esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding the Seismic Resilience of Metallic Cylindrical Tanks Through Parametric Analysis
## 4 Discussions on FE Results and Parametric Sensibility
### Tank H/R = 3.0  
As observed in Figure 9a, the pressure profiles for the five different \(R/t\) ratios show a similar trend. As in previous cases, significant pressure peaks occur at the top of the wall due to convective waves both for rigid and flexible wall tanks. The largest peak occurred for the flexible tank (\(R/t\) = 1000) at approximately 0.9 normalized height (\(z/H\)), and this peak decreased as the rigidity of the wall increased, reaching its lowest point for the rigid tank (\(R/t\) = 250--Figure 9b). In the lower part of the tank, the pressure profiles remained relatively consistent, except for the most flexible wall (\(R/t\) = 1000), which exhibited very large peaks due to the low resistance of the shell to horizontal excitations. It is evident that the \(R/t\) = 1000 tank configuration failed through the classical elastic-plastic elephant foot buckling mechanism just above the base of the wall, highlighting the vulnerability of highly flexible tanks under seismic loading. The predictions made using the international regulations (EC8-4, NZSEE, and AWWA) consistently underestimate the envelope of pressures observed from the FE analyses. While the small discrepancies in the lower part of the tank can be accounted for in design, the significant underestimation in the upper portion of the tank, caused by the convective pressure component, may lead to an unsafe design, particularly if a substantial reduction in shell thickness is implemented along the tank height. Finally, it is important to note that the contribution of sloshing motion increased clearly as the radius-to-thickness ratio (\(R/t\)) rose. However, current analytical proposals are not fully capable of accounting for this behavior, which is observed to become more pronounced as the flexibility of the tank increases. This underscores the need for a more accurate prediction of fluid-structure interaction in tanks particularly in cases involving highly flexible structures that are more susceptible to large convective forces.  
Figure 8: Horizontal pressure distribution of \(H/R\) = 1.5 tank ratio considering different \(R/t\) thickness ratios: (**a**) FE simulations and (**b**) predictive pressures computed according the international standards compared with FE simulations.
## 3 Proposed ML Method
### Feature Extraction
#### 3.1.1 Discrete-Time Signal Analysis  
The non-stationary time series data recorded from the 2012 PHM Data Challenge bearing dataset are presented as a 2-D vector. The vibration amplitude sampled at 25.6 kHz by the Dytran Model 3035B accelerometer was transferred from the discrete-time domain to the frequency domain using the Short-Time Fourier transform (STFT) parameters detailed in Table 2.  
The Short-Time Fourier transform (STFT) can be defined as a sequence of Fourier transforms of a windowed input signal. STFT is used to extract time-localized frequency  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sampling Frequency (\(f_{s}\))** & 25,600 Hz \\
**Window Length (\(L_{\text{w}}\))** & 1024 \\
**Hop Size (\(L_{\text{h}}\))** & 256 (75\%) \\
**No. of FFT Points (\(nfft\))** & 1024 \\
**Window Type (\(w\))** & Blackman \\
**Amplitudes** & dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: STFT parameters.  
Figure 4: (**a**) STFT feature extraction approach with feature compression and (**b**) the three feature extraction approaches investigated for Envelope Analysis (EA).  
information, for situations in which frequency components of a signal vary drastically over time, such as non-stationary bearing vibration signals [67]. The STFT, shown in Equation (1), involves calculating a windowed Fast Fourier transform (FFT) of the discrete time samples, with each window overlapping the previous by a factor of 75% to obtain the complex feature vector signatures across time. The average value of these absolute complex feature vectors for each sample is calculated to extract a spectral feature vector consisting of 512 spectral points (bins).  
The spectral points are spaced at 25 Hz intervals and represent the spectral amplitudes content over a range of 0 to 12,800 Hz (Nyquist frequency). This frequency range is determined by specifying the number of Discrete Fourier transform (DFT) points calculated for each window and by the sampling frequency of the DFT. The sampling frequency was matched to the frequency of the accelerometers sampled the bearing signals at, 25.6 kHz, and the number of DFT points calculated for each window was 1024 points (the same value as the length of the sample window \(g(n)\)).  
\[\mathcal{F}\Big{\{}x[n]\Big{\}}=X[m,f]=\sum_{n=-\infty}^{\infty}x[n]g[n-mR]e^ {-f2\pi fn} \tag{1}\]  
where \(\mathcal{F}\Big{\{}x[n]\Big{\}}\) is the DFT of windowed data centred about time \(mR\), \(g(n)\) is a window function, and \(R\) is the hop size between successive DFTs.  
The STFT has been favoured as a feature extraction method to obtain useful features for both RUL estimations and fault classification and achieved extremely high results as seen in [34; 35; 68; 69].
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
###### Abstract  
Mental health disorders constitute a significant global challenge, compounded by the limitations of traditional management approaches that rely heavily on subjective self-reports and infrequent professional evaluations. This study presents a groundbreaking IoT-based system that integrates big data analytics, fuzzy logic, and machine learning to revolutionise mental health monitoring. In contrast to existing solutions, the proposed system uniquely incorporates environmental factors, such as temperature and humidity in enclosed spaces--critical yet often overlooked contributors to emotional well-being. By leveraging IoT devices to collect and process large-scale ambient data, the system provides real-time classification and personalised visualisation tailored to individual sensitivity profiles. Preliminary results reveal high accuracy, scalability, and the potential to generate actionable insights, creating dynamic feedback loops for continuous improvement. This innovative approach bridges the gap between environmental conditions and mental healthcare, promoting a transformative shift from reactive to proactive care and laying the groundwork for predictive environmental health systems.  
m m  
rate variability and sleep patterns, while neglecting the impact of external environmental conditions, including temperature, humidity, air quality, and lighting. Research has shown that such environmental factors significantly influence emotional well-being and mental health, particularly in enclosed spaces where people spend a substantial portion of their time [5,6]. Ignoring these factors limits the comprehensiveness and efficacy of current monitoring solutions. Studies have shown that temperature, humidity, and other ambient factors can profoundly affect mental health [7]. However, they remain underutilised in treatment and prevention strategies.
# Improved DQN for Dynamic Obstacle Avoidance and Ship Path Planning
## 5 Discussion  
The complexity of the environment puts forward higher requirements for the rapidity and flexibility of path planning. The rapid development of artificial intelligence provides an effective way for path planning of complex problems. This paper improves the experience replay of DQN algorithm, and it is applied to path planning in complex marine environments. By sorting and sampling the samples (\(s_{j}\), \(a_{j}\), \(r_{j}\), \(s_{j+1}\)) in the experience playback pool, the higher the value of the samples, the greater the probability of the sampling motivating the agent to learn correctly. The multi-step update reduces the inaccurate estimation of Q value and accelerates training. The improved DQN enhances the algorithm's performance by training network parameters based on non-uniform sampling by setting sample priority. In the simulation environment, the unit length of the shortest collision avoidance path planned by the improved DQN is 21, which is superior to other comparative algorithms. In the simulation environment of dynamically generating random obstacles, the shortest collision avoidance path planned by the improved DQN outperforms other comparative algorithms regarding exploration steps and rewards. The improved DQN algorithm is applied to ship path simulation and can plan reliable paths in dynamic and static obstacle environments. Experiments show that this improved DQN reinforcement learning algorithm can be used to solve the path planning problem in complex environments. The ship simulation environment designed in this article is relatively simple, and exploring DQN planning collision avoidance paths in more complex environments is more meaningful. Exploring other technologies to improve DQN performance in solving collision avoidance path problems is worthy of future research.  
Conceptualization, X.Y. and Q.H.; methodology, X.Y.; software, X.Y.; validation, X.Y.; formal analysis, Q.H.; investigation, X.Y.; resources, Q.H.; data curation, X.Y.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y.; visualization, X.Y.; supervision, Q.H.; project administration, Q.H.; funding acquisition, Q.H. All authors have read and agreed to the published version of the manuscript.  
Figure 17: Episode via cost of the improved DQN for scenario 2.  
**Funding:** This work was funded by the National Key R&D Program of China under Grant No. 2020 YFB1710200.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request due to restrictions, e.g., privacy or ethical.  
**Acknowledgments:** Thanks for the support of the laboratory for the simulation experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**All Features**} & **All Except** & **All Except** & **All Except Features** & **All Except** & **Wind Speed** \\  & & **Theoretical** & **Previous Real** & **Based on Date and** & **Seasonal Binary** & **Only** \\  & & **Power** & **Power** & **Time** & **Features** & **Only** \\ \hline LR & 183.70 & 229.58 & 252.52 & 184.31 & 184.16 & 459.71 \\ SVR & 277.08 & 298.01 & 334.36 & 192.77 & 282.59 & 254.69 \\ kNN & 133.31 & 164.85 & 130.63 & 136.88 & 132.38 & 260.12 \\ DT & 135.31 & 137.86 & 194.46 & 149.64 & 144.40 & 256.70 \\ RF & 121.32 & 121.59 & 169.68 & 132.16 & 125.52 & 243.65 \\ ET & 125.04 & 137.96 & 182.74 & 134.58 & 128.11 & 240.89 \\ AB & 125.04 & 123.24 & 135.58 & 138.95 & 125.10 & 611.49 \\ GB & 112.59 & 113.87 & 144.43 & 134.91 & 116.80 & 252.59 \\ XGB & 114.34 & 113.08 & 131.13 & 138.76 & 114.04 & 240.06 \\ CB & 113.25 & 114.98 & 123.81 & 132.88 & 116.67 & 239.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE (kW) obtained at the test set. from the previous time step received experimental confirmation. It should also be noted that wind direction and month (as a feature of seasonality) have a slight influence, as shown in Figure 10.  
Figure 11 displays a scatter plot of real and predicted wind turbine power, while Figure 12 presents a comparison of real, theoretical, and predicted power. Figure 11: Real vs. predicted power. Figure 10: CatBoost feature importance for using all features.
## 3 Background of Study
### Proposed Intelligent Monitoring Framework
#### 3.2.2 Standard DL-Based Diagnostic/Classification Models
The output \(O_{i_{\text{out}}}\) from each of the output nodes \(O_{i}\) is obtained by passing the inner product \(O_{i_{\text{in}}}\) through a nonlinear activation function \(f\) using Equation (6):  
\[O_{i_{\text{out}}}=f\big{(}O_{i_{\text{in}}}\big{)} \tag{6}\]  
The automatic (supervised) learning process of the CNN and MLP enables the minimizing of the squared error in the predicted outputs and the actual target labels using Equation (7):  
\[E=(y-O_{iou})^{2} \tag{7}\]  
where \(E\) is the prediction error (cost function), and \(y\) is the desired output label. More often, the _ReLU_ activation function is preferred because of its faster learning advantages on DNNs. Further, because it returns the corresponding output value for positive values whereas, for negative input values, it returns a zero value [28]; and ideally, most input variables--extracted features are usually greater than zero--the _ReLU_ is often preferred.
## 5 Simulation Result and Discussion
### Simulation Verification of Improved Adaptive Control Method  
Under the same conditions as Section 5.2, the simulation verification of the improved NDI control method is carried out. Similarly, in the simulation of this section, all links including recursive detrending, identification, and step pitch angle command are also used, and only the image of the step command tracking stage is displayed.  
Figure 10 shows the step response tracking effect of the improved NDI control method, conventional NDI control, and PID control.  
It can be seen from the simulation diagram that compared with the PID controller and the NDI controller, the improved NDI controller has a further improvement in response speed and overshoot. Similarly, it can be seen from the elevator deflection angle image and the pitch angle velocity image that the improved NDI controller has a better response effect of the elevator deflection angle. In order to compare the differences among the two numerically, three indicators of rise time, peak time, and overshoot are selected to quantitatively compare the control effects of the three. Table 5 shows the quantitative comparison of simulation results.  
Under the same simulation conditions, compared with the improved NDI, adaptive NDI and PID control methods, it can be seen that the improved NDI has faster response speed and convergence speed than the adaptive NDI, and the overshoot is smaller. It has the best control effect among the three, which shows the effectiveness of the series lead link.  
Figure 10: Cont.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **PID** & **NDI** & **Improver NDI** \\ \hline rise time(s) & 0.39 & 0.36 & 0.35 \\ peak time(s) & 0.61 & 0.55 & 0.49 \\ overshoot (\%) & 35.21 & 26.78 & 11.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Control effect index comparison (INDI vs. NDI vs. PID).
## 5 Experiment and Evaluation
### Discussion of Application Scenario  
On the basis of experiment and evaluation, it is found that the apprenticeship learning-based cross-layer routing protocol has the following characteristics: Firstly, its learning speed is sufficiently  
Figure 12: (**a**,**b**) Effectiveness properties versus probability of PU arrival.  
high with the guidance of expert demonstration data. Secondly, it achieves high energy efficiency by reducing the trial-and-error cost and applying the DAR. Thirdly, the routing latency of the scheme is relatively low, and it accommodates the maximal latency requirement. Fourthly, it releases memory space periodically to store more data caches or high-precision data. Fifthly, by using DRL, the proposed algorithm is able to solve joint routing and resource management for large-scale networks. With all these advantages, the proposed scheme can be used in wireless sensor networks such as the Internet of Things (IoT) and the Wireless Environmental Monitoring Network (WEMN). In these applications, the number of sensor nodes is large, and every node has limited storage resources. Moreover, the network lifetime is limited, since the nodes are energy-constrained and the environment of their deployment area is complicated. In addition, the proposed scheme can also be applied in cognitive radio ad hoc networks such as Military Ad Hoc Networks (MAHN) and Personal Area Networks (PAN). These applications require the network to have fast self-organizing capability, and the routing protocol has to ensure that the end-to-end delay is within the tolerance and meets the application requirement. Furthermore, since most radios rely on batteries for power supply, energy efficiency should be taken into account. Consequently, the proposed scheme would satisfy all of the requirements for these application scenarios and produce better network performance.
## 3 Next Generation Sequencing (NGS)
In fish phylogenetic and phylogeographic studies, the most popular NGS methods are restriction site-associated DNA sequencing (RAD-seq), transcriptome sequencing (RNA-seq), targeted amplicon sequencing (TAS) and hybrid enrichment using probes. In salmonid population genetic studies, the RAD-seq technique has been widely used [64]. RAD-seq technologies facilitate overcoming some limitations of traditional methods by increasing the number of genes analyzed [6]. The main applications of the RAD-seq method are the identification and annotation of novel SNPs and the genotyping of thousands of SNPs in population genetic studies. The RAD-seq method proposed by [PERSON] [65] showed some limitations, and therefore, subsequent modifications were proposed (ddRAD [66] and 2b-RAD [67]). In [PERSON]'s method [65], sequences between known restriction sites and random sites are obtained, while with the ddRAD method, sequences between two known restriction sites are obtained. The results of RAD-seq analysis can be analyzed using several software programs, such as STACKs [68], IQ-TREE [69], UNEAK (TASSEL) [70], PyRAD [71], dDOCENT [72] and AftrRAD [57; 73]. The most popular software is the STACKs program. The results obtained in the first analytical step using this software are then transferred to the GENEPOP or STRUCTURE programs for further analysis. The ddRAD method was used by [PERSON] et al. [6] to obtain a dense association map for _S. trutta_ by crossing parents of the AT and ME lineages. [PERSON] et al. [74] used a RAD-seq method to study the different genera of the subfamily _Salmonidae._ [PERSON] et al. [75] analyzed the introgression of the AT lineage into local ME populations in southern France using the ddRAD technique, and [PERSON] et al. [76] used the same method for different populations of the _S. trutta_ complex in Italy (northern Italy, the Tyrrenian area, Sardinia and Corsica). [PERSON] et al. [77] and [PERSON] et al. [33] reported improved genetic analyses using RAD-seq methods compared to those using microsatellite markers, particularly in resolving population structures. [PERSON] et al. [78] used a RAD-seq method to identify 12,204 SNPs throughout the genome of _S. trutta_ and a low-density SNP array (192 SNPs). Transcriptome sequencing (RNA-seq) is used to analyze the sequences of coding regions, which usually account for approximately 1.5% of the total genome. [PERSON] et al. [54] obtained 35,736 protein-coding transcripts for the AT lineage.
# A Hybrid MCDM Model for Live-Streamer Selection via the Fuzzy Delphi Method, AHP, and TOPSIS
## 3 Research Method
### Topsis  
Established by [PERSON] and [PERSON] in 1981, TOPSIS is an MCDM approach, based on the idea that a chosen alternative should be the closest to the positive ideal solution (PIS) and the farthest from the negative ideal solution (NIS). This method assumes that each element is either monotonically increasing or decreasing. If the element is a benefit, the greater the performance value, the greater the preference value. Conversely, if the element is a cost, the smaller the performance value, the greater the preference value. The PIS is composed of all the best values attainable by the elements, while the NIS consists of all the worst values attainable by the elements. Based on the Euclidean distance, alternatives are selected by calculating their closeness to the ideal solution. Its process is carried out based on the following steps [49]:  
1. Create a normalized evaluation matrix.
2. Create a weighted evaluation matrix.
3. Determine the PIS and NIS.
4. Compute the separation.  
The separation of each evaluation matrix in each alternative from the PIS and NIS is computed based on the Euclidean distance formula.
5. Compute the relative closeness to the ideal solution.  
When the relative closeness approaches 1, an alternative is closer to the PIS and farther from the NIS. Therefore, the chosen alternative is the best.  
6. Rank the alternatives.  
We rank the alternatives based on their relative closeness.  
TOPSIS has been successfully used for MCDM problems in many subjects. It involves a simple but effective mechanism to handle multiple criteria and is computationally efficient [50]. Additionally, TOPSIS is a useful approach for evaluating alternatives [51]. Hence, the alternatives were ranked in this study based on TOPSIS.
## 2 Materials and Methods
### Experimental Setup  
_Chlorella vulgaris_ was obtained from the Department of Botany, University of Dar es Salaam, in Tanzania, where it was isolated from the fish pond using a standard plating method. The species was batch cultured in triplicate for 20 days in 2 L conical flasks using BBM (control), BCM and ANM growth media. The cultures were maintained under laboratory conditions with temperature of \(28\pm 1\)\({}^{\circ}\)C, light intensity \(5000\pm 10\) lux (measured using a VXLM-636 light meter, Vertex, San Diego, CA, USA), photoperiod (16:8 light: dark cycle) and stirred by continuous aeration. The pH was adjusted to between 9 and10 (AB 15 Accumet Basic, Fisher Scientific, Singapore) by adding 5 M sodium hydroxide or 1 M hydrochloric acid. The microalgae biomass was harvested prior to attaining the stationary growth phase by centrifugation at 978.02\(\times\)\(g\) for 10 min. The biomass was thereafter rinsed with distilled water, air-dried and stored at 4 \({}^{\circ}\)C until further analysis.