esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Quasi-Standardized 2-D Dynamic Time Warping
### Variance Homogeneity Test  
Variance homogeneity test is a test to verify whether the \(k\) samples have equal variances [23; 24]. In other words, it is used to test the null hypothesis that the \(k\) samples are drawn from the same distribution. Based on the assumption of the distribution the samples came from, the test can be divided into two types; normal distribution-based test and non-normal distribution-based test:  
Bartlett Test is one popular test of variance homogeneity [25]. It has high sensitivity towards the non-normality, thus, it works best if the distribution the sample came from is the normal distribution. The test statistic is defined as  
\[V=\frac{(N-k)\ln s_{p}^{2}-\sum_{i=1}^{k}(N_{i}-1)\ln s_{i}^{2}}{1+\Big{(}\frac {1}{(3(k-1))}\Big{)}\Big{(}\Big{(}\sum_{i=1}^{k}\frac{1}{(N_{i}-1)}\Big{)}- \frac{1}{(N-k)}\Big{)}} \tag{4}\]  
where \(s_{i}^{2}\) is the variance of the \(i\)th group, \(N\) is the sample size of the \(i\)th group, \(k\) is the number of groups, and \(s_{p}^{2}\) is the pooled variance, defined as  
\[s_{p}^{2}=\sum_{i=1}^{k}\frac{(N_{i}-1)s_{i}^{2}}{(N-k)} \tag{5}\]  
Levine Test [26] is a homogeneity test with lower sensitivity towards non-normality, thus it works better than the Bartlett Test if the samples are drawn from non-normal distribution. The test statistic, \(V\) is defined as follows  
\[V=\frac{(N-k)\sum_{i=1}^{k}N_{i}\big{(}\overline{Z}_{i,\cdot}-\overline{Z}_{ \cdot}\big{)}^{2}}{(k-1)\sum_{i=1}^{k}\sum_{j=1}^{N_{i}}\big{(}Z_{ij}-\overline {Z}_{\cdot_{i}}\big{)}^{2}} \tag{6}\]  
Figure 2: Comparison of performance between 1-D DTW and 2-D DTW. The top plot shows the comparison between the unstandardized versions of the two, while the bottom one shows the comparison between the standardized versions of them.  
where \(\overline{Z}_{i.}\) is the group means of \(Z_{ij}\) and \(\overline{Z}_{.}\) is the overall mean of \(Z_{ij}\). \(Z_{ij}\) can be represented in a variety of different definitions, however, the one that is known as more robust than the other is called the Brown-Forsythe test which defines \(Z_{ij}\) in terms of the median, that is  
\[Z_{ij}=\big{|}Y_{ij}-\overline{Y}_{i.}\big{|} \tag{7}\]  
where \(\overline{Y}_{i.}\) is the median of \(i\)th subgroup.  
Variance homogeneity tests accept the null hypothesis that all \(k\) samples are drawn from the distributions with the same variance if  
\[V<F_{a,k-1,N-k} \tag{8}\]  
where \(F_{a,k-1,N-k}\) is the upper critical value of \(F\)-distribution with \(k-1\) and \(N-k\) degrees of freedom at significance level \(\alpha\).
## 3 Results  
### Participants' Responses about Their Organizations and Its Involvement  
This survey had been open for the period June 2020 to April 2021. Upon concluding the survey, we found that 24 participants responded to our invitation, providing a 32% response rate. Figure 2 shows the type of participants' affiliated organizationsand quantifies whether their organizations have collaborated/cooperated/coordinated with others during the 2016 HRF event and received any further training/capacity building programs after the disaster.  
Upon analysing the responses to the question entitled \"Have your organization collaborate/cooperate/coordinate with others during and after the 2016 Horse River Fire\", we found that at least 67% of the participants' organizations mentioned such activities (Figure 2b). In these cases, they also briefly explained the extent of their organizational efforts, and these activities could be summarized as follows:  
* working with Provincial and Federal Governments, including Alberta Emergency Management Agency, Alberta Forestry, Regional Operations Centre, etc.;
* cooperating with the Local Government;
* providing support to the fire victims;
* involving in the evacuation responses;
* monitoring of the regional air quality; and
* assessing the environmental impacts of fire on canopy structure, among others.  
Additionally, in response to the question asked \"Have your organization received any further training/capacity building programs after the 2016 Horse River Fire in wildland fire-induced risk management?\", we found that at least 46% of the participant's organization had attended such programs (see Figure 2c). When the responses were 'yes', the participants also briefly explained the extent of further training and/or capacity building programs in their organization that were received after the 2016 HRF event. Those included: (i) development of business continuity plan, which came into effect during the wildfire and again COVID-19 lockdown making sure that these were prepared to respond in case of an emergency; (ii) adaptation of a nationally recognized program like FireSmart, including rehabilitation of fire affected cities in forests and support for personal property redevelopment; (iii) training on Incident Command System, i.e., a standardized management system to organize and manage a scalable response to any emergency incidents [50]; and (iv) performing Table Top simulations [51], among others.
## 5 Performance Evaluation
### Performance Evaluation
#### 5.2.3 Bottleneck Link Bandwidth Influence  
Figure 9 shows the influence of the bottleneck link network bandwidth on the content acquisition time. When other parameters remain the same, it demonstrates the influence of the bottleneck bandwidth on the transmission delay. It can be seen that NDN-BestRoute is most affected by the bandwidth. NDN-BestRoute does not choose the path in the middle of the butterfly network (router 3 to router 2) for requests from two edge nodes. The exact path will return all interest message requests to the edge nodes. The optimal path will be congested if the network bandwidth is too small. With the increase in bandwidth, this congestion situation will be resolved. When NDN-Multicast is adopted for multipath forwarding, each intermediate node will forward each interest message through two paths.  
Figure 8: Transmission performance under different cache sizes. (**a**) Average cache hit rate. (**b**) Average cache hit distance.  
The content provider needs to respond to the content request separately on each forwarding path, leading to request latency and transmission redundancy.  
As shown in Figure 9, IDEANE achieved an average reduction in the download time of nearly 42% compared to NDN-Multicast and approximately 8.3% compared to NetCodCCN. IDEANE does not need to perform encoding and decoding operations on encoded blocks in the intermediate nodes. Instead, it directly forwards these packets.
## 6 Conclusions  
To understand better the earthquake response and damage concentration of setback buildings, numerical investigations were conducted and a practical method to evaluate the damage distribution was proposed.  
First, two previous experimental tests of reinforced concrete buildings comprising a towered setback and a stepped setback were modeled and analyzed by STERA_3D and the test results were successfully simulated.  
Then, to study the building damage distribution, 2D reinforced concrete frame models containing 20 stepped and 15 towered setback frames were generated to inspect the relationship between the damage index ratio and the irregularity indices under safety level earthquake input ground motions. Based on the results, two nonlinear regression equations were proposed as an alternative to the dynamic analysis procedures.  
Finally, the proposed equations were applied to the previous experimental tests, and it was demonstrated that the proposed formulas could predict the damage to vertically irregular reinforced concrete moment-resisting frame with reasonable accuracy. Although the prediction formula works, it is important to be noted that the setback buildings considered in this study are limited for low- to mid-rise reinforced concrete frame buildings with uniaxial seismic loading.  
Conceptualization, T.S., B.E. and T.I.M.; methodology, B.E., T.I.M.; software, T.S.; writing--original draft preparation, T.I.M.; writing--review and editing, T.S.; supervision, T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The first author appreciates AUN/SEED-Net JICA for the support through scholarship of doctoral degree.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Frame** & \(\mathbf{\varphi_{b}}\) & \(\mathbf{\varphi_{s}}\) & **Natural Period T (s)** & **Calculated \(DI_{ratio}\)** & **Predicted \(DI_{ratio}\)** & **Accuracy (\%)** \\ \hline Wood [18] & 2.75 & 1.25 & 0.118 & 1.2691 & 1.3552 & 93.22 \\ [PERSON] and [PERSON] [19] & 2.00 & 1.20 & 0.248 & 1.1641 & 0.7766 & 66.71 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical analysis result compared to prediction result using Regression Equation.
## 2 Research Background
### Optimization Control Strategy for Single Cooling Machine of Chilled Water System
#### 2.3.2 Fitness Value Calculation and Reproduction Mechanism  
Individuals in an initial population are substituted into a target function to calculate a fitness value. The aim of the target function is to obtain the minimum total power consumption of the water system. The total power consumed by the water system is composed of the power consumed by the cooling machine, water pump, and cooling tower. The power consumed by the cooling tower and water pump can be calculated according to the operating frequency, whereas the power consumed by the cooling machine can be calculated using a multiple linear regression model. Such a model can be established by referring to various water system parameters, including chilled water input and output temperatures, chilled water flow, cooling water input and output temperatures, cooling water flow, cooling machine power consumption, water pump frequency, and outdoor temperature and humidity. Furthermore, such a model can be used to estimate the effects of control conditions in different environments on the power consumption of the cooling machines. The reproduction mechanism involves the application of roulette wheel selection, in which each feasible solution is assigned a roulette wheel slot whose area is proportional to the fitness value of the solution. Thus, a solution with a high fitness value has a high probability of being selected. A power consumption model for chilled water systems applies principal component analysis to select the most influential parameters to establish a regression model for evaluating the power consumption of such systems. Such a model is presented in Equation (8):  
\[kW_{chi}=a(Tin_{chi})-b(Tout_{chi})+c(Tin_{cw})-d(Tout_{cw})-e(Tout)-f(Hout) \tag{8}\]  
where \(a,b,c,d,e\), and f represent regression parameters; \(Tin_{chi}\) represents chilled water input temperature; \(Tout_{chi}\) represents chilled water output temperature; \(Tin_{cw}\) represents cooling water input temperature; \(Tout_{cw}\) represents cooling water output temperature; \(Tout\) represent outdoor temperature; and \(Hout\) represents outdoor humidity.
## 2 Material & Methods
### Statistical Analysis  
Statistical analysis was performed using SPSS (Version 25.0, SPSS Inc., Armonk, NY, USA). Graphical representations were created using GraphPad Prism (Version 8.0.0, GraphPad Software, San Diego, CA, USA) in addition to SPSS.  
The Kolmogorov-Smirnov test was applied to test variables for normal distribution. Normally distributed metric data were expressed as the mean \(\pm\) standard deviation (SD) and analyzed using an unpaired Student's t-test. Abnormally distributed metric data were expressed as the median and interquartile range (IQR), and the Mann-Whitney U test was applied for statistical analysis. Frequencies/percentages were used for categorial clinical data and compared using the chi-squared test. The Friedman test with pairwise comparison was used to test dependent samples (biomarker change over time within the sPAP group \(<\) 40 mmHg and within the sPAP group \(\geq\)40 mmHg).  
To determine the optimal cut-off value according to an sPAP \(\geq\) 40 mmHg and different serum biomarker levels over time, the area under the receiver operating characteristics (AUROC)-curves and a separate analysis of the Youden index (YI) was performed.  
To recognize possible influencing factors regarding the presence of PH (sPAP \(\geq\) 40 mmHg), a univariate binary logistic regression analysis was first completed. For better comparability, a Z transform was absorbed for metric data. Subsequently, multivariate binary logistic regression was performed to assess the independent factors regarding the prediction of PH. Therefore, the covariates associated with the detection of PH in the univariate analysis (\(p\) = 0.100) were entered, and a backward variable elimination was carried out.  
A \(p\)-value \(<\) 0.050 was considered statistically significant.
## 4 Similarity Measurement Model and Clustering Model  
In the task of time series clustering, existing similarity measurement methods frequently encounter challenges such as the improper alignment of time series or excessive computational complexity, which directly affect the accuracy of the clustering results. Therefore, optimizing the current similarity measurement technology is of importance and practical value. The Shape-Based Distance (SBD) algorithm can more effectively capture the patterns and trends in time series, especially when the amplitudes and baselines of time series may be inconsistent [41]. The time complexity of the SBD algorithm is \(O(n\log n)\), which is superior in computational efficiency to traditional distance-based measurement methods represented by the Dynamic Time Warping (DTW) algorithm, especially when dealing with longer time series. Therefore, we apply the SBD algorithm to microseismic waveform recognition to improve the recognition speed and clustering effect.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 3 Cognitive HSTN
### Spectrum and Power Allocation
To maximize the energy efficiency of the cognitive satellite user and ensure the interference at the terrestrial primary user is below a certain threshold, energy-efficient optimal power-allocation schemes for real-time and non-real-time applications in underlay-cognitive HSTNs were proposed in [112] using an average interference power (AIP) constraint for the primary terrestrial user and average transmit power (ATP) and peak transmit power (PTP) constraints for the secondary satellite user. For optimal power control in cognitive HSTNs, the case of imperfect CSI for both satellite and terrestrial interference links was discussed in [116]. Pilot-based channel estimation and a back-off interference power constraint were adopted for the satellite and terrestrial interference links. Another power allocation scheme for a cognitive two-path successive-relay (TPSR) HSTN with full-interference cancellation (FIC) operating in AF mode and maximizing system capacity under predetermined co-channel interference to the primary user network was proposed in [142]. Taking a more general approach from the models presented in [115, 128], the authors of [117] proposed an alternating direction method of multipliers (ADMM)-based optimal power control scheme for an underlay uplink-cognitive HSTN. Both interference power and interference outage constraints were considered in real- and non-real-time applications. By using game-theory-based maximization of throughput for global networks, the power control problem of a cognitive HSTN was studied. A distributed power control algorithm based on perfect channel estimation was proposed in [118] to obtain the Nash equilibrium (NE) of the game while ensuring the SINR requirements of the satellite links. The authors of [119] proposed a short-term and a long-term optimal power control scheme to maximize the delay-limited capacity and minimize the outage probability for a cognitive LEO constellation with a terrestrial network by taking LEO satellite mobility into consideration. Similarly, an energy-efficient optimal power control scheme for an underlay LEO constellation-based cognitive HSTN with outdated CSI was presented in [120]. Introducing an effective energy efficiency metric employing delay QoS exponent for a cognitive HSTN with instantaneous CSI of a secondary transmitter and primary receiver, ref. [122] proposed a power allocation scheme by taking both the energy efficiency requirements and delay QoS provisioning into account. A sensing-based power allocation scheme for an integrated wireless sensor network-cognitive HSTN was introduced in [108]. Perfect and imperfect sensing scenarios for seamless channel access of the cognitive satellite's secondary user were considered.
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 4 Experimental Results and Analysis
### Experimental Result Analysis
To further validate the performance of the proposed CIMKG, we conducted ablation experiments, and the results are shown in Table 3.  
By removing the intent attention layer from the CIMKG, we obtained a new model named _\"without intent attention layer\"_, in which the original hidden states \(H\) are utilized to replace the hidden states \(H_{I}\) that would have been processed by the intent attention layer. Similarly, another model referred to as _\"without slot attention layer\"_ was obtained by solely removing the slot attention layer. As shown in Table 3, compared with the CIMKG, the performance of slot filling and intent detection in these two modules decreased, indicating that explicit intent and slot representations are crucial for the co-interactive layer between the two tasks. Subsequently, to verify the role of bidirectional information flow, only one direction of the information flow was retained. The models with only one direction of information flow were named the _\"with intent-to-slot\"_ and the _\"with slot-to-intent\"_. As shown in Table 3, the CIMKG performed better with a bidirectional information flow configuration. The ablation experimental results indicate that modelling the interaction between intent detection and slot filling can enhance the performance of both tasks, further demonstrating the strong correlation between intent detection and slot filling.
### Computational Complexity of the HFA-K-Means Algorithm  
The proposed HFA-K-means clustering method is simple in terms of complexity, making it easy to implement for the problem at hand. The HFA-K-means is designed to retain the two inner loop structures of the classical FA when going through the population \(n\) and one outer loop for evaluation in terms of the maximum number of function evaluations (MaxFE). So, the complexity in the extreme case is as of the standard FA, which is \(O\big{(}n^{2}MaxFE\big{)}\). Moreover, as n is small (typically the value of \(n=25\) in our case), and MaxFE is large (MaxFE = 50,000), the computation cost is relatively inexpensive because the algorithm complexity is linear in terms of MaxFE. Therefore, considering the clustering objective, in this case, the algorithm's overall computational complexity is \(O(n.K.MaxFE)\).
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 3 Spectrum Sharing Radar and Communications Systems  
### Common Transmit Waveform of Coexisting Systems  
We considered two coexisting systems that share a spectrum in the S band. The first system is a monostatic pulse-Doppler radar used to determine the range and velocity of a nonfluctuating deterministic (Swerling-0 model) target. The second one is a wireless multiuser (MU) communications system used to transmit multiple symbols for \(M\) users over each pulse repetition interval (PRI) of the radar. The receivers of the two systems are assumed to be collocated.  
We assumed that both systems use a transmit waveform with the same time-varying amplitude and phase functions. In particular, both systems use the OLFM waveform in Equation (4) with fixed bandwidth \(B\) but with varying FM rate \(b\) and duration \(T\). We denoted the transmit OLFM waveform for the radar as \(s_{\rm r}(t)=s_{\rm r}(t;b_{\rm r},T_{\rm r})\) and for the \(m\)th communications user as \(s_{\rm c,m}(t)=s_{\rm c,m}(t;b_{m},T_{\rm c})\), \(m=1,\ldots,M\). The radar parameter set, with cardinality 2, is given by:  
\[\Psi_{\rm r}=\left\{T_{\rm r},\ \ b_{\rm r}\right\}. \tag{7}\]  
Assuming all \(M\) communications users have OLFM waveforms with the same finite duration \(T_{\rm c}\) but unique FM rates, then the MU communications parameter set, with cardinality \((M+1)\), is given by:  
\[\Psi_{\rm c}=\left\{T_{\rm c},\ \ b_{1},\ \ b_{2},\ \ldots,\ \ b_{M}\right\}. \tag{8}\]  
Thus, overall, there are \((M+3)\) parameters that must be designed at each coherent processing interval (CPI) of the radar. Our proposed coexisting transmit waveform scheme (CoWS) design is based on optimizing waveform-dependent system performance metrics; such metrics include system-specific ones, including multiple access interference (MAI), gross bit rate bit rate, transmission bit-error rate (BER), and range resolution, and mean-squared error (MSE) of parameter estimation, as well as joint metrics, such as interference between the two systems (see Section 4). We first provide some background on the processing of the received waveforms for each system that is required when optimizing the waveform-dependent performance metrics.
## 4 Performance Evaluation and Comparison  
The performance evaluation and comparison of our LSTM-based intrusion detection system (IDS) represent a crucial phase in validating the effectiveness and robustness of our proposed model. In this section, we present a detailed analysis of the IDS's performance using three distinct datasets: CICIDS2017, NSL-KDD, and UNSW-NB15. The primary aim is to assess the model's ability to accurately detect network intrusions across various real-world scenarios. To achieve this, we employ a set of standard evaluation metrics, providing valuable insights into the model's accuracy, precision, recall, F1-score, and AUC-ROC. Additionally, we utilize confusion matrices to gain a deeper understanding of the model's performance by analyzing true positive, false positive, true negative, and false negative predictions. By systematically evaluating and comparing the IDS on different datasets, we aim to validate its effectiveness in real-world intrusion detection scenarios, addressing the ever-evolving challenges of network security.