esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### FEM Creation and Updating

Table 4: Cross-section of truss members. Figure 11: Two types of bearing were used in the eighth span of the Chuong-Duong Bridge: (**a**) type 1; (**b**) type 2. The Modal Assurance Criterion (MAC), a statistical indicator, is particularly sensitive to significant discrepancies in the mode shapes and is comparatively insensitive to smaller differences [55]. This results in a reliable statistical indication and consistency between the mode shapes. This study used the MAC value to evaluate and update the finite element model. The Formula (26) was used to calculate the MAC value:  
\[MAC=\frac{\left|\sum\limits_{k=1}^{n}{{{{\left({{\widetilde{\varphi}_{k}}}\right)} ^{T}}}\times{\varphi_{k}}}\right|^{2}}{\left\{{\sum\limits_{k=1}^{n}{{{{\left({{ \widetilde{\varphi}_{k}}}\right)}^{T}}}\times{\varphi_{k}}}}\right\}\times{ \left\{{\sum\limits_{k=1}^{n}{{{{\left({{\varphi_{k}}}\right)}^{T}}}\times{ \varphi_{k}}}}\right\}}} \tag{26}\]  
where \(n\) is the number of modes shape considered, \(MAC\) is the modal assurance criterion, \({\varphi_{k}}\), \(\stackrel{{\sim}}{{\varphi_{k}}}\) is the modes shape FEM and experimental, and \(T\) represents the transposed matrix. The MAC values were determined through Formula (26), comparing the FE model results and the actual measurements. The first three MAC values greater than 0.85 showed good agreement between each pair of mode shapes. However, other MAC values did not reach this minimum value. The correlation between the calculated and measured mode shape vectors was not guaranteed. The frequency values were also significantly different. This is a common situation for initial FE models, most of which have not been able to extract modes with high accuracy. Meanwhile, depending on the calculation requirements of the structure, some structures needed high accuracy to structure health monitoring, diagnose damage. There were many uncertain parameters, such as material properties and stiffness parameters. For this reason, we recommend performing a model update procedure to reduce errors.
# Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM
## 5 Fault Detection of Wind Turbine-Based Pitch Control System
### Experimental Results  
To confirm the superiority of the IHHO-LightGBM in the pitch control system fault detection, yearly WT operation data of the SCADA system for a 1.5 MW wind turbine in a wind power plant in Inner Mongolia is selected, and the datasets, including pitch control emergency stop fault, pitch control motor fault, and pitch control power supply alarm, are selected from the normal working condition data of SCADA, which are recorded as dataset 1-3. To compare with the following three established fault detection methods: GBDT, XGBoost, and LightGBM, and various evaluation standards in three various datasets are employed.  
The FNR and FAR of those three failure datasets are displayed in Figures 6 and 7. The result showed that compared with the other three algorithms, the IHHO-LightGBM algorithm works better than the IHHO-LightGBM in FAR (around 0.6-1.69%) and FNR (around 012-0.47%). The XGBoost algorithm works the worst with FNR, it reaches 3.57%, and the LightGBM algorithm works the worst with FAR, in the case of a large amount of data, it reaches 16.92%. Compared with the GBDT, XGBoost, and LightGBM algorithms, the FAR of IHHO is reduced by 0.08-15.34%, and the FNR is reduced by 0.14-3.3%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{2}{c}{**Forecast**} \\ \cline{2-3}  & **Forecast Faulty** & **Forecast Normal** \\ \hline Actual Faulty & TP & FN \\ Actual Normal & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix of classification results.  
The F1-scores of those three failure datasets are displayed by Figure 8. The F1-score is available for assessment in the case of a large amount of data. One algorithm will have a better performance when the F1-score gets closer to one. It is clear that the fault detecting ability of IHHO-LightGBM is very stable with an F1-score around 98.52-99.32%.  
Figure 6: FAR of four algorithms for fault detection.  
Figure 7: FNR of four algorithms for fault detection.  
In conclusion, IHHO-LightGBM has shown a better FAR, FNR, and F1-score. It is found, through comparison, that the fault detecting ability of LightGBM optimized by the improved HHO algorithm is obviously improved.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 6 Performance Evaluation
### Resistivity to DoS and DDoS Attacks
#### 6.4.1 Average Delays under DDoS Flooding Attacks  
One of the outstanding features of the 5 GSBA is the alleviation of the flooding of DDoS attacks. The 5 GSBA, since all authentication tasks are distributed at various gNBs, avoids congestion and processing at the single authentication entity AUSF. Hence, to illustrate the performance of the protocols under the flooding of many authentication requests, we can model all centralized protocols, such as 5G-AKA [4] and SE-AKA [20], as M/M/1 queuing models, while the blockchain-based protocol of the 5 GSBA can be modeled as a M/M/c  
Figure 9: Energy consumption for UE.  
queuing model (c is the number of gNBs). For simplicity, it is assumed that the arrival and location of UE devices follow Poisson distribution, such that all devices within 1 km\({}^{2}\) are located uniformly and can initiate authentication requests randomly. For the centralized protocols, since the AUSF in the 5 GC should be a computationally powerful server, it is assumed that they run on the server 80% faster than the performance listed in Table 2. For blockchain-based protocols, as the computational resources for each gNB are limited, it is assumed that its computational time is the same as that in Table 2. Furthermore, one AUSF only serves the gNBs within the 1 km\({}^{2}\) area, and the ISD of every gNB is 200 m according to the TS 22.261 5G dense urban service requirement [36]. Consequently, each server needs to process the requests from 1 km\({}^{2}/\pi 0.1^{2}=31.8\approx 32\) gNBs (i.e., c = 32).  
Finally, the average time to complete the execution of the protocol is shown in Figure 10. It shows that although the decentralized 5 GSBA takes slightly longer time than the other centralized schemes when the network traffic is low, as the number of requests increases, the 5 GSBA outperforms the others by maintaining an almost constant delay. In other words, the 5 GSBA can serve more authentication requests with the same amount of delay than the centralized schemes. In future, as the number of gNBs keeps increasing, it is expected that the 5 GSBA will be able to perform even better by accommodating more users. Therefore, this shows that the 5 GSBA is more robust in terms of serving more users, and thus is more effective in lessening the effects of flooding attacks. Moreover, since the 5 GSBA can accommodate more requests, this also gives network operators more time to detect DDoS attacks and deploy countermeasures. For example, network operators can use the time to analyze network traffic and block potential DDoS attackers without impacting overall service quality. By doing so, attackers would find it more challenging to perform a successful DDoS attack in the 5G network.
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 4 Results and Discussion
### Dispersion Curves  
The dispersion curves obtained by applying the f-k and SPAC techniques are shown in Figure 5. The curves are plotted within the theoretical wavenumber limits (Kmin-Kmax), have a frequency range from 6 to 15 Hz, and a corresponding phase velocity range between 250 and 900 m/s.  
The obtained frequency results have been compared with previous works carried out in other regions of the study basin. In the work of [22], where the same methods and array geometry were applied, they found frequency values ranging from 5 to 12 Hz. In this case, the lithology in the area under study was similar, except for the recent Quaternary alluviums (topmost layer) that exist in the Middle-Chelif but not in the Oran region. However, it is important to note that an array aperture of 30 m does not allow for investigating higher depths, but only the first tens of meters of the soil column. One of the main limitations of the array techniques with regard to single-station-based techniques is that the array techniques are limited in terms of the investigated depths and depend on the array aperture, so it can be very difficult to find open spaces to perform large aperture array when working on urban areas. Hopefully, the targeted depth in this study is 30 m, and for that, an array aperture of 30 m is enough.  
In a previous different work [27], one circular array with a radius of 50 m was deployed in the west part of the study basin. In this case, they obtained fundamental frequency values ranging from 2.7 to 14 Hz. The variation in the frequency range was maybe due to the different local geological conditions of both sites, as well as the different aperture of the arrays.  
Figure 5: The dispersion curves estimated for each recording site using the f-k analysis (black line) and the SPAC method (red line). The dispersion curves are plotted in terms of the Rayleigh wave phase velocities (m/s) as a function of frequency (Hz). Each plot corresponds to the results obtained at the different analyzed sites (P1 to P15). In Figure 2, the position of these sites is shown.
# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 2 Overview of Classifiers  
### Linear Discriminant Analysis  
Linear discriminant analysis (LDA) is one of the most popular classifiers in BCI research, as it is computationally efficient and provides robust results. Despite the fact that this trivial algorithm was proposed in the 1980s [18], it is still one of the most useful methods applied for classification of various data, including multichannel EEG time-series. LDA can be applied for both a supervised and unsupervised P300 speller [19].  
LDA assumes that the covariance matrices of each class are identical and full rank matrices, which results a linear structure when using Bayes' rule. Different solving methods can be applied for LDA implementation, such as singular value decomposition (SVD), eigenvalue decomposition (ED), or the least squares solution (LSS). SVD is applied for LDA in our case as the EEG data vectors have a large number of features.  
LDA is a classical method for EEG time-series classification, as it is good for dimensionality reduction and classification. LDA usually provides stable results for BCI systems, for instance, when using EEG and electrooculography (EOG) combined for detecting a user's response, LDA can achieve an accuracy of more than 97% [20]. Despite the fact that LDA may not be as efficient when applied to small high-dimensional datasets, it provides good results when the amount of the user data is sufficient. Nevertheless, LDA-based methods, such as group sparse discriminant analysis [21] can be applied to overcome the undersampling problem. In order to improve the results obtained by LDA, some complex dimension reduction methods, such as bond graph analysis, may be applied as a preprocessing step [22].
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 4 Results
### TVOC Content Prediction (SGP3 Sensor Response)  
Figure 9 compares the performance of MLTs in predicting TVOC content (evaluated using SGPC3 sensor) in the living room based on IAQ monitoring in the kitchen. The following performance metrics were used: R\({}^{2}\) (Figure 9a), MAPE (Figure 9b), and RMSE (Figure 9c). Their median values are shown in Table 3.  
As shown in Figure 9a, all MLTs applied to predict TVOC content, evaluated using the SGPC3 sensor, demonstrated lower quality (R\({}^{2}\) = 0.16-0.54) than the naive approach (R\({}^{2}\) = 0.56). These values indicated the modest quality of models in general. Still, linear models MLR and MPL2 were featured by the highest R\({}^{2}\), with R\({}^{2}\) = 0.54 and R\({}^{2}\) = 0.55, respectively. The quality of nonlinear MLTs was very small (R\({}^{2}\) = 0.16-0.44). Based on Figure 9b, all the MLTs offered lower errors (MAPE = 0.93-1.49%) than the naive approach (MAPE = 3.70%). In the case of linear MLTs, i.e., MLR and MPL2, the errors were smallest, with MAPE = 0.93% and MAPE = 0.95%, respectively. Also, regarding the RMSE, all the MLTs (RMSE = 255-353 s.r.u.) offered lower errors compared to the naive ap  
Figure 9: Comparison of the MLTs’ performance in predicting TVOC content (evaluated using the SGPC3 sensor) in the living room based on IAQ monitoring in the kitchen. The metrics are (**a**) R\({}^{2}\), (**b**) MAPE, and (**c**) RMSE. The dots indicate the means.  
proach (RMSE (naive) = 715 s.r.u.) (see Figure 9c). The errors attained using linear MLTs, i.e., MLR and MPL2, were the smallest (RMSE = 255 s.r.u. and RMSE = 257 s.r.u., respectively). In conclusion, it is possible to predict TVOC content, evaluated using an SGPC3 sensor, in the living room based on IAQ monitoring in the kitchen, using linear models, with a percentage error of less than 1%. Similar to T, RH, and TVOC content based on the SGP30 sensor it is crucial to note that this result was achieved by prediction models trained on IAQ monitoring data collected in different apartments and not where the prediction was realized. The best prediction was achieved using MLR, which was also one of the highest-quality models. Although the quality of the naive approach was the highest, it was outperformed by all the MLTs in terms of prediction errors.
## 4 Simulation Results
### Simulation Response for a Varied External Load  
The simulation response for a varied external load is presented in Figure 14. The load variation is given at 0.45 p.u-speed. The motor was initially loaded with 0.1 of rated torque, and the load was increased to 0.2 of rated torque at \(t\) = 25 s. At \(t\) = 35 s, the load was reduced back to 0.1 of the rated torque.  
As the load was increased, a spike occurred in the rotor-speed reduction to the minimum value of 0.443 p.u-speed at \(t\) = 25.174 s. Simultaneously, the current began to increase from 0.0313 to 0.0610 p.u-current. Under the decreased load condition (\(t\) = 35 s), the rotor speed spiked to a maximum value of 0.458 p.u-speed at \(t\) = 35.165 s, causing the current to decrease from 0.0610 to 0.0313 p.u-current. Although a spike in the motor speed occurred in response to a change in the load, the motor speed could return to its initial value, indicating that the proposed system is robust to load variations.
###### Abstract  
The internal overburden movement after coal mining may cause many disasters to the on-site production. It is of great guiding significance for the engineering treatment such as separation layer grouting and gas extraction to master the evolution law of separation layer and fracture in the overburden. Combined with the full-columnar overburden of a certain working face, this study established a number of models using 3 DEC simulation software and analyzed the influence of different mining heights and widths on the distribution law of separation layer and fracture after strata movement. The simulation results show that the evolution of separation layer in the overburden after mining roughly consists of three stages, namely, initial generation, reaching peak, and tending to close (stable). The development of the separation layer is positively correlated with the mining height and negatively correlated with the mining width. When the mining height increases from 3 m to 5 m, the peak value of cumulative separation increases from 0.7 m to 2.1 m. On the contrary, when the mining width increases from 250 m to 350 m, the peak value of cumulative separation decreases from 2.8 m to 1.1 m. The pre-bearing stress concentration will be formed in the mining process of the working face. The influence of mining width change on the peak of stress concentration is greater than that of mining height change, and the subsidence is mainly affected by mining height. A quantitative analysis method of water-flowing fracture development height was developed by using the penetration height of joint shear displacement. The calculated fracture zone height 117.33 m was in good agreement with the actual measured results 120 m, verifying the validity of this method. These findings are of great reference for mastering the distribution law of separation layer and fracture in the mining overburden.  
mining parameters; separation layer; water-flowing fracture; key strata; 3 DEC Article
# Factors Influencing the Mental Health Consequences of Climate Change in Canada
## 4 Discussion
### Access to Care: Financial  
Psychosocial wellbeing in a changing climate may be thwarted by limited financial or physical access to mental health care [11; 42; 50; 51; 52; 53; 54; 54; 55; 56; 57; 58; 59; 64]. Lack of access may stem from economics, geography,poor government planning, lack of trust, and/or poorly trained health professionals who may not recognize mental health symptoms or inappropriately triage patients with mental health needs.  
While Canada has a universal, single-player health care system, access to mental health care can still be impeded by financial constraints and availability of services depending on where an individual lives [50; 68; 69]. Mental health services vary by province and many mental health services are not covered under Canada's universal health care program [50]. Services covered by universal health care in Canada include in-hospital mental health care and out-patient services from primary care physicians, nurses, and psychiatrists [50]. Patients seeking support outside of these professional domains will require private insurance to cover these expenses or they will need to pay for these services, which can make them inaccessible for many Canadians [50; 69].
## 4 Conclusions  
In this study, we thoroughly discussed the real-time BDS-3 clock estimation with multi-frequency uncombined signals. The multi-frequency UC model is first developed with B1I/B3I IF combined clock datum and then the GFIF model is derived for IFCB estimations. With data from about 80 global IGS network stations over one week, the simulated real-time numerical experiments are conducted for validation and analysis. The results indicate that the satellite clock estimation from multi-frequency UC models achieves high agreement with that from the traditional dual-frequency IF combined model. The STD of DD clocks reaches about 0.03 ns with respect to post-processed solutions. Different from the steady satellite IFCBs between the B1C and B1I/B3I signals, the satellite IFCBs between the B2a and B1I/B3I signals showed apparent periodical variations with multi-frequency UC models. Further investigation with the GFIF model using different PCO correction strategies imply the variations mainly result from imperfect receiver PCO corrections. Therefore, we address the urgency for receiver antenna information to achieve utmost accuracy in the multi-frequency data processing.  
Conceptualization, K.K. and J.W.; methodology, K.K.; software, K.K.; validation, K.K.; formal analysis, K.K.; investigation, K.K.; resources, K.K.; data curation, K.K.; writing--original draft preparation, K.K.; writing--review and editing, K.K., J.W. and H.H.; visualization, K.K.; supervision, J.W.; project administration, J.W. and K.K.; funding acquisition, J.W., H.H. and K.K. 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 (Grant No. 41874029, Grant No. 41904029) and the Young Teachers Research Capability Enhancement Program of Beijing University of Civil Engineering and Architecture (Grant No. X21020).  
The observation data and precise products used in the research are available on the FTP of Wuhan University ([[ftp://igs.gnsswhu.cn/pub/](ftp://igs.gnsswhu.cn/pub/)]([ftp://igs.gnsswhu.cn/pub/](ftp://igs.gnsswhu.cn/pub/)) accessed on 29 January 2021).  
The authors gratefully acknowledge IGS for providing the observation data and GFZ for providing the precise products.  
The authors declare no conflict of interest.
# Detecting Out-of-Distribution Samples in Complex IoT Traffic Based on Distance Loss
## 3 Proposed Approach
### Distance Loss Optimization Design
In this setting, the group-wise mean vectors \(\hat{\mu}i\)and \(\hat{\mu}m_{i}\) are derived from the same-dimensional representations, ensuring that Euclidean distance computations remain geometrically meaningful. Moreover, by treating features from other groups as out-of-group samples within the same projection dimension, we effectively enforce worst-case margin separation, encouraging the model to avoid overlapping feature regions across groups. This group-based contrast enforces a more discriminative feature organization even under complex, overlapping traffic distributions. The complete training procedure is described in Algorithm 2. In contrast to traditional Mahalanobis-based approaches, which require computation of high-dimensional covariance matrices, our group-level strategy significantly reduces computational complexity while maintaining strong discriminative capability. As shown in Figure 3, the proposed method yields superior OOD detection performance compared to classification-based baselines such as MSP, particularly in complex and overlapping IoT traffic scenarios. ```
0: Training set \(X_{\text{train}}\), class-group labels \(Y_{\text{train}}\), penultimate layer feature representations \(\vec{f}\), grouping results \(group_{\text{slice}}\)
0: Optimized feature representations for OOD detection
1:for each mini-batch do
2:for\(i=1\) to \(G\)do
3: Extract feature vectors belonging to group \(i\):
4:\(\vec{f}_{i}\leftarrow\vec{f}[group_{\text{slice}}[i][0]:group_{\text{slice} }[i][1]]\)
5: Compute mean feature vector for group \(i\) samples:
6:\(\hat{\mu}_{i}=\frac{1}{|G_{i}|}\sum_{j\in G_{i}}\vec{f}_{j}\)
7: Compute intra-group loss:
8:\(\mathcal{L}_{\text{in-group}_{i}}=\frac{1}{|G_{i}|}\sum_{j\in G_{i}}\left\| \vec{f}_{j}-\hat{\mu}_{i}\right\|^{2}\)
9: Identify samples outside group \(i\):
10:\(m_{i}=\{\text{samples}\text{ in mini-batch not in }G_{i}\}\)
11: Compute mean feature vector for out-of-group samples:
12:\(\hat{\mu}_{m_{i}}=\frac{1}{|m_{i}|}\sum_{j\in m_{i}}\vec{f}_{j}\)
13: Compute inter-group loss:
14:\(\mathcal{L}_{\text{group}_{i}}=\|\hat{\mu}_{m_{i}}-\hat{\mu}_{i}\|^{2}\)
15:endfor
16: Compute the average intra-group loss:
17:\(\mathcal{L}_{\text{in-group}}=\frac{1}{|G|}\sum_{k=1}^{|G|}\mathcal{L}_{\text {in-group}_{k}}\)
18: Compute the minimum inter-group loss:
19:\(\mathcal{L}_{\text{group}}=\text{min}_{k}\mathcal{L}_{\text{group}_{k}}\)
20: Compute overall loss:
21:\(\mathcal{L}=\mathcal{L}_{\text{in-group}}-\mathcal{L}_{\text{group}}+\mathcal{ L}_{\text{CE}}\)
22:endfor
```  
**Algorithm 2** Loss Function Optimization