rm m}^{\rm min}\), \(T_{\rm m}^{\rm avg}\), and \(T_{\rm m}^{\rm max}\) were all linearly and positively correlated with the average air temperature. The correlation ratios (\({}^{\circ}\)C/\({}^{\circ}\)C) were, respectively, 0.97, 1.02, and 1.08, indicating that the higher the average air temperature within a day, the wider the fluctuation range of \(T_{\rm m}\).
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Endocrine Disrupting Chemicals (EDCs)
#### 5.4.3 Brominated Flame Retardants  
Brominated flame retardants are also considered as halogenated analogs of Bisphenol A. Tetrabromobisphenol A (TBBPA) is widely utilized in circuit boards and construction materials, while tetrachloropisphenol A (TCBPA) is used in flame retardant. Both TBBPA and TCBPA can be found in water, soil, sediments, and home dust, as well as in birds, marine life, and humans [170; 171]. They were reported to be toxic to the immune and reproduction systems and may also cause endocrine disruption and neurodevelopment [172].  
A paper-based analytical device (PAD) has been developed using molecularly imprinted polymers (MIPs) for TBBPA as a template, and a metal-organic framework of UiO-66-NH\({}_{2}\), as shown in Figure 11. The adsorption capacity was found to be 120.94 mg g\({}^{-1}\) with an imprinting factor of 4.07. Due to the PAD's specific recognition capabilities, TBBPA, TCBPA, and BPA could be effectively separated using paper chromatography with fluorescence detection technique. A photocatalyst, UiO-66-NH\({}_{2}\), produces reactive oxygen species (ROS) that degrade TCBPA, TBBPA, or BPA in imprinted cavities, and the fluorescence  
Figure 10: A schematic illustration of the composite of three-doped carbon quantum dots (B, N, F-CQDs) and silver nanoparticles (AgNPs) modified glassy carbon electrode (GCE) for the detection of Bisphenol S [148]. (Reproduced under permission from Elsevier, doi:10.1016/j.aca.2019.03.051).  
intensity of 2\({}^{\prime}\),7\({}^{\prime}\)-dichlorohydrofluorescein diacetate (H\({}_{2}\)DCFDA), added as an ROS probe, supported the indirect detection of the target substances. For BPA and its halogenated analogues, the detection limits range from 0.14 to 0.30 ng kg\({}^{-1}\). In total, 91.0 to 105.6% of the analytes were recovered, with RSD values of less than 7.5% [173].  
Another recent method for the determination of TBBPA was also reported [150]. Surface imprinted TBBPA polymers on wrinkled silica nanoparticles (WSNs) and CdTe quantum dots (QD) hybrid particles have been used to produce a fluorescent probe for the determination of TBBPA. When coated with MIP, wrinkled silica-QD hybrid particles (WSNs-QDs-MIPs) had a adsorption capacity of 96.5 mg g\({}^{-1}\) and an imprinting factor of 7.9 towards TBBPA. At optimum conditions, the fluorescence intensity was quenched in the range of 25 to 5000 nM of the TBBPA concentration, at an LOD of 5.4 nM.
## 3 Results and Discussion  
### Calculation of Different Extraction Yields  
The solvent extraction yield was calculated by Equation (1):  
\[\mathrm{E}=100\mathrm{m_{1}}/[(100-\mathrm{Aad}-\mathrm{Mad})\cdot\mathrm{m} /100] \tag{1}\]  
In Equation (1), \(\mathrm{m_{1}}\) is the dried amount (g) obtained from the extracted solvent phase; \(\mathrm{m}\) is the quality (g) of the starting raw coal; \(\mathrm{Aad}\) is the ash content of the raw coal in air-dry base (%); \(\mathrm{Mad}\) is the moisture content of raw coal in air drying base (%). The extraction yields obtained by different methods are 1.7% for UAE, 1.5% for SE, and 2.0% for MAE, respectively. The yield of SE is the worst because the solvent is always difficult to penetrate into the coal matrix. Solvent extraction of coal is a process where solvent diffusion occurs without external assistance. The ultrasonic-assisted process can promote solvent permeation into fractures of crosslinked bonds of the coal. The microwave-assisted process is more powerful for extraction than the ultrasound-assisted one, since microwave penetration is much stronger; consequently, the networks of coal could be opened and more organic substances were dissolved.
## 5 Discussion  
Remote monitoring systems with sensor networks in smart homes for the elderly have the potential to provide a non-invasive way to detect and respond to potential emergencies. One of the key elements of these systems is the use of routing protocols in the sensor devices. These protocols are used to ensure that the data collected by the sensors are transmitted to the central hub or cloud-based platform in a timely and efficient manner. This is critical for ensuring that the data are available for analysis and can be used to detect potential issues or changes in the individual's health or wellbeing.  
One of the most important aspects of these routing protocols is their ability to adapt to changing conditions in the network. For example, if a sensor goes offline or the network becomes congested, the routing protocol will automatically adjust to ensure that the data continues to be transmitted. This can be especially important in smart homes for the elderly,  
Figure 5: Measurement of usability metrics.  
where the individual may be at risk of falls or other accidents and prompt intervention is critical. The use of routing protocols that are designed to be robust and reliable can help to ensure that the data is transmitted even in the event of a network failure.  
Another important aspect of routing protocols in remote monitoring systems is their ability to optimize the use of network resources. This can be achieved by using algorithms that are designed to minimize the energy consumption of the sensor devices, reduce the amount of data transmitted over the network or improve the overall efficiency of the system. These algorithms can be especially important in smart homes for the elderly, where the individual may have limited mobility and may be unable to replace batteries or other power sources. By optimizing the use of network resources, these algorithms can help to prolong the battery life of the sensor devices and reduce the need for maintenance.  
Overall, remote monitoring systems with sensor networks in smart homes for the elderly have the potential to provide a non-invasive, efficient and reliable way to detect and respond to potential emergencies. The use of routing protocols that are designed to be robust, adaptive and energy-efficient can help to ensure that the data are transmitted in a timely and efficient manner and that the system can adapt to changing conditions in the network. Additionally, these protocols are able to optimize the use of network resources, prolong the battery life of the sensor devices and reduce the need for maintenance, which could be critical for elderly individuals who may have limited mobility.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 3 System Model
### Training Results  
We train the AE shown in Figure 2 in an end-to-end manner through the channel model to minimize the binary cross-entropy loss, which was shown to maximize the bitwise MI [10]. The system is trained for various SNRs to obtain different mapper constellations. As framework for the initial system, we use PyTorch together with the Adam optimizer [33] and AMSgrad [34]. The learning rate is fixed to 0.01 while the training iterations are set to 2000 and 200 for batch size 10,000 and 100,000, respectively. The model is trained for SNRs starting from \(-6\) to 4 dB in steps of two. The resulting constellation diagrams obtained at the mapper's output are shown in Figure 4. We only trained the model for up to 4 dB as for higher SNR the constellations are similar to 16-QAM.  
As can be seen, the higher the SNR the more similar the learned constellation is to a conventional 16-QAM. With lower SNR, the spacing between each point becomes non-uniform, up until multiple messages are mapped to the same symbol for \(E_{b}/N_{0}=-6\) dB. This can be explained by the fact that the AE is trained to optimize the bitwise MI; therefore, it increases the reliability for specific bit channels in the low-SNR-region by maximizing the spacing between them, taking into account the information loss for other bit channels that cannot be conveyed reliably at low SNR anyway.  
The gain of the AE's constellations becomes clear when observing the MI as shown in Figure 5. It can be seen that the maximum MI for the given test-SNR is always achieved by the constellation that is also trained for this SNR. The learned constellations consistently  
Figure 4: Constellations learned by autoencoder for 4 bits per symbol.  
Figure 3: ANN topology optimization for different number of neurons compared to 16-QAM.  
outperform the conventional 16-QAM at the SNR they are optimized for. In further analysis, we validate the functionality of our hardware implementation described in Section 4 by reproducing the software results of Figure 5 on FPGA. However, it should be noted that the advantage of the AE compared to conventional systems is not only the gain observed by comparing the MI but primarily the possibility to adapt to varying channel conditions during runtime.
## 5 FPGA Implementation
### FPGA Implementation of the Carrier Synchronization Module  
The filter, module, phase discrimination, integral accumulation and NCO modules are the main modules of the carrier loop in our FPGA implementation. The overall FPGA framework of the carrier synchronization module is shown in Figure 25. After integrating and adjusting the above modules, we used the logic analyzer of Quartus II for debugging. The signal waveform obtained from the logic analyzer is shown in Figure 26. From fil_cos and fil_sin in Figure 26, we see that the baseband signal has been restored, which shows that the FPGA implementation of the carrier synchronization module was for the most part successful.  
Figure 24: The Physical map of the hardware platform.  
Figure 25: The overall FPGA framework of the carrier synchronization module.
# Regularized Urdu Speech Recognition with Semi-Supervised Deep Learning
## 4 Results and Analysis
### Evaluation of LLE and Self-Training  
The WER keeps decreasing after the self-training of the neural network for 15 iterations using test samples with confidently predicted clusters by label propagation. However, the WER starts to increase after self-training for more iterations as few errors in clustering keep getting emphasized. The optimum of the probability to measure confidence in label propagation clustering is found to be 0.95. For lower cluster probability values, a significant number of samples are added for self-training but more of them include errors, deteriorating the WER. For probability higher than 0.95, a very small set is obtained for self-training, which does not cause significant improvement.  
The optimization and generalization of the model are compared before and after adding LLE components to speech features. The optimization improves as LLE helps to extract useful features from the input data set and using structure of both training and test data improves generalization. Figure 7 shows the optimization over 9 speakers' data used for training with and without adding LLE dimensions to speech features. The optimization is faster using LLE as it extracts key features. Figure 8 shows the test error with and without LLE in the same setting. LLE extracted dimensions appended with speech features improves the steady state validation error on test data due to incorporating the structure of the complete data set, including the test data portion.  
Figure 6: Test error vs. iterations for network architectures.  
Figure 7: Training error vs. iterations with and without Locally Linear Embedding (LLE) (10% test and 90% training data).  
Figure 9 shows the optimization of 5 speakers' data used for training and 5 speakers reserved as the validation set. LLE extracted dimensions improve optimization. Figure 10 shows the test error in the same setting. With an increase in the unlabeled validation data size, the improvement in generalization after incorporating LLE becomes more significant.  
Figure 8: Test error vs. iterations with and without LLE (10% test and 90% training data).  
Figure 10: Test error vs. iterations with and without LLE (50% test and 50% training data).  
Figure 9: Training error vs. iterations with and without LLE (50% test and 50% training data).
## 2 Results
### Antibiofilm Activities Against S. Aureus  
In our previous studies [23], we found that the two Trp- and Arg-rich hexapeptides listed in Table 1 displayed excellent activities against a Gram-positive _S. aureus_ MBEC strain. Hence, it was of interest to us to determine if this activity could be improved upon by utilizing the 13-residue Tritrp peptides for eradicating biofilms formed by this Gram-positive strain. Our data are shown in Figure 3 and in Table 4. Taken together, these data show that the Tritrp peptides do not offer a serious improvement in the MIC, MBIC and MBEC values over the two hexapeptides described earlier. Given that several of the Tritrp analogs have relatively high hemolytic activities [37], while the hemolytic activity of the shorter hexapeptides is negligible [51], the latter are clearly preferred for potential clinical applications against _S. aureus_ and potentially for other Gram-positive strains.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Peptides (uMol/L)**} & \multicolumn{6}{c}{**Sa GFP (10\% TSB and 0.1\% Glucose)**} & **Sa GFP** \\ \cline{2-7}  & **MIC at 600 nm** & **MBIC at 488 and 530 nm** & **MBEC at 488 and 530 nm** & **MBEC Agar Plate** \\ \cline{2-7}  & **LD50** & **LD90** & **LD50** & **LD90** & **LD50** & **LD90** \\ \hline RRRWWW-NH2 & 1 & 1 & 1 & 1 & 0.5 & 4 & 4 \\ \hline RRWWWR-NH2 & 1 & 1 & 1 & 1 & 1 & 4 & 4 \\ \hline Tritrp-Arg & 0.5 & 2 & 1 & 2 & 1 & 2 & 2 \\ \hline Tritrp-Lys & 1 & 2 & 2 & 2 & 1 & 2 & 4 \\ \hline Tritrp-P59A & 1 & 2 & 2 & 2 & 1 & 2 & 4 \\ \hline Tritrp-P59A-Lys & 2 & 2 & 2 & 2 & 2 & 4 & 4 \\ \hline Tritrp-W678Y & 1 & 2 & 1 & 2 & 2 & 8 & 16 \\ \hline Tritrp-F410Y & 1 & 2 & 2 & 2 & 1 & 2 & 4 \\ \hline Tritrp-W678F & 0.5 & 0.5 & 1 & 1 & 2 & 4 & 8 \\ \hline Tritrp-P5A & 1 & 2 & 1 & 4 & 2 & 4 & 4 \\ \hline Tritrp-P9A & 0.25 & 1 & 0.25 & 2 & 0.5 & 2 & 2 \\ \hline Tritrp-P9A-Lys & 1 & 4 & 2 & 4 & 1 & 8 & 8 \\ \hline Tiritrp-W678 hW & 2 & 4 & 2 & 4 & 4 & 8 & 16 \\ \hline Indolicidin & 4 & 8 & 4 & 8 & 2 & 8 & 8 \\ \hline PuroA-Arg & 4 & 4 & 4 & 4 & 8 & 16 & 8 \\ \hline PuroA-Lys & 2 & 4 & 2 & 4 & 8 & 8 & 8 \\ \hline \hline \end{tabular} Note: MIC, minimum inhibition concentration; MBC, minimum bactericidal concentration; MBEC, minimum biofilm inhibitory concentration; MBEC, minimum biofilm eradication concentration. The MBC was defined as the lowest concentration of peptide with no bacterial growth seen on the spot agar plate.  
\end{table}
Table 4: The activities measured by the fluorescence method for different tryptophan-rich peptides against _S. aureus_ GFP.  
Figure 2: The MIC (**A**), MBIC (**B**) and MBEC (**C**) values for the tritrptic-Lys derivative peptides were assessed against Pa PAO1 GFP. The MIC values were determined by measuring the absorbance of bacterial cells at 600 nm. The MBIC and MBEC values were determined by recording the fluorescence at 480 nm and 530 nm from the biofilms of GFP-expressing PAO1. Three replicates were run for each sample, the values were averaged and the error bars represent the standard deviation (\(\pm\)SD).
## 3 Results
### Effects of Platonin on NF-\(\upkappa\)B Activation in LPS/IFN-\(\gamma\)-Induced VSMCs  
To clarify the anti-inflammatory mechanisms of platonin in LPS/IFN-\(\gamma\)-treated VSMCs, we determined the effect of platonin on the NF-\(\upkappa\)B signaling cascade, a critical transcriptional factor for driving the expression of inflammatory genes such as iNOS and mIL-1\(\upbeta\)[23]. An NF-\(\upkappa\)B luciferase reporter assay was employed to evaluate the effect of platonin on NF-\(\upkappa\)B activity. As may be observed in Figure 3A(a), treatment with LPS/IFN-\(\gamma\) for 6 h significantly stimulated NF-\(\upkappa\)B activity in the VSMCs. The administration of platonin (2 \(\upmu\)M) substantially attenuated NF-\(\upkappa\)B activation in LPS/IFN-\(\gamma\)-stimulated VSMCs (Figure 3A(b)). We next determined the effect of platonin on the degradation of the \(\upkappa\)B protein, which we observed to be considerably reduced after exposure to LPS/IFN-\(\gamma\) for 30 min (Figure 3B(a)). Nevertheless, pretreatment with platonin (0.5-2 \(\upmu\)M) did not reverse the degradation of \(\upkappa\)Ba in the LPS/IFN-\(\gamma\)-stimulated VSMCs (Figure 3B(b)). We then determined whether NF-\(\upkappa\)B/p65 contributed to the inhibitory effect of platonin on LPS/IFN-\(\gamma\)-stimulated NF-\(\upkappa\)B activation. As is indicated by Figure 3C(a), p65\({}^{\text{Ser536}}\) phosphorylation was remarkably increased in VSMCs after incubation with LPS/IFN-\(\gamma\) for 60 min. The LPS/IFN-\(\gamma\)-induced p65\({}^{\text{Ser536}}\) phosphorylation was significantly suppressed by pretreatment with platonin (2 \(\upmu\)M; Figure 3C(b)). Our previous study indicated that p65 phosphorylation regulated by protein phosphatase 2A (PP2A) may modulate NF-\(\upkappa\)B activation in an IxB\(\upalpha\) -independent pathway [24]. Thus, we confirmed the role of PP2A in the anti-inflammatory mechanism of platonin in VSMCs by using okadaic acid, a selective inhibitor of PP2A. As is presented in Figure 3C(c), pretreatment with okadaic acid (10 nM) did not restore the downregulation of iNOS by platonin in the LPS/IFN-\(\gamma\)-stimulated VSMCs.  
Figure 2: Platonin suppresses ox-LDL-induced production of monocyte chemoattractant protein-1 (MCP-1) in vascular smooth muscle cells (VSMCs). VSMCs were pretreated without (**A,B**) or with (**C**) platonin (0.5–2 \(\mu\)M) for 30 min and then stimulated with (**A,B**) ox-LDL (10–40 \(\mu\)g/mL) or (**C**) ox-LDL (20 \(\mu\)g/mL) for 48 h. Western blotting assay was used to determine the cytosolic expression of iNOS and mIL-1ß content in the medium. MCP-1 production was determined using the ELISA kit as described in “Materials and Methods.” Data are presented as mean \(\pm\) S.E.M. (\(n=3\)); * \(p<0.05\), **\(p<0.01\)**, compared with the resting (R) group; # \(p<0.05\), ## \(p<0.01\) compared with the positive control group.
# SESAM Mode-Locked Yb:Ca3 Gd2(BO3)4 Femtosecond Laser
## 5 Conclusions  
In conclusion, we present a comprehensive characterization of a diode-pumped Yb:GdCB laser operating both in the CW and passively ML regimes. Pumped with a single-transverse mode fiber-coupled laser diode at 976 nm, the CW laser generated a maximum output power of 382 mW at 1053 nm, corresponding to a very low threshold of 25 mW and high slope efficiency of 55.2%. Continuous wavelength tuning between 1006 and 1074 nm (a tuning range of 68 nm) was achieved. A SESAM ML Yb:GdCB laser emitted pulses as short as 96 fs at a central wavelength of 1045 nm with an average output power of 205 mW and a pulse repetition of \(\sim\)67.3 MHz. To the best of our knowledge, this is the first report on passively ML operation of the Yb:GdCB crystal. Our results indicate the possibility for power scaling and pulse shortening, thus accessing the sub-50  
Figure 5: SESAM ML Yb:Ca\({}_{3}\)Gd\({}_{2}\)(BO\({}_{3}\))\({}_{4}\) laser with \(T_{\text{OC}}\) = 2.5%: (**a**) optical spectrum and (**b**) SHG-based intensity autocorrelation trace with a sech\({}^{2}\) fit. _Inset_: simultaneously measured long-scale SHG-based intensity autocorrelation trace for the time span of 50 ps.  
Figure 6: Radio-frequency (RF) spectra of the SESAM ML Yb:GdCB laser: (**a**) fundamental beat note at 67.3 MHz recorded at a resolution bandwidth (RBW) of 300 Hz and (**b**) harmonics on a 1-GHz frequency span, measured with an RBW of 100 kHz.  
fs time domain for Yb:GdCB lasers by using high-power laser diodes and the Kerr-lens mode-locking technique.  
Conceptualization, W.C. and H.L.; methodology, G.Z. and L.W.; validation, Z.-L.L., W.-Z.X. and H.-J.Z.; investigation, H.-J.Z. and W.C.; resources, Z.P.; writing--original draft preparation, H.-J.Z. and W.C.; writing--review and editing, P.L., X.M., L.W. and V.P.; supervision, W.C.; project administration, W.C.; funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61975208, 51761135115, 61850410533, 62075090, 52032009, 52072351); the Sino-German Scientist Cooperation and Exchanges Mobility Program (M-0040); the Foundation of the President of China Academy of Engineering Physics (YZJILX2018005); the Foundation of Key Laboratory of Optoelectronic Materials Chemistry and Physics, Chinese Academy of Sciences (2008 DP173016); and Foundation of State Key Laboratory of Crystal Materials, Shandong University (KF2001).  
Not applicable.  
All the data reported in the paper are presented in the main text. Any other data can be provided on request.  
The authors declare no conflict of interest.
## 2 Development of Identification Procedure  
This study developed and demonstrated a procedure for identifying debonding damage using an ANN on the changes in the natural frequency characteristics of a wind turbine blade, as shown in Figure 1. The procedure consists of (1) selecting the target blade and constructing a corresponding FE model, (2) defining the debonding damage configuration through selecting proper debonding damage variables, (3) constructing a debonding damage map using the natural frequency changes obtained through modal analysis, (4) selecting important key features using a preprocessing technique such as PCA, and (5) identifying debonding damage using an ANN. In this study, the authors employed the blades of the 5-MW class Sandia offshore wind turbine [21].