esa-sceva/satcom-chunk-collection huggingface dataset:
# Batch and Column Adsorption of Phosphorus by Modified Montmorillonite
## 4 Conclusions  
This research shows that montmorillonite modified by Keggin Al\({}_{13}\) complexes and HDTMA can more effectively remove phosphorus than MMt in aqueous solution. The high adsorption efficiency of phosphorus onto Al\({}_{13}\)-O-MMt was due to the larger interlayer distance and greater number of adsorption sites for phosphorus provided by the modifiers, Keggin Al\({}_{13}\) complex and HDTMA. The adsorption efficiency increased as the pH increased to 8, which implied the better adsorption of phosphorus in PO\({}_{4}\)\({}^{2-}\) form, which was transformed from HPO\({}_{4}\)\({}^{-}\) with increasing pH. The decrease in the adsorption efficiency with the further increase in pH to 9 was due to the increase in the negative charge on the surface, resulting in more electrostatic repulsion between the adsorbent and phosphorus. Since the phosphorus adsorption process by Al\({}_{13}\)-O-MMt is spontaneously exothermic, the adsorption ability of Al\({}_{13}\)-O-MMt decreased with increasing temperature. The breakthrough time was 4794.67 min, verifying that the adsorption of phosphorus onto Al\({}_{13}\)-O-MMt in the column experiment met the expected adsorption capacity in the batch experiment.  
The conceptualization was by [PERSON] and H.D.R.; methodology, J.C. and H.D.R.; validation, H.D.R.; formal analysis, Z.H.; data curation, Z.H.; writing--original draft preparation, Z.H.; writing--review and editing, J.C., J.L., L.S., S.Y.J., C.H.L. and H.D.R.; supervision, H.D.R.; project administration, H.D.R.; and funding acquisition by H.D.R. and S.Y.J. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by Guangdong Department of Education (2020-2023) of the \"Research Platform and Research Project\" No. 2020 ZDZX1022, and UIC Research Grant No. UICR5202018 at BNU-HKBU United International College, Zhuhai, PR China; Guangdong High Education Youth Creation Grant No. 2020 KQNCX099, and UIC Research Grant No. UICR5202016 at BNU-HKBU United International College, Zhuhai, PR China; and UIC Research Grants Nos. R202011, R25202016, R5202018, and R201805 at BNU-HKBU United International College, Zhuhai, PR China.  
Not applicable.  
The authors thank [PERSON], [PERSON], and [PERSON], who provided comments for this work.  
Figure 7: XRD pattern for comparison of MMt, Al\({}_{13}\)-O-MMt, and Al\({}_{13}\)-O-MMt after batch and column adsorption. 26.58\({}^{\circ}\) 20 is the quartz diffraction used as the internal standard for spacing identification.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Issues with Modeling a Tunnel Communication Channel through a Plasma Sheath
## 2 Resonant Tunneling at Oblique Incidence
Comparing data in Figure 2a,b one can notice that, unlike the TE wave, the filling factor for the TM wave grows slightly with an angle increase, and the resonant position shifts slightly to the left. In particular, this effect is associated with the direction of the electric field strength vector. Due to the presence of the _E_-field component normal to the surface, the TM wave interacts more efficiently with the plasma layer. As a result, better signal tunneling is observed near the resonance frequency region. From a practical point of view, both the angle of incidence and the contribution of the TE and TM components can be varied during the signal transferring to the vehicle. This means that the filling factor should be calculated for mixed polarization. It is important to notice that for the case of mixed polarization, the differences between the TE and TM cases partially compensate each other: for the case of the equal contribution of both polarizations (such a case corresponds to the unpolarized GHz pulse), the data presented in Figure 2c demonstrate a very weak filling factor dependence on the angle of wave incidence.
## 3 Experimental Verification
### Experimental Results and Analysis
#### 3.2.2 Visualization Analysis of SE-Res Block  
DNNs have always been subject to the stereotype that they are unreliable in the field of device health management because their internal training process is not visible to the users. In this section, we focus on visualizing the Squeeze-Excitation operation in the SE-Res block to show the effectiveness of SRMANet.  
The intermediate process is visualized with planetary gearbox data, as shown in Figure 9. The product of the horizontal and vertical coordinates plus one is the corresponding number of channels, i.e., the three intermediate layers in Figure 9 contain 256, 64 and 25 channels, respectively. Obviously, the convolution kernel extracts a set of feature maps corresponding to the more haphazard distribution of channel attention weights. Each channel is only concerned with local features at each time step of the convolution kernel, lacking an understanding of global information and thus containing more redundant information.  
After the Squeeze-Excitation operation, the distribution of weights among different feature maps is more discrete. A large number of fault-independent feature maps are adaptively and dynamically eliminated, preserving the information dependencies between different channels. After the excitation operation, the relationship between channels has stronger sparsity and redundant information is effectively removed.
## 3 Methodology
### Training and Evaluation
#### 3.4.2 Evaluation Criteria  
The root mean square error (RMSE) is utilized as both the loss function of the model and the fitness function of the PSO in the training process. The test set is imported into the well-trained model to obtain the SOE. Three metrics are selected to evaluate the performance of the model: RMSE, maximum absolute error (MAXE), and mean absolute error (MAE). The specific formulae of these metrics are as follows:  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{(SOE_{ip}-SOE_{ir})^{2}}}} \tag{23}\]  
\[MAXE=max\big{|}SOE_{ip}-SOE_{ir}\big{|} \tag{24}\]  
\[MAE=\frac{1}{N}{\sum\limits_{i=1}^{N}{\big{|}SOE_{ip}-SOE_{ir}\big{|}}} \tag{25}\]  
where N is the length of time series, SOE\({}_{\text{ip}}\) represents the estimated SOE, and SOE\({}_{\text{ir}}\) is the actual SOE.
## 4 Analysis and Discussion
### Validation of Instantaneous Baseline  
In the early stage of on-site monitoring, both Segment 1 and Segment 2 were in a normal state, and Segment 2 seemed to have slight and negligible marginal debonding between track slab and CA mortar layer. The vibration characteristics of Segment 1 and Segment 2 were analyzed to examine the effectiveness of setting the two segments as mutual instantaneous baselines. The methods described in Section 3.1 were applied. The time-domain feature values are presented in this section, and the frequency-domain and time-frequency-domain feature values are omitted because their findings were accordant with those of the time-domain analysis. The six time-domain feature values and their differences between the two segments in the early monitoring stage are summarized in Table 2. Each value in this table is the average of all feature values of the responses from sensors No. 1-No. 3, induced by 39 passing-by vehicles in a typical day. The high similarity  
Figure 7: (**a**) Contrast between raw signal and de-noised signal. (**b**) Enlarged view of details.  
between their vibration characteristics demonstrates that the two selected segments could be an instantaneous baseline for each other.  
Specifically, one of the time-domain features, peak acceleration, is illustrated in Figure 8 to observe their consistency. In Figure 8a,b, the horizontal coordinate displays the number of passing-by vehicles in a typical day, and the vertical coordinate exhibits the vertical acceleration of three sensors. The black rectangle and blue triangle represent the vertical accelerations of sensor No. 1 and sensor No. 3 in the cable trough of cross-sections 1-1 and 3-3 of the track segments, respectively, and the red circle stands for the vertical acceleration of sensor No. 2 in the cable trough of the cross-section 2-2 of the track segments. Figure 8a shows the scatter plot of the peak acceleration at the three measurement positions of Segment 1. Figure 8b shows the scatter plot of the same feature of Segment 2. The consistency in vibration levels between the two segments can be observed from Figure 8a,b.
# Sustainable Building Tool by Energy Baseline: Case Study
## 4 Analysis and Results
### EnBL Time Series Models  
The time series models proposed for validating energy value measurement models and the ratio of measured values are applied to establish an energy baseline. This application is illustrated through the case study of Classroom 4 on the university campus, progressing from simple models to those that account for seasonality and segmented data analysis. Over a three-year period, an analysis was conducted using each proposed time series model, revealing variations in the Mean Absolute Percentage Error (MAPE) ranging from 1.1% among the Historical Average, Weighted Historical Average, and Seasonality Indices models. Conversely, the Moving Average model (n = 12) exhibited the highest error rate, underestimating consumption values during low-demand months. The models considering the weekly schedule and daily consumption demonstrated improved accuracy  
Figure 5: Monthly electricity consumption of the lecture hall building of the UAO University campus. Figure 6: Seasonal performance of final energy consumption of the lecture hall building. by incorporating relevant variables such as the academic scheduling of classrooms and average daily consumption. The six time series models obtained for forecasting the energy consumption of the building are summarized below. Table 3 and Figure 7 show the values obtained for the different models compared with the actual energy consumption. To compare the six different time series forecasting models used in order to obtain the LBEn, the Mean Absolute Percentage Error (MAPE) was used. MAPE provides the deviation in percentage terms and is presented in Table 4. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Method** & **January** & **February** & **March** & **April** & **May** & **June** & **July** & **August** & **September** & **October** & **November** & **December** \\ \hline Historical average & 9.369 & 19.136 & 18.186 & 18,346 & 18.855 & 8.774 & 11.086 & 19.269 & 18.496 & 16.742 & 13.979 & 7.494 \\ Weighted & 10.110 & 19.050 & 18.508 & 18.350 & 18.993 & 9.682 & 11.980 & 19.149 & 18.448 & 17.044 & 14.199 & 8.222 \\ historical average & 9.918 & 20.257 & 19.252 & 19.422 & 19.960 & 9.288 & 11.736 & 20.398 & 19.581 & 17.723 & 14.799 & 7.934 \\ seasonality indices & 15.699 & 15.381 & 15.358 & 15.476 & 15.182 & 15.236 & 15.376 & 15.921 & 16.346 & 16.715 & 17.038 & 17.281 \\ Moving (n = 12) & & & & & & & & & & & \\ Considering weekly schedule & 10.054 & 17.857 & 19.770 & 16.129 & 19.770 & 12.334 & 12.640 & 17.459 & 16.896 & 17.459 & 16.896 & 7.909 \\ Daily consumption & 10.179 & 17.857 & 19.228 & 16.359 & 20.159 & 11.667 & 12.152 & 17.830 & 16.811 & 16.725 & 17.100 & 7.920 \\ Actual value & 8.157 & 18.339 & 19.271 & 15.445 & 18.876 & 12.135 & 20.271 & 24.242 & 22.723 & 21.390 & 17.109 & 10.418 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Six forecasting models implemented to determine the energy consumption in kWh and the actual value of energy consumption. Figure 7: Results of the different forecasting methods to obtain an annual LBEn for the building.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 2 Signal Model
### Multichannel Linear Prediction Reverberation Model  
By modeling reverberation components using the MCLP and predicting reverberation components of the current frame using the signals coming from the previous (\(L-D\)) frames, where \(D\) and \(L\) are the frame numbers prior to the current frame, respectively, the speech signal with reverberation can be expressed as follows [12]:  
\[\mathbf{x}(l)=\sum_{n=D}^{L}\mathbf{B}_{\mathbf{r},n}(l)\mathbf{x}(l-n)+ \mathbf{x}_{\mathrm{e}}(l) \tag{16}\]  
where \(\mathbf{B}_{\mathbf{r},n}\) (\(l\)) denotes the multichannel linear prediction coefficients, and \(L>D\geq 1\). The reverberation is divided into two parts: the early reverberation and the late reverberation. The result \(\mathbf{r}\) (\(l\)) represents the late reverberation components. Here, the desired signal \(\mathbf{x}_{\mathrm{e}}\) (\(l\)), which is the direct signal with early reverberation components, is also called the prediction error in the linear prediction model. The prediction error is expressed as \(\mathbf{x}_{\mathrm{e}}(l)=\left[x_{\mathrm{e}}^{1}(l),x_{\mathrm{e}}^{2}(l), \cdots,x_{\mathrm{e}}^{M}(l)\right]^{T}\), which contains the desired signal of each microphone. Note that \(\tau=\left(L-D\right)\) is the number of the delayed frames that are used to predict the late reverberation components. The selection of \(\tau\) should make the correlation between the late reverberation and the expected signal approach zero.
## 3 Proposed Methodology
### Data Balancing  
The dataset's class imbalance was remedied using Dynamic Proportional Class Adjustment (DPCA). By taking data links into account, this method dynamically scales underrepresented classes. In order to provide synthetic data for underrepresented classes and to decrease overrepresented classes proportionally, DPCA uses weighted resampling [32]. The amount of oversampling or undersampling that is required in synthetic sampling is determined by a weight factor, \(\zeta_{k}\), that is assigned to each class, \(k\). Equation (4) is used to compute the weight factor.  
\[\zeta_{k}=\frac{\overline{n}-n_{k}}{\max(n_{k},\delta)} \tag{4}\]where \(\overline{\pi}\) represents the average frequency across all classes, \(\delta\) is a small constant used to prevent zero division, and \(n_{k}\) is the frequency of class \(k\). Classes that are below \(\overline{\pi}\) are oversampled because they receive positive weights, whereas classes that are above \(\overline{\pi}\) are undersampled because they receive negative weights.  
Oversampling minority classes is achieved by Weighted Instance Synthesis (WIS), which interpolates between data points. Two samples, \(v_{a}\) and \(v_{b}\), belonging to the same class are used to create a synthetic sample, \(v_{\text{new}}\), in Equation (5).  
\[v_{\text{new}}=\lambda v_{a}+(1-\lambda)v_{b},\quad\lambda=\frac{1}{1+e^{- \xi}} \tag{5}\]  
In this equation, \(\xi\) indicates the feature distance between \(v_{a}\) and \(v_{b}\), weighting closer samples more during interpolation.  
Frequency-depending Sampling Adjustment (FBSA) decreases the sampling probability, \(\rho_{k}\), of each class, \(k\), depending on its relative frequency to control over-represented classes. Equation (6) defines sampling probability:  
\[\rho_{k}=\frac{\overline{\pi}}{n_{k}+\delta} \tag{6}\]  
Proportionately downsampling higher-frequency classes achieves balance without losing crucial information. DPCA, WIS, and FBSA represent all classes proportionately while retaining dataset variety and integrity. This method dynamically adapts to class distributions by solving multi-label DDoS attack detection problems.
## 5 Analysis of Factors Influencing Node Ductility and Energy Consumption
### Axial Pressure Ratio  
In this study, four different axial pressure ratios were considered, namely, 0.2 (432 kN), 0.3 (648 kN), 0.4 (864 kN), and 0.5 (1080 kN). The corresponding analytical calculations are shown in Table 4. It can be seen that as the axial pressure ratio increased, the ultimate bearing capacity of the joint also increased, which was due to the fact that the increase in vertical force inhibited, to a certain extent, the development of horizontal cracks in the yield phase of the joint core under a reciprocal load, thus increasing the ultimate bearing capacity of the joint. The joint ductility decreased with the increase in the axial compression ratio because the joint entered the plastic damage stage with the application of the load. The larger vertical force increased the unfavorable bending moment of the joint under the horizontal load, which led to the earlier damage of the joint and thus reduced the joint's ductility. The energy dissipation coefficient of the joint increased and then decreased with the increasing axial pressure ratio. The reason for the increase was that the increase in the ultimate bearing capacity of the joint increased the area of the hysteresis loop, which led to an increase in the energy dissipation coefficient, but as the axial pressure ratio continued to increase, the horizontal displacement capacity of the joint was suppressed, so the area of the hysteresis loop started to decrease, which led to a decrease in the energy dissipation coefficient.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Axial Pressure** & **Ultimate Bearing** & **Ductility** & **Energy Dissipation** \\
**Ratio** & **Capacity (kN)** & **Ductility** & **Coefficient** \\ \hline
0.2 & 130.04 & 16.67 & 0.603 \\
0.3 & 134.50 & 12.07 & 0.616 \\
0.4 & 141.04 & 8.57 & 0.625 \\
0.5 & 144.59 & 5.98 & 0.609 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Influence of axial compression ratio on joint.  
Figure 11: Damage nephogram for each phase of cast-in-place joint: (**a**) 2 s damage nephogram; (**b**) 6 s damage nephogram; and (**c**) 15.5 s damage nephogram.
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 3 Network Model and Problem Formulation
### Energy Model  
In the HMMN-EC, ARNs and ECNs usually have sufficient energy supply with no stringent capacity limitation; while battery-powered underwater nodes, i.e., SSNs, URNs, and MPs, are typically of small sizes with limited battery capacity. Thus, to provision quality marine monitoring services of the HMMN-EC, it is of critical importance to improve the operation time of the underwater nodes.  
Generally, the states of the battery of a node include sending, receiving, idle, and sleeping. It is reported in [33] that the communication module consumes the most energy, i.e., around 80% of the total energy consumption. Energy consumption during idle and sleep modes is only related to time. In our system model, energy consumption per unit of time during idle and sleep mode is regarded as a constant. Here, an URN communicates to other URNs or SSNs over underwater acoustic channels.  
The transmission and receiving energy consumption of node \(i\) over a communication channel, i.e., either an acoustic channel [34] or a radio channel [35], are denoted as \(\mathrm{E}_{tr}^{i}\) and \(\mathrm{E}_{re}^{i}\), respectively, which are given by  
\[\mathrm{E}_{tr}^{i}(l_{ij},d_{ij})=\left\{\begin{array}{ll}\mathrm{E}_{0}d_{ ij}^{k}10^{d_{ij}\frac{\pi(f)}{10}}l_{ij},&\text{acoustic channel,}\\ \mathrm{E}_{\mathrm{elec}}l_{ij}+\varepsilon_{amp}l_{ij}d_{ij}^{2},&\text{ radio channel,}\end{array}\right. \tag{1}\]  
and  
\[\mathrm{E}_{re}^{i}(l_{ii})=\left\{\begin{array}{ll}\mathrm{E}_{r}l_{ti},& \text{acoustic channel,}\\ \mathrm{E}_{\mathrm{elec}}l_{ti},&\text{radio channel,}\end{array}\right. \tag{2}\]  
where \(\mathrm{E}_{\mathrm{elec}}\) is the energy consumption of the transmitter circuit; \(\varepsilon_{amp}\) is the energy consumption of power amplifier; \(E_{0}\) is the energy consumption of transmitting one bit of data with a certain communication radius; \(E_{r}\) is the energy consumption of receiving one bit of data; \(k\) is the energy diffusion factor; \(l_{ij}\) is the size of the data packet from node \(i\) to node \(j\) in bits; and \(d_{ij}\) is the transmission distance from node \(i\) to node \(j\). \(\alpha(f)\) is the Doppler frequency-absorption coefficient of signal frequency \(f\), which is given by Throp [34],  
\[\alpha(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100+f^{2}}+2.75\times 10^{-4 }f^{2}+0.003.\]  
Thus, the communication energy consumption of node \(i\) is  
\[\mathrm{E}_{i}=\sum_{j\in\mathrm{V}}\mathrm{E}_{\mathrm{tr}}^{i}(l_{ij},d_{ij })e_{ij}+\sum_{t\in\mathrm{V}}\mathrm{E}_{\mathrm{re}}^{i}(l_{ti})e_{ti}, \tag{3}\]  
where \(e_{ij}=1\) indicates that node \(i\) can directly communicate with node \(j\) and vice versa. The total energy consumption of node \(i\) is  
\[\mathrm{E}_{i}=\mathrm{E}_{i}+\mathrm{E}_{\mathrm{idle}}^{i}+\mathrm{E}_{ \mathrm{sleep}}^{i}, \tag{4}\]  
where \(\mathrm{E}_{\mathrm{idle}}^{i}\) and \(\mathrm{E}_{\mathrm{sleep}}^{i}\) are the energy consumption of node \(i\) during idle and sleep mode, respectively.
## 2 Materials and Methods
### Western Blotting  
Following completion of the animal experiment, colon tissue was immediately ground using liquid nitrogen. Ground colon tissue (0.15 g) was homogenized in RIPA buffer (10 nM NaF, 2 mM phenylmethyl sulfonyl fluoride, 0.1 mg/L aprotinin, 10 mM sodium pyrophosphate, 1 mg/mL pepstatin, and 1 mg/mL leupeptin). Reagents for RIPA homogenized buffer were purchased from Sigma-Aldrich Chemical (St. Louis, MO, USA). Homogenized samples were then centrifuged and resulting supernatants were collected. Protein concentrations of samples were determined using the Bradford method.  
For sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), proteins (100 mg) were mixed with tricine sample buffer (Bio-Rad Laboratories, Inc., Hercules, CA, USA) and subjected to heat treatment before loading onto SDS-PAGE gel. Separated proteins were transferred from the gel to a polyvinylidene fluoride (PVDF) membrane using a semi-dry transfer system. Following the transfer, the PVDF membrane was blocked with Tris-buffered saline containing 0.05% Tween 20 (TBST) plus 3% bovine serum albumin (BSA). Subsequently, the membrane was incubated with primary antibodies, including anti-TNF-\(\alpha\) (ab205587, Abcam plc., Cambridge, UK), anti-IL-1\(\beta\) (ab9722, Abcam plc., Cambridge, UK), anti-NF-\(\kappa\)B (PA1-41089, Thermo Fisher Scientific Inc., Waltham, MA, USA), and anti-GAPDH (PA1-987, Thermo Fisher Scientific Inc., Waltham, MA, USA). After incubating with primary antibodies, the membrane was washed multiple times with TBST buffer. The membrane was then incubated with a secondary antibody, horseradish peroxidase (HRP)-conjugated goat anti-rabbit immunoglobulin G. Following another round of washing with TBST buffer, protein bands were visualized using an enhanced chemiluminescence (ECL) reagent (Santa Cruz Biotechnology, Dallas, TX, USA) and captured using an iBright FL1500 imaging system (Invitrogen, Waltham, MA, USA). Intensities of protein bands were quantified using ImageJ software (NIH, Bethesda, MD, USA). To ensure equal protein loading, GAPDH was utilized as a loading control.
# Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network
## 4 Experimental Results
### Data Pre-Processing  
To determine the condition of a bearing, a total of sixty-four 12-kHz vibration signals were pre-processed to establish a bearing fault diagnosis model. Each signal was split into several nonoverlapping fragments containing 1024 points. Subsequently, each fragment was formed into a 32 \(\times\) 32 image as the CNN input data. The data pre-processing procedure is illustrated in Figure 6. The total number of fragments in each category is listed in Table 1, and the corresponding fragments and processed images are presented in Figure 7.  
Figure 5: Vibration signals of (**a**) normal bearing and fault in (**b**) inner race, (**c**) ball, and (**d**) outer race under 1797-rpm motor speed.  
Figure 6: Data pre-processing procedure.