esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Results
### Analysis of Results
#### 3.3.1 Comparison with IEMs
However, it is important to note that the proposed algorithm does not require any preset parameters, making it more convenient and intelligent compared to the IEMs. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & **Classifiers** & **Metrics** & **InfoGain** & **GainRatio** & **SU** & **ChiSquare** & **Fisher** & **MCMI** \\ \hline \multirow{8}{*}{BPEx} & NB & OA & 0.851 & 0.872 & 0.678 & 0.859 & 0.850 & **0.887** \\  & SN & FN & 15 & 25 & 40 & 20 & 30 & 8 \\  & SVM & OA &  
Figure 3: Feature quality of the IEMs on BPEx: (**a**) Rel. (**b**) NRF. Figure 2: Classification accuracy of the IEMs on BPEx: (**a**) 10-fold cross-validation accuracy by using NB. (**b**) 10-fold cross-validation accuracy by using SVM. (**c**) 10-fold cross-validation accuracy by using C4.5. (**d**) 10-fold cross-validation accuracy by using RF.
# Carbon Nanotube-Based Composite Filaments for 3D Printing of Structural and Conductive Elements
## 3 Results
### Electrical Properties
Nanocomposites containing CNT are characterized by a negative temperature coefficient of resistivity (NTC effect), which means that the electrical resistivity of composite decreases during heating. The results obtained coincide with numerous literature reports related to nanocomposites [48; 49; 50]. The current-voltage characteristics for increasing and decreasing voltage were also prepared. It is noted that increasing and decreasing voltage measured current curves do not follow the same path, and electrical hysteresis is visible. As shown in Figure 9, a small increase in measured current is observed during the decreasing voltage cycle. In the increasing voltage cycle, electrical conductivity is lower. This phenomenon is caused by the occurrence of hysteresis in the I-V characteristic of electron tunneling [51; 52; 53]. The electrical potential required to start the electron tunneling process is higher than the minimum potential needed to sustain the process. This results in improved conductivity of the composite when a higher voltage is applied first, and the tunneling mechanism is initiated in the higher number of connections between the CNTs. When a low voltage is applied initially, the tunneling process cannot start in such a large number of connections. It was also observed that the difference in conductivity is smaller for composites with higher CNT content. This is related to the higher number of CNT connections and explains that the tunneling distance is drastically reduced while the content of nanoparticles increases. Figure 7: Schematic of percolation mechanisms. (**a**) CNT amount below the percolation threshold (**b**) CNT amount above the electrical but below the mechanical percolation threshold. Circles show examples of places where electron tunneling may occur due to a thin layer of insulating polymer between nanotubes. (**c**) CNT amount above the mechanical percolation threshold. Circles show examples of places where physical contacts of nanotubes are visible. Nanotube bonds result in a long carbon fiber, which improves the mechanical properties of the composite. Figure 8: Electrical resistance values of conductive composite samples under different values of voltage applied for I–V measurements. Figure 9: _Cont._
## 3 Results
### RQ4. How Has the Relationship Between Owners and Their Pets Changed with the Implementation of IoT?
Additionally, some of the solutions provided secure access management, such as automated doors, reducing the risks of pets escaping or facing hazards (e.g., S15, S16, S21, S22, S39, S46, S56, and S59). The shared objective among these studies was to ensure pet safety by controlling both the physical environment and the pet's location. * Clean Habitat. Fifthly, in the habitat cleaning category, one study focused on aquarium management, aiming to reduce human error in tank maintenance and stabilize water conditions (e.g., S53 and S58). Other studies centered on automated cleaning, such as waste removal and feeding station cleanliness (e.g., S22, S41, S51, S59, S60, and S62). Studies were also proposed that focused on cleaning or managing the area where pets defecate, especially for devices designed for cats (e.g., S12, S16, and S20). Collectively, these studies aimed to ensure that pet habitats are safe and clean, enhancing both pet well-being and owner convenience. * Physical Activity. Sixthly, studies focused on monitoring pet physical activity helped owners ensure their animals maintain adequate exercise levels (e.g., S6, S7, S11, S17, S33, and S35). Certain devices were relevant for pets with limited movement, like hamsters, by assessing physical activity throughout their lifespan or by season (e.g., S11). Other devices tracked pet movement actively, ensuring they obtained sufficient exercise for health. This group of studies emphasized physical activity as crucial for overall health, helping to prevent inactivity-related issues like obesity and cardiovascular disease (e.g., S7) or providing data for diagnostics in pet hospitals (e.g., S6). * Well-Being. Finally, some of the studies prioritized enhancing pet well-being. These studies leveraged sensors and connected devices to monitor key variables such as health, feeding, and behavior. One device detected anomalies in pet behavior and notified the owner, while others used the IoT to monitor physical activity and pet location (e.g., S4, S6, S7, S21, S23, S39, and S43). The common theme here was the automation of monitoring and management, providing owners with real-time information and reducing the need for constant supervision. All of the proposals under analysis in this study contributed to improving pet welfare in one or more of the categories described herein.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 4 Federated Optimisation (FedOpt)
### Efficiency and Privacy in FedOpt
#### 4.3.3 Aggregation Phase  
Once all the gradients \(G_{\rm II}\) are received by the cloud server, it initialises the secure aggregation process as given below:  
\[\begin{array}{l}E_{add}=\sum_{\rm II=1}^{n}E_{\rm II}\\ =j^{-1}j(\sum_{\rm II=1}^{n}E_{\rm II,k})^{k}+k^{-1}k(\sum_{\rm II=1}^{n}E_{\rm II,j})^{j}\mod M\end{array} \tag{10}\]  
Afterwards, the cloud server begins communication with all the local users and broadcasts the encrypted global gradient \(E_{add}\), in order to avoid collusion from adversaries.
# Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
## 2 Related Work
### Federated Machine Learning and Deep Learning  
Machine learning (ML)-based anomaly detection [4] requires sufficient amounts of data to establish a baseline of normal behavior, expected noise, and abnormal deviations. In complex environments where multiple entities must be monitored for anomalies, centralizing all data for training and testing purposes may not be feasible due to (1) resource constraints, (2) security and privacy concerns, or (3) unacceptable communication latencies to transfer the raw data. Federated ML [6] distributes the workload by enabling multi-party on-device learning. As federated learning is still a relatively new technique, its applicability to optimize anomaly detection for resource- constrained environments and the implications on the achieved performance and accuracy trade-off is still unclear and needs to be studied. Deep learning has been investigated for federated ML [7] and has been applied to find anomalies in system logs [8]. While federated ML minimizes the amount of raw data to be shared or centralized, it can be considered as a tactic to preserve security and privacy. However, recent research shows that data leakage is theoretically still possible in such collaborative learning environments [9].
# A Malware Detection Framework Based on Semantic Information of Behavioral Features
## 2 Related Work
### Static Analysis  
The static analysis method has the advantages of high detection security, fast detection, and low detection overhead, because no samples are executed, but it is sensitive to obfuscation techniques.  
[PERSON] et al. [33] proposed a malicious code classification methodology on the basis of opcode sequences, using N-gram sequences with different dimensions, 1-g, 2-g,... and 6-g, to construct feature sets. Using different methods, document frequency, information gain and Fisher Score, they finally determined that the 2-g-based opcode sequences obtain better classification performance. This method solved the problem of low accuracy when the data were unbalanced. [PERSON] et al. [34] focused on a detection method based on extracting static feature of malicious codes, then classified them by a machine learning model, achieved high accuracy while keeping a low false positive rate. The method extracted the instruction layer and byte-level features of malicious codes through the N-grams algorithm, trained them with multiple classifiers. An active learning mechanism, as well as a weighting algorithm, were used for the classification results of these classifiers to retain a high detection accuracy. However, this method was the result of combining several simple machine learning classification models, and the accuracy of detection needed to be improved. [PERSON] et al. [35] proposed a dynamic prototype network (DPNSA) that can learn adaptively based on samples to detect malware. The method took images from fixed-size malware binaries and used them as input to the embedding module. Then, introduced the dynamic convolutional neural network, which employed the SE module in the convolution. They proposed a new activation function based on the dynamic activation function, i.e., the dual-sample dynamic activation. The distance between malware and query samples was calculated using a metric approach to achieve malware detection. [PERSON] et al. [36] introduced data mining into malware detection. The method analyzed and extracted the API calls of PE files, then selected the most distinguishing API calls by Fisher score to improve the classification accuracy. Random forest was used for classification, which achieved an accuracy of 0.983. Although the method achieved good performance in an unbalanced dataset, handling unreachable code and fake API calls needed further improvement.
## 4 Conclusions  
This paper presents an experimental and analytical study on the effects of the amount of WRA on the rheological properties of UHPFRC and the relationship between the rheological properties, fiber distribution, and mechanical behavior of UHPFRC. A series of experiments including a mini-slump flow test, rheology test, compressive strength test, flexural test, and image analysis were performed. From the test results, the following conclusions were drawn:  
1. The flowability and rheological properties were measured for the fresh UHPFRC, and the results revealed that the positive effect of increasing the amount of WRA on the flowability as well as the rheological properties of fresh UHPFRC could be achieved only with an amount less than a threshold, and a higher amount of WRA exceeding the threshold contributed little to improving the flowability and the rheological properties.
2. The compressive strength test results revealed that adding WRA up to a certain amount did not adversely affect the compressive strength while providing a beneficial effect on the workability, whereas an excessive amount of WRA had a negative influence on the compressive strength of UHPFRC.
3. The flexural performance showed that the flexural strength and the CMOD at peak load decreased as the amount of WRA increased. Through the comparison between the tendency of the compressive strength and flexural strength, it could be surmised that the flexural strength due to the amount of WRA might be influenced by the fiber distribution.
4. A quantitative investigation using the image analysis of the fiber distribution proved that the flexural behaviors with four different mixtures depended in part on the compressive strengths of the matrices and in part on the fiber orientation distributions due to the rheological properties. Furthermore, it was observed that the effect of the rheological property on the fiber orientation is more significant than that on the fiber dispersion.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
This research received no external funding.  
The authors declare no conflict of interest.
# Design and Implementation of a Tether-Powered Hexacopter for Long Endurance Missions
## 5 Conclusions  
The proposed tether-powered hexacopter demonstrates successful outdoor operation with an endurance of 4 h at a height of 50 m. This system achieves the longest flight time and the highest altitude among surveyed experiments. A mathematical model is also proposed to estimate the optimal tether's size and its theoretical length limit. The model error is 3.88% compared with the measured data. The reasons may be the extra force exerted by the tether, imbalance of the hexacopter, and the sidewind. The influences of above reasons can be considered in future works to increase model accuracy.  
Conceptualization, S.-K.H.; methodology, S.-K.H. and K.-H.C.; software, K.-H.C.; validation, S.-K.H. and K.-H.C.; formal analysis, K.-H.C.; investigation, S.-K.H. and K.-H.C.; resources, S.-K.H.; data curation, K.-H.C.; writing--original draft preparation, S.-K.H. and K.-H.C.; writing--review and editing, S.-K.H.; visualization, K.-H.C.; supervision, S.-K.H.; project administration, S.-K.H. funding acquisition, S.-K.H. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Ministry of Science and Technology, Taiwan, grant number 108-2221-E-009-084-MY2.  
Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Measured ground supply voltage & 98.1 V \\ Measured ground supply current & 6.6 A \\ Calculated ground supply power & 648 W \\ Measured tether resistance & 4.02 O \\ Calculated power consumption by tether & 175 W \\ \hline Measured power consumption of hexacopter & 421 W \\ Theoretical power consumption of hexacopter & 438 W \\ Theoretical power consumption model inaccuracy & 3.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Endurance test data and data from model.  
**Acknowledgments:** The authors would like to thank Force Precision Instrument Co. Ltd., Taiwan for technical support.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
###### Abstract  
In view of the characteristics of various types of information system integrated hardware and software systems, complex network topology, complex causes of fault alarm and uncertainty, this paper studies the communication fault maintenance decision-making based on the inverse symmetry algorithm. Based on the principle of the inverse symmetry algorithm, the modulation and demodulation process of information system is determined, the redundant system attributes in the information system are reduced by rough set, and the Bayesian network model with minimum diagnosis set is obtained by combining the prior knowledge in the operation process of the information system. The input and output of the network are the condition attributes and decision attributes of the decision table, respectively. Through the above process, the optimal fault diagnosis rules are established. After the communication fault is diagnosed, different communication fault maintenance decisions of the information system are used to eliminate the fault. The experimental results show that this method can effectively diagnose the communication fault of the information system and make effective maintenance decisions. The average accuracy of data transmission of the information system using this method is over 99.5% under different operating distances, which has better communication performance.  
inverse symmetry algorithm; information system; communication; fault; maintenance; decision +
Footnote †: journal: Computer Science
## 3 Underwater Routing Protocols
### VBF, HH-VBF and FBR  
The authors in [35] performed a comparative study of the VBF, HH-VBF and FBR protocols. They concluded that for the selected underwater scenario, when the transmission range of a sensor node equals the total depth of the network, VBF has the longest end-to-end delay and network lifetime. Conversely, the HH-VBF has the highest number of packet receptions at the sink, but has the shortest network lifetime.
## 2 Materials and Methods
### Eligibility Criteria and Search Strategy  
The research question was formulated using a PICOS approach--Patient (P); Intervention (I); Comparison (C); Outcome (O), and Study design (S). This study selects those articles that described orthopaedic surgical procedures (P) assisted by VR, AR and AI (I). The aim was to assess the role of these technologies in orthopedic surgery and their potential future applications (O). For this purpose, randomized studies (RCTs) and non-randomized controlled studies (NRCTs), as prospective, retrospective, cross-sectional, observational studies, case-series, pilot and case-control studies, were included (S).  
Figure 1: Study selection process and screening according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow chart [17].  
A comprehensive search on the databases PubMed, Medline, Cochrane, CINAHL and Embase databases was conducted from inception to February 2021. The following keywords were used isolated and combined--Virtual Reality; Augmented Reality; Artificial Intelligence; Orthopedic Surgery; Reality; preoperative planning; training; intraoperative; and mixed reality. All the keywords were searched isolated and combined with their MeSH terms. More studies were searched among the reference lists of the selected papers. The exclusion criteria included reviews, books, case reports, technical notes, letters to editors, instructional courses, in vitro and cadaver studies.
## 5 Case Study
### Experimental Results and Analysis
#### 5.2.1 Pointing Accuracy Analysis  
The pointing accuracy's error bar is used to express the different results under different assembly methods, as shown in Figure 16. The antenna's pointing accuracy is affected by various factors such as the assembly positioning error, hinge clearance, and rod distortion. The pointing accuracy cannot be precisely determined before the assembly is completed because each hinge's positioning error is not definite until the assembly is completed, though it is in a certain tolerance range. Moreover, the antenna's actual pointing accuracy in space is almost impossible to measure directly when measured by humans on the ground. However, it can be indirectly predicted using methods such as the assembly error model that uses the hinges' positioning error as the input and the pointing angle as the output. Therefore, the antenna's pointing accuracy in space can be predicted using the assembly error model built in Section 3. As the assembly progresses, the pointing accuracy's range is gradually decreased, but it does not eventually become a definite value because each hinge center in space is uncertain when hinge clearances are exists.  
As shown in Figure 16, the pointing accuracy is predicted using the assembly error model built in Section 3. For the assembled hinge, the positioning error measured using the laser tracker is an input for the assembly error model. For the not-yet-assembled hinge, the tolerance is an input for the assembly error model. The blue error bars represent the antenna pointing accuracy using the previous method, regardless of gravity's effect during ground assembly. Additionally, the orange error bars represent the antenna's pointing accuracy using the assembly error model proposed in this paper, which considers the different environments between the ground assembly and service in space. Both methods perform gravity compensation for the LSDM's key components. There is an initial error of 0.003\({}^{\circ}\) at the beginning of assembly when using the previous assembly method, which is caused by the gravity during the ground assembly. Additionally, the antenna's pointing accuracy can be improved from 0.068\({}^{\circ}\) to 0.045\({}^{\circ}\) using the method proposed in this paper.  
Figure 15: Tolerance collaborative allocation software.
## 3 Experimental Results
### Variation of Cone Penetration with Water Content
Later, the liquid limits of mixtures were obtained based on the 20 mm cone penetration. In the experiment, the increase in water content was continued, and the cone penetration exceeded 20 mm, and the soil reached a liquid state. The experiments were continued until the cone penetration depth reached 40 mm by means of the added water content. For each mixture shown in Table 1, the Fall cone test was completed in a minimum of 30 steps and a maximum of 48 steps, depending on the water-holding capacity of the mixtures. The validity of the log-log relationship can be observed in Figure 3a-c over a wide range of cone penetrations and water contents. Figure 3a-c shows that the cone penetration depth (\(d\)) seems to be dependent on soil mineralogy, soil type and soil plasticity. The variation of water content and cone penetration on a logarithmic scale for mixtures of _NaM-CaM_, _NaM-K_ and _NaM-S_ are given in Figure 3a-c, respectively. Since the _NaM-CaM_ mixtures have very high water-absorption capacity, they vary in a narrower band, in comparison with the other two mixtures for liquid state (cone penetration higher than 20 mm, Figure 3a). The water contents corresponding to the liquid state in the _NaM-CaM_ mixtures given in Figure 3a vary between 240% and 490%. In the _NaM-K_ mixtures given in Figure 3b, the change in water content with cone penetration occurred in a wider band range due to the lower water-holding capacity of kaolin clay compared to _NaM-CaM_m mixtures. While the water content corresponding to the 20 mm cone penetration, which is the lower limit in the liquid state, is 300% for _NaM_, this value decreases considerably as the kaolinite content of the mixture increases, resulting in a 60% value for 100% \(K\) content. The same relationship for _NaM_-_S_ mixtures is seen in Figure 3c, and the decrease in water content of _NaM_-_S_ mixtures is much more pronounced than the other two mixtures. In this case, it also showed that \(S\) has the lowest water-holding capacity compared to _CaM_ and \(K\) clays. As a result, the water retention of the mixtures decreased significantly as the amount of sepiolite clay, with the lowest water affinity, was increased by weight. Thus, the variation of water content in the liquid state occurred in a very wide band. As shown in Figure 3c, the water content, which is the lowest limit of the liquid state for 100% \(S\) and corresponds to 20 mm penetration, decreased to approximately 45%. Comparing all clay mixtures from Figure 3a-c shows that the reduction in water-holding capacity makes a significant difference in the case of mixtures of clays with different mineralogical properties. Figure 3: _Cont._
## 6 MIMO Antenna Performance Enhancement Techniques
### Dielectric Resonator  
An antenna that contains a dielectric resonator is called as dielectric resonator antenna (DRA). DRAs are capable of providing low loss, high gain, and high radiation efficiency. Dual-band property as well as high isolation value can be achieved using DRAs [88].