esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Result Analysis
The resistivity distribution in the lower Quaternary loose layers is relatively uniform, with resistivity values around 800 \(\Omega\)-m. At depths of \(-\)10 m to \(-\)12 m, there a high-resistivity anomaly (circled by red circle in the Figure 9) with resistivity values exceeding 3000 \(\Omega\)-m. These anomalies are isolated and enclosed, with one having a relatively larger area, approximately 2 m in diameter, and the other with an anomaly about 0.5 m in size. Drilling verification confirmed that these two anomalies are indeed isolated boulders. The influence of different geological environments and rock composition on ERT (resistivity tomography) results is complex and significant. In terms of geological environment, the distribution and content of groundwater, the porosity of soil and rock, and the complexity of geological structure all have an important impact on ERT detection results. The existence of groundwater can significantly reduce the resistivity of the medium and form a low resistivity anomaly area, especially in the aquifer or groundwater flow channel. At the same time, the larger the porosity of soil and rock, the easier it is to be filled by water or other conductive media and then change the resistivity distribution. Geological structures such as faults and folds may lead to the nonuniformity of resistivity distribution, providing important geological information for ERT detection. The difference in rock composition is also a key factor affecting ERT results. Rocks with different mineral compositions have different resistivity characteristics. For example, rocks with a high content of conductive minerals have lower resistivity, while rocks with a high content of insulating minerals have higher resistivity. In addition, the chemical composition of rocks, such as salt content, will also affect their resistivity. Rocks rich in salt usually show low resistivity. As the channels of groundwater and other conductive media, the cracks and pores in the rock will also reduce the resistivity of the rock and form an obvious low-resistivity anomaly area on the ERT image. To sum up, different geological environments and rock compositions have a  
Figure 9: Inversion results of Ground 2D ERT. significant impact on the ERT detection results by affecting the resistivity distribution of underground media. In the process of ERT detection, these factors should be fully considered, and comprehensive analysis and judgment should be carried out in combination with the knowledge of geological exploration, petrology, geochemistry, and other disciplines so as to improve the accuracy and reliability of the detection results.
# Evolution of Technology and Technology Governance
## 2 Theoretical Background
### The Technological Waves Concept
Experts expect that the synthesis of these areas will eventually lead to quantum computing and artificial intelligence, and provide access to a fundamentally new development level of governmental, societal, and economic systems. Experts predict that the sixth technological paradigm will enter the maturity phase after 2040. It is expected that a new scientific, technical and technological revolution based on achievements in the aforementioned basic technological areas will take place during 2020-2025. There are reasons for making such estimations: in 2010, the most economically developed countries had 20 % of their productive power in fourth technological paradigm, 60 % in the fifth, and about 5 % in the sixth [2]. Currently we are observing the structural reorganization of the world economy. We can try to predict the birth of a new technological paradigm out of IT and communication technologies and bioengineering with a certain measure of nano-technological solutions in well-developed economies of the 'first world', which will ultimately lead to a beneficial 'long wave' of growth. A drop in oil prices is a characteristic sign of the end of the 'delivery' period; and a new technological paradigm will grow exponentially in no small part thanks to a 'diffusion' of innovative, resource-efficient technologies and overall production energy intensity reduction.
# Temporal Encoding to Reject Background Signals
## 3 Discussion  
We have demonstrated a transmission scheme suitable for single photon level optical wireless communications. By transmitting temporally correlated signals, data communications can be performed at extremely low light levels, with received power on the order of pico-Watts. Signals can be transmitted using an LED, and received with a single SPAD. A 100 kb/s link has been achieved with a BER of less than \(10^{-3}\) at a received power of 8.25 pW. The scheme is robust to background light, with only a minor increase in required power for very high background conditions. Modulated background signals appear to have little additional influence over that of continuous background, suggesting the scheme could be used in parallel with other optical communications with minimal interference. Furthermore, multiple transmission systems using this scheme could operate without interfering with each other, simply by using different pulse time separations. The modest data rates presented in this work are dominated by two key factors, firstly the requirement of this protocol for correlating many repetitions of a pulse pattern, and, secondly, the dead time of the SPAD detector itself.  
A real time transmission setup has been demonstrated, showing a method for clock synchronisation and determination of a threshold level. The current, unoptimised implementation allows a data rate up to 20 kb/s, with only a minor reduction in performance when compared to offline processed transmission. The real time transmission link has been demonstrated in a simulated satellite environment, providing data transmission at a received optical intensity of 9.5 mW/m\({}^{2}\) under simulated nanosatellite power systems. Additionally, GaN LEDs at low current densities show higher wall-plug efficiencies than laser diode counterparts [28], further enhancing the power consumption characteristics of the system. In future satellite-focussed experiments, an optimum transmission wavelength will be chosen, based on LED performance, detector response and solar-blind wavelengths.  
Data rate and photons per bit efficiency can both be improved through relatively straightforward modifications to the system. By using a SPAD array as a receiver rather than a single device, the dead time limitation can be overcome and therefore higher data rates achieved. In addition, by implementing a form of pulse position modulation, with powerful FEC codes, the photons per bit transmission efficiency can be improved. Finally, data rates may be enhanced by using a form of pulse amplitude modulation, however the received power requirement would also increase.  
This transmission protocol has clear applications in communications systems for long range or high loss environments, but is also equally applicable in microscopy or low light level imaging systems when coupled with a SPAD imaging array, and can be implemented using a wide range of pulsed optical sources dependent on the application.  
Figure 7: BER performance of the real-time communication link under simulated satellite power.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 3 Results
### Model Batch By Bundle  
#### 3.1.1 Service Time Modelling  
In this section, we consider messages that correspond to a bundle of XML documents, which we call \"Batch By Bundle\". Our aim is to investigate the fitting of a parametric distribution to the service times accounting for these \"Batch by Bundle\" messages.  
To recap on modelling \"Batch By Bundle\", we take all messages and count the number of XML documents associated with a message. If a message has only one XML document, we take the service times for these messages for modelling. For any messages where the XML document count associated with a message is greater than one, we take the first and last message of this bundle and discard all messages in between. We note that all messages in between are all zero seconds in duration.  
Using the model in Figure 12, we applied data from the \"normal period\" using the tail of the data and removing zeros from the model. Our model came close to fitting a log-normal parametric distribution but failed the AD GoF test.  
We partitioned the data into a head and tail, using a boundary of 1 s. The model results of fitting the log-normal distribution to the tail of the data are shown in the chart to the left of Figure 13. The histogram represents a shape close to a log-normal distribution but again fails the AD test. The chart to the right shows the results of the Q-Q plot for log-normal distribution fitting. We observe from the Q-Q plot that the lower quantile regions are more fitting to the line than the upper quantile regions, but there are clear systematic mismatches.  
Figure 11: Batch by category discrete values, ST, head.  
Figure 12: Batch by bundle classification model.  
Table 11 shows our best AD test result. We applied a constant of 1 to the tail of the service times and applied a log transform on the data to improve the fit.  
To model the head of the data, Figure 14 shows a histogram with a probability density drawn on the service times. We note several peaks at the left of the estimated density that again suggests quantisation. Consequently, it is unlikely that these data would fit a parametric distribution. Possibly, it could be further partitioned, KDE could be applied, or adjustments could be made for quantisation.  
We now attempt to model our data using \"Batch By Split Count\".
## 4 Case Study
### Simulation Results and Discussion  
In each scenario, three models are compared. The first model (BA_MIN) forms the cable connection layout scheme according to the minimum cross-sectional cable selection rule, which means the thinnest cable that satisfies the cable current constraint will be selected. The second model (BA_FREE) considers the impact of the cable sectional area selection on the objective function. The last model (MBA) is designed based on the BA_FREE while taking the impacts of crossed cables on the final layout into account. To compare their performances, all the algorithms are run with the same termination criterion: that is, terminated at 800 s. The input parameters of BA are identical for each model: that is Q = 400, \(\theta=150\), \(\alpha=0.999\), \(\gamma=0.001\). Each algorithm is run 10 times independently, and the best results are selected as the global optimal solution. In order to illustrate the selected cable sectional area, different colors are adopted in the following figures. The simulation results of each scenario are included in Table 1.
## 4 Discussion  
Vegetation cover changes (as represented by LAI) play an essential role in simulating Africa's regional surface climate and the terrestrial carbon cycle. Regarding the surface climate, this role has been extensively covered in previous studies (e.g., [3; 4; 6]) using a coupled regional climate-vegetation model. Concerning the terrestrial carbon fluxes, ref. [4] also used a regional coupled climate-dynamic vegetation model to examine the influence of the modified GPP parameterization on the simulated GPP with respect to MTE using different physical configurations. In addition, ref. [17] examined the role of vegetation cover and soil moisture changes in simulating GPP and observed that GPP is more sensitive to vegetation-soil-moisture-coupled systems than when only vegetation cover changes are considered.  
Moreover, ref. [6] reported that the LAImod improves the simulated surface energy balance and surface climate compared to the LAIorg formula against reanalysis products. Recently, RegCM4 has been upgraded to work as an Earth system model ([18]). Therefore,  
Figure 5: The figure shows the monthly mean GPP (in gC m\({}^{-2}\) day\({}^{-1}\)) for the LAIorg (OLD; in blue), LAImod (NEW; in red) and Fluxnet observation (in green) for the locations: SD-DEM (**first row**), ZM-MON (**second row**) and CG-TCH (**third row**).  
optimizing the RegCM4 performance concerning the terrestrial carbon fluxes is important. In the present study, the influence of LAI parameterization on the terrestrial carbon fluxes of Africa was examined by conducting two 13-year simulations using the RegCM4 model. The results are summarized in the following points:  
1. Compared with the MODIS, LAImod succeeds in alleviating the LAI bias relative to the LAIorg, particularly in JJA and SON.
2. LAImod reduces the SABV, FPSN and LEAFC and increases ST10 relative to LAIorg.
3. LAImod improves the simulated GPP and NEE bias concerning the CARDAMOM product. Additionally, LAImod improves the simulated HR with respect to the CARDAMOM product over the Savanna regions but not over the Congo basin.
4. Concerning the Fluxnet observation, the RegCM4 model performance varies with respect to the location. Yet, LAImod shows better performance than LAIorg.
# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 3 Materials and Methods
### Week Learners  
#### 3.1.1 Logistic Regression  
Logistic regression is a machine learning algorithm often used for classification. Similar to linear regression, the difference is the function used to fit the data. Linear regression is used to calculate a linear function to fit the data according to the characteristics of the learned data, so as to predict a new point. Conversely, logistic regression uses a sigmoid function to fit the data, which is more efficient than a linear function. Since the value range of a sigmoid function is (0, 1), in the binary classification task, the value of a sigmoid function at a point can be regarded as the probability of taking a positive sample, and then the prediction result at that point can be judged by comparing it with the threshold set.  
The form of the fitting function of multiple linear regression is \(y=\omega_{0}x_{0}+\omega_{1}x_{1}+\cdots+\omega_{n}x_{n}\) or \(y=W^{T}X\), and the fitting function of logistic regression, which is \(g(z)=\left(\frac{1}{1+w^{T}X}\right)\), can be obtained by substituting it into the sigmoid function. At this time, the probability that a point is predicted to be a positive sample is obtained, and the probability of successful prediction at a certain point can be further obtained:  
\[P(true)=(g(\omega,x_{i}))^{y^{i}}*(1-g(\omega,x_{i}))^{1-y^{i}} \tag{1}\]  
where \(y^{i}\) refers to the predicted value of a sample, which is either 0 or 1 in the binary classification task.  
After obtaining the probability of successful prediction on a sample, one may wish to maximize the prediction success rate on all samples, that is, maximize the product of the prediction success rate on all points. Using the maximum likelihood estimation method to solve this, let \(h_{\theta}(x)=\frac{1}{1+e^{-x}}\). Then, the maximum likelihood estimation function is expressed as follows:  
\[L(\theta)=\prod_{i=1}^{m}(h_{\theta}(x^{(i)}))^{y^{(i)}}(1-h_{\theta}(x^{(i)} ))^{1-y^{(i)}} \tag{2}\]  
We take the logarithm and negative sign on both sides at the same time to obtain:  
\[I_{log}(\omega)=\sum_{i=1}^{m}-y_{i}\log(p(x_{i};\omega))-(1-y_{i})\log(1-p(x _{i};\omega)) \tag{3}\]  
Therefore, our goal is to minimize Equation (3), which is the loss function of logistic regression, also known as the cross-entropy loss function. Then, the gradient descent method is used to solve the minimum value and \(\omega\).
## 4 Results and Discussion
### Load-Displacement Curve
#### 4.3.1 Beam S1-2  
The crack pattern width in the RC beam was only reported at five positions in the study conducted by [PERSON] et al. [16]. Therefore, the comparison between the simulated and experimental results for the crack patterns of the RC beam was performed at these five positions based on the available experimental data.  
Figure 12 illustrates the crack patterns observed in both experimental and simulated results, with each crack numbered from (1) to (5) to facilitate the comparison of crack parameters. Based on the output data, the plastic strain, tension damage variable, and stress of the element at the position of the crack were derived after the service load, as shown in Table 5.  
Detailed measurements of the height (h), width (w), and distance between cracks at the soft of the RC beam (s) are presented in Table 6. The comparison shows good agreement  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crack ID** & \(\epsilon_{\text{t}}\)PI & \(\mathbf{d_{t}}\) & \(\mathbf{\sigma_{t}}\) **(MPa)** & \(\epsilon_{\text{t}}\)**(ck)** & **w (mm)** \\ \hline
1 & 0.009789 & 0.952 & 1.071 & 0.010667 & 0.16 \\ \hline
2 & 0.005112 & 0.952 & 1.071 & 0.00612 & 0.09 \\ \hline
3 & 0.004351 & 0.761 & 0.969 & 0.004478 & 0.07 \\ \hline
4 & 0.003774 & 0.936 & 1.071 & 0.004424 & 0.07 \\ \hline
5 & 0.009796 & 0.952 & 1.071 & 0.010667 & 0.16 \\ \hline \hline \end{tabular} Note: \(\epsilon_{\text{t}}^{\text{I}}\), \(d_{t}\), \(\sigma_{\text{t}}\) denote the plastic strain, tension damage variable, and stress of the crack element at the service load extracted from the ODB file in the ABAQUS program. \(\epsilon_{\text{t}}^{\text{K}}\) is the cracking strain, which was calculated from Equation (13). w is the width of the crack was calculated based on Equation (9).  
\end{table}
Table 5: Calculating the crack width from plastic strain, tensile stress, and tension damage variable.  
Figure 12: Comparison of crack patterns in simulated and experimental results of the beam S1-2.  
between the simulated and experimental crack patterns in terms of their position, width, and height, with a difference of 12.1% observed in mean height and mean distance between cracks. The mean width difference between the simulation and experiment at the service load is around 3.38%, while the maximum crack width difference is about 12.6%.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 6 Performance Analysis and Computational Complexity
### Performance Analysis
#### 6.1.5 Performance with Small Buffer Threshold  
Case 2: If the buffer threshold \(B_{c}\) is less than \(2B_{1}\) and greater than \(2\lambda t_{1}\), the transmission action is shown in Figure 10. During \(t_{0}\) to \(t_{1}\), the node does not transmit; during \(t_{1}\) to \(t_{2}\), the transmission power is \((\mu-\frac{1}{c_{t}})^{+}\); during \(t_{2}\) to \(t_{4}\), the transmission rate is equal to the data arrival rate; during \(t_{4}\) to \(t_{5}\), the transmission power is \((\mu-\frac{1}{c_{t}})^{+}\).  
The average energy consumption per unit of data is  
\[AE_{2}=E_{2}/(\lambda(t_{5}-t_{0})). \tag{56}\]  
The average buffer length is  
\[AB_{2}=B_{c}/2. \tag{57}\]  
The average transmission delay is  
\[AD_{2}=B_{c}/2\lambda. \tag{58}\]  
Case 3: If buffer threshold \(B_{c}\) is less than \(2\lambda t_{1}\), the transmission action is shown in Figure 11. During \(t_{0}\) to \(t_{7}\), the node does not transmit; during \(t_{7}\) to \(t_{8}\), the transmission rate is equal to the data arrival rate; during \(t_{8}\) to \(t_{5}\), the transmission power is \((\;\mu-\frac{1}{c_{t}})^{+}\). From \(t_{0}\) to \(t_{7}\), the amount of arrival data is \(B_{2}\), which is half of the buffer threshold \(B_{c}\). \(t_{7}\) is calculated as  
\[t_{7}=B_{2}/\lambda, \tag{59}\]  
where \(B_{2}=B_{c}/2\). From \(t_{8}\) to \(t_{5}\), the buffer length decreases from \(2B_{2}\) to \(B_{2}\). Similar to (51), \(t_{8}\) can be calculated as  
\[t_{8}=t_{5}-\frac{\rho log_{2}(\mu c_{t_{5}})-\lambda-\sqrt{\left(\rho log_{2 }(\mu c_{t_{5}})-\lambda\right)^{2}-2k_{\mu}\rho D_{2}B_{2}}}{\rho k_{h}D_{2}}. \tag{60}\]  
Similar to case 2, the energy consumption is  
\[E_{3}=\int_{t_{7}}^{t_{8}}P_{t}dt+\int_{t_{8}}^{t_{5}}\left(\mu-\frac{1}{c_{t }}\right)dt. \tag{61}\]  
Figure 11: Transmission action when \(B_{c}<2\lambda t_{1}\), for case 3.  
The two items are calculated as follows:  
\[\int_{l_{7}}^{l_{8}}P_{t}dt =\int_{l_{7}}^{l_{8}}\frac{2^{\frac{3}{p}}-1}{c_{t}}dt=(2^{\frac{1}{p }}-1)(\frac{-10}{k_{h}ln10}\bigg{(}\frac{1}{c_{l_{8}}}-\frac{1}{c_{l_{7}}}\bigg{)}), \tag{62}\] \[\int_{l_{8}}^{l_{5}}\bigg{(}\mu-\frac{1}{c_{t}}\bigg{)}dt =\int_{l_{8}}^{l_{8}}\bigg{(}\mu-10^{-\frac{h}{10}}\bigg{)}dt=(t_{5} -t_{8})\mu+\frac{10}{k_{h}ln10}\bigg{(}\frac{1}{c_{l_{5}}}-\frac{1}{c_{l_{8}}} \bigg{)}. \tag{63}\]  
The average energy consumption per unit of data is  
\[AE_{3}=E_{3}/(\lambda(t_{5}-t_{0})). \tag{64}\]  
The average buffer length is  
\[AB_{3}=B_{c}/2. \tag{65}\]  
The average transmission delay is  
\[AD_{3}=B_{c}/2\lambda. \tag{66}\]  
These quantitative analysis will be calculated in simulation Section 7.4, and impacts of buffer threshold and data arrival rate on communication performance will be presented.
## 3 Results and Discussion  
### Anti-Biofilm Efficiency of PAH  
The antibacterial activity of PAH against the _S. aureus_ biofilms after various incubation time was evaluated (Figure 1B). DIW and H\({}_{2}\)O\({}_{2}\) solution were chosen as negative controls, results indicated that they had no significant (\(p>0.05\)) effect on the inactivation of the biofilm. For the PAH treatment, the number of viable bacteria decreased significantly (\(p<0.05\)) from 7.25 to 3.21 log CFU/mL after the prolonged incubation time from 0 to 30 min, then the reduction showed a flat tendency with increasing incubation time. The total amount of biofilms was determined by the crystal violet staining method (Figure 1B).  
The higher value of the OD 570 nm, the more amount of biofilm combined with the crystal violet. The decreasing trend in OD values was consistent with the decreasing trend of the bacterial cells. Therefore, the following investigation mainly focused on the influence of PAH on the biofilms after incubation for 0-30 min. Results indicated that the PAH was very effective in the inactivation of _S. aureus_ cells (ATCC 6538) in the biofilms. Reactive species contributing to the antimicrobial efficacy of the PAH have been attributed mainly to hydrogen peroxide (H2O2), ozone (O3), hydrogen radicals (OH*), nitrite (NO2\({}^{-}\)), and nitric oxide (NO*) [10], which are generated through the following pathways [21; 22]:  
\[\mathrm{H_{2}O+e^{-}\to H^{\bullet}+OH^{\bullet}+e^{-}}\]  
\[\mathrm{OH^{\bullet}+OH^{\bullet}\to H_{2}O_{2}}\]  
\[\mathrm{O+O_{2}+O_{2}\to O_{3}+O_{2}}\]  
\[\mathrm{NO+NO_{2}+H_{2}O\leftrightarrow 2 NO_{2}{}^{-}+2H^{+}}\]  
\[2\mathrm{HNO_{2}\to NO^{\bullet}+NO_{2}{}^{\bullet}+H_{2}O}\]  
The ROS, such as OH*, possess high penetrability against extracellular polymeric substances and bacterial cells in the biofilms. Meanwhile, reactive nitrogen species, which are responsible for the decrease in the pH value of PAH [23], affect the stability of biofilm structure [24]. After plasma treatment, the pH of PAH decreases to about 2.28, which might affect the hydrophobicity and surface adhesion of the biofilms. When the pH value of the external environment of bacterial cells decreases, the activity of the membrane proton pump increases, and H+ is expelled from the cytoplasm. Therefore, cell energy is mainly used for avoiding internal acidification, which reduced the adhesion of the biofilm [25].