esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application  
The current work is motivated by the need to analyze and understand big datasets arising in the manufacturing of organic electronics (OEs). OE is a new sustainable class of device, spanning organic transistors [26; 27], organic solar cells [28; 29], diode lighting [30; 31], flexible displays [32], integrated smart systems such as RFIDs (Radio-frequency Identification) [33; 34], smart textiles [35], artificial skin [36], and implantable medical devices and sensors [37; 38]. The critical and highly desirable features of OEs are their cost, and their rapid and low-temperature roll-to-roll fabrication. However, many promising OE technologies are bottleneck at the manufacturing stage, or more precisely, at the stage of efficiently choosing fabrication pathways that would lead to the desired material morphologies, and hence device properties.  
The final properties of OEs (e.g., electrical conductivity) are a function of more than a dozen material and process variables that can be tuned (e.g., through evaporation rate, blend ratio of polymers, final film thickness, solubility, degree of polymerization, atmosphere, shearing stress, chemical strength, and frequency of patterning substrate), leading to the combinatorial explosion of manufacturing variants. Because the standard trial-and-error approach, in which many prototypes are manufactured and tested, is too slow and cost inefficient, scientists are investigating in silico approaches [39; 40]. The idea is to describe the key physical processes via a set of differential equations, and then perform high-fidelity numerical simulations to capture the process dynamics in relation to input variables. Then, the problem becomes to identify and simulate some initial set of manufacturing variants, and use analytics of the resulting process data to first understand the process dynamics (e.g., rate of change in domain size or the transition between different morphological classes), and then identify new promising manufacturing variants.  
The key scientific breakthroughs that improved organic solar cell (OSC) performance were closely related to the manufacturing pathway. Most advances have been achieved by nontrivial and nonintuitive (and sometimes very minor) changes in the fabrication protocol. Classic examples include changing the solvent [41], and thermal annealing [42], which together resulted in a two orders of magnitude increase in the efficiency of OSCs. These advances have reaffirmed the importance of exploring processing conditions to impact device properties, and have resulted in the proliferation of manufacturing variants. However, these variants are invariably chosen using trial-and-error approaches, which has resulted in the exploration of very scattered and narrow zones of the space of potential processing pathways due to resource and time constraints.
# Extracting Searching as Learning Tasks Based on IBRT Approach
## 4 Experiment Evaluation
### Subjective Metrics  
In this section, we applied subjective analysis to identify the performance of the proposed method.  
We first discuss the subjective analysis setup. On the basis of the experiments we discussed in Section 4.1, we convened the participating students from the UWP course to conduct a subjective analysis of IBRT and other baseline methods. Each student was asked to evaluate their own search log. Students were asked to recall their own learning process and to evaluate the performance of the different algorithms. For a fair comparison, we anonymized the algorithm names, and we also shuffled the order of the extraction results when displaying them to students. Specifically, each student was asked to score the performance according to the following two aspects.  
(1) The accuracy of SAL task extraction  
For this, the participants were asked to score the accuracy of each method. To this end, each student was asked to answer the following question:  
_\"Does the task extraction result match the real situation when you are learning?\"_  
We list the scoring principles in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Score** & **Scoring Principles** \\ \hline
0 & Mismatches the real situation \\
1 & Only a small amount matches the real situation \\
2 & Mostly matches the real situation \\
3 & Completely matches the real situation \\ \hline \hline \end{tabular}
\end{table}
Table 2: The scoring principles for question 1.  
Figure 4: F1 score comparison on UWP dataset.  
2. The accuracy of the hierarchy For this, the participants were asked to evaluate the hierarchy of each method. To this end, each student was asked to answer the following question: _\"Do you think the hierarchy of your learning process is valid?\"_ We list the scoring principles in Table 3.  
Table 4 presents the evaluating results for the accuracy of SAL task extraction. A higher score indicates that the method had a better performance. The score of IBRT was the best with 2.7 (about 85% of students believed that the extraction results completely match their learning process). BRT performed second best with 2.5. This result also validates the performance of the BRT model in task extraction. In addition, the student evaluations were consistent with our experimental results in Section 4.2.  
Table 5 presents the evaluating results for the accuracy of the hierarchy. A higher score indicates that the method had a better hierarchy. The score of IBRT was the best with 2.3 (about 69% of students believed that the extraction results completely matched their learning hierarchy). BRT performed second best with 2.0. BHC presented the worst score, as it adopts a binary tree structure, which is usually not the case for real learning tasks.
# Development of a Model Material for Dynamic Geotechnical Model Tests
## 3 Experimental Design
### Sample Preparation Process and Process  
To prepare the samples, first, the quartz sand, barite powder, ferric powder, gypsum and cement were weighed and blended evenly in a blender; tap water was added and stirred until the mixture was uniform, where the water-solid ratio was 3:20; then we dissolved 4% glycerine per 100 mL water. The water-solid ratio and the glycerine ratio were selected based on a large number of preliminary experiments and previous experience. Then, the mixture was transferred to a cylindrical mould with inner diameter \(\phi\) 50 \(\times\) 100 mm or \(\phi\) 50 \(\times\) 50 mm, and the mould underwent uniform vibrations on a vibration table. Second, after curing for 4 h, the samples were demoulded and allowed to cure under ambient conditions. Finally, after curing for 1 week, each sample was ground finely on both sides, numbered and reserved.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rock** & \(\rho\)**/g\(\cdot\)cm\({}^{-3}\)** & \(\alpha_{c}\)**/MPa** & \(\sigma_{t}\)**/MPa** & **E/GPa** & **c/MPa** & \(\varphi^{c}\) \\ \hline Sandstone & 2.17\(\sim\)2.70 & 2.5–200 & 4–25 & 0.63\(\sim\)12.5 & 4–50 & 25–50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of physico-mechanical parameters of sandstone [33].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group Number**} & \multicolumn{4}{c}{**Analytical Factor**} \\ \cline{2-5}  & **A/\%** & **B/\%** & **C/\%** & **D/\%** \\ \hline
1 & 2 & 60 & 25 & 15 \\
2 & 2 & 70 & 35 & 20 \\  
A total of 25 groups of tests were designed for this experiment, each of which required wave velocity detection, uniaxial compression, direct shear and splitting tests. The sample size used for wave velocity detection was the same as that used for the uniaxial test or direct shear test, and no new sample preparation was needed. To reduce the error, the average value was taken as the final result, including 3 specimens in the splitting test group, 3 specimens in the uniaxial test group, 6 specimens in the direct shear test group, and a total of 225 long cylindrical specimens (\(\upphi\) 50 \(\times\) 100 mm) and 75 short cylindrical specimens (\(\upphi\) 50 \(\times\) 50 mm), as shown in Figure 1.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the CIC-IoT2023 Dataset Using a Supervised Machine Learning Approach
#### 4.3.3 Research Analysis According to [51]  
Research [51] has investigated anomaly detection in IoT networks, utilizing the CIC-IoT 2023 dataset. The methodology encompasses several key steps.  
Step 1: Data Preparation and Processing  
The dataset was subjected to preprocessing, which included activities related to the removal of redundant features, conversion of categorical features to numerical ones, and normalization of numerical features. Random oversampling techniques were applied to balance the class proportions. Finally, the collection was divided into training and testing.  
Step 2: Model utilization  
The RF method, as one of the ML algorithms, was chosen to classify the attacks.  
Step 3: Model evaluation  
Model performance was assessed using standard metrics.  
Step 4: Result verification  
Analysis of the results showed that RF performed best in terms of accuracy and other metrics when compared to other models.
# Compact Fuzzy Systems Based on Boolean Relations
## 6 Application Example of Architecture II: PID Control  
Fuzzy logic systems allow link-up system inputs and outputs using linguistic terms in the description [63]. This feature allows using fuzzy logic in control systems development [22]; in addition, the FIS systems allows handling nonlinearities like products between variables, saturations, and power functions, among others present in nonlinear dynamic systems [64]. This section aims to show that the II architecture established in Section 4 through the procedure of Section 3 and the fundamentals of Section 2 can be used to control systems. As a study case, it is shown the application of a hydraulic control system employing compact FIS-BBR with architecture II.
# Analysis of Urban Spatial Accessibility of Museums within the Scope of Seoul
## 3 Analysis and Discussion
### Polynomial Curve Fitting Analysis and Discussion  
As shown in Tables 6 and 7, the variances according to the global integration of the total urban space of museums in Seoul and the weighted travel angle and limit distance setting range were T1024 and R500 (F = 7.385, \(p<0.01\)), T1024 and R750 (F = 10.105, \(p<0.001\)), T1024 and R1000 (F = 17.661, \(p<0.001\)), T1024 and R1250 (F = 23.390, \(p<0.001\)), T1024 and R1500 (F = 29.271, \(p<0.001\)), T1024 and R1750 (F = 34.944, \(p<0.001\)), and T1024 and R2000 (F = 36.314, \(p<0.001\)), respectively, and the ANOVA results of the local integration and weighted travel angle and limit distance setting range were T1024 and R250 (F = 18.684, \(p<0.001\)), T1024 and R500 (F = 19.096, \(p<0.001\)), T1024 and R750 (F = 20.975, \(p<0.001\)), T1024 and R1000 (F = 16.869, \(p<0.001\)), T1024 and R1250 (F = 18.078, \(p<0.001\)), T1024 and R1500 (F = 17.030, \(p<0.001\)), T1024 and R1750 (F = 15.888, \(p<0.001\)), and R2000_I (F = 14.642, \(p<0.001\)). The global integration and T1024 and R500 had a 0.01 significance level, and all others seemed to have a 0.001 significance level. So, it was confirmed that the independent variables according to the travel angle of museums and the limit distance setting range could explain the dependent variables (global integration and local integration) by a regression equation. Accordingly, Tables 8 and 9 show the trend function  
Figure 6: Urban hierarchy graph –Seoul: (**a**) Global integration graph; (**b**) Local integration graph; (**c**) Intelligibility. The Residual Sum of Squares (RSS) and the coefficient of determination (R'2) according to the global integration of the total urban space of museums in Seoul and the weighted travel angle and limit distance setting range were T1024 and R500 (10.687, 0.077), T1024 and R750 (11.459, 0.102), T1024 and R1000 (11.488, 0.166), T1024 and R1250 (11.486, 0.209), T1024 and R1500 (11.435, 0.249), T1024 and R1750 (11.446, 0.283), and T1024 and R2000 (11.294, 0.291). The RSS of the independent variables T1024 and R250, T1024 and R500, T1024 and R750, T1024 and R750, T1024 and R1000, T1024 and R1250, T1024 and R1500, T1024 and R1750, and T1024 and R2000 corresponding to the local integration were 9.187, 9.524, 10.321, 11.574, 12.059, 12.762, 13.536, and 13.667, and the coefficients of determination were 0.174, 0.177, 0.192, 0.160, 0.170, 0.161, 0.152, and 0.142, respectively. The RSS of the global integration group maintained a relatively stable value, but the RSS of the local integration group gradually increased as the limit distance setting range was extended.
## 3 Experiments and Analysis of Results
### Experiment Conditions and Procedure
Before the experiment, participants were informed that they could express cold discomfort, such as stamping feet, narrowing shoulders, crossing arms, blowing into hands, putting on a coat, and rubbing hands, as well as a list of hot discomfort body language including shaking clothing, scratching head, fanning with an object, taking off a coat, wiping sweat, or rolling up sleeves. Participants were also given the option not to perform any of these actions. A general camera was used to automatically capture subjects' thermal discomfort body language in office scenes. By collecting these data, we can better understand people's thermal discomfort body language expression performance under different temperature conditions. This provided a reliable theoretical basis for thermal environment assessment and regulation. Thermal comfort vote: During the experiment, the thermal comfort vote was collected from subjects at 25 min intervals, and the questionnaire included subjects' personal information and thermal comfort vote, personal information including subjects' name, gender, age, height, weight, body mass index, and clothing. The thermal comfort vote is shown in Table 3. It was characterized by seven levels of thermal sensation index, which was one of the most commonly used evaluation indicators, and the room temperature was kept constant when users voted. Each vote was not affected by the last temperature. In the experiment, the thermal comfort vote was used to calculate the actual prediction of dissatisfaction (_APD_).
## 2 Moving Window Decision-Making Algorithm
This extra cost term can be calculated by integrating \(Q_{F}(t)\), which is the extra energy consumption depending on the difference between the actual and the target temperature of the crude oil entering the furnace, together with the cost of fuel (\(C_{E}\)) and the furnace efficiency (\(\eta_{f}\)). The cleaning cost term, in the meantime, can be calculated from the control actions \(y_{np}\) with the cleaning cost (\(C_{\mathrm{C}}\)). Here, \(N_{P}\) is the number of the discretized time periods and \(N_{E}\) is the number of heat exchangers. \[Obj=\int\limits_{0}^{t_{F}}\frac{C_{E}Q_{F}(t)}{\eta_{f}}\ dt+\sum\limits_{p=1 }^{N_{P}}\sum\limits_{n=1}^{N_{E}}C_{\mathrm{C}}\ \big{(}1-y_{np}\big{)} \tag{10}\]  
The moving window decision-making algorithm presented in this study basically adopts the multi-period OCP framework described above. However, we have combined the moving window method with the OCP framework in the context of its extension, which is the distinct feature of our decision-making algorithm. Figure 1 shows the scheme of our moving window algorithm. On the contrary to the previous method where an optimal cleaning schedule is determined in a single lump based on the simulation results obtained for the whole time periods of 0 to \(N_{P}\), our algorithm sequentially determines cleaning decision only for the very first time period within the moving window. With this scheme, the complexity of the optimization problem can be greatly reduced because the number of control variables to be optimized is decreased from \((N_{P}-1)\times N_{E}\) to \(N_{E}\). Moreover, the simulations can be conducted only for the time periods corresponding to the moving window size (\(S_{MW}\)), which makes it possible that the OCP is solved very fast at each moving step. After the moving window reaches the final step \((N_{P}-S_{MW})\), the optimal cleaning schedule is constructed by combining each decision in a whole. It should be noted that the presented moving window decision-making algorithm is different from the GrA [21] or its modified versions [32], although they are very similar in that they use a sliding time horizon. While the GrAs makes the cleaning decisions based on a \"threshold heuristic\" or \"greedy threshold\", our moving window method makes decisions simply using the gradient-descent algorithm within the OCP framework, which is not a heuristic approach.
## 2 Materials and Methods
### Influence of PAM on the Cell Adhesion and Viability of HaCaT  
To determine the effect of PAM on the adhesion of HaCaT cells, we used the xCELLigence system. This system allows the real-time monitoring of living cell adhesion, spreading, and vitality. As shown in Figure 4, the adhesion of HaCaT cells in PAM over 1.5 h was significantly reduced compared with cells cultivated in the DMEM control, whereas no altered adhesion was observed for cells cultivated in AAM. This effect was the same for both PAM30 and PAM60, i.e., independent of the plasma treatment time used to produce the PAM. In PAM30 and PAM60, the normalized cell index after 1.5 h reached values of about 0.197 and 0.189, respectively, whereas, in DMEM, the value was about 0.397. Interestingly, HAdase-treated cells showed only slight impairment of the cell adhesion within 1.5 h, and the normalized cell index was 0.302. This effect was not significant.  
The cell viability was determined after 48 h culture in PAM by MTS assay. Figure 5 shows that the viability was significantly reduced compared with DMEM. This effect was stronger in PAM60 than in PAM30. The viability of HAdase-treated cells was also reduced after 48 h and was in a range similar to the cells cultivated in PAM30. But this effect was not significant.  
In summary, PAM treatment impaired the HA pericellular coat of HaCaT cells. Furthermore, the influence of PAM on the viability and the time-dependent adhesion was higher than after HAdase treatment. HAdase reduced the HA pericellular coat of the HaCaT cells almost completely, but this deficiency did not lead to a comparable loss of cell adhesion, suggesting additional effects of PAM on cell adhesion mechanisms.  
Figure 3: Immune fluorescence images of the HA pericellular coat of HaCaT cells (HA—green, nuclei —blue) after cultivation in PAM or control media for 6 h. Note that the HA coat was reduced in PAM30 and PAM60. (LSM780, Carl Zeiss, objective 63 \(\times\) oil, zoom 2.5, bar = 5 \(\upmu\)m). Abbr.: DMEM: Dulbecco’s modified Eagle’s medium, PAM30: 30 s plasma-treated DMEM, PAM60: 60 s plasma-treated DMEM, AAM: 60 s argon gas-treated DMEM, HAdase: DMEM with hyaluronidase.
## 2 Heat Transfer Boundary Condition
### Solar Radiation Effect
#### 2.1.1 Calculation of Sun Position Parameters  
The position parameters of the sun relative to the earth, such as the declination angle \(\delta\), elevation angle \(\alpha_{s}\), and azimuth angle \(\gamma_{s}\), can be calculated using the law of solar operation in astronomical knowledge [21].  
\[\delta=23.45^{\circ}\sin\left[\frac{360^{\circ}}{365}(284+N)\right] \tag{4}\]  
\[\sin(\alpha_{s})=\cos(\varphi)\cos(\delta)\cos(\tau)+\sin(\varphi)\sin(\delta) \tag{5}\]  
\[\gamma_{s}=sign(\tau)\mid\cos^{-1}\left[\frac{\sin(\alpha_{s})\sin(\varphi)- \sin(\delta)}{\cos(\alpha_{s})\cos(\varphi)}\right]\mid \tag{6}\]  
In the above formula, \(N\) is the annual accumulated day, that is, the total number of days from January 1 to the current date; \(\alpha_{s}\) is the solar elevation angle, which is \(0^{\circ}\) at sunrise and sunset. \(\varphi\) is the geographical latitude, which is positive in the Northern Hemisphere and negative in the Southern Hemisphere, and the value range is \(-90^{\circ}\sim 90^{\circ}\); \(\tau\) is the solar hour angle, and the solar hour angle at noon is exactly \(0^{\circ}\), \(\tau=(t-12)\times 15^{\circ}\), where \(t\) is the true solar hour; the \(\gamma_{s}\) is positive to the west and negative to the east, and the value range is \(-180^{\circ}\sim 180^{\circ}\); the \(sign\) is a symbol function.  
The relative position relationship between the surface of the structure and the sun is shown in Figure 2. The angle between the sunlight and the exterior normal \(n\) of the surface, that is, the incident angle \(i\), is calculated by the following formula:  
\[\cos\theta=\sin\alpha_{s}\cos\beta+\cos\alpha_{s}\sin\beta\cos(\gamma_{s}-\gamma) \tag{7}\]  
where \(\beta\) is the inclination angle of the structural surface relative to the horizontal plane, the value range is \(0^{\circ}\sim 180^{\circ}\), and when it is greater than \(90^{\circ}\), the surface is downward; \(\gamma\) is the angle between the exterior normal \(n\) of the surface and the positive south direction. The direction is positive to the west and negative to the east, and the value range is \(-180^{\circ}\sim 180^{\circ}\).
## 4 Experiments
### Effectiveness of SSL  
Well-labeled remote sensing data are very expensive to acquire, but unlabeled remote sensing scene images are easy to obtain. Using SSL for model initialization is one of the keys to the success of our proposed framework. In this section, to verify the effectiveness of SSL as a model initialization strategy, we report experiments conducted on several representative backbone networks, including ResNet, ResNeXt, and WRN. The models were pretrained on the ImageNet dataset for 120 epochs and on the NAP and NAP+ datasets using SSL for 300 epochs. To ensure fair comparisons, all methods were fine-tuned on the NWPU dataset for 100 epochs using the default data augmentation strategy on ImageNet. It is important to note that other components of GFSC are not used at this stage.
## 3 Analysis and Discussion
### Prediction on Testing Dataset  
The method of ensemble learning [87] is used for testing. Specifically, it is done in four steps: (a) Select the five best models from the \(M_{3}\) model with complex networks; (b) predictions are made for each model on the test dataset; (c) counting all the predictions; (d) the final prediction is determined by the probability of happening (note that, the highest probability of damage state is selected if there are different probabilities of damage states. Otherwise the median of different damage states is selected). Furthermore, the confusion matrix is selected as an index to evaluate the prediction performance, and the results are shown in Figure 15b. Most predictions correctly classify the damage states for the GM test dataset, and the overall prediction accuracy is 93.3%. Meanwhile, all predictions are over-predicted or under-predicted within a damaged state in the respective damage state sample data. For the test dataset (Figure 15a), the data imbalance between categories is similar to the training set, which allows the network to more accurately predict low-damage states (at least 91%). In comparison, medium- and high-damage (moderate and extensive) will be less accurate. Furthermore, limited by the amount of data in the severe damage (complete) sample (0.63%), the predictions have a certain amount of chance. However, although the \(M_{3}\) model slightly understates seismic damage, the overall prediction accuracy is very promising. The prediction accuracy will be further improved if further network tuning is performed.  
Figure 15: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Method** & **Features** & **Non-Linearity** & **Accuracy \%** & **Increase \%** & **Training Times (h)** \\ \hline \(M_{3}\) & AFEM & Complex & Log & 93.7 & 7.0 & 3.2 \\  & & Power & 95.1 & 8.6 & 3.1 \\ \hline \hline \end{tabular} _Note: The increase is relative to the network (3 layers 32 cells) with hand-crafted features as the input._  
\end{table}
Table 8: The effect t of complex neural network. (\(\gamma=-1/7\), Epoch = 200).