esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Problem Statement and Contribution  
Nevertheless, realizing these widely recognized spectrum sharing techniques as shown in Figure 1 for an MNO and evaluating their relative performances under a common scenario is not obvious. Since the ABS-based eICIC technique has already been well adopted by the standardization bodies due to its effectiveness in addressing the co-channel interference and hence improving the spectral and energy efficiencies of heterogeneous networks by sharing the spectrum spatially with small cells, we consider realizing these spectrum sharing techniques using the eICIC technique. More specifically, unlike addressing individually (e.g., [21]), in this paper, we focus on realizing all these major spectrum licensing policies mentioned in Figure 1 to share both the licensed and unlicensed spectrum with in-building small cells of an MNO using the ABS-based eICIC technique in order to address the ever increasing demand of high user data rate, network capacity, and spectral and energy efficiencies of the MNO. To realize these techniques, we consider multiband, specifically, dual-band, enabled in-building small cell base stations (SBSs) as shown in Figure 2 where one of the two transceivers operates at the default licensed spectrum of the concerned MNO, namely MNO 1. However, the second transceiver of each SBS is explored to operate at the spectrum of another system, which can be either the same homogeneous mobile system or a different heterogeneous system (e.g., space-satellite system) based on the spectrum sharing technique. For example, since LSA is applied to a heterogeneous system where the spectrum of other systems than that of a mobile system is shared with the mobile system (i.e., MNO 1), as shown in Figure 2, LSA is shown as a technique requiring both the mobile spectrum and the satellite spectrum operating respectively at the transceiver 1 and transceiver 2. Note that in Figure 2, even though a number of spectrum bands are shown for the transceiver 2, only one of the spectrum bands can operate in any time depending on the spectrum sharing technique. Further, if the second transceiver is not used, then an SBS can be considered as a single-band enabled SBS. In such cases, because the transceiver 1 operates at the same spectrum as that of the MNO 1, this kind of sharing is referred to as intra-system spectrum sharing. However, in all other cases so long as the transceiver 2 is explored, such a kind of spectrum sharing is termed as inter-system spectrum sharing. Hence, in short, the following three major attributes must be in place to realize the spectrum sharing techniques shown in Figure 1:  
* Dual-band enabled SBSs,
* A set of small cell deployed 3D buildings, and
* TD ABS based eICIC technique.
# A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe
## 6 Conclusions  
This study involved basic experimental assessments of the ability of modified smart bolts to detect water pipe valve leakage and the loosening of bolts, which are among the main causes of leakage in municipal water supply networks.  
The developed smart bolt had a strain gauge inserted in a customized hole and an acceleration sensor attached to the top of the bolt. Through FEA and tensile strength tests, the strength of the bolt met the standard specification for water supply pipe connections in Korea (KCS 57 30 20: 2017) and also met the ISO 898-1 requirement to perform within the allowable tensile strength range of such bolts. Therefore, the smart bolt can be safely applied to the water supply valve fastening mechanism, and we determined that it is possible to monitor the loosening state of the bolt by setting the threshold value of the strain gauge according to the torque.  
In addition, we analyzed the frequency response of the simulated leakage pipe system depending on the leakage of the valve and the main pipe in 10 scenarios. An acceleration sensor installed in the smart bolt detected a leak signal in the 60-Hz band when the valve leaked. When the main pipe leaked, a higher-order mode frequency response occurred than when the valve leaked, probably due to the characteristics of the cast iron pipe, the rubber packing (which plays a direct damping role), and the simulated leakage pipe system being in an unconstrained state.  
This paper presents a basic experiment to detect water leakage and monitor bolt loosening by applying the developed smart bolt, and the applicability of the smart bolt in water supply valve was confirmed. Development for the system construction such as the power modules, wireless communication modules, monitoring programs, and leak detection algorithms is in progress. In the future, comparative experiments with various sensors and field application experiments of a real water distribution system would be required.  
**Author Contributions:** Conceptualization, Y.K. and H.J.; Methodology, Y.K.; Validation, Y.K. and J.C.; Visualization, Y.K.; FFT analysis, Y.K.; Finite element analysis J.C.; Supervision H.J. and J.R.; Writing--original draft, Y.K.; Writing--review and editing, H.J. and J.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was conducted with the support of the Korea Agency for Infrastructure Technology Advancement (KAIA) as \"A development of replacing system of valve without cut off water using smart bolt based on BLE\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is available within the manuscript.  
**Acknowledgments:** This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure, and Transport (Grant 18-CTAP-C143381-03).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions
Furthermore, the proposed SBAS-InSAR and GNSS collaborative monitoring technology provides a safety guarantee for the high-quality development of China's South-North Water-Diversion Follow-up Project as well as an effective technical means for accurately carrying out the deformation monitoring of large-scale banded water-conservancy projects. Conceptualization, Q.H., Y.K., W.L. ([PERSON]) and X.L. ; methodology, Q.H., P.H. and X.L. ; software, J.Y., J.L., S.L., W.L. ([PERSON]) and Y.K. ; validation, Q.H., P.W., K.M. and J.Y. ; formal analysis, Q.H. and J.L. ; investigation, Q.H. ; resources, Q.H., K.M. and J.Y. ; data curation, H.H. and Y.L. ; writing--original draft preparation, Q.H., J.Y., S.L. and Y.K. ; writing--review and editing, K.M. and P.H. ; supervision, Q.H. and W.L. ([PERSON]); project administration, Q.H., X.L. and W.L. ([PERSON]); funding acquisition, Q.H., X.L. and W.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (nos. 42277478, 52274169, and 41301598) and the Joint Funds of the National Natural Science Foundation of China (no. U21A20109). The authors would like to thank the editor and reviewers for their contributions to this paper. Not applicable. The authors declare no conflict of interest.
## 5 Simulations
### Simulation Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Feature \#** & **Fitness Value** & **CPU Time** \\ \hline \multirow{8}{*}{Breastcancer} & BBA & **0.9786** & 6.0000 & **0.0272** & 69.5141 \\  & BDA & 0.9767 & **5.8000** & 0.0289 & 68.7234 \\  & BGWO & 0.9767 & 6.5000 & 0.0296 & 75.3848 \\  & BPSO & **0.9786** & 6.0000 & **0.0272** & 69.2956 \\  & EBBA & 0.9786 & 6.0000 & **0.0272** & 69.926 \\  & BBA & 0.9613 & 6.6000 & 0.0405 & 73.3552 \\  & BDA & 0.9589 & **5.1667** & 0.0424 & 67.3963 \\ BreastEW & BGWO & 0.9532 & 14.6667 & 0.0512 & 79.7631 \\  & BPSO & 0.9612 & 8.5000 & 0.0413 & 75.4869 \\  & EBBA & **0.9614** & 5.4333 & **0.0400** & **63.4576** \\  & BBA & **0.9793** & 6.9000 & 0.0248 & 58.3419 \\  & BDA & 0.9743 & **5.1667** & 0.0287 & 55.9260 \\ Congress & BGWO & 0.9750 & 8.8000 & 0.0303 & 61.5544 \\  & BPSO & 0.9785 & 6.7333 & 0.0255 & 59.3514 \\  & EBBA & **0.9793** & 6.4000 & **0.0245** & **49.2745** \\  & BBA & **1.0000** & **6.0000** & **0.0046** & 91.2861 \\  & BDA & 0.9197 & 6.6667 & 0.0846 & 100.3068 \\ Exactly & BGWO & 0.9040 & 8.0333 & 0.1012 & 116.4705 \\  & BPSO & 0.9999 & 6.0333 & 0.0048 & 103.6469 \\  & EBBA & **1.0000** & **6.0000** & **0.0046** & **88.3184** \\  & BBA & 0.7854 & 2.2667 & 0.2142 & 86.8956 \\  & BDA & 0.7847 & 1.7667 & 0.2145 & 90.0558 \\ Exactly2 & BGWO & 0.7693 & 7.5333 & 0.2342 & 117.3080 \\  & BPSO & **0.7890** & **1.0667** & **0.2097** & 101.6048 \\  & EBBA & 0.7868 & 1.9667 & 0.2126 & **81.4770** \\  & BBA & 0.8511 & 5.0333 & 0.1513 & 45.4301 \\  & BDA & 0.8391 & **4.9333** & 0.1631 & 44.0686 \\ HeartEW & BGWO & 0.8357 & 6.6667 & 0.1678 & 45.2137 \\  & BPSO & **0.8527** & 5.0000 & 0.1497 & 44.8358 \\  & EBBA & 0.8520 & 5.0667 & **0.1504** & **39.0150** \\  & BBA & 0.9100 & 27.3000 & 0.0937 & 49.9454 \\  & BDA & 0.9010 & **20.5000** & 0.1015 & 48.1458 \\ SonarEW & BGWO & 0.9899 & 40.6667 & 0.1069 & 55.5145 \\  & BPSO & 0.9046 & 27.7333 & 0.0991 & 51.0521 \\  & EBBA & **0.9117** & 27.0667 & **0.0919** & **42.8199** \\  & BBA & 0.7379 & 10.5000 & 0.2643 & 46.546 \\  & BDA & 0.7185 & **8.9667** & 0.2827 & 44.8928 \\ SpectEW & BGWO & 0.7219 & 14.3667 & 0.2819 & 47.5410 \\  & BPSO & 0.7335 & 10.5000 & 0.2687 & 46.3761 \\  & EBBA & **0.7407** & 10.7000 & **0.2615** & **38.4262** \\  & BBA & 0.8493 & 8.8667 & 0.1590 & 97.9119 \\  & BDA & 0.8099 & 7.1667 & 0.1962 & 97.8065 \\ tic-tac-toe & BGWO & 0.8465 & 8.8000 & 0.1618 & 91.4816 \\  & BPSO & **0.8521** & **9.0000** & **0.1564** & **88.2232** \\  & EBBA & **0.8521** & **9.0000** & **0.1564** & **84.4753** \\  & BBA & **0.9519** & 5.2000 & **0.0509** & 46.4947 \\  & BDA & 0.9457 & 5.7667 & 0.0574 & 45.8363 \\ Vote & BGWO & 0.9440 & 8.5333 & 0.0608 & 47.9505 \\  & BPSO & 0.9493 & **4.9000** & 0.0532 & 46.5221 \\  & EBBA & 0.9513 & 5.4000 & 0.0516 & **38.9119** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimization results achieved by various algorithms. (The best values are highlighted in bold). EBBA, EBBA-IF1, EBBA-IF2, and EBBA-IF3 outperform conventional BBA, which means that the proposed improved factors are non-trivial and effective.
## Appendix A Decoding PPP-B2b Corrections  
Each PPP-B2b message has a length of 486 bits, with the highest 6 bits indicating the message type, the lowest 24 bits for the cycle redundancy check (CRC), and the remaining 456 bits being the message data. After the 64-ary non-binary low-density parity check (LDPC), encoding and concatenating with 6 symbols of the reserved flags, 6 symbols of the PRN, and 16 symbols of the preamble, the data frame consisted of 1000 symbols. Becausethe data were broadcast at a rate of 1000 symbols per second, the broadcast time of each data frame was 1 s.  
Although 7 message types had been designed, currently only message types 1-4 can be decoded by users; these types include satellite masks (Type 1), satellite orbit corrections and user range accuracy indices (Type 2), DCBs (Type 3), and satellite clock corrections (Type 4). Message type 63 was a null message used to fill in the blank period when there was no information available. As shown in Figure 11[82], the broadcast time of a complete PPP-B2b message was 48 s. The broadcast times of a complete set of clock correction parameters, orbit correction parameters, and DCBs were 6 s, 7 s, and 4 s, respectively. During a 48-s broadcast period, the type 4 messages were updated eight times, and messages 2 and 3 were updated only once, which means that the update intervals for clock corrections, orbit corrections, and DCBs were 6 s, 48 s, and 48 s, respectively.  
As shown in Table 12, to ensure the interrelationship among the information content of different message types, the information needed to be matched based on a group of issue of data (IOD), including: IOD of SSR (IOD SSR) for defining the issue number of the SSR; IOD of PRN (IODP) for matching the satellite clock correction and satellite mask; IOD of navigation (IODN); and IOD of correction (IODC) for matching the broadcast navigation message and corrections of the satellite clock and orbit.
## 5 Experiment Verification
### Experiment Equipment
#### 5.1.3 The Power Supply System:  
1. This study utilizes three power supplies to provide the power required by standard heat source generators; supply to the cooling fan and thermoelectric cooling chip is as shown in Figure 17, and heat generator is as shown in Figure 18.
2. Power supply conditions, cooling fan 23 VDC/0.9 A, thermoelectric cooling chip 15.3 VDC/4.51 A, and heat generator 20 VDC/2.4 A.
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 2 Related Work
### Existing APT Detection Methods  
[PERSON] [24] proposed a systematic framework called IDNS, which identifies malicious C&C domains by analyzing DNS traffic and establishes a reputation evaluation system for judging whether the traffic is malicious or not by combining anomalous traffic detection techniques. The features used to evaluate malicious traffic mainly include the characteristics of the DNS domain name itself, lacking features about the time and the relationship between request and response messages.  
[PERSON] [25] proposed a method to discover suspicious APT based on unknown domain names. The unknown suspected domain names are derived by analyzing DNS logs and traffic, using a similarity comparison of indicators of compromise (IOC) that have appeared in real APT activities and other steps. The difficulty of this method is collecting IOCs of real APT activities.  
[PERSON] [26], [PERSON] [27], [PERSON] [28], and [PERSON] [29], cover most machine learning algorithms, including supervised and unsupervised learning, integrated learning, and deep learning. The accuracy of the classifiers of these improved machine learning algorithms is generally high, but they still need to be validated by datasets constructed in realistic network environments, and the massive amount of traffic data in the experimental part also affects the efficiency of model training. [PERSON] [30] established an APDD framework to collect a large amount of DNS request data and extracted all the features consistent with APT attacks. It uses change vector analysis to build a credit scoring system for DNS domains and outputs the ranking of suspicious domains according to the credit score. The method relies on massive DNS data to determine the credit score of APT attacks; however, the credit score cannot directly determine whether an APT attack has occurred.  
[PERSON] [31] compared normal traffic and anomalous traffic by a time-transformed feature approach to distinguish APT attacks, by capturing the signal of malicious traffic, and then inferring the presence of APT attacks, and then using machine learning methods to detect APT attacks in big data.  
[PERSON] [32] proposed an SVM-based APT attack detection system using the NSL-KDD database to implement method validation, in which principal component analysis (PCA) was used for feature sampling and then compared using multiple machine learning classifiers for classification.  
The comparison of the existing APT detection methods is shown in Table 1. Most of the existing detection approaches mentioned above rely on massive amounts of DNS data and lack features about the relationship between DNS requests and responses, which can lead to low detection efficiency and low accuracy for machine learning. The sketch-based APT detection scheme proposed in this paper can significantly reduce the training time of various machine learning models by counting and compressing DNS traffic while guaranteeing the accuracy of the model, which allows defenders to detect APT attacks faster and improve the efficiency of the scheme.
## 3 System Design and the Proposed SEAC-Based Resource Allocation Methodology
### Network State Representation
#### 3.1.1 Distance to Neighboring Vehicles (\(\Delta d\))  
This parameter represents the distance between the ego vehicle and its neighboring vehicles. This is crucial for the reuse of radio resources, as distant vehicles can use the same resources without causing interference  
Figure 3: Framework for resource allocation strategies in 5G-NR-V2X Mode 2.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Urban Area**} & \multirow{2}{*}{\begin{tabular}{c} **Time of the** \\ **Day** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Minimum** \\ **Measured** \\ **Temperature** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Maximum** \\ **Measured** \\ **Temperature** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Predicted** \\ **Temperature** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Maximum** \\ **Temperature** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Difference in** \\ **Maximum** \\ **Temperature** \\ \end{tabular} } \\  & & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) \\ \hline Burlington & Afternoon & 31.41 & 35.12 & 31.48 & 35.03 & 0.07 & \(-\)0.09 \\ \hline Burlington & Evening & 28.47 & 30.99 & 28.51 & 30.97 & 0.04 & \(-\)0.02 \\ \hline Burlington & Night & 25.92 & 27.82 & 25.96 & 27.8 & 0.04 & \(-\)0.02 \\ \hline Cedar Rapids & Afternoon & 32.5 & 35.97 & 32.62 & 35.62 & 0.12 & \(-\)0.35 \\ \hline Cedar Rapids & Evening & 25.95 & 29.47 & 26.04 & 29.38 & 0.09 & \(-\)0.09 \\ \hline Cedar Rapids & Night & 22.36 & 25.57 & 22.54 & 25.47 & 0.18 & \(-\)0.1 \\ \hline Council Bluffs & Afternoon & 27.84 & 32.64 & 27.88 & 32.18 & 0.04 & \(-\)0.46 \\ \hline Council Bluffs & Evening & 23.4 & 26.6 & 23.42 & 26.38 & 0.02 & \(-\)0.22 \\ \hline Council Bluffs & Night & 18.66 & 21.59 & 18.72 & 21.5 & 0.06 & \(-\)0.09 \\ \hline Des Moines & Afternoon & 35.62 & 39.77 & 35.76 & 39.5 & 0.14 & \(-\)0.27 \\ \hline Des Moines & Evening & 29.75 & 32.91 & 29.8 & 32.78 & 0.05 & \(-\)0.13 \\ \hline Des Moines & Night & 26.95 & 29.3 & 27.09 & 29.26 & 0.14 & \(-\)0.04 \\ \hline Fort Dodge & Afternoon & 30.34 & 34.6 & 30.49 & 34.38 & 0.15 & \(-\)0.22 \\ \hline Fort Dodge & Evening & 22.09 & 27.6 & 22.33 & 27.49 & 0.24 & \(-\)0.11 \\ \hline Marshalltown & Afternoon & 32.79 & 36.9 & 32.89 & 36.58 & 0.1 & \(-\)0.32 \\ \hline Marshalltown & Night & 21.33 & 22.42 & 21.36 & 22.37 & 0.03 & \(-\)0.05 \\ \hline Sioux City & Afternoon & 32.75 & 35.85 & 32.84 & 35.64 & 0.09 & \(-\)0.21 \\ \hline Sioux City & Evening & 27.15 & 30.4 & 27.29 & 30.22 & 0.14 & \(-\)0.18 \\ \hline Sioux City & Night & 22.04 & 26.05 & 22.06 & 25.95 & 0.02 & \(-\)0.1 \\ \hline WaterlocCF & Afternoon & 32.35 & 36.57 & 32.49 & 36.49 & 0.14 & \(-\)0.08 \\ \hline WaterlocCF & Evening & 23.15 & 28.81 & 23.65 & 28.77 & 0.5 & \(-\)0.04 \\ \hline WaterlocCF & Night & 22.14 & 23.19 & 22.16 & 23.17 & 0.02 & \(-\)0.02 \\ \hline Waverly & Afternoon & 34.73 & 37.66 & 34.8 & 37.37 & 0.07 & \(-\)0.29 \\ \hline Waverly & Evening & 28.27 & 30.52 & 28.49 & 30.48 & 0.22 & \(-\)0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Measured vs. predicted temperature by urban area/time of the day. Figure 9: Modeled raster surface for Waterloo/Cedar Falls night.
## 5 Case Studies
### Analysis of Group Decision Making on Electricity Sales Packages
#### 5.2.3 Sample Customer Clustering  
In order to further verify the rationality and feasibility of the decision-making method proposed in this paper, the decision-making method in this paper is compared with the following two decision-making methods. And the decision-making results for each decision-making method are calculated, as shown in Figure 2.  
Method 1: Without clustering customers. The decision-making arrangement of electricity sales packages will be based on two aspects, that is, the satisfaction of sample set customers and the similarity between target customers and sample set customers.  
Method 2: The evaluation attribute of the electricity sales package only considers the cost and ignores other attributes. Based on TSDC and the minimum adjustment distance consensus, the electricity sales package group decision-making method is reached.  
We assess the accuracy of the different methods by calculating the average RMSE of all customers of the same type, \(\phi\). We find that \(\phi=\frac{\sum\limits_{i=1}^{N}\varphi_{i}}{N}\), where \(N\) represents the number of customers of a particular type.  
As can be seen from Figure 5, the \(\phi\) of the proposed method is less than 1, while the \(\phi\)s of method 1 and method 2 are larger than that of the proposed method. \(\phi\) represents the difference between the results obtained by this method and the simulated actual selection results. And the larger \(\phi\) is, the larger the deviation is and the worse the method is. On the contrary, the smaller \(\phi\) is, the smaller the deviation is and the better the method is. Therefore, compared with method 1 and method 2, the method proposed in this paper is more effective.  
The result of method 1 without customer clustering is the largest, and the \(\phi\) is 2.429. This shows that without performing customer clustering, the exceptional characteristics of individual customers within the sample set can have a significant influence on the target customers. This can result in a larger overall decision-making error, as the decision making does not account for the diversity of the target customer base.  
The result of method 2 that only considers the cost is the second largest, and the maximum \(\phi\) is 1.975. This shows that by solely focusing on cost and neglecting other important factors, the decision making for electricity sales packages will fail to capture the nuanced and varied requirements of different customer types. This situation can lead to inferior decision results.