esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Adversarial Outliers  
In this section, we expand on our results corresponding to the identified outlier classifications-specifically, \"No ROI\" and \"No ROI Split\". To deepen the analysis and provide clearer insights, we compare these outcomes with the baseline results, enabling a more comprehensive comparative evaluation.  
In Figure 10, we noticed identical scores from the 'Baseline' and 'No ROI' models for KL0. This observation suggests either that the absence of a large region of interest (ROI) does not affect KL0 classification or that true class region confounds are visible. Additionally, the 'No ROI Split' model demonstrated performance remarkably similar to the 'Baseline' model for KL4. Although it did not achieve complete alignment with the 'Baseline,' its relative success hinted at similar influences as observed in KL0. Most notably, we observed a clear performance boost for KL1 in the 'No ROI Split' model. This class is historically the most challenging for classification in Knee-Osteoarthritis. Remarkably, this score was the highest individual KL1 score across all examined models in this study.  
To delve deeper into these paradoxical results, we applied the Grad-CAM technique to the top examples from each of the previously mentioned classes. As shown in Figure 11, in the first row (No ROI KL0), we observed activations focused on texture and potential outlines of the patella. On the other hand, the second row (Baseline KL0) displayed control images that distinctly highlighted the knee joint. However, it is essential to note the broad spread of activation extending across and above the knee joint. Observations in the third row (No ROI Split KL4) revealed unclear patterns, primarily centered around what seemed to be a focus on wear-related texture. Despite the ambiguity, the controls (row 4) highlighted the knee joint, albeit with significantly less spread than the KL0 control (second  
Figure 10: Confusion matrices for the test set using No ROI, baseline, and No ROI split base data augmentations.  
Figure 9: Using a one-vs-all scheme, ROC curves are shown for the No ROI condition (**left**) and the No ROI Split condition (**right**).  
row). Examining the KL1 focus in the first image of the last set (fourth row) revealed what appeared to be a part of the patella outline. In contrast, the other two images (fourth row) depicted a non-specific texture focus. Finally, all controls (KL0, KL4, KL1) presented a wide activation range that extended slightly over the knee joint. Overall, our observations suggested that the baseline models for KL0 tended to rely not only on the knee joint but also incorporated broader areas for their classifications. This starkly contrasted with the KL1 baseline models, which seemed to concentrate more on the knee joint.  
Figure 11: Grad-CAM displays for outlier conditions with baseline comparisons. ‘Confidence’ denotes the output layer’s value on the correct class.
# Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm
## 5 Gas Cyclone Design
### Pressure Drop Calculation  
The energy consumed in a cyclone is most frequently expressed as the pressure drop across the cyclone. This pressure drop is the difference between the gas static pressure measured at the inlet and outlet of the cyclone. Many models have been developed to determine this pressure drop. Some of the commonly used equations to calculate the pressure drop are:  
(a) The Koch and Licht Pressure Drop Equation.  
[PERSON] and [PERSON] (1977) expressed the cyclone pressure drop as  
\[\Delta\mathrm{P}=0.003\rho_{\mathrm{g}}\mathrm{V}_{\mathrm{\beta}}\mathrm{N} _{\mathrm{\Pi}} \tag{10}\]  
where:  
\(\rho_{\mathrm{g}}\) = gas density (lbm/ft\({}^{3}\));  
\(\mathrm{V}_{i}\) = inlet velocity (ft/s);  
\(\mathrm{N}_{\mathrm{\Pi}}\) = number of velocity heads (inches of water) and is expressed as  
\[\mathrm{N}_{\mathrm{\Pi}}=\mathrm{K}\left(\frac{a\cdot b}{D_{\mathrm{\epsilon} }^{2}}\right) \tag{11}\]  
\(\mathrm{K}=16\) for no inlet vane 7.5 with neutral inlet vane;  
\(a\), \(b\) = inlet height and width, respectively.  
(b) The Ogawa Equation.  
Another pressure drop equation due to [PERSON] (1984) takes the form of  
\[\Delta\mathrm{P}=\frac{1}{2}\rho_{\mathrm{g}}\mathrm{V}_{i}\mathrm{N}_{ \mathrm{\Pi}} \tag{12}\]  
The prediction of the performance of the cyclone separators is a challenging problem for the designers owing to the complexity of the internal aerodynamic process and dust particles. Hence, modern numerical simulations are needed to solve this problem. Fluid flows have long been mathematically described by a set of nonlinear, partial differential equations, namely the Navier-Stokes equations.  
A cyclone is required to remove carbon dust particles from affluent air coming from a thermal power station at a rate of 5.0 m\({}^{3}\)/s at 65 \({}^{\circ}\)C. The dust particles are assumed to have a normal size distribution with an MMD of 20 \(\upmu\)m and a GSD of 1.5 \(\upmu\)m. For energy cost consideration, the pressure drop in the cyclone is required to be not more than 1000 N/m\({}^{2}\). The density of the dust particle is 2250 kg/m\({}^{3}\), and the cyclone is operating at atmospheric pressure. To attain overall efficiency of 70% and above, the recommended required geometry/size and cut size of the cyclone are: Q = 5 m\({}^{3}\)/s, temp = 65 \({}^{\circ}\)C, MMD = 20 \(\upmu\)m, GSD = 15 \(\upmu\)m, pressure 1000 N/m\({}^{2}\), and \(\rho\) dust particle = 2250 kg/m\({}^{2}\).  
This study develops PSO, DE, and DEPSO models for the design optimization of a 5.0 m\({}^{3}\)/s gas cyclone instrument.
## 6 Experimental Results
### Anomaly Injection in Datasets  
Figure 10 shows the distribution of anomalies and normal data for temperature, humidity and airflow features of the Fumehood subsystem in both the training and testing datasets. As expected, the anomalies (in red) are more frequent in the testing set compared to the training set for each feature. Additionally, the anomalies in the testing dataset exhibit a broader distribution, indicating greater variance in anomaly values compared to the training dataset for each feature.  
Figures 11 and 12 illustrate the distribution of temperature and humidity values in the training and testing datasets for the Vacuum Pump and Lithography Unit CPSs, respectively, distinguishing between normal readings and anomalies. For both CPS components of the cleanroom, the training datasets show anomalies concentrated around specific values, while the testing datasets exhibit a broader, more dispersed anomaly distribution. This shift indicates changes in operational conditions or anomaly patterns between the training and testing phases. The normal data distribution in the training datasets has pronounced peaks, whereas the testing datasets show a flatter distribution.  
Table 3 represents the Kolmogorov-Smirnov (KS) test's \(p\)-values used to quantify the difference in anomaly distributions between the training and testing datasets for each feature of the three CPS components (Fumehood, Vacuum Pump and Lithography Unit). The KS statistic is a non-parametric statistical method for measuring the equality between two probability distributions with different shapes [51]. A \(p\)-value from the KS test indicates the probability that the observed differences between the distributions could have occurred by chance. In the literature [52; 53; 54; 55], a \(p\)-value less than a conventional threshold (commonly 0.05 or 0.01) suggests a significant difference between the two distributions, implying that the samples do not come from the same distribution. In Table 3, the results show significant differences in the distributions of anomalies, particularly in features like temperature and airflow, where very low \(p\)-values indicate substantial discrepancies, indicating the existence of concept drift between anomalies of the training and testing datasets. These findings highlight the impact of different noise injection methods on the anomaly distribution, which is crucial for evaluating the performance and sensitivity of adaptive drift adaptation models for accurate anomaly classification models under concept drift conditions.  
Figure 10: Distribution of anomalies and normal data for different Fumehood features (temperature, humidity and airflow) in both training and testing datasets.  
Figure 11: Distribution of anomalies and normal data for Vacuum Pump’s feature (temperature) in both training and testing datasets.  
Figure 12: Distribution of anomalies and normal data for different Lithography Unit’s features (temperature and humidity) in both training and testing datasets.
### Objectives and Scope of Work  
The majority of the published literature is directed to UHI effects in Delhi followed by Bengaluru, Mumbai, Chennai, Pune, Hyderabad, and Ahmedabad [91]. There are few studies considering the spatial, seasonal and diurnal variability of SUHI on the major cities in India [88; 89; 90]. To date, no long-term surface urban heat island study has been conducted covering all the important cities in and surrounding India. One of the prime objectives of the current study is to understand the spatial and seasonal extent of UHI effect and its intensity over the twenty major cities in India and its surroundings. This is one of the first studies that uses a long-term MODIS satellite-based LST dataset (2000-2023) to study the temporal increase or decrease in the UHI effect over India and its surrounding region. The study also explores the \"day\" and \"night\" variability and range of UHI intensity (urban heat island canopy effect) and its variability across months and seasons of core urban region (within the perimeter of cities) compared with the surrounding region (a buffer of 10 km along the periphery). The study explores changes in UHI intensity over months (seasons) along with the total change in LST over specific city regions based on a linear trend for a period of 23 years (2000-2023) using satellite observations.
## 3 Experimental Results
### Bond Behavior
#### 3.3.2 Bond Stress-Slip Relation  
There was a clear difference in the bond strength between the deformed steel rebar and concrete made with natural and recycled aggregates of different qualities.  
With the 13-mm-diameter steel rebar, the majority of the concrete specimens did not fail at the ultimate load, and the post-peak behavior of the bond stress-slip curve could be observed. The average bond stress-strain relationships between the steel rebar and concrete, made with different water-to-cement ratios are plotted in Figure 14a-d. The bond behavior between the steel rebar and the concrete with natural and recycled aggregate and with different water-to-cement ratios are highly similar, except for the concrete with a water-to-cement ratio of 0.30. Although the ultimate bond strengths of the HC30 and the MC30 specimens were higher than that of the N30, the bond stresses of the HC30 and MC30 specimens decreased rapidly in the post-peak region and became lower than that of N30. In contrast, the ultimate bond strength of LC30 was the lowest but became the largest in the post-peak region. When the water-to-cement ratio decreased, the stiffness in the elastic region tended to increase, resulting in a higher ultimate bond strength. The adhesive strength between the concrete and steel rebar also increased with an increase in the water-to-cement ratio, but the magnitude was small, compared to the anchorage mechanism of the rib of the deformed bars.  
With the 19-mm-diameter steel rebar, all of the specimens broke at the maximum tensile load. Therefore, the post-peak behavior of the bond between the concrete and the steel rebar could not be obtained for this diameter. The experimental results are shown in Figure 15a-d. The slope of the bond stress-slip curve was steeper for the lower water-to-cement ratios. The slip value of specimens with the low-quality recycled aggregate was larger, and a clear difference could be observed for the case when the water-to-cement ratio was 0.45. However, although the bond strength of the specimens with a water-to-cement ratio of 0.45 was nearly the same as that of the specimens with a water-to-cement ratio of 0.60, there was no significant improvement in strength.  
Figure 13: Bond strength between the steel rebar and concrete with different water-to-cement ratios and bar diameters. (**a**) DB13 mm and (**b**) DB19 mm.  
Figure 14: Bond stress-slip relationship of the concrete with a 13-mm-diameter steel rebar with different water-to-cement ratios. (**a**) For w/c = 0.75; (**b**) for w/c = 0.60; (**c**) for w/c = 0.45; (**d**) for w/c = 0.30.  
Figure 15: Bond stress-slip relationship of the concrete with a 19-mm-diameter steel rebar with different water-to-cement ratios. (**a**) For w/c = 0.75; (**b**) for w/c = 0.60; (**c**) for w/c = 0.45; (**d**) for w/c = 0.30.
## 3 Results and Discussion
### Photocatalytic Degradation
The presence of various interfering species forming a complex matrix, along with the observed catalytic performance achieved without any effluent pre-treatment, highlights the capability of the materials to address real-world challenges. Therefore, we investigated the behavior of the catalysts in a real effluent to validate their practical applicability and identified certain limitations that could guide future optimization, such as morphological tuning, doping strategies, and the construction of heterostructures. To contextualize the performance of the materials investigated in this study, a comparison with other photocatalysts reported in the literature was carried out. Table 4 presents a selection of materials used for real effluent degradation, including heterostructures such as Cu\({}_{2}\)O/ZnO/Ag\({}_{3}\)PO\({}_{4}\), ZnO/Fe\({}_{2}\)O\({}_{3}\)/MnO\({}_{2}\), and ZnO-iron oxide. These systems generally exhibit high degradation efficiencies due to synergistic effects between their components. Despite being single-phase materials, the iron-based oxides evaluated in this study demonstrated promising photocatalytic activity under visible light irradiation within 120 min, highlighting their potential for practical wastewater treatment applications. Additionally, Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\) and FeWO\({}_{4}\) are low-toxicity semiconductors composed of naturally abundant elements, which contribute to their economic viability. These features suggest a favorable cost-benefit ratio for potential industrial implementation. However, further studies are required to evaluate the environmental impact of the photocatalytic process, including possible ecotoxicity and the risk of secondary pollution. Figure 5 presents a schematic representation of the degradation process of real effluents using iron-based oxides as catalytic agents. This model aims to provide a simplified illustration of the proposed mechanism for the studied system.
## 5 Experimental Setup  
### Dataset  
In this paper, we select the check-in data from six cities--New York (NY), Tokyo (TKY), Sao Paulo (SP), Jakarta (JK), Istanbul (IST), and Kuala Lumpur (KL)--from the Foursquare dataset, based on a comprehensive consideration of geographic coverage, cultural diversity, data completeness, and city size. These cities represent different geographic regions (North America, Asia, South America, Europe, and Southeast Asia), encompass diverse cultural backgrounds (Western, East Asian, and Islamic), and provide complete user check-in and social relationship data. This enables the dataset to adequately reflect the diversity of user behavior and social relationships in LBSNs. To ensure a balanced ratio of positive and negative samples, negative samples are randomly selected from pairs of users without friendship relationships within the LBSN.  
In the data preprocessing stage, UTC time is first converted to Unix timestamps and parsed into structured time information to normalize the raw check-in records, supporting subsequent time modeling. Next, POI data are enhanced through standardizing identifiers, adding semantic information, and other processing. The total number of check-ins for each POI and user in the dataset is calculated, and, based on prior research [29; 72], low-frequency POIs with fewer than 10 check-ins are removed to suppress noise and enhance the model's ability to capture stable user patterns. At the same time, users with fewer than or equal to 2 check-ins are filtered out to ensure the data effectively reflect social behavior patterns. Finally, based on the cleaned data, three types of heterogeneous edge relations are constructed: user-POI check-in relations, user-user friendship relations, and POI-POI co-category relations. Different edge features (such as visit frequency, geographic distance, category labels, etc.) are defined to comprehensively quantify user preferences, social relationships, and potential associations between POIs, providing high-quality input for subsequent heterogeneous graph modeling. The data are then randomly split into training, validation, and test sets at a 7:2:1 ratio. The details of the preprocessed data are shown in Table 1.
## 3 Material Test and Results for LLFD
### LLFD Material Test Loading and Measurement Methods  
Figure 7 shows the installation of the LLFD specimens and the details of the measuring instruments. The horizontal load was applied by controlling the displacement using a 500-kN actuator. Linear variable differential transformers (LVDTs) were installed on each specimen to measure the displacement and base slip of the friction damper. The loading was applied to the LLFD specimens based on the measured displacement of the vibration control system, [LVDT (1) + LVDT (2)]/2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Shear Surface** \\ **(N\({}_{\mu}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Number** \\ **of Bolt** \\ **(N)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Diameter** \\ **of Bolt** \\ **(\({}_{\lambda}\)) [mm]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Torque** \\ **(T\({}_{0}\)) [N-m]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Clamp Force of** \\ **Bolted Joint** \\ **(\({}_{\mathcal{D}}\)**\({}_{\mathcal{R}}\)) [kN]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Design Strength of** \\ **Friction Joints** \\ **(\({}_{\mathcal{R}}\)) [kN]** \\ \end{tabular} } \\ \hline FP-LLFD-1 & \multirow{2}{*}{2} & \multirow{2}{*}{8} & \multirow{2}{*}{16} & \multirow{2}{*}{100} & \multirow{2}{*}{41.6} & \multirow{2}{*}{93.3} \\ FP-LLFD-2 & & & & & 200 & 83.3 & 186.6 \\ \cline{5-5}  & & & & 300 & 125 & 280.0 \\ \hline GS-LLFD-1 & \multirow{2}{*}{2} & \multirow{2}{*}{8} & \multirow{2}{*}{16} & \multirow{2}{*}{100} & \multirow{2}{*}{41.6} & \multirow{2}{*}{84.0} \\ GS-LLFD-2 & & & & & 200 & 83.3 & 168.0 \\ \cline{5-5}  & & & & 300 & 125 & 252.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specimens of the LLFD used in the device test and calculation results.  
Figure 6: Details of LLFD used in device test (Unit: mm).  
The loading point of the actuator was matched to the center of the hardware for loading at the top of the specimen, with cyclic loading applied 10 times at 0.33 times the system displacement expected under the maximum considered earthquake (MCE), 5 times at 0.67 times this displacement, and 3 times at 1.0 times this displacement, based on the results of the 17.6 prototype test from the Korean seismic design standard [23] (see Figure 8). An allowable story drift of 1% (33 mm) for school buildings (special grade), which were the target buildings, was set as the maximum displacement expected under the MCE [23].
## 3 Results and Discussion
### Atr-Ftir  
The absorption peaks in the 1600-1700 cm\({}^{-1}\) band caused by the stretching vibrations of C=O are called amide I bands, and these characteristic absorption peaks are mainly caused by vibrations of the secondary structures of peptides and proteins. For the amide I band second derivative and deconvolution techniques were used for peak splitting, and the secondary structure of the protein was quantified by curve fitting. Figure 7 showed the IR spectra of cuttlefish by different freezing methods during frozen storage. Figure 8 shows the relative content of MP secondary structure of cuttlefish treated with different freezing methods during freezing. The protein secondary structures in cuttlefish flesh were mainly \(\alpha\)-helix (1650-1660 cm\({}^{-1}\)), \(\beta\)-fold (1660-1700 cm\({}^{-1}\)), \(\beta\)-fold (1600-1640 cm\({}^{-1}\)) and irregular convolution (1640-1650 cm\({}^{-1}\)). Figure 8A,8D show the contents of secondary structures of cuttlefish at 3 and 90 days of freezing storage, respectively. The comparison revealed that the \(\alpha\)-helix content of cuttlefish myofibrillar protein decreased significantly after 90 days of freezing storage, while the irregular curl content increased significantly, and the trends of \(\beta\)-fold and \(\beta\)-flip angles did not show a clear pattern. On the 90 th day of storage, the FF group had the least \(\alpha\)-helix content, which decreased by 56.20% compared with the 3 rd day, and the corresponding irregular curl content was the highest, which increased by 52.70% compared with its 3 rd day. \(\alpha\)-helix content of TF, BF and LF groups were not significantly different (\(p<0.05\)), and the LF group had the least irregular curl content. This is consistent with the previous results of microstructure under electron microscopy. The decrease of \(\alpha\)-helix indicates that the hydrolysis, oxidation and interaction of cuttlefish protein during frozen storage exposed the original hydrophobic groups in the molecule and increased the hydrophobicity of the protein surface, thus affecting the protein structure. At the same time, frozen storage resulted in an aggregation of proteins and disruption of intermolecular hydrogen bonds, which transformed the secondary structure into an irregular one.  
Figure 7: ATR-FTIR spectra of cuttlefish with different freezing methods during freezing at \(-18\)\({}^{\circ}\)C. (**A–D**, represent the samples stored for 3 d, 30 d, 60 d, 90 d, respectively).