esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison of the Stresses in Surrounding Rock of Loess Tunnel  
When the buried depth of unlined loess tunnel is 50 m, the uniform in situ stress \(\sigma_{\text{s}}\) is 750 kPa. Thus, the distribution of stresses in the surrounding rock of the unlined loess tunnel of the modified Fenner formula based on M-C strength and the newly modified Fenner formula based on joint strength can be calculated and is shown in Figures 4 and 5.  
Figure 4: Comparison of radial stresses in surrounding rock of loess tunnel.  
Figure 5: Comparison of tangential stresses in surrounding rock of loess tunnel.  
The radial stresses of the modified Fenner formula based on M-C strength and the newly modified Fenner formula based on joint strength increase when the distance is far from the axis of the loess tunnel. However, the radial stress of surrounding rock of loess tunnel determined by modified Fenner formula based on joint strength is less than that determined by M-C strength. This is consistent with the conclusion in the literature [28] that the M-C strength criterion overestimates the tensile strength of loess, resulting in a deviation in the calculation results.  
The tangential stresses of the modified Fenner formula based on M-C strength and the newly modified Fenner formula based on joint strength increase firstly and then decrease when the distance is far from the axis of the loess tunnel. In the meantime, the maximum tangential stresses occur at the interface of the elastic-plastic zone.  
The tangential stress in the plastic zone of the surrounding rock determined by the modified Fenner formula based on joint strength is obviously smaller than that determined by the M-C strength, but the phenomenon is reversed in the elastic zone. The largest difference in tangential stress in the two kinds of modified Fenner formulas is observed at the elastic-plastic interface.
## Data and Methods
### Methodology
#### 3.3.4 Enhanced Vegetation Index  
The Enhanced Vegetation Index (EVI) [61; 62] utilizes three spectral bands that summarize radiometric and biophysical vegetation characteristics. EVI includes the blue band in addition to the vegetation-sensitive red and near-infrared bands. A set of coefficients and the blue band enhances the vegetation signal by reducing the influence of soil reflectance and atmospheric scattering. [PERSON] et al. [21] and [PERSON] et al. [63] found that EVI follows the seasonal dynamics of EC GPP better than the MODIS-derived GPP in Sahelian and Australian seric savannas, respectively.  
\[EVI=2.5\times\frac{NIR-RED}{NIR+6\times RED-7.5\times BLUE+1} \tag{6}\]  
where \(RED\), \(NIR\) and \(BLUE\) represent MCD43A4 NBAR Level 3 surface reflectance acquired in the red (620-670 nm), near infrared (841-876 nm) and blue (459-479 nm) portions of the electromagnetic spectrum, respectively.
## 4 The Results and Discussion of Experiment
### The Experiments on Classical Benchmark  
#### 4.1.1 The Convergence Analysis  
In order to evaluate the advantages of the MGTO algorithm on the benchmark functions, the MGTO is compared with traditional GTO, AOA, SSA, WOA, GWO, PSO, ROL-GWO, DSCA and HSCAHS algorithms. The results in Figure 4 show that the proposed MGTO can achieve more efficient and better results compared with other optimization algorithms. Furthermore, this paper selects \"semi-logarithms\" to draw the convergence curve with the purpose of making the difference of curve convergence obviously. Because \"0\" has no logarithms, the iterative curve is interrupted in the figure. As shown in F1-F4, F9 and F11, the modified algorithm does not display curves in the subsequent process, just because it converges to 0, which reflects the high convergence accuracy of this algorithm. In addition, the proposed algorithm is superior to other algorithms in the benchmark-functions (F1\(\sim\)F13) experiments. It is obvious that the MGTO has the excellent property of keeping balance between exploration and exploitation when solving complicated problems. In the benchmark functions (F14-F23), the MGTO can obtain great superiority, thus indicating that the proposed algorithm is competitive in solving composite functions. However, the results of the benchmark functions (F17-F18) and the DSCA and PSO algorithms demonstrate that these algorithms give an excellent performance to obtain high-quality results. In summary, the MGTO performs well in benchmark functions (F14-F23). What is more, the MGTO can find excellent solutions compared with other algorithms in most cases. Thus, in the comparative experiment, the MGTO has certain advantages over the other algorithms. The effectiveness of the MGTO can be proved by the experiment results.  
Figure 4: _Cont_.  
Therefore, as far as numerous unimodal functions are concerned, the MGTO still remains the excellent searching performance, thus clearly proving the superiority of the exploitative ability in the MGTO. In contrast to unimodal functions, multimodal functions have multiple optimal solutions, which have many local optimal solutions. These multimodal functions are usually adopted to evaluate the convergence ability of algorithms. As Table 1 shows, the results can indicate that the proposed model has slight advantages in getting out of the local solutions. The primary cause is that the basic GTO has already provided competitive results. The functions F14-F23 can be used to test the stability and searching capability of algorithms. From Table 1, it can be seen that the performance of MGTO surpasses the traditional GTO, and the solution results of multiple functions are extremely close to the true value. Evidently, it can be concluded that the MGTO maintains high stability and exploitative ability consistently in the test.
## 2 Principles and Model Establishment
### The Precipitation Cross Sectional Model  
To simplify the precipitation model and reduce the method complexity, a lot of assumptions are made as follows. The microwave pulses emitted by the radar are treated as plane waves. Among all the hydrometeors, only the rainfall and snowfall are considered and seen as independent, which means the melting layer is ignored. The ground is considered as a plane terrain. The simplified cross sectional model of rainfall and snowfall is shown in Figure 1.
## 3 Results and Accuracy Analysis  
### Landslide Susceptibility Modelling  
In the susceptibility mapping, landslide susceptibility index was considered for the probability of landslide occurrence (landslide: 1, non-landslide: 0). Before landslide susceptibility modelling, the data of landslide causal factors should be normalized. In this study, we normalized the factors into the range of [0.01, 0.99] on the basis of their information values. The normalized value was used as input data, whereas the susceptibility index was used as output data.  
In order to test the performance of the used methods, the landslide locations were randomly divided into two parts. A total of 50% of the landslide locations were utilized for the training model, and the remaining 50% were applied to verify the model performance. In the training process of the models, too much or too little training data of any kind would lead to the imbalance of model training. Therefore, the same number of data was randomly selected from the non-landslide area as the training samples. Three machine learning models (SVM, ANN, and CART) and the multivariate statistical model (LR) were used for landslide susceptibility modelling with nine important causal factors. The modelling process of the four models was completed in Clementine 12.  
Furthermore, the parameters of SVM and ANN were obtained by error and trial algorithm (Table 6). The CART model did not need any parameter in modelling. In the LR model, the formula of LR model for calculating the landslide susceptibility index (LSI) was as follows:  
\[\begin{array}{ll}LSI=&(-6.651)+(SPI*(-0.055))+(TRI*1.826)+(Lithology*1.417)\\ &+(Slope*1.458)+(Gully*0.806)+(Aspect*0.384)\\ &+(River*3.792)+(Faults*0.174)\end{array} \tag{14}\]  
The landslide susceptibility index was calculated by SVM, ANN, CART, and LR model, and then was divided into four levels: high (20%), moderate (20%), low (20%) and very low (40%), respectively. The results are shown in Figure 4.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Models** & **Parameters** & **Notes** \\ \hline SVM & \(c=20\), \(\gamma=1.3\) & \(c\) is the penalty factor, \(\gamma\) is the parameter of the kernel function \\ ANN & \(n=5\), \(\alpha=0.9\) & \(n\) is the neurons number, \(\alpha\) is the momentum \\ \hline \hline \end{tabular}
\end{table}
Table 6: The parameters of support vector machine (SVM) and artificial neural network (ANN) models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Eliminating Less Important Factors** & **Accuracy** \\ \hline Model 1 & Without eliminating any factor & 0.918 \\ Model 2 & TWI & 0.918 \\ Model 3 & TWI, profile curvature & 0.920 \\ Model 4 & TWI, profile curvature, plan curvature & 0.919 \\ Model 5 & TWI, profile curvature, plan curvature, curvature & 0.922 \\ Model 6 & TWI, profile curvature, plan curvature, curvature, aspect & 0.908 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The prediction accuracy with elimination of the less important factors.
## 3 Experimental Setup  
### Dataset Generation  
Voice data for this study were obtained from the open-source Chinese speech corpus, zhvoice, which comprises eight datasets. The corpus, following noise reduction and silenceremoval, includes more than 3200 speakers and approximately 900 h of audio, all recorded at a 16 kHz sampling rate. Two versions of the zhvoice corpus are adapted for this paper, tailored to conference room conditions. One version simulates scenarios with both noise and reverberation (herein referred to as NR-zhvoice), while the other version represents conditions with noise only (referred to as N-zhvoice). For NR-zhvoice, the simulation of reverberant speech is a preliminary step. The room impulse response is simulated using the wham_room library [56], where the configuration of parameters for reverb is shown in Table 2. In addition, wham_room utilizes a dual-source and dual-microphone configuration by default, in contrast to the minimalist speaker verification approach, which operates with monaural audio. Consequently, identical sets of sound sources and microphone parameters are input into wham_room, with only the left channel of the first microphone being employed for reverb output.  
In the second processing step, reverberant speech is corrupted by adding a randomly chosen noise at a randomly determined SNR within the range of [0, 0.5] dB. In cases where the noise duration is shorter than that of the speech, the noise is looped or extended by concatenating its beginning to ensure matching lengths. The noises are derived from two primary collections. The first is the soundsnap website, which encompasses a diverse array of sounds, including air conditioners, fans, cell phone vibration, coughing, sneezing, walking, flipping through documents, keyboards, and more. The dataset includes 1950 records across twelve categories. Additionally, as shown in Figures 7 and 8, actual recordings (A total of 480 records are sampled at 16 kHz, all lengths are cropped to 3 s) were made in a working conference room using a U-PHORIA UMC202 HD sound sensor manufactured by Behringer. N-zhvoice is generated by a process that follows the second step of NR-zhvoice, involving the addition of noise while excluding reverberation to the zhvoice.
# A 0.3 V, Rail-to-Rail, Ultralow-Power, Non-Tailed, Body-Driven, Sub-Threshold Amplifier
## Appendix A  
### CMFF Analysis  
The \(CMFF\) circuit can be analyzed with reference to Figure 3 and exploiting the equation for the subthreshold conduction of the MOS transistor [21]:  
\[I_{d}=I_{D_{0}}\exp\left(\frac{V_{gs}-V_{th}}{nU_{T}}\right)\left[1-\exp\left( \frac{-V_{ds}}{U_{T}}\right)\right] \tag{1}\]  
where  
\[U_{T}=\frac{kT}{q} \tag{2}\]  
is the thermal voltage. We assume balanced supply voltage \(\pm V_{DD}\), and no input common mode signal, so that \(V_{ic}=0\).  
The analysis of the leftmost branch of the circuit provides  
\[I_{ref}=I_{D_{0n}}\frac{W_{b_{2}}}{L_{b_{2}}}\exp\left(\frac{V_{B}+V_{DD}-V_{ Th_{B}}}{nU_{T}}\right)\left[1-\exp\left(-\frac{V_{B}+V_{DD}}{U_{T}}\right)\right] \tag{3}\]  
Let \(I_{1}\) the current flowing in \(M_{b_{4}}\): an expression for \(I_{1}\) similar to (3) can be written, but now \(V_{ds}=V_{A}+V_{DD}\). By exploiting Taylor expansion, the ratio between \(I_{1}\) and \(I_{bias}\) can now be expressed as  
\[\frac{I_{1}}{I_{bias}}=\frac{1+\exp\left(\frac{V_{A}+V_{DD}}{U_{T}}\right)}{ 1+\exp\left(\frac{V_{B}+V_{DD}}{U_{T}}\right)}\approx 1+\frac{V_{A}-V_{B}}{U_{T}} \tag{4}\]  
The voltage \(V_{A}\) is set by \(M_{b_{6}}\), since it results  
\[I_{1}=I_{D_{0p}}\frac{W_{b_{6}}}{L_{b_{6}}}\exp\left(\frac{V_{DD}-V_{A}-V_{ Th_{p}}}{nU_{T}}\right)\left[1-\exp\left(-\frac{V_{DD}-V_{A}}{U_{T}}\right)\right] \tag{5}\]\(V_{A}\) and \(V_{B}\) are applied to the gates of \(M_{1}\) and \(M_{3}\), respectively; the currents of \(M_{1}\) and \(M_{3}\) can be derived by exploiting the same approximation in (A4) as  
\[I_{M1}=I_{1}\bigg{(}1+\frac{V_{A}-V_{o}}{U_{T}}\bigg{)}\approx I_{ ref}\bigg{(}1+\frac{V_{A}-V_{B}}{U_{T}}\bigg{)}\bigg{(}1+\frac{V_{A}-V_{o}}{U_{T}} \bigg{)}\] (A6)  
\[I_{M_{3}}=I_{bias}\bigg{(}1+\frac{V_{o}-V_{B}}{U_{T}}\bigg{)}\] (A7)  
By imposing \(I_{M_{1}}=I_{M_{3}}\) we obtain \(V_{o}=V_{A}\).
## 2 Concept of Micro-Grid Systems
### Power Electronics Converter Control  
The hierarchical control architecture of a microgrid system includes the primary, secondary, and tertiary controllers. A primary controller consists of the preliminary power sharing and regulation of current/voltage, whereas a secondary controller is higher than the primary controller and helps to compensate for the voltage and improve the performance sharing. Finally, a tertiary controller focuses on power management, energy management, and economic dispatch [35].
# Short-Form Videos for Public Library Marketing: Performance Analytics of Douyin in China
## 5 Discussion
### Marketing Performance of Mainland Public Libraries on Douyin Channels (Rq2)  
Our findings indicated a positive correlation between the number of fans and the number of likes but no significant correlation between the number of fans and posts. For example, Jiangxi libraries have the highest number of fans and likes, but the number of posts is average. Jilin Library has 90,000 more likes than Chongqing Library but has 10,000 fewer followers than Chongqing Library. The high number of likes for Jilin Library is just due to more videos posted. Although the total number of likes for Jilin Library is higher than that of Chongqing Library, the average number of likes for its videos is lower than that of Chongqing Library.  
Thus, the effectiveness of new media promotion is closely related to the quality of the promoted content and whether patrons are interested in them [55]. It is more effective to produce quality videos popular with users than to rely on the number of videos to accumulate likes. Data from another library support this view. The Shanghai Library produces videos at the rate of one per day, eight times that of the Jiangxi Library, but receives only nine one-thousandths of the likes and nine percent of the followers of the Jiangxi Library. Therefore, a better strategy for libraries is to post high-quality videos that are more popular with patrons to achieve good marketing results [55].  
Notably, there is still much room for progress in short-form videos for libraries. The overall Douyin marketing performance of mainland public libraries was low. The highest DMI of 626 for the Guangdong Provincial Library compares favorably to other cultural and educational accounts, with a DMI of up to 900 [29].
# A Case Study of Floating Offshore Super-Long Steel Pipeline Combing with Field Monitoring
## 2 Project Characteristics
### Key Engineering Problems
#### 2.2.2 The Particularity of Materials  
The deformation test of steel-3 PE composite material shows that the steel pipeline and 3 PE would be separated from each other after large deformation. The indoor test material was the Q345 steel plate as same as the field steel pipeline material. The length, width and thickness of the steel plate were 40 cm, 20 cm and 2 cm respectively. Its surface was coated with 3 PE anticorrosive and was tested on the universal material testing machine. During the testing process, the steel plate was on the top and the 3 PE anticorrosive coating was on the bottom. The strain gauge was attached to the position corresponding to the steel plate and the 3 PE anticorrosive coating (as shown in Figure 3), and the force was applied on the middle line of the steel plate.  
The steel-3 pe deformation curve is shown in Figure 4. It can be seen that the strain at the centerline of the steel plate is the largest. When the load is about 30 kN, the center point of the steel plate (the measuring point 2-1) reaches the elastic limit state, and there is a significant difference between the steel plate and 3 PE strain at the same position. The maximum strain of the corresponding steel plate is 437 \(\mu\)e. If the load on the composite continues to increase, the steel plate and the 3 PE material will be separated from each other. Based on the test situation and considering that the compressive strength of general welds is similar to that of the base metal, the monitoring tester suggests that the safety range of the monitoring and warning strain value could be 260 \(\mu\)e. As soon as the monitoring strain approaches or reaches this value, the monitoring tester should immediately send out early warning signals and location information to the operators on the construction site.  
Figure 3: Laboratory test drawing of steel-3 PE.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 5 Experiment and Evaluation
### Experiment Design  
#### 5.3.1 First Experiment: Query Newest Data  
The first experiment is designed to measure the response time of the proposed CFC framework and the traditional cloud architecture when query the newest data. iFogSim [25] is utilized for simulation. According to the results of the experimental setup, 2000 clients were employed to make connections with the fog server. To simulate reality, the clients were divided into two categories: we assumed that 1600 clients uploaded data in the speed of 760 Mbytes/s and 400 clients sent query requests (as in reality the number of clients uploading data is greater than the number of clients requesting data). Overall, 1600 clients employ 1600 gateways for collecting the IoT devices' data. In this experiment, we assume that there were 10,000 devices and every device sends its location x, y, and z, its identifier and a timestamp which form 76 bytes in total. Consequently, a gateway will collect device data from 10,000 devices making the speed of data upload data 760 Mbytes/s.  
For evaluation, a child abduction alert service (i.e., latency-sensitive and location awareness service) was simulated. The threshold of the fog server is configured first. The locations of parents and children will be uploaded to the fog server continuously. The fog server computes the distance between children and their parents to check whether the distance increases over the configured threshold or not. An alert with the current location of the child will be sent to the parent by the fog server whenever the computed distance is over the defined threshold. Data will be synchronized to the cloud periodically; they will be filtered first and synchronized when the change is out of threshold to reduce the amount of data being transferred to the cloud and thus reducing cloud overhead.  
We measured the response time of these 400 clients who sent query requests regarding getting the newest data. The response time means the time difference between sending the request and receiving the response. Note that when requesting the newest data, the fog server will return the response. In contrast, requests that query historical data need a connection with the cloud. In this experiment, the response time and the delay time of the proposed framework were compared with three fog computing-based frameworks [26; 27; 28] that were published in the GitHub website [29].
## 4 Discussion and Conclusions  
This paper introduces the principles of the \(\Phi\)-OTDR system and describes how to complete a fast and accurate reconstruction of nonlinear and non-stationary signals based on CEEMDAN-KL. Then, it describes the AMNBP method of encoding, adaptive adjustment, location, and recognition. By designing three types of typical events and comparing the experimental results of four types of signals, the AMNBP values achieved an average gain of 30 times. This method takes into account the efficiency and accuracy of events. In multiple sets of experiments, the average recognition rate of the four types of events reached 94.03%, and the calculation efficiency increased by 20% compared with the two other conventional methods, namely SVM based on wavelets and frequency-domain analysis. The experimental results show that this method obtained a performance enhancement of the \(\Phi\)-OTDR system. It should be pointed out that this method based on nonlinear signal enhancement and adaptive coding is a very feasible development direction for a \(\Phi\)-OTDR system in practical applications.  
The gradual maturity of 5G technology will strongly promote the development of the optical fiber field. Therefore, there is an urgent need for \(\Phi\)-OTDR systems to overcome associated challenges. Due to the characteristics of high stability, low price, and long distance, research based on optical fiber transmission and sensing will receive a further burst. However, it should be mentioned that the current research also has its limitations that motivate further study. For example, since this method is only tested in intrusion events, whether this method can be applied to pipeline leakage fields needs further study. Another question is whether multi-value coding can achieve excellent performance in terms of both efficiency and accuracy. The encoding length and window length also need further research. In the future, more research on the applications of \(\Phi\)-OTDR system in practical scenes needs further study and discussion. All of this research will aim toward increasing the performance and providing more practical applications in various fields.  
Conceptualization and methodology, Y.H. ([PERSON]) and Z.M.; formal analysis, validation, and writing--original draft, Z.M.; validation, Y.Z. and X.A.; review and editing, Y.S. and Y.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was supported by the Beijing Municipal Natural Science Foundation (no. 4192042), National Science Fund subsidized project (no. 61627816), Beijing Science and Technology Plan Project (no. Z191100001419001), and the Fundamental Research Funds for the Central Universities. The authors would like to thank the anonymous reviewers for their constructive comments. The authors declare no conflict of interest.