esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Studies and Analysis
### Test Results and Analysis for PFG at SCP3  
#### 3.4.1 Test Results and Analysis in Two-Dimensional Planes  
According to the theoretical analysis, the PFG at SCP3 have the most degrees of freedom for regulations and the most complicated changing rules. As shown in (a) of Figure 7, the PFG to CIV1 at SCP3 in the normal operation conditions present similar curves ranging within \(-2.0\) p.u. to \(3.0\) p.u., except that the PFG in the severe condition have larger ranges to own more high regulation efficiencies. In (b) and (c) of Figure 7, the APFG and RPFG to CIV2 show similar changing rules as those at SCP2, which can also be inferred by Equations (5) and (8). In (d) of Figure 7, all the curves of PFG to CIV3 at SCP3 show nearly the same shapes, and the magnitudes and phases of the curves in different regulation scenarios have only small differences.  
From another point of view, in (e) of Figure 7, the curves of APFG against RPFG to CIV1 are analogous to certain nearly closed shapes, which much different to those at SCP1 and SCP2, and can be  
Figure 6: Spatial surfaces of PFG to CIV3 at SCP2 in spaces: (**a–d**) Spatial surfaces of PFG to CIV3 in four regulation scenarios.  
Figure 7: Curves of PFG to each CIV at SCP3 in planes: (**a**) Curves of the PFG to CIV1; (**b**) Curves of the APFG to CIV2. (**c**) Curves of the RPFG to CIV2; (**d**) Curves of APFG and RPFG to CIV3; (**e**) Curves of APFG against RPFG to CIV1; (**f**) Curves of APFG against RPFG to CIV2.  
more adaptive to different operation conditions. As for (f) of Figure 7, the curves of APFG against RPFG to CIV1 in two different operation conditions will form certain closed shapes, but the two curves in the same operation condition by Reg.1 and Reg.2 modes present similar changing rules, indicating the PFG to CIV2 can be regulated within larger overall regions and have more regulation tendencies.
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 4 Results and Discussion  
This section presents the model specifications, performance metrics, and outcomes derived from the implementation analysis of deep neural networks. The results are organized into three subsections: firstly, the implementation of the Multilayer Perceptron (MLP) model in Python; secondly, an assessment of the feasibility of generating synthetic data using CopulaGAN; and lastly, the training and evaluation of the MLP model with two distinct datasets.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 1 Introduction
Now, the installed capacity of PV in northwest China is increasing rapidly. However, due to the restriction of distribution grid, PV power generation does not have the ability to feed back the distribution network, but only be absorbed locally. This is a great waste of PV resources. On the other hand, intelligent agriculture and protected agriculture, as new modes of electricity use in agriculture, are more diversified and gradually promoted in rural areas. The coordinated application of multi-energy in IES has created favorable conditions for local PV absorption. Meanwhile, the protected agriculture load is usually time-shifting and schedulable, which is an excellent consumer-side response resource. Therefore, reasonable dispatching and planning of time-shifting load in agricultural production is also a measure of DR with rural characteristics.
# DroidPortrait: Android Malware Portrait Construction Based on Multidimensional Behavior Analysis
## 2 Related Work
### Dynamic Analysis-Based Malware Detection  
Unlike static analysis-based approaches, dynamic analysis-based approaches aim at capturing run-time behaviors from Android apps during the execution process of the apps on Android emulator or real devices. The examples in this category include taint analysis based approaches [12; 23] and behavior-based detection strategies [24; 25].  
[PERSON] et al. proposed a malware detection solution named DroidScope, where both the operating system level and programming language level semantics were rebuilt and allowed the instrumentation of the Dalvik and native instructions [20]. [PERSON] et al. proposed a host-based malware detection system that analyzed Android app features at the levels of the kernel, application, user and package [25]. In [26], a framework named AdDroid was studied to analyze malicious behaviors in Android applications based on different combinations of mobile app behaviors such as network access information, file uploading to a server or package installation on the device, among others. [PERSON] et al. proposed a malware detection approach named component traversal that automatically executes the code routine of the individual application effectively [27]. [PERSON] et al. applied fuzzy C-means clustering to the generated network traffic for Android malware detection [28], and lastly, DL-Droid is a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation [29].  
The proposed approach has the following significant innovations when compared to the existing static and dynamic analysis-based approaches, and they are twofold. The former is, we extract far more behavioral characteristics, which include both static and dynamic behaviors, while as the latter, we build a multidimensional behavioral portrait for Android malware to depict them accurately and systematically.
# Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest
## 6 Conclusions  
In this study, the RF and ANN algorithms were used to examine the capability in predicting the bearing capacity of piles. An unprecedented database containing 2314 instances from on-field measurements of pile load test was used to develop and evaluate the two proposed ML models. The results showed that RF outperformed ANN with a satisfactory accuracy (R\({}^{2}\) = 0.866, RMSE = 98.161 kN, MAE = 2.924 kN using RF compared with R\({}^{2}\) = 0.809, RMSE = 116.366 kN, MAE = 3.190 kN using ANN). Moreover, the results of this study indicated that the RF algorithm was more accurate in predicting the pile bearing capacity than those obtained from traditional approaches, namely the formulas or empirical equations from the work of [PERSON] (1976), [PERSON] (1982), [PERSON] (1995), [PERSON] (2008), [PERSON] (2004), and a classical MVR. In addition, a sensitivity analysis using RF indicated that the average SPT value along pile shaft N\({}_{\text{s}}\), pile tip elevation, and L\({}_{2}\), L\({}_{1}\), E\({}_{\text{t}}\) had the most significant effect on the predicted bearing capacity of piles.  
Overall, the RF algorithm, like many other machine learning algorithms, has an additional advantage over conventional methods, which is, once the model is constructed, it can be used as an accurate, quick numerical tool for estimating the bearing capacity of piles. Thus, the performance of such a numerical tool is crucial in foundation engineering. Therefore, improving the prediction accuracy is one perspective of the present work, for instance, using hybrid ML algorithms or deep neural network to predict the bearing capacity of piles.  
Figure 10: Feature importance of 10 variables used in this study.  
**Author Contributions:** Conceptualization, H.-B.L. and T.A.P.; Methodology, T.A.P., V.Q.T. and H.-L.T.V.; Software, T.A.P. and H.-B.L.; Validation, T.A.P.; Formal Analysis, T.A.P. and H.-B.L.; Investigation, T.A.P. and H.-B.L.; Resources, L.V.G., H.-A.T.D.; Data Curation, T.A.P. and L.V.G.; Writing-Original Draft Preparation, T.A.P., H.-B.L., V.Q.T. and H.-L.T.V.; Writing-Review & Editing, H.-B.L. and T.A.P.; Visualization, T.A.P. and H.-B.L.; Supervision, T.A.P., H.-B.L. and V.Q.T.; Project Administration, T.A.P. and H.-B.L.; Funding Acquisition, T.A.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# EchoIA: A Cloud-Based Implicit Authentication Leveraging User Feedback
## 5 Evaluation
### Authentication Accuracy  
EchoIA has the highest authentication accuracy in most of the tests, as shown in Figure 3. Multi-Sensor-IA also has a high authentication accuracy compared to other IA schemes. In the experiment, most of the schemes reach to more than 90% accuracy after using 80% of the data except SilentSense-IA. Note that there are some fluctuations in different IA schemes due to behavior changes, where both Multi-Sensor-IA and Gait-IA have an accuracy drop at the point of 30%. We analyzed the data of Multi-Sensor IA at that point, which shows that most users traveled to different places that did not appear in the training phase. The machine learning model cannot separate users based on the given training data. Since, for some users, the traveling and staying time is non-negligible, it becomes harder for Multi-Sensor IA to make a decision based on the previous training data. After the behavioral data in this new location is collected and stored in training data, the accuracy of Multi-Sensor IA eventually increases and becomes similar to other IA schemes. However, this behavior change does not affect the proposed EchoIA since the system automatically updates users' personal features during usage. In such a case, the accuracy curve for EchoIA is much smoother than the other schemes. As literature usually does in template updating related research [22, 29, 30], we measured the EERs and ROCs for EchoIA, Multi-Sensor-IA, Gait-IA, SilentSenseIA, and Shi-IA, which provide more reliable results. The EERs for EchoIA, Shi-IA, Muti-Sensor-IA, Gait-IA, and SilentSense-IA are 0.1428, 0.2200, 0.1635, 0.2700, and 0.2720, respectively; and corresponding AUCs are 0.8991, 0.8588, 0.9244, 0.8103, and 0.8013, respectively.  
In EchoIA, we calculated an average authentication accuracy for 17 users by utilizing all the data spanned two years. The result is shown in Figure 4, where the average accuracy across all users is 93.23%.  
Figure 4: The accuracy for each user.  
Figure 3: Accuracies for different IA schemes.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 3 System Model
### Energy Model  
The energy consumption of WSNs is generally composed of many parts, such as monitoring, data storing, and data transmitting. However, the energy used for data transmission takes up a large proportion of the total energy consumption. Therefore, we only consider the energy consumption used in transmission in this paper. We use the same energy model as literature [38; 39] adopted. The following formulas are used to calculate the energy consumption for sending \(k\)-bit data.  
\[E_{Tx}(k,dis(s_{m},s_{n}))=E_{elce}\cdot k+\varepsilon_{amp}\cdot k \tag{1}\]  
where \(dis(s_{m},s_{n})\) represents the distance between node \(m\) and node \(n\) and it can be calculated using Formula (2).  
\[dis(s_{m},s_{n})=\sqrt{\left(x_{m}-x_{n}\right)^{2}+\left(y_{m}-y_{n}\right)^{2}} \tag{2}\]  
\(E_{elce}\) represents the energy to run the transmission circuit. \(\varepsilon_{amp}\) denotes the energy consumption for the amplifier to strengthen the signal for further transmission and it can be further divided into Formula (3).  
\[\varepsilon_{amp}=\left\{\begin{array}{l}\varepsilon_{fs}dis^{2}if\;dis\leq d _{0}\\ \varepsilon_{mp}\cdot dis^{4}if\;dis>d_{0}\end{array}\right. \tag{3}\]  
where \(\varepsilon_{fs}\) represents the power for close range communication and \(\varepsilon_{mp}\) represents the power for long distance communication. Those two schemas are also called free space model and multi-path fading model, respectively. The threshold value \(d_{0}\) is used to judge which model should be adopted and it can be calculated as:  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{4}\]  
The energy dissipates for receiving \(k\)-bit data can be figured out by Formula (5).  
\[E_{Rx}=E_{elce}\cdot k \tag{5}\]  
Figure 4: Network model.
## 2 Materials and Methods
### OneBitPitch Algorithm
The building blocks of the OBP algorithm are represented in Figure 4 and described as follows:  
* Input: The input signal is assumed to be limited in band, with no DC components, which can be obtained before ever entering the digital realm by analog filtering; this also guarantees the zero average. * Sign: From the original signal, only the sign is extracted; in any common digital environment, this is obtained by just retaining the MSB. For different kinds of representation, a comparator would be required. * Buffer: It is needed to store a fixed amount of the previous samples of the signal and its length is related to the minimum frequency that needs to be detected (_Fmin_). With _Fs_ being the sampling rate, a buffer length of _Fs/Fmin_ samples is required. * Modified autocorrelation: The difference autocorrelation previously defined is applied to the sign signal within the buffer. Instead of performing a circular correlation, like other algorithms (namely, YIN), a linear correlation is applied on a total frame length of one buffer and a half to avoid phase jumps. For periodic signals, autocorrelation tends to rise and fall from/to the minimum symmetrically: for this reason, the center of the minimum is to be considered to evaluate the period. * Threshold: The search for the minimum of the difference autocorrelation function is simply performed by thresholding the signal with a fixed threshold. This can be visualized with a \"Thresholding\" logical signal that is 1 only when the autocorrelation is under the threshold. Although more sophisticated methods can be implemented, such a naive implementation is computationally efficient and has been empirically observed to be a good trade-off between accuracy and speed. * Output: As previously stated, the very minimum point is the center of the sections below the thresholds, which is equal to a logical 1 in the \"Thresholding\" signal. The frequency result is simply obtained by counting the number of samples between two minima. Figure 5 displays the progression of the algorithm by showing each internal signal. Figure 4: Block diagram of the OneBitPitch (OBP) pitch detection algorithm.
## 5 Conclusions  
According to the results obtained, a-tomatine and chlorhexidine compounds were found to achieve potentially strong inhibition of the MMP-2 enzyme. For the MMP-8 enzyme, it was determined that a-tomatine achieves a potential strong inhibition and the chlorhexidine compound achieves a potential inhibition or partial inhibition. For the MMP-9 enzyme, it was determined that the chlorhexidine compound caused no inhibition and had no potential to have any effect on enzyme functioning. The a-tomatine compound was found to achieve either potentially no inhibition or potentially partial inhibition of the MMP-9 protein structure. According to molecular dynamic analysis regarding the affinity for MMP enzymes, it is anticipated that the system is in equilibrium, and the existing affinities will be preserved over time. The finding that a-tomatine exhibited higher bond strength values in all subgroups compared to chlorhexidine and the control group (regardless of dentine type) in the in vitro aspect of this study corroborates the findings of the in silico method. In line with the findings of this study, it can be said that the tomatine material that was found to increase bond strength showed better results than chlorhexidine.  
Figure 11: Alpha-carbon RMSF plots of the halo form and apo form of the MMP-9 protein.  
**Author Contributions:** M.K.U.: conceptualization, methodology, data curation, writing--original draft, and reviewing and editing. A.B.O.: formal analysis, methodology, data curation, writing--original draft, and reviewing and editing. S.B.: data curation, methodology, and resources. E.Y.: methodology, validation, visualization, supervision, project administration, and reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The present work was supported by the Research Fund of Istanbul University, Project No. 32045.  
**Institutional Review Board Statement:** This article does not contain any studies with human participants or animals performed by any of the authors. All procedures performed involving human tissues were in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki and its later amendments or comparable ethical standards. The study was approved by the Ethics Committee of the Istanbul University Faculty of Dentistry Clinical Research (File no: 2017/66).  
**Informed Consent Statement:** Written informed consent has been obtained from patients to publish this paper.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors do not have any financial interest in the companies whose materials are included in this article.
# Improving Steerability Detection via an Aggregate Class Distribution Neural Network
## 3 Detecting the Steerability by AGGNN
### Comparing the Classification Models Trained with Different Features  
In Figure 3a-d, we show the classification models with the F1 feature with a quantum state with whole information and classification models with features F2-F4 with partial information. Naturally, whole information F1 is better than partial information F2-F4. In partial information, F3 and F4 have a better performance than F2. In this part, we compared the classification models trained with features F\(i\) (\(i\) = 1,2,3,4) and different measurements \(m\).  
It is easy to speculate that the well-trained model will become more accurate and better at learning steerable samples as \(m\) rises. To prove this prediction, we randomly took 1000 steerable and 1000 unsteerable samples from the datasets for \(m=8\) to test the classification models of different \(m\). Figure 6 shows the classification accuracy for 1000 random steerable and 1000 random unsteerable samples from the datasets for \(m=8\) and the blue-plus line, orange-circle line, green-cross line, and red-star line depict features F1-F4, respectively. Figure 7 shows the classification accuracy for 1000 random unsteerable samples  
Figures 6-8 show that the classification accuracy of 1000 random steerable and 1000 random unsteerable samples increases as \(m\) rises. The classification accuracy of 1000 random unsteerable samples does not change much; the classification accuracy of 1000 random steerable samples is the main factor affecting the total classification accuracy. Although some inflection points exist, this may be due to inaccurate data or an uneven data distribution. Furthermore, F3 has the best performance.  
Figure 9 illustrates the classification accuracy for the Werner state validation sets \(\theta=\{\frac{\pi}{4},\frac{\pi}{6},\frac{\pi}{8},\frac{\pi}{72}\}\) for each AGGNN classification models with the features F1-F4, respectively. The first subgraph for F1 features shows that the classification accuracy for the Werner state validation sets are all above 99% except for \(m=6\). The second subgraph shows the steerability classification model of F2 features. The classification accuracy increases as the parameter \(\theta\) decreases and is over 76%. The third and fourth subgraphs show the classification accuracy for the Werner state validation sets of features F3 and F4. F3 and F4 have similar and correct trends; the accuracy increases with a increase in \(m\), and the accuracy decreases with a decrease in \(\theta\). Furthermore, the steerability classification models of features F3 and F4 perform with a relatively higher classification accuracy on the Werner state validation sets.  
Figure 8: Classification accuracy of 1000 random steerable samples for \(m=8\) with different classification models.  
Figure 9: Classification accuracy for the Werner state validation sets for each AGGNN classification model for the features F1–F4, respectively.
# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
## 4 WASN Design
### Logical Topology
For instance, in Figure 7, S4 does not have direct connection to the core, but it can reach the core using multiple paths, the fastest ones being the following four: 1. S4-S2-S1-Core 2. S4-S5-S3-Core 3. S4-S1-S3-Core 4. S4-S1-Core  
The fastest path is the fourth option, as it only has two jumps to reach the core. The third one will probably not be used, as S1 will receive the data and will send them directly to the core. Although, in case S1 does not have connectivity to the core, it can send the data to S3. Once the core receives the data, it will forward it to the router and, lastly, the router will forward it to the Internet. The percentage of frames received by the platform during the experimental evaluation in the real-world deployment is shown in Table 4. It can be observed that the mesh strategy achieves better results (Table 4) than the direct connection, with a percentage of received frames higher than 98% for S5 and higher than 99% for the other nodes. Nevertheless, the connection with S5 was lost after 9 days of operation. To solve that issue, a weekly reboot of S5 was programmed using _crontab_. This way, the loss of connectivity of S5 was avoided and a percentage of 99% was achieved as it can be observed in the third row of Table 4.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 6 System Implementation
### Security Trusted Operation Module  
In the security trusted operation function module, the operation processes such as data deletion, outsourcing, copy and modify are visually presented and analyzed through components such as thermal calendar chart, radar chart, line chart and box chart, as shown in Figure 11.  
After selecting the operation that needs supervision, the security trusted operation function module immediately starts the multi-dimensional operating status calculation mechanism based on risk analysis, abnormal behavior detection and event extraction, which can quickly judge the credibility of data operation and identify potential abnormal operation or network attack risk.  
Taking copy operation management as an example, by analyzing the log file of each operation, a quantitative evaluation model is established, and the risk degree of each operation is visually presented in the form of a thermal calendar diagram. In the calendar chart, different color gradients correspond to different risk levels. The red area represents high risk, and the green area represents low risk. Managers can quickly identify high-risk periods. When managers select a certain date on the thermal calendar chart, the interface triggers the intelligent linkage mechanism and automatically jumps to the radar chart interface. This radar chart is centered on the selected date and covers the 10-day data cycle before and after, building a three-dimensional operation management index evaluation system. In this way, managers can fully perceive the real time status and historical trend of the operation event. In the line chart interface, the system takes time as the horizontal axis and dynamically displays the changes in the number of times of this operation in the form of curves. Through an analysis of the curve trend, managers can clearly observe the fluctuations of indicators at different time nodes. The box chart clearly shows the concentration trend and dispersion degree of the number of operations through statistics such as median, quartile, minimum and maximum. The length of the box reflects the distribution interval of the number of operations, the dotted line defines the normal fluctuation range of the data, and the abnormal values are separately marked in the form of discrete points. In this way, potential abnormal operations or network attack risks can be quickly identified.  
Figure 11: Security trusted operation module interface in secure data asset sharing and circulation systems.