esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
### Robustness to Illumination Variations  
This study focuses on fall detection in an outdoor environment using equipment such as CCTV cameras. Therefore, the tracking algorithm needs to be robust against illumination changes in an outdoor environment to ensure optimum performance of the algorithm. Therefore, we tested our method by artificially controlling the intensity values of images of testing datasets to simulate illumination changes and then evaluated the accuracy of the tracking. Figure 19 shows a set of testing images simulating illumination changes from \(-30\) pixel (slightly darker) to \(-150\) pixel (darkest). In Figure 20, each line shows the tracking results with the testing images for each scene, respectively. As seen in the images, our tracking algorithm can accurately track a human's state in the darkest image. Table 4 presents the tracking success rate and fall detection success rate when experimenting with four illumination changes. As changes in artificial illumination cannot adequately represent illumination  
Figure 18: Results of tracking and fall detection in the presence of multiple other people (two to four from the top) in a scene. A person in a green ellipse is a target for tracking and fall detection.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**TP** & **TN** & **FP** & **FN** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** & **Error (\%)** \\ \hline
977 & 2106 & 27 & 44 & 95.7\% & 98.7\% & 97.7\% & 2.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification result of our system for all experiments.  
changes in real environments, we also conducted tracking experiments with illumination changes in real environments. Figure 21 shows the tracking results at 12 pm and 6 pm. Sunset time is 6 pm in Korea and it is darker than when artificially subtracting 150. However, in this case, the ellipses more accurately represent the human, which means that artificially subtracting intensity value causes more information loss in the image than the actual illumination change. Therefore, in real environments, this algorithm can track people more accurately. Hence, we can confirm that this algorithm works robustly with changes in illumination, which is an important variable for an outdoor environment. Moreover, it is expected that the proposed method will be able to utilize the fall detection algorithm successfully in outdoor environments, considering it is more difficult to track black clothes in a dark environment.  
Figure 19: Testing images synthetically generated by reducing the pixel intensity for illumination changes.  
Figure 20: Tracking results with the synthetic testing images for illumination changes.  
Figure 21: Human tracking results in real illumination variations from midday light (noon) to evening time (6 pm right after sunset) in Korea.
## 4 Case Study  
In this section, two typical industrial process case studies were used to verify the effectiveness of the proposed MOSE-MLV-VSPLS algorithm. One was to predict the penicillin content in the penicillin fermentation process, and the other was the butane content in the debutanizer process.  
The methods used for comparison were as follows:  
(1) EPLS (ensemble partial least squares regression): an ensemble soft sensor modeling method that ensemble all base models generated by the PLS algorithm on the diversified subsample space obtained based on bootstrap resampling.  
(2) VSEPLS (variable selection-based EPLS): an ensemble soft sensor model that uses the MIC algorithm for input variable selection on each subsample set of the constructed EPLS.  
(3) LV-VSEPLS (latent variable space-based VSEPLS): an ensemble soft sensor modeling method that diversified base models are constructed on the latent variable space, which built based on an adaptive single kernel function on each variable space of VSEPLS.  
(4) MLV-VSEPLS (multiple latent variable space-based VSEPLS): an ensemble soft sensor modeling method that diversified base models are constructed on the latent variable space which built base on adaptive multiple kernel functions on each variable space of VSEPLS.  
(5) MOSE- MLV-VSPLS (selective ensemble MLV-VSPLS based on the muti-objective optimization algorithm): a selective ensemble soft sensor modeling method that ensemble those base models selected by the ensemble pruning method proposed in this paper that content diversity and accuracy simultaneously.  
Take the root-mean-square error (RMSE) and coefficient of determination (R\({}^{2}\)) as the prediction performance indicators of the model established by the above method.  
\[\text{RMSE}_{test}=\sqrt{\frac{1}{n_{t}}\sum\limits_{i=1}^{n_{t}}(\hat{y}_{ ti}-y_{ti})^{2}} \tag{25}\]\[R^{2}=1-\frac{\sum\limits_{i=1}^{n_{t}}{(\hat{y}_{ti}-y_{ti})^{2}}}{\sum\limits_{i= 1}^{n_{t}}{(\hat{y}_{ti}-\overline{y}_{t})^{2}}} \tag{26}\]  
where \(n_{t}\) means the total number of sample points in the test set, \(\hat{y}_{ti}\) and \(y_{ti}\) are the predicted output value and actual output value of the \(i\)th test sample, and \(\overline{y}_{t}\) represents the mean value of the sample points.
## 2 Methods
### Analytical Model of the Normal Air-Gap Magnetic Field  
#### 2.1.1 Model of the Equivalent Magnetic Circuit of the Rotor  
The flux is produced by the scalar magnetic potential on the rotor surface, and the part entering the air-gap is solved by using the equivalent magnetic circuit network method. The equivalent magnetic circuit is shown in Figure 1.  
The permanent magnet magnetomotive force \(F_{M}\), equivalent reluctance \(R_{M}\), extreme flux leakage \(\Phi_{b}\) and interpolar flux leakage \(\Phi_{i}\) can be expressed by the motor parameters as  
\[F_{M}=\frac{B_{M}\cdot h_{M}}{\mu_{0}\mu_{r}} \tag{1}\]  
\[R_{M}=\frac{h_{M}}{\mu_{0}\mu_{r}L_{M}L_{Z}} \tag{2}\]  
Figure 1: The equivalent magnetic circuit.  
\[\Phi_{b}=B_{s}h_{b}L_{Z} \tag{3}\]  
\[\Phi_{i}=B_{s}\frac{h_{i}}{2}L_{Z} \tag{4}\]  
where \(B_{M}\) is the residual magnetic induction intensity of the permanent magnet, \(h_{M}\) is the thickness of the permanent magnet, \(\mu_{0}\) is the vacuum permeability, \(\mu_{r}\) is the relative permeability of the permanent magnet, \(L_{M}\) is the width of the permanent magnet, \(L_{Z}\) is the axial length of the iron core, \(B_{S}\) is the magnetic induction intensity of bridge saturation, \(h_{b}\) is the thickness of the end of bridge, and \(h_{i}\) is the thickness of the interpolar magnetic in the bridge. The scalar magnetic potential of the rotor surface can be expressed as  
\[\Omega_{r}=F_{M}-(\Phi_{b}+\Phi_{i}+\Phi_{gr})R_{M} \tag{5}\]  
where \(\Phi_{gr}\) is the magnetic flux entering the air-gap. It can be calculated as  
\[\Phi_{gr}=\frac{F_{M}}{R_{M}}-\frac{\Omega_{r}}{R_{M}}-\Phi_{b}-\Phi_{i} \tag{6}\]
## 3 Results and Discussion
### Antioxidant Activity by DPPH Method
From Figure 1 it can be seen that for all the samples analyzed, the inhibition recorded is directly proportional to the concentration. The ascending order of the minimum concentrations determining 50% DPPH inhibition compared with ascorbic acid (control) was as follows: PT < LMC < AS < LML < ALC < AP < LMS < ascorbic acid < LML < PC. IC50 values ranging from 0.3 mg/mL to 5.6 mg/mL were reported by [PERSON] et al. [51] for 13 ethanolic extracts of propolisiles collected from beehives in Transylvania. Chinese propolis was analyzed by [PERSON] et al. [52] and its antioxidant activity was reported with IC50 values ranging from 0.633 mg/mL to 13.798 mg/mL for different proposi extracts. [PERSON] et al. [53] reported strong DPPH scavenging activity (IC50 = 16.55 \(\pm\) 0.87 ug/mL) in a study carried out on extracts of Mexican propolis and ethyl acetate. Strong antioxidant activity was reported by [PERSON] et al. [54] for four Algerian propolis methanolic extracts with IC50 values between 0.007 and 0.066 mg/mL, but lower than control ascorbic acid (0.184 mg/mL). Brazilian propolis has been reported to have antioxidant activity, with DPPH scavenging activity varying from 23.7% to 43.5% at different doses, using ascorbic acid as \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Samples** & **PS** & **PC** & **PT** & **LMS** & **LML** & **LMC** & **AS** & **ALC** & **AP** & \
\begin{tabular}{} \end{tabular} \\ \hline IC50 \(\pm\) & 3.661 \(\pm\) & 3.979 \(\pm\) & 1.720 \(\pm\) & 2.276 \(\pm\) & 1.696 \(\pm\) & 1.832 \(\pm\) & 1.669 \(\pm\) & 1.830 \(\pm\) & 2.058 \(\pm\) & 2.284 \(\pm\) \\ (mg/mL) & 0.002 & 0.001 & 0.002 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\ \hline R\({}^{2}\) & 0.9788 & 0.9752 & 0.9488 & 0.9497 & 0.9067 & 0.9556 & 0.9027 & 0.904 & 0.9017 & 0.9913 \\ \hline
\begin{tabular}{c} Hill \\ Slope \\ \end{tabular} & 9.925 & 8.864 & 11.398 & 11.066 & 12.166 & 10.326 & 15.914 & 16.932 & 12.615 & 17.548 \\ \hline \hline \end{tabular} Results are expressed as the three-determination mean \(\pm\) standard deviation (SD).  
\end{table} 
Table 7: The IC50 values of samples vs ascorbic acid. a positive control [55], with values similar to ours at concentrations of 0.5 (mg/mL). A study by [PERSON] et al. [56] on propolis extracts from 14 countries around the world evaluated their antioxidant activity and reported a wide range of DPPH radical scavenging activity (from 10% to 90%), values similar to those obtained for the three propolis samples. According to that study, the strongest results were obtained by propolis from countries such as Australia, China, Hungary, and New Zealand. In the case of propolis originating in India, IC50 determinations for 10 ethanolic extracts varied between 0.3334 mg/mL and 0.6008 mg/mL, while for ascorbic acid the value was 0.2849 mg/mL [57]. IC50 values ranging from 0.043 to 0.269 mg/mL for Korean propolis were reported by [PERSON] et al. [58] for 20 samples investigated.
## 4 Use Cases  
To examine the usefulness and applicability of the visualization system, we applied it to a spatiotemporal learning dataset (described in detail in Section 4.1). The variety and scale of this dataset made it challenging to analyze and interpret, which could be solved by our visualization tools. We invited five educators with expertise in mathematics education and extensive experience in online teaching. All of them possessed a PhD or master's degree in education. They were asked to use MultiScaleAnalyzer to analyze students' problem-solving processes and identify any notable patterns or outliers. Their analysis processes were recorded, and some representative findings were organized and described as use cases in this section.
## 3 Results
### Structure after Oxidation
#### 3.3.2 Cross-Section Analysis after 100 h  
After 100 h, remarkable changes in the constitution of the cross-sections were revealed. The OL of the HfCo alloy became Co-rich, thicker (\(\sim\)70 \(\upmu\)m), and contained some porosity (Figure 4a, Table 2). The IRZ deepened (with a total thickness of \(\sim\)750 \(\upmu\)m) and developed into two zones with no clear difference in chemical composition between them. The only difference was the O content: the much-thicker IRZ_1 was richer in O compared to IRZ_2. In the bottom part of IRZ_1 and inside IRZ_2, we found thin lateral cracks, the surfaces of which were covered with Co-rich layers. Similar to 1 h-oxidation, the interface between the IRZ and the unoxidized core was decorated with the dense Co-rich layer (Figure 4a, Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Elements, at. \%**} \\ \cline{2-6}
**Area of EDS Analysis** & **Hf** & **Co** & **Nb** & **Mo** & **O** \\ \cline{2-6}  & & \multicolumn{5}{c}{**HfCo**} \\ \hline OL & 48.9 & 10.0 & - & - & 41.1 \\ IRZ & 42.8 & 36.0 & - & - & 21.2 \\ Co-rich layer & 21.7 & 78.3 & - & - & - \\ \hline  & \multicolumn{5}{c}{(HfCo)\({}_{90}\)(NbMo)\({}_{10}\)} \\ \hline OL & 2.1 & 41.0 & 0.1 & 0.2 & 57.6 \\ IRZ\_1 & 21.5 & 4.4 & 2.7 & 1.0 & 70.4 \\ IRZ\_2 & 21.1 & 22.5 & 1.2 & 3.1 & 52.1 \\ Co-rich layer & 21.3 & 61.7 & 1.2 & 1.9 & 13.9 \\ \hline  & \multicolumn{5}{c}{(HfCo)\({}_{75}\)(NbMo)\({}_{25}\)} \\ \hline OL & 0.8 & 35.6 & 1.9 & 10.9 & 50.9 \\ IRZ\_1 & 42.8 & 5.2 & 5.4 & 0.2 & 46.4 \\ IRZ\_2 & 32.1 & 36.8 & 4.0 & 4.5 & 22.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average chemical compositions of the characteristic layers formed in the cross-sections of the HfCo, (HfCo)\({}_{90}\)(NbMo)\({}_{10}\), and (HfCo)\({}_{75}\)(NbMo)\({}_{25}\) alloys oxidized at 1000 \({}^{\circ}\)C for 1 h.  
However, we found numerous large lateral cracks in the central part of IRZ_1. The upper surfaces of these cracks were covered with continuous Co-rich layers and discontinuous Mo-rich islands. In turn, the specific elemental distribution inside IRZ_2 resembled that of 1 h-oxidation, with a clear separation between Hf-rich and Co-rich (and O-lean) regions. Beneath, there was also the Co-rich layer that delineated the IRZ from the unoxidized core (Figure 4b, Table 2).  
Since the (HfCo)\({}_{75}\)(NbMo)\({}_{25}\) alloy experienced multiple spallation events, no external OL was found and only a part of IRZ could be identified (Figure 4c). The \(\sim\)1300 \(\upmu\)m IRZ was composed of numerous alternate layers separated by large cracks, the surfaces of which were framed with thick Mo-rich layers and thin Co-rich islands (Figure 4b, Table 2). Notably, the top of the IRZ was depleted of Mo, while its bottom part was lean in Co, in contrast to the HfCo and (HfCo)\({}_{90}\)(NbMo)\({}_{10}\) alloys.
# Are You Depressed? Analyze User Utterances to Detect Depressive Emotions Using DistilBERT
## 2 Literature Review
### Text-Based Mental Illness Detection  
Several studies use various Natural Language Processing (NLP) and machine learning algorithms to analyze social media data for detecting depression and other mental illnesses. For example, [PERSON] et al. [15] analyze Twitter data of users diagnosed with MDD using the Center for Epidemiologic Studies Depression Scale (CES-D) questionnaire and develop an MDD classifier to predict which users are susceptible to depression. Similarly, [PERSON] et al. [4] use machine learning algorithms, including regression analysis, Naive Bayes (NB), and Support Vector Machines (SVM) to analyze data collected from the subreddits 'r/SuicideWatch' and 'r/depression'. [PERSON] et al. [16] also use Reddit data to classify mental illnesses such as anxiety, bipolar disorder, dementia, and psychosis and develop an ensemble model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN). [PERSON] et al. [17] propose a Recurrent Neural Network (RNN) to analyze text data and detect depression early. Moreover, [PERSON] et al. [5] develop a deep-learning-based prediction model for the early detection of depression in high-risk groups using social media data. The model consisting of Bi-LSTM and 1-D CNNs classifies depressed and non-depressed posts.  
In recent years, pre-trained language models such as Bidirectional Encoder Representations from Transformers (BERT) have gained popularity in detecting depression based on social media data. For instance, [PERSON] et al. [18] employ BERT and DistilBERT models to classify depression and its severity in four categories (non-depressed, mild, moderate, and severe) using tweets. [PERSON] et al. [19] used two separate BERT-based classifiers to detect users' depression based on social media texts. In another study, [PERSON] et al. [20] customize BERT and RoBERTa models for the mental health care domain through training them on mental-health-related subreddits, including 'r/depression', 'r/SuicideWatch', 'r/Anxiety', 'r/offmychest', 'r/bipolar', 'r/mentalillness', and 'r/mentalhealth', resulting in improved performance in mental health detection tasks.
## 4 Current Management Strategies for Corneal Scarring
### Limitations in Space  
Adapting terrestrial transplantation techniques to space missions presents formidable challenges. Traditional eye banking is constrained by the short viability of donor tissues, typically limited to 14 days. While advancements like the Active Storage Machine extend tissue viability to three months, these remain experimental and unsuitable for long-duration missions [61]. Moreover, risks of microbial contamination during tissue storage and transport greatly detract from the feasibility of using donor corneas in microgravity environments [62].  
In situ corneal transplants aboard spacecraft face significant additional obstacles. Maintaining sterile conditions during surgical procedures is nearly impossible due to the constraints of spacecraft design. The absence of sophisticated surgical tools, operating microscopes, and intensive postoperative care resources further limits the practicality of conventional transplantation in space [57; 58]. Moreover, limited access to immunosuppressive medications during missions increases the risk of graft rejection, making traditional approaches less viable [58].  
To address these barriers, bioengineered corneal substitutes and stem cell-based regenerative therapies are emerging as promising alternatives. These approaches leverage advancements in tissue engineering to produce biocompatible corneal constructs tailored to individual needs. For example, hydrogels derived from decellularized corneal extracellular matrix have shown potential in promoting scarless healing and could serve as off-the-shelf solutions for acute injuries in space [62]. Similarly, bioreactors capable of mimicking physiological cues and supporting extended tissue maturation are pivotal in overcoming the resource limitations of space exploration [53].  
The integration of bioprinting technologies and compact bioreactors aboard spacecraft represents a critical innovation. These systems allow astronauts to produce corneal constructs on-demand using their own stem cells, mitigating the risks of immune rejection and reducing dependency on terrestrial resources. Furthermore, optimizing bioinks with enhanced viscoelastic properties ensures structural stability under microgravity conditions, paving the way for practical implementation during space missions [60; 63].  
By bridging terrestrial advancements with space-specific adaptations, these innovations hold the promise of revolutionizing ocular healthcare in space, addressing the unique demands of long-duration missions and habitation on other planets while advancing terrestrial ophthalmology practices.
## 4 Results
### Regional Disparities and Infrastructure Challenges  
There are pronounced regional disparities in Mexico, particularly between the industrialized northern and central states, and the southern and more rural areas. These disparities are often reflected in both GDP figures and access to telecommunications services.  
Northern and central states such as Nuevo Leon, Jalisco, Mexico City, and Queretaro typically have higher GDPs and better telecommunications infrastructure. We would expect these states to exhibit consistently high penetration rates across all services (fixed telephony, internet, and restricted TV).  
Southern states such as Chiapas, Guerrero, and Oaxaca often struggle with lower GDPs and insufficient infrastructure. These states may show lower penetration rates across the board, particularly for fixed internet and restricted television, which are less accessible due to both economic and logistical challenges in these regions.
## 4 Security Analysis of the MAPP Protocol
### Resistance to Attacks  
The proposed protocol is secure against some common attacks described below.
## 2 Wave Force Tests
### Wave Conditions and Sea Levels  
The support structure models were tested under the wave conditions listed in Table 2 including five regular waves and three irregular waves. The wave variables adopted for the wave force test are the wave periods (T\({}_{\rm w}\)) and wave heights (H\({}_{\rm w}\)). Accordingly, three wave periods (T\({}_{\rm w}\)) of 7.5 s, 9.5 s, and 13.7 s and three wave heights (H\({}_{\rm w}\)) of 3.435 m, 6.87 m, and 12.78 m were selected for the full-scale models, where H\({}_{\rm w}\) was determined from significant wave height for the southwest sea of South Korea. These values were then scaled down to respectively, 1.5 s, 1.9 s, and 2.74 s, and 0.137 m, 0.275 m, and 0.511 m for the test models using a [PERSON] scale law of 1/\(\lambda\) for the wave height (H\({}_{\rm w}\)) and 1/\(\lambda\) for the wave period (T\({}_{\rm w}\)). The three irregular waves were generated with the Pierson-Moskowitz spectrum according to the three peak wave periods (T\({}_{\rm w}\)) of 1.5 s, 1.9 s. and 2.74 s for the test model, where an irregular wave was generated by setting a random number 200 times on the wave maker. Reliability of generated irregular wave was secured by comparing the energy of the Pierson-Moskowitz spectrum with the energy of the measured wave data for 5 min. Note that a wave height of 9.50 m was applied instead of 12.78 m for LSWL to prevent wave breaking at shallow water depth [22].  
Along with these wave variables, three sea levels were also applied in the wave force tests of Table 1. These sea levels (h) were selected with respect to the design guideline for the southwest sea of South Korea and were 24.25 m, 20.0 m, and 15.75 m for HSWL, MSL, and LSWL, respectively. These values were also scaled down to 0.97 m, 0.80 m, and 0.63 m by the scale factor of 1/\(\lambda\).
# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 4 Traditional and Modern Processes of Architectural Design
### Advantages and Challenges of AI Technology in Architectural Design  
AI is widely used in the architectural design industry. It transforms architectural design by automating repetitive tasks like generating documents and construction drawings, which reduces errors, shortens design cycles, and allows designers to focus on creativity. AI-supported project management tools can efficiently allocate resources and schedules to ensure timely completion of projects [83].  
AI is outstanding in enhancing design innovation by analyzing vast datasets and cases to inspire designers [84]. It diversifies algorithm-driven and parametric methods, while its simulation capabilities aid in material selection, structural optimization [85], and evaluating energy-saving performance, unlocking new design possibilities.  
AI also improves design accuracy, offering precise control over details to optimize building performance and functionality. For example, when designing energy efficiency, it can accurately calculate heat load and light mode, providing customized solutions to improve accuracy and calculation efficiency.  
However, AI faces many difficulties in the field of construction. In terms of technology, the quality and quantity of data are the key issues. AI requires high-quality data, but architectural factors are complex, making it difficult to obtain accurate, comprehensive datasets. Algorithm stability and complexity are also concerns, as developing robust algorithms that account for multiple variables is difficult, and unstable outputs may arise. In addition, integration with other technologies is also a problem; architectural design requires a variety of software and tools, and achieving seamless integration of AI with existing technologies to optimize the design process is a challenge.  
The use of AI in construction raises ethical concerns, including shifts in designer roles and over-reliance on AI, which may lead to loss of control in the design process. AI generation schemes may raise copyright and intellectual property issues, especially if they resemble existing works. The AI decision-making process is opaque, making it difficult for designers and customers to understand and evaluate its rationality and to determine responsibility when things go wrong. The widespread use of similar AI tools risks homogenizing designs, reducing diversity and innovation.  
User acceptance [86] is an important challenge for AI in the construction sector. While some teams embrace AI-driven solutions, some designers worry that the AI-generated solution is not user-friendly and personalized and have doubts about its reliability and stability.