esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
### Performance  
We evaluated the experimental performance using four metrics: precision, recall, accuracy, and F2 score. These metrics were calculated using binary classification data from ARMA, the implementation of which was added into the ARMA script. Precision is a measure of positive predictive value. In our case, it represented the proportion of ARMA flagged hours that were actually anomalous. Recall is a measure of sensitivity and indicated the proportion of anomalous hours that were correctly identified by ARMA. Accuracy indicated the proportion of correctly predicted hours, normal and anomalous combined. Finally, F score takes into account both recall and precision into a harmonic  
Figure 2: Baseline ARMA output for the ERGM “sum” term’s adjusted time series over 66 h and predicted on 6 h, with a prediction window of 1 standard deviation in this case. mean. Its related function, the F2 score, gives two times more consideration to recall than precision. We preferred F2 score because in the field of anomaly detection, it is more important to actually flag anomalous hours. In other words, the sensitivity of the model must be appropriate. Often times, precision alone does not provide a clue as to the overall sensitivity of the model. Table 1 demonstrates the ARMA performance on the sum term, whereas Table 2 shows the performance on the nodecov term. Some cells are empty due to division by zero. ARMA is known to have decreasing predictive ability as the test set increases in size. ARMA predicted the first four hours of the baseline time series test set correctly, and the terminal two hours incorrectly, so we thus decided to split the data analysis, considering the test set's first three hours' and last three hours' performance separately. The last three hours' performance can be considered statistically irrelevant due to poor baseline performance. Precision was positively correlated with the threshold window size for all three modes, which was expected since as the model's range of normal values increased, there were less false positives. On the other hand, recall is inversely correlated with the ARMA threshold across all terms and modes, since a smaller threshold is more selective about which hours are normal, resulting in more false and true positives. In the single injection mode, accuracy was positively correlated with threshold, but this was due to the fact that most hours were already normal, and there was only a single anomalous hour. In the other modes, accuracy was either consistent across window sizes or slightly inversely correlated. F2 scores were inversely correlated with threshold size, since recall was also inversely correlated with threshold size. The sum and nodecov term do appear to have some discrepancy in their descriptive power. Nodecov performed much better than sum in multiple injection mode, while performing about equally in single and random mode. In summary, the reduced performance of nodecov could be attributed to multiple factors, but it is likely that due to the use of the HTTP protocol across multiple devices, the effect of this statistic in detecting anomalies is slightly suppressed.
## 3 The Proposed Range-Division User Relay Selection Scheme  
In COM-NOMA, the density of SU is larger than the conventional CM-OFDMA scheme. Moreover, USU chooses UR from SUs who are within his/her ETR. When USU is near the cell center, the density of SU in his/her vicinity is high; otherwise, it is low. At the same time, every UR is able to provide larger SINR than \(\sigma_{0}\) for the USU served by him (her). Therefore, near the cell center, the probability that multiple URs serve the same one USU is high, when \(M_{i}\) is large. This is also verified by simulation in Section 5. To reduce UR redundancy and improve coverage efficiency, RDUR scheme is proposed in this section.  
In RDUR, the circular coverage area of BS is divided into continuous AAs. The width of AA is decided by the optional radius of ERSR, which is no longer a fixed value \(R_{0}\). RDUR is composed by three steps and shown in Figure 2:  
1. Find the starting inner diameter of RDUR.
2. Allocate radius of ERSR to AA.
3. Determine the ERSR radius set.
## 2 Related Work
### IoT Device Identification Methods
[PERSON] et al. proposed an identification of IoT device models in the home domain using IP flow information export (IPFIX) records [12]. The aim of this method based on IPFIX, which is a standard for flow information, is to reduce data volume for communication costs. In this method, IoT traffic captured on a gateway router was converted to IPFIX information, and the converted data were sent to the device identification server. Then, feature extraction from the IPFIX records and training of an identification model using LightGBM were operated on the server. As a result, this method identified 25 IoT devices with 98.48% precision. Moreover, using IPFIX records reduced the data volume to approximately 11% compared to traffic data. On the other hand, since all procedures for the IoT device identification were performed on a single server, there are concerns about the concentrated load. [PERSON] et al. proposed a method to mitigate frequent retraining for IoT device identification models [13; 14]. A Siamese neural network (SNN) was trained to generate embeddings corresponding to the similarities of feature values. In this method, a database of embedding vectors corresponding to IoT traffic was created using an SNN. In the identification, 95 feature values extracted from IoT traffic were input to the identification model using an SNN, and an embedding vector was output. Then, the closest embedding to the output vector was searched from the database, and the input traffic was classified according to the IoT device corresponding to the retrieved embedding. When a new device was added, this IoT traffic was also input to the identification model using an SNN, and an embedding corresponding to the device was generated. And then, this embedding was added to the database. Therefore, the database was extended to include the new device type, and the model could recognize the new device without the updating. Based on the results in [14], this method yielded an 85.8% F-measure even when 28 unknown IoT device data were added after training. However, this method also processed traffic data at once, whereas all traffic data should be shared with the cloud servers in the real environments.
# Accounting for Resilience in the Selection of R Factors for a RC Unsymmetrical Building
## 3 Design Concept and Structural Details of the Building
### Estimation of Building Resilience from Functionality Curves  
To estimate the building resilience, functionality curves for each building case were developed with respect to each design level, using various recovery functions. The analytical recovery functions, such as linear (RP-1), exponential (RP-2) and trigonometric (RP-3) recovery paths, were proposed by [PERSON] et al. [57] and [PERSON] et al. [58]. The functionality curve in terms of resilience was found as follows:  
Functionality:  
\[Q(t)=1-\{L(I,T_{RE})\times[H(t-t_{OE})-H(t-(t_{OE}+T_{RE}))]\times f_{rec}(t,t_ {OE},T_{RE})\} \tag{3}\]  
The time of occurrence of a seismic event is designated as \(\underline{t_{OE}}\), its recovery time is designated as \(T_{RE}\), and its Heaviside step function is designated as \(\overline{H}()\). For the purposes of this study, \(\underline{t_{OE}}\) is set at 50 days, and total recovery time is calculated as 65 days with a total control time period (\(T_{\text{LC}}\)) of 140 days. The analytical equations for each recovery function are the following:  
\[\text{Linear function}:\ f_{rec}(t,t_{OE},T_{RE})=\left[1-\frac{t-t_{OE}}{T_{ RE}}\right] \tag{4}\]  
\[\text{Exponential function}f_{rec}(t,t_{OE},T_{RE})=\exp\left[-\frac{(t-t_{OE})( \ln 200)}{T_{RE}}\right] \tag{5}\]  
\[\text{Trigonometric function}:\ f_{rec}(t,t_{OE},T_{RE})=0.5\bigg{\{}1+\cos \biggl{[}\Pi\frac{(t-t_{OE})}{T_{RE}}\biggr{]}\bigg{\}} \tag{6}\]  
Along with conventional, two different recovery paths named modified trigonometric path (RP-4) and combined recovery path (RP-5) were also considered.  
The idea behind the parameter introduction was that, under realistic conditions, recovery could not begin after a seismic event had happened (Figure 16). Real-world situations necessitate a careful assessment of the structural damage before the recovery procedure can begin. The functionality and recovery equations were adjusted as follows in light of this.  
Figure 16: Various recovery paths considered (RP-1 to RP-5).  
In addition to the 'toi' parameter, the modified trigonometric path is the fourth recovery path (RP-4) which has an initial delay in the recovery process, whereas the combined recovery path is the fifth recovery path (RP-5) which has a certain breakdown in the recovery process (Figure 16). The functionality Equation (3) now includes the new argument 'toi'. Based on this, the functionality and recovery equations were modified as follows:  
\[Q(t)=1-\left\{L(I,T_{RE})\times[H(t-t_{OE}-t_{oi})-H(t-(t_{OE}+T_{RE})-t_{oi}) ]\times f_{rec}(t,t_{OE},T_{RE},t_{oi})\right\} \tag{7}\]  
\[f_{rec}(t,t_{OE},T_{RE},t_{oi})=0.5\bigg{\{}1+\cos\bigg{[}\Pi\frac{(t-t_{OE}-t_ {oi})}{T_{RE}}\bigg{]}\bigg{\}} \tag{8}\]
## 4 Verification and Discussion of the Proposed Method
### The Fault Analysis of Different Breakages  
In this subsection, different gear faults are analyzed using the dynamical model of the gearbox system, and side-band frequency features are obtained by using different analytical methods. The analytical results are shown in Figure 7.  
According to the parameters of the gearbox, the rotational frequency of the faulty gear shaft is \(f_{r}=f_{in}(Z_{1}/Z_{2})=48 Hz*(17/81)=10.07 Hz\). The first-order frequency value of the analytical result is 10.1 Hz, which is close to the theoretical value. The variation of each order frequency amplitude is consistent with the experimental data, the comparative results are shown in Figures 5 and 8b,c. Hence, the dynamical model can reflect the fault characteristics of the tooth breakages. The proposed method can still clearly extract the side-band frequency feature with the 1/3 tooth breakage, but other methods have lost the ability to detect the gear fault. The result is shown in Figure 8a.  
The results from Table 2, the amplitude value of frequency feature for EMD method is much bigger than LMD and DWT method with the different tooth breakage. However, the amplitude value obtained by the proposed method is 53%, 45%, and 32% higher than that of EMD with the different tooth breakage, respectively. Especially 1/3 tooth breakage, other methods are very difficult to detect the side-band frequency features. The results indicate that the proposed method can clearly extract the side-band frequency features without the external noise, and the amplitude value is enlarged with increasing tooth breakage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Type** & **EMD** & **LMD** & **DWT** & **Proposed Algorithm** & **Percentage** \\ \hline
1/3 breakage & 0.037 & 0.011 & 0.013 & 0.079 & 53\%, 86\%, 84\% \\
2/3 breakage & 0.22 & 0.24 & 0.21 & 0.32 & 45\%, 33\%, 52\% \\ Total breakage & 0.25 & 0.29 & 0.22 & 0.37 & 32\%, 22\%, 41\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the first-order frequency amplitude value of different analysis methods.  
Figure 8: The results comparison (**a**) 1/3 breakage, (**b**) 2/3 breakage, (**c**) total breakage, (**d**) the proposed method with different breakage.
###### Abstract  
Commercial pure Mg specimens were processed through equal channel angular pressing (ECAP) using two dies with die angles of 90\({}^{\circ}\) and 120\({}^{\circ}\). Mg billets were processed up to four passes via different route types. Machine learning (ML) techniques were adopted to estimate the ECAP parameters and verify the experimental findings. Several ML techniques were employed to estimate the effect ECAP parameters of pure Mg on microstructural evolution, Vicker's microhardness (HV), and tensile properties for ECAP billets and their as-annealed (AA) counterparts. Electron back-scatter diffraction (EBSD) was applied to determine the structural evolution and crystallographic texture both prior to and following the ECAP process for the Mg billets. EBSD analysis showed that route Bc is the most effective route in grain refinement, and four passes of route Bc experienced a significant refinement of 86% compared to the AA condition. Furthermore, the crystallographic texture showed that four passes of route Bc produced the most robust texture that was greater than 26.21 times random. ML findings revealed that the grain size demonstrated a strong correlation of \(-0.67\) with rising number of passes, while \(\phi\) affected the grain size strongly with 0.83. When adopting a 90\({}^{\circ}\)-die to accumulate the plastic strain up to 4 Bc, the subsequent HV was indeed 111% higher than that of the AA equivalent. From ML findings it was clear that the number of passes was the most significant parameter on the Mg HV values, while ECAP channel angle (\(\phi\)) revealed high correlation factor with HV values as well. Furthermore, four passes of route Bc with \(\phi\) = 90\({}^{\circ}\) and 120\({}^{\circ}\) led to a significant increase of the tensile strength by 44.7% and 35.7%, respectively, compared to the AA counterpart. ML findings revealed that the tensile strength was affected by the increasing number of passes with a strong correlation of 0.81, while affecting ductility moderately with 0.47.  
p 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion
### Characterization of Films Processed at Pilot Scale
#### 3.2.3 Thermal Properties  
The thermal properties of the pilot scale processed films were evaluated in terms of DSC and TGA analysis. DSC parameters for multilayer LDPE based films containing \(\beta\)-carotene and for the control films are shown in Table 3. In ML and ML/LDPE_CAR samples, two endothermic peaks were obtained at about 102-105 \({}^{\circ}\)C and 111 \({}^{\circ}\)C, respectively, related to the melting process of LDPE, which can be attributed to several factors such as two different crystallographic forms, crystallites of varying degrees of perfection, and differences in crystallite size, among others [19; 33]. The same behaviour was observed in the crystallisation process, and significant differences were obtained in this parameter when comparing the control films with the ones containing \(\beta\)-carotene. In these multilayer samples, two additional melting peaks were distinguished at around 180 \({}^{\circ}\)C and 254 \({}^{\circ}\)C, respectively, attributed to the presence of EVOH and PET, as was expected [19; 23].  
In relation to the role of \(\beta\)-carotene as a protector against polymer oxidation, in previous works it has been postulated that the stabilising effect of it depends upon their ability to form stable products during the thermooxidative degradation of different polymers such as ABS, among others [19]. This type of stabiliser is considered to have a multifunctional role in many polymers. Especially for polyolefins, OIt tests are well established for quality control purposes as a quick screening method to check the performance of the stabilisation systems. This induction time can be related to the amount of antioxidant present in the polymer or the effectiveness of any particular antioxidant in the formulation [32]. The addition of \(\beta\)-carotene to the studied multilayer LDPE based films resulted in a significant 9.6 min increase in the OIt when compared to the material without the antioxidant. This effect could be attributed to the amount of \(\beta\)-carotene that remained in the films after the extrusion process (3.07 wt.%). This is a clear indication of the good stabilisation obtained with the addition of low amounts of \(\beta\)-carotene to polyolefins, as indicated by other authors who studied the effect of natural antioxidants as polyolefins stabilisers [24].  
The thermogravimetric results confirmed a principal degradation step for all the analysed samples. The TGA T\({}_{\rm ini}\) and T\({}_{\rm max}\) values related to all the analysed samples are presented in Table 3. The incorporation of \(\beta\)-carotene to the formulation brings about a significant effect on the thermal stability of the obtained active films (\(p<0.05\)). In this sense, the presence of 3 wt.% \(\beta\)-carotene to ML_LDPE films resulted in an increase in the T\({}_{\rm max}\) of 1%.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 4 Double-Scale Channel State Prediction
### Small-Scale Channel State Prediction
#### 4.2.1 Small-Scale Channel Fluctuating Features  
Due to the complex fluctuation of underwater acoustic channel state, direct prediction usually suffer from large prediction error. Over-estimation of the channel state will lead to packet loss, while under-estimation will lead to energy efficiency deterioration.  
To improve the accuracy of prediction, a decomposition-based prediction model is proposed. The advantage of this model is that it adopts a series decomposition method with low complexity to improve the prediction accuracy, and a large-scale channel state only needs to be predicted once in a large-scale epoch.  
Firstly, the large-scale channel state \(\mathbf{H}^{L}\) is subtracted from the original channel state series \(\mathbf{H}\) to obtain the high frequency residual series \(\mathbf{H}^{re}\), as shown in Figure 6.  
\[\mathbf{H}^{re}=\mathbf{H}-\mathbf{H}^{L}. \tag{20}\]  
The residual series can represent small-scale fluctuating features of the underwater acoustic channel. Although prediction models based on other decomposition methods, such as discrete wavelet decomposition [31] and empirical mode decomposition [32; 33; 34], can further improve the prediction accuracy, the advantage of the decomposition method in this paper is its lower complexity.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 5 Conclusions  
This paper proposes an end-to-end insider threat detection method based on deep neural networks. The proposed method firstly employs the encoder-decoder model to effectively learn feature representation of user behavior sequences. Subsequently, a deep clustering network is constructed, which iteratively refines cluster centers using a clustering objective function and adjusts the parameters of the deep neural network through the stochastic gradient descent (SGD) algorithm. Abnormal user behaviors are then identified  
Figure 6: Different detection results when adjusting the number of class labels \(k\) of user multi-source behavior sequences.  
Figure 7: Different detection results when adjusting the dimensionality \(h\) of user behavior event entity embedding feature vectors.  
based on the degree of behavior deviation from the cluster centers. The proposed method improves insider threat detection by jointly optimizing user behavior clustering and feature representation, thus addressing the challenge of learning an optimal behavior sequence representation suitable for clustering. The experimental evaluation, conducted on the publicly available CMU-CERT insider threat dataset, shows that the proposed method achieved 98% AUC and 99.8% _Recall_ for the second type of malicious insiders in the dataset. The overall comparison with the existing detection methods demonstrates that the proposed method obtains superior detection performance.  
In this study, the proposed method can be applied to any organization in which a substantial amount of user behavior data can be collected. It is particularly effective for datasets with diverse types of user behavior logs, including host-based and network-based behavior logs. A significant factor influencing detection accuracy is the predetermined number of clusters required by the deep clustering algorithm. Future research will focus on developing a method to automatically select the optimal number of clusters based on the characteristics of the dataset in use.  
[PERSON] and C.Z.: methodology, formal analysis, writing--original draft, writing--review and editing. [PERSON], [PERSON] and C.Z.: software, English spelling check. [PERSON] and C.Z.: conceptualization, validation, data curation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61931019 and the Xiejialin Project of Institute of High Energy Physics under Grant no. E25467U2.  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.
In Japan, the existing 66 kV transmission line faced two main technical issues to uprate to 154 kV line that involved clearance around tower and insulation between conductors in span, and it was found that the proposed techniques of insulator supported jumper and phase-to-phase jumper succeeded to fulfill the electrical clearance requirements [16]. According to [10], the different technique of voltage uprating was used by converting from double circuit to single circuit in order to achieve adequate safety margin of electrical clearances from the original level voltage of 230 kV to new level voltage of 500 kV lines requirement. Meanwhile, another study reported that a major challenge for voltage uprating is to provide sufficient air clearance for the new level of voltage, however, this study found that by adjusting insulator length and its creepage distance had achieved sufficient electrical clearances of the new voltage lines' needs [13]. In recent years, the country of Malaysia has been growing steadily, especially in several leading sectors, and has indirectly required sufficient power supply capability. On the other hand, transmission lines are the backbone for power transfer and one of the crucial assets for electric utility operators to maintain and be fit to operate [17]. However, it was noticed that the transmission lines are fully exposed to environmental factors, including transient overvoltages, i.e., lightning activities [18; 19; 20; 21]. This is portrayed where adverse weather, which represents lightning-related events, is one of the causes of transmission line interruptions. In Malaysia, a study regarding the uprating of the 132 kV transmission line proposed current uprating techniques. It was mainly focused on the reconductoring, i.e., conductor type, to improve conductor sag [22]. However, there is still a lack of knowledge regarding the voltage uprating of the existing transmission lines in Malaysia. In this paper, the main contribution is to propose the voltage uprating method associated with techniques and processes on the existing 132 kV transmission lines. In this study, a significant concern of voltage uprating is a technical issue, including a phase-to-earth electrical clearance. The study demonstrated that the voltage uprating of the existing 132 kV transmission line is likely to be considered a minimal structural modification in order to fulfill electrical clearances requirements. However, it is worth mentioning that voltage uprating is a preeminent option rather than new transmission line development. The FEM simulations were carried out on the proposed voltage uprating model by considering transient overvoltages and power frequency magnitude to examine the withstand capabilities under various conditions (i.e., still air and wind conditions) on the insulation strength. The simulations results were compared with the estimated critical flashover voltage (CFO) as the benchmark.