esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Energy Neutral Operation
### Optimal SWIPT Ratio  
For a given CH \(i\), the optimal SWIPT ratio that maximizes \(R^{\text{swipt}}\) is formulated as  
\[\begin{array}{rl}\overrightarrow{\alpha}^{*}&=\underset{\overrightarrow{ \alpha}^{*}}{\arg\max}R^{\text{swipt}}\\ &=\underset{\overrightarrow{\alpha}^{*}}{\arg\max}\min\left[\min_{j\in \mathcal{N}\setminus\{i\}}\left\{R^{\text{swipt}}_{ij}\right\},R^{\text{ swipt}}_{i}\right]\end{array} \tag{19}\]  
where \(\overrightarrow{\alpha}^{*}=\{\alpha_{j}\},\forall j\in\mathcal{N}\setminus\{i\}\), and \(R^{\text{swipt}}_{ij}=R^{\text{PS}}_{ij}\) or \(R^{\text{TS}}_{ij}\) according to the SWIPT method used. From Equations (7), (8), and (11), as \(\overrightarrow{\alpha}\) increases, \(R^{\text{swipt}}_{ij}\) increases but \(R^{\text{swipt}}_{i}\) decreases, and vice versa. Thus, there is a tradeoff between \(\min_{j}\left\{R^{\text{swipt}}_{ij}\right\}\) and \(R^{\text{swipt}}_{i}\) according to \(\overrightarrow{\alpha}\). In this case, the optimal solution of the given max-min problem is found when \(\min_{j}\left\{R_{ij}^{\text{swipt}}\right\}=R_{i}^{\text{swipt}}\)[44]. To find the optimal \(\overrightarrow{a}^{*}\) that satisfies \(\min_{j}\left\{R_{ij}^{\text{swipt}}\right\}=R_{i}^{\text{swipt}}\), we design an iterative algorithm, as shown in Algorithm 1. Since SWIPT is performed when \(\min_{j}\left\{R_{ij}\right\}>R_{i}\), we set the initial target rate \(R_{i}^{\text{tar}}\) to \(\min_{j}\left\{R_{ij}\right\}\) and adjust the target rate to make all of the link rates equal. In every step, \(\alpha_{j}\) is determined to satisfy \(R_{i}^{\text{tar}}\) according to the PS or TS method. The computational complexity of the proposed SWIPT algorithm is given by \(O(L|\mathcal{N}|)\) where \(L\) is the number of iterations for the first loop and \(|\mathcal{N}|\) is the number of nodes in cluster.  
```
1:\(i\) is CH and \(j\in\mathcal{N}\setminus\left\{i\right\}\)
2:Initialize \(R_{i}^{\text{tar}}\leftarrow\min_{j}\{R_{ij}\}\)
3:repeat
4:if PS is usedthen
5:\(\alpha_{j}\leftarrow\left(2^{R_{i}^{\text{tar}}/T_{s}}-1\right)\frac{\sigma^ {2}T_{s}}{\eta_{j}\zeta_{j}\beta_{j}\eta_{j}P_{\ell}}\) from Equation (7)
6:elseif TS is usedthen
7:\(\alpha_{j}\leftarrow\frac{R_{i}^{\text{tar}}}{T_{s}\log_{2}\left(1+\frac{ \eta_{j}\zeta_{j}\zeta_{j}\beta_{j}\eta_{j}P_{\ell}}{\sigma^{2}T_{s}}\right)}\) from Equation (8)
8:endif
9: Update \(E_{i}^{\text{add}}\) according to Equation (9)
10: Update \(R_{i}^{\text{swipt}}\) according to Equation (11)
11:\(R_{i}^{\text{tar}}\leftarrow\frac{R_{i}^{\text{swipt}}+R_{i}^{\text{tar}}}{2}\)
12:until\(R_{i}^{\text{swipt}}\geq R_{i}^{\text{tar}}\)
13:Return \(\alpha_{j},\forall j\in\mathcal{N}\setminus\left\{i\right\}\)
```  
**Algorithm 1**SWIPT algorithm
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 5 Performance Evaluation
### Comparison with State-of-the-Art Methods  
To evaluate the performance of our proposed mechanism we selected few state-of-the-art protocols like SEED [11], ABC [9], ZBR [27] and CEEC [15] for comparison purposes. The selected methods are very recent in the literature of WSN and working of these protocols is somehow related to our proposed model. We preferred CEEC [15] and ABC [9] for comparison because the CH selection and cluster formation process of ABC are centralized and controlled by the BS. Moreover, both the protocols are trying to overcome the problem of control overhead. The SEED and ZBR are selected for comparison with our proposed model because both the protocols have good cluster stability and good energy management. For a fair comparison, all the conditions such as the initial network energies, the simulation environment, the node distribution, and the node densities in the network are taken same for all the methods. The average results included in these simulations have 90% confidence interval which is acquired after running the simulation 5 times.
# Galile Acid Induces S and G2 Phase Arrest and Apoptosis in Human Ovarian Cancer Cells In Vitro
## 3 Results
### GA Causes S and G2 Phase Arrest in Both OC Cell Lines  
To elucidate the underlying mechanism promoting GA-mediated OC cell viability suppression and apoptosis stimulation, we evaluated the consequence of GA exposure on the cell cycle of both OC cell lines. Both OC cell lines were exposed to t GA treatment (5, 10, 20 \(\upmu\)M) or DMSO control for 48 h and the cell cycle profile was assessed with flow cytometry.  
The control, untreated OVCAR-3 cells showed 56.69% G1 phase, 34.65% S phase and 8.67% G2 phase. With GA treatment at 20 \(\upmu\)M, the treated cells showed 49.62% G1 phase, 39.89% S phase and 10.49% G2 phase (Figure 4b). Based on these data, there was a small elevation in the % of OVCAR-3 cells in the S or G2 phases (\(p<0.05\)), along with a simultaneous reduction in the % of OVCAR-3 cells in the G1 phase (\(p<0.01\)). The cell cycle results of control, untreated A2780/CP70 cells showed 63.79% G1 phase, 27.76% S phase and 8.46% G2 phase. With GA treatment at 20 \(\upmu\)M, the treated cells showed 55.95% G1 phase, 32.93% S phase and 11.13% G2 phase. Therefore, GA exposure also produced a small elevation in the % of /CP70s cells in the S or G2 phases (\(p<0.01\)), along with a simultaneous decrease in the % of A2780/CP70s cells in the G1 phase (Figure 4c) (\(p<0.01\)). Collectively, these data indicate that GA exposure can halt both OC cell line cell cycle at the S or G2 phase.  
Figure 3: GA induces OVCAR-3 apoptosis by activating p53-dependent signaling. (**a**) Evaluation of Caspase 3/7 activity after 48 h exposure with GA. (**b**) Western blot image. (**c**) Protein expression quantization of Bad, Bax, cleaved caspase-3 and p53.
# On Analyzing Beamforming Implementation in O-RAN 5G
## 3 Challenges of Implementation: Splitting Options
The beamforming is performed in this flow to be placed before IFFT in case of digital beamforming and after analog conversion in case of analog beamforming. Split Option 7-2x implements functions up to the resource element mapping in the O-DU and supports both precoding in O-DU (Category A) and precoding in O-RU (Category B). **Category A:** the precoding (digital beamforming) function is performed in O-DU, allowing the O-RU design to be simple. The front-haul interface will carry spatial streams in the downlink. Category A fits well in the case of low-frequency and latency-insensitive services. _Category B_: precoding (digital beamforming and analog beamforming) is performed in O-RU, which makes the O-RU design complex, and the front-haul interface will carry spatial layers. The advantage of this category is the reduced downlink throughput achieved thanks to modulation compression in the downlink so that only the bits equivalent to the constellation points are sent there. Category B fits well for high-frequency and high-traffic networks [32]. Figure 4: DL and UL block diagram in O-RAN. In Split 7-2x, the uplink process supposes that the signal received in the time domain by the O-RU is digitally conversed, FFT-processed, and IQ-sampled in the frequency domain. Then, the signal will access the O-DU through resource element demapping, equalizing processing, inverse discrete Fourier transform (IDFT) processing, and channel estimation, demodulation, descrambling, and decoding. Then, the user bit sequence will be sent to the MAC layer. The beamforming is placed after FFT processing in the case of digital beamforming and before digital conversion in analog beamforming. As we explained above, one of the advantages of Split Option 7-2x is to reduce the front-haul required bandwidth caused by over-sampling applied to the OFDM signal in the time domain. This reduction can be higher when more functions are assigned to the O-RU. For example, in the case of downlink, placing the precoding in O-RU can prevent an increase in the required front-haul bandwidth when the number of MIMO spatial streams is greater than the number of MIMO layers. Another example: when performing resource element mapping/demapping on the O-DU side, data will be transmitted after user multiplexing through simplifying control signals on the Front-haul so that it may be simpler to achieve multi-provider RAN [26; 29].
## 2 Flow-based electrochemical detection
### Amperometric detection
The rep ormulations using tubular cell composite biosensors has been reported [30]. reducibility of the system in terms of its standard deviation was 2.9% with a 95% confidence level with a lifetime of over 100 days. Four types of Barbera wines from different origins in northern Italy were characterized using an electronic tongue based on amperometric detection in a flow injection system. A commercial electronic nose has also been used. All data from both E-nose and E-tongue were compared and elaborated on together with those of chemical analysis and color evaluation. All data were treated by multivariate data processing based on principal component analysis (PCA), linear discriminant analysis (LDA), and classification and regression trees (CART) analysis [31]. A sequential injection analysis/amperometric biosensor for the enantioselective analysis of the S-enantiomer of enalapril, ramipril, and pentopril has also been reported [32]. The biosensor used can be prepared by immobilization of L-amino acid oxidase in carbon paste. By using the proper enzymatic reactions to prepare the biosensors, the same system can be used for the determination of L- and D-carnitine [33] or L- and D-methotrexateate [34] with a sample throughput of 34 samples per hour. The determination of tramadol hydrochloride using a flow injection analysis system with amperometric detection has been developed [35]. Tramadol could be determined in concentrations between 9 and 50 \(\upmu\)M with a detection limit of 1.7 \(\upmu\)M at 90 samples per hour. These methods were successfully applied to the determination of tramadol in pharmaceutical dosage forms, without any pre-treatment of the samples. Recovery trials were performed to assess the accuracy of the results; the values were between 97 and 102%. The use of microfluidic cells for micro-flow analysis has been reported [36]. These cells can be designed and assembled by thermal transfer of laser printed masks and CD-Rs. Microprocessor-controlled electronic pipettors can be used as liquid propulsion devices instead of conventional oversized peristaltic pumps. This system can be operated in three different modes similar to FIA, SIA, and direct injection analysis (DIA). Various analytes such as promethazine, dipyone, and chlorpromazine in pharmaceutical formulations can be determined at low picomole quantites with an excellent linearity and high repeatability.
## 5 Discussion

Table 3: _Cont._was poor in Si, Mg, Fe, and Mn (Figure 5). The CaO-MgO-SiO\({}_{2}\) ternary diagram used for the classification of marble indicates that the studied samples are plotted in the field of pure calcite marble (Figure 6). The chemical analysis of Gahirat Marble revealed the presence of high concentrations of CaO, but low concentrations of other elements such as SiO\({}_{2}\), TiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), MgO, Na\({}_{2}\)O, K\({}_{2}\)O, and P\({}_{2}\)O\({}_{5}\). However, in most cases, the mineral impurities exhibited in chemical analyses, such as oxides of silica, aluminum, titanium, sodium, potassium, and manganese, were too low to affect the density of the rock. Geotechnical investigations of the Gahirat Marble depicted that it is a suitable stone for use as a dimension/building stone. The soundness test values ranged from 0.14% to 0.65%, as presented in the Table 3. The maximum soundness value was observed in sample GMC1 from the Chinar vicinity (0.65%), whereas the minimum value was found in sample GMKB1 from Gahirat Gol (0.14%). The average values of UCS range from 72 MPa to 100 MPa. The maximum value of UCS was measured in sample GMGG2 (100 mpa) from the Khairabad area, while the minimum value of UCS was observed in sample GMA1 (72 mpa) from the Ayun area. The values of UCS for Gahirat Marble were higher than the values of Nauseri Marble of the Muzzafarabad area [1] and Nikani Ghar Marble of the Buner area [26], Northwest Himalayas, Pakistan (Figure 7c). The measurements of the BTS tests range from 7.79 MPa to 12.80 MPa. The maximum value of the BTS was determined in sample GMGT2 (13.47 MPa) from Gahirat Gol, and the minimum value was determined in sample GMA1 (7.79 MPa) from the Ayun area. The values of BTS for Gahirat Marble were higher than the values of Nauseri and Nikani Ghar Marbles (Figure 7e) [27]. The UCS results showed that the Gahirat Marble can be classified as strong to very strong using ISRM's (1978) strength classification. Due to the interlocking texture of the marble and less fracturing in the mineral grains, it shows less water absorption and high values of specific gravity. The specific gravity values show its ability to bear the impact of the degree of polishing and grinding. Using the Marble Institution of America [52] and ASTM C503 [53] marble soundness classification, the studied marble has been classified as \"Group A marbles\". Finally, this integrated approach revealed that the Gahirat Marble is a more suitable building and dimension stone when compared to Nauseri and Nikani Ghar Marbles of the Northwest Himalayas, Pakistan.
## 3 Results
### Spatial Correlation  
The spatial relation of the E-field with the TMS-evoked activity is studied with the brain topographic plots of CEA. Figure 3 shows the projection of the E-field on the individual subjects sensor space, along with CEA(25,80) and CEA(25,275) estimated on the records of dataset B (120%LT). As expected, we see that the E-field is higher near the head vertex which coincides with the stimulation location. We also see that the extent of the field differs among subjects, similarly to the observations of Figure 1, where the projection is on the whole cortex. The CEA measures are also higher near the vertex, but in most cases, they are concentrated in a smaller area. Additionally, the CEA in the early window (25-80 ms) exhibits higher variability than in the window (25-275 ms). Overall, we observe remarkable consistency between E-field and CEA, with only notable exceptions the CEA(25,80) in subject S1, where there seems to be left frontal activation, instead of central. We note that for this subject, the E-field indeed had some high values at the left frontal, albeit being localized in a very small area.  
Figure 4 presents the average E-field and CEA measures across subjects. Here, it becomes more evident that the high E-field values span a larger area than that of the area of correctly evoked activity. In essence, from the brain regions where the induced E-field is high, only a part is ultimately activated. The center of the activated region is the Cz  
Figure 3: Topographic plots of the E-field, and CEA(25,80) and CEA(25,275) (rows) for all subjects (columns), as indicated on the top and left.  
Figure 2: (**a**) Average TEP across subjects on top and GFP on bottom, (**b**) Correlation \(\rho\) of CSD and E-field values for all subjects on top and their average on bottom, (**c**) same as (**b**), for covariance \(\sigma\).  
electrode, and the region extends to the adjoined electrodes. In Figure 4, as a check of validity of the brain activation in the two windows, we also present CEA topographies for time windows prior to TMS (mirrored to the CEA what we use in our analysis), where we see very small CEA values and no discernable pattern. Similar results were observed for other pre-TMS windows, or for post-TMS windows after the 500 ms mark.
# Effective Assessment of Inelastic Torsional Deformation of Plan-Asymmetric Shear Wall Systems
## 4 Comparison between Predictions by the Proposed Method and Time History Analysis Results
### Analysis Results and Interpretation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\alpha\)} & \multirow{2}{*}{\(\beta\)} & \multicolumn{3}{c}{**Time History Analysis**} & \multicolumn{3}{c}{**Proposed Method**} & \multicolumn{3}{c}{**Linear Elastic Analysis**} \\ \cline{3-10}  & & \(\Delta_{cent\_TH}\) & \(\varphi_{cent\_TH}\) & \(\Delta_{flex\_TH}\) & \(\varphi_{cent\_pron}\) & \(\Delta_{flex\_pron}\) & **Relative** & \(\varphi_{cent\_das}\) & **\(\Delta_{flex\_class}\)** & **Relative** \\  & & **(m)** & **(rad)** & **(m)** & **(rad)** & **(m)** & **Error (\%)** & **(rad)** & **(m)** & **Error (\%)** \\ \hline \multirow{3}{*}{1.30} & 0.50 & 0.161 & \(-\)0.012 & 0.221 & \(-\)0.009 & 0.205 & \(-\)7.28 & \(-\)0.004 & 0.180 & \(-\)18.45 \\  & 1.00 & 0.162 & \(-\)0.008 & 0.204 & \(-\)0.005 & 0.189 & \(-\)7.48 & \(-\)0.004 & 0.180 & \(-\)11.96 \\  & 2.00 & 0.162 & \(-\)0.009 & 0.209 & \(-\)0.003 & 0.177 & \(-\)15.31 & \(-\)0.003 & 0.177 & \(-\)15.36 \\ \hline \multirow{3}{*}{1.60} & 0.50 & 0.157 & \(-\)0.011 & 0.211 & \(-\)0.017 & 0.241 & 14.13 & \(-\)0.007 & 0.192 & \(-\)9.13 \\  & 1.00 & 0.157 & \(-\)0.012 & 0.216 & \(-\)0.010 & 0.209 & \(-\)3.25 & \(-\)0.006 & 0.189 & \(-\)12.59 In Figure 19, the two sets of the results presented in Figure 18 are plotted with respect to the ratio of the stiff wall stiffness to that of the flexible wall (\(\alpha\)), and the linear regression analysis is performed again. The coefficients of determination obtained by the regression results by the proposed and linear elastic methods are 0.0473 and 0.063, respectively. It can be seen from the figure that the linear regression line of the proposed method gets closer to the horizontal axis with increasing \(\alpha\). This means that the proposed method is able to provide more accurate solutions when the level of plan-asymmetry gets higher. Similarly, Figure 20 plots the same data as in Figure 18; Figure 19 with respect to \(\beta\). The coefficients of determination obtained by the regression results by the proposed and linear elastic methods are 0.2191 and 0.0961, respectively. Since the regression line of the proposed method gets closer to the horizontal axis with decreasing \(\beta\), the proposed method can provide more accurate solutions as the level of torsional constraint gets smaller, and thus the same conclusion can be made as in Figure 19.  
Figure 19: Comparison between the relative errors of the flexible side wall maximum displacement computed by the two methods with respect to \(\alpha\). Figure 18: Comparison between the relative errors of the flexible side wall maximum displacement computed by the two methods with respect to the target displacement.
# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 5 Conclusions  
In this paper, a MBCNN-BiLSTM model is presented for the prediction of the RUL of bearings, and the effectiveness of the model is verified on the PHM2012 bearing degradation public dataset. The results of the study are as follows:  
* The proposed method is an end-to-end bearing RUL prediction method, in which the original data are directly input into the network for RUL prediction without manual extraction of degradation features;
* The spatial feature extraction of the bearing input data is realized by MBCNN, and then BiLSTM further mines the data for temporal features. The GAM attentional mechanism is more conducive to focusing on the key information in the features, which improves the prediction accuracy of the bearings' RUL;
* It has been shown that the average absolute error and root mean square error of prediction are reduced by \"22.2%\" to \"50.0%\" and \"26.1%\" to \"52.8%\", respectively, compared with some existing prediction methods, which proves the effectiveness and feasibility of the proposed method. It is proved that the method proposed in this paper can reduce the prediction error in bearing RUL and has certain advantages over other methods.  
The dataset used in this paper contains constant speed and constant load data, but the bearings in practical engineering applications usually operate under variable speed and variable load. In addition, we consider the RUL prediction of bearings for only one operating condition in this paper. One assumption of the deep learning approach used in this paper is that the predicted data need to follow a similar distribution as the historical  
Figure 16: Feature Dimensionality Reduction Visualization.  
data. However, the distribution is often different for different operating conditions and when the bearing data are dynamic. So, feature extraction becomes difficult to achieve in real industrial processes because the distribution changes. Transfer learning is a learning method in which similarities between tasks are used to apply knowledge gained in an old domain to a new domain. In future work, we will consider the application of transfer learning methods. In detail, sensor data from devices with different operating conditions or data from different devices can be used to achieve transferability of knowledge and thus improve the performance of RUL predictions.  
[PERSON] developed the method, conducted the experiment and wrote the manuscript. [PERSON] and [PERSON] validated the method. [PERSON] and [PERSON] supervised the research work and the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The research work in this paper uses a publicly available dataset, which can be accessed here: [[https://hal.archives-ouvertes.fr/hal-00719503](https://hal.archives-ouvertes.fr/hal-00719503)]([https://hal.archives-ouvertes.fr/hal-00719503](https://hal.archives-ouvertes.fr/hal-00719503)) (accessed on 16 October 2022).  
The authors declare no conflict of interest.